Handbook of Nutrition, Diet, and Epigenetics [1st ed.] 978-3-319-55529-4, 978-3-319-55530-0

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Vinood B. Patel Victor R. Preedy Editors

Handbook of Nutrition, Diet, and Epigenetics

Handbook of Nutrition, Diet, and Epigenetics

Vinood B. Patel • Victor R. Preedy Editors

Handbook of Nutrition, Diet, and Epigenetics With 416 Figures and 143 Tables

Editors Vinood B. Patel School of Life Sciences University of Westminster London, UK

Victor R. Preedy Diabetes and Nutritional Sciences Research Division Faculty of Life Sciences and Medicine King’s College London London, UK

ISBN 978-3-319-55529-4 ISBN 978-3-319-55530-0 (eBook) ISBN 978-3-319-59052-3 (print and electronic bundle) https://doi.org/10.1007/978-3-319-55530-0 Library of Congress Control Number: 2018953716 # Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The well-being of humankind is not only dependent upon individuals receiving adequate nutrition but also upon their genetic makeup. Genes may encode proteins responsible for structural components (e.g., membranes, subcellular organelles) and dynamics (e.g., enzymes, receptor–postreceptor cascades). Many of these components will require, from the outset, an adequate diet. For example, some antioxidant enzymes are critically dependent on the diet. This is illustrated by the role of dietary selenium which is necessary for glutathione peroxidase activities, while copper and zinc are necessary for superoxide dismutase activities. However, there is an increasing body of evidence to suggest that nutrition itself may alter the way in which genes are expressed via the process of epigenetics. Definitions of epigenetics vary and include modifications in the functional expression of DNA. This may involve changes in, or the influence of, DNA methylation, noncoding RNA, chromatin, histone acetylation or methylation, genomic imprinting, and other processes. There are many dietary components that impose epigenetic changes including folate, B vitamins, betaine, choline, and other extracts from plants, foods, and beverages. In fact, the knowledge base of how dietary components impact epigenetic processes has increased markedly over the past few years. As a prelude to understanding the role of epigenetics, it is also necessary to understand the basics of cellular and molecular biology, as well as the clinical basis of health and disease. However, marshaling all the information on the complex relationships between cellular and molecular biology, diet and nutrition, health and disease, and epigenetic processes is somewhat difficult due to the myriad of material. To address this, the editors have compiled the Handbook of Nutrition, Diet, and Epigenetics. The book is divided into the following parts: Part I. Introductory Material and Foundations Part II. Organs, Disease, and Life Stages Part III. Influence of Diet and Nutrition on Epigenetics Part IV. Practical Techniques and Applications Part I Introductory Material and Foundations covers biology of the cell, overviews, and comparative epigenetics. Part II Organs, Disease, and Life Stages covers weight control, metabolic syndrome and obesity, diabetes, insulin and v

vi

Preface

glucose, the cardiovascular system, the nervous system, cancers and immune function, the intestinal tract, kidney, muscle and bone, life stages, pregnancy, development and programming, transgenerational effects, and aging. Part III Influence of Diet and Nutrition on Epigenetics covers energy, general treatments and nutritional modifications, lipids and proteins as macronutrients and their components, vitamins and minerals, combinations (mixtures of components), specific foods and nutraceuticals, and nutritional toxicology and adverse effects. Part IV Practical Techniques and Applications covers multilocus methylation assays, beadchips, bioinformatics databases, microRNAs, mass spectrometry, embryonic stem cells, and molecular pathways and resources. It is difficult to list all the chapters as there are just over 120, and some cover numerous analytical or disease-based domains. The editors recognize the difficulties in assigning chapters to specific parts of the book as some chapters may well be suitable for two or more sections. Nevertheless, there is a wide breadth of material available. There are also unique features in this handbook, whereby each of the chapters includes the following sections: • Dictionary of Terms • Key Facts • Summary Points These features enable the transdisciplinary and transintellectual divides to be bridged. Contributors are authors of international and national standing, leaders in the field, and trendsetters. Emerging fields of epigenetics in relation to diet and nutrition are also incorporated in the Handbook of Nutrition, Diet, and Epigenetics. This represents essential reading for nutritionists, dietitians, health care professionals, research scientists, molecular and cellular biochemists, physicians, general practitioners, public health practitioners, as well as those interested in health in general. Vinood B. Patel Victor R. Preedy

Contents

Volume 1 Part I 1

Introductory Material and Foundations . . . . . . . . . . . . . . . . .

1

Environmental Effects on Genomic Imprinting in Development and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rakesh Pathak and Robert Feil

3

............

25

2

Effect of Epigenetic Differences in Identical Twins Tanya L. Schwab and Tara L. Hogenson

3

Nutrition, DNA Methylation, and Developmental Origins of Cardiometabolic Disease: A Signal Systems Approach . . . . . . . Zachary M. Laubach, Christopher D. Faulk, Andres Cardenas, and Wei Perng

43

4

Folate and Epigenetics: Colorectal Cancer Risk and Detection . . . Nancy Lévesque, Daniel Leclerc, and Rima Rozen

61

5

Epigenetics and the Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . Meirav Pevsner-Fischer, Niv Zmora, Sofia Braverman, and Eran Elinav

79

6

Implications of Genotype and Environment on Variation in DNA Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ives Y. Lim, Xinyi Lin, and Neerja Karnani

7

8

Socioeconomics, Obesity, and Early-Life Nutrition on the Role of DNA Methylation in Biological Embedding . . . . . . . . . . . . . . . . Christiana A. Demetriou, Karin van Veldhoven, Caroline Relton, Silvia Stringhini, Kyriacos Kyriacou, and Paolo Vineis Linking Enhancer to Epigenetics: New Way to Think About Human Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhuojuan Luo and Chengqi Lin

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Contents

Peroxisome Proliferator-Activated Receptor-Gamma Coactivator-1Alpha and DNA Methylation as Epigenetic Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuemei Xie and Xiaoping Luo Role of PIWI-Interacting RNA (piRNA) as Epigenetic Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Danielle Queiroz Calcagno, Elizangela Rodrigues da Silva Mota, Fabiano Cordeiro Moreira, Stefanie Braga Maia de Sousa, Rommel Rodríguez Burbano, and Paulo Pimentel Assumpção

165

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Epigenetic Targeting of Vascular Endothelial Growth Factor (VEGF) Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Steven G. Gray

211

Epigenetic Aspects of Nuclear Receptor Coregulators: How Nutritional and Environmental Signals Change Gene Expression Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fawaz Alzaïd, Tomas Jakobsson, Eckardt Treuter, and Nicolas Venteclef

233

13

Diversity of Human CpG Islands . . . . . . . . . . . . . . . . . . . . . . . . . . Isabel Mendizabal and Soojin V. Yi

265

14

DNA Demethylation and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . Xiaofei Zhang, Thomas E. Witzig, and Xiaosheng Wu

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15

Guanine-Quadruplexes and Possible Role in Nutritional Epigenetics and Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paniz Tavakoli, Wayne Leifert, Michael Fenech, and Maxime François

16

Role of SIRT1 in Epigenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhipeng A. Wang, Willie Hsu, and Wenshe R. Liu

17

The Epigenetically Modulated Circadian System: Implications for Nutrition and Health. Nutritional Modulation of the Circadian Epigenome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lidia Daimiel

18

19

20

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331

Epigenetic Regulation of Fat Deposition: A Focus on Krüppel-Like Factor 14 (Klf14) . . . . . . . . . . . . . . . . . . . . . . . . . . . Robert A. Koza

351

Regulatory Roles of PARP-1 and Lipids in Epigenetic Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Rosaria Faraone-Mennella, Annalisa Masi, and Carla Ferreri

369

Methylation in CPT1A, Lipoproteins, and Epigenetics . . . . . . . . . . Stella Aslibekyan and Steven A. Claas

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21

Histone Deacetylase HDAC8 and Insulin Resistance . . . . . . . . . . . Vincent Wai-Sun Wong, Myth Tsz-Shun Mok, and Alfred Sze-Lok Cheng

22

Prenatal Programming and Epigenetics of Obesity Metabolic Phenotype: Pre- and Postnatal Metabolic Phenotypes and Molecular Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antonio Gonzalez-Bulnes and Susana Astiz

23

Perinatal Malnutrition and Epigenetic Regulation of Long-Term Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel B. Hardy

24

Epigenetics of Undernutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Omar Ramos-Lopez, Jose Ignacio Riezu-Boj, Fermin I. Milagro, and J. Alfredo Martinez

25

Cancer Epigenomics on Precision Medicine and Immunotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Javier I. J. Orozco, Diego M. Marzese, and Dave S. B. Hoon

405

423

439 457

483

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Epigenetics of Systemic Sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . Nezam Altorok, Vivek Nagaraja, and Bashar Kahaleh

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Traces of Life’s Experiences: Epigenetics (DNA methylation) in Forensics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meenu Ghai, Dyfed Lloyd Evans, and Shailesh Joshi

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Epigenetics, Dietary Restriction, and Insects: Implications for Humankind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ting Lian, Uma Gaur, and Mingyao Yang

549

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Nutritional Programming and Effect of Ancestor Diet in Birds . . . Mireille Morisson, Vincent Coustham, Laure Frésard, Anne Collin, Tatiana Zerjal, Sonia Métayer-Coustard, Loys Bodin, Francis Minvielle, Jean-Michel Brun, and Frédérique Pitel

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Volume 2 Part II 30

Organs, Disease, and Life Stages . . . . . . . . . . . . . . . . . . . . .

583

Molecular Biology of Human Obesity: Nonepigenetics in Comparison with Epigenetic Processes . . . . . . . . . . . . . . . . . . . . . . David Albuquerque, Licínio Manco, and Clévio Nóbrega

585

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Epigenetics in Hyperphagia Minati Singh

..............................

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Adipogenesis and Noncoding RNAs . . . . . . . . . . . . . . . . . . . . . . . . Pang-Kuo Lo, Benjamin Wolfson, and Qun Zhou

623

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MicroRNA-Regulated Immune Cell Function in Obese Adipose Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beiyan Zhou, Wei Ying, Chuan Li, and Anthony T. Vella DNA/Histone Methylation and Adipocyte Differentiation: Applications to Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yangmian Yuan, Chengyu Liu, Danyang Wan, Kun Huang, and Ling Zheng Nutritional Programming of Metabolic Syndrome: Role of Nutrients in Shaping the Epigenetics . . . . . . . . . . . . . . . . . . . . . . . Sonal Patel, Arpankumar Choksi, Richa Pant, Aftab Alam, and Samit Chattopadhyay

647

665

683

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MicroRNAs in Metabolic Syndrome . . . . . . . . . . . . . . . . . . . . . . . . Juan Francisco Codocedo and Nibaldo C. Inestrosa

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Sperm Epigenome in Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nur Duale, Oliwia Witczak, Gunnar Brunborg, Trine B. Haugen, and Birgitte Lindeman

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Carbohydrate-Responsive Histone Acetylation in Gene Body Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kazuki Mochizuki, Natsuyo Hariya, Kazue Honma, and Toshinao Goda

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40

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Epigenetic and Metabolism: Glucose and Homeotic Transcription Factor PREP1 VRP Suggested Epigenetics and Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luigi Albano, Paolo Emidio Macchia, and Paola Ungaro Impact of Epigenetic Mechanisms on the Regulation of Gene Expression During Intrauterine Programming of the Endocrine Pancreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ana Laura Ortega-Márquez, Angélica Morales-Miranda, and Sumiko Morimoto Butyrate, a Short-Chain Fatty Acid and Histone Deacetylases Inhibitor: Nutritional, Physiological, and Pharmacological Aspects in Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sabbir Khan, Krishna Prahlad Maremanda, and Gopabandhu Jena

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761

777

793

Insulin Action, Insulin Resistance, and Their Link to Histone Acetylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aneta Balcerczyk, Sabrina Chriett, and Luciano Pirola

809

Distribution of Methylated Regions Within gDNA in Acute and Chronic Phases of Diabetes Mellitus . . . . . . . . . . . . . . . . . . . . Alexey A. Leontovich and Michael P. Sarras Jr.

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Long-Term Complications in Diabetes Mellitus and the Interrelationship of Blood Vessel Formation, Endothelial Progenitor Cells, and gDNA Methylation . . . . . . . . . . . . . . . . . . . . Michael P. Sarras Jr. and Alexey A. Leontovich

849

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Epigenetics of Diabetic Nephropathy . . . . . . . . . . . . . . . . . . . . . . . Harvest F. Gu

865

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Multiple miRNA Regulation of Lipoprotein Lipase . . . . . . . . . . . . Sybil Charriere and Philippe Moulin

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Nutritional Stress and Fetal Epigenetics in the Brain Qingyi Ma and Lubo Zhang

..........

899

48

Brain Hypothalamic Proopiomelanocortin and High-Fat Diet on Methylation in Offspring as Epigenetic Modifications . . . . . . . Jia Zheng and Xinhua Xiao

923

Epigenetic Effects of Nutrients Involved in Neurodevelopmental and Mental Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takeo Kubota and Kazuki Mochizuki

935

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Psychosocial Impact of Epigenetics in Pediatrics . . . . . . . . . . . . . . Xiaoming Gong and Lewis P. Rubin

949

51

MicroRNAs as Neuroregulators . . . . . . . . . . . . . . . . . . . . . . . . . . . Ketan S. Patil and Simon G. Møller

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Diet, Epigenetics, and Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . Andrea Fuso and Cristina Domenichelli

987

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Epigenetic Alterations in Stomach Cancer: Implications for Diet and Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005 Carolina Oliveira Gigek, Elizabeth Suchi Chen, and Marilia Arruda Cardoso Smith

54

Epigenetics of Dietary Methyl-Group Donor Deficiency and Liver Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023 Aline de Conti and Igor P. Pogribny

55

Nuclear Receptors and Epigenetic Regulation . . . . . . . . . . . . . . . . 1039 Ornella I. Selmin, Alberto PG Romagnolo, and Donato F. Romagnolo

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Epigenetic Regulation of Early Nutrition on Immune System . . . . 1067 Lorella Paparo, Rosita Aitoro, Rita Nocerino, Carmen di Scala, Margherita Di Costanzo, Linda Cosenza, Viviana Granata, and Roberto Berni Canani

57

miRNAs and Their Role in the Pathogenesis of Celiac Disease: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1079 Donatella Barisani

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Diet and Epigenetic Alteration of Renal Function . . . . . . . . . . . . . 1101 Eva Nüsken, Kai-Dietrich Nüsken, and Jörg Dötsch

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Liver Diseases: Epigenetic Mechanisms, Oxidative Stress, and Use of Alpha-Lipoic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Aleksandra Uskoković, Svetlana Dinić, Jelena Arambašić Jovanović, Goran Poznanović, Melita Vidaković, and Mirjana Mihailović

60

High-Fat Diet and Maternal Obesity-Associated Epigenetic Regulation of Bone Development . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 Jin-Ran Chen

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Epigenetic Alterations in Human Sperm . . . . . . . . . . . . . . . . . . . . 1161 Naoko Miyauchi, Akane Kitamura, Hitoshi Hiura, Hiroaki Okae, Norio Kobayashi, Hiromitsu Hattori, Souta Takahashi, and Takahiro Arima

62

Gaps in Knowledge and Missing Evidence in the Role of DNA Methylation in Biological Embedding . . . . . . . . . . . . . . . . . . . . . . . 1177 Christiana A. Demetriou, Karin van Veldhoven, Caroline Relton, Silvia Stringhini, Kyriacos Kyriacou, and Paolo Vineis

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Interplay Between Maternal Micronutrients, DNA Methylation, and Brain Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1193 Richa Rathod and Sadhana Joshi

64

Gestational Betaine, Liver Metabolism, and Epigenetics . . . . . . . . 1217 Demin Cai, Haoyu Liu, Yun Hu, Yuqian Jiang, and Ruqian Zhao

65

Maternal Methyl Supplemented Diets and Epimutations in Offspring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231 Cheryl S. Rosenfeld

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Epigenetic Consequences of Low Birth-Weight and Preterm Birth in Adult Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263 Qihua Tan

67

Influence of Maternal Nutrition on Genomic Imprinting and Fetal Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1277 Emily Chapman, Jia Chen, and Maya A. Deyssenroth

68

Epigenetic Mechanisms in Food Allergy . . . . . . . . . . . . . . . . . . . . . 1293 David J. Martino

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Epigenetic Programming of Water Drinking and Sodium Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1307 Andre Souza Mecawi, Michael Paul Greenwood, and Juan Arguelles

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Igf1 DNA Methylation, Epigenetics, and Low-Salt Diet in Fetal Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1329 Flávia Ramos de Siqueira, Luzia Naôko Shinohara Furukawa, and Joel Claudio Heimann

71

Preventing and Diagnosing Diabetic Complications: Epigenetics, miRNA, DNA Methylation, and Histone Modifications . . . . . . . . . 1347 Daoyin Dong, E. Albert Reece, and Peixin Yang

72

Embryopathy as a Model for the Epigenetics Regulation of Complications in Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1361 Daoyin Dong, E. Albert Reece, and Peixin Yang

73

Slow Growth Period and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . 1381 Lars Olov Bygren and Gunnar Kaati

74

Developmental Programming and Transgenerational Transmission of Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395 Mark H. Vickers

75

Epigenetics and Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1413 Carolina Soriano-Tárraga, Jordi Jiménez-Conde, and Jaume Roquer

Volume 3 Part III

Influence of Diet and Nutrition on Epigenetics . . . . . . . . .

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Energy Metabolism and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . . 1437 Scott J. Bultman

77

Milk Exosomes and MicroRNAs: Potential Epigenetic Regulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1467 Bodo C. Melnik and Foteini Kakulas

78

Nutritional Regulation of Mammary miRNome: Implications for Human Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1495 Christine Leroux, Dragan Milenkovic, Lenha Mobuchon, Sandrine Le Guillou, Yannick Faulconnier, Bruce German, and Fabienne Le Provost

79

Diet-Induced Epigenetic Modifications and Implications for Intestinal Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1513 Elodie Gimier, Nicolas Barnich, and Jérémy Denizot

80

Nutritional and Epigenetics Implications in Esophageal Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1535 Danielle Queiroz Calcagno, Kelly Cristina da Silva Oliveira, and Nina Nayara Ferreira Martins

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The Methyl-CpG-Binding Domain (MBD) Protein Family: An Overview and Dietary Influences . . . . . . . . . . . . . . . . . . . . . . . 1555 Carolina Oliveira Gigek, Elizabeth Suchi Chen, Gaspar Jesus Lopes-Filho, and Marilia Arruda Cardoso Smith

82

Epigenetic Effects of N-3 Polyunsaturated Fatty Acids . . . . . . . . . 1571 Christine Heberden and Elise Maximin

83

Threonine Catabolism: An Unexpected Epigenetic Regulator of Mouse Embryonic Stem Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . 1585 Ruta Jog, Guohua Chen, Todd Leff, and Jian Wang

84

The Role and Epigenetic Modification of the Retinoic Acid Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1605 Yukihiko Kato

85

Histone Deacetylase Inhibitor Tributyrin and Vitamin A in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1615 Renato Heidor, Ernesto Vargas-Mendez, and Fernando Salvador Moreno

86

Association Between MicroRNA Expression and Vitamin C in Ovarian Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1637 Yong Jin Kim, Yoon Young Kim, and Seung-Yup Ku

87

Rewriting the Script: The Story of Vitamin C and the Epigenome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1671 Tyler C. Huff and Gaofeng Wang

88

Vitamin C and DNA Demethylation in Regulatory T Cells . . . . . . 1691 Varun Sasidharan Nair and Kwon Ik Oh

89

Cobalamin, Microbiota and Epigenetics . . . . . . . . . . . . . . . . . . . . . 1707 Joan Jory

90

Maternal Folate and DNA Methylation in Offspring . . . . . . . . . . . 1727 Emma L. Beckett, Mark Lucock, Martin Veysey, and Bonnie R. Joubert

91

Modulation of microRNA by Vitamin D in Cancer Studies . . . . . . 1747 Emma L. Beckett, Martin Veysey, Zoe Yates, and Mark Lucock

92

Epigenetics and Minerals: An Overview Inga Wessels

93

Calcium-Deficient Diets in Pregnancy and Nursing: Epigenetic Change in Three Generations of Offspring . . . . . . . . . . . . . . . . . . 1789 Junji Takaya

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Selenoproteins and Epigenetic Regulation in Mammals . . . . . . . . . 1803 Hsin-Yi Lu, Berna Somuncu, Jianhong Zhu, Meltem Muftuoglu, and Wen-Hsing Cheng

95

DNA Methylation in Anti-cancer Effects of Dietary Catechols and Stilbenoids: An Overview of Underlying Mechanisms . . . . . . 1819 Megan Beetch and Barbara Stefanska

96

Epigenetic Drivers of Resveratrol-Induced Suppression of Mammary Carcinogenesis: Addressing miRNAs, Protein, mRNA, and DNA Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1845 E. R. Sauter

97

PARylation, DNA (De)methylation, and Diabetes Melita Vidaković, Anja Tolić, Nevena Grdović, Mirunalini Ravichandran, and Tomasz P. Jurkowski

98

Extra Virgin Olive Oil and Corn Oil and Epigenetic Patterns in Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1877 Raquel Moral and Eduard Escrich

99

Natural Polyphenol Kaempferol and Its Epigenetic Impact on Histone Deacetylases: Focus on Human Liver Cells . . . . . . . . . . . . 1897 Sascha Venturelli, Christian Leischner, and Markus Burkard

100

Dietary Methylselenocysteine and Epigenetic Regulation of Circadian Gene Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1915 Helmut Zarbl and Mingzhu Fang

101

Proanthocyanidins and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . 1933 Cinta Bladé, Anna Arola-Arnal, Anna Crescenti, Manuel Suárez, Francisca I. Bravo, Gerard Aragonès, Begoña Muguerza, and Lluís Arola

102

Application of Nutraceuticals in Pregnancy Complications: Does Epigenetics Play a Role? . . . . . . . . . . . . . . . . . . . . . . . . . . . 1957 Luís Fernando Schütz, Jomer Bernardo, Minh Le, Tincy Thomas, Chau Nguyen, Diana Zapata, Hitaji Sanford, John D. Bowman, Brett M. Mitchell, and Mahua Choudhury

103

Polyphenols and Histone Acetylation . . . . . . . . . . . . . . . . . . . . . . 1977 Anna K. Kiss

104

Ginkgo biloba, DNA Damage and DNA Repair: Overview Daniela Oliveira, Bjorn Johansson, and Rui Oliveira

105

Plant Monoterpenes Camphor, Eucalyptol, Thujone, and DNA Repair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2017 Biljana Nikolić, Dragana Mitić-Ćulafić, Branka Vuković-Gačić, and Jelena Knežević-Vukčević

. . . . . . . . . . . . . 1857

. . . . . 1997

xvi

Contents

106

Modulatory Role of Curcumin in miR-Mediated Regulation in Cancer and Non-cancer Diseases . . . . . . . . . . . . . . . . . . . . . . . 2035 Sayantani Chowdhury, Jyotirmoy Ghosh, and Parames C. Sil

107

Epigenetic Impact of Indoles and Isothiocyanates on Cancer Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2053 Pushpinder Kaur and Jaspreet Kaur

108

Epigenetic Phenomena of Arsenic and Histone Tail Modifications: Implications for Diet and Nutrition . . . . . . . . . . . 2069 Qiao Yi Chen and Max Costa

109

Arsenic and microRNA Expression . . . . . . . . . . . . . . . . . . . . . . . 2085 Elena Sturchio, Miriam Zanellato, Priscilla Boccia, Claudia Meconi, and Silvia Gioiosa

110

Epigenetic Effects of Bisphenol A (BPA): A Literature Review in the Context of Human Dietary Exposure . . . . . . . . . . . . . . . . . 2105 Luísa Camacho and Igor P. Pogribny

111

Ochratoxin A and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2125 Alessandra Mezzelani

112

Silver and Histone Modifications Yuko Ibuki

113

High-Fructose Consumption and the Epigenetics of DNA Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2161 Hiroya Yamada, Eiji Munetsuna, and Koji Ohashi

Part IV

. . . . . . . . . . . . . . . . . . . . . . . . . 2145

Practical Techniques and Applications . . . . . . . . . . . . . . . .

2179

. . . . . . . . . . . . . . . 2181

114

Multilocus Methylation Assays in Epigenetics Thomas Eggermann

115

Illumina HumanMethylation BeadChip for Genome-Wide DNA Methylation Profiling: Advantages and Limitations . . . . . . 2203 Kazuhiko Nakabayashi

116

Bioinformatics Databases and Tools on Dietary MicroRNA Juan Cui

117

MicroRNAs and Reference Gene Methodology Petra Matoušková

118

Mass Spectrometry and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . 2251 Luciano Nicosia, Roberta Noberini, Monica Soldi, Alessandro Cuomo, Daniele Musiani, Valeria Spadotto, and Tiziana Bonaldi

. . . . 2219

. . . . . . . . . . . . . . 2233

Contents

xvii

119

Forward and Reverse Epigenomics in Embryonic Stem Cells . . . 2269 Ilana Livyatan and Eran Meshorer

120

MiRImpact as a Methodological Tool for the Analysis of MicroRNA at the Level of Molecular Pathways . . . . . . . . . . . . . . 2289 Anton A. Buzdin and Nikolay M. Borisov

121

Resources in Diet, Nutrition, and Epigenetics . . . . . . . . . . . . . . . . 2309 Rajkumar Rajendram, Vinood B. Patel, and Victor R. Preedy

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2315

About the Editors

Vinood B. Patel School of Life Sciences University of Westminster London, UK Vinood B. Patel (B.Sc., Ph.D., FRSC) is a Reader in Clinical Biochemistry at the University of Westminster and honorary Fellow at King’s College London. Dr. Patel graduated from 1992 the University of Portsmouth with a Degree in Pharmacology and completed his Ph.D. in protein metabolism from King’s College London in 1997. His postdoctoral work was carried out at Wake Forest University Baptist Medical School studying structural-functional alterations to mitochondrial ribosomes, where he developed novel techniques to characterize their biophysical properties. Dr. Patel is a nationally and internationally recognized scientist, and in 2014 he was elected as a Fellow to the Royal Society of Chemistry. He presently directs studies on metabolic pathways involved in diabetes and liver disease, particularly related to mitochondrial energy regulation and cell death. He is currently conducting research to study the role of nutrients, antioxidants, phytochemicals, iron, alcohol, and fatty acids. His other areas of interest include identifying new biomarkers that can be used for the diagnosis and prognosis of liver disease and understanding mitochondrial oxidative stress in Alzheimer’s disease and gastrointestinal dysfunction in autism. Dr. Patel has edited biomedical books in the areas of nutrition and health prevention and biomarkers and has published over 150 articles.

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xx

About the Editors

Victor R. Preedy (B.Sc., Ph.D., D.Sc., FRSB, FRSPH, FRCPath, FRSC) is a senior member of King’s College London, where he is also Director of the Genomics Centre and a member of the Faculty of Life Sciences and Medicine. Professor Preedy graduated in 1974 with an Honors Degree in Biology and Physiology with Pharmacology. He gained his University of London Ph.D. in 1981. In 1992, he received his Membership of the Royal College of Pathologists, and in 1993 he gained his second doctoral degree for his outstanding contribution to protein metabolism in health and disease. Professor Preedy was elected as Fellow to the Institute of Biology in 1995 and to the Royal College of Pathologists in 2000. Since then, he has been elected as Fellow to the Royal Society for the Promotion of Health (2004) and the Royal Institute of Public Health (2004). In 2009, Professor Preedy became Fellow of the Royal Society for Public Health and, in 2012, Fellow of the Royal Society of Chemistry. In his career, Professor Preedy has carried out research at the National Heart Hospital (part of Imperial College London), the School of Pharmacy (now part of University College London), and the MRC Centre at Northwick Park Hospital. He has collaborated with research groups in Finland, Japan, Australia, the USA, and Germany. He is a leading expert in the science of health and has a long-standing interest in food and nutrition for over 30 years, especially related to tissue pathology and cellular and molecular biology. He has lectured nationally and internationally. To his credit, Professor Preedy has published over 600 articles, which include peer-reviewed manuscripts based on original research, abstracts and symposium presentations, reviews, and numerous books and volumes.

Contributors

Rosita Aitoro Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Aftab Alam Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India Luigi Albano Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy David Albuquerque Department of Life Sciences, Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, Portugal Nezam Altorok Division of Rheumatology, Department of Internal Medicine, University of Toledo Medical Center, Toledo, OH, USA Fawaz Alzaïd Sorbonne Universités, Université Pierre et Marie-Curie, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR_S 1138 Cordeliers Research, Paris, France Gerard Aragonès Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Juan Arguelles Departamento de Biología Funcional, Área de Fisiología, Facultad de Medicina y Ciencias de la Salud, Universidad de Oviedo, España, Oviedo, Asturias, Spain Takahiro Arima Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Lluís Arola Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Nutrition and Health Research Group, Technological Center for Nutrition and Health (EURECAT-CTNS), Tecnio, Campus of International Excellence Southern Catalonia (CEICS), Reus, Spain xxi

xxii

Contributors

Anna Arola-Arnal Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Stella Aslibekyan Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA Paulo Pimentel Assumpção Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil Susana Astiz Comparative Physiology Group. SGIT-INIA, Avda. Puerta de Hierro s/n, Madrid, Spain Aneta Balcerczyk Faculty of Biology and Environmental Protection, Department of Molecular Biophysics, University of Lodz, Lodz, Poland Donatella Barisani School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy Nicolas Barnich M2iSH, Université Clermont Auvergne, Inserm U1071, USCINRA 2018, Clermont-Ferrand, France Emma L. Beckett School of Medicine and Public Health, The University of Newcastle, Ourimbah, NSW, Australia Megan Beetch Land and Food Systems, Food, Nutrition and Health, University of British Columbia, Vancouver, BC, Canada Jomer Bernardo Texas A&M Irma Lerma Rangel College of Pharmacy, Kingsville, TX, USA Cinta Bladé Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Priscilla Boccia Department of Technological Innovation and Safety of Plants, Product and Anthropic Settlements (DIT), Italian Workers’ Compensation Authority (INAIL), Rome, Italy Loys Bodin GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France Tiziana Bonaldi Department of Experimental Oncology, European Institute of Oncology, Milan, Italy Nikolay M. Borisov Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre “Kurchatov Institute”, Moscow, Russia Department of Personalized Medicine, First Oncology Research and Advisory Center, Moscow, Russia John D. Bowman Texas A&M Irma Lerma Rangel College of Pharmacy, Kingsville, TX, USA

Contributors

xxiii

Sofia Braverman Department of Immunology, Weizmann Institute of Science, Rehovot, Israel Francisca I. Bravo Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Jean-Michel Brun GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France Gunnar Brunborg Department of Molecular Biology, Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway Scott J. Bultman Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Rommel Rodríguez Burbano Laboratório de Biologia Molecular, Hospital Ophir Loyola, Belém, Pará, Brazil Markus Burkard Department of Vegetative and Clinical Physiology, Institute of Physiology, University Hospital Tuebingen, Tuebingen, Germany Anton A. Buzdin Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre “Kurchatov Institute”, Moscow, Russia OmicsWay Corporation, Walnut, CA, USA Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia Lars Olov Bygren Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden Demin Cai Key Laboratory of Animal Physiology and Biochemistry, Ministry of Agriculture, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China Danielle Queiroz Calcagno Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil Luísa Camacho Division of Biochemical Toxicology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA Roberto Berni Canani Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy European Laboratory for the Investigation of Food Induced Diseases (ELFID), University of Naples “Federico II”, Naples, Italy CEINGE Advanced Biotechnologies, University of Naples “Federico II”, Naples, Italy Andres Cardenas Department of Population Medicine, Harvard Medical School, Boston, MA, USA

xxiv

Contributors

Emily Chapman Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA Sybil Charriere Claude Bernard University, Lyon, France Department of Endocrinology, Diabetology, Metabolic Diseases and Nutrition, Cardiovascular Hospital Louis Pradel, Bron Cedex, France Samit Chattopadhyay Indian Institute of Chemical Biology, Kolkata, West Bengal, India Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India Qiao Yi Chen Department of Environmental Medicine, New York University School of Medicine, Tuxedo, NY, USA Elizabeth Suchi Chen Division of Genetics, Department of Morphology and Genetics, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil Guohua Chen Department of Pathology, Wayne State University School of Medicine, Detroit, MI, USA Jia Chen Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Medicine, Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA Jin-Ran Chen Arkansas Children’s Nutrition Center and the Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA Alfred Sze-Lok Cheng School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Wen-Hsing Cheng Department of Food Science, Nutrition and Health Promotion, Mississippi State University, Mississippi State, MS, USA Arpankumar Choksi Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India Mahua Choudhury Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, College Station, TX, USA Sayantani Chowdhury Division of Molecular Medicine, Bose Institute, Kolkata, India Sabrina Chriett INSERM U1060, Oullins, France

Contributors

xxv

Steven A. Claas College of Public Health, University of Kentucky, Lexington, KY, USA Juan Francisco Codocedo CARE UC Biomedical Research Center, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile Anne Collin INRA – URA, INRA, Nouzilly, France Linda Cosenza Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Max Costa Department of Environmental Medicine, New York University School of Medicine, Tuxedo, NY, USA Vincent Coustham INRA – URA, INRA, Nouzilly, France Anna Crescenti Nutrition and Health Research Group, Technological Center for Nutrition and Health (EURECAT-CTNS), Tecnio, Campus of International Excellence Southern Catalonia (CEICS), Reus, Spain Juan Cui Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA Alessandro Cuomo Department of Experimental Oncology, European Institute of Oncology, Milan, Italy Lidia Daimiel Nutritional Genomics of the Cardiovascular Disease and Obesity, Foundation IMDEA Food CEI UAM + CSIC, Madrid, Spain Department of Nutrition and Bromatology, Facultad de Farmacia, Universidad San Pablo-CEU, CEU universities, Boadilla del Monte, Madrid, Spain Aline de Conti Division of Biochemical Toxicology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA Flávia Ramos de Siqueira Department of Internal Medicine, Laboratory of Experimental Hypertension, School of Medicine, University of Sao Paulo, Sao Paulo, SP, Brazil Department of Internal Medicine, University of São Paulo School of Medicine, Butantã, São Paulo, SP, Brazil Department of Internal Medicine, Nephrology Division, School of Medicine, University of São Paulo, São Paulo, SP, Brazil Stefanie Braga Maia de Sousa Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil Christiana A. Demetriou Neurology Clinic D, The Cyprus Institute of Neurology and Genetics, Ayios Dhometios, Nicosia, Cyprus The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Ayios Dhometios, Nicosia, Cyprus

xxvi

Contributors

Jérémy Denizot M2iSH, Université Clermont Auvergne, Inserm U1071, USCINRA 2018, Clermont-Ferrand, France Maya A. Deyssenroth Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA Margherita Di Costanzo Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Carmen di Scala Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Svetlana Dinić Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia Cristina Domenichelli Largo Alessandria del Carretto, Rome, Italy Daoyin Dong Department of Obstetrics, Gynecology and Reproductive Sciences, University of Maryland School of Medicine, Baltimore, MD, USA Jörg Dötsch Department of Pediatrics, University of Cologne, Cologne, Germany Nur Duale Department of Molecular Biology, Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway Thomas Eggermann Institute of Human Genetics, University Hospital, RWTH Technical University Aachen, Aachen, Germany Eran Elinav Department of Immunology, Weizmann Institute of Science, Rehovot, Israel Eduard Escrich Multidisciplinary Group for the Study of Breast Cancer, Department of Cell Biology, Physiology and Immunology, Physiology Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain Dyfed Lloyd Evans School of Life Sciences, University of KwaZulu-Natal, Westville Campus, Durban, KwaZulu Natal, South Africa Mingzhu Fang Department of Environmental and Occupational Health, School of Public Health Environmental and Occupational Health Sciences Institute, Rutgers, The State University of New Jersey, Piscataway, NJ, USA Maria Rosaria Faraone-Mennella Department of Biology, University of Naples “Federico II”, Naples, Italy Yannick Faulconnier Herbivore Research Unit -Biomarkers Team, French Institut of Agricultural Research (INRA), St Genès-Champanelle, France Christopher D. Faulk Department of Animal Sciences, University of Minnesota, St. Paul, MN, USA

Contributors

xxvii

Robert Feil Institute of Molecular Genetics (IGMM), Centre National de Recherche Scientifique (CNRS), UMR-5535, University of Montpellier, Montpellier, France Michael Fenech CSIRO Health and Biosecurity, Personalised Nutrition and Healthy Ageing, Adelaide, SA, Australia Carla Ferreri Institute of Organic Synthesis and Photoreactivity (ISOF), CNR, Bologna, Italy Maxime François CSIRO Health and Biosecurity, Personalised Nutrition and Healthy Ageing, Adelaide, SA, Australia Department of Molecular and Cellular Biology, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia Laure Frésard Department of Pathology, Stanford University, Stanford, CA, USA Luzia Naôko Shinohara Furukawa Department of Internal Medicine, Laboratory of Experimental Hypertension, School of Medicine, University of Sao Paulo, Sao Paulo, SP, Brazil Department of Internal Medicine, University of São Paulo School of Medicine, Butantã, São Paulo, SP, Brazil Andrea Fuso Department of Surgery “P. Valdoni”, Sapienza University of Rome, Rome, Italy Uma Gaur Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, Sichuan, China Bruce German Department of Food Science and Technology, University of California Davis, Davis, CA, USA Meenu Ghai School of Life Sciences, University of KwaZulu-Natal,Westville Campus, Durban, KwaZulu Natal, South Africa Jyotirmoy Ghosh Department of Chemistry, Banwarilal Bhalotia College, Ushagram Asansol, West Bengal, India Carolina Oliveira Gigek Division of Genetics, Department of Morphology and Genetics, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil Division of Surgical Gastroenterology, Department of Surgery, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil Elodie Gimier M2iSH, Université Clermont Auvergne, Inserm U1071, USCINRA 2018, Clermont-Ferrand, France Silvia Gioiosa Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy

xxviii

Contributors

Toshinao Goda Graduate School of Nutritional and Environmental Sciences, University of Shizuoka, Shizuoka, Japan Department of Nutrition, School of Food and Nutritional Sciences, The University of Shizuoka, Shizuoka, Japan Xiaoming Gong Department of Pediatrics, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA Antonio Gonzalez-Bulnes Comparative Physiology Group. SGIT-INIA, Avda. Puerta de Hierro s/n, Madrid, Spain Viviana Granata Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Steven G. Gray Thoracic Oncology Research Group, Trinity Translational Medical Institute, St James’s Hospital, Dublin, Ireland Nevena Grdović Institute for Biological Research, University of Belgrade, Belgrade, Serbia Michael Paul Greenwood School of Clinical Sciences, University of Bristol, Bristol, UK Harvest F. Gu Department of Clinical Science, Intervention and Technologies, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden Sandrine Le Guillou Génétique Animale et Biologie Intégrative, French Institut of Agricultural Research (INRA), Jouy-en-Josas, France Daniel B. Hardy Departments of Obstetrics and Gynecology and Physiology and Pharmacology, The Children’s Health Research Institute and The Lawson Health Research Institute, The University of Western Ontario, London, ON, Canada Natsuyo Hariya Department of Nutrition, Faculty of Health and Nutrition, Yamanashi Gakuin University, Kofu, Yamanashi, Japan Hiromitsu Hattori Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Trine B. Haugen Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Oslo, Norway Christine Heberden Micalis Institute, INRA, AgroParisTech, Université ParisSaclay, Jouy-en-Josas, France Renato Heidor Laboratory of Diet, Nutrition and Cancer, Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, Brazil

Contributors

xxix

Joel Claudio Heimann Department of Internal Medicine, Laboratory of Experimental Hypertension, School of Medicine, University of Sao Paulo, Sao Paulo, SP, Brazil Hitoshi Hiura Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Tara L. Hogenson Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, MN, USA Kazue Honma Graduate School of Nutritional and Environmental Sciences, University of Shizuoka, Shizuoka, Japan Department of Nutrition, School of Food and Nutritional Sciences, The University of Shizuoka, Shizuoka, Japan Dave S. B. Hoon Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA Willie Hsu Department of Chemistry, Texas A&M University, College Station, TX, USA Yun Hu Key Laboratory of Animal Physiology and Biochemistry, Ministry of Agriculture, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China Kun Huang Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China Tyler C. Huff Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA Yuko Ibuki Graduate Division of Nutritional and Environmental Sciences, University of Shizuoka, Shizuoka, Japan Nibaldo C. Inestrosa CARE UC Biomedical Research Center, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia Centro de Excelencia en Biomedicina de Magallanes (CEBIMA), Universidad de Magallanes, Punta Arenas, Chile Tomas Jakobsson Department of Laboratory Medicine, Karolinska Institutet, Huddinge, Sweden Gopabandhu Jena Facility for Risk Assessment and Intervention Studies, Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Mohali, Punjab, India

xxx

Contributors

Yuqian Jiang Department of Biochemistry and Molecular Medicine, University of California at Davis, Sacramento, CA, USA Jordi Jiménez-Conde Department of Neurology, Hospital del Mar; Neurovascular Research Group, IMIM (Institut Hospital del Mar d’Investigacions Mèdiques), Barcelona, Spain Ruta Jog Department of Pathology, Wayne State University School of Medicine, Detroit, MI, USA Bjorn Johansson Centre of Molecular and Environmental Biology (CBMA), Department of Biology, University of Minho, Braga, Portugal Joan Jory Guelph, ON, Canada Sadhana Joshi Department of Nutritional Medicine, Interactive Research School for Health Affairs, Bharati Vidyapeeth Deemed University, Pune, India Shailesh Joshi School of Life Sciences, University of KwaZulu-Natal,Westville Campus, Durban, KwaZulu Natal, South Africa Bonnie R. Joubert Population Health Branch, National Institute of Environmental and Health Sciences, Durham, NC, USA Jelena Arambašić Jovanović Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia Tomasz P. Jurkowski Institute of Biochemistry, University of Stuttgart, Stuttgart, Germany Gunnar Kaati Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden Bashar Kahaleh Division of Rheumatology, Department of Internal Medicine, University of Toledo Medical Center, Toledo, OH, USA Foteini Kakulas School of Medicine and Pharmacology, The University of Western Australia, Crawley, WA, Australia Neerja Karnani Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore, Singapore Yukihiko Kato Department of Dermatology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan Jaspreet Kaur Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Chandigarh, India Pushpinder Kaur USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Contributors

xxxi

Sabbir Khan Facility for Risk Assessment and Intervention Studies, Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Mohali, Punjab, India Yong Jin Kim Department of Obstetrics and Gynecology, Korea University Medical College, Korea University Guro Hospital, Seoul, South Korea Yoon Young Kim Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea Anna K. Kiss Department of Pharmacognosy and Molecular Basis of Phytotherapy, Faculty of Pharmacy, Medical University of Warsaw, Warsaw, Poland Akane Kitamura Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Jelena Knežević-Vukčević Microbiology, Center for Genotoxicology and Ecogenotoxicology, Faculty of Biology, University of Belgrade, Belgrade, Serbia Norio Kobayashi Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Robert A. Koza Center for Molecular Medicine, Maine Medical Center Research Institute, Scarborough, ME, USA Seung-Yup Ku Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea Takeo Kubota Faculty of Child Studies, Seitoku University, Matsudo, Chiba, Japan Kyriacos Kyriacou Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, The Cyprus School of Molecular Medicine, The Cyprus School of Molecular, Nicosia, Cyprus Zachary M. Laubach Department of Integrative Biology and Program in Ecology, Evolutionary Biology, and Behavior, Michigan State University, East Lansing, MI, USA Minh Le Texas A&M Irma Lerma Rangel College of Pharmacy, Kingsville, TX, USA Daniel Leclerc Departments of Human Genetics and Pediatrics, McGill University, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada Todd Leff Department of Pathology, Wayne State University School of Medicine, Detroit, MI, USA Wayne Leifert CSIRO Health and Biosecurity, Personalised Nutrition and Healthy Ageing, Adelaide, SA, Australia

xxxii

Contributors

Department of Molecular and Cellular Biology, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia Christian Leischner Department of Vegetative and Clinical Physiology, Institute of Physiology, University Hospital Tuebingen, Tuebingen, Germany Alexey A. Leontovich Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA Christine Leroux Herbivore Research Unit -Biomarkers Team, French Institut of Agricultural Research (INRA), St Genès-Champanelle, France Department of Food Science and Technology, University of California Davis, Davis, CA, USA Nancy Lévesque Departments of Human Genetics and Pediatrics, McGill University, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada Chuan Li Department of Immunology, School of Medicine, University of Connecticut Health Center, Farmington, CT, USA Ting Lian Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, Sichuan, China Ives Y. Lim Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore, Singapore Chengqi Lin Institute of Life Sciences, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, China Xinyi Lin Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore, Singapore Birgitte Lindeman Department of Toxicology and Risk, Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway Chengyu Liu Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China Haoyu Liu Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden Wenshe R. Liu Department of Chemistry, Texas A&M University, College Station, TX, USA Ilana Livyatan Department of Genetics, The Hebrew University of Jerusalem, Jerusalem, Israel Pang-Kuo Lo Greenebaum Cancer Center, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA

Contributors

xxxiii

Gaspar Jesus Lopes-Filho Division of Surgical Gastroenterology, Department of Surgery, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil Hsin-Yi Lu Department of Food Science, Nutrition and Health Promotion, Mississippi State University, Mississippi State, MS, USA Mark Lucock School of Environmental and Life Sciences, The University of Newcastle, Ourimbah, NSW, Australia Xiaoping Luo Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Zhuojuan Luo Institute of Life Sciences, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, China Sonia Métayer-Coustard INRA – URA, INRA, Nouzilly, France Qingyi Ma Center for Perinatal Biology, Division of Pharmacology, Department of Basic Sciences, Loma Linda University, School of Medicine, Loma Linda, CA, USA Paolo Emidio Macchia Department of Clinical Medicine and Surgery, University of Naples “Federico II”, Naples, Italy Licínio Manco Department of Life Sciences, Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, Portugal Krishna Prahlad Maremanda Facility for Risk Assessment and Intervention Studies, Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Mohali, Punjab, India J. Alfredo Martinez Department of Nutrition, Food Science and Physiology, and Centre for Nutrition Research, University of Navarra, Pamplona, Spain Department of Nutrition and Dietetics, King’s College London, London, UK Madrid Institute of Advanced Studies (IMDEA Food), Madrid, Spain David J. Martino Gastro and Food Allergy, Murdoch Children’s Research Institute, The University of Melbourne Department of Paediatrics, The Royal Children’s Hospital, Melbourne, VIC, Australia Nina Nayara Ferreira Martins Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belem, Brazil Diego M. Marzese Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA Annalisa Masi Institute of Organic Synthesis and Photoreactivity (ISOF), CNR, Bologna, Italy Petra Matoušková Department of Biochemical Sciences, Charles University, Faculty of Pharmacy, Hradec Králové, Czech Republic

xxxiv

Contributors

Elise Maximin Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France Andre Souza Mecawi Department of Physiological Sciences, Institute of Medical and Biological Sciences, Federal Rural University of Rio de Janeiro, Seropedica, RJ, Brazil Claudia Meconi Research Organization CRF (Cooperativa Ricerca Finalizzata Sc), Tor Vergata University Science Park, Rome, Italy Bodo C. Melnik Department of Dermatology, Environmental Medicine and Health Theory, University of Osnabrück, Osnabrück, Germany Isabel Mendizabal School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country UPV/EHU, Leioa, Spain Eran Meshorer Department of Genetics and the Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel Alessandra Mezzelani National Research Council, Institute of Biomedical Technologies (CNR-ITB), Segrate (MI), Italy Mirjana Mihailović Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia Fermin I. Milagro Department of Nutrition, Food Science and Physiology, and Centre for Nutrition Research, University of Navarra, Pamplona, Spain Dragan Milenkovic School of Medicine, Division of Cardiovascular Medicine, University of California Davis, Davis, CA, USA Department of Human Nutrition, French Institut of Agricultural Research (INRA), St Genès-Champanelle, France Francis Minvielle UMR INRA/AgroParisTech – GABI, Jouy-en-Josas, France Brett M. Mitchell Texas A&M, Department of Medical Physiology, College Station, TX, USA Dragana Mitić-Ćulafić Microbiology, Center for Genotoxicology and Ecogenotoxicology, Faculty of Biology, University of Belgrade, Belgrade, Serbia Naoko Miyauchi Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Lenha Mobuchon Herbivore Research Unit -Biomarkers Team, French Institut of Agricultural Research (INRA), St Genès-Champanelle, France Kazuki Mochizuki Department of Local Produce and Food Sciences, Faculty of Life and Environmental Sciences, University of Yamanashi, Kofu, Yamanashi, Japan

Contributors

xxxv

Myth Tsz-Shun Mok School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Simon G. Møller Department of Biological Sciences, St. John’s University, New York, NY, USA Norwegian Center for Movement Disorders, Stavanger University Hospital, Stavanger, Norway Raquel Moral Multidisciplinary Group for the Study of Breast Cancer, Department of Cell Biology, Physiology and Immunology, Physiology Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain Angélica Morales-Miranda Department of Reproductive Biology, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Mexico City, Mexico Fabiano Cordeiro Moreira Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil Fernando Salvador Moreno Laboratory of Diet, Nutrition and Cancer, Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, Brazil Sumiko Morimoto Department of Reproductive Biology, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Mexico City, Mexico Mireille Morisson GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France Elizangela Rodrigues da Silva Mota Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil Philippe Moulin Claude Bernard University, Lyon, France Department of Endocrinology, Diabetology, Metabolic Diseases and Nutrition, Cardiovascular Hospital Louis Pradel, Bron Cedex, France Meltem Muftuoglu Department of Molecular Biology and Genetics, Acibadem University, Istanbul, Turkey Begoña Muguerza Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Eiji Munetsuna Department of Biochemistry, Fujita Health University School of Medicine, Toyoake, Aichi, Japan Daniele Musiani Department of Experimental Oncology, European Institute of Oncology, Milan, Italy Vivek Nagaraja Division of Rheumatology, Department of Internal Medicine, University of Toledo Medical Center, Toledo, OH, USA

xxxvi

Contributors

Varun Sasidharan Nair Department of Pathology, Hallym University, College of Medicine, Chuncheon, Gangwon-Do, Republic of Korea Kazuhiko Nakabayashi Division of Developmental Genomics, Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, Japan Chau Nguyen Texas A&M Irma Lerma Rangel College of Pharmacy, Kingsville, TX, USA Luciano Nicosia Department of Experimental Oncology, European Institute of Oncology, Milan, Italy Biljana Nikolić Microbiology, Center for Genotoxicology and Ecogenotoxicology, Faculty of Biology, University of Belgrade, Belgrade, Serbia Roberta Noberini Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia, Milan, Italy Clévio Nóbrega Department of Biomedical Sciences and Medicine, Center for Biomedical Research (CBMR), University of Algarve, Faro, Portugal Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal Rita Nocerino Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Eva Nüsken Department of Pediatrics, Pediatric Nephrology, University of Cologne, Cologne, Germany Kai-Dietrich Nüsken Department of Pediatrics, Pediatric Nephrology, University of Cologne, Cologne, Germany Kwon Ik Oh Department of Pathology, Hallym University, College of Medicine, Chuncheon, Gangwon-Do, Republic of Korea Koji Ohashi Department of Clinical Biochemistry, Fujita Health University School of Health Sciences, Toyoake, Aichi, Japan Hiroaki Okae Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Daniela Oliveira Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Department of Biology, University of Minho, Braga, Portugal Rui Oliveira Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Department of Biology, University of Minho, Braga, Portugal Centre of Biological Engineering (CEB), Department of Biology, University of Minho, Braga, Portugal

Contributors

xxxvii

Kelly Cristina da Silva Oliveira Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belem, Pará, Brazil Javier I. J. Orozco Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA Ana Laura Ortega-Márquez Department of Reproductive Biology, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Mexico City, Mexico Richa Pant Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India Lorella Paparo Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy Sonal Patel Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India Vinood B. Patel School of Life Sciences, University of Westminster, London, UK Rakesh Pathak Institute of Molecular Genetics (IGMM), Centre National de Recherche Scientifique (CNRS), UMR-5535, University of Montpellier, Montpellier, France Ketan S. Patil Department of Biological Sciences, St. John’s University, New York, NY, USA Wei Perng Department of Nutritional Sciences, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA Meirav Pevsner-Fischer Department of Immunology, Weizmann Institute of Science, Rehovot, Israel Luciano Pirola INSERM U1060, Oullins, France Frédérique Pitel GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France Igor P. Pogribny Division of Biochemical Toxicology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA Goran Poznanović Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia Victor R. Preedy Diabetes and Nutritional Sciences Research Division, Faculty of Life Science and Medicine, King’s College London, London, UK Fabienne Le Provost Génétique Animale et Biologie Intégrative, French Institut of Agricultural Research (INRA), Jouy-en-Josas, France

xxxviii

Contributors

Rajkumar Rajendram Department of Medicine, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia Diabetes and Nutritional Sciences Research Division, Faculty of Life Science and Medicine, King’s College London, London, UK Omar Ramos-Lopez Department of Nutrition, Food Science and Physiology, and Centre for Nutrition Research, University of Navarra, Pamplona, Spain Department of Molecular Biology in Medicine, Civil Hospital of Guadalajara “Fray Antonio Alcalde”, Guadalajara, Jalisco, Mexico Richa Rathod Department of Nutritional Medicine, Interactive Research School for Health Affairs, Bharati Vidyapeeth Deemed University, Pune, India Mirunalini Ravichandran Institute of Biochemistry, University of Stuttgart, Stuttgart, Germany E. Albert Reece Department of Obstetrics, Gynecology and Reproductive Sciences, University of Maryland School of Medicine, Baltimore, MD, USA Caroline Relton MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK Jose Ignacio Riezu-Boj Department of Nutrition, Food Science and Physiology, and Centre for Nutrition Research, University of Navarra, Pamplona, Spain Alberto PG Romagnolo University of Arizona Cancer Center, College of Medicine, University of Arizona, Tucson, AZ, USA Donato F. Romagnolo Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA University of Arizona Cancer Center, College of Medicine, University of Arizona, Tucson, AZ, USA Jaume Roquer Head of the Department of Neurology, Hospital del Mar; Neurovascular Research Group, IMIM (Institut Hospital del Mar d’Investigacions Mèdiques), Barcelona, Spain Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain Cheryl S. Rosenfeld Department of Biomedical Sciences, Bond Life Sciences Center Investigator, Thompson Center for Autism and Neurobehavioral Disorders, University of Missouri, Columbia, MO, USA Rima Rozen Departments of Human Genetics and Pediatrics, McGill University, The Research Institute of the McGill University Health Centre, Montreal, QC, Canada

Contributors

xxxix

Lewis P. Rubin Departments of Pediatrics and Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA Hitaji Sanford Texas A&M Irma Lerma Rangel College of Pharmacy, Kingsville, TX, USA Michael P. Sarras Jr. Department of Cell Biology and Anatomy, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA E. R. Sauter Hartford HealthCare Cancer Institute, Hartford, CT, USA Luís Fernando Schütz Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, College Station, TX, USA Tanya L. Schwab Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA Ornella I. Selmin Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA University of Arizona Cancer Center, College of Medicine, University of Arizona, Tucson, AZ, USA Parames C. Sil Division of Molecular Medicine, Bose Institute, Kolkata, India Minati Singh Department of Pediatrics, The University of Iowa, Iowa City, IA, USA Marilia Arruda Cardoso Smith Division of Genetics, Department of Morphology and Genetics, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil Monica Soldi Department of Experimental Oncology, European Institute of Oncology, Milan, Italy Berna Somuncu Department of Molecular Biology and Genetics, Acibadem University, Istanbul, Turkey Carolina Soriano-Tárraga Neurovascular Research Group, IMIM (Institut Hospital del Mar d’Investigacions Mèdiques), Barcelona, Spain Valeria Spadotto Department of Experimental Oncology, European Institute of Oncology, Milan, Italy Barbara Stefanska Land and Food Systems, Food, Nutrition and Health, University of British Columbia, Vancouver, BC, Canada Silvia Stringhini Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital, Lausanne, Switzerland

xl

Contributors

Elena Sturchio Department of Technological Innovation and Safety of Plants, Product and Anthropic Settlements (DIT), Italian Workers’ Compensation Authority (INAIL), Rome, Italy Manuel Suárez Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili (URV), Tarragona, Spain Souta Takahashi Department of Informative Genetics, Tohoku University Graduate School of Medicine, Aoba-ku, Sendai, Japan Junji Takaya Department of Pediatrics, Kawachi General Hospital, HigashiOsaka, Osaka, Japan Department of Pediatrics, Kansai Medical University, Moriguchi, Osaka, Japan Qihua Tan Epidemiology and Biostatistics, Department of Public Health, Unit of Human Genetics, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark Paniz Tavakoli CSIRO Health and Biosecurity, Personalised Nutrition and Healthy Ageing, Adelaide, SA, Australia Department of Molecular and Cellular Biology, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia Tincy Thomas Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, College Station, TX, USA Anja Tolić Institute for Biological Research, University of Belgrade, Belgrade, Serbia Eckardt Treuter Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden Paola Ungaro Institute for Experimental Endocrinology and Oncology, “G. Salvatore”(IEOS), National Research Council (CNR), Naples, Italy Aleksandra Uskoković Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia Karin van Veldhoven Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK Ernesto Vargas-Mendez Laboratory of Diet, Nutrition and Cancer, Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, Brazil Anthony T. Vella Department of Immunology, School of Medicine, University of Connecticut Health Center, Farmington, CT, USA Nicolas Venteclef Sorbonne Universités, Université Pierre et Marie-Curie, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR_S 1138 Cordeliers Research, Paris, France

Contributors

xli

Sascha Venturelli Department of Vegetative and Clinical Physiology, Institute of Physiology, University Hospital Tuebingen, Tuebingen, Germany Martin Veysey School of Medicine and Public Health, The University of Newcastle, Gosford Hospital, Gosford, NSW, Australia Mark H. Vickers Liggins Institute, University of Auckland, Auckland, New Zealand Melita Vidaković Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia Paolo Vineis Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK Branka Vuković-Gačić Microbiology, Center for Genotoxicology and Ecogenotoxicology, Faculty of Biology, University of Belgrade, Belgrade, Serbia Danyang Wan College of Life Sciences, Wuhan University, Wuhan, China Zhipeng A. Wang Department of Chemistry, Texas A&M University, College Station, TX, USA Gaofeng Wang Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA Department of Human Genetics, Dr. Nasser Ibrahim Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA Jian Wang Department of Pathology, Wayne State University School of Medicine, Detroit, MI, USA Cardiovascular Research Institute, Wayne State University School of Medicine, Detroit, MI, USA Inga Wessels Institute of Immunology, RWTH Aachen University Hospital, Aachen, Germany Oliwia Witczak Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Oslo, Norway Thomas E. Witzig Division of Hematology, Mayo Clinic, College of Medicine, Rochester, MN, USA Benjamin Wolfson Greenebaum Cancer Center, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA Vincent Wai-Sun Wong Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

xlii

Contributors

Xiaosheng Wu Division of Hematology, Mayo Clinic, College of Medicine, Rochester, MN, USA Xinhua Xiao Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China Xuemei Xie Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China Hiroya Yamada Department of Hygiene, Fujita health University School of Medicine, Toyoake, Aichi, Japan Mingyao Yang Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, Sichuan, China Peixin Yang Department of Obstetrics, Gynecology and Reproductive Sciences, University of Maryland School of Medicine, Baltimore, MD, USA Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA Zoe Yates School of Biomedical Sciences and Pharmacy, The University of Newcastle, Ourimbah, NSW, Australia Soojin V. Yi School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA Wei Ying Department of Medicine, Division of Endocrinology and Metabolism, University of California, San Diego, La Jolla, CA, USA Yangmian Yuan College of Life Sciences, Wuhan University, Wuhan, China Miriam Zanellato Department of Technological Innovation and Safety of Plants, Product and Anthropic Settlements (DIT), Italian Workers’ Compensation Authority (INAIL), Rome, Italy Diana Zapata Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, College Station, TX, USA Helmut Zarbl Department of Environmental and Occupational Health, School of Public Health Environmental and Occupational Health Sciences Institute, Rutgers, The State University of New Jersey, Piscataway, NJ, USA Tatiana Zerjal UMR INRA/AgroParisTech – GABI, Jouy-en-Josas, France Lubo Zhang Center for Perinatal Biology, Division of Pharmacology, Department of Basic Sciences, Loma Linda University, School of Medicine, Loma Linda, CA, USA Xiaofei Zhang Fudan University Shanghai Cancer Center, Shanghai, China

Contributors

xliii

Ruqian Zhao Key Laboratory of Animal Physiology and Biochemistry, Ministry of Agriculture, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China Jia Zheng Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China Ling Zheng College of Life Sciences, Wuhan University, Wuhan, China Beiyan Zhou Department of Immunology, School of Medicine, University of Connecticut Health Center, Farmington, CT, USA Qun Zhou Greenebaum Cancer Center, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA Jianhong Zhu Department of Preventive Medicine, Department of Geriatrics and Neurology at the Second Affiliated Hospital, and Key Laboratory of Watershed Science and Health of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China Niv Zmora Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

Part I Introductory Material and Foundations

1

Environmental Effects on Genomic Imprinting in Development and Disease Rakesh Pathak and Robert Feil

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roles of Genomic Imprinting in Development, Growth and Metabolism . . . . . . . . . . . . . . . . . . . . . . Imprinting (De)regulation, the INS/IGF Signaling Pathway and Diabetes . . . . . . . . . . . . . . . . . . . . . Imprinting Disorders and Effects of Assisted Reproductive Technology . . . . . . . . . . . . . . . . . . . . . . . Nutrition and Toxic Components Influence Genomic Imprinting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Broadening Outlook on Imprinting Deregulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 6 12 13 15 17 19 19 20 20

Abstract

Genomic imprinting mediates the parent-of-origin-specific, mono-allelic expression of many protein-coding genes and noncoding RNAs. This paradigm for epigenetic gene regulation plays diverse roles in mammalian development, growth and behavior. Mechanistically, it involves parentally inherited DNA methylation marks that control clusters of imprinted genes. Perturbation of these epigenetic imprints affects embryonic and postnatal development and leads to complex diseases in humans, including different types of diabetes. This chapter discusses imprinted genes, with emphasis on those that control metabolism and cellular proliferation, several of which encode proteins of the insulin-like growth factor/insulin signaling pathway. Nutrition, chemical pollutants, and other environmental cues can readily perturb DNA methylation imprints, not only during development, but sometimes even in adults. Such epigenetic alterations R. Pathak · R. Feil (*) Institute of Molecular Genetics (IGMM), Centre National de Recherche Scientifique (CNRS), UMR-5535, University of Montpellier, Montpellier, France e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_92

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(“epimutations”) may affect imprinted gene expression and, hence, can have deleterious effects on phenotype. In the future, clinical and environmental imprinting studies will gain from taking a broader approach that considers not only the imprinted gene loci themselves, but also similarly controlled loci located elsewhere in the genome. Keywords

Epigenetics · Environment · Genomic imprinting · DNA methylation · Growth · Metabolism · IGF/Insulin pathway · Endocrine disruptor List of Abbreviations

ART BPA BWS ICR IGF INS IUGR ncRNA SRS TNDM

Assisted Reproductive Technology Bisphenol A Beckwith-Wiedemann Syndrome Imprinting control region Insulin-like growth factor Insulin Intra-uterine growth restriction Noncoding RNA Silver Russell Syndrome Transient neonatal diabetes mellitus

Introduction Epigenetic mechanisms contribute to the establishment and maintenance of stable patterns of gene expression during development and throughout postnatal life. DNA methylation at cytosine residues is the best studied epigenetic modifications in mammals. It plays essential roles in cells and tissues. These include stable repression of endogenous retroviruses and the tissue-specific silencing of developmental genes, particularly germ line genes, which become silenced in the embryo. DNA methylation also contributes to X-chromosome inactivation. This is a gene dosage mechanism in female embryos that leads to the repression of most genes on one of the two X chromosomes. The current chapter focuses on another gene dosage mechanism for which cytosine methylation is essential, namely genomic imprinting (Bartolomei and Ferguson-Smith 2011). It introduces the roles of imprinted gene expression in mammalian development and its perturbation in disease and makes the link with the insulin/IGF signaling pathway, metabolism and growth control. The chapter also discusses how diet and environmental cues may perturb the epigenetic regulation of imprinted genes and that this can have long-lasting phenotypic consequences (Feil and Fraga 2012). Mammalian genomic imprinting evolved coincident with the emergence of viviparity and the growing importance of placentation and the evolution of defense mechanisms against transposable elements. The oldest imprinted genes arose about 170 million years ago, during the Jurassic period. Today, more than 100 protein-

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Environmental Effects on Genomic Imprinting in Development and Disease

5

coding genes and hundreds of noncoding RNAs (ncRNAs) are known to be imprinted in mice and humans (Peters 2014). To understand what the mono-allelic expression of imprinted genes entails, it is important to remember that imprinting was discovered only 30 years ago, when two different laboratories found that both the maternal and the paternal genome are strictly required for embryonic development (McGrath and Solter 1984; Surani et al. 1984). Particularly, parthenogenetic (with two maternal genomes and lack of the paternal genome) and androgenetic embryos (with two paternal genomes) were found to not develop to term and had gross developmental abnormalities. About half of all the known imprinted genes are expressed from the maternal genome only, whereas the other half are expressed from the paternally inherited genome only. In mono-parental embryos, consequently, individual imprinted genes are either not expressed at all or are overexpressed, and this explains the gross developmental effects observed in the mono-parental embryos. Also maternal or paternal uniparental disomy of individual chromosomes is associated with developmental phenotypes (Cattanach and Kirk 1985). Imprinted genes are organized in evolutionarily conserved clusters. At each “imprinting cluster,” there is an essential regulatory region that acquires allelic DNA methylation in one of the two parental germlines (Peters 2014; Sanli and Feil 2015). The mono-allelic methylation marks at these “imprinting control regions” (ICRs) are maintained throughout development and mediate the monoallelic imprinted expression of genes at the respective domains (Fig. 1). Most ICRs acquire their allelic DNA methylation in the oocyte; at only some it is acquired during spermatogenesis. What makes ICRs unique is that, exceptionally, they maintain their differential methylation throughout pre- and postimplantation development (Kelsey and Feil 2013). This contrasts with the bulk of the genome, which undergoes global waves of demethylation and de novo methylation during preimplantation development and gastrulation. How precisely this remarkable epigenetic stability is controlled is not well understood. DNA methylation imprints at ICRs are consistently associated with repressive histone modifications, however, including histone H3 lysine-9 trimethylation (H3K9me3) and H4 lysine-20 trimethylation (H4K20me3), and show allelic binding of heterochromatin protein 1 (HP1g) as well (Kelsey and Feil 2013). Furthermore, the histones H3 associated with DNA methylation imprints are not canonical histones, but a variant histone called H3.3 (Voon et al. 2015). This variant histone and regulatory protein complexes are recruited onto the chromatin in an allele-specific manner and collectively contribute to the stability of the allelic methylation state of ICRs in the embryo. The epigenetic maintenance of ICRs is essential to ensure faithful mono-allelic expression of imprinted genes. In case this maintenance process is perturbed, this causes pathological phenotypes. In humans, losses or gains of DNA methylation at ICRs are causally involved in a growing number of congenital “imprinting disorders” (Eggermann et al. 2015, Hirasawa and Feil 2010). Given their essential roles in development, growth metabolism and behavior there has been a tremendous interest in imprinted gene domains. Of particular interest have been the well-conserved imprinting clusters that control fetal- and postnatal growth, and the imprinted

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Embryo

Zygote

Adult

Maintenance

Maintenance

Embryonic Development

Postnatal Development

Sperm Oocyte

Paternal genome

PGC (female) PGC (male)

Maternal genome DNA methylation Imprint

Fig. 1 Germline establishment and somatic maintenance of parental imprints. Shown is the establishment of paternal DNA methylation imprints and maternal DNA methylation imprints during spermatogenesis and oogenesis, respectively. After fertilization of the oocyte, these parental DNA methylation imprints are maintained in all somatic cells, during embryonic development and after birth. During early embryonic development the imprints are relatively susceptible to environmental and stochastic methylation changes, which may affect imprinted gene expression and phenotype.

genes that are part of the insulin-like growth factor/Insulin (IGF/INS) signaling pathway (Delaval et al. 2006; Moore et al. 2015; Peters 2014). Though ICRs are epigenetically stable, even slight changes in DNA methylation can be readily detected in molecular assays. Once aberrant methylation changes have occurred, they persist in the embryo and this perturbs imprinted gene expression. These unique characteristics and the causal link with diseases including diabetes and intrauterine growth disorders have made genomic imprinting an excellent paradigm to explore the epigenetic effects of the environment and nutrition (Feil and Fraga 2012). This constitutes the overall theme of the chapter, which focuses on imprinted gene loci that control growth and metabolism, their involvement in specific human diseases and the effects of nutrition and toxic components (Tables 1 and 2).

Roles of Genomic Imprinting in Development, Growth and Metabolism Ever since the discovery in mice of the first imprinted genes – Igf2r, Igf2, and H19 (Barlow et al. 1991; Bartolomei et al. 1991; DeChiara et al. 1991) – a main focus of research has been on epigenetic regulation and how at individual domains, the ICR brings about mono-allelic expression in cis (Abramowitz and Bartolomei 2012).

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Table 1 Human imprinting disorders

Imprinting disorder (ID) BeckwithWiedemann Syndrome (BWS) Pseudohypo parathyroidism Type-1B (PHP1B)

Silver-Russell Syndrome (SRS)

Transient neonatal diabetes (TNDM) Kagami-Ogata Syndrome (KOS14)

Temple Syndrome (TS14)

Angelman Syndrome (AS) Prader-Willi Syndrome (PWS)

Chromosome/ imprinted domain

Epigenetic/genetic alteration

Chr. 11p15: IGF2-H19: ICR

Hypermethylation

KCNQ1: ICR

Hypomethylation

Chr. 20q13:

Paternal UPD(20)

GNAS

Aberrant methylation

Chr. 11p15: IGF2-H19: ICR IGF2-H19: IGF2

Hypomethylation Point mutations

KCNQ1: CDKN1C

Point mutations

Chr. 6q24:

Paternal UPD(6) paternal duplication

PLAGL1: alt-TSS Chr. 14q32:

Methylation defect Paternal UPD(14)

DLK1-DIO3: ICR

Maternal deletion, hypermethylation Maternal deletion

DLK1-DIO3: MEG3 Chr. 14q32:

Maternal UPD(14) Paternal deletion

DLK1-DIO3: ICR DLK1-DIO3: MEG3

Aberrant methylation

Chr. 15q11-q13:

Paternal UPD(15)

SNRPN: UBE3A

Maternal deletion Point mutations

Chr. 15q11-q13:

Maternal UPD(15) Paternal deletions Loss of methylation Loss of methylation

SNRPN: ICR SNRPN: NECDIN

Clinical features

Pre- and postnatal overgrowth, organomegaly, macroglossia, omphalocele, neonatal hypoglycemia, hemihypertrophy, increased tumor risk Resistance to PTH and other hormones; Albright’s hereditary osteodystrophy, subcutaneous ossifications, feeding behavior anomalies, abnormal growth IUGR, postnatal reduced growth, macrocephaly at birth, hemihypotrophy, prominent forehead, triangular face, feeding difficulties IUGR, transient neonatal diabetes, hyperglycemia without ketoacidosis, macroglossia, omphalocele IUGR, polyhydramnios, abdominal and thoracic wall defects, bellshaped thorax, coat-hanger ribs

IUGR, reduced postnatal growth, neonatal hypotonia, feeding difficulties in infancy, truncal obesity, scoliosis, precocious puberty, small feet and hands Feeding problems, developmental delays, pronounced speech impairment, hyperactivity, severe movement and balance disorders, bouts of laughter Obesity, short stature, decreased muscle tone, hypogonadism, decreased mental capacity

A lot is known also about the biological functions of imprinted gene expression, particularly in development, growth, and metabolism (Peters 2014). For instance, the insulin-like growth factor-2 (Igf2) gene on mouse chromosome 7/human chromosome 11p15 (DeChiara et al. 1991) is a major regulator of fetal growth. It is expressed from the paternal genome in mesodermal and endodermal tissues. Igf2 is flanked by the insulin gene (Ins2), which is also imprinted and expressed from the paternal allele only in the yolk sac (Deltour et al. 1995; Duvillie et al. 1998;

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Table 2 Nutritional and toxicological effects on imprinted genes

Species

Developmental window

Epigenetic alteration/ expression change

Peri-conception Altered methylation in (first stages of adult offspring development) (IGF2, INS, MEG3, GNAS) Early gestation Altered imprinted gene expression

Tissues/ cell types Phenotypic affected observations

References

Blood (adult)

Correlates with metabolic and mental phenotypes in adults

Tobi et al. (2014)

Various tissues

Reduced fetal growth, Type-2 diabetes in offspring

Morita et al. (2014), SferruzziPerri et al. (2013) Ingrosso et al. (2003)

Gestational starvation

Human

High-fat diet

Rat, mouse

Increased “methyl donor” (folate-rich diet) Alcohol consumption in pregnant females, in adult males

Human

Adult life

Increased DNA Blood methylation, including at IGF2-H19

Human, mouse

Embryonic development, male gametogenesis

Reduced DNA methylation at IGF2-H19 locus and ICRs

Lead/ cadmium Pollution exposure during pregnancy Bisphenol-A Experimental exposure of pregnant females

Human

Embryonic development

Altered methylation at ICRs in offspring

Mouse

Periconceptual, embryonic dev.

Vinclozolin Exposure of pregnant females

Mouse Rat

Embryonic development

Methoxychlor Mouse Exposure of pregnant females

Embryonic development

Expression changes and loss of methylation (Snrpn, Kcnq1ot1, Cdkn1c) Reduced methylation at Igf2-H19, increased methylation at maternal ICRs Reduced methylation at Igf2-H19, gain of methylation at maternal ICRs Gains and losses of DNA methylation at ICRs

Assisted reproduction technologies

Mouse, farm animals, human

Periconception, embryonic development

Folate treatment given to hyperhomocysteinaemia patients

Masemola et al. (2015), Ouko et al. (2009), Stouder et al. (2011) Park et al. Correlates with educed fetal growth (2017) and childhood obesity

Male germ cells, blood, frontal cortex Embryo, placenta

Fetal alcohol syndrome in off spring, reduced fertility in alcoholic males

Embryo, placenta

Expression and methylation changes can be inherited to next generation

Susiarjo et al. (2013, 2015)

Germ cells of male offspring

Expression and methylation changes can be inherited to next generation

Stouder and PaoloniGiacobino (2010)

– Germ cells of male offspring

Stouder and PaoloniGiacobino (2011)

Tissues, blood

Dean et al. (1998), Dias and Maher (2013), Feil and Fraga (2012)

In animals, diverse phenotypic abnormalities. In humans, small increase in occurrence of IDs

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Insulin IGF2

IGF1 IGF1 Insulin

IGF1R

IR

GRB10

GRB2

IRS1/2

AKT

Cell Survival

Metabolism

Growth

Fig. 2 The IGF/INS signalling pathway is controlled by genomic imprinting. The IGF/INS pathway activates intracellular signalling cascades that control the proliferation and survival of cells, metabolism and growth. Several proteins that are encoded by imprinted genes (blue shapes) play key roles in enhancing, or repressing, the IGF/INS trans-membrane signaling. Whereas IGF2 and Insulin have positive effects on the pathway, and hence on metabolism and cellular proliferation, GRB10 and IGF2R have negative effects. This is a simplified figure which shows only some of the key proteins of the downstream intracellular cascades.

Moore et al. 2001). In the embryo proper and in adult pancreas, Ins2 is expressed from both alleles. Igf2 and Ins2 are part of the IGF/INS signaling pathway (Fig. 2) and are part of a small imprinted domain. This “Igf2-H19 domain” also comprises the long non-coding RNA (lncRNA) gene H19, which is expressed from the maternal allele and has a negative effect on fetal growth (Gabory et al. 2010). The Igf2-H19 domain is structurally conserved in humans, where its perturbation causes two different disease syndromes. Its intergenic ICR is methylated on the paternal copy (Fig. 3). On the maternal chromosome, this ICR acquires a specialized chromatin structure through binding of a structural protein called CTCF and that of cohesin complexes. This differential chromatin structuration at the ICR mediates the allelic expression of Igf2, Ins2, and H19 (Abramowitz and Bartolomei 2012). Another locus that controls fetal growth is the Kcnq1 domain, located next to the Igf2-H19 domain. This 1-Megabase domain comprises several genes that are imprinted in the embryo, placenta, and after birth. These include Cdkn1c (also called p57Kip2), a negative regulator of the cell cycle, expressed from the maternal

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a KCNQ1 domain

WT

d IGF2-H19 domain

ICR

CDKN1C

ICR

IGF2

KCNQ1 ICR

WT ZAC1/PLAGL1 domain

M H19

ICR

ZAC1/PLAGL1

P

M

P

KCNQ1OT1 ncRNA

b

ICR CDKN1C

KCNQ1 ICR

e

SRS

BWS M

ICR

IGF2

H19 ICR

P

c

TNDM

M ZAC1/PLAGL1

P

M P

BWS ICR IGF2

H19 ICR

M

P

Fig. 3 Epigenetic alterations cause imprinting disorders (IDs). (a) The KCNQ1 and IGF2-H19 imprinted gene domains both control cellular proliferation, metabolism and growth. The KCNQ1 domain has an intragenic ICR that is marked by maternally-inherited DNA methylation (lollipops). On the paternal chromosome, this ICR produces a long ncRNA that mediates gene repression in cis. The flanking IGF2-H19 domain has an intergenic ICR that is marked by paternal (P) DNA methylation. B, Embryonic loss of this maternal (M ) imprint at the ICR of the KCNQ1 domain in the embryo induces BWS. Loss of methylation at the ICR of the IGF2-H19 domain leads to SRS. (c) Conversely, aberrant gain of biallelic methylation at this ICR gives BWS. (d) The ZAC1/ PLAGL1 imprinted locus is controlled by maternal DNA methylation, which induces paternal allele-specific expression of ZAC1. (e) Loss of the maternal methylation imprint leads to biallelic ZAC1 expression, and an increased dosage of this transcription factor, which causes transient neonatal diabetes mellitus (TNDM).

chromosome only. Loss of Cdkn1c expression enhances fetal growth, and its overexpression leads to growth restriction. The domain comprises several placenta-specific imprinted genes as well. These include the transcription factor gene Ascl2, which controls spongiotrophoblast development (Guillemot et al. 1995), a process which is influenced by the imprinted Cdkn1c gene as well (Zhang et al. 1998). The domain’s ICR is intragenic and it expresses a long ncRNA, called Kcnq1ot1, from its unmethylated paternal copy (Fig. 3). In the preimplantation embryo, the allelic expression of the long ncRNA induces gene repression on the paternal chromosome, through a process that involves repressive histone methylation (Umlauf et al. 2004). Grb10 on mouse chromosome 11/human chromosome 7 is another imprinted growth regulator. In the mouse, Grb10 is expressed from the maternal chromosome

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in placenta and in mesodermal and endodermal tissues. It encodes a growth-factor binding protein (GRB10) that antagonizes the insulin/IGF pathway, by binding to the cytoplasmic phase of the insulin (IR) and IGF1-receptor (IGF1R) (Fig. 2). Grb10 knockout mice show severe overgrowth, whereas a double dose of expression due to deletion of the domain’s ICR reduces fetal growth (Charalambous et al. 2003; Shiura et al. 2009). The maternal Grb10 expression in the placenta is conserved in humans, which emphasizes the evolutionary importance of Grb10 imprinting in extra-embryonic lineages (Monk et al. 2009). Grb10 expression in the brain influences social behavior. In brain, an alternative transcript is expressed from the paternal chromosome only, both in mice and in humans (Monk et al. 2009; Sanz et al. 2008). The comparative studies on Grb10 emphasize that imprinted genes can have different functions depending on where and from which promoter they are expressed and that their mono-allelic expression is not always from the same parental chromosome in different tissues. Igf2r (also called the mannose-6-phosphate receptor (Barlow et al. 1991)) on mouse chromosome 17 encodes a nonfunctional, antagonistic receptor of IGF2. Consequently, its expression has a negative effect on IGF/INS signaling (Fig. 2). This imprinted gene is expressed from the maternal chromosome only and mutations lead to severe overgrowth and embryonic lethality. This phenotype can be “rescued” by either loss of Igf2 expression or loss of the nonimprinted IGF1-receptor (Ludwig et al. 1996; Wutz et al. 2001). The extensive research on the imprinted Igf2, Ins2, Igf2r, and Grb10 genes has established that sets of imprinted genes control common pathways of growth and metabolism. Targeting studies in the mouse have revealed other roles for imprinted genes as well. Observed phenotypic effects have often been tissue-specific and imprinting is particularly important in placental development and function and in neurogenesis and behavior (Peters 2014). Worth mentioning is Zac1 (also called Plagl1), a transcription factor gene which is paternally expressed in humans and mice. Its genetic deletion was shown to lead to reduced fetal growth and perinatal death. Interestingly, ZAC1 controls several other imprinted genes of a network that comprises nonimprinted genes as well (Al Adhami et al. 2015; Arima et al. 2005; Varrault et al. 2006). ZAC’s imprinted gene targets include Igf2 and H19, Cdkn1c, and also the lncRNA Kcnq1-ot1 expressed by the ICR of the Kcnq1 imprinted domain. Recent studies have unraveled the details of this trans-regulation. For instance, ZAC1 binds to an enhancer that is shared by Igf2 and H19 (Iglesias-Platas et al. 2014). Other links between growth-related imprinted genes are known as well. Loss of H19 expression at the Igf2-H19 domain leads to upregulation of six other imprinted genes. Transgenic H19 overexpression corrects this phenotype, which indicates that the H19 ncRNA itself is involved. Possibly, the trans regulation by H19 ncRNA occurs through interaction with a chromatin repressor protein called MBD1 (Monnier et al. 2013). These and other examples show that imprinted genes evolved common biological functions and influence each-other within intricate networks of imprinted genes.

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Imprinting (De)regulation, the INS/IGF Signaling Pathway and Diabetes Four imprinted genes directly control the IGF/INS pathway (Fig. 2). Multiple others affect cellular proliferation and glucose metabolism in other ways (Peters 2014). In the mouse, Igf2 is expressed from the paternal chromosome, both in the embryo and placenta, whereas Igf2r is expressed from the maternal genome only in the same tissues. Ins2 is imprinted during uterine development and shows, like Igf2, paternal expression in the yolk sac. The receptor binding protein Grb10 is expressed from the maternal genome only. In humans, the situation is similar to that in rodents, but for IGF2R. In humans, this gene is imprinted in a polymorphic manner: a minority of people shows maternal IGF2R expression, the majority express both parental alleles. INS, however, is consistently imprinted in the yolk sac, as it is in mice (Monk et al. 2006; Moore et al. 2001). Glucose metabolism is influenced by other imprinted genes as well. Studies on transient neonatal diabetes mellitus (TNDM) revealed a key role of ZAC1. TNDM is a transitory form of diabetes in newborns, with hyperglycemia and low insulin levels during the first year of life. Unlike in type-I diabetes, there is no evidence for autoimmunity against the pancreatic Β cells. In more than half of the patients, there is loss of the methylation imprint at the ICR of the ZAC1 locus. This leads to biallelic ZAC1 expression (Fig. 3). The increased gene dosage impairs glucose-stimulated insulin secretion in B cells at fetal and postnatal stages, when ZAC1 is expressed most highly. Particularly, ZAC1 is thought to induce a pituitary adenylate cyclase-activating polypeptide, which is an activator of glucose-stimulated insulin secretion. In adult pancreatic Β cells, ZAC1 expression is much lower and functionally less important. In the mouse, loss of Zac1 expression gives rise to altered expression of multiple other imprinted genes, including that of Igf2 and H19 (Al Adhami et al. 2015). In the placenta, interestingly, the H19 long ncRNA produces a micro-RNA (miR-675) that reduces the expression of Igf1r. These different findings functionally link the essential ZAC1 transcription factor to Igf2, H19, and Igf1r expression and to glucose metabolism and cellular proliferation. A “variable number of tandem repeat” polymorphism upstream of the INS gene in humans is genetically linked to the clinical progression of type-1 diabetes, which involves the insulin-producing pancreatic B cells (Zhang et al. 2015). Another example is the Krüppel family transcription factor KLF14. This imprinted gene is maternally expressed in adipose tissues (Parker-Katiraee et al. 2007) and is genetically linked to an increased risk of type-1 diabetes and metabolic phenotypes (Small et al. 2011; Voight et al. 2010). Another developmentally essential imprinted locus, the DLK1-DIO3 domain on human chromosome 14q32.2, is genetically linked to type-1 diabetes as well (Wallace et al. 2010). The maternally expressed KCNQ1 gene, which encodes a potassium channel, is important for B cells as well. In humans, single nucleotide polymorphisms (SNPs) at KCNQ1 are linked to type-2 diabetes following maternal inheritance only (Voight et al. 2010).

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Loss of expression of the imprinted Grb10 gene in the mouse is associated with increased glucose tolerance and increased inulin sensitivity. Increased Grb10 expression, on the other hand, gives rise to insulin resistance and impaired glucose tolerance. At the guanine nucleotide binding protein a-stimulating (Gnas) imprinted locus on mouse chromosome-2, mutations that affect its maternal expression bring about insulin resistance, hyperinsulinemia, and hyper-glycaemia associated with obesity (Peters 2014). Combined, the different studies show that many imprinted genes are involved in different types of diabetes, but often the underlying mechanisms remain to be discovered.

Imprinting Disorders and Effects of Assisted Reproductive Technology A growing number of diseases are known to be caused by imprinted genes. Several “imprinting disorders” (IDs) are characterized by intra-uterine and postnatal growth defects and are linked directly or indirectly to the INS/IGF signaling pathway (Fig. 3). Already introduced above, TNDM (OMIM 601410) is a rare form of diabetes (incidence 70%. More serious, but less common, is the dcSSc subset. Patients with dcSSc may present with an abrupt onset of Raynaud’s phenomenon, puffy hands, with rapid progression to extensive skin fibrosis in the extremities and trunk with early appearance of internal organ involvement. Anti-topoisomerase-1 (Scl-70) antibodies occur predominately in these patients. This subset is characterized by rapid disability and organ dysfunction and poor prognosis with a 10-year survival of 40 % (Korn 2003). The risk factors for worse prognosis include male gender, African American ethnicity, onset at an older age, presence of interstitial lung disease or pulmonary hypertension, and rapid progression of skin involvement. Classification. The most widely accepted classification criterion in the past was the preliminary American College of Rheumatology (ACR) Criteria published in 1980 (1980). One major skin thickening proximal to the metacarpal phalangeal joints (MCPs) or two minor (sclerodactyly, digital tip ulcers, and pulmonary fibrosis) criteria were required for classification. In 2013, a joint committee of the ACR and European League Against Rheumatism (EULAR) published a new set of classification criteria that aimed to incorporate new advances in the diagnosis of SSc, to capture a wider range of patients, and to have greater clinical relevance (van den Hoogen et al. 2013). Skin thickening proximal to the MCPs alone is sufficient for classification, but if not present, a point system was applied based on seven criteria with a score of 9 sufficient for classification. Validation of the new criteria found it to be superior to the 1980 criteria with sensitivity and specificity of 91% and 92%, respectively. Inclusion of SSc-specific autoantibodies as well as nailfold capillaroscopy clearly resulted in the improved sensitivity and specificity. Pathogenesis. The etiology and pathogenesis of SSc remain largely unknown. The initial event that triggers the disease is believed to be vascular injury that results from exposure to certain chemical or microbial agents or from immunologic insult or

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both. A series of interrelated inflammatory-immune and vascular events follows that result in the emergence of myofibroblast in involved tissues. Fibrosis is assumed to be mediated by profibrotic cytokines such as transforming growth factor-beta (TGF-β) and platelet-derived growth factor (PDGF) that stimulate myofibroblast production and subsequent deposition of extracellular matrix that result in tissue fibrosis (Gabrielli et al. 2009). Genetic influence is suggested by the finding of strong association with the major histocompatibility complex (Zhou et al. 2009). Increased familial incidence is reported, with a relative risk among first-degree relatives of 13 (95% CI, 2.9–48.6; P < 0.001), with incidence rate of 1.6% within families vs 0.026% in the general population (Arnett et al. 2001). However, the study of twin pairs reported an overall concordance rate of disease in only 4.7%, a rate that is the same for both monozygotic and dizygotic pairs, suggesting stronger association with the environment over genetic defects in disease susceptibility (Feghali-Bostwick et al. 2003). Circumstantial evidence has implicated a role for environmental triggers, including silica and organic solvents (reviewed in this chapter). Moreover, an immune response to cancer may trigger the disease in a subset of patients (Shah et al. 2010). At the cellular level, it is now well established that SSc fibroblast (FB) produces excessive amounts of collagen and other components of the extracellular matrix (EMC) in association with reduced levels of matrix metalloproteinases, both in vivo and in vitro, leading to excessive deposition of ECM (Abraham and Varga 2005). Similarly, microvascular endothelial cell (MVEC) dysfunction in SSc is characterized by a shift in endothelial function toward an inflammatory and vasospastic functional profile that is best illustrated by dysregulated vascular tone control and progressive disorganization of vascular architecture leading to vascular obliteration and diminished blood flow to the involved organs (Altorok et al. 2014b). Defective endothelial-dependent relaxation is well described and believed to be related to underexpression of nitric oxide synthase. The basis for the abnormal FB and MVECs phenotype has not been determined, but primary metabolic abnormalities, responses to abnormal environmental signals, and clonal selection have all been hypothesized to play a role. However, the fact that SSc-MVECs and FB cellular abnormalities persist for multiple generations in vitro suggests permanent imprinting of disease phenotype in cellular pathways. In this chapter, we will present data that implicate epigenetic regulation in the emergence and unremitting SSc cellular abnormalities both in vivo and in vitro.

Clinical Manifestations of SSc SSc is a multisystem autoimmune disease. The skin and peripheral vasculature are the most common organs involved in SSc; however, other organ systems are frequently involved as shown in Table 1. Constitutional. Fatigue, arthralgia, weakness, and weight loss are common manifestations in SSc, particularly in the diffuse subset. Fatigue is a major

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Table 1 Key clinical features of SSc Organ involved Skin

Vasculature

Lung

Kidney Heart

Gastrointestinal

Musculoskeletal

Nerves

Clinical manifestation Skin fibrosis Digital tip pitting scars, ulcers, and gangrene Cutaneous calcinosis Hyper-/hypopigmentation of the skin Telangiectasias Raynaud’s phenomenon Pulmonary hypertension Abnormal nailfold capillaries Macroangiopathy Interstitial lung disease Pulmonary hypertension Pleural effusion Scleroderma renal crisis Heart block Fibrotic cardiomyopathy Pericardial effusion Pericarditis Gastroesophageal reflux disease Dysphagia Gastric antral vascular ectasia (GAVE) Gastroparesis Small bowel dysmotility Bacterial overgrowth Malabsorption Joint contractures Tendon friction rubs Myopathy, myositis Bone resorption (osteolysis) Synovitis Compression neuropathies

contributor to poor function and equal in severity to what is seen in malignant disorders (Thombs et al. 2008). Raynaud’s phenomenon. RP is a common disorder characterized by reversible vasospasm of the digits induced by cold exposure. Clinically, RP consists of episodic attacks of triphasic color changes of the digits after cold exposure with blanching (Fig. 1), cyanosis, and redness followed by numbness, pain, and often-functional disability with significant impact on the quality of patient’s life. Moreover, RP can be associated with digital ulceration and autoamputation. RP affects up to 11% of women and 8% of men in the United States. The etiology and pathogenesis of RP are incompletely understood. Nonetheless, an imbalance between vasodilatory (nitric oxide, prostacyclin) and constrictive (endothelin-1) signals is believed to be

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Fig. 1 Raynaud’s phenomenon (RP) is one of the earliest manifestations of systemic sclerosis. The figure demonstrates blanching of the fingers, which is usually a reversible phase in RP

involved in RP pathogenesis. Digital ulcerations can cause significant pain, disability, and decreased quality of life. Gastrointestinal (GI) involvement. Nearly 90% of patients with either dcSSc or lcSSc have evidence of gastrointestinal involvement. Esophageal dysmotility and incompetence of the lower esophageal sphincter are the earliest visceral manifestations of SSc. GI dysmotility represents a key dysfunction creating severe chronic complications in SSc. Autonomic dysfunction, vascular ischemia, autoimmunity, and fibrosis can all affect normal GI physiology leading to dysmotility that can cause decreased digestion, emptying, and absorption with resultant hypomotility and dilation that may lead to malabsorption (Marie et al. 2001). Lung involvement. Pulmonary involvement is seen in more than 70% of patients with SSc. Two distinct clinical manifestations are recognized: interstitial lung disease (ILD) and pulmonary vascular disease, leading to pulmonary arterial hypertension (PAH). ILD and PAH now account for most of the mortality of scleroderma (Tyndall et al. 2010). ILD occurs in >70% of patients with SSc, while PAH occurs in 10–15% of cases and is more common in patients with lcSSc. Cardiac involvement. Cardiac involvement is a major contributor to mortality in SSc. Patients with SSc and symptomatic cardiac involvement have a poor prognosis, with 5-year mortality rates of approximately 75%. Cardiac involvement includes myocarditis, pericardial effusion, myocardial fibrosis with stiff heart and diastolic dysfunction, and arrhythmias. Patchy myocardial fibrosis is the pathological

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hallmark of cardiac involvement in SSc that may result from repeated episodes of ischemia-reperfusion injury (RP). Renal involvement. Scleroderma renal crisis (SRC) is a microangiopathic process that most commonly occurs early in dcSSc. SRC presents with acute rise in blood pressure followed by acute renal failure seen in approximately 10% of patients. Mortality due to SRC was the leading cause of death in the disease before current emphasis on early detection and treatment with angiotensin converting enzyme inhibitors. Prolonged steroid therapy even at modest doses can precipitate SRC (Steen and Medsger 1998). Treatment of SSc. No drug or combination of drugs is of proven therapeutic value in adequately controlled prospective trials. Treatment of SSc typically requires both systemic and organ-based approaches. Effective therapy for SSc renal crisis, pulmonary fibrosis, digital ulcers, PAH, and GI involvement is available. The use of immunosuppression with cyclophosphamide, mycophenolate mofetil, rituximab, or stem cell or lung transplantation is indicated in patients with severe and lifethreatening complications. The recent description of rigorous epigenetic regulation in SSc pathogenesis, as discussed in this chapter, will hopefully introduce newer therapeutics that may transform this disease into an easily manageable chronic disorder.

Environmental Exposures Associated with SSc Exposure to environmental factors has been proposed to play a role in SSc pathogenesis, including occupational exposure to chemicals and pollutants such as crystalline silica, white spirit, aromatic solvents, chlorinated solvents, trichloroethylene, ketones, and welding fumes (Marie et al. 2014). Here we will review data that support a possible role for environmental factors in SSc. Table 2 summarizes key environmental factors that may have a role in SSc pathogenesis.

Occupational Exposures 1. Silica. Crystalline silica exposure has long been recognized as an occupational hazard with more than 100,000 workers in high-risk jobs such as abrasive blasting, foundry work, stonecutting, rock drilling, quarry work, and tunneling. In a review of medical records in the Michigan silicosis surveillance system (1985–2006), individuals with a confirmed diagnosis of silicosis had greater than 28-fold increased risk of developing SSc compared to the general population (Makol et al. 2011). Clinically, patients exposed to crystalline silica more often exhibit dcSSc, ILD, digital ulcers, myocardial dysfunction, and cancer (lung cancer being most common) (Marie et al. 2015). Serological testing is often positive for anti-ScL70 in these patients. The pathophysiology of silica-induced SSc is not well understood. Crystalline silica may act as an immunoadjuvant, the exposure to which may lead to activation of the innate immune system resulting

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Table 2 Environmental factors potentially associated with SSc and scleroderma mimickers Drugs

Viral infections

Nutritional Occupational exposures

Others

Bleomycin Sex hormones Methysergide Pentazocine Cocaine Carbidopa Cytomegalovirus (CMV) Parvovirus Helicobacter pylori L-Tryptophan Denatured rapeseed oil Silica and related dusts Organic solvents: benzene, toluene, trichloroethylene, vinyl chloride Epoxy resins Welding fumes Pesticides UV radiations Gadolinium Silicon

in proinflammatory cytokine production and inflammasome activation in the lungs, which may further lead to activation of adaptive immunity, breaking of tolerance, and autoantibody production (Pollard 2016). Given the frequency of occupational exposure to silica, men are more likely to develop silica-induced SSc. 2. Silicone implants. The association between silicone implants and SSc is not clear. Cases of SSc-like syndromes were reported after silicone gel breast implantation started in the 1960s. Some of these patients tested positive for rheumatoid factor, antinuclear antibodies, or antibodies against DNA. In the 1990s, more case reports discussed the associations between silicone implants and the development of SSc. However, the Institute of Medicine reviewed most of the available literature and concluded that there was insufficient evidence to support an association of silicone breast implants with any defined CTD including SSc (Rohrich 1999). 3. Vinyl chloride (VC). VC is an organo-chloride compound used to produce the polymer polyvinyl chloride (PVC), with the United States being the largest international producer. In the 1960s, first reports emerged for individuals working in PVC manufacturing, who experienced finger paresthesia; cold sensation; Raynaud’s symptom; clubbed fingers; watch-glass nails; osteolytic lesions in distal phalanges; edema and hardening of the skin of the fingers, hands, and forearms; and pulmonary changes (Gama and Meira 1978). The risk of developing these symptoms increased with exposure frequency, and an

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improvement was noted after cessation of exposure. While laboratory studies indicated increased levels of cryoglobulins, IgG, and complement system activation, clinical studies in patients with moderate to severe VC-related SSc did not consistently show raised autoantibodies (like ANA, anti-centromere, or anti-ScL 70) (Ward et al. 1976; Black et al. 1983). 4. Organic solvents. Exposure to industrial organic solvents, such as perchloroethylene (PCE), trichloroethylene (TCE), trichloroethane (TCA), benzene, and carbon tetrachloride, has been linked to development of SSc since the 1950s (Reinl 1957). In a carefully conducted case-control study to determine the link between occupational exposure to organic solvents and increased risk of developing SSc, the risk of SSc was increased almost threefold in men who had a high-intensity and high cumulative exposure to some solvent (TCE, TCA, benzene, or carbon tetrachloride) during their career (Nietert et al. 1998). The exposure to solvents and SSc association was stronger in SSc patients who tested positive for antiScl-70. Certain cytochrome P2 polymorphisms (CYP) are believed to increase the susceptibility to SSc following exposure to organic solvents. CYP alleles at two loci, 2E1 and 2C19, are reported to occur at a greater frequency in SSc patients with solvent exposure compared to sporadic SSc patients (Povey et al. 2001). 5. Other agents. Epoxy resins and pesticides are other occupational agents implicated in the development of SSc. Previous studies reported a 2.5-fold higher risk of SSc associated with epoxy resins; however, in recent larger studies, there was no significant difference between epoxy resin exposure and development of SSc (Marie et al. 2014). Toxic oil syndrome (TOS) and eosinophilia-myalgia syndrome (EMS). These two syndromes are clinically similar, and their etiology is linked to consumption of contaminated food. TOS was described in the 1980s in Spain where individuals in Madrid province and 13 other provinces in Spain were affected by an illness that was eventually labeled TOS by the World Health Organization. In less than 2 years, over 20,000 people were affected, and around 350 people died from this disease (Philen et al. 1997). TOS was linked to the consumption of contaminated rapeseed oil, and chemical analysis of the case-associated oil identified brassicasterol, a marker for rapeseed oil, and trace amounts of aniline, oleyl anilide (OA), and other contaminants (Posada de la Paz et al. 1989). The contaminated oil was traced back to a batch of rapeseed oil produced by a specific company. However, a number of companies imported rapeseed oil denatured with 2% aniline from France, refined the oil, mixed it with other oils, and fraudulently sold the product as pure olive oil (Tabuenca 1981). EMS is a chronic multisystem disorder that was first recognized in 1989 in the United States (Hertzman et al. 1990). EMS is linked to the consumption of L-tryptophan, a popular dietary supplement at the time. EMS occurred in an epidemic outbreak affecting >1,500 individuals and was associated with 36 deaths over a 2-year period (Swygert et al. 1993). Once the sale of tryptophan-containing products was banned by FDA, there was a decline in the incidence of EMS.

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An isolated case was reported in 2005, again associated with L-tryptophan (Allen et al. 2011). Initially, tryptophan was identified as a causative agent for EMS; however, further investigation revealed a strong association linked to the consumption of L-tryptophan produced by one company (Allen et al. 2011). Over 60 minor impurities were identified in this company’s products, six of which were linked to EMS. Clinically, TOS and EMS are quite similar. The presentation of these two entities includes myalgia, arthralgia, dermal infiltration, fever, asthenia, and intense eosinophilia in the acute stage. In the chronic stages, patients tend to develop scleroderma like skin disease, liver disease, PAH, neuropathy, and sicca syndrome. While the initial treatment with glucocorticoids helped in controlling the eosinophilia, it did not prevent the progression into the chronic phase. Nephrogenic systemic fibrosis (NSF). NSF is a fibrosing disorder that mimics SSc. NSF is exclusively seen in patients with advanced renal impairment. It is characterized by thickening and hardening of the skin overlying the extremities and trunk. The majority of NSF cases are linked to a previous exposure to gadolinium-based contrast agents. The mechanism of pathogenesis of NSF is unclear, although gadolinium has been found in the skin of affected patients. The resemblance of a tissue injury reaction and presence of myofibroblasts are suggestive of a fibrogenic process. In all patients, skin involvement in the form of plaques, papules, and/or nodules is the main presentation; the affected skin becomes thick and firm. The onset of the skin disease is usually within 2–4 weeks of gadolinium exposure. Some patients have systemic manifestations in the form of muscle induration and contractures, joint contractures, and rarely involvement of the lungs (fibrosis), pleura, myocardium, pericardium, and dura mater (Galan et al. 2006). The diagnosis is usually confirmed based on careful history and physical examination, supplemented by histopathologic findings. While there is no specific treatment of the disease, stabilization or improvement in the skin disease occurs with improvement of the renal function. Prevention is the mainstay by avoiding gadolinium exposure in individuals with estimated glomerular filtration rate less than 30 mL/min.

Epigenetic Alterations in SSc While the genetic code is the same throughout all somatic human cells, there are several regulatory mechanisms that tightly differentiate the function of one cell type from another. These regulatory mechanisms are known as epigenetic mechanisms. Aberrations in the epigenetic regulatory mechanisms can be associated with cellular and tissue dysfunction through alterations of gene expression patterns, which in turn can lead to a disease state, most notably cancer and autoimmune diseases. An interesting aspect of epigenetics is the interaction between the genome and the environment, where certain environmental factors can influence the expression of genes within a cell without altering the genetic code, but instead through modifying epigenetic marks. Indeed, a role for environmental factors has been proposed as a

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central factor in the pathogenesis of SSc, which we have discussed earlier in this chapter. Moreover, a role for environmental-epigenetic factors in pathogenesis of SSc is demonstrated by the observations of geographic clustering of SSc and presence of substantial epigenetic aberrancies in specific gene regions and at the genome-wide level. Therefore, it is plausible that environmentally induced epigenetic dysregulation plays a significant role in the pathogenesis of SSc. Here, we will review the evidence for epigenetic regulation in FB, MVEC, and lymphocytes. The role of these cell types in mediating tissue fibrosis, vascular dysfunction, and activation of the immune system is well established.

Alteration DNA Methylation in SSc-FB The role of FB activation in the pathogenesis of SSc is well established. FBs are capable of producing ECM including collagen. There is a growing evidence to support a role of DNA methylation in the generation and persistent of SSc-FB phenotype. Table 3 summarized DNA methylation aberrancies in SSc.

Different Methylation Profiles of Diffuse Cutaneous SSc (dcSSc) and Limited Cutaneous SSc (lcSSc) At the genome-wide level, there is evidence for significant differences in DNA methylation patterns between dcSSc and lcSSc FB that is represented by divergence of the methylome in these two subtypes of SSc (Altorok et al. 2014a). The common differentially methylated CpG sites represent only 6% out of the total differentially methylated CpG sites between dcSSc and lcSSc. This observation suggests that the difference between the two subsets of SSc is not only related to clinical manifestations and outcome but also suggests a difference in the underlying epigenetic machinery that maintains the disease phenotype, as reflected by different gene ontologies and pathways that were enriched by the differentially methylated genes. This observation has an important implication on future epigenetic work in the field of SSc, where we suggest studying epigenetic dysregulation of each subtype of SSc separately due to the difference in FB methylome. Altered DNA Methylation Maintenance Factors in SSc There is evidence of altered levels of epigenetic maintenance mediators in SSc-FB, which may partly explain persistence of the profibrotic phenotype of SSc-FB. DNMT1, the maintenance DNA methylation enzyme, is overexpressed in SSc-FB, as well as methyl-CpG DNA-binding protein 1 (MBD-1) and MBD-2 and methyl-CpG-binding protein 2 (MeCP-2) in SSc-FB (Wang et al. 2006). Aberrancies of DNA Methylation in Transcription Factors that Are Involved in Collagen Gene Expression Fli-1, which is encoded by FLI1 gene, is a transcription factor that negatively regulates collagen gene expression by FB. Previous studies demonstrated downregulation of FLi-1 in SSc skin and cultured SSc-FB (Kubo et al. 2003), which

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Table 3 Summary of key DNA methylation aberrancies in systemic sclerosis Gene/ pathway FLI1

Epigenetic defect Hypermethylation

DNMT1

Overexpressed

FB, MVEC

DNMT1

Downregulated

DNA demethylase activity MBD1

Downregulated

CD4+ T cells MVEC

Overexpressed

FB

CD40L

Hypomethylation

CD70 (TNFSF7) BMPRII

Hypomethylation

CD4+ T cells CD4+ T cells MVECs

ITGAL

Hypomethylation

CD4+ T cells

DKK1, SFRP1

Hypermethylation

FB

CTNNA2, Hypomethylation CTNNB1 and CTNNA3 ADAM12 Hypomethylation

FB

ITGA9

Hypomethylation

FB

COL23A1, COL4A2

Hypomethylation

FB

Hypermethylation

Cell type FB

FB

Target/consequence Overexpression of collagen genes in SSc-FBs Increased expression of Dnmt1 could be contributing to hypermethylation of certain genes, such as FLI1 Global hypomethylation in CD4+ T cells Hypermethylation and repression of FLI1 Interference with transcriptional machinery, recruitment of HDACs. Unfavorable chromatin structure Costimulatory molecule, role is not clear in SSc Costimulatory molecule, role is not clear in SSc Failure of the inhibitory mechanism for cell proliferation and induction of apoptosis Integrin alpha L (ITGAL) encodes for CD11a, a costimulatory molecule T cell proliferation Endogenous Wnt inhibitors, recapitulation of Wnt/β-catenin pathway Recapitulation of Wnt/βcatenin pathway Overexpressed in SSc-FB. Probably involved in activation of TGF-β signaling pathways Overexpressed in SSc-FB. Probably involved in activation of TGF-β signaling pathways Overexpressed in SSc-FB. Tissue fibrosis

Reference Wang et al. (2006) Wang et al. (2006), Qi et al. (2009), and Kahaleh and Wang (2012)

Lei et al. (2009) Wang et al. (2006 and Kahaleh and Wang (2012) Wang et al. (2006)

Lian et al. (2012) Jiang et al. (2012) Wang and Kahaleh (2013)

Stummvoll et al. (2004)

Dees et al. (2013)

Altorok et al. (2014a) Altorok et al. (2014a)

Altorok et al. (2014a)

Altorok et al. (2014a) (continued)

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Table 3 (continued) Gene/ pathway NOS

Epigenetic defect Hypermethylation

Cell type MVEC

AIF1

Hypomethylation

PBMC

Target/consequence Reduced NOS activity in MVECs, increased expression of proinflammatory and vasospastic genes Overexpression in SScPBMC. Probably involved in promoting macrophage activation and growth of vascular smooth muscle cells and T lymphocytes

Reference Matouk and Marsden (2008)

Li et al. (2015)

Modified with permission (Altorok et al. 2015)

suggests that reduced levels of Fli-1 may partially be responsible for increased collagen synthesis and expansion of the ECM in SSc. The promoter region of FLI1 is heavily methylated in SSc-FB (Wang et al. 2006). Indeed, the addition of 5-azacytidine (5-AZA), which is a universal demethylating agent (DNMT1 inhibitor), resulted in increased FLI1 expression and simultaneous reduction in type I collagen expression levels in SSc-FB. These observations demonstrate that DNA methylation aberrancies contribute to excessive collagen production in SSc-FB by epigenetic repression of an antifibrotic transcription factor.

Altered DNA Methylation in the TGF-b Signaling Pathway Activation of the TGF-β signaling pathway has been established as a key player in FB activation and myofibroblast differentiation. ITGA9, which encodes for an alpha integrin 9, is hypomethylated and overexpressed in SSc-FB compared to controls (Altorok et al. 2014a). There is a bidirectional interaction between integrins and TGF-β signaling in fibrosis, with TGF-β inducing integrin expression, and several integrins directly control TGF-β activation (Margadant and Sonnenberg 2010). Moreover, ADAM12 was found to be hypomethylated and overexpressed in SScFB (Altorok et al. 2014a). ADAM12 contributes to the fibrosis through augmenting TGF-β signaling (Atfi et al. 2007). Thus, in light of these observations, there appears to be a role for DNA methylation aberrancies in persistent activation of the TGF-β pathway leading to tissue fibrosis. Alterations of DNA Methylation in the Wnt/b-Catenin Signaling Pathway The Wnt/β-catenin signaling pathway is considered a key profibrotic pathway in SSc (Wei et al. 2011). The canonical Wnt/β-catenin signaling is activated by the overexpression of Wnt proteins and by the downregulation of the endogenous Wnt antagonists. Epigenetic defects in the Wnt/β-catenin pathway in SSc-FB were demonstrated at two levels: (i) epigenetic silencing of genes encoding the

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endogenous Wnt inhibitors Dickkopf-related protein 1 (DKK1) and secreted frizzled-related protein 1 (SFRP1) mediated by hypermethylation of the promoter region of DKK1 and SFRP1 in SSc-FB (Dees et al. 2013) and (ii) hypomethylation of CTNNA2 and CTNNB1 and CTNNA3 in SSc-FB compared to control-FB (Altorok et al. 2014a). These findings suggest that epigenetic alteration leads to decrease expression of Wnt antagonists and increase expression of Wnt ligands, which results in persistent activation of Wnt/β-catenin pathway signaling in SSc.

Aberrancies of DNA Methylation in SSc-MVECs MVEC injury is a critical event in the pathogenesis of SSc, and epigenetic dysregulation can contribute to MVEC dysfunction (Altorok et al. 2014).

DNA Methylation Alterations in Nitric Oxide Synthesis Nitric oxide (NO) is produced in the vascular endothelium largely by endothelial nitric oxide synthase (NOS3), and it is pivotal for the endothelium function. It has been demonstrated that there are intrinsic defects in the production of NO by MVECs isolated from SSc patients (Romero et al. 2000). NO is a potent vasodilator and an inhibitor of smooth muscle cell proliferation. There is evidence for underexpression of NOS3 in SSc-MVECs and that the promoter region of NOS3 is hypermethylated in SSc-MVECs compared to controls (Wang and Kahaleh 2007). This finding suggests a crucial role for DNA methylation in SSc-MVEC dysfunction. MVECs Apoptosis SSc is characterized by enhanced MVEC apoptosis. Bone morphogenetic proteins (BMPs) are a group of proteins that constitute morphogenetic signals and orchestrate tissue architecture through coordinating cell survival and differentiation. Bone morphogenetic protein receptors (BMPR) are signaling molecules that belong to the transforming growth factor-β superfamily. BMP signaling through bone morphogenetic protein receptor II (BMPRII) favors MVEC survival and apoptosis resistance. BMPRII is underexpressed in SSc-MVECs, and the promoter region is heavily methylated in comparison to healthy controls. Of interest, treatment with 5-AZA normalized BMPRII expression levels and restored SSc-MVEC response to apoptosis triggers to normal levels (Wang and Kahaleh 2013). Therefore, DNA methylation appears to play a role in MVEC responses to apoptosis in SSc by epigenetic repression of BMPRII.

DNA Methylation Defects in Lymphocytes Female Sex Predominance and CD40L Methylation in SSc T Lymphocytes DNA methylation is a natural physiological process that maintains silencing of genes that are not particularly needed for a specific cell type, and it is also an innate process for inactivation of one X-chromosome in order to keep a balance among genes encoded on the X-chromosome in females (Lyon 1961). The ligand for

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CD40 (CD40L), which activates CD40 costimulatory signal, is located on the X-chromosome. CD40L is overexpressed in female SSc patients in association with demethylation of the promoter region of CD40L in CD4+ T cells, which is not the case in men with SSc (Lian et al. 2012). This data suggests a role of epigenetic dysregulation in reactivation of genes on the X-chromosome in autoimmune diseases that may partially explain female predominance, such as the case in SSc.

The Role of DNA in Expression of Costimulatory Molecules in SSc T Lymphocytes The CD70/CD27 axis is involved in regulating activation of the immune system (Denoeud and Moser 2011). CD70 is a costimulatory molecule that is involved in regulating B and T cell activation (Denoeud and Moser 2011). CD70 is overexpressed in SSc-CD4+ T cells in association with hypomethylation of CD70 promoter gene (Jiang et al. 2012), which suggests a role for DNA methylation in expression of costimulatory molecules that in turn leads to a break in immune tolerance in SSc. Integrin alpha L (ITGAL) encodes for CD11a, which is the α-chain subunit of the lymphocyte function-associated antigen-1. CD11a is one of the costimulatory molecules expressed in CD4+ T cells, as well as B cells, neutrophils, and macrophages that contribute to T cell proliferation and the recruitment of inflammatory cells. Overexpression of ITGAL by SSc-CD4+ T cells has been confirmed and is associated with lower methylation levels in the promoter region of ITGAL (Stummvoll et al. 2004). Furthermore, treatment of CD4+ T cells with 5-AZA decreased ITGAL promoter methylation levels and increased ITGAL expression to a level comparable to normal CD4+ T cells. Moreover, the co-culture of 5-AZA treated CD4+ T cells with B cells, and FB led to increased production of IgG and collagen genes, respectively (Wang et al. 2014). DNA Methylation in Peripheral Blood Lymphocytes Combined genome-wide DNA methylation and transcription analysis using peripheral SSc blood monocyte cells (PBMCs) demonstrated hypomethylation and increased expression of 405 genes, indicating a potential role for epigenetics in expression of these genes. Among hypomethylated and overexpressed genes in SScPBMCs are a group of autoimmune-related genes, such as allograft inflammatory factor 1 (AIF1), which is a protein that plays a role in promoting macrophage activation and growth of vascular smooth muscle cells and T lymphocytes. Interestingly, polymorphism of AIF1 has been associated with SSc. Other genes that were hypomethylated and overexpressed include ARID3A, IFI44L, PARP9, RSAD2, EPSTI1, EIF2AK2, and CYFIP2 (Li et al. 2015).

Aberrancies of the Histone Code in SSc The most commonly studied histone modifications in SSc are acetylation and methylation, mostly in FB and MVECs. Table 4 summarized defects in histone code related to SSc.

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Table 4 Summary of key histone code aberrancies in systemic sclerosis Gene/ pathway H3, H4 acetylation H3K27me3

Epigenetic defect Reduced Increased

FB, murine dermal FBs

Global H4 acetylation, H3K methylation

Increased H4 act, decreased H3K meth

B cells

FLI1 H3 and H4 acetylation SIRT1

Reduced

FB

Decreased expression

Skin

HDAC5

Increased expression

MVEC

Cell type FB

Target/consequence Unfavorable chromatin structure for target gene expression May contribute to inhibition of collagen suppressor genes and, therefore, collagen deposition Favor target gene expression in B cells, could be contributing to activation of genes in the immune system and antibody production Repression of FLI1, therefore, overexpression of collagen genes

Reference Wang et al. (2006) Kramer et al. (2013) Wang et al. (2013)

Sirtuin 1 is a histone deacetylase. SIRT1 targets TGF-β signaling pathway Antiangiogenic effects, probably by repressing proangiogenic genes

Wei et al. (2015)

Wang et al. (2006)

Tsou et al. (2016)

Modified with permission (Altorok et al. 2015)

Aberrancies of Histone in SSc-FB We have described DNA hypermethylation and repression of FLI1 in SSc-FB earlier in this chapter. There are also a significant reduction in histones H3 and H4 acetylation in SSc-FB (Wang et al. 2006), which favors permissive chromatin architecture for gene expression, suggesting the presence of defects in the histone code in SSc-FB and a cross talk between DNA methylation and histone modification changes that contribute to the generation of the activated FB phenotype in SSc. Trimethylation of lysine 27 on histone H3 (i.e., H3K27me3) represents a potent repressive mark that is associated with an unfavorable chromatin structure for gene transcription. Increased levels of H3K27me3 have been demonstrated in SSc-FB (Kramer et al. 2013). Inhibition of H3K27me3 stimulates the release of collagen in SSc cultured fibroblasts and in bleomycin-induced experimental fibrosis model (Kramer et al. 2013). In a genome-wide expression study, it was determined that the histone deacetylase sirtuin 1 (SIRT1) is reduced in SSc skin tissues, associated with a negative correlation with severity of skin fibrosis (Wei et al. 2015). SIRT1 targets the transforming growth factor-beta (TGF-β) signaling pathway, and its activation attenuated fibrotic responses in skin fibroblasts and skin organ cultures. The antifibrotic effects of sirtuin 1 were due in part to decreased expression and function of the acetyltransferase p300, which is involved in activation of TGF-β signaling in SSc. Of note, the expression levels of p300 in lesional tissues of SSc are increased, and forced expression of p300 increased H4 hyperacetylation and collagen transcription by FB (Ghosh et al. 2013).

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Histone Code Aberrances in SSc-MVEC The histone deacetylase HDAC5 is significantly overexpressed in SSc-MVECs (Tsou et al. 2016). HDAC5 has significant antiangiogenic effects (Urbich et al. 2009), probably by repressing proangiogenic genes (Tsou et al. 2016); silencing HDAC5 in SSc-MVECs restored normal angiogenesis, which is impaired in SSc. HDAC5 knockdown induces an open chromatic architecture in several genes mostly involved in regulation of angiogenesis and fibrosis. Theoretically, blocking HDAC5 might ameliorate SSc vascular disease.

Aberrant Expression of MicroRNAs in SSc MicroRNAs (miRNAs) have emerged as key regulators of gene expression in general. Aberrant expression of miRNAs in SSc is likely to have an important role in the pathogenesis of SSc, based on findings of aberrant expression of miRNAs that are associated with pro-/anti-fibrosis effect. Table 5 summarizes key miRNAs that appear to be involved in SSc.

Reduced Expression of MiR-29 in SSc-FB Strong antifibrotic effects for miR-29 have been demonstrated in many organs including the heart, the kidney, and the lung. It is suggested that miR-29 target genes involved in the expansion of the extracellular matrix and the development of tissue fibrosis (Kriegel et al. 2012). Downregulation of miR-29a and miR29b was demonstrated in SSc-FB and the skin, as well as in FB from bleomycin-induced skin fibrosis model, associated with overexpression of collagen genes that increased upon Table 5 Summary of key miRNA expression profiles in systemic sclerosis Gene/ pathway miR-29

Epigenetic defect Downregulated

miR-21

Overexpressed

miR-31

Overexpressed

miR-145

Downregulated

miR-146

Overexpressed

miR-152

Downregulated

miR-503

Overexpressed

Cell type FB, murine dermal FB Skin tissue, FB, murine dermal FB Skin tissue, FB Skin tissue, FB Skin tissue, FB MVECs Skin tissue, FBs

Modified with permission (Altorok et al. 2015)

Target/ consequence Antifibrotic factor, putative target is type 1 collagen Profibrotic factor, target SMAD7 Putative target is type 1 collagen Putative target is SMAD3 Putative target SMAD4 Overexpression of DNMT1 Unclear, putative target SMAD7

Reference Fabbri et al. (2007), Maurer et al. (2010), and Zhu et al. (2012) Zhu, Li et al. (2012) and Zhu, Luo et al. (2013) Zhu et al. (2012) Zhu et al. (2012) Zhu et al. (2012) Wang and Kahaleh (2010) Zhu et al. (2012)

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further downregulation of miR-29 by the knockdown of miR-29 (Maurer et al. 2010). Forced overexpression of miR-29a significantly reduces collagen expression levels (Maurer et al. 2010). Taken together, these data argue for an antifibrotic role for miR-29 and indicate that this and other miRNAs may prove to be valuable options to explore as a therapeutic strategy for SSc in the future.

miR-21 and miR-145 TGF-β signaling pathway can mediate fibrosis by the activation of its downstream mediators, SMAD2 and SMAD3, but it also can negatively regulate fibrosis by activation of the inhibitory factor SMAD7. MiR-21 is upregulated in SSc-FB and the skin (Zhu et al. 2012). The putative targets for miR-21 are SMAD7 and COL1A1. Overexpression of miR-21 in SSc-FB results in reduced expression of SMAD7, whereas the knockdown of miR-21 increased SMAD7 expression levels (Sing et al. 2012; Zhu et al. 2013). On the other hand, miR-145, which targets SMAD3, is downregulated in SSc-FB (Zhu et al. 2012). Therefore, it appears that downregulation of antifibrotic miRNAs like miR-145 and upregulation of profibrotic miRNAs like miR-21 are important in shifting the balance of TGF-β signaling toward a profibrotic one. Divergence of MicroRNA Regulation Between the Two SSc Subsets We discussed earlier in this chapter divergence of the methylome between dcSSc and lcSSc. Interestingly, divergence of miRNAs in skin biopsies from patients with dcSSc and lcSSc has also been established. Only 21 miRNAs out of the 42 differentially expressed miRNAs in dcSSc and 60 miRNAs differentially expressed in lcSSc were common miRNAs between the two groups (Zhu et al. 2012). This data supports the notion of significant divergent epigenetic regulation in the two subsets of SSc, similar to divergence of the methylome in FB from dcSSc and lcSSc subsets (Altorok et al. 2014a). miRNA Aberrant Expression in MVECs Studies have shown downregulation of miR-152 in SSc-MVECs and the target for miR-152 is DNMT1 (Wang and Kahaleh 2010). Forced expression of miR-152 in control MVECs led to decrease expression level of DNMT1, whereas inhibition of miR-152 expression in control MVECs led to enhanced DNMT1 expression and lower expression levels of NOS3 to levels similar to what is seen in SSc-MVEC. These data indicate that miR-152 plays a role in SSc-MVEC phenotype probably through the maintenance of DNA methylation inheritance pattern.

Dictionary of Terms • Autoimmune diseases – A group of disorders characterized by activation of the immune system in association with loss of self-tolerance. • Fibrosis – Thickening and scarring of the connective tissue.

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• Raynaud’s phenomenon – A disorder characterized by reversible vasospasm of the digits induced by cold exposure that is manifested by cold fingers, numbness, and phasic color changes of the digits (white, blue, and red color). • Scleroderma – A group of diseases that involve the hardening and tightening of the skin and connective tissues. • Eosinophilia-myalgia syndrome – A clinical condition characterized by severe muscle pain and skin rash. This disease has been associated with the ingestion of contaminated L-tryptophan, which is a dietary supplement. • Nephrogenic systemic fibrosis – A disease that mimics systemic sclerosis, characterized by fibrosis of the skin and occasionally internal organs. It has been associated with exposure to gadolinium, which is used in magnetic resonance imaging.

Key Facts • Scleroderma, what is known now as systemic sclerosis (SSc), was first described by Hippocrates around 400 BC. • Scleroderma is derived from the Greek words skleros (hard or indurated) and derma (skin). • Scleroderma is a complex autoimmune disease that is associated with high risk of morbidity and mortality. • It is a multisystem disease that is manifested by skin thickening, lung fibrosis, pulmonary hypertension, and others. • Vascular injury, activation of the immune system, and tissue fibrosis leading to organ dysfunction are the hallmark pathologic findings in SSc. • The trigger for SSc is not clear; it is suggested that there is a role for epigeneticenvironmental factors in SSc pathogenesis. • Environmental exposures, such as exposure to silica and organic solvents, have been associated with development of SSc.

Summary Points • SSc is a multisystem autoimmune disease associated with high morbidity and mortality. • There is a need to better understand SSc pathogenesis in order to identify better and more effective therapeutic options. • The initial trigger for SSc is not clear, but it is thought that an environmental factor may serve as the initial trigger for SSc. • Case-control studies have identified several environmental factors that are associated with SSc, such as silica, organic solvents, and epoxy resin. • The evidence is mounting for alteration of DNA methylation, histone code modification, and abnormal expression of microRNAs in key pathways that are involved in pathogenesis of SSc.

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• Observations in SSc suggest that autoimmunity, vascular disorder, and tissue fibrosis may result from cell-specific epigenetic regulation of gene expression. • There is divergence of DNA methylation signature between dcSSc and lcSSc; same observations were noted in miRNA expression in the two subsets. • The role of DNA methylation aberrancies in SSc has been confirmed at the genome-wide level and at candidate gene level. • There is evidence of aberrant expression of DNA methylation maintenance enzymes. • Hypermethylation of FLI1, a negative regulator of collagen expression, is associated with excessive collagen production in SSc-FB. • Hypermethylation of BMPRII plays a role in increased susceptibility of SScMVEC to apoptosis. • There are epigenetic alterations in key pathways involved in fibrosis, such as TGF-β and β-catenin signaling pathways. • Downregulation of miR-29 and upregulation of miR-21 seem to be contributing to the SSc-FB phenotype.

References Abraham DJ, Varga J (2005) Scleroderma: from cell and molecular mechanisms to disease models. Trends Immunol 26(11):587–595 Allen JA, Peterson A, Sufit R, Hinchcliff ME, Mahoney JM, Wood TA, Miller FW, Whitfield ML, Varga J (2011) Post-epidemic eosinophilia-myalgia syndrome associated with L-tryptophan. Arthritis Rheum 63(11):3633–3639 Altorok N, Tsou PS, Coit P, Khanna D, Sawalha AH (2014a) Genome-wide DNA methylation analysis in dermal fibroblasts from patients with diffuse and limited systemic sclerosis reveals common and subset-specific DNA methylation aberrancies. Ann Rheum Dis 74(8):1612–1620 Altorok N, Wang Y, Kahaleh B (2014b) Endothelial dysfunction in systemic sclerosis. Curr Opin Rheumatol 26(6):615–620 Altorok N, Wang YQ, Kahaleh B (2014c) Endothelial dysfunction in systemic sclerosis. Curr Opin Rheumatol 26(6):615–620 Altorok N, Almeshal N, Wang Y, Kahaleh B (2015) Epigenetics, the holy grail in the pathogenesis of systemic sclerosis. Rheumatology (Oxford) 54(10):1759–1770 Arnett FC, Cho M, Chatterjee S, Aguilar MB, Reveille JD, Mayes MD (2001) Familial occurrence frequencies and relative risks for systemic sclerosis (scleroderma) in three United States cohorts. Arthritis Rheum 44(6):1359–1362 Atfi A, Dumont E, Colland F, Bonnier D, L’Helgoualc’h A, Prunier C, Ferrand N, Clement B, Wewer UM, Theret N (2007) The disintegrin and metalloproteinase ADAM12 contributes to TGF-beta signaling through interaction with the type II receptor. J Cell Biol 178(2):201–208 Black CM, Welsh KI, Walker AE, Bernstein RM, Catoggio LJ, McGregor AR, Jones JK (1983) Genetic susceptibility to scleroderma-like syndrome induced by vinyl chloride. Lancet 1 (8314–5):53–55 Dees C, Schlottmann I, Funke R, Distler A, Palumbo-Zerr K, Zerr P, Lin NY, Beyer C, Distler O, Schett G, Distler JH (2013) The Wnt antagonists DKK1 and SFRP1 are downregulated by promoter hypermethylation in systemic sclerosis. Ann Rheum Dis 73(6):1232–1239 Denoeud J, Moser M (2011) Role of CD27/CD70 pathway of activation in immunity and tolerance. J Leukoc Biol 89(2):195–203 Fabbri M, Garzon R, Cimmino A, Liu Z, Zanesi N, Callegari E, Liu S, Alder H, Costinean S, Fernandez-Cymering C, Volinia S, Guler G, Morrison CD, Chan KK, Marcucci G, Calin GA,

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Meenu Ghai, Dyfed Lloyd Evans, and Shailesh Joshi

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methylation for Body Fluid Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identification of Monozygotic Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methylation Differences Between Ethnic Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation of Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methylation Changes Associated with Behavior: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of DNA Methylation in Forensics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Since the development of methylation-based diagnostic biomarkers, the application of DNA methylation in forensic investigation is also rapidly gaining ground. DNA methylation patterns are established during early embryonic development and are influenced by both genetic and environmental factors like diet, age, stress, socioeconomic status, and habitat. Identification of differentially methylated regions (DMRs) which differ between tissues or phenotypes can be targeted for forensic applications. Tissue-specific methylation differences can be used for accurate identification of body fluid/ tissue source found at a crime scene. Agespecific methylation changes in repetitive genomic regions have been used to develop epigenetic clocks for age estimation. DNA methylation patterns differ

M. Ghai (*) · D. L. Evans · S. Joshi School of Life Sciences, University of KwaZulu-Natal,Westville Campus, Durban, KwaZulu Natal, South Africa e-mail: [email protected]; [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_14

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even between monozygotic twins and can assist with the challenge of their identification. Recent development of whole genome methylation analysis platforms like Illumina whole genome methylation bead chips and single-cell reduced bisulfite sequencing has opened the doors for large-scale survey of methylation differences in both CpG islands and non-CpG regions. Future research could predict an individual’s social behavior and activities by applying DNA methylation indicators. Advancements in DNA methylation analysis for forensics will complement the current STR analysis and provide robust inferences for forensic evidence and human identification. Keywords

DNA methylation · Forensics · Differentially methylated regions (DMRs) · Tissue-specific differentially methylated regions (tDMRs) · Body fluid identification · Monozygotic twins · Forensic age estimation · Behavioral epigenetics · Population epigenetics List of Abbreviations

CpG DmAM DMRs DNAm DZ ICR LINE-1 MeCAP-seq MeDIP-seq MSRE-PCR MS-SNuPE MZ sjTRECs SNP STR WGBS

Cytosine–phosphate–guanine DNA methylation age measures Differentially methylated regions DNA methylation Dizygotic Imprinting control regions Long interspersed elements Methylated DNA capture by affinity purification sequencing Methylated DNA immunoprecipitation sequencing Methylation-specific restriction enzyme polymerase chain reaction Methylation-sensitive single-nucleotide primer extension Monozygotic Signal joint TCR excision circles Single-nucleotide polymorphism Simple sequence repeat Whole genome bisulfite sequencing

Introduction DNA methylation is the best characterized epigenetic modification. Cytosine methylation at CpG dinucleotides is essential for the regulation of gene expression, cellular differentiation, and maintenance of cell-type-specific gene expression patterns. DNA methylation patterns are erased and reprogrammed during early embryonic development. The methyltransferase enzymes, namely, DNMT1, DNMT3A, DNMT3B, and DNMT3L, are responsible for the introduction of de novo methylation, as well as the maintenance of methylation, by copying methylation marks

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Fig. 1 Enzymes involved in maintenance and de novo methylation. Genetic and epigenetic factors affecting methylation patterns

from the parental strand onto newly synthesized daughter strands (Fig. 1). Human genome contains about 30 million CpG dinucleotides, of which 70–80% are methylated. The distribution of CpGs in human genes is shown in Fig. 2. Most cell types display stable DNA methylation patterns (Ziller et al. 2013). However, methylation patterns undergo changes during one’s lifetime due to genetic and environmental factors (diet, stress, habitat, drugs, socioeconomic status) (Fig. 1). Recent research on genome-wide DNA methylation has revealed differentially methylated regions (DMRs) that vary between phenotypes. DMRs may occur throughout the genome but have been identified mainly around the promoter regions of genes, in gene body, and at intergenic regulatory regions (Peters et al. 2015) (Fig. 2). Differentially methylated regions could be tissue specific (tDMRs), cancer specific (cDMRs), age specific (aDMRs), or population specific (PopDMRs) (Rakyan et al. 2011; Hernando-Herraez et al. 2015). tDMRs display different methylation profiles according to cell or tissue type. Methylation patterns at tDMRs are stable and specific thus making them excellent markers for tissue differentiation (Eckhardt et al. 2006). Aberration in DNA methylation has been identified in diseases such as cancer and has been exploited as diagnostic biomarkers (Wittenberger et al. 2014; Wielscher et al. 2015). Human aging is also associated with DNA methylation modifications. Methylation patterns at CpG islands of various genes have been reported to show variation in individuals with different ages and have been

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Fig. 2 Landscape of DNA methylation in the human genome

investigated as a biomarker and a tool for age estimation (Bocklandt et al. 2011; Zbiec-Piekarska et al. 2015). In addition, DNA methylation contributes to phenotypic differences between human populations (Fraser et al. 2012; Heyn et al. 2013) (Fig. 3). Recently, the use of DNA methylation in forensics has generated considerable attention. The advantages of using DNA methylation-based assays include the stability of the epigenetic mark, ease of DNA isolation and modification, and availability of sensitive methods to quantify methylation. DNA methylation analysis is totally compatible with standard STR profiling and allows for the analysis of multiple tissues in a single reaction. When compared to mRNAs or proteins, the methylation of DNA is considered more chemically and biologically stable, making it an ideal biomarker for forensic applications. The advancement in technology has developed high-throughput methylation analysis methods like whole genome bisulfite sequencing (WGBS) and whole genome methylation chips (Yong et al. 2016) (Table 1). The Illumina infinium human methylation 450 (450 K) bead chip has allowed for the analyses of >450,000 CpG sites across the human genome at singlebase resolution. The present chapter reviews the various potential applications of DNA methylation to forensic analysis.

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Fig. 3 Types of differentially methylated regions (DMRs) identified and their potential forensic application

DNA Methylation for Body Fluid Identification Biological evidence found at a crime scene can include body fluids and tissues like blood, semen, saliva, sweat, vaginal fluid, nails, tears, skin tissue, or a mixture of body fluids and tissues. Accurate identification of the source of the tissue/body fluid can strengthen the forensic investigation and aid in reconstruction of the crime scene (Kayser and de Knijff 2011; Choi et al. 2014). Current methods for body fluid identification include catalytic, enzymatic, and immunological tests. Presumptive tests are used for initial screening. However, these tests can consume precious evidence and also have specificity limitations. Confirmatory tests are used for absolute tissue identification, but there is not a single test which can identify all likely evidential body fluids. Recently, mRNA- and micro-RNA-based body fluid identification assays have been developed (Haas et al. 2014, van den Berge et al. 2014). mRNAs suffer from stability issues and expression of most RNA markers overlaps between single body fluids. Even though micro-RNAs are more stable than mRNA, analysis of a mixture of body fluids could be difficult (Hanson et al. 2009). Also, RNA expression may not be fully cell type specific due to background transcription (Sijen 2015) and data interpretation requires considerable bioinformatics input. Body fluid identification by the analysis of tissue-specific DNA methylation pattern is a promising development in forensic DNA analysis. The earliest report of the application of DNA methylation for the differentiation of body fluids was by Frumkin et al. (2010). Random CpG islands containing a HhaI restriction site were targeted for primer design. A total of 38 loci, out of 205 genomic

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Table 1 Commonly used methods of DNA methylation analysis Methods Methylation-specific restriction enzyme PCR (MSRE-PCR) Methylation-sensitive PCR(MSP)

Whole genome bisulfite sequencing

MeDIP-seq: methylated DNA immunoprecipitation sequencing

MeCAP-seq: methylated DNA capture by affinity purification sequencing MS-SNuPE: methylation-sensitive single-nucleotide primer extension Reduced bisulfite sequencing Human methylation bead chip arrays

Description DNA digested by methylation-sensitive restriction enzyme, followed by PCR DNA is bisulfite converted and PCR performed with two primer pairs, which detectmethylated and unmethylated DNA, respectively Allows genome-wide DNA methylation profiling, uses bisulfite treatment combined with highthroughput sequencing Involves immunoprecipitation and enrichment for single-stranded methylated DNA fragments using anti-methylcytosine antibody, followed by sequencing Extraction of the methylated sections of the genome by protein binding, followed by immunoprecipitation and sequencing Following bisulfite conversion, SNuPE is performed with primers designed to hybridize immediately upstream of the target CpG site(s) Involves sequencing of reduced and representative section of the whole genome Microarray technology for genome-wide methylation analysis. Bisulfite-treated DNA is hybridized to arrayed probes, combined with a single-nucleotide extension to measure methylation at the genomic hybridization site for a single CpG dinucleotide

CpG loci, showed significant differential methylation pattern between blood, saliva, semen, and skin epidermis. From the 38 loci, 16 were selected for MSRE–PCR, wherein 1 ng of DNA was digested with a Hha1 methylation-sensitive enzyme. The methylated DNA was not cleaved, giving strong peaks on an electropherogram, while the unmethylated DNA was cleaved. The methylation ratio for each pair of differentially methylated loci was calculated as the rfu (relative fluorescence unit) of locus A divided by the rfu of locus B. The ratio of locus L91762/L68346 was lower in semen samples (0.04–0.53) and was higher in blood, saliva, skin epidermis, urine, and vaginal secretion. Thus, the low methylation ratio of L91762/L68346 confirmed the presence of semen. The ratio of loci L76138/L26688 was low in blood and saliva (0.08–1.54) and higher in semen and skin epidermis. Madi et al. (2012) identified four loci which displayed differential methylation in sperm, blood, saliva, and epithelial cells. Unlike Frumkin, this group targeted CpG loci within coding genes. The CpGs at locus ZC3H12D, FGF7, were hypomethylated in semen and differentiated sperm from the hypermethylated CpGs in blood, saliva, and epithelial cells. The locus C20orf117 allowed for the differentiation of blood from saliva, semen, and epithelial cells with highest level of methylation in blood as compared to the other tissues. Target CpGs at locus BCAS4 were hypermethylated in saliva in comparison to the other three tissues.

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Similarly, Lee et al. (2012) identified novel gene-based tDMRs in blood, saliva, semen, menstrual blood, and vaginal fluid. DNA methylation profiles for five tDMRs in the genes DACT1, USP49, HOXA4, PFN3, and PRMT2 were selected. The CpG loci (tDMRs) for DACT1 and USP49 were hypomethylated in semen and hypermethylated in other tissues. Thus, it was possible to differentiate semen from blood, saliva, menstrual blood, and vaginal fluid. The tDMRs for the genes HOXA4, PFN3, and PRMT2 could not differentiate any single body fluid from the others but displayed varying degrees of methylation. For example, tDMR analysis for PRMT2 showed methylation in semen but hypermethylation in menstrual blood and vaginal fluid. An et al. (2013) analyzed the effect of age on the DNA methylation pattern of the semen-specific tDMRs (DACT1, USP49, and PRMT2) used by the Lee et al. (2012) study. This was important because DNA methylation patterns are susceptible to change due to increasing age. However, they found that the age of the donors did not alter the hypomethylation status of the tDMRs. In a recent study by Bai et al. (2015), the H19 imprinting control region (ICR) loci showed differential methylation in sperm and blood. Sperm showed hypermethylation (55.27  8.36%) and blood displayed hypomethylation (101.94  11.66%). Genome-wide DNA methylation profiling was performed using the human methylation 450 K bead array which includes 450,000 CpG sites, to identified tDMRs in blood, saliva, and vaginal secretions (Park et al. 2014). The most comprehensive study on methylation markers for forensically relevant body fluids was reported by Forat et al. (2016). The study not only identified new tDMRs but also tested the effect of genetic and environmental factors on differential DNA methylation patterns. Genome-wide DNA methylation profiling was performed using the Illumina human methylation bead chips 27 and 450, to identify new CpG loci for the identification of body fluids. A total of 485,179 CpG sites were interrogated in peripheral blood, menstrual blood, saliva, vaginal fluid, and sperm. Results identified a total of 150 candidate differentially methylated CpG sites. From this initial set of 150, nine loci were selected as being most differentially methylated. To simulate forensic conditions, the body fluid samples were exposed to dry, humid, and outdoor conditions for 6 months prior to testing. Interestingly, humid conditions resulted in maximum change in methylation signals. To evaluate the influence of commonly occurring cancers on the methylation pattern of the nine selected markers, body fluids were collected from cancer patients. All tumor samples showed only slight differences in methylation patterns, and the discriminatory ability of the markers was retained. The only exception was the vaginal fluid-specific markers which showed marked reduction in the methylation level of the markers as compared to healthy vaginal fluid samples. Specific cis-SNPs were also found to affect methylation pattern of four target CpG sites, e.g., the sequence variant G167A was found to decrease methylation of the target CpG in saliva-specific markers. To evaluate the feasibility of applying the above findings in a forensic setting, most of the studies reported above were performed with a mixture of various body fluids and with low quantities of DNA (1 ng).

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Though body fluid identification using methylation primers is proving to be a robust technique, the candidate markers must be tested across ethnic groups and geographical locations to ensure their stability and sensitivity. With increased development of foolproof methods of methylation analysis, methylation markers for body fluid identification will soon become a mainstay of forensic investigations.

Identification of Monozygotic Twins Monozygotic twins (MZ) account for 1 in 250 live human births, and despite their common genetic background, such twins frequently demonstrate considerable phenotypic discordance (Pirazzini et al. 2012; Li et al. 2013a). However, discriminating MZ twins is still not easy in the field of forensic DNA analysis. Epigenetic differences in MZ twins are present since birth (Ollikainen et al. 2010). Small differences in methylation patterns in neonates become magnified with age (Fraga et al. 2005). Recently, large-scale studies on MZ twins have been conducted to demonstrate the roles that inheritance of methylation patterns and environmental factors has in determining the phenotypes of complex diseases and how these findings can be applied to forensics. The first large-scale MZ twin study examined DNA methylation and histone acetylation at multiple genomic regions in 30 male and 50 female Spanish MZ twin pairs with age range 3–74 years and mean (SD) age 30.6 (14.2). MZ twins had very similar epigenetic profiles, indicative of high epigenetic heritability. However, epigenetic variability increased with age across multiple tissues, and interestingly, the greatest differences were observed post hoc in twins who differed most in lifestyle (e.g., smoking habits, physical activity, or diet). The authors coined the term “epigenetic drift” for the increasing epigenetic divergence observed with age (Fraga et al. 2005). Another large-scale twin study of DNA methylation used a high-resolution DNA methylation array in three tissues [buccal, gut, and white blood cells (WBC)] in approximately 20 MZ twin pairs and across two tissues (buccal and WBC) in 20 dizygotic age-matched twin pairs. Overall, it was found that MZ twins have more similar DNA methylation patterns than dizygotic twins across tissues. The most heritable CpG sites were correlated with functional regions and promoters, suggesting that the more functionally relevant methylation signals were under stronger genetic control (Kaminsky 2009). Xu et al. (2015b) studied methylation levels of three CpG sites in the LINE-1 promoter region of 119 healthy, MZ twins. A total of 12.61% of the MZ pairs showed differences in LINE-1 methylation. The differences could be attributed to diet, environment, habit, etc. Differential methylation in MZ twins has also been reported at various other loci (Li et al. 2013a; Du et al. 2015). Various studies on MZ twins in relation to autoimmune and psychiatric disorders have also reported DNA methylation variation. Since monozygotic (MZ) twins share a common DNA sequence, their study is ideal for exploring the contribution of DNA methylation to disease etiology.

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More twin-based studies have been performed in respect of psychiatric disorders (clinical depression, bipolar disorder, schizophrenia) than any other disease type. Dempster et al. (2011) working on schizophrenia and bipolar disorder analyzed whole methylome data with an Illumina 27 K methylation array. Five genes – GGN, SLC117A, SMUG1, SOX1, and TCF7L2 – highly discordant for methylation were significantly associated with psychosis. Differential methylation was also observed in the CTNNA2 gene, which contains an intron-encoded gene, LRRTM1, previously associated with psychosis in a parental-origin-specific manner. A recent study of Córdova-Palomera et al. (2015) looked at both differential and variable methylation patterns between MZ twins concordant and discordant for major depressive disorder. This was a whole genome study using bead chip technology. Several differentially methylated probes associated with genes previously associated with major depressive disorder (most notably WDR26, which has been proposed as a biomarker for depression) were identified. Variably methylated probes were located in genes such as CACNA1C, IGF2, and MAPK11 (p38 MAP kinase), showing enrichment for biological processes such as glucocorticoid signaling. The study of Castellani et al. (2015), focused on schizophrenia, revealed a total of 26 genes with specific differentially methylated regions (DMRs) – some of these regions were shared with parents; others were novel. The majority of genes could be categorized as belonging to the “cell death and survival” or the “cellular movement and immune cell trafficking” networks. Thus, methylation studies in a disease setting have confirmed the disease association of previously known genes, while indicating that differential methylation may have a significant effect on the etiology of the disease. However, in terms of forensic utility, additional, large-scale studies on the differences between monozygotic twins are needed. Current large-scale twin-based studies include EpiTwin (http://www.twinsuk.ac.uk/), a study that aims to discover methylated genes responsible for discordance of ten common traits and diseases. The study is using MeDIP-seq on blood samples to assay epigenomic differences in 5,000 adult UK twins aged 16–85, discordant and concordant for a wide variety of diseases and environments. Another ongoing large-scale prospective study consists of a cohort of Australian newborn twins (Craig 2010). These data will prove invaluable to unravelling the timing of methylation changes over the lifetime of an individual. A Norwegian study is exploring healthy twins for DNA methylation and histone modification pattern variability across the genome. Another ongoing project that presents perhaps a more cost-effective approach to next-generation epigenomic studies is that undertaken by the ENGAGE consortium (http://www.euengage.org/), where MeCAP-seq is being performed by sample pooling across multiple traits in discordant twins. Although it must be stated that twins are no more likely to be criminals than the general population, the legal cases involving MZ twins are high profile. For example, the genetic identity of MZ twins can allow twins to provide each other with alibis in criminal cases. Differences in epigenetic profiles between MZ twins, if consistently replicated, could in future lead to closing this particular legal loophole.

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DNA Methylation Differences Between Ethnic Groups Several studies have reported epigenetic differences among ethnic groups (Fraser et al. 2012; Heyn et al. 2013). DNA methylation analysis of different human populations has revealed that African Americans have predominantly lower genome-wide levels of methylation as compared to Caucasians (Adkins et al. 2011; Terry et al. 2008). Methylation aberrations associated with diseases such as cancer and diabetes also show differential methylation patterns among different ethnicities (Cappetta et al. 2015; Wang et al. 2012). For example, Kwabi-Addo et al. (2010) examined the methylation status of the prostate cancer-related genes, GSTP1, AR, RARβ2, SPARC, TIMP3, and NKX2-5 in African American and Caucasian American populations. The study revealed higher methylation levels of genes such as NKX2-5 and TIMP3 in African Americans as compared to Caucasians. Heyn et al. (2013) analyzed DNA methylation differences in lymphoblastoid cell lines (LCLs) from African Americans (AF), Caucasian Americans (CA), and Asian Americans (AS). More than 406,021 CpG sites in the human genome were analyzed. Particularly, 172 AF, 129 CA, and 138 AS CpG sites revealed DNA methylation that differed significantly between the ethnic groupings. These CpG sites, displaying population-specific differential methylation, were termed pop-CpGs (Heyn et al. 2013). The presence of DNA methylation differences between an African and a European population using a 27,000-CpG-site microarray platform has been previously reported (Fraser et al. 2012). Genetic and environmental factors contribute to the racial/ethnic difference in global DNA methylation. Most of the DNA methylation differences among ethnic groups are attributed to variation in allele frequencies between populations and complex GxG or GxE interactions. Similar differences were observed across tissues (Fraser et al. 2012). Exogenous factors affecting DNA methylation also include socioeconomic status, diet, and drug use. Nielsen et al. (2010) reported DNA methylation differences between heroin addicts and controls within three ethnic groups. The CpG island, upstream of the promoter of the μ-opioid receptor gene (OPRM1), consisting of 16 CpG dinucleotide sites was analyzed. The overall methylation level of the CpG sites was significantly higher in former heroin addicts when compared with the controls (point-wise P = 0.0457). However, in African Americans, the degree of methylation was significantly decreased in former heroin addicts at the +12 CpG location. In Hispanics, the degree of methylation was increased in former heroin addicts at the 25, 14, and +27 locations. The inference of ethnic background by the use of DNA methylation markers adds a new dimension in medical research and forensic casework. However, definition of race and ethnicity is not stable. The ancient human introgression into the parental genomes introduces a large amount of heterogeneity between ethnic groups. For example, Caucasians have up to 6% Neanderthal DNA, Asiatic populations have up to 12% Denisovan as well as up to 4% Neanderthal DNA, and West Africans have up to 8% AA (Ancient African) DNA (Ericksson and Manica 2012). The concept of human classification is also dismissed by others due to legal and ethical issues.

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Predominantly, using epigenetic variation for a classification or inference of ethnicity of the perpetrator of a crime may lead to stigmatization of certain groups within society (Koops and Schellekens 2008). However, in association with other AIM markers (ancestry-specific markers), DNA methylation may be able to not only indicate ethnicity (at least in the broad terms of geographic origin) but may also provide insight into phenotypes such as behavioral traits.

Estimation of Age Aging is a complex biological process characterized by an overall decline in physiological functions and an increased risk of disease over time (Berdasco and Esteller, 2012). Biological age is also influenced by additional parameters such as genetic background and lifestyle. Age estimation is very important in the field of forensics. Some of the methods routinely used are based on dental morphology and bone elongation (Thevissen et al. 2012). Telomere length has been believed to be a promising biomarker for age estimation; however, assays currently used to determine average telomere length are not precise or reproducible and are prone to the influence of additional variables that can alter the estimation (Barrett et al. 2013). The most recently described method of age estimation is to measure sjTRECs accumulation in T-cells; however, this method can only be used to test peripheral blood (T-cells) and therefore is not applicable to other forensically relevant tissues (Kayser and de Knijff 2011). The aging process is associated with specific epigenetic modifications. In fact, the current most promising age predictive biomarker is DNA methylation (Lee et al. 2015). Studies involving identical twins show that epigenetic drift is associated with aging, with the global DNA methylation level decreases with aging. However, many genes or genomic regions have been reported to be hypo- or hypermethylated in an age-dependent manner (Florath et al. 2014). DNA methylation patterns have also been correlated with several age-linked diseases and cancers as well as diseases characterized by premature aging such as Werner’s syndrome and Hutchinson–Gilford progeria (Heyn et al. 2013; Bennett et al. 2015). Several research groups have used Illumina bead array technology to identify numerous CpG sites that are significantly correlated with age; some of which have been used to infer age with a linear correlation model using a select set of DNA methylation markers in a single or within multiple tissues (Bekaert et al. 2015). These studies have shown that DNA methylation patterns of about a third of all CpG dinucleotides in the human genome are influenced by age. These sites are also influenced by ageassociated hypermethylation at CpG islands which preferentially targets genes that are not expressed in blood tissue. In contrast, age-associated hypomethylation targets more highly expressed genes (Yuan et al. 2015). According to Xu et al. (2015a), to establish a rapid and reliable age prediction model in forensic practice, it would be advantageous to employ fewer CpG sites while maintaining the mean absolute deviation value (MAD) at a minimum. Generally, linear regression is used to construct regression models. However,

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correlation between DNA methylation and age is too complicated to be considered as linear. Simple linear regression may not model the data, and the use of higher dimensional methylation data (i.e., a more complex model) is a further challenge for prediction. Xu et al. (2015a) compared four different models including multivariate linear regression, multivariate nonlinear regression, back propagation neural network, and support vector regression (SVR) and found that the most robust model was SVR. Based on just six CpG loci, the model showed the least mean absolute deviation from real chronological age and an average accuracy of 4.7 years. This method is promising in that it provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. Bekaert et al. (2015) also reported four CpG DNA methylation markers that were capable of producing highly accurate age predictions for blood samples from deceased and living individuals. Horvath (2013) developed a multi-tissue predictor of age that allows one to estimate the DNAm age of most tissues and cell types. The clock may be used to measure the DNAm age of human tissues, organs, and cell types – including the brain, breast, kidney, liver, lung, and blood, as well as prenatal brain samples. The “Horvath Clock” was developed using 7,844 samples from 51 healthy tissues and cell types from 82 Illumina DNA methylation array datasets and 5,826 cancer samples from 32 individual cancer datasets. A total of 353 CpG sites were identified that, together, form an aging clock. Of these 160 CpGs correlated negatively with age while the remaining 193 correlated positively with age. Vidal-Bralo et al. (2016) developed and validated biomarkers based on specific changes in DNA methylation referred to as DNA methylation age measures (DmAM). The study targeted the eight most informative CpG sites, based on a training set of 390 healthy subjects. Results were not significantly influenced by sex, smoking, or variation in blood cells. Thus, the DmAM could be used for age indication in blood of adults. Further research needs to be directed toward finding more age-related methylation markers for age prediction from various biological samples. Also, if models of age prediction can be improved with a lower error, we can confidently envisage that one-day biological age might be predicted with accuracy as great as chronological age.

DNA Methylation Changes Associated with Behavior: According to Lester et al. (2011), “Behavioral epigenetics is described as the application of the principles of epigenetics to the study of physiological, genetic, environmental, and developmental mechanisms of behavior in humans and nonhuman animals” where environment consists of the external and internal influences that occur in the mother’s womb, at birth, and in adulthood like social experience, nutrition, hormones, thermal stress, and toxicological exposures. Behavioral epigenetics is an emerging field which explores epigenetic effects on normal brain development, developmental disorders, and psychopathology. However,

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there are only a limited number of studies focusing on behavior epigenetics in humans. Hence, application of behavioral epigenetics to forensics is still a very distant concept. However, it is interesting to know that stress, maternal care, and drugs can alter epigenome which can influence behavior. Several studies have established that both DNA methylation and histone posttranslational modifications are essential for cellular memory (Lattal and Abel 2001; Lindbo and Dougherty 2005). Miller and Sweatt (2007) reported interesting results where the expression of DNA methyltransferases (DNMTs) increased in the adult rat hippocampus following fear conditioning (Feng et al. 2010). Furthermore, blocking DNMT activity blocked fear conditioning. Also, fear conditioning induced methylation and transcriptional silencing of the memory suppressor gene protein phosphatase 1(PP1) and demethylation and transcriptional activation of the plasticity gene reelin. Thus, genes constantly undergo demethylation or remethylation, which can be driven by environment or by experience, potentially leading to temporary or permanent functional changes at the gene level (Li et al. 2013). Several studies in mice indicate epigenetic changes due to maternal care. Frequent licking/grooming was associated with increased methylation of the exon 17 GR promoter in the brain and increased their pup response to stress as adults (Meaney and Ferguson-Smith 2010). Maze et al. (2010) reported that chronic cocaine intake led to downregulation of G9a and H3K9me2, which increased a person’s vulnerability to stressful conditions and increased development of behavioral abnormalities associated with depressive disorders. Behavioral epigenetics may be able to elucidate some tricky questions about numerous complex medical conditions, including but not limited to schizophrenia, bipolar disorder, clinical depression, neurodegenerative disorders, and even social challenges such as cognitive changes during aging, suicide, etc. But, due to the complex nature of these disorders, the problem arises in terms of applying wellcontrolled study data to complex diseases. Moreover, it is also very tricky to translate research conducted on heterogeneous groups of people to each individual person with in the group. There will be individual variation even within the study groups and much more between the groups and across studies (Gunter 2015). Epigenetic modifications related to behavior changes may in future provide additional information on suspect’s behavior.

Future Directions DNA methylation-based forensic investigation promises immense potential. However, comprehensive study needs to be done into environmental factors influencing the stability of the methylation marker. Also, tissue specificity of DNA methylation needs to be considered when analyzing different fluids/tissues found at the crime scene. To allow development of an epigenome forensic database, transgenerational inheritance of DNA methylation patterns within coding genes and target loci has to

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be quantified. Additionally, population epigenetics should take center stage to highlight population-specific DMRs. Methylation-specific chips have significantly lowered the bar for whole genome methylation screening. Though most chips tend to represent only one or two CpG islands per gene and are often biased toward cancer-associated genes, soon chips covering the whole human exome will be available. Based on these chips, and identification of forensically important genes and alternatively methylated sites, forensically informative methylation chips should soon be developed. As the technologies needed to advance our knowledge of epigenetics and its effect on the human genome and phenome have advanced, the bioinformatics tools needed to analyze these data have lagged behind. This is particularly the case in the forensic sphere. All current applications and tools are either bespoke or have been repurposed. This is particularly a problem in epigenomics as most effects are differential rather than being binary. Strong statistical tools are needed to discriminate signal from noise. Bisulfite sequencing though gaining in popularity requires a reference genome for assembly (Fig. 4). In addition, as unmethylated sequences are converted to uracil (which reads as thymine on sequencing) and methylation can be strand specific, not only do bisulfite-treated sequences not match the original reference but also the two strands may not be complementary. This requires new methods of mapping bisulfite-treated assembly fragments to a reference potentially in a strand-specific manner. Though such applications exist, they are in their infancy. This makes reassembly of whole genome bisulfite-treated sequence extremely difficult. Currently, there is no assembler for bisulfite-treaded DNA so that a reference has to be sequenced first before reassembly of bisulfitetreated DNA onto this template can be done. There is a dire need for new bioinformatics tools in this area, particularly when dealing with the thymine bias in the data (which throws most read mappers and all current assemblers). Before routine bisulfite treatment can be used in forensics, this gap in software needs to be addressed.

Dictionary of Terms • tDMRs – DNA methylation patterns vary depending on the type of tissue. tDMRs regulate tissue-specific gene expression. • CpG islands – Are regions of the genome about 200 bp – few kb in length, with high density of the CpG dinucleotides. Most CpG islands associated with gene promoters are unmethylated. Methylated CpG islands are associated with transcriptional repression. • Epigenetic drift – Refers to changes in epigenome with changes in lifestyle, environment, and aging. Aging results in small defects in transmission and maintenance of epigenetic information.

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Fig. 4 Mapping of bisulfite sequenced DNA to a reference genome. After bisulfite conversion and sequencing, regions of interest are PCR amplified prior to library preparation and sequencing. Using bioinformatics tools, reads are cleaned by removing adapters, eliminating poor-quality reads, and ensuring that both reads of a paired end are present. Quality checked and cleaned reads are mapped to a reference genome with bisulfite-specific read mappers, such as Bismark, BSMap, Walt, Bison, BRAT, and BAM-ABS. A consensus sequence is then exported, and differences between the assembled and reference sequence can be called as methylated/unmethylated bases

• Bisulfite conversion – Is a process of deamination of unmethylated cytosine to uracil in DNA after treatment with sodium bisulfite. Methylated cytosines are protected from the conversion to uracil. Following conversion, direct sequencing can be used to determine the methylation status of CpGs at single-nucleotide resolution. • Trans-generational epigenetic inheritance – Epigenomic changes that are transmitted from one generation to future generations. Environmental conditions like diet, stress, and socioeconomic status can alter the epigenetics of the germline and be inherited.

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Key Facts of DNA Methylation in Forensics • DNA methylation is one of the well-characterized epigenetic modifications. The stability of methylation patterns makes it a good biomarker for diagnostic and forensic application. • Methylation patterns differ between human tissues and between phenotypes. This differential methylation can be targeted to design markers for identification of forensically relevant body fluids, estimation of age, and identification of monozygotic twins. • DNA methylation marks modify with the changing environment. Diet, stress, social conditions, age, emotions, and habitat can bring about changes in DNA methylation. • Not only genes but also methylation marks can be passed on from the grandfather to the grandson. • Bisulfite conversion and sequencing is considered the best method to study methylation profile.

Summary Points • DMRs which differ between phenotypes have been used for development of methylation markers for use in forensics. • Gene-based tDMRs have been targeted to identify and differentiate blood, saliva, semen, and vaginal fluid found at a crime scene. • The CpG loci (tDMRs) for DACT1 and USP49 genes are semen-specific hypomethylation markers and can differentiate semen from blood, saliva, semen, menstrual, and vaginal fluid. • Monozygotic twins can be accurately differentiated based on methylation differences in target genes. • Epigenetic variability among MZ twins increases with age across multiple tissues. This process is known as epigenetic drift. • DNA methylation differences due to aging can be targeted to estimate age of forensic samples. • Epigenetic clock has been developed that allows one to estimate the DNAm age of most tissues and cell types. • DNA methylation differences are also observed in ethnic groups. • Genetic and environmental factors contribute to the racial/ethnic difference in global DNA methylation. • Most of the DNA methylation differences among ethnic groups are attributed to variation in allele frequencies between populations and complex GxG or GxE interactions. • Whole genome methylation analysis has paved the way for identification of novel, stable, and inheritable CpG methylation sites which can assist in development of forensic methylation database.

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• New bioinformatics tools for bisulfite sequencing data analysis needs to be developed for efficient interpretation of differential methylation and to aid in development of stable methylation markers.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetic Changes Associated with Dietary Restriction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insect Epigenomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methylation with Response to Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Histone Epigenetic Marks Dependent on Nutritional Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Epigenetic Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insects as Models for Epigenetics-Mediated Diseases in Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insects Own Completely Sequenced Genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trans-generational Effects Can Be Facilitated in Insects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Diet nutrition has been confirmed to influence health for decades. Proper total nutrition intake is beneficial for organisms’ health from yeast, insects, rodents, to humans. Epigenetic factors are considered to be one of the mediators of the dietary effects, which make the effects remembered from one cell generation to the next by marking on the genome. In this chapter, we will review the accumulative evidences about the association between epigenetic factors (including DNA methylation, histone modifications, and other epigenetic factors), and diet

T. Lian · U. Gaur · M. Yang (*) Institute of Animal Genetics and Breeding, Sichuan Agricultural University, Chengdu, Sichuan, China e-mail: [email protected]; [email protected]; [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_25

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nutrition especially dietary restriction, and its implications for humankind. At the same time, we suggest that insects can be employed as efficient models to investigate the fundamental basis of human diseases especially the involvement of epigenetic mechanisms, because insects owe inexpensive cost, easy accessibility, shorter generations, along with conserved epigenetic mechanisms and signaling pathways with humans. Keywords

Epigenetics · Dietary restriction · Nutrition · DNA methylation · Histone modifications · Insect · Diseases · Trans-generational effects · Genome List of Abbreviations

5mC 6mA 10-HDA ChIP-seq DMGs Dnmts DR EGFR H3K27ac HAD HDAC HDACi LC-MS/MS lnc-RNAs LPHC OCM RJ SAM Sir2 TEs WJ

5-Methylcytosine N6-Methyladenine (E)-10-hydroxy-2-decenoic acid Chromatin immunoprecipitation sequencing Differentiated methylated genes DNA methyltransferases Dietary restriction Epidermal growth factor receptor Histone H3 at lysine 27 10-hydroxy-2-decenoic acid Histone deacetylase Histone deacetylase inhibitor Liquid chromatography coupled with tandem mass spectrometry Long noncoding RNAs Low-protein, high-carbohydrate diet One-carbon metabolism Royal jelly S-adenosylmethionine Sirtuin-2 Transposable elements Worker jelly

Introduction The term epigenetics was first introduced by Conrad H. Waddington in 1942, which is broadly defined as heritable changes in genetic expression without change in the DNA sequence itself (Waddington 2012). DNA methylation, chromatin modifications, and RNA-based mechanisms (noncoding RNAs account the most) constitute the important epigenetic mechanisms out of which, most of the epigenetic changes are based on DNA methylation and histone modifications. The most studied and important epigenetic change is DNA methylation which is also the very first discovered epigenetic mechanism. Histone modifications are covalent

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posttranslational modifications to histone proteins and mainly include methylation, phosphorylation, and acetylation. Epigenetics is, reversible, susceptible to changes, and is shaped by various environmental factors including nutrition composition of diet, some kinds of stress, environmental exposure (such as chemical pollutants), temperature, etc. Epigenetic variations contribute to the phenotypic plasticity, resetting the aging clock and aging rate, and most importantly, it plays an important role in cancer. Sometimes if the establishment and maintenance of epigenetic modifications are changed, the effect will be inherited by subsequent generations. Accumulating evidences suggest the role of diet in modifying the epigenetic changes (Fig. 1) and thereby inducing the beneficial effect in organisms’ phenotype including the improvement in some aging-related diseases, healthspan, and lifespan. Food phytochemicals such as polyphenols in tea, isoflavone, resveratrol, curcumin, and some nutrients found in vegetables and fruits (Chen et al. 2011) play an important role in anticancer mechanisms through epigenetic alterations. In addition, various dietary interventions have been conducted to explore its role in epigenetic modifications, which ultimately influence the aging rate and prevent the cancer. So far, the most common and useful dietary intervention is dietary restriction (DR) (it includes calorie restriction), which is a dietary regimen involving the reasonable reduction in total nutrition intake without causing health problems. Generally, it is

Fig. 1 Effects of diet on epigenetic changes. Food phytochemicals and dietary/nutritional interventions modify DNA or histone protein, then influence gene transcription and expression, and finally exert the effects on phenotypes including aging, aging-related diseases, lifespan, and healthspan.

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carried out by two ways, which are change in the nutrient composition of diet (such as the ratio of protein and carbohydrates, the content of certain amino acid such as methionine) and intermittent fasting (reduced feeding frequency). DR has been applied on various organisms ranging from yeast, C. elegans, to insects, rodents, and humans, showing its role in antiaging and anticancer therapy (Fontana and Partridge 2015; Kim et al. 2016a; Mattocks et al. 2017). Here, we will highlight insects as the model to discuss the impacts of dietary nutrition on epigenetics, followed by its implications in humankind.

Epigenetic Changes Associated with Dietary Restriction Insect Epigenomics DNA Methylation DNA methylation is one of the epigenetic mechanisms, which is responsible for the transcription factor-DNA association and consequently regulating the gene expression and various cellular processes (Smith and Meissner 2013). There are several kinds of methylated bases of DNA in chromosome including 5-methylcytosine (5mC), N6-methyladenine (6mA), and N4-methylcytosine (Zhang et al. 2015), which have been found in a variety of species (Fig. 2). These methylated bases are generated during the post-replicative DNA modification by specific DNA methylases (Wion and Casadesus 2006). Among these, 5mC is the most commonly studied covalent modification which is generated by adding a methyl group to the fifth carbon of the pyrimidine ring of cytosines by DNA methyltransferases (Dnmts). In mammals, Dnmts are of three types based on the difference in structure and function: Dnmt1, Dnmt2, and Dnmt3 (Bestor 2000). Cytosine methylation is catalyzed by two types of Dnmts (Dnmt1 and Dnmt3). Dnmt1 is responsible for maintaining the methylation from the template strand to the newly synthesized strand, while Dnmt3 works in de novo methylation (Bird 2002). However, in insects, Dnmts vary in different taxa. Many insect species own the complete set of DNA methyltransferases including Apis mellifera, Acyrthosiphon pisum, Camponotus floridanus, Solenopsis invicta, Pogonomyrmex barbatus, Linepithema humile, Atta cephalotes, Daphnia pulex, and Nasonia vitripennis (Lyko and Maleszka 2011), whereas some species lack one or two DNA methyltransferases. For example, Drosophila only possesses Dnmt2 (Marhold et al. 2004); Bombyx mori (Xia et al. 2004), Tribolium castaneum (Zemach et al. 2010), and Schistocerca gregaria (Falckenhayn et al. 2013) possess Dnmt1 and Dnmt2. In invertebrate genomes especially in insect species including Diptera, Hemiptera, Hymenoptera, Lepidoptera, Coleoptera, Odonata, and Orthoptera (Kim et al. 2016a), the cytosine methylation level is very low (0–10%) compared to mammals (70–80% of CpG sites are methylated) (Bird 2002) (Table 1). In contrast, vertebrate genomes are globally methylated, and cytosine methylation is mostly found within CpG dinucleotides. Invertebrate methylomes show relatively sparse cytosine methylation distribution, mainly confined to the genic regions (promoters,

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Fig. 2 Representative methylated bases of DNA in chromosome. (a) 5-methylcytosine (5mC); (b) N6-methyladenine (6mA); (c) N4-methylcytosine. SAM S-adenosylmethionine.

exons, and introns). Most of the cytosine methylation in insects occurs within CpG dinucleotides except Drosophila, where low level of cytosine methylation is found in CHH trinucleotides (mainly refined to CpA and CpT) (Lyko et al. 2000; Capuano et al. 2014). Besides, in hymenopteran genomes such as ants and bees, transposable elements (TEs) are also methylated with very low methylation level (Lyko et al. 2010; Bonasio et al. 2012). In addition, the alternative splicing role of cytosine methylation in insects has shown its impact in behavioral regulation and caste specificity in eusocial insects (Bonasio et al. 2012; Foret et al. 2012; Li-Byarlay et al. 2013; Terrapon et al. 2014).

Histone Modifications In addition to cytosine methylation, posttranslational modifications of histone proteins constitute the second class of important epigenetic mechanisms. Histones are the building blocks of the nucleosome, comprised of 147 base pairs of DNA wrapped around a protein octamer containing two copies each of histone subunits H2A, H2B, H3, and H4 (Bednar et al. 1998). Chromatin compact form can affect some molecular processes such as DNA repair, DNA replication, and gene transcription (Cedar and Bergman 2009). Histone posttranslational modifications mainly include histone

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Table 1 Similarity and difference of cytosine methylation in representative invertebrates and vertebrates

Species Apis mellifera

Nasonia vitripennis Silkworm (Bombyx mori) Drosophila melanogaster embryo Drosophila melanogaster adult Caenorhabditis elegans Camponotus floridanus Harpegnathos saltator Desert locust (Schistocerca gregaria) Mus musculus

Homo sapiens

Dnmts Dnmt1, Dnmt2, Dnmt3 Dnmt1, Dnmt2, Dnmt3 Dnmt1, Dnmt2 Dnmt2

Dinucleotides CG, CHG, CHH

DNA methylation level 0.30%

Sequencing category Bisulfite sequencing

References Zemach et al. (2010)

CG, CHG, CHH

0.18%

Bisulfite sequencing

Beeler et al. (2014)

CG, CHG, CHH CG, CHG, CHH

0.11%

MethylCseq Bisulfite sequencing

Xiang et al. (2010) Zemach et al. (2010)

Dnmt2

CG, CHG, CHH

3.40%

LC-MS/MS

N/A

N/A

0.0019–0.0033%

LC-MS/MS

Dnmt1, Dnmt2, Dnmt3 Dnmt1, Dnmt2, Dnmt3 Dnmt1

CG, CHG, CHH

0.30%

Bisulfite sequencing

Capuano et al. (2014) Hu et al. (2015) Bonasio et al. (2012)

CG, CHG, CHH

0.21%

Bisulfite sequencing

Bonasio et al. (2012)

CG, CHG, CHH

1.6–1.9%

EST database

Boerjan et al. (2011)

Dnmt1, Dnmt2, Dnmt3 Dnmt1, Dnmt2, Dnmt3

CG, CHG, CHH

7.60%

LC-MS/MS

CG, CHG, CHH

3–6% (70–80 CpG)

Bisulfite sequencing

Capuano et al. (2014) Bird (2002)

0.10%

methylation, acetylation, phosphorylation, and ubiquitination (Fig. 3). They play important roles in regulating various cellular processes such as the establishment, the maintenance, and the reversal of transcriptional programs. Acetylation and methylation of histone H3 and H4 have been quantified by mass spectrometry in honeybee, which is the first identification and quantitation of histone posttranslational modifications in eusocial insect (Dickman et al. 2013). After that, the chromatin structure of another eusocial insect – ant (Camponotus floridanus) – was determined by employing chromatin immunoprecipitation sequencing (ChIP-seq) which revealed the difference in histone modifications between castes (Simola et al. 2013). Furthermore, the identification of histone-modifying enzyme systems in Drosophila and

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Fig. 3 Histone posttranslational modifications. Histone posttranslational modifications mainly include histone methylation, acetylation, phosphorylation, and ubiquitination

migratory locust provides important cues for understanding the epigenetic mechanisms in insects (Guo et al. 2016).

DNA Methylation with Response to Nutrition DNA methylation is reversible and influenced by various environmental factors including nutrition (Fig. 4), thereby inducing long-term changes on gene expression and the phenotype. Previous studies have revealed that folate-mediated one-carbon metabolism (OCM) pathway has an effect on DNA methylation. As we know, folate plays a primary role in the transfer of one-carbon moieties. It plays a role in DNA methylation by providing methylene and methyl groups for the synthesis of S-adenosylmethionine (SAM), which is the methyl donor for DNA methylation during transmethylation reactions. S-adenosylhomocysteine (SAH) is a by-product, which is the potent inhibitor for DNA methyltransferases. SAH is hydrolyzed to homocysteine and adenosine by SAH hydrolase, and folate deficiency reduces the synthesis of SAM, thereby causing hypomethylation. Folate deficiency is found to be associated with cancer and anemia (Sukla et al. 2014; Mason and Tang 2017). Betaine is the derivative of choline, which is involved in the synthesis of SAM by increasing the remethylation of homocysteine into methionine. Polyphenols, isoflavones, resveratrol, and curcumin inhibit the activity of Dnmts. Among them, high polyphenol content in green tea extends the lifespans in various experimental animal models including Drosophila (Lopez et al. 2016). Insect is considered to be an ideal model to study how nutrients influence DNA methylation due to the most extreme changes in their phenotypes with response to

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Fig. 4 Nutrition effects on DNA methylation cycle. Folate can provide folate B12 to facilitate the formation of methionine through one-carbon metabolism. Betaine is derived from choline. Polyphenols, isoflavones, resveratrol, and curcumin inhibit the function of DNA methyltransferases and halt the S-adenosylhomocysteine (SAH) production

diet. Polyphenism is when two or more distinct phenotypes are produced from a single phenotype (Fig. 5). Honeybee caste development displays an interesting model to study the effect of diet on DNA methylation. It is the larval diet that determines two female castes, workers and queens. Larvae which feed on royal jelly (RJ), a kind of rich nutrient diet, develop into queens, while the larvae that feed on worker jelly which is more dilute, and equivalent to restricted diet, develop into workers. Interestingly enough, when Dnmt3 was knocked down by RNA interference in honeybee larvae (Kucharski et al. 2008), the queen phenotype was induced. Similarly, when larvae were fed on royal jelly, it resulted in reduced Dnmt3 expression (Shi et al. 2011) and also expressed the queen phenotype. These reports demonstrate a link between nutrition, DNA methylation, gene expression, and phenotype, and also the knocking down of Dnmt3 in larvae has been confirmed to mimic the royal jelly effect on postembryonic development. But how nutrition alters DNA methylation? Does diet directly influence DNA methylation? It has been reported that the specific factor of RJ is a 57-kDa protein, royalactin, which induces the differentiation of honeybee larvae into queens with an epidermal growth factor receptor (EGFR)-mediated signaling (Kamakura 2011), but the direct association between EGFR-mediated signaling and DNA methylation is not elucidated in honeybee (Buttstedt et al. 2016), which suggests an indirect role of diet nutrition in DNA methylation. The role of DNA methylation in diet-nutrition-associated longevity is also demonstrated in honeybee. The lifespan of the queen bee is almost 20 times more, compared to workers, which is only determined by the diet, and the workers’ larval head contained a much higher number of differentiated methylated genes (DMGs) than queen larvae (Foret et al. 2012). It is evident from all the above-stated

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Fig. 5 Schematic representation of dietary effect on DNA methylation, gene expression, and phenotype honeybee queen phenotype. HAD 10-hydroxy-2-decenoic acid, RJ royal jelly, WJ worker jelly, HDAC histone deacetylase, EGFR epidermal growth factor receptor. Dashed line shows the EGFR-mediated differentiation of honeybee larvae into queens

information that DNA methylation plays an important role in honeybee lifespan with response to royal jelly (Ford 2013), which is similar to the effect of DR in other organisms (Fontana and Partridge 2015). In addition, DNA methylation is also linked to larval nutrition in horned beetles (Snell-Rood et al. 2013) and caste determination in ant (Bonasio et al. 2012), further supporting the hypothesis that DNA methylation could be one of the mechanisms underlying phenotypic plasticity. Furthermore, DNA methylation can also have an effect on hexamerins, one of the amino acid sources during metamorphosis, which is known to be responsible for regulating caste differentiation in wasps and termites (Zhou et al. 2006; Hunt et al. 2007).

Histone Epigenetic Marks Dependent on Nutritional Stimuli Besides DNA methylation, histone modifications are also one of the epigenetic markers, which stand at the crossroad of nutrition and development (Romanoski et al. 2015). In honeybee, (E)-10-hydroxy-2-decenoic acid (10-HDA) is the major component of royal jelly, which can regulate Fas gene expression during early honeybee larval development and also facilitates caste switch by changing histone deacetylase inhibitor (HDACi) activity (Spannhoff et al. 2011). The study of chromatin modifications in carpenter ant (Camponotus floridanus) also suggests the role of HDA in regulating the distribution or amount of histone H3 at lysine 27 (H3K27ac), thereby facilitating the caste switch (Simola et al. 2013).

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The expression of Sirtuin-2 (Sir2), which is one of the histone deacetylases, is higher in the heads of queens than that in worker bees (Guan et al. 2013). It is unclear whether the difference is originated from the differential nutritional status during larval development. Paoli et al. in 2014 found that Sir2 is nutritionally responsive in honeybees, and it displayed an elevated mRNA expression with a specific ratio of dietary amino acids to carbohydrate (1:500), which demonstrated the potential role of Sir2 in adult worker honeybee lifespan in response to dietary restriction (Paoli et al. 2014). Also overexpression of Sir2 in fat body and neurons can increase Drosophila lifespan under dietary restriction (Bauer et al. 2009; Hoffmann et al. 2013). Drosophila Sir2 mutant heterozygotes also displayed an extended lifespan during amino acid starvation (Slade and Staveley 2016). These results indicated the changes in histone modification with response to nutrition, but whether histone modification is a cause or mere effect needs to be explored further.

Other Epigenetic Mechanisms In addition to DNA methylation and histone methylation, other epigenetic mechanisms including PIWI RNA and long noncoding RNAs (lnc-RNAs) are also influenced under the different nutritional conditions. In honeybee, there are two genes encoding PIWI-like proteins, Am-aub and Am-ago3, and expression of these proteins is found to be higher in queen than workers. Furthermore, these also encode some small noncoding RNAs (known as piRNAs), which are differentially methylated in queens and workers (Liao et al. 2010). These results highlighted the fact that the difference in early nutrition can induce the changes in piRNAs, but it is not clear whether the impact is direct. Although there is no comprehensive identification of lnc-RNAs in any eusocial insects, we generated the report of lnc-RNA difference in Drosophila under dietary restriction and fully fed diet by employing RNA sequencing (Yang et al. 2016), which provided the fundamental source for understanding the nutritional impact on lnc-RNA expression and the functional involvement of lnc-RNAs in certain phenotypes including aging and aging-related diseases.

Insects as Models for Epigenetics-Mediated Diseases in Humans Insects Own Completely Sequenced Genomes Following the rapid development of next-generation sequencing, many insect species methylomes and very few chromatin structures have been characterized as stated above. Among them, Tribolium castaneum is a potential alternative model to screen the epigenetic effects of drugs, which are difficult to detect in mammals (Bingsohn et al. 2016). Fruit fly and honeybee have been well established as models to study the pathomechanisms of human diseases, especially the epigenetic basis of these

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diseases (Bergman et al. 2016; Mukherjee et al. 2015). Other insects including ant, aphids, and greater wax moth are attractive and tractable models for studying the host-microbe metabolism of human pathogens (Kim et al. 2016b). As stated above, insects have already been considered as the ideal model to investigate the epigenetic basis of human diseases including aging, cancer, neurodegenerative disorders, inflammation, and sepsis. Among these, fruit fly has been widely used in Alzheimer’s disease (behavior (Prüßing et al. 2013) and clinical drugs such as HDAC6 (Xiong et al. 2013)), Parkinson’s disease (epigenetic plasticity and therapy drugs such as HDAC inhibitors including sodium butyrate (Bayersdorfer et al. 2010)), Huntington’s disease (treatment methods such as class I and II HDAC inhibitors (Lee et al. 2013)), and tumor formation and metastasis (Tipping and Perrimon 2014). Silkworm (Bombyx mori) has also been used to explore the potential epigenetic plasticity of Parkinson’s disease (Tabunoki et al. 2013). Greater wax moth and mosquito have been employed to investigate the epigenetic mechanism of inflammation and sepsis (Freitak et al. 2014; Mukherjee and Vilcinskas 2014).

Trans-generational Effects Can Be Facilitated in Insects Trans-generational effects transmitted via the male germline have received broad attention recently, which involve imprinting, DNA methylation, histone modifications, and noncoding RNA transcripts. Although human cell lines have been used as a surrogate system to study the molecular basis of many diseases, the investigation of trans-generational effects is not allowed, for example, the epigenetic transmission of gene regulation states through meiosis. Insects bridge the gap to facilitate the study of trans-generational effects and screen the novel drugs for human diseases. Mainly because, firstly, the life cycle of insects is very short so the researchers can control and assess the corresponding parameters easily and rapidly. For example, the life cycle of the red flour beetle is about 30 days which allows the researchers to study as many as 10–12 generations within a year. Drosophila’s life cycle is around 10 days under 25  C, which is much quicker, thereby accelerating the development of scientific experiments. Secondly, polyphenism in insects provide the opportunity to study the environmental effects on human development and longevity (Paoli et al. 2014). Thirdly, fecundity is easy to assess and study in insects. Based on this, researchers can measure the eggs across several generations, gender ratio, and body weight. All three of these parameters are influenced by environmental stimuli and regulated by epigenetic mechanisms that are conserved between insects and mammals. Furthermore, as stated above, insects also regulate the same pathophysiological processes as mammals. Finally, insects make the epigenetic experimental manipulation accessible to study the inheritance through meiosis, for example, following exposure to pathogens, chemicals, and other environmental stimuli (Mukherjee et al. 2015).

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Summary Epigenetic mechanisms including DNA methylation, histone modifications, and noncoding RNAs are influenced by various environmental factors including nutrition especially dietary restriction. The impact can convert into gene expression, thereby leading to changes in phenotype, which is further heritable through meiosis. But some phenotypes are complex such as fecundity, longevity, and susceptibility to diseases and are difficult to detect accurately in humans. Insects can be employed as efficient models to investigate the fundamental basis of human diseases, because of inexpensive cost, easy accessibility, shorter generations, and conserved epigenetic mechanisms and signaling pathways.

Dictionary of Terms • Dietary restriction (DR) – Dietary restriction is a nutritional intervention for delaying aging and aging-related diseases via reducing specific or total nutrient intake without causing malnutrition. • Phenotypic plasticity – When an organism can change its phenotype in response to the change in environment, it is called phenotypic plasticity. • Chromatin immunoprecipitation sequencing (ChIP-seq) – ChIP-seq is a method to study the interaction between protein and DNA, which is aimed at identifying the location of DNA binding sites for a target protein. • Polyphenism – When one phenotype develops into two or more phenotypes under different environmental stresses, which is an irreversible alternative phenotype, it is called polyphenism. • Trans-generational effects – Trans-generational effects refer to the heritable genetic information which is inherited from the parental generations to the offsprings.

Key Facts • Epigenetic mechanisms include DNA methylation, histone modifications, and noncoding RNAs, which are influenced by various environmental factors including nutrition. • Nutrition during early development can have long-lasting effects. • Nutrients from our food are channelized into a biochemical pathway that extracts methyl groups and then attaches them to our DNA.

Summary Points • This chapter focuses on dietary restriction-induced epigenetic changes in insects and the implications in humankind.

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• Insect epigenetic genome is influenced by nutritional stress especially dietary restriction. • Insects have the advantage in studying some complex phenotypes such as fecundity and longevity. • Insects own the completely decoded genomes. • Trans-generational effects can be studied easily in insects. • Insects can be employed as the model to study epigenetics-modulated diseases in humans.

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Nutritional Programming and Effect of Ancestor Diet in Birds

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Mireille Morisson, Vincent Coustham, Laure Frésard, Anne Collin, Tatiana Zerjal, Sonia Métayer-Coustard, Loys Bodin, Francis Minvielle, Jean-Michel Brun, and Frédérique Pitel

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutritional Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Maternal Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manipulation of Egg Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Post-hatch Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multigenerational Influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implication for Humankind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Epigenetic Inheritance in Birds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Phenotype variability depends on genetics and environmental factors. Improving farm animal performances relies on genetic variability, but the possible improvement of selection schemes taking into account nongenetic transgenerational M. Morisson · L. Bodin · J.-M. Brun · F. Pitel (*) GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France e-mail: [email protected]; [email protected]; [email protected]; [email protected] V. Coustham · A. Collin · S. Métayer-Coustard INRA – URA, INRA, Nouzilly, France e-mail: [email protected]; [email protected]; [email protected] L. Frésard Department of Pathology, Stanford University, Stanford, CA, USA e-mail: [email protected] T. Zerjal · F. Minvielle UMR INRA/AgroParisTech – GABI, Jouy-en-Josas, France e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_40

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inheritance has become a topic of choice. Indeed, the parental diet may influence the adult phenotype of the offspring, and more and more studies suggest that information acquired from environmental exposures may be transmitted across generations. In this review, we focus on nongenetic inheritance of diet effects in birds, either as parental effect, that is, “nutritional programming,” or through the transmission of information across several generations, via “transgenerational epigenetic inheritance.” Compared to mammal models with regard to their closer proximity with humans, bird models have the added benefit to minimize maternal confounding effects by a direct manipulation of the egg content. Keywords

Nutritional programming · Diet · Epigenetic inheritance · Transgenerational · Birds · Chicken · Duck · Quail · Poultry · Genetics · Epigenetics · Farm animals · Selection List of Abbreviations

ABCA1 ACC-alpha BDNF CPT-I CYP7A1 DHA DOHaD EPA IGF-I IGF-IR NAFLD PGC TAG

ATP Binding Cassette Subfamily A Member 1 Acetyl-CoA Carboxylase Alpha brain-derivated neurotrophic factor Carnitine Palmitoyltransferase 1A Cytochrome P450 Family 7 Subfamily A Member 1 docosahexaenoic acid Developmental Origins of Health and Disease eicosapentaenoic acid Insulin Like Growth Factor 1 Insulin Like Growth Factor 1 Receptor Non-alcoholic fatty liver disease primordial germ cells triacylglyceride

Introduction Genetic selection has been used for centuries to improve farm animal performances through animal breeding. It relies on the prediction of the genetic value of future offspring, whereas actual performance will depend on both genetic value and environmental effects. In poultry, the increase of performances in the last decades was mainly due to genetics, while improvement due to progress in nutrition affected production traits marginally: in the broiler industry, 85–90% of the increase in carcass yield in the last 50 years came from genetic selection (Havenstein et al. 2003). Yet, one of the current questions in animal breeding is to evaluate if the transmission of information to the next generation is through the DNA only (Feeney et al. 2014; Goddard and Whitelaw 2014), because recent studies have shown that epigenetic marks could carry information between generations (Miska and

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Ferguson-Smith 2016). The prenatal environment is known to influence the adult phenotype in several species (Ho and Burggren 2010), and although the mechanisms of transmission of epigenetic marks induced by environmental exposure, via “transgenerational epigenetic inheritance,” are not yet fully known (Jablonka and Raz 2009), a number of recent studies have highlighted the involvement of histone modification, DNA methylation, or small RNAs in germline-dependent epigenetic effects (Chen et al. 2016; Miska and Ferguson-Smith 2016). Birds are different from other vertebrates with regard to epigenetic phenomena: genomic imprinting seems to be absent (Fresard et al. 2014; Wang et al. 2015) and sex chromosome dosage compensation is not a chromosome-wide mechanism (Ellegren et al. 2007; Itoh et al. 2007). Moreover, germ cells seem to isolate very early in the first cell divisions (Tsunekawa et al. 2000), but the methylation state of their genome or its dynamics of reprogramming during embryogenesis is still unknown. On the other hand, the easy access to the egg content, the possibility of testing several diet compounds for environmental toxicology, and the simple control of embryonic development conditions make avian species models of choice for epigenetic studies, as they could bring interesting results that can be applied more widely, including to humankind. The possibility of improvement of offspring performances through the modification of parental environment opens new perspectives in animal breeding. This can be achieved by including nongenetic transmission information in the quantitative genetics models of artificial selection programs. This review will focus on nongenetic inheritance of dietary treatment effects in birds, from parent to progeny (nutritional programming), or through transgenerational diet effect.

Nutritional Programming The rationale behind nutritional programming is that nutritional perturbations (i.e., variation in the quality or quantity of nutrients) during critical periods of early life have long-term impacts on physiological or morphological states and may even predispose to health problems. It is being actively explored in many species including social insects such as honeybees and mammalians including rodent laboratory models, farm animals, and humans. A remarkable example comes from honeybees’ female larvae which can develop into either long-lived reproductive queens or shortlived sterile workers, depending on the diet (Vaiserman 2014; Mukherjee et al. 2015). DNA methylation and histone modifications are epigenetic modifications which affect gene expression, leading to distinct phenotypes from identical genomes. However, it is not known to what extent these epigenetic modifications can contribute to the final adult phenotype construction in complement to the genetic information. Also, different tissues are impacted by the early diet. The perinatal epigenomes, which are set up during cellular differentiation, are very susceptible to the nutritional environment. Epigenomes are transmitted through mitoses, contributing to maintain the metabolic state and tissue specificity throughout life as discussed by Skinner (2011). For example, pups of rats having received methyl donor-deficient diet during

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gestation and lactation showed cardiomyopathy (Garcia et al. 2011), liver steatosis (Pooya et al. 2012), as well as impaired small intestinal differentiation and barrier function (Bressenot et al. 2013). As a matter of fact, in mammals, the nutritional environment of the developing embryo directly depends on the maternal diet, and a large number of animal models have been developed to study nutritional programming. It is now established that maternal nutritional imbalance, through global overnutrition, undernutrition, or deficiencies in certain nutrients, leads to impaired physiological or morphological states and may even predispose to disease susceptibility in adult life (see Canani et al. 2011; Gabory et al. 2011; Lillycrop and Burdge 2011; Feeney et al. 2014; Jang and Serra 2014; Langley-Evans 2015; ChavattePalmer et al. 2016; Murdoch et al. 2016 for reviews). In human, epidemiological long-term studies of progeny from mothers who were fed different diets during embryonic or fetal development led to the hypothesis of the Developmental Origins of Health and Disease (DOHaD) in which epigenetic marks, such as DNA methylation or histone modifications, could provide a persistent memory of earlier nutritional states (see Vickers 2014; Fleming et al. 2015; Langley-Evans 2015; Reynolds et al. 2015; Chavatte-Palmer et al. 2016 for reviews). The knowledge gained from these surveys led to prevention actions with a strong focus on the 1000 first days extending from the conception to the second birthday, as defined by the United Nations and the World Health Organization. In farm animals, the impact of nutritional programming on the adult phenotype has also been reported (see Ibeagha-Awemu and Zhao 2015; Murdoch et al. 2016; Sinclair et al. 2016 for reviews). It should be carefully considered in farm production designs and possibly taken into account in programs for improving economically relevant production traits. On the other hand, far less studies have been published in birds (see Fresard et al. 2013; Dixon et al. 2016 for reviews), although the egg – i.e., the direct embryo nutritional environment – is directly accessible for manipulations in avian species. Indeed, the nutritional environment of the developing avian embryo or hatchling can be modified in three different ways: (a) through the maternal diet which influences the supply of nutrients in the egg, (b) through the manipulation of egg content by removal or injection of different substances, and (c) through the post-hatch nutrition during the very early growing period (Table 1).

Effects of Maternal Diet Changes to the nutritional resources contained in the egg can have an impact on newborn fitness and later on the adult phenotype (see Ho et al. 2011; Reed and Clark 2011 for review). The consequences of an inadequate maternal diet are decrease of egg production, embryonic mortality, or chick weakness at hatch. A large number of studies have been conducted to evaluate the impacts of supplementation or deficiency of a wide variety of nutrients – such as vitamins, minerals, proteins, fatty acids, or antioxidants – to optimize the diet of female avian breeders. For example, hens fed inadequately may produce eggs with deficiencies at different stages of

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Table 1 References for nutritional programming in chicken Type of early nutrition perturbation (a) Maternal diet

Species Chicken

(b) Manipulation of egg content

Supplementation of nutrient

Chicken

Albumen removal

Chicken

Noble 1986; Everaert et al. 2013; Willems et al. 2013, 2014, 2015, 2016

Chicken

Li et al. 2007; Yang et al. 2010; Richards et al. 2010; Xu et al. 2012; Jiang et al. 2016;

(c) Post-hatch nutrition

References Calini and Sirri 2007; Moran 2007; Ni et al. 2007; Rao et al. 2009; Van der Waaij et al. 2011; Aigueperse et al. 2013; Cherian 2015; Koppenol et al. 2015a, b; Duan et al. 2015; Rosa et al. 2016 Calini and Sirri 2007; Wei et al. 2011; Ni et al. 2012; Kadam et al. 2013; Hu et al. 2015; Roto et al. 2016

References are given according to the type of early nutrition manipulation which can be applied through either (a) the modification of the maternal diet, (b) the manipulation of egg content by injection or removal of different substances, or (c) the post-hatch nutrition. The chicken drawings are inspired from https://www.google.fr/search?q=dessinþpoulets&client=firefoxb&tbm=isch&tbo=u&source=univ&sa=X&ved=0ahUKEwj9wYj3k4DSAhUGS RoKHXCZCp4QsAQIGw&biw=1680&bih=915

embryonic development until hatch (Moran 2007). Indeed, most of the work on breeder hen diet has focused on egg production, hatchability, and hatchling vitality, this last trait being very important because the day-old chick is not fed for 48–72 h post-hatch when being shipped from hatching facilities. These studies were very useful to improve production traits in laying and broiler hens, but more recently, an attention has been given to the offspring performances. Thus, an extensive review was published about the effects of breeder nutrition on offspring phenotypes in the poultry industry (Calini and Sirri 2007), showing the influence of broiler breeder’s diet on offspring’s body composition, body weight, growth rate, or health traits . More recently, it was found that 4-week-old chicks from mothers fed a low-protein diet had significantly heavier body weight and pectoralis major muscle weight (Rao et al. 2009). Similarly, it was found that essential omega-6 (n-6) and omega-3 (n-3) fatty acids added to maternal diet on meat-type broiler chickens modified cell fatty acid composition in progeny chicks and may notably impact offspring health, as reviewed by Cherian (2015). Supplementation of n-3 polyunsaturated fatty acids in maternal diet influenced docosahexaenoic acid (DHA) and arachidonic acid concentration in the brain and liver on offspring up to 40 days of age. Moreover, it impacted production of inflammatory mediators until 21 days of age and dampened (~50-fold)

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delayed hypersensitivity test response at 28 days of age. These data show that it is possible to improve broiler health conditions by manipulating their dam’s diet. Also, offspring of dams fed n-3 fatty acid-enriched diet conserved a higher hepatic eicosapentaenoic acid (EPA) concentration until 28 days of age (Koppenol et al. 2015a). Yet, supplementation of EPA or DHA did not affect the production traits at slaughter at 38 days of age (Koppenol et al. 2015b). In another study, adding daidzein – a phytoestrogen present in soybeans – in broiler breeder diet improved water-holding capacity of the breast muscle in offspring marketed at 63 days of age (Ni et al. 2007). In broilers, the antioxidant capacity of offspring is affected by digestible arginine level (Duan et al. 2015) or canthaxanthin added to the dam’s diet (Rosa et al. 2016). Nutrition of the hen may also impact offspring behavior, as feed bearing menhaden (fish) scent given to the dam modified feeding behavior and fearfulness in young chicks (Aigueperse et al. 2013). The quantity of feed offered to the dam may also have an impact on the progeny performance. In the current broiler system of production, breeders undergo feed restriction as a mean to improve fertility and viability, and offspring are fed ad libitum. But it was found that maternal feed restriction before conception had a negative influence on the offspring’s body weight at 6 weeks of age (van der Waaij et al. 2011). Finally, the dietary protein level during the rearing period also impacts the offspring performances in broilers (van Emous et al. 2015).

Manipulation of Egg Content In the studies investigating the impact of maternal diet on the progeny, it is hard to control for other changes that may occur in the egg, such as alterations of the maternally transmitted hormones. In ovo manipulation of nutrients is a more direct way to impact the offspring phenotype (Calini and Sirri 2007). Achieved by either injection of a nutrient or removing a part of a component, in ovo manipulation has the advantage to affect only the target of interest. Thus, a large number of articles reporting the use of in ovo supplementation of nutrients to improve hatchability, growth, and health of chickens have been published (see Kadam et al. 2013; Roto et al. 2016). As an example, a study showed that equol – a metabolite of daidzein – when injected in the albumen of fertile eggs at 7 days of embryonic stage has the same effect as daidzein added in maternal diet (Ni et al. 2007) and also improves water-holding capacity of the breast muscle in broilers (Wei et al. 2011). This result is important to the broiler industry because cooking drip loss has a large effect on meat tenderness and juiciness. The authors also noted a significant effect on lipid metabolism, with a decrease in serum triacylglycerol and total cholesterol and an increase in serum high-density lipoprotein cholesterol in 49-day broilers. They finally reported a hepatic upregulation of CPT1 and CYP7A1 genes and a downregulation of FAS gene in female broilers from injected eggs (Ni et al. 2012). In another study, epigenetic mechanisms involved in hepatic cholesterol metabolism regulation after in ovo betaine injection were analyzed (Hu et al. 2015) in newly

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hatched chicks. Betaine injection increased the serum concentration and the hepatic content of cholesterol. Accordingly, CYP7A1 – which converts cholesterol in bile acids – was downregulated and the methylation level of its promoter was increased, while ABCA1, which mediates cholesterol counter transport, was upregulated and the methylation level of its promotor was decreased. In addition, the hepatic content of DNA methyltransferase 1 and the global genomic DNA methylation level were increased, while the level of hepatic H3K27me3 was decreased in betaine-treated chicks. These results suggest the existence of a nutritional epigenetic programming initiated by betaine injection in newly born chicks, but the long-term impact on adult chicken cholesterol metabolism is still to be demonstrated. Another way to manipulate egg content is to remove a part of the albumen content. Recent studies explored the impact of protein restriction by albumen removal and replacement by sterile saline. The direct effect of protein deficiency was evaluated by comparing the chicks hatched from treated eggs to a control group – a hole was made to eggs without removing albumen – and an untreated group, which did not undergo any manipulation. In broilers, for example, protein undernutrition was explored by removing 10% of the albumen from fertilized eggs before incubation (Everaert et al. 2013). The chick weight at hatch was decreased in the treated group. No differences were found in plasma glucose and triacylglyceride (TAG) concentration, but lower plasma uric acid and amino acid concentrations were observed in albumen-deficient hatchlings indicating an altered protein metabolism. Nevertheless, no data were given concerning the long-lasting impacts of the protein undernutrition on growing and adult broilers. The same method was used to explore the lasting impacts of protein undernutrition in layertype chickens (Table 2). During the rearing phase, from hatch to 17 weeks of age, the treated “albumen-deprived” pullets weighed less and had a lower feed intake than the two other groups. During the laying phase, from 18 to 55 weeks of age, they were heavier and had a comparable feed intake as the two other groups, but they laid smaller eggs and had a lower laying rate and a higher number of second grade eggs, a consequence of early protein undernutrition. It is remarkable that the albumen-deprived hens also laid eggs with a lower proportion of albumen (Willems et al. 2013). This observation was confirmed by a second experiment, during which the eggs of the albumen-deprived hens were incubated to explore the impact on the offspring. The albumen-deprived hens gave rise to chicks with a reduced body weight from hatch to 3 weeks of age, suggesting multigenerational impacts of prenatal protein undernutrition due to albumen removal (Willems et al. 2015). Recently, hepatic proteome changes in hatchlings from albumen-deprived eggs were reported (Willems et al. 2014). They were not only related to amino acid metabolism but also to energy and glucose metabolism. Furthermore, differential gene expression was described in the hepatic transcriptome of the adult hens from albumen-deprived eggs (Willems et al. 2016). Altogether, this group has demonstrated that the removal of albumen at day 1 of incubation is a valuable model to investigate the long-lasting effects of nutritional programming induced by protein undernutrition in layer-type chickens and their progeny. However, the underlying epigenetic mechanisms are still to be explored.

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Table 2 Nutritional programming in layer-type chicken (Willems et al. 2013, 2014, 2015, 2016)

Step 1: An average of 7.45% of the albumen was removed from fertilized eggs at day 1 of incubation and replaced with the same volume of saline inducing protein undernutrition. Step 2: At hatch, hepatic proteome changes were observed. Step 3: During the rearing phase, the treated pullets weighed less and had a lower feed intake. They also showed a decreased glucose tolerance. Step 4: During the laying phase, they laid smaller eggs with a lower proportion of albumen. They also showed hepatic transcriptome changes. Step 5 and 6: Finally, the altered egg composition of the albumen-deprived hens resulted in reduced body weights from hatch to 3 weeks of age in the offspring. The chicken drawings are inspired from https://www.google.fr/search?q= dessinþpoulets&client=firefox-b&tbm=isch&tbo=u&source=univ&sa= X&ved=0ahUKEwj9wYj3k4DSAhUGSRoKHXCZCp4QsAQIGw&biw=1680&bih=915

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Post-hatch Nutrition The newly hatched chick is still maturing its immune system and gastrointestinal tract and undergoes transition to a carbohydrate-rich feed (Moran 2007; Lamot et al. 2014), which makes this critical period particularly susceptible to the nutritional environment. The evolution of hepatic lipogenesis gene expression has been explored during this crucial stage of initiation of feeding – from hatch to 8 days of life – in broilers (Richards et al. 2010). When submitting the hatchlings to fasting during the first 48 h, thus mimicking the absence of feeding during transportation, no lasting effects of fasting on hepatic lipogenesis gene expression at 8 days of life were observed, suggesting a limited impact on this pathway. Another group (Xu et al. 2012; Jiang et al. 2016) reported that 3-day-old chicks after a 24-h fasting showed a global histone H3 dimethylation and trimethylation at lysine 27 (H3K27me3) along with an increase of the histone methyltransferase mRNA expression in the hypothalamic paraventricular nucleus that controls feed intake. Furthermore, they described a decrease in brain-derived neurotrophic factor (BDNF) protein, which is known to have an anorexigenic effect. This decrease in BDNF was associated with a decrease in mRNA level and an increase of dimethylated and trimethylated H3K27 level at the BDNF promotor. This work clearly reported a relationship between early life 24-h fasting and epigenetic histone modifications in the brain at both global and specific levels. In commercial broilers, early feed restriction is used to increase feed efficiency and decrease fat deposition and metabolic disorders such as ascites. As an example, newly hatched broiler chicks were submitted to early feed restriction from hatch to 14 days of age – with feed provided on alternate days – followed by ad libitum feeding until 63 days of age (Li et al. 2007). At 14 days, the body weight was reduced by almost half, and at 63 days, the body weight was still reduced as well as the lateral gastrocnemius muscle weight. This was accompanied by altered muscle myofiber composition and modified mRNA expression of the two growth-related genes IGF-I and IGF-IR. Moreover, higher serum total cholesterol level was associated with downregulation of ACC-alpha mRNA expression and upregulation of CPT-I mRNA in the liver of the early feed restriction group (Yang et al. 2010). These works provide another example of the impact of early life nutritional programming on different tissues and metabolisms, later in life. In conclusion, several studies on birds have demonstrated the long-lasting effects of nutritional programming on the final adult phenotype, but only very few of them tried to decipher the underlying epigenetic mechanisms. However, elucidating the ways nutritional programming – and therefore the epigenetic marks – influences the phenotypes in birds represents an exciting challenge for further improvement of productivity within the poultry sector. If direct manipulations of the egg content have the advantage to exclude possible confounding effects compared to manipulations of maternal diet, they are generally less amenable to farm conditions. Incidentally, experimental studies are always conducted under strictly controlled conditions, which make their transfer difficult to the much more variable environment of livestock production (Sinclair et al. 2016). In domesticated avian species, however,

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husbandry of breeding flocks and incubation of eggs are highly controlled in specific units, and the in ovo supplementation method (Uni and Ferket 2004; Kadam et al. 2013; Roto et al. 2016) might be successfully adapted from experimental settings to poultry breeding and incubation company environment. Although further studies will be required to study the multigenerational impact of nutritional programming under real commercial breeding conditions, the scientific literature reviewed here demonstrates that nutritional programming might be valuable to improve broiler production.

Multigenerational Influences The parental environment has an effect on the F1 generation, as described in the previous section, and it is particularly obvious in mammals, since the mother directly hosts the developing offspring. The maternal diet effects may also occur in the F2 generation, since the F1 generation developing during pregnancy carries the primordial germ cells which will develop into an F2 animal. As a consequence, the maternal environment can directly affect the next two generations, F1 and F2, but not the F3 unless there has been transgenerational epigenetic transmission (Fig. 1). Thus, incomplete erasure of epigenetic marks between generations resulting in nongenetic transmission from one generation to another is undisputable only if the effect is detected in generation F3 or beyond (Skinner 2011). Here we also consider the multigenerational effect, affecting the F1 or F2 generations: multigenerational diet effects have been documented by epidemiological studies in human or animal experiments in rodents, either from paternal (reviewed in Soubry 2015) or maternal (reviewed in Aiken et al. 2016) diets. Several analyses in human have demonstrated a multigenerational effect of the diet, at least until the second generation. Epidemiological studies have shown that Fig. 1 The maternal environment may impact F1 and F2 individuals (From Frésard et al. 2013). In birds, the maternal diet has an impact on individuals of the F1 generation through the egg content. However, it can also impact individuals of the F2 generation, since the developing offspring bears the primordial germ cells (PGC) that later differentiate into gamete precursor cells and finally lead to the individuals of the F2 generation

Nutritional perturbation

Mother F0

Embryo = F1 Germ line cells = F2

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the paternal grandfather’s food supply affects the mortality rate of grandsons but not of granddaughters, while the food supply of the paternal grandmother affects the mortality rate of granddaughters but not grandsons (Kaati et al. 2007; Pembrey 2010). Another well-documented example is the impact of high-fat diet in male rats, for which a recent study brought demonstration of the modification of epigenetic marks in F0 and F1 males (de Castro Barbosa et al. 2016). Other examples exist, as the well-known Dutch Hunger Winter, where the undernutrition of the maternal (Stein and Lumey 2000) or paternal (Veenendaal et al. 2013) grandmother may affect F2 phenotypes, but these phenomena may be rare. Above all, in most cases, it is not possible to exclude phenomena other than transmission of epigenetic marks, such as sampling biases or differences within the DNA sequence itself. More experiments demonstrating the involvement of epigenetic phenomena are needed to show the existence of this “nongenetic heredity,” at least in animals. Some studies have been published in farm animals (see Feeney et al. 2014), and one of them reported an effect of the grandsire diet on carcass conformation, lipid metabolism, and DNA methylation in F2 pigs (Braunschweig et al. 2012). Several studies on multigenerational effects of stress or age of the parents have been conducted in chicken (Goerlich et al. 2012; Ericsson et al. 2016) or wild house sparrows (Schroeder et al. 2015). In birds, a recent paper reported that information related to diet disruption in zebra finch was transmitted, but only over one generation: females exposed to high maternal corticosterone levels produced offspring with higher growth rates and showed a sex-biased offspring mortality (Khan et al. 2016). Similar effects on potential transmission from the paternal grandam were investigated in the Muscovy duck (Brun et al. 2015), in a study testing if maternal methionine deficiency could affect grand-progeny phenotypes through the paternal path of transmission (Table 3). This study is part of a broader one, including a common duck lineage, and focusing on the G2 or G3 offspring, which are either purebred Muscovy and common ducks or their hybrid, the so-called mule duck Table 3 Summary table of experiments related to the effects of ancestors’ diet in birds

a

Species Muscovy duck

Nutritional effector Methionine feed content G0 females

Zebra finch Japanese quail Common duck

Corticosterone level of G0 females Genistein G0 egg injected Methionine feed content G0 females

Generation investigated G2 Purebreds þ hybridsa G2 G3 G3 Purebred þ hybrida

Impacted traits Growth Force feeding traits Blood lipid metabolites Sex ratio Growth Reproductive traits Behavioral traits Growth Force feeding traits Blood lipid metabolites

Reference Brun et al. 2015

Khan et al. 2016 Leroux et al. 2017 Brun et al. in preparation

The Mule duck, the sterile hybrid of a Muscovy drake and a common duck female

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Common duck lineage G0

Dietary Met restriction

Muscovy duck lineage

G1

G0

G2

G1

G3

G2 Purebred common duck

Mule ducks

Purebred Muscovy duck

(= Muscovy male x Common duck female)

Brun et al., in preparation

Brun et al. (2015)

Fig. 2 Experimental design for the study of multigenerational effects of a dietary methionine restriction in the duck. The methionine restriction was applied to G0 females. Last-generation and force-feeding traits were focused. In the common duck lineage, two transmission paths were investigated: through the maternal grandsire vs. the maternal grandam. Gray color indicates an ancestor with a dietary methionine restriction

(Fig. 2). In the Muscovy duck lineage, two groups of progeny were compared, coming from grandams fed a methionine-deficient diet or a control diet. In purebred Muscovy grand-offspring, the grandam-deficient diet decreased the growth rate between the ages of 4 and 12 weeks, and the body weight at the end of force-feeding in male only, and increased the carcass proportion of the liver in both sexes. In mule duck grand-offspring, the grandam-deficient diet decreased body weights at 4, 8, and 12 weeks but increased the weight gain during force-feeding and the final weight of abdominal fat. Several metabolic markers were also affected. Whether the epigenetic nature of the information transmitted by the sire due to the grandam diet was epigenetic or not remains to be demonstrated. Some avian species have very short generation intervals and are quite appropriate for multigenerational research, but to the best of our knowledge, no “nutritional programming” study has been performed until the F3 generation in birds. As the egg composition may be modified by the mother diet, a pilot study was performed in quail by direct modification of the egg content and showed that some effects induced by environmental stressors could be transmitted across generations (Leroux et al. 2017). To test for the existence of epigenetic transmission in third-generation birds, two quail lines were produced using fertilized eggs from the same founder population. Genistein – a natural isoflavone in soybean products which is also a methylation modifier – was used to model nutritional programming: it was injected or not into the eggs of a quail population before incubation to create two founder quail lines (Epiþ

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line issued from injected eggs and the Epi line issued from non-injected eggs). A “mirrored” mating strategy was used to minimize between-line genetic variability by maintaining similar ancestor contributions across generations in each line. After three generations of parallel within-line breeding, and without further injection, several traits were affected by the ancestors’ treatment, including both reproductive (e.g., age at first egg delayed by 8 days) and behavioral traits (e.g., social responses affected). The equal relative genetic contribution of ancestors to the two lines, maintained across generations through the “mirror” single-pair mating design, should have reduced the effect of between-line genetic variability, so observed differences were likely to be due, at least in part, to epigenetic transgenerational inheritance.

Implication for Humankind As reviewed in this book, epigenetics is in close relation to diet and nutrition in human and can have major consequences on health. This field receives more and more attention from the scientific community, and a broad range of animal models are proposed to study nutritional programming. The advantage compared to human studies is that animal models allow full control over the dietary experimental design, genetic factors, and other confounding factors and biases which impact human retrospective cohort studies. Although mammals’ models are preferred to transpose the results on humans, avian models offer the advantage to minimize maternal confounding effects by a direct manipulation of the egg content. For instance, an avian model for a human disease (nonalcoholic fatty liver disease, NAFLD) has been used to establish the liver protection effects of in ovo injection of betaine (Hu et al. 2017). Of course more studies are needed to conclude whether the results obtained in birds may be extrapolated to mammalian species, including human. Avian models have additional advantages: many individuals can be analyzed in parallel with a strict control of the environment of the developing embryo and of the animal throughout its lifetime, allowing homogenous developmental conditions between samples. This raises the possibility of accurately testing several diet compounds for environmental toxicology. Moreover, the short generation interval, as is the case in quail, for example, allows transgenerational studies to be performed in a relatively short period of time. Independently of the model used, the challenge for the coming years will be to identify the mechanisms involved in nutritional programming to enhance our understanding on the factors influencing the physiological development of the embryos and of production and health conditions of adults.

Conclusion The importance of the ancestor diet may be more critical than previously thought in poultry production due to its direct or transgenerational effect on the offspring phenotype. Given the current climate evolution and the increasing demand for

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food of animal origin, a better understanding of the epigenetic mechanisms that governs the embryo’s response to parental diet changes could open new avenues to improve production efficiency, animal welfare, and food quality. This knowledge could help optimize the nutrition of breeders and thus improve the performance of future broilers and layers. An important issue in animal breeding programs is the extent to which “nongenetic” inheritance might also affect the efficiency of genetic selection. After decades spent on optimizing the genetic selection of farm animals on the basis of Mendelian inheritance, studying epigenetic transgenerational transmission of diet effects offers the opportunity to assess whether selection schemes and production could be improved by taking into account nongenetic transgenerational inheritance. Even if extrapolation has to be undertaken with caution, improving our knowledge of multigenerational effects of the ancestors’ diet on further generations through epigenetics phenomena in birds could be of great interest for understanding the impact of nutrition on human health.

Dictionary of Terms • Genotype – All the genetic information present on the DNA of an individual. • Phenotype – All traits of an individual, resulting from genotype and environment. • Genetic selection – Improvement of farm animal performances through animal breeding. • Nutritional programming – Influences exerted by maternal diet at early stages of development. • Multigenerational – Affecting at least two generations. • Transgenerational – Affecting at least three generations.

Key Facts of Epigenetic Inheritance in Birds • Genetic selection may be improved by taking into account epigenetic phenomena. • Parental diet may impact offspring phenotypes through epigenetic nutritional programming. • Maternal diet, modification of egg content, and early nutrition may affect adult phenotypes. • Diet effects may be passed on to more than one generation in duck. • Modification of the egg content in quail has an influence on the third-generation phenotypes.

Summary Points • Phenotype variability depends on genetics and environmental factors. • Improving farm animal performances mostly relies on genetic variability.

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• A novel question appears about the putative improvement of selection schemes taking into account nongenetic transgenerational inheritance. • The parental diet may influence the adult phenotype of the offspring in several species, through nutritional programming. • In birds, the nutrients required for the embryo development must be provided by the hen through the egg. • While breeder hen diet is optimized for egg production, hatchability, and hatchling vitality, the performances of offspring at adult stages could be taken into account in the parental diet formulation, to improve health conditions or production traits. • Modification of egg content may trigger similar effects as parental diet changes, either by adding or removing nutritional components. • Early life nutrition may impact adult epigenetic marks. • Information acquired from environmental exposures may be transmitted across generations. • In birds, diet effects may be passed on to more than one generation.

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Part II Organs, Disease, and Life Stages

Molecular Biology of Human Obesity: Nonepigenetics in Comparison with Epigenetic Processes

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Homeostasis and Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genes Involved in Energy Homeostasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetic Factors Associated with Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Obesity Susceptibility Risk Cannot Be Explained by All Genetic Factors . . . . . . . . . . . . . . . Epigenetic Changes Related to Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brief Overview of Epigenetic Processes Related to Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methylation Studies on Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Histone Modification Studies on Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAS and Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fetal Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Complex Interplay Between Genetics, Epigenetics, and Nutrition . . . . . . . . . . . . . . . . . . . . . . . Conclusion Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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D. Albuquerque (*) · L. Manco Department of Life Sciences, Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, Portugal e-mail: [email protected]; [email protected]; [email protected] C. Nóbrega Department of Biomedical Sciences and Medicine, Center for Biomedical Research (CBMR), University of Algarve, Faro, Portugal Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_7

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Abstract

The rapid increase in the prevalence of obesity worldwide is undoubtedly linked to a “social globalization”; however, a genetic component also accounts for individual differences in the predisposition to weight gain. The contribution of candidate gene studies identified several mutations related to obesity in the leptin/ melanocortin pathway, which is involved in the regulation of food intake and energy expenditure. Other studies including genome-wide association study (GWAS) found genetic variants across the genome associated with the susceptibility risk to develop obesity. However, until now, all these genetic variations explain only a small fraction of the estimated heritability of obesity. Furthermore, our genome is not likely to change profoundly through mutations in few generations as to explain the rapid increase in the prevalence of obesity. More recently, epigenetic regulation of gene expression emerged as a potential factor that might explain differences in obesity risk. Several genes have been found whose expression is controlled by epigenetic factors. Diet and nutrition appear to be the most important factors influencing epigenetic mechanisms leading to an obese phenotype. Effectively, our diet suffered drastic changes in the last decades with the incorporation of new nutrients and bioactive molecules. Several studies performed both in humans and animal models found differences at different epigenetic mechanisms between obese and non-obese individuals. However, our knowledge on which and how nutrients affect epigenetic mechanisms remains limited. Currently, it is thought that the obesity condition might be a consequence of an interplay between genetic, epigenetic, and lifestyle factors. In the near future, studies based on alterations on gene expression due to environmental signals will help to draw a more complete picture of the obesity etiology. Keywords

Obesity · Genetic of obesity · Mutations · Genome-wide association study (GWAS) · Energy homeostasis · Leptin/melanocortin pathway · Singlenucleotide polymorphism (SNP) · Heritability · Copy number variation (CNV) · DNA methylation · Histone modification · MicroRNA · Fetal programming List of Abbreviations

α-MSH AHRR Avy BDNF BMI cAMP CH3 CNS CNVs CpG DNA

Alpha-melanocyte-stimulating hormone Aryl-hydrocarbon receptor repressor Yellow agouti allele Brain-derived neurotrophic factor Body mass index Cyclic adenosine monophosphate Methyl group Central nervous system Copy number variations 50 —C—phosphate—G—30 Deoxyribonucleic acid

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Dnmt DOHaD GWAS HIF3A LEP LEPR MC4R NNMT NPY NTRK2 PCSK1 POMC PWS RNA SIM1 TrkB

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DNA methyltransferases Developmental origins of health and disease Genome-wide association study Hypoxia-inducible factor 3 alpha subunit Leptin Leptin receptor Melanocortin 4 receptor Nicotinamide N-methyltransferase Neuropeptide Y Neurotrophic receptor tyrosine kinase 2 Proprotein convertase subtilisin/kexin type 1 Pro-opiomelanocortin Prader-Willi syndrome Ribonucleic acid Single-minded homolog 1 Tropomyosin receptor kinase B

Introduction Obesity is one of the world’s greatest health public challenges, contributing to the increased risk of many chronic diseases: hypertension, type 2 diabetes, cardiovascular diseases, and other comorbidities (Ng et al. 2014) (Fig. 1). Global trends in obesity measures show that adult BMI increased from 21.7 kg/m2 in 1975 to 24.2 kg/m2 in 2014 in men and from 22.1 kg/m2 in 1975 to 24.4 kg/m2 in 2014 in women. If this trend continues to increase, the worldwide obesity prevalence will reach 18% in men and surpass 21% in women by 2025 (Singh et al. 2016). The global rise of obesity led some authors to consider it as the result of a “social globalization” (Goryakin et al. 2015). In fact, it is widely accepted that obesity results from an energy imbalance, where the energy intake exceeds the energy expenditure. From an evolutionary point of view, this current pandemic is the result of a more sedentary lifestyle together with higher availability of caloric foods (Albuquerque et al. 2015b). Nevertheless, the individual genetic profile also accounts for observed differences in the predisposition to weight gain. Over the last two decades, studies performed within families, twins, and adopted children identified several genes as cause of different monogenic forms of obesity. Mutations found in LEP, LEPR, POMC, PCSK1, MC4R, SIM1, BDNF, and NTRK2 genes represent around 10% of monogenic cases with earlyonset obesity. Other studies, especially those using an approach based on genome-wide association study (GWAS), discovered several variants associated with polygenic forms of obesity. Until now, more than 50 loci have been undoubtedly linked with an obesity-related trait (Albuquerque et al. 2015b). However, it is difficult to explain the rapid spread of obesity worldwide based only on genetic background.

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Diet Behavior

Type 2 Diabetes Cardiovascular disease

Physical activity

Genetics and Epigenetics mechanisms

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Obstructive sleep apnea Cancer

Birth weigh Environment Fig. 1 Different factors accounting to an obese phenotype, and their possible effects in obesity comorbidities

Significant progresses were made in the last decades in the understanding of epigenetic mechanisms regulating gene expression as a consequence of environmental factors. DNA methylation, histone modifications, or microRNAs are some epigenetic mechanisms, which can modify the gene expression. In the context of obesity, nutrition appears to be the most influential factor that can directly induce epigenetic changes, thus impacting body weight (Albuquerque et al. 2015a). Several studies found evidence that parental diet and early-life nutrition modify gene expression. This suggests that epigenetic factors might contribute to parental gamete modification and prenatal and early postnatal development programming, which can influence obesity propensity throughout the life course (van Dijk et al. 2015). However, further studies are needed to unravel the effective contribution of nutrients or bioactive food components in health status as modifiers of gene expression through epigenetic mechanisms. Moreover, a better understanding of the combination of genetic, epigenetic, and nutrition factors as obesity contributors might help to identify novel interactions within phenotype-associated loci, thus providing new ways to understand the obesity causes.

Energy Homeostasis and Obesity Energy homeostasis involves the control of energy intake and energy expenditure, which is in part regulated by the thermogenesis of brown adipose tissue via the central nervous system (CNS). One of the key players for this regulation of energy balance in CNS is the leptin/melanocortin pathway of the hypothalamus. Leptin is a hormone secreted by fat cells apparently involved in the regulation of food intake and energy expenditure. An increase in leptin levels leads to its attachment to leptin receptors in the hypothalamus, which sends a signal to melanocortin 4

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receptor (MC4R) activating the expression of POMC gene in the hypothalamus (Lee 2009). The α-melanocyte-stimulating hormone (α-MSH), product of POMC gene, binds to the MC4R protein inducing a satiety effect (Oswal and Yeo 2007). The MC4R is a membrane-bound G-protein-coupled receptor that activates the adenylate cyclase in response to the α-MSH, inducing the production of cAMP (Hinney et al. 2013). The expression of MC4R is restricted to the brain, where it is found in hypothalamic nuclei involved in food intake regulation. It was found that MC4R activation leads to a decreased food intake and increased energy expenditure (Hinney et al. 2013). Reflecting the importance of the leptin/melanocortin signaling pathway is the fact that mutations causing monogenic forms of obesity occur in the genes involved in this pathway. All these genes code for proteins with a central role in the leptin/melanocortin signaling pathway present in the hypothalamus, therefore affecting the regulation of food intake and energy expenditure.

Genes Involved in Energy Homeostasis Studies performed in cases with monogenic forms of obesity identified more than 200 mutations in genes related to energy imbalance. All these mutations are located in only ten genes, being eight of them well described: LEP, LEPR, POMC, PCSK1, MC4R, BDNF, NTRK2, and SIM1 and explaining around 10% of cases with early-onset obesity (Albuquerque et al. 2015b). Mutations in LEP and LEPR genes have been found in individuals with obesity and hyperphagia, affecting levels of leptin in serum (Montague et al. 1997). A homozygous frameshift mutation in LEP gene was associated with undetectable leptin in serum and extreme obesity (Montague et al. 1997; Rau et al. 1999). In the LEPR gene, a splice site mutation was associated with leptin receptor deficiency and producing extreme obesity (Clément and Ferré 2003). Mutations in MC4R gene account for 2–6% of the overall patients with severe obesity (Albuquerque et al. 2014). The MC4R deficiency results in hyperphagia, which start early in life. For this reason, it is highly recommended the screening for MC4R mutations in children with morbid obesity in the first year of life (Hinney et al. 2013). Until now, more than 150 mutations were found in this gene leading to a reduced function of the wild-type MC4R (Hinney et al. 2013). Two mutations (p.R147G and p.G323E) were found at heterozygous state in individuals suffering from eating disorders (Albuquerque et al. 2014). These patients demonstrated an impaired MC4R response to α-MSH, suggesting that this could be the mechanism underlying obesity. The screening of POMC gene also revealed several mutations associated with extreme weight gain (Pritchard et al. 2002). These mutations may cause deficiency of all POMC-derived peptides like α-MSH, which will then result in MC4R deficiency, obesity, and hyperphagia (Krude et al. 1998; Coll et al. 2007). Another mutation at the homozygous state (p.Y77X) was found in the POMC gene causing a premature stop codon and also leading to obesity (Mendiratta et al. 2011). Mutations located in the gene encoding BDNF are also reported leading to early onset of obesity. However, these patients develop other complex syndromes characterized by impaired cognitive function (Gamero-Villarroel et al. 2014). The SIM1 gene is also

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involved in the control of food intake. Mutations such as p.T481K and p.A517V suggest a loss of function responsible for SIM1 haplo-insufficiency causing obesity (Zegers et al. 2014). Mutations found in NTRK2 gene, which encodes TrkB, seem to contribute to severe hyperphagia and obesity (Xu and Xie 2016). It is the case of a missense mutation in the heterozygous state resulting in p.Y722C substitution and affecting the ability of TrkB to promote neurite outgrowth in response to BDNF (Gray et al. 2007).

Genetic Factors Associated with Obesity Several studies pointed out an important familial component contributing to the risk to develop obesity (Albuquerque et al. 2015b). Clusters of cases within a family show high-risk predictors of obesity in childhood. For example, children born from obese mothers are more likely to be obese early in childhood (Whitaker 2004), and children whose both parents are obese increased twice the risk of childhood obesity (Whitaker et al. 1997). However, familial effects can be explained by sharing both the same environments (cultural factors) and genetic factors, which are common to parents and their children. Trying to weight the importance of these factors, several studies based on twins and adopted children were performed to access the importance of the genetic component to obesity (Feinleib et al. 1977; Stunkard et al. 1986a, b). These studies suggested that genetic factors might play a stronger effect than environmental factors on BMI. It was shown evidences that approximately 40–70% of the BMI heritability has been attributed to genetic variations (Silventoinen et al. 2010). Non-syndromic monogenic forms of obesity result from inherited or de novo mutations in single genes. As mentioned above, most of these mutations have been discovered in genes that play roles in food intake and energy homeostasis as we referred previously. On the other hand, in the past decades, several studies based on the genome-wide association study (GWAS) approach identified several common genetic variants associated with the risk of obesity, thus increasing our knowledge on the genetic basis of obesity.

The Obesity Susceptibility Risk Cannot Be Explained by All Genetic Factors It is now consensual that there is an important genetic component underlying the obesity susceptibility risk. However, until now, all these described loci can only explain part of the obesity heritability, accounting for around 2–3% of the total genetic variance in BMI. This value is far from the BMI heritability estimates, which are around 40–70%. More recently, some investigators pointed to different types of genetic variations such as rare and low allele frequency or copy number variations (CNVs) as a possible source for the “missing heritability” of obesity. In the near future and with the advance of massive sequencing technology, a more

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complete picture of the genetic variants accounting for the obesity risk will be achieved. It will be important to unravel the variants associated with obesity, but more importantly will be the understanding on how all these genes work together to affect body weight. Anyway, genetic variations cannot explain by itself the growth rate of obesity, as genomes do not change profoundly through mutations in few generations. Furthermore, important questions arose from an evolutionary point of view: (a) why or how natural selection favored the spread of genes that increase the risk to develop an obese phenotype? and (b) if there was a natural selection, how this predisposition to obesity evolved? (see an extensive discussion on these questions in Albuquerque et al. (2015b). Gene expression is altered by epigenetic factors in response to environmental exposures throughout the life course. Several studies found evidences of a prenatal and perinatal period of development, which plays a substantial role in the programming of human tissues all over the body. In a simple analogy, we might explain genetics referring to the genes “writing” while epigenetics to the genes “reading.” Thus, alternative phenotypes can be originated from individuals genetically identical due to different gene expression, as a response of environmental factors. Epigenetics is emerging as perhaps the possible mechanism, which could explain the “missing heritability” to the obesity pandemic.

Epigenetic Changes Related to Obesity Nutrients are composed of bioactive molecules, which might play an important role in the interaction between genome and epigenome. In 1942, Conrad Waddington formulated the concept of epigenetics (see Waddington 2014 for more informations). Nowadays, epigenetics could be defined as heritable changes that are mitotically stable (and potentially meiotically) and affect gene function without altering the underlying DNA sequence through processes as DNA methylation, changes in chromatic organization by histone modifications, and regulatory action of miRNAs (Russo et al. 1996). Several studies reflected the importance and existence of a complex interplay between the genetic profile and life experiences. Take for example the case of the Agouti mouse model. The Agouti yellow (Avy) allele is responsible for the coat color of mice. Interestingly, despite a widerange variation of coat color, it all results from the same gene. It was discovered that a different expression of the Avy gene due to DNA methylation early in development is responsible for the variation in the coat color (Dolinoy 2008). When the Avy gene is methylated, it switches from the off position, and the mouse has brown fur with no health problems. On the other hand, if the gene is not methylated, it is expressed or turned on, and the mouse presents a yellow fur with some associated health problems such as obesity, diabetes, and cancer. Furthermore, the wide-range spectrums from full yellow through brown fur result from varying degrees of the methylation of the Avy gene. From this example it became clear that epigenetic modifications will later profoundly alter the gene expression, which will be also translated into phenotypic alterations.

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Brief Overview of Epigenetic Processes Related to Obesity DNA Methylation Studies on Obesity As mentioned before, several genes were positively associated with obesity in the last years. More recently, studies started looking for gene expression changes, as a result of epigenetic regulation as contributors for obesity. One of the epigenetic mechanisms, the DNA methylation, consists in the addition of a methyl group (CH3) to the cytosine on CpG (cytosine-guanine) dinucleotides, by different DNA methyltransferases (Dnmts), and represents so far the most widely studied in the obesity context (Fig. 2). Since the start of epigenetic research in the obesity field, several studies reported a significant association of methylation loci and obesity-related traits (Dick et al. 2014; Aslibekyan et al. 2015; Demerath et al. 2015). Moreover, it was recently found that several obesity-related SNPs associated with alterations in DNA methylation probably impact in a significant way the obesity risk allele genotypes, both in adults (Yang et al. 2012; Voisin et al. 2015; Volkov et al. 2016) and in children (Wang et al. 2015). These studies on DNA methylation are particularly focused in obesity-related genes (involved in metabolic regulation or genetically associated with obesity CH3

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Fig. 2 DNA methylation consist in the addition of a methyl group to the DNA base cytosine in the context of the CpG dinucleotides

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measures). For example, it was shown that leukocyte methylation levels of obese men might reflect the epigenetic regulation of two important appetite-regulatory genes, NPY and POMC (Crujeiras et al. 2013). Authors speculate that alteration in the methylation levels of these genes might be implicated in the weight regain process after dieting. It was also shown that methylation in a variably methylated region (VMR) in the POMC gene was strongly associated with individual BMI (Kühnen et al. 2016). Another important aspect on studying DNA methylation in the context of obesity is the type of cells or tissue analyzed. Several studies focused on the impact of DNA methylation in adipocytes and the adipose tissue. For example, a study showed that DNA methylation patterns remained stable during adipocyte differentiation, which might indicate they are not crucial in the gene expression regulation during this process (van den Dungen et al. 2016). Nevertheless, in a genome-wide analysis of DNA methylation, other authors found significant differences in methylation between human pre-adipocytes and mature adipocytes (Zhu et al. 2012). Interestingly, several studies showed alterations in DNA methylation patterns in the adipose tissue and related those changes with obesity or obesity-related comorbidities. For example, Keller et al. (2014) found evidences that global DNA methylation levels in the adipose tissue were related to fat distribution and glucose homeostasis. Another study reported differences in the DNA methylation pattern between insulin-resistant from insulin-sensitive obese subjects (Crujeiras et al. 2016). Further studies are needed to establish if these alterations in DNA methylation patterns are in fact relevant for the obesity phenotype and if they could constitute future targets for therapeutic intervention. For example, it was shown that a 6-month exercise intervention influences the genome-wide DNA methylation pattern in human adipose tissue, potentially affecting adipocyte metabolism and, in the last instance, impacting obesity (Rönn et al. 2013). Probably the hot topic in obesity epigenetic research is the connection between early-life exposure (either prenatal or infancy) and adult life outcomes (HernándezAguilera et al. 2016; Gillberg et al. 2016; Navarro et al. 2016). A recent study related obesity in males with epigenetic alteration in the sperm, which might impact the future offspring (Soubry et al. 2013). It was also shown that alterations in HIF3A gene methylation before birth are related to adiposity, with a possible effect in later stages of life (Pan et al. 2015). In fact, several reports provided evidences showing that in utero nutrition overexposure to specific compounds has a profound effect in the alteration of DNA methylation patterns, with an important impact in obesity development susceptibility (Vickers 2014; McKay and Mathers 2016; Williams et al. 2016). Finally, several studies also reported significant alterations in DNA methylation patterns related to obesity in parents, which are then also observed in the offspring (Soubry et al. 2013). For example, DNA methylation in the aryl-hydrocarbon receptor repressor gene (AHRR) of offspring is related to maternal BMI, gestational age, and birth weight (Burris et al. 2015). Another study found DNA methylation differences between children born to obese parents and children born to non-obese parents (Soubry et al. 2015).

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Histone Modification Studies on Obesity DNA is wrapped in proteins with globular and N-terminal tail domains called histones (Fig. 3, 1A). Different types of histone modifications like acetylation, methylation, or phosphorylation might influence the behavior of transcriptional factors and thus influence gene expression (Fig. 3, 1B). There is a lack of studies concerning histone modifications in the context of human obesity. In fact, most of the available data result from studies in animal models, particularly mice where several modifications in histone were related to obesity, feeding behavior, or exercise (Tateishi et al. 2009; Wheatley et al. 2011). Other studies focused on the role of histone modifications in cell differentiation. For example, several acetylation signals were detected in H3K56 in human adipocytes (Lo et al. 2011), and another study found several histone modifications during adipogenesis, suggesting an important role in regulating the transcriptional network during that process (Zhang et al. 2012). Another study, using adipocytes from obese individuals, found several different histone modifications, being 23 detected in two or more individuals, although the vast majority were alterations detected in a single individual (Jufvas et al. 2011). Only recently some studies related epigenetic alterations in histone and an increased risk for obesity or obesity-related traits in humans. It was found that methylation of the histone nicotinamide N-methyltransferase (NNMT) in the serum

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was positively correlated with BMI and waist circumference and negatively correlated with high-density lipoprotein (Liu et al. 2016). Authors found that elevated serum me-NAM was associated with a higher risk for overweight/obesity and diabetes. In a combinatory analysis between GWAS and epigenetic data, several epigenetic alterations were recently found in the promoter regions of obesity-related genes, which we will however need further studies to validate their real impact in obesity (Dong et al. 2016).

MicroRNAS and Obesity Another type of epigenetic mechanism, the microRNAs (miRNAs), is endogenous short single-stranded nonprotein-coding RNAs with about 21/25 nucleotides in length, which are involved in posttranscriptional regulation of gene expression by partially complementary binding to the 30 untranslated region (30 UTR) of target mRNAs (Fig. 4). In the last years, several studies pointed to an important role of miRNAs in adipogenesis, either stimulating or inhibiting the differentiation of adipocytes and regulating specific metabolic and endocrine functions. Several miRNAs were found to be altered in different stages of adipocyte differentiation and involved in the regulation of important genes for that process (see Arner and Kulyté 2015; Zhou and Li 2014 for a review on this subject). Another type of studies focused on the miRNA levels in the context of the obese phenotype. For example, Mehanna et al. (2015) found that the C allele of miRNA146a rs2910164 showed positive association with increased susceptibility to metabolic

Protein assembly Typical gene Protein

DNA mRNA microRNA gene

microRNA Binds to mRNA Protein silencing

Fig. 4 MicroRNA contributes to the regulation of gene expression at the post-transcriptional level, resulting in their silencing via translational repression or target degradation

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syndrome. Using data from gene expression studies, Li et al. (2015) identified a total of 16 miRNAs that showed a significantly differential expression in obese subjects compared to non-obese subjects. Meerson et al. (2013) found that the expression levels of miR-221 were positively correlated with BMI (particularly in women) and fasting insulin concentrations, while the levels of miR-193a-3p and miR-193b-5p were negatively correlated with BMI. Other study found that the expression of miR-802 is increased in the liver of obese human subjects (Kornfeld et al. 2013).

Fetal Programming Some epidemiological studies suggest that adult health and disease may have some type of origin or conditioning in utero. This concept is known as Developmental Origins of Health and Disease (DOHaD) (previously referred as fetal programming) (Chavatte-Palmer et al. 2016). Basically, it suggests that the fetus adapts in response to intrauterine environment cues, resulting in permanent readjustments in homeostatic systems. Nevertheless, some of these adaptations in early development could result in disadvantageous signals in postnatal life. For example, nutrition and environmental exposure early in life might play an important role in obesity susceptibility risk. Effectively, some biochemical process through the genome can occur both during early and later stages of embryonic development, affecting gene expression in response to environmental events (Perera and Herbstman 2011). It is now well established that both early nutrition and maternal metabolic status during pregnancy have an important role on determining offspring’s health. Several studies show that maternal obesity during pregnancy is associated with an increase in obesity rates in the offspring (Drake and Reynolds 2010). Furthermore, early postnatal period is also a critical stage; it was suggested that breastfeeding might be a protective factor against obesity in children (Yan et al. 2014). The major part of studies focused their interest on maternal environment and maternal-infant interactions. However, some studies have demonstrated that paternal obesity can also influence and affect offspring development by imprinted marks during spermatogenesis (Soubry et al. 2013). For example, Soubry et al. (2015), when comparing newborns of obese parents with children born from non-obese parents, found altered DNA methylation patterns at several imprinted genes. All together, these studies suggest that the pre-conception stage and the first year of life appear to be critical for long-term risks of developing obesity. Epigenetic mechanisms will probably be behind this condition, but more follow-up studies are needed to understand its role and importance.

The Complex Interplay Between Genetics, Epigenetics, and Nutrition Obesity is associated with changes in gene expression across different tissues throughout the body. As all gene expression changes are thought to have associated epigenetic modifications, it means that the links between obesity and epigenetics will

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undoubtedly be very wide (Youngson and Morris 2013). On the other hand, the progress in identifying genetic variations associated with obesity helps us to better understand the inheritance, development, and possible treatments for this condition. Until now, more than 50 genetic loci have been found associated with an obesityrelated trait (Albuquerque et al. 2015b). However, the proportion of all trait variance explained by these loci is very modest (less than 3%). Furthermore, studies on epigenetic mechanisms underlying obesity have revealed environmental interactions that can contribute significantly to the obese phenotype. Nutrition appears to be one of the most influential factors that can modify patterns of gene expression. The human diet and eating habits have drastically changed within the last half century (Haggarty 2013). New technologies combined with innovations in food production increased the use of novel ingredients, supplements, and other nutrients changing completely our diet. There are several awareness campaigns to encourage healthy eating, but how these changes may affect the health of current and next generation remains poorly understood. Epigenetics has emerged as perhaps one of the most important mechanisms through which diet and nutrition can directly be linked to genome alterations. In fact, several studies have demonstrated how nutrients and bioactive food components can influence epigenetic mechanisms by inhibiting enzymes that catalyze DNA methylation or histone modifications (Choi and Friso 2010). For example, folate; vitamins B2, B6, and B12; methionine; choline; and betaine are methyl donors affecting DNA methylation patterns (Albuquerque et al. 2015a). Knowledge on which nutrients affect epigenetic mechanisms and how these interactions can lead to obesity is needed to better understand the role of these bioactive foods in health and to create new forms of prevention and treatment based on these data. Furthermore, the reversible nature of epigenetic modifications makes them a source of possible therapeutic epigenetic targets for treatment of obesity. The use of “epigenetic drugs,” compounds that are able to interfere with epigenetic processes, for the treatment of obesity could become a reality in the near future.

Conclusion Remarks Many features of modern societies have changed dramatically over the past 40 years, and, for example, the prevalence of obesity duplicated. In terms of genetics, alterations in the human genome difficultly explain obesity prevalence, as it is not likely that rapid changes occur in a short period of time. This increase in obesity prevalence could be partially explained by our diet; however, it is a well-established genetic component related to weight gain. Recently, a third component emerged which might help to explain the increase in obesity prevalence: epigenetic changes. The interplay between environmental factors (like nutrition) and the genetic background could contribute significantly to the obese phenotype. Alterations in gene expression due to environmental signals both in embryonic development and in postnatal life are now emerging as an important player in the obesity risk. The next few years will be exciting in the obesity field. It is expected that the identification of CNVs and low/ rare allele frequency, the detection of epigenetic marks, and functional studies will

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help draw a more complete picture of obesity etiology, which will undoubtedly contribute to the development of prevention strategies and therapeutic interventions.

Dictionary of Terms • Adipose tissue – A connective tissue composed of fat cells, called “adipocytes.” • Bioactive molecules – Is a substance that has a biological activity with an effect on human health. • Codon – Is a triplet of adjacent nucleotides in the messenger RNA chain that codes for a single amino acid during the protein synthesis. • DNA methylation – Is a heritable epigenetic mark which methyl group is added to DNA. • Frameshift mutation – A genetic mutation caused by an insertion or deletion of nucleotides that shift in the translational reading frame. • Histone – Proteins that form the unit around which DNA is coiled in the nucleosomes of eukaryotic chromosomes. • Homeostasis – Is the maintenance within a normal range value of internal physiological conditions in higher animals under fluctuating environmental conditions. • microRNA – Small noncoding RNA molecules that play a role in the regulation of gene expression. • Missense mutation – A genetic mutation caused by a single-nucleotide change in a codon which codes for a different amino acid.

Key Facts Key fact of energy homeostasis • Body weight is regulated by energy intake and energy expenditure. • Energy intake is characterized by food and drinks. • Resting energy expenditure (basal metabolism) represents the energy needed to support minimal daily function. • Caloric restriction is associated with a compensatory decrease in energy expenditure, which is difficult to maintain weight loss by dieting. • Energy balance is achieved by incorporating both long-term adiposity and shortterm satiety signals. • The two major signals involved in energy homeostasis are leptin and insulin. Key fact of fetal programming • Evidences were observed between nutritional insufficiency during embryonic and fetal development to metabolic syndromes in adulthood. • In 1990, the Barker hypothesis postulates that a number of organ structures and associated functions will undergo programming during embryonic and fetal life,

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determining the set points of physiological and metabolic responses that continue throughout adult life. Epigenetic mechanisms play an important role on gene expression and could be involved in fetal reprogramming. Nutrition is one of the principal major intrauterine environmental factors that alters expression of the fetal genome and may have future lifelong consequences. Epigenetic alteration of gene expression could be attributed to an altered nutrient environment. Regulation of appetite and satiety function must develop in utero to prepare the newborn life.

Summary Points • Obesity results from an imbalance between energy intake and energy expenditure; however, a genetic background also accounts for the susceptibility to develop obesity. • Several mutations associated with obesity were found in genes related to the leptin/melanocortin pathway, which is involved in energy thermogenesis. • The advance of genome-wide association study (GWAS) identified several genes associated with the susceptibility risk to develop common obesity. • Until now, all genetic variants found associated with obesity (more than 50) only explain a small part of the genetic variance in BMI; however, new studies emerged to identify new genetic variations trying to explain this “missing heritability.” • Environmental factors such as nutrition have been implicated in the influence of the genetic background contributing to the increase of obesity through epigenetic mechanisms. • Some studies detected differences on DNA methylation status between lean and obese individuals. • Other factors such as preconceptual and in utero conditions might play a significant role in obesity susceptibility risk. • Our diet and lifestyle also changed drastically these past three decades with the incorporation of new molecules and bioactive nutrients which may influence our epigenome. • In the near future, new studies based on genetics, epigenetics, and nutrition will undoubtedly be a further step to unveil the condition to develop an obese phenotype.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Epigenetic Influences Hyperphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RNA Epigenetic Influences Hyperphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RNA Adenosine Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RNA Adenosine Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The central dogma states that the genetic information, which is contained in the DNA, is transcribed and translated into proteins. We now know that a recently identified novel phenomenon, known as epigenetics, alters gene expression without altering DNA sequences. Thus, this phenomenon alters the central dogma hypothesis. Some of these epigenetic changes are reversible, while some of these changes are heritable; both have the potential to influence every aspect of biology. Furthermore, epigenetic changes happen naturally during environmental changes, during aging, and during disease states. Consequently, epigenetics impacts our daily lives. One such biological process that is impacted by epigenetics is the feeding behavior. Epigenetics supports the theory that life experience can alter your feeding behavior irrespective of one’s genetic makeup. This is

M. Singh (*) Department of Pediatrics, The University of Iowa, Iowa City, IA, USA e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_78

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because some life experience leaves physical marks on DNA, or epitranscriptome changes alter biological functions of proteins that are involved in feeding behavior. To date, at least five systems have been identified to be involved in epigenetic processes: DNA methylation, histone modification, noncoding RNA (ncRNA) regulation, RNA methylation, and RNA editing. All these processes initiate and sustain epigenetic changes independently. This chapter highlights various epigenetic changes known to regulate and alter gene expressions and how some of these epigenetic changes can directly or indirectly affect an overeating behavior known as hyperphagia, which leads to obesity. Keywords

Hyperphagia · Epigenetics · DNA methylation · RNA methylation · Histones · RNA editing · Epitranscriptome · Noncoding RNA · microRNA · Obesity List of Abbreviations

5HT2CR A to I ADAR DNMT FTO HCRT m6A MC2R miRNA ncRNA OXTR POMC PWS rRNA snoRNA snRNA SNRPN tRNA

Serotonin 2C receptor Adenosine to inosine RNA editing Adenosine deaminase that acts on RNA DNA methyltransferase Fat mass and obesity-associated gene Hypocretin (orexin) N6-methyladenosine Melanocortin receptor microRNA Noncoding RNA Oxytocin receptor Pro-opiomelanocortin Prader-Willi Syndrome Ribosomal RNA Small nucleolar RNA Small nuclear RNA Small nuclear ribonucleoprotein Transfer RNA

Introduction Around mid-century research uncovered that a combination of genetics and developmental biology plays a role in a phenotype, and these findings have evolved into a field known as epigenetics (Holliday 2006). Epigenetics in Drosophila melanogaster was described as the influence of genetic processes on development which later was discovered as the molecular basis of environmental stresses influencing certain phenotypic characteristics (Holliday 2002). All cells contain the same DNA with certain sets of genes that are either turned on or off depending on the cell’s requirement. Epigenetics is described as a

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phenomenon where all mitotic and meiotic heritable changes alter gene expressions without the alteration of DNA sequence (s). DNA methylation, histone modification, chromatin remodeling, and RNA modifications are all examples of new processes that have evolved the epigenomic landscape of all cell types in the human body (Goldberg et al. 2007). Surprisingly, although they are independent events, all combinatorial processes initiate and sustain epigenetic changes, while interacting with each other to affect gene expression and protein function. What is surprising is that if any of these processes that tweak the DNA system is altered, then the consequent epigenetic changes lead to epigenetic diseases (Egger et al. 2004). One such epigenetic disease is the imprinting phenomenon that manifests in hyperphagia or overeating of Prader-Willi syndrome (PWS) (Cassidy et al. 2011). In PWS, hyperphagia leads to morbid obesity (Feigerlova et al. 2008). However, hyperphagia may also arise from many other causes such as impaired satiety, overeating in the absence of hunger, and psychic distress (Theodoro et al. 2006; Feigerlova et al. 2008; Singh 2014). Thus, epigenetics has the potential to govern the DNA system without altering DNA sequence affecting a phenotype outcome. Animal models and the human neurodevelopmental disorder of PWS demonstrate that epigenetic dysregulation causes morbid obesity (Cassidy et al. 2011; Ivanova and Kelsey 2011; Singh 2014). However, the impact of epigenetic mechanisms on obesity which is a worldwide epidemic remains unclear. This is mostly due to the inherent tissue specificity of epigenetic regulation. Deciphering the process and molecular mechanism of epigenetics is complicated due to multiple interactions of the genetics, environment, epigenetics, and obesity in human obesity. When epigenetic changes are heritable, then these changes contribute to evolution, and this has been documented (Burggren 2016). Studies show that the variation of lifestyles and the genetic makeup of a person influence the risk of obesity; this is partly due to epigenetics and developmental pathways (Gluckman and Hanson 2008). Developmental pathways during gestation, perinatal, overnutrition or undernutrition, and exposure in early infant life promote epigenetic changes that lead to a higher risk of metabolic disorder and obesity (Gluckman and Hanson 2008). Hunger epigenetics can also explain how genetics can be altered by the availability of nutrition that can impact several generations (Roseboom et al. 2006; Veenendaal et al. 2013). Such changes have been well documented in the Cambodian hunger of 1975–1979 and the Dutch famine in 1940–1945 where their starvation led to modification in their DNA leaving physical marks which impacted the next two generations. For example, altered DNA methylation affecting the growth-regulated genes led to populations with smaller infants during that famine. Thus, the famine episode showed the early example of epigenetic changes that impacted inheritance without the inherited traits coming from DNA sequences. Of all the epigenetic modifications, the most well-characterized and broadly studied are DNA methylations followed by other major modifications of chromatin remodeling, histone modifications, noncoding RNA, RNA methylation, and RNA editing that provide in depth of their role in cellular and whole organism functions. Since the epigenetic field has grown as an area of interest over the years, the relationship between epigenetic changes and a host of disorders is better understood.

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chromatin remodeling

Phenotype Fig. 1 The landscape of epigenetics in hyperphagia. Environment, genotype, and epitranscriptome can either independently or in synergy regulate the outcome of hyperphagia phenotype. Epigenetic processes lead to a dynamic epitranscriptome profile where RNA expression and protein functions are altered. Because of altered protein functions in the feeding pathway, this process leads to a phenotype of hyperphagia-mediated obesity

This includes different types of cancers, mental disorders, immune disorders, neuropsychiatric disorders, hyperphagia, and obesity. Obesity is a multifactorial disease influenced by environment and genetic vulnerability (Singh 2014; Steiger and Thaler 2016). The genetic vulnerability is due to certain portion of the DNA sequence that is expressed from the encoded region in the DNA sequence. This altered expression is due to epigenetic regulation influencing epitranscriptome (RNA) profile which leads to altered biological functional or nonfunctional proteins. Besides the genetic component, other environmental cues such as diet, physical activity, intrauterine environment, infection, sleep patterns, and endocrine-disrupting chemicals can all influence epigenetic changes. Further epigenetic changes such as DNA methylation, histone modification, small RNAs, RNA editing, and methylation influence and shape the outcome of a phenotype. Thus, combination of interaction between environment, epigenetics, and genetics all shapes the phenotype outcome (Fig. 1). Hyperphagia is a condition of excessive hunger that leads to overeating (Heymsfield et al. 2014). Overeating can be present in the absence of physiological hunger. The loss of control, regarding food intake, is defined as “binge eating disorder” in the DSM-IV-R diagnostic criteria (Wallin and Rissanen 1994; Mirch et al. 2006; Singh 2014). Lost control of regulated eating can move from episodic binge eating in the absence of hunger to extreme forms of overeating known as hyperphagia (Mirch et al. 2006; Berthoud et al. 2011). Furthermore, hyperphagia has been observed in many conditions of human disease and animal models of hyperphagia-mediated obesity, glucocorticoid hormone imbalance, defective leptin

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Diet Methyl rich Diet

DNA with methyl marks

Environmental Stress mirRNA in milk

Mood disorder and hormone imbalance

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Ghrelin

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Increased ghrelin signaling in the brain

Serotonin Dopamine

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Hyperphagia Fig. 2 Effect of environmental influence on epigenetic-mediated hyperphagia. Everyday life outcomes of different diets and kinds of stress can modulate DNA and RNA modifications, leading to epigenetic-mediated hyperphagia

signaling, obesity syndrome, and cognitive impairment as observed in Prader-Willi syndrome (PWS) (Theodoro et al. 2006; Gluckman et al. 2008; Ivanova and Kelsey 2011). Thus, hyperphagia is the main contributor to obesity. Maternal nutrition can induce nutrition-induced altered DNA methylation leading to modified gene expression (Fig. 2) (Zheng et al. 2015). A growing number of studies in the field of epigenetics have provided insights into the interaction of environment, gene, and epigenetics where both the environment factors such as gestation distress, early life stress, nutritional factors, and individual lifestyle can all interact with the genome to directly influence the epigenetic outcome such as eating disorders (Fig. 2) (Singh 2014; Steiger and Thaler 2016). These changes may be reflected during various stages of person’s life and in a later generation. Early and postnatal exposure from environmental factors can lead to behavioral disorders and chronic disease such as obesity and coronary heart disease as seen in children that were born during the period of the Dutch famine between 1940 and 1945 (Roseboom et al. 2006; Veenendaal et al. 2013). A very well-characterized epigenetic locus of the insulin-like growth factor II (IGF2) gene shows reduced DNA methylation following under malnutrition of famine (Roseboom et al. 2006). Further, epigenetic changes in cognition that are associated with learning and memory either early or late in life can also play an important role in sustained eating disorder (Bali et al. 2011; Day and Sweatt 2011). Thus, numerous factors contribute to epigenetic changes that can be either temporary or long lasting that are transmittable to the next generation (Figs. 3 and 4). All these changes impact growth and feeding behavior.

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G

G G

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METTL3 and METTL14

SS-RNA

SS-and DS-RNA Adenosine methylation G

SS-and DS-RNA Adenosine demethylation

G I ADARs

Hyperphagia DS-RNA DS-RNA Adenosine RNA editing

Fig. 3 Effect of RNA modifications, adenosine methylation, and RNA editing on epigeneticmediated hyperphagia. Adenosine demethylation by FTO enzyme affects a hunger hormone ghrelin, which leads to increased ghrelin hormone and hyperphagia. Adenosine RNA editing of the 5HT2CR results in reduced serotonin signaling and leads to hyperphagia

Epigenetic transmission Environmental stress

Imprinting\ DNA methylation

Altered chromatin structure

Epitranscriptome

Regulatory Region

Histone modification and chromatin structure

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Fig. 4 Effect of chromatin structure, DNA, and histone modifications on epigenetic-mediated hyperphagia. DNA methylation in the regulatory regions of imprinted genes Prader-Willi locus regulates parent-of-specific gene expression that plays an important role in hyperphagia and obesity in PWS

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Histones are proteins that package the DNA. Nucleosomes comprise of DNA, histone protein, and RNAs that are organized as units in the chromatin structure (Fig. 4) (Bernstein et al. 2007). Histones that are wrapped around 147 bp DNA can be modified chemically to alter the chromatin structure and affinity for chromatininteracting proteins such as transcription factors. Heterochromatin in general has high levels of DNA methylation and modified histones which is a posttranslational modification that is conducive to gene silencing. By contrast, euchromatin has reduced DNA methylation and modified histones that promote gene expression. Generally, acetylated histones mark an active and transcriptionally competent region, while hypo-acetylation of histones is found in both heterochromatin and euchromatin regions. A hallmark of silent DNA results from globally distributed lysine 9 methylation on the N-terminus region of histone H3 (H3-K9) which is seen in heterochromatin region of centromeres, telomeres, inactive X chromosome, and promoter regions of genes. On the other hand, methylated lysine 4 histone H3 (H3-K4) marks activity and is mostly found in promoter regions of active genes. Besides methylation, posttranslational phosphorylation, ubiquitination, and SUMOylation of histones also play a role in epigenetic gene regulation. Thus, histone modification contributes to an important role in epigenetic gene regulation (Fig. 4). The regulation of eukaryotic mRNA expression involves transcription, mRNA processing, and degradation of the transcripts. The transcript homeostasis is regulated, coordinated, and maintained by extracellular and intracellular signaling pathways (Bentley 2014). The gene regulation is influenced by RNA expression that can be modified epigenetically giving rise to epitranscriptome (Fig. 3) (Table 1) (Saletore Table 1 Types of RNA modifications found in eukaryotic mRNA. In eukaryotic mRNAs several base modifications have been found in the transcripts including adenosine, guanosine, cytosine, and uracil modifications (Li and Mason 2014). m6A RNA methylation has been linked to impacting epigenetics of hyperphagia and obesity (Karra et al. 2013) Adenosine mRNA modifications N6-methyladenosine (m6A) 20 -O-methyladenosine (Am) N6, 20 -O-demethyladenosine (m6Am) N6, N6, 20 -O-trimethyladenosine (m62Am) Guanosine mRNA modifications 7-methylguanosine (m7G) 20 -O-methylguanosine (Gm) N2, 70 -dimethylguanosine (m2,7G) N2, N2, 70 -trimethylguanosine (m2,2-7G) Cytosine mRNA modifications 20 -O-methylcytidine (Cm) 5-methylcytidine (m5C) Uracil mRNA modifications 20 -O-methyluridine (Um) 3,20 -O-dimethyluridine (m3Um)

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Table 2 Types of RNA editing. Insertion and deletion type of RNA editing does not involve the breakage of double-stranded RNA backbone, while base modification type of RNA editing does involve base modification with the breakage of double-stranded RNA backbone (Emeson and Singh 2001; Bass 2002). A to I RNA editing in the 5HT2CR mRNA has been linked to hyperphagia and obesity (Kawahara et al. 2008; Morabito et al. 2010) A. Insertion and deletion type of RNA editing Nucleotides in mRNA are either inserted or deleted without the breakage of double-stranded RNA B. Base modification type of RNA editing Nucleotides in mRNA are modified in RNA by breakage of double-stranded RNA 1. The enzyme cytidine deaminase catalyzes the modification of cytidine base to a uridine base (C to U) 2. ADAR family of enzymes catalyzes the modification of adenosine base to inosine base in the double-stranded RNA (A to I) 3. Alternative U to C mRNA editing and non-classic G-A mRNA changes have been proposed to be catalyzed by the enzyme cytidine deaminase APOBEC3A

et al. 2012). Further, the findings of numerous different types of RNA modification such as RNA methylation, RNA editing, pseudouridylation in rRNA, tRNA, and spliceosomal RNA have added to the excitement surrounding epitranscriptome in the gene regulation (Li and Mason 2014). Besides these modifications and several types of base modification in RNA (Table 1), there are other fine-tuning transcript processing that takes place, including RNA methylation and RNA editing which have shown to play a role in feeding behavior (Table 2) (Emeson and Singh 2001; Jia et al. 2011; Karra et al. 2013; Alarcon et al. 2015). Integrative analysis of N6methyladenosine methylation (m6A) and A to I RNA editing shows that these processes are mutually exclusive (Witkin et al. 2015). This suggests that m6A methylation has the potential to alter A to I RNA editing. Epitranscriptome that includes RNA methylation and RNA editing leads to altered translational efficiency. These processes allow immediate response to ever-changing environmental cues. Thus, RNA editing and methylation can change the pathway of activation of transcriptional factors that regulate gene expression and have been shown to play a key role in hyperphagia (Fig. 3). RNA editing in the epitranscriptome is a posttranscriptional modification where genomically encoded sequences are altered at the level of mRNA. Some of these changes in mRNAs are an absolute requirement for normal physiology of the animal. There are two types of RNA editing: adenosine to inosine (A to I) and cytidine to uracil (C to U) (Table 2). A to I RNA editing is the most abundant type of RNA editing that occurs in double-stranded RNAs, and the process is catalyzed by a family of enzymes known as adenosine deaminase that acts on RNAs (ADARs) (Bass 2002). During the A to I editing, adenosine is modified to inosine, and since inosine has the same base pairing properties as guanosine, both the transcriptional and translational machineries recognize inosine as a guanosine. Hence the nucleotide adenosine in DNA is changed to guanosine, and a silent mutation is created at the level of RNA. A to I editing has been shown to alter gene expression, regulation, RNA stability, splicing, localization, microRNA (miRNA) function, and protein translation (Bass

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2002; Nishikura 2016). A to I editing processes are dynamic and irreversible, depending on the intracellular and extracellular stimuli (Nishikura 2016; Tajaddod et al. 2016). A to I editing is also variable where barely detectable level to 100% level is observed in a tissue-specific and developmental stage-specific manner. These changes occur in coding and noncoding region such as introns and 30 UTRs of mRNA. All together this suggests that editing changes respond to both intra- and extracellular stimuli and that having A to I editing process provides an advantage of flexibility over a hardwired DNA to quickly respond to specific needs to regulate gene expression. The second most abundant RNA editing is cytidine to uracil (C to U) editing that is catalyzed by different types of cytidine deaminase enzymes that can target both RNA and DNA (Rosenberg et al. 2011). C to U changes are observed in cellular processes such as antiviral defense, host cell defense, immunity, and recoding of transcripts. APOBEC I and APOBEC3A cytidine deaminases have been observed to change many cytidine residues by editing in mRNAs (Prohaska et al. 2014). C to U editing mediated by APOBEC3A is induced under inflammatory and hypoxia conditions. N6-methyladenosine RNA methylation (m6A) is present in 1–2% adenosine of mRNA (Meyer et al. 2012). There are many factors that write, erase, and read m6A methylation in RNA. These proteins are involved in development, metabolism, and circadian rhythm (Jia et al. 2011; Fustin et al. 2013). METTL3 and METTL14 are the writers, while fat mass and obesity-associated gene (FTO) and ALKBH5 are the erasers, and YTHDF1-YTHDF3, HuR, and HNRNPAB1 are m6A binders. Each of these fine-tunes RNA modification expression (Jia et al. 2011; Meyer et al. 2012). m6A methylation increases during development in the brain, and methylation pattern is tissue specific (Meyer et al. 2012). Circadian rhythm is known to affect feeding behavior (Berthoud and Morrison 2008). m6A is also involved in circadian rhythm where overexpression of METTL3 shortens and low expression of METTL3 lengthens the circadian rhythm by retaining Arntl and Per2 transcripts in the nucleus (Turek et al. 2005). Furthermore, the mRNA export from the nucleus to the cytoplasm is affected by ALKBH5 and METTL3 expression where they have an opposing effect on the transcript export (Fustin et al. 2013). Interestingly m6A methylation that is found in 3’UTR affects the binding of miRNAs. Furthermore, m6A methylation promotes miRNA biogenesis leading to mature miRNAs that in turn modulates METTL3 activity to decrease m6A methylations (Zheng et al. 2013; Alarcon et al. 2015). All together these studies indicate that m6A has many roles from development to miRNA regulation, circadian rhythm, and feeding behavior that are regulated in a tissue-specific manner. There are several classes of noncoding RNAs (ncRNAs) that are involved in the epigenetic regulation of gene expression. microRNA (miRNA) 23 nucleotides in length are noncoding RNAs (ncRNAs) that have the ability to target 30 mRNA and alter posttranscriptional gene regulation and protein translation. All DNMTs are targeted by mir-29 targets and reduce CpG methylation in DNA. Altered CpG methylation affects feeding-related gene expression (Fabbri et al. 2007). Small RNA molecules known as small nucleolar RNA (snoRNA) are another class of RNA involved in epigenetics of gene regulation. snoRNAs primarily guide

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chemical modifications of other RNAs, such as ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and small nucleolar RNAs (snoRNAs). The two main classes of snoRNAs are the H/ACA box snoRNAs, which are associated with pseudouridylation, and the C/D box snoRNAs, which are associated with RNA methylation. snoRNAs are organized in a diverse manner, ranging from intergenic regions, UTR, to open reading frames of protein coding genes. However, vertebrate snoRNAs are mostly encoded in introns of protein coding which are synthesized by RNA polymerase II. They are involved in ribosome synthesis or translation (Scott and Ono 2011). snoRNAs are also processed into smaller molecules with different functionality that have extensive similarities with microRNAs (miRNA) which regulate gene expression and protein function (Kishore et al. 2010). Thus, noncoding RNAs play a role in epigenetic gene regulation.

DNA Epigenetic Influences Hyperphagia Methylation occurs at C5 position at CpG sites or islands in DNA cytosine. In general, CpG methylation is well known for its role in epigenetic gene silencing. Imprinted genes in general have a CpG-rich region that is methylated differentially which leads to altered gene expression. Methylation of CpG islands is observed in many key regulatory regions of genes. Approximately 1% of mammalian genes that are imprinted affect the success of normal growth, development, and viability (Bittel and Butler 2005; Gluckman and Hanson 2008; Ivanova and Kelsey 2011; Peters 2014; Burggren 2016). The chromosome 15q-113 region contains about eight million DNA base pairs that includes large clusters of non-imprinted and imprinted regions, where genes are equally expressed from maternal or paternal chromosome. However, few genes show paternal bias (Bittel and Butler 2005). The PWS genomic region is inherited in a Mendelian fashion. When paternally expressed genes are lost from the 15q11-q13 region, then PWS arises. This is due to the fact that maternal genes from this region are silenced because they have imprinted DNA methylation. On the contrary, when maternal genes from this region are lost, then Angelman syndrome is observed, as paternal genes from this region are silenced due to imprinting (Peters 2014). Such imbalance of imprinting and many genes that are inactivated in PWS locus has been reported to lead to compulsive overeating in PWS of human disease (Bittel and Butler 2005, Cassidy and Driscoll 2009). Despite an extensive amount of research, PWS with extreme hyperphagia is still poorly understood. What is known so far is that hyperphagia and morbid obesity are associated with imprinting, snoRNAs, and RNA editing disorder of PWS (Doe et al. 2009; Butler 2011). Thus, DNA methylation in genomic imprinting regulates obesity of PWS. Furthermore, these studies suggest that dysregulated gene expression of imprinted maternal and paternal specific genes from this locus causes PWS and Angelman disease. DNA methyltransferases (DNMTs) are enzymes that methylate DNA. In humans, DNMT3A and DNMT3B methylate different DNA regions during embryonic development where DNMT1 methylates DNA following mitosis (Egger et al. 2004).

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Methylated DNA attracts methyl-cytosine binding proteins that promote chromatin condensation and repressive conformation. In general cytosine preceding guanine (CpG) is highly methylated in mammals and is found mostly in noncoding regions or in clusters, upstream of genes’ coding sequence known as CpG islands. DNA CpG methylation in promoter regions also leads to reduced gene expression as seen in feeding-regulated genes, leptin, POMC, and insulin genes that can affect adipogenesis and glucose homeostasis and influence hyperphagia-mediated obesity (Kuroda et al. 2009; Palou et al. 2011). In addition, there are feeding-related genes that are imprinted and paternally expressed in PWS males that affect feeding behavior which include hypocretin (orexin, HCRT), pro-opiomelanocortin (POMC), oxytocin receptor (OXTR), MC2R (melanocortin receptor), and serotonin receptors 3A, 1B, and 2B (Bittel and Butler 2005). Furthermore, there are also several transcripts in the 15q11-q13 region that are involved in RNA processing that include small nuclear ribonucleoprotein (SNRPN, protein coding sequence SNURF, or SNRPN upstream reading frame), as well as multiple copies of C/D box small nucleolar RNAs (snoRNAs) or SNORDs. The HBII-52 also known as SNORD 115 regulates RNA editing and splicing of the serotonin 2C receptor (5HT2CR) and is known to affect mood and feeding behavior, specifically hyperphagia (de Smith et al. 2009; Doe et al. 2009; Kishore et al. 2010). All together these findings suggest that there are many levels of gene regulations that are involved in the PWS locus promoting hyperphagia-mediated obesity in PWS.

RNA Epigenetic Influences Hyperphagia RNA Adenosine Editing A to I RNA editing is a posttranscriptional regulation of RNA catalyzed by a family of enzymes known as ADARs, where individual adenosine nucleotide is base modified to inosine with altered protein function (Bass 2002), thereby creating a silent mutation. RNA editing affects gene expression and protein functions (Emeson and Singh 2001). Several changes include feeding behavior that is affected by A to I RNA editing of the 5HT2CR, where five adenosine residues are modified to inosine at the level of RNA. RNA editing of the five adenosine residues in the 5HT2CR leads to 14 different types of RNA transcripts, hence generating receptor isoforms, each with a distinct protein signaling function (Burns et al. 1997). In general, with increasing numbers of adenosine modification in the 5HT2CR, there is a concomitant reduction of serotonin signaling strength of the 5HT2CR isoforms (Marion et al. 2004; Werry et al. 2008). The enzymes that catalyze RNA editing in mammalian system are known as ADARs (Bass 2002). Both ADAR1 and ADAR2 from the ADAR family edit the 5HT2CR transcript at five different adenosine sites. This is a process that occurs naturally. Thus, increased RNA editing of the 5HT2CR reduces serotonin signaling, and this loss of 5HT2CR function in the serotonin signaling affects the feeding pathway which leads to hyperphagia-mediated obesity (Kawahara et al. 2008; Werry et al. 2008; Morabito et al. 2010).

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Loss of the 5HT2CR specifically in the POMC neuron leads to hyperphagia and obesity in mice (Xu et al. 2008). Interestingly, mis-expression of RNA editing enzyme ADAR2 in mice results in hyperphagia and obesity (Singh et al. 2007; Akubuiro et al. 2013). Furthermore, studies show mood, food, and obesity are linked in humans and in animals (Singh 2014). Intriguingly, heterodimerization of the ghrelin receptor and non-edited 5HT2CR attenuates food intake (Schellekens et al. 2013). This suggests that 5HT2CR editing plays a significant role in fine-tuning both serotonin and ghrelin signaling in feeding pathways. In addition, RNA editing of the 5HT2CR provides a fine-tuning mechanism for feeding behavior without altering DNA sequences. Of note, feeding behavior is also linked to mood in humans, and RNA editing of the 5HT2CR has been found to alter mood and feeding behavior in both animals and humans (Wallin and Rissanen 1994; Singh et al. 2007, 2009, 2011; Singh 2014). It is also worth noting that both mood and hyperphagia in human disease of PWS are associated with altered serotonin signaling and 5HT2CR editing (Sahoo et al. 2008; Doe et al. 2009). The 5HT2CR RNA editing is regulated by other processes such as snoRNA methylation that affects splicing and editing of the 5HT2CR which is also known to play a role in PWS (Kawahara et al. 2008; Doe et al. 2009; Glatt-Deeley et al. 2010; Morabito et al. 2010). Furthermore, hyperghrelinemia is associated with PWS obesity (Feigerlova et al. 2008). Recently, it has been shown that ghrelin and serotonin systems interact via non-edited 5HT2CR to regulate food intake (Schellekens et al. 2013). The 5HT2CR also interacts with dopamine system and ghrelin system by their receptors to influence reward (Skibicka et al. 2012; Schellekens et al. 2013). Thus, RNA editing of the 5HT2CR is associated with mood, hunger, satiety, and reward. All three systems of dopamine, ghrelin, and serotonin are also associated with human disease of PWS. Thus, 5HT2CR editing may provide the crucial link between these three systems in PWS (Skibicka et al. 2012). Together these studies show that genomic imprinting, RNA methylation, and RNA editing all play a role in PWS phenotype.

RNA Adenosine Methylation The most common and abundant modification in RNA molecules of eukaryotes is the RNA methylation present in N6-methyladenosine (m6A) methylation (Meyer et al. 2012). m6A mRNA affects metabolism processes such as splicing, nuclear export, RNA transcription, translation ability, and stability. Comprehensive analysis of mRNA methylation reveals that these changes are mostly enriched in 30 UTRs and near stop codons (Meyer et al. 2012). More than 80% of all RNA base methylations in various species show that m6A are distributed in mRNA and in noncoding RNA such as transfer RNA (tRNA), ribosomal RNA (rRNA), and small nuclear RNA (snRNA) (Dominissini et al. 2012; Meyer et al. 2012). A methyltransferase complex METTL3 catalyzes the m6A modification. There are other enzymes known as m6A RNA demethylases or erasers, fat mass and obesity-associated gene (FTO), and ALKBH5 that catalyze m6A demethylation in an α-ketoglutarate (α-KG)- and Fe2+dependent manner (Liu et al. 2016). These enzymes have been shown to play an

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important role in many biological processes, development, metabolism, and fertility (Jia et al. 2011; Zheng et al. 2013). Increased FTO activity in patients with FTO mutations leads to abnormal m6A demethylation, dysfunctional RNA, and diseases (Church et al. 2010; Jia et al. 2011; Karra et al. 2013; Merkestein et al. 2014). Single nucleotide polymorphism in the first intron of FTO enhances FTO expression that is associated with reduced RNA adenosine (m6A) methylation. A reduction in methylation leads to the altered mRNA stability and altered protein expression. It is observed that abnormally low levels of m6A in target mRNAs contribute to the onset of obesity and related diseases (Jia et al. 2011; Karra et al. 2013). Such is the case of reduced level of m6A in ghrelin mRNA leading to increased ghrelin levels that promotes hyperphagia, and interestingly high ghrelin level is exclusive to PWS obesity (Karra et al. 2013). Ghrelin is also linked to stress and mood disorder in PWS (Feigerlova et al. 2008; Schellekens et al. 2012). It is notable that serotonin and ghrelin system are linked via the RNA editing of the 5HT2CR and that both these systems are dysregulated in PWS (Dimitropoulos et al. 2000; Schellekens et al. 2015). Thus, RNA editing of 5HT2CR is pivotal to feeding behavior of PWS (Kawahara et al. 2008; Morabito et al. 2010). Recently, it has been implicated that FTO expression can also be modified by an epigenetic amplifier such as milk (Fig. 2) (Melnik 2015). Bovine and human milk carry a substantial amount of noncoding microRNA-29 (mir-29). mir-29 targets all DNMTs leading to reduced methylation of the FTO gene. Concomitantly reduced methylation of FTO leads to increased FTO expression and reduced m6A levels in targeted mRNA (Karra et al. 2013; Merkestein et al. 2014). Numerous genes are affected by increased FTO expression, and one such gene is ghrelin that regulates feeding behavior (Karra et al. 2013). Thus, imprinting, RNA methylation, and RNA editing all could be involved in PWS (Fig. 1). Thus, hyperghrelinemia is linked to epigenetic interactions of methylation and editing proteins that may influence mood, reward, and hyperphagia-mediated obesity in PWS.

Conclusion A combinatorial regulation of genetics and epigenetics shapes the outcome of many phenotypes, one such being hyperphagia. Epigenetic regulation plays an important role in hyperphagia. Therefore, future research on understanding the various regulations of epigenetics in hyperphagia will help in designing better targeted therapeutic drugs for a worldwide epidemic of hyperphagia-mediated obesity.

Future Directions Recognizing that many factors contribute to epigenetics is a step forward in the right direction to understanding silent mutations that brings about a phenotype that cannot be explained by genetics alone. Epigenetic research is growing, but research to promote understanding the epigenetic process in relation to hyperphagia is slow.

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There are many reasons, but mainly due to lack of brain studies of humans. Postmortem tissues have their own problems, but having access to healthy and diseased brain samples could pave the way to the area of epigenetics in overeating. Many studies have focused on DNA methylation, while less time has been spent on other components of epigenetics. Increased studies of histone modification, the enzymes that control these processes, and changes in RNA modification, which all affect gene expression involved in feeding, could benefit the understanding of hyperphagia. Most studies involving epigenetics of feeding behavior have used peripheral tissue, such as blood to examine the levels of DNA and RNA methylations. However, in relation to peripheral changes at the DNA or RNA level, a need to examine the correlation of peripheral changes with brain changes is also equally important. Therefore, using animal models of hyperphagia on whole brain, brain region specific, and peripheral tissue comparison should be made to provide a better comparison and valuable insight into the dynamics of epigenetic regulation which regulates feeding-related genes and their pathways. Reversible chemical m6A modification in RNA serves as a novel epigenetic marker as they are silent changes without altering DNA information and have profound biological significance. Therefore, using high-throughput screening assays examining m6A RNA methylation levels and their distribution on RNA transcripts would be more useful for a better understanding that could benefit diagnostics and therapeutics of disease. Furthermore, an examination of the influence of adenosine methylation on adenosine RNA editing process that is irreversible requires a better understanding of their influence over each other. This influence ultimately alters the genotype outcome. Integrative studies will provide a better biological understanding the underpinning of gene-environment interactions playing a role in hyperphagia-mediated obesity that is so common in human population. This will lead to better targeted therapeutic drugs for hyperphagia-mediated obesity. Additionally, thorough investigations are required on the impact of genes from the region of 15q11-q13 that are involved with coding or noncoding protein production and function. In addition, genes outside of PWS locus that may influence and regulate genes of PWS locus and consequently impact the PWS phenotype need to be examined. Finally, data obtained from systematic epigenetic studies using animal models of hyperphagia will provide better clinical diagnostics of hyperphagia and advance the field of psychiatric studies associated with eating disorders.

Dictionary of Terms • Epitranscriptome – Base modification in noncoding RNA and mRNA leading to altered RNA expression. • Hyperphagia – Excessive overeating. • A to I RNA editing – Adenosine to inosine base modification in RNA.

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• Chromatin remodeling – The modification of chromatin structure that allows access to transcriptional factors to bind to DNA and regulate gene expression. • Genomic imprinting – DNA methylations and histone modifications govern certain gene expression in a parent-of-origin-specific manner. • Hyperghrelinemia – A condition where plasma ghrelin levels are high.

Key Facts • Gene expression can be altered epigenetically without altering DNA sequence (Fig. 1). • Epigenetic changes occur at the level of DNA, histone, and RNA (Fig. 4). • Epigenetic changes are influenced by environmental factors (Fig. 2). • Epitranscriptome alters RNA expressions and biological function of proteins (Fig. 3). • Noncoding RNA regulates RNA methylation, RNA stability, and translation. • Imprinting, RNA editing, and RNA methylation alter feeding pathways. • Epigenetics regulates hyperphagia and obesity (Fig. 1).

Summary Points • A dynamic combination of genetics and epigenetics shapes our lives and alters the way we look at the central dogma hypothesis. • Epigenetic changes can be regarded as silent mutations. • Epigenetics changes gene expression from one generation to the next without altering the DNA sequence. • Altered RNA expression from changes in epigenetics represents the epitranscriptome. • Dysregulated epigenetics following cell division can lead to many diseases including hyperphagia and obesity. • Epigenetics provides a tool for the cells to respond to environmental changes. • Epigenetic changes can occur at the level of DNA, histone, and RNA. • Imprinting, RNA methylation, and RNA editing are associated with hyperphagia. • Hyperphagia is the main contributor to obesity in the general population and in PWS. • Environmental factors such as maternal nutrition, milk, and early life stress can also change the marking of DNA and affects the offspring’s behavior and wellbeing. • Epigenetic changes may be temporary or long lasting and can pass to the next generation. • Finally, the outcome of interpreted DNA information in a living cell comes from the environmental cues orchestrating the epigenetic factors in the cell bringing about epigenetic changes that give rise to unique phenotypes (Fig. 1).

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Pang-Kuo Lo, Benjamin Wolfson, and Qun Zhou

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Overview of White and Brown/Beige Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Overview of Noncoding RNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Noncoding RNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs Involved in White Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs Function to Promote Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs Function to Inhibit Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs Involved in Brown/Beige Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs Promote Brown/Beige Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs Inhibit Brown/Beige Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LncRNAs Involved in White Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . lncRNAs Involved in Brown/Beige Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Noncoding RNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Adipogenesis is the cellular process through which pluripotent mesenchymal stem cells and preadipocytes differentiate into mature adipocytes, which are the predominant cell in white or brown/beige adipose tissue. The complicated

P.-K. Lo · B. Wolfson · Q. Zhou (*) Greenebaum Cancer Center, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA e-mail: [email protected]; [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_41

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process of adipogenesis is stringently controlled by multiple layers of regulators that include a myriad of transcription factors, hormones, and signaling pathway molecules. In addition to these well-known protein regulators, a growing evidence has shown that adipogenesis is also controlled by nonprotein regulators called noncoding RNAs (ncRNAs) that account for the majority of the mammalian transcriptome. Two major classes of ncRNA, microRNAs and long noncoding RNAs, have been identified to play crucial roles in the regulation of a variety of biological processes, including adipogenesis. In this chapter, we review recent advances in regulatory roles of ncRNAs in adipogenesis and discuss how these ncRNA regulatory networks contribute to the development and functions of white and brown/beige adipose tissues. Keywords

Adipogenesis · White adipose tissue · Brown/beige adipose tissue · Noncoding RNAs · MicroRNAs · Long noncoding RNAs List of Abbreviations

ADSCs BAT C/EBPα/β DGCR8 ECM lncRNAs miRNAs MSCs PPARγ WAT

Adipose tissue-derived stem cells Brown adipose tissue CCAAT-enhancer-binding protein α/β DiGeorge syndrome critical region 8 Extracellular matrix Long noncoding RNAs MicroRNAs Mesenchymal stem cells Peroxisome proliferator-activated receptor γ White adipose tissue

Introduction Obesity results from abnormal or excessive fat accumulation in adipose tissue and is a worldwide epidemic that predisposes individuals to a greater risk of the development of several serious diseases, including type 2 diabetes, cardiovascular disease, and certain types of cancers (Després and Lemieux 2006). Therefore, increased research to further our understanding of the molecular mechanisms involved in adipose biology and obesity is critical and could propel the identification of new therapeutic targets for combating obesity and obesity-related diseases. Adipose tissue plays an integral role in the storage and release of biological energy as well as serving as an immune and endocrine organ. Adipose tissues are classified into two different types: white adipose tissue (WAT) and brown adipose tissue (BAT). WAT mainly functions in the secretion of adipokines and the storage of fatty acids/triglycerides, whereas the primary functions of BAT are energy expenditure and non-shivering thermogenesis (Rosen and Spiegelman 2014). Both types of

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Fig. 1 Cell origins of white, beige, and brown adipocytes

adipose tissues are composed of mature adipocytes that are developed from the differentiation of preadipocytes or pluripotent mesenchymal stem cells, a process called adipogenesis (Fig. 1). Classical brown adipocytes present in the interscapular brown adipose tissue of mice have been known to originate from myoblastic-like Myf5-positive progenitor cells, which are also able to differentiate into myocytes (Rosen and Spiegelman 2014). Therefore, there is a close developmental relationship between brown adipocytes and myocytes (Fig. 1). In addition to BAT, brown-like adipose tissue (also called “beige” or “brite” adipose tissue) is found to arise from de novo differentiation of WAT precursors or preexisting white adipocytes (Rosen and Spiegelman 2014) (Fig. 1). Elucidating the molecular mechanisms governing the adipogenic process is crucial for understanding how obesity develops and how this abnormality can be prevented and cured. Although there have been significant advances in deciphering the molecular regulations of adipogenesis, our understanding of the regulatory mechanisms involved in adipose development is rudimentary. Among the discovered categories of noncoding RNAs (ncRNAs), recent investigations have shown that both microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are implicated in the regulatory networks of adipogenesis and play a key role in regulating the adipogenic process (Sun et al. 2013; Peng et al. 2014). This review chapter summarizes the most prominent recent advances concerning miRNA and lncRNA regulation of adipogenesis, providing novel insights into possible solutions for obesity prevention and therapy. Due to the limitations of space, it is impossible for us to discuss all publications and findings in this rapidly growing field. Therefore, we only focus on

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seminal, pioneering studies with clear mechanisms and in vivo/physiological evidence. We apologize for those studies that are not included.

An Overview of White and Brown/Beige Adipogenesis Adipogenesis involves a highly orchestrated series of events including lineage commitment, clonal expansion, and terminal differentiation. Adipocytes originate from pluripotent mesenchymal stem cells (MSCs) that have the multipotent ability to develop into various tissue cell types, such as adipocytes and osteoblasts. Commitment or determination of MSCs’ fate to differentiate into the adipogenic cell lineage is triggered by adipogenic differentiation signaling cues, which are still not well known. After this step, MSCs committed to the adipogenic lineage lose their ability to differentiate along other lineages. Upon exposure to hormones and other stimuli, committed preadipocytes developed from MSCs undergo the mitotic clonal expansion and differentiation steps to become mature adipocytes (Rosen and Spiegelman 2014). Adipogenesis is elicited by various stimuli such as nutrients, hormones, and temperature and is regulated by complicated mechanisms that involve numerous signaling pathways as well as transcriptional and posttranscriptional programs. Several signaling pathways elicited by cytokines such as insulin, bone morphogenic proteins (BMPs), TGFβ, and Wnt have been shown to be key regulators in driving MSC commitment to the adipogenic lineage and differentiation into mature adipocytes (Christodoulides et al. 2009; Margoni et al. 2012). For instance, insulin is critically involved in adipogenic regulation, predominantly through stimulating insulin growth factor-1 receptor and triggering the insulin receptor substrate (IRS)/ phosphoinositide 3-kinase (PI3K)/PDK1/AKT signaling cascade. The insulin signaling cascade is known to regulate adipogenesis via several different mechanisms. One mechanism is to promote the transcriptional activity of cAMP response element binding (CREB) via enhancing its phosphorylation, thereby activating adipogenic gene expression to elicit adipogenesis (Tseng et al. 2005). Another adipogenic mechanism underlying insulin signaling is through AKT-/PKB-mediated phosphorylation of forkhead box protein O (FOXO) transcription factors, leading to inhibition of anti-adipogenic FOXO factors (Nakae et al. 2003). Moreover, the mitogenactivated protein kinase (MAPK) pathways have been revealed to play a dual role in adipogenesis. It has been shown that ERK1 is activated during clonal expansion to positively regulate adipogenesis, whereas its activity declines at later adipogenic stages, allowing adipogenesis to proceed (Bost et al. 2005). While white and brown/beige adipogenesis occur in part through common mechanisms, there are also molecular mechanisms and factors specific to each. Both PPARγ (peroxisome proliferator-activated receptor gamma) and C/EBPs (CCAAT/ enhancer binding proteins) are known to be common transcriptional regulators driving WAT and brown/beige adipogenesis (Farmer 2006). In contrast, several transcriptional regulators, such as UCP1, peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC1α), early B cell factor 2 (EBF2), and PR-

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domain containing protein 16 (PRDM16), are only involved in driving the process of BAT-specific adipogenesis (Seale et al. 2008; Rajakumari et al. 2013). With regard to beige adipocytes present in WAT tissue, they contain high mitochondrial contents but manifest low levels of UCP1 when unstimulated. Upon cold exposure, UCP1 expression is induced in beige adipocytes, which subsequently activates respiration and energy expenditure programs that are similar to those found in classic brown adipocytes. These events result in thermogenesis and the anti-obesity effect (Wu et al. 2012).

An Overview of Noncoding RNAs MicroRNAs Small RNA deep sequencing analyses have unraveled that miRNAs are transcribed from exonic, intronic, or intergenic regions of the human genome (Rodriguez et al. 2004). miRNA biogenesis is known to be a multistep process. Primary miRNAs (primiRNAs) are transcribed by RNA polymerase II and are subsequently processed into precursor miRNAs (pre-miRNAs, ~70 nucleotides in length) by the RNase III Drosha-DGCR8-DDX5 microprocessor complex (Chua et al. 2009). After RNA processing, pre-miRNAs are exported to the cytoplasm by exportin (a Ran-GFPdependent transporter). In the cytoplasm, pre-miRNAs are further processed by the RNase Dicer-TAR RNA-binding protein (TRBP) complex to generate mature, single-strand miRNAs that are 19–23 nucleotides in length (Chua et al. 2009) (Fig. 2). According to computational analyses, miRNAs are estimated to regulate the expression of over 60% of human genes through guiding a various set of multiprotein RNA-induced silencing complexes (RISC) for specific, enzymatic processing of mRNA targets (Schraivogel and Meister 2014). The miRNA-associated RISC complexes are composed of the argonaute (Ago), glycine-tryptophan (GW) repeat-containing protein of 182 kDa (GW182) families of proteins, and other accessory proteins (Diederichs and Haber 2007). miRNAs are known to silence gene expression through driving the decay or translational repression of mRNA targets, which is determined by the combinatory nature of the RISC complex components and the degree of the complementarity between the 8-nt miRNA seed sequence and the miRNA-targeting site in the 30 -untranslated region (30 -UTR) of mRNA (Diederichs and Haber 2007).

Long Noncoding RNAs LncRNAs have recently been revealed to be the most numerous and functionally diverse class of ncRNAs, which are defined based on their lack of protein-coding capacity and their length over 200 nucleotides (Lo et al. 2016). LncRNAs are found to be transcribed from the sense or antisense DNA strands of introns or intergenic

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Fig. 2 The illustrative diagram for microRNA biogenesis

regions relative to protein-coding genes. The majority of lncRNAs are transcribed by RNA polymerase II and subsequently polyadenylated. In contrast, lncRNAs lacking poly(A) tails are mostly transcribed by RNA polymerase III (Lo et al. 2016). The primary nucleotide sequences and complex secondary structures of lncRNAs enable them to target genomic loci or to interact either with other RNA molecules (mRNA or ncRNAs) or with proteins for regulating a variety of biological processes in a temporal and spatial manner (Lo et al. 2016). LncRNAs exploit diverse mechanisms to influence chromatin remodeling, protein functionality, and gene expression. Some identified lncRNAs function as molecular scaffolds to assist the binding of nuclear transcriptional regulators to specific DNA elements, the formation of cellular substructures (e.g., paraspeckles),

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or the assembly of protein complexes (Lo et al. 2016). Moreover, some nuclear lncRNAs have been identified to interact with splicing factors or directly with premRNAs to regulate RNA splicing (Lo et al. 2016). Recently, some lncRNAs have been found to function as endogenous miRNA sponges to suppress the inhibitory effects of miRNAs on gene expression through their primary RNA sequences containing miRNA-targeting sites (Lo et al. 2016). These interactions between lncRNAs and miRNAs constitute a ncRNA regulatory network critically implicated in a variety of cellular processes, including cell differentiation and pluripotency (Lo et al. 2016). Through these diverse molecular mechanisms at multiple levels, lncRNAs are important molecules in regulation of gene expression and protein functionality, and their dysregulation can critically contribute to the development of diseases.

MicroRNAs Involved in White Adipogenesis MicroRNAs Function to Promote Adipogenesis Numerous miRNAs have been identified to regulate the commitment of mesenchymal stem cells (MSCs) to the adipogenic lineage, mitotic clonal expansion, and terminal adipocyte differentiation. Pro-adipogenic miRNAs function to promote these differentiation stages (summarized in Table 1). Regarding lineage commitment regulation, miR-124 has been shown to promote adipogenesis of MSCs through suppression of Dlx5 expression, a pro-osteogenic transcription factor (Qadir et al. 2013). Therefore, miR-124 facilitates MSC adipogenesis through inhibiting osteoblast differentiation. miR-30a, miR-30d, miR-204, miR-211, and miR-320 have also been found to promote the adipogenic lineage commitment of MSCs via targeting the pro-osteogenic transcription factor RUNX2 (Huang et al. 2010; Zaragosi et al. 2011; Hamam et al. 2014). An in vivo study has revealed age-related induction of miR-188 in bone marrow mesenchymal stem cells (BMSCs) and the critical role of miR-188 in bone loss and fat accumulation during aging (Li et al. 2015). The study further demonstrates that miR-188 directs the commitment of BMSCs to the adipogenic lineage, likely involving targeting of histone deacetylase 9 (HDAC9) and RPTOR-independent companion of MTOR complex 2 (RICTOR) (Li et al. 2015). These data indicate that miR-188 regulates an age-related switch between osteoblast and adipocyte differentiation of BMSCs. Similar to miR-188, both miR705 and miR-3077-5p function as inhibitors of BMSC osteoblast differentiation and promoters of adipocyte differentiation by targeting HOXA10 and RUNX2, respectively (Liao et al. 2013). These results are consistent with the observation that expression of these two miRNAs is increased in BMSCs from osteoporosis patients (Liao et al. 2013). Moreover, miR-140, a BMP4-induced miRNA, is reported to promote the BMP4-mediated adipocyte lineage commitment of C3H10T1/2 pluripotent stem cells via targeting osteopetrosis-associated transmembrane protein 1 (Ostm1), an anti-adipogenic factor (Liu et al. 2013b).

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Table 1 Pro-adipogenic microRNAs involved in white adipogenesis microRNA miR-124

Target Dlx5

Expression in adipogenesis Upregulated

miR-30a miR-30d miR-204 miR-211 miR-320

RUNX2

Upregulated

RUNX2

Upregulated

RUNX2

Upregulated

miR-188

HDAC9 RICTOR

Upregulated

miR-705 miR-3077-5p

HOXA10 RUNX2

Upregulated

miR-140

Ostm1

Upregulated

miR-181d

Adamts1

Upregulated

miR-17-92

Rb2/p130

Upregulated

miR-143

Upregulated

miR-375

MAP2K5 ERK5 ERK1/2

miR-125a-3p miR-483-5p

RhoA ERK1

Upregulated

miR-210

TCF7L2 SHIP1

Upregulated

miR-148a

WNT1

Upregulated

miR-20a miR-21

TGFBR2

Upregulated

miR-181a-5p

Smad7 Tcf7l2 SIRT1

Upregulated

miR-146b

Upregulated

Upregulated

Functional role Inhibit osteogenic commitment of MSCs Inhibit osteogenic commitment of MSCs Inhibit osteogenic commitment of MSCs Inhibit osteogenic commitment of MSCs Regulates age-related switch between osteoblast and adipocyte differentiation of BMSCs Inhibit BMSC osteoblast differentiation and promote adipocyte differentiation Promote the BMP4-mediated adipocyte lineage commitment Promote the adipogenic lineage commitment Enhance mitotic clonal expansion and adipocyte differentiation Repress anti-adipogenic MAPK signaling Repress anti-adipogenic MAPK signaling Inhibit anti-adipogenic inhibit RhoA/ROCK1/ERK1/2 signaling Suppress anti-adipogenic WNT signaling and activate pro-adipogenic PI3K/Akt signaling Inhibit anti-adipogenic WNT signaling Inhibit anti-adipogenic TGFβ signaling

Suppress both anti-adipogenic WNT and TGFβ signaling Inhibit the transcriptional activity of anti-adipogenic FOXO1

References Qadir et al. (2013) Zaragosi et al. (2011) Huang et al. (2010) Hamam et al. (2014) Li et al. (2015)

Liao et al. (2013) Liu et al. (2013b) Chen et al. (2016) Wang et al. (2008) Chen et al. (2014c) Ling et al. (2011) Chen et al. (2015) Qin et al. (2010), Liang et al. (2013) Shi et al. (2015) Kang et al. (2013), Zhou et al. (2015) Ouyang et al. (2016) Ahn et al. (2013) (continued)

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Table 1 (continued) microRNA miR-519d

Target PPARα

Expression in adipogenesis Upregulated

miR-342-3p

CtBP2

Upregulated

Functional role Inhibit fatty acid metabolism Activate the transcriptional activity of adipogenic C/EBPα

References Martinelli et al. (2010) Wang et al. (2015)

Furthermore, miR-181d has been shown to induce adipogenic lineage commitment via targeting matrix-associated metalloproteinase Adamts1 (a disintegrin and metalloproteinase with thrombospondin motifs 1) (Chen et al. 2016). Adamts1 overexpression impairs the adipogenic commitment process via its metalloprotease activity, which drives remodeling of extracellular matrix (ECM) components followed by activating FAK-ERK signaling. In vivo inactivation of Adamts1 in adipose tissue results in increased adipose tissue mass, reduced insulin sensitivity, and disrupted lipid homeostasis (Chen et al. 2016). These results suggest that miR181d instigates adipogenesis in part through ECM modulation by targeting Adamts1. Regarding the regulation of mitotic clonal expansion, the miR-17-92 cluster has been reported to directly repress expression of Rb2/p130 (a RB family protein), likely accounting for miR-17-92-enhanced mitotic clonal expansion and adipocyte differentiation. Consistently, expression of the miR-17-92 is upregulated during the clonal expansion stage of adipogenesis (Wang et al. 2008). Several miRNAs have been reported to facilitate adipogenesis through targeting signaling transduction pathways that inhibit adipogenesis through promoting cell proliferation (e.g., MAPK signaling) and maintaining cell stemness (e.g., WNT and TGFβ signaling). For instance, miR-143 expression increases during adipogenesis of 3T3-L1 preadipocytes and adipose tissue-derived stem cells (ADSCs), and it promotes adipocyte differentiation through repression of MAPK signaling by targeting MAP2K5 and ERK5 (Chen et al. 2014c). Similarly, pro-adipogenic miR-375 acts as a MAPK signaling inhibitor through inhibiting phosphorylation of ERK1/ERK2. miR-375 induction during adipogenesis is required for increased expression of adipogenic genes (C/EBPα, PPARγ, and aP2) and the formation of lipid droplets (Ling et al. 2011). Through the study of a rare disease called multiple symmetric lipomatosis (MSL) that is characterized by symmetric and abnormal distribution of subcutaneous adipose tissue (SAT), miR-125a-3p and miR-483-5p have been found to be upregulated in the SAT of MSL patients, and it has been shown that their ectopic expression promotes human ADSC adipogenesis. Mechanistic studies show that miR-125a-3p and miR-483-5p promote adipogenesis through inhibiting antiadipogenic RhoA/ROCK1/ERK1/ERK2 signaling by targeting RhoA and ERK1, respectively (Chen et al. 2015). In addition to regulating MAPK signaling, miR-210 is reported to stimulate adipogenesis via targeting TCF7L2, a key transcription factor that triggers downstream responsive genes of WNT signaling (Qin et al. 2010). Moreover, another

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study has identified SHIP1, a negative regulator of PI3K/Akt pathway, as a new miR-210 target (Liang et al. 2013). These findings suggest that miR-210 facilitates adipogenesis via inhibition of WNT signaling and concomitant activation of PI3K/ Akt signaling. miR-148a enhances adipogenesis by targeting WNT1, a WNT signaling ligand (Shi et al. 2015). miR-20a and miR-21 activate adipogenesis by targeting TGFβ receptor 2 (TGFBR2), a protein receptor mediating TGFβ signaling (Kang et al. 2013; Zhou et al. 2015). miR-181a-5p has been identified as an adipogenic enhancer via suppression of both WNT and TGFβ signaling. Mechanistic studies show that miR-181a-5p targets the 30 -UTRs of Smad7 and Tcf7l2 mRNAs, both of which encode the important components of TGFβ and Wnt signaling pathways, respectively. Through this mechanism, supplementation of miR-181a-5p in 3T3-L1 cells promotes adipogenesis (Ouyang et al. 2016). Some pro-adipogenic miRNAs have been shown to target transcriptional regulators that control adipogenic transcriptional programming. miR-146b has been identified as a pro-adipogenic miRNA that targets sirtuin 1 (SIRT1) (Ahn et al. 2013). SIRT1 is a deacetylase that mediates FOXO1 deacetylation, an anti-adipogenic forkhead box transcription factor. miR-146b is highly expressed in differentiated adipocytes and in the adipose tissue of obese mouse models, and it is required for expression of adipogenic marker genes and visceral fat development (Ahn et al. 2013). Furthermore, miR-519d has been shown to target PPARα and promote adipogenesis (Martinelli et al. 2010). PPARα is a transcriptional regulator that plays a key role in enhancing fatty acid oxidation in the liver, muscles, and heart. Therefore, miR-519d likely promotes adipogenesis by downregulating PPARα, resulting in inhibition of fat burning. In addition, pro-adipogenic miR-342-3p has been reported to target the transcriptional co-repressor CtBP2. miR-342-3p expression is elevated during adipogenesis and in the adipose tissue of obese mice (Wang et al. 2015). Due to the repressive effect of CtBP2 on C/EBPα, these findings suggest that miR-342-3p stimulates adipogenesis through inhibiting CtBP2 and releasing the key adipogenic transcription factor C/EBPα from CtBP2 binding, which activates the transcriptional activity of C/EBPα to induce expression of adipogenic programming genes.

MicroRNAs Function to Inhibit Adipogenesis A number of miRNAs have been shown to function as anti-adipogenic regulators that inhibit adipogenesis from commitment to terminal differentiation (summarized in Table 2). By targeting PPARγ, a master transcription factor that determines adipogenic lineage commitment, miR-27a, miR-130a, and miR-302a have been shown to function as anti-adipogenic regulators. Functional studies demonstrate that these three miRNAs inhibit adipogenic lineage commitment as well as adipocyte differentiation and concomitantly cause PPARγ downregulation (Kim et al. 2010; Lee et al. 2011; Jeong et al. 2014a). Consistent with the functional data, the expression levels of these three miRNAs are decreased during adipogenesis, inversely correlating with expression levels of PPARγ. Another study of

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Table 2 Anti-adipogenic microRNAs involved in white adipogenesis microRNA miR-27a

Target PPARγ

Expression in adipogenesis Downregulated

miR-130a

PPARγ

Downregulated

miR-302a

PPARγ

Downregulated

miR-194

Downregulated

miR-363

COUPTFII E2F3

Downregulated

Let-7

HMGA2

Downregulated

miR-33b

HMGA2

Downregulated

miR-135a-5p

APC

Downregulated

miR-448

KLF5

Downregulated

miR-25

KLF4 C/ EBPα

Downregulated

miR-195a

Zfp423

Downregulated

miR-224-5p

EGR2

Downregulated

miR-138

EID-1

Downregulated

Functional role Inhibit adipogenic lineage commitment of MSCs and adipocyte differentiation Inhibit adipogenic lineage commitment of MSCs and adipocyte differentiation Inhibit adipogenic lineage commitment of MSCs and adipocyte differentiation Inhibit adipogenic lineage commitment of MSCs Inhibit the transition from mitotic clonal expansion to terminal differentiation during ADSC adipogenesis Inhibit the transition from clonal expansion to terminal differentiation Inhibit the transition from clonal expansion to terminal differentiation Activate anti-adipogenic WNT signaling Downregulate the adipogenic transcription factor KLF5 to inhibit adipogenesis Downregulate the adipogenic transcription factor KLF4 and C/ EBPα to inhibit adipogenesis Inhibit adipocyte differentiation and the maintenance of white adipocyte identity Suppress the early stages of adipogenesis Attenuates ADSC adipogenesis through targeting EID-1

References Kim et al. (2010) Lee et al. (2011) Jeong et al. (2014a) Jeong et al. (2014b) Chen et al. (2014b)

Sun et al. (2009) Price et al. (2016) Chen et al. (2014a) Kinoshita et al. (2010) Liang et al. (2015) Shao et al. (2016) Peng et al. (2013) Yang et al. (2011)

mesenchymal C3H10T1/2 and primary bone marrow stromal cells has shown that miR-194 is a pro-osteogenic miRNA that targets COUP-TFII, a ligand-inducible transcription factor determining the adipogenic lineage commitment of MSCs. miR194 provokes osteoblast differentiation of MSCs through blocking adipogenic commitment by repressing COUP-TFII expression (Jeong et al. 2014b). Therefore, miR-194 determines the differentiation fate of MSCs via regulation of COUP-TFII. Some anti-adipogenic miRNAs inhibit the mitotic clonal expansion of adipogenesis. For example, anti-adipogenic miR-363 has been shown to downregulate E2F3

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expression during ADSC adipogenesis (Chen et al. 2014b). E2F3, a member of the E2F transcription factor family, transactivates a set of genes involved in promoting the G1-S transition of the cell cycle. Therefore, miR-363-mediated E2F3 downregulation may account for the inhibitory effect of miR-363 on the transition from mitotic clonal expansion to terminal differentiation during ADSC adipogenesis. Another study shows that the anti-adipogenic miRNA let-7 targets high mobility group AT hook 2 (HMGA2) (Sun et al. 2009). HMGA2 is a transcription factor that regulates growth and proliferation. Transgenic and knockout animal studies indicate that HMGA2 is a proadipogenic factor required for fat tissue development (Zhou et al. 1995). Expression of let-7 is inversely correlated with HMGA2 during 3T3-L1 adipogenesis, suggesting that let-7 inhibits the transition from clonal expansion to terminal differentiation through targeting HMGA2 (Sun et al. 2009). In addition to let-7, miR-33b has also been shown to target HMGA2 to inhibit adipogenesis (Price et al. 2016). Several miRNAs have been found to function as anti-adipogenic regulators via targeting genes involved in signaling transduction and transcriptional processes crucial for multiple stages of adipogenesis. miR-135a-5p is downregulated during 3T3-L1 adipogenesis and inhibits preadipocyte differentiation through activating WNT signaling by targeting the WNT signaling inhibitor APC (adenomatous polyposis coli) (Chen et al. 2014a). Moreover, several anti-adipogenic miRNAs have been found to target key adipogenic transcriptional regulators. miR-448 has been shown to target Krüppel-like factor 5 (KLF5), a key transcription factor that facilitates adipogenesis by activating expression of adipogenic genes (e.g., C/EBPα and PPARγ). KLF5 expression levels inversely correlate with miR-448 levels, suggesting that inhibition of adipogenesis by miR-448 involves miR-448mediated downregulation of KLF5 expression (Kinoshita et al. 2010). Similarly, anti-adipogenic miR-25, which is downregulated during 3T3-L1 adipogenesis, has been shown to target two key adipogenic regulators, KLF4 and C/EBPα (Liang et al. 2015). miR-195a is a newly identified anti-adipogenic miRNA with a suppressive effect on adipogenic differentiation of 3T3-L1 and C3H10T1/2 cells in part through inhibiting expression of its target zinc finger protein 423 (Zfp423), which functions as a transcription factor (Yun et al. 2015). Zfp423 is required for maintaining white adipocyte identity through suppression of the beige cell thermogenic gene program (Shao et al. 2016). Therefore, miR-195a/Zfp423 signaling plays a critical role in regulating adipogenic transcriptional programming and determining adipocyte identity. Moreover, during early adipogenesis, antiadipogenic miR-224-5p inhibits early growth response 2 (EGR2, also known as Krox20) expression, a transcription factor that enhances early stages of adipogenesis through C/EBPβ-dependent and C/EBPβ-independent mechanisms (Peng et al. 2013). Furthermore, it has been shown that miR-138 is significantly downregulated during adipogenesis of ADSCs, which is reversely correlated with expression of adenovirus early region 1-A-like inhibitor of differentiation 1 (EID-1) that functions as a nuclear receptor coregulator involved in modulating the activities of other transcriptional activators and repressors (e.g., CBP/p300). Functional studies suggest that miR-138 attenuates ADSC adipogenesis through targeting EID-1 (Yang et al. 2011).

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MicroRNAs Involved in Brown/Beige Adipogenesis To reveal the in vivo role of miRNAs in adipose tissue development, several mouse strains with adipose tissue-specific knockout of DGCR8 (DiGeorge syndrome critical region 8) or Dicer, two key regulators required for miRNA biogenesis, have been created (Kim et al. 2014; Mori et al. 2014). Both DGCR8- and Dicer-knockout mice exhibit defects in BAT development and WAT browning upon exposure to cold, indicating that miRNAs play a crucial role in browning adipogenesis. Indeed, recent investigations have identified several miRNAs functionally implicated in different stages of brown/beige fat adipogenesis (summarized in Table 3).

Table 3 MicroRNAs involved in regulation of brown/beige adipogenesis Expression in adipogenesis Upregulated

microRNA miR-193b-365

Target Cdon, Igfbp5, Runx1t1

miR-378

Pde1b

Upregulated

miR-196a

Hoxc8

Upregulated

miR-455

Upregulated

miR-26a

HIF1an, Runx1t1, Necdin ADAM17

Upregulated

miR-133

PRMD16

Downregulated

miR-155

C/EBPβ

Downregulated

miR-34a

FGFR1

Downregulated

Functional role Inhibit myogenesis and activate browning adipogenesis Enhance lipolysis and increase BAT activity via increasing cAMP levels Inhibit Hoxc8 to activate C/ EBPβ expression, resulting in promoting browning adipogenesis of WAT Enhance the browning program and mitochondrial biogenesis Promote browning adipogenesis through downregulation of antibrowning TNFα converting enzyme ADAM17 Inhibit brown/beige lineage commitment via downregulating the browning transcription factor PRMD16 Mediate the inhibitory effect of TGFβ1/Smad signaling on browning adipogenesis through downregulating C/EBPβ Inhibits FGF21-mediated browning transcriptional programming and promotes diet-induced obesity and insulin resistance

References Sun et al. (2011), Timmons et al. (2007) Pan et al. (2014) Mori et al. (2012)

Zhang et al. (2015) Gelling et al. (2008)

Trajkovski et al. (2012), Liu et al. (2013a) Chen et al. (2013)

Fu et al. (2014)

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MicroRNAs Promote Brown/Beige Adipogenesis The bicistronic transcript miR-193b-365 is found to be significantly upregulated during brown adipogenesis through transactivation by browning activators PRDM16 and PPARα. miR-193b-365 is an activator of brown adipogenesis via targeting two pro-myogenic factors (Cdon and Igfbp5) and a browning inhibitor (Runx1t1) (Sun et al. 2011). Hence, miR-193b-365 promotes the transition of myoblasts to brown adipocytes (Timmons et al. 2007). miR-378 is a browning activator through increasing cAMP levels by targeting phosphodiesterase 1b (Pde1b), which catalyzes cAMP turnover (Pan et al. 2014). miR-378 transgenic mice manifest elevated cAMP levels, enhanced lipolysis, and increased BAT activity (Pan et al. 2014). Moreover, miR-196a induction is required for browning of WAT upon some stimuli (e.g., cold exposure), but not for BAT development (Mori et al. 2012). This browning effect results from miR-196a targeting of Hoxc8. As Hoxc8 inhibits C/EBPβ expression, miR-196a induction leads to increased C/EBPβ expression, resulting in increased browning of WAT (Schulz et al. 2011). These discoveries are relevant as miR-196a transgenic mice exhibit higher beige tissue generation in WAT, enhanced energy expenditure, improved glucose metabolism, and resistance to obesity. The recently identified BAT-specific miRNA miR-455 functions to promote brown adipogenesis and thermogenesis through targeting hypoxia-inducible factor 1 alpha subunit inhibitor (HIF1an). HIF1an downregulation activates AMPKα1, enhancing the browning program and mitochondrial biogenesis. Adipose-specific miR-455 transgenic mice exhibit remarkable browning of subcutaneous white fat upon cold exposure, indicating the physiological role of miR-455 in enhancing WAT browning. In addition, miR-455 targets the adipogenic suppressors Runx1t1 and Necdin to stimulate adipogenesis (Zhang et al. 2015). By targeting ADAM metallopeptidase domain 17 (ADAM17, also called TNFα-converting enzyme TACE), miR-26a, a browning-induced miRNA, promotes browning adipogenesis (Karbiener et al. 2014). Given that ADAM17-deficient mice exhibit increased browning fat and a lean phenotype (Gelling et al. 2008), these findings suggest that miR-26/ADAM17 signaling has a critical role in regulating energy-dissipating of thermogenic adipocytes.

MicroRNAs Inhibit Brown/Beige Adipogenesis miR-133 is a browning inhibitor whose expression levels decline during browning adipogenesis and in BAT after cold exposure. miR-133 targets the 30 -UTR of PRMD16 mRNA to suppress PRMD16 expression, a key browning transcription factor (Trajkovski et al. 2012; Liu et al. 2013a). In addition to suppressing brown/ beige adipogenesis, miR-133 facilitates myogenesis. As myocyte enhancer factor 2 (MEF2) positively regulates miR-133 expression, downregulation of MEF2 expression in BAT by cold exposure leads to miR-133 downregulation and PRDM16 upregulation, which promotes brown/beige lineage commitment (Trajkovski et al. 2012). Consistently, miR-133-knockout mice exhibit an anti-obesity phenotype,

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increased energy expenditure, browning of WAT, and increased insulin sensitivity (Liu et al. 2013a). Moreover, a recent study indicates that miR-155 is a TGFβ1-induced miRNA and mediates the inhibitory effect of TGFβ1/Smad signaling on browning adipogenesis through targeting C/EBPβ (Chen et al. 2013). Importantly, studies of miR155 transgenic and knockout mice demonstrate that miR-155 is a bona fide, negative regulator of brown/beige adipogenesis (Chen et al. 2013). Through directly targeting fibroblast growth factor receptor 1 (FGFR1) to attenuate fibroblast growth factor 21 (FGF21)/sirtuin 1 (SIRT1)-dependent deacetylation of PGC-1α, miR-34a inhibits FGF21-mediated browning transcriptional programming and promotes diet-induced obesity and insulin resistance (Fu et al. 2014). Consistently, miR-34a expression has been shown to be downregulated in preadipocytes upon cold exposure. Antagonizing miR-34a in vivo leads to a browning induction in all types of WAT, increased BAT formation, and reduced adiposity (Fu et al. 2014).

LncRNAs Involved in White Adipogenesis Several lncRNAs have been identified to play critical roles in the regulation of white adipogenesis (summarized in Table 4). SRA (steroid receptor RNA activator) is the first lncRNA reported to be involved in white adipogenesis. SRA expression is induced during preadipocyte differentiation. SRA promotes white adipogenesis by enhancing expression of adipocyte master regulators PPARγ and C/EBPα (Xu et al. 2010). Mechanistically, SRA binds PPARγ and enhances its transcriptional activity to promote 3T3-L1 adipogenesis. The pro-adipogenic effect of SRA may also involve its regulatory roles in additional signaling pathways including insulin/AKT/FOXO1, cell cycle regulation, and TNFα/JNK signaling (Xu et al. 2010). SRA expression has been consistently shown to be physiologically upregulated in the WAT of obese mice induced by high-fat diet. Moreover, SRA knockout mice display anti-adipogenic and anti-obesity phenotypes (Liu et al. 2014). The lncRNA PU.1 AS is transcribed from the antisense DNA strand of the PU.1 transcription factor gene and has been reported to serve as an adipogenic activator (Pang et al. 2013). Since PU.1 functions as an inhibitor of white adipogenesis (Wang and Tong 2008), PU.1 AS facilitates adipogenesis through preventing PU.1 mRNA translation by forming an mRNA/AS lncRNA duplex (Pang et al. 2013). Through deep sequencing analysis, 175 lncRNAs have been found to be differentially expressed during adipogenesis. Ten out of these identified lncRNAs have been revealed to be required for adipogenesis and are termed lnc-RAP-n (lncRNA regulated in adipogenesis) (Sun et al. 2013). Among them, lnc-RAP-1 (or Firre, functional intergenic repeating RNA element) has been reported to interact with the nuclear-matrix factor hnRNP-U (heterogeneous nuclear ribonucleoprotein U) via its 156-bp repeating sequence, and this complex mediates trans-chromosomal interactions (Hacisuleyman et al. 2014). As hnRNP-U is also required for adipogenesis, these hnRNP-U-Firre-mediated interactions between trans-chromosomal loci may be required to maintain the nuclear architecture for the proper transcriptional

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Table 4 Long noncoding RNAs involved in regulation of white and brown/beige adipogenesis lncRNA SRA

Target PPARγ

Tissue expression WAT

PU.1 AS

PU.1

WAT

lnc-RAP-1/ Firre

hnRNP-U

WAT

ADINR

PA1

WAT

NEAT

na

WAT

Blnc1

EBF2

brown/ beige fat tissue

lnc-BATE1

hnRNP-U

BAT

Functional role SRA binds PPARγ and enhances its transcriptional effect to promote white adipogenesis PU.1 AS promotes adipogenesis through inhibiting expression of the anti-adipogenic transcription factor PU.1 Firre binds hnRNP-U to form FirrehnRNP-U complexes that mediate interactions between transchromosomal loci to maintain the nuclear architecture for the proper transcriptional programming of adipogenesis ADINR binds PA1to recruit MLL3/4 histone methyltransferase complexes, resulting in elevated C/EBPα expression and enhanced adipogenesis through increasing H3K4me3 and decreasing H3K27me3 histone modification at the C/EBPα locus miR-140-mediated upregulation of NEAT1 facilitates white adipogenesis Blnc1 binds to EBF2, which promotes EBF2 expression and EBF2-mediated induction of thermogenic gene expression lnc-BATE1 specifically maintains expression of BAT program genes and concomitantly inhibits expression of WAT program genes through its binding to hnRNP-U

References Xu et al. (2010) Pang et al. (2013)

Sun et al. (2013)

Xiao et al. (2015)

Gernapudi et al. (2015) Zhao et al. (2014)

AlvarezDominguez et al. (2015)

na not applicable

programming of adipogenesis. Another mRNA-lncRNA-combined microarray study has identified 1423 differentially expressed lncRNAs during human MSC adipogenesis (Xiao et al. 2015). ADINR (adipogenic differentiation-induced noncoding RNA), one of the most highly induced lncRNAs identified in the study, is required for lipid accumulation and expression of master adipogenic transcription factors (C/EBPα and PPARγ) and adipogenic markers, indicating its pivotal role in adipogenesis. ADINR functions by binding to PA1 (a component of the multiprotein histone methyltransferase complex) and recruiting MLL3/MLL4 histone methyltransferase complexes to increase H3K4me3 and decrease H3K27me3 histone modification at the C/EBPα locus, leading to elevated C/EBPα expression and enhanced adipogenesis (Xiao et al. 2015).

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The lncRNA NEAT1 (nuclear-enriched abundant transcript 1) has recently been linked to white adipogenesis. NEAT1 is a nuclear architectural lncRNA required for paraspeckle formation in the nucleus. Paraspeckles are a type of subnuclear structure that functions to sequester A-to-I hyperedited RNAs and nuclear protein factors. Studies of ADSCs from miR-140 knockout mice show that loss of miR-140 impairs adipogenesis of ADSCs and downregulates Neat1 expression (Gernapudi et al. 2015). Restoration of Neat1 expression in miR-140-deficient ADSCs rescues adipogenesis, suggesting that NEAT1 downregulation contributes to the defect in adipogenic differentiation of miR-140-/- ADSCs. Mechanistically, miR-140 stabilizes NEAT1 through binding to the cognate miR-140-binding site within the NEAT1 lncRNA (Gernapudi et al. 2015). However, further investigations are needed to address how miR-140 stabilizes NEAT1 and how NEAT1 promotes adipogenesis.

lncRNAs Involved in Brown/Beige Adipogenesis Two recent studies have revealed that lncRNAs play key roles in brown/beige adipogenesis (summarized in Table 4). The lncRNA called brown fat lncRNA 1 (Blnc1) has been found to be enriched in BAT tissue. Blnc1 enhances brown/beige adipocyte differentiation via promoting expression of thermogenic genes such as UCP1 (Zhao et al. 2014). This effect is mediated by a ribonucleoprotein complex that is formed by the interaction of Blnc1 with EBF2, which promotes EBF2 expression and EBF2-mediated induction of thermogenic gene expression. Interestingly, there is a feedforward relationship between Blnc1 and EBF2 as EBF2 positively regulates Blnc1 expression (Zhao et al. 2014). A RNA-seq study has identified BAT-enriched lncRNAs called lnc-BATEs (Alvarez-Dominguez et al. 2015). Many lnc-BATEs are the targets of key adipogenic regulators such as PPARγ and C/EBPα. Among these lnc-BATEs, lnc-BATE1 expression is highly induced during browning adipogenesis, and its induction is required for browning adipogenesis and the maintenance of brown adipocyte features in vitro and in vivo. Mechanistic studies show that lnc-BATE1 specifically maintains expression of BAT program genes and concomitantly inhibits expression of WAT program genes through its binding to hnRNP-U, which is also required for browning adipogenesis (Alvarez-Dominguez et al. 2015). However, the mechanisms by which this ribonucleoprotein complex exerts its function in browning adipogenesis remain elusive and require further investigation.

Conclusions and Perspectives In this chapter, we have summarized the functional roles of miRNAs and lncRNAs in white and brown/beige adipogenesis and their physiological functions in WAT and BAT development. A large body of research has revealed the critical role of the miRNA-regulated signaling network in controlling each step of adipogenic differentiation from MSCs to mature white or brown adipocytes and the browning process

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in white adipocytes. In contrast to the relatively unifying molecular mechanism underlying miRNA actions, the mechanisms exploited by lncRNAs to regulate white and brown adipogenesis are more diverse. Interactions between miRNAs and lncRNAs in adipogenesis have also been identified, indicating that the miRNA-lncRNA regulatory network plays a pivotal role in regulating adipogenesis. It is expected that more functional, adipogenesis-related lncRNAs with different mechanisms will emerge in the near future. These advances provide new insights into pathogenesis of metabolic disorders (e.g., obesity and diabetes), cancer and agerelated osteoporosis, and will lead to the development of innovative therapeutic targets and strategies for combating these diseases.

Dictionary of Terms • Adipogenesis – The differentiation process from stem cells or preadipocytes to adipocytes. • Preadipocytes – Undifferentiated precursor cells that can be stimulated to differentiate into adipocytes. • Osteogenesis – The developmental process of bony tissue formation, including osteoblast differentiation and osteoblast-mediated bone generation. • Myogenesis – The formation of muscle tissue through the differentiation of myoblasts into myocytes. • Mesenchymal stem cells (MSCs) – MSCs are multipotent stromal cells that can differentiate into various types of tissue cells, such as osteoblasts, chondrocytes, myocytes, and adipocytes. • Adipose tissue-derived stem cells (ADSCs) – ADSCs are a type of MSCs present in the stromal vascular fraction (SVF) isolated from processed adipose tissue. • 3T3-L1 cells – A preadipocyte cell line derived from mouse 3T3 fibroblast cells. • C3H10T1/2 – A mouse MSC model for cell differentiation studies.

Key Facts Key Facts of Adipogenesis • Adipogenesis is a differentiation process from pluripotent mesenchymal stem cells or preadipocytes to mature adipocytes, which are the major cell type in white or brown/beige adipose tissue. • White adipose tissue functions to store bioenergy into fat, whereas brown/beige adipose tissue consumes fat to generate heat (thermogenesis). • Preventing white adipose tissue accumulation and increasing brown adipose tissue function are potential strategies to combat obesity and other related diseases.

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• The discovery of noncoding RNAs opens up a new research era in adipogenic biology and related medicine. • Advances in the understanding of noncoding RNAs in adipogenic biology have propelled the development of adipogenesis-related disease prevention and therapy.

Key Facts of Noncoding RNAs • Noncoding RNAs (ncRNAs) have been identified as a new class of RNA-based regulators that modulate various biological processes. • New-generation sequencing technology has accelerated the discovery of ncRNAs. • The current studies of ncRNAs mainly focus on the roles of microRNAs (small ncRNAs) and long ncRNAs (lncRNAs) in biological processes. • MicroRNAs regulate gene expression through promoting mRNA degradation and/or inhibiting mRNA translation. • lncRNAs regulate gene expression and biological processes through diverse mechanisms.

Summary Points • Adipogenesis is a complicated differentiation process involving commitment, clonal expansion, and terminal differentiation. • MicroRNAs and long noncoding RNAs (lncRNAs) have been identified to regulate both white and brown adipogenesis. • MicroRNAs regulate adipogenesis through modulating expression of multidisciplinary genes involved in controlling adipogenesis. • Unlike microRNAs, lncRNAs exploit diverse mechanisms to regulate adipogenesis. • MicroRNAs/lncRNAs involved in obesity development are potential targets of therapeutic interventions in obesity prevention and treatment. • Brown/beige fat tissue plays an anti-obesity role through promoting energy expenditure and non-shivering thermogenesis. • MicroRNAs/lncRNAs involved in browning adipogenesis are promising targets for combating obesity. • Some age-related miRNAs implicated in osteoporosis are potential targets for prevention and therapy of osteoporosis. • The interactions between microRNAs and lncRNAs form a new regulatory circuit in adipogenesis. Acknowledgments This work was supported by Grants from the American Cancer Society (ACS) and NIH/NCI R01 (CA163820A1 and CA157779A1).

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Beiyan Zhou, Wei Ying, Chuan Li, and Anthony T. Vella

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Obesity-Induced Inflammation and Insulin Resistance in Adipose Tissue . . . . . . . . . . . . . . . . . Obesity-Induced Adipocyte Hypoxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adipose Tissue Macrophage Recruitment and the Regulatory Role of MicroRNA . . . . . . . MicroRNA-Regulated Macrophage Maturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activation and Function of Adipose Tissue Macrophage and the Regulatory Role of miRNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Function of Adipose Tissue T Cells and the Potential Roles of miRNAs . . . . . . . . . . . . . . . . . miRNA-Mediated Function in Adipose Tissue B Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . miRNA Secretion from Adipose Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Low-grade tissue inflammation is a key driver for the development of obesityinduced insulin resistance with a remarkable accumulation of various immune cells in adipose tissues. The proinflammatory activations of immune cells play

B. Zhou (*) · C. Li · A. T. Vella Department of Immunology, School of Medicine, University of Connecticut Health Center, Farmington, CT, USA e-mail: [email protected]; [email protected]; [email protected] W. Ying Department of Medicine, Division of Endocrinology and Metabolism, University of California, San Diego, La Jolla, CA, USA e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_26

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critical roles in exacerbating the tissue inflammation and insulin resistance in obesity. Here, this review features the regulatory roles of microRNAs on the maturation, recruitment, and activation of both innate and adaptive immune cells in response to obesity, which could reveal the mechanisms underlying the pathogenesis of obesity-related metabolic syndrome. Keywords

MicroRNAs · Obesity · Adipose tissue inflammation · Insulin resistance · Macrophage polarization · Hematopoiesis List of Abbreviations

AML1 ANT ARNT ATMs BCR BMI C/EBPσ CCL2 CDR3 Dusp5 ETS1 Foxp3 HFD HIF1α IL-4 Irak1 IRF4 LPS LTB4 M-CSFR MAX MHC II miRNAs NFκB Pknox1 Ptpn11 T2DM Th1 Th2 Traf6 Tregs VATs

Acute myeloid leukemia-1 Adenine nucleotide translocase Aryl hydrocarbon nuclear translocator Adipose tissue macrophages B cell receptor Body mass index CCAAT/enhancer binding protein-σ Chemokine (C-C motif) ligand 2 Complementary determining region 3 Dual-specificity protein phosphatase 5 V-ets erythroblastosis virus E26 oncogene homolog 1 avian Forkhead box P3 High-fat diet Hypoxia-inducible factor 1α Interleukin-4 IL-1 receptor-associated kinase 1 Interferon regulatory factor 4 Lipopolysaccharide Leukotriene B4 Macrophage colony-stimulating factor receptor MYC-associated factor X Major histocompatibility complex class II MicroRNAs Nuclear factor κB PBX/knotted 1 homeobox 1 Protein tyrosine phosphatase type 11 Type 2 diabetes mellitus T helper type 1 T helper type 2 TNF receptor-associated factor 6 T regulatory cells Obese visceral adipose tissues

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Introduction Obesity-Induced Inflammation and Insulin Resistance in Adipose Tissue Over the past several decades, obesity has become an epidemic and is now one of the most common causes of insulin resistance. Insulin resistance is the key etiological feature mediating the pathogenesis of metabolic syndrome (Johnson and Olefsky 2013). Prolonged status of metabolic syndrome drives the development of type 2 diabetes mellitus (T2DM) (Romeo et al. 2012). There is a well-known parallel between the global obesity epidemic and the great number of obese patients with type 2 diabetes. Indeed, in the United States, about 29.1 million people or 9.3% of the population have type 2 diabetes, and more than 80% of those with T2DM are overweight (BMI >25) (Prevention 2014; Flegal et al. 2016). Therefore, the obesity epidemic is the underlying driver of the T2DM epidemic. Chronic low-grade tissue inflammation is a major causal factor for the pathogenesis of insulin resistance through interrupting insulin signaling in key metabolic organs such as the adipose tissues, liver, and skeletal muscle (Hotamisligil et al. 1995; Xu et al. 2003; Gregor and Hotamisligil 2011; Lumeng and Saltiel 2011). This is a hallmark of obesity in both rodents and man, and yet current research is only beginning to understand the complexity of this inflammatory process. Other metabolic abnormalities such as adipose tissue hypoxia, lipotoxicity, endoplasmic reticulum stress, and mitochondrial dysfunction ultimately converge in the development of tissue inflammation in the context of obesity.

Obesity-Induced Adipocyte Hypoxia During long-term excessive nutrient intake, adipocytes undergo hypertrophy and hyperplasia (Drolet et al. 2008; Wang et al. 2013). In mice, the increase in adipose tissue mass is mainly due to the hypertrophy of adipocytes after 4 weeks of feeding a high-fat diet (HFD), while prolonged HFD exposure leads to a significant increase in adipogenesis in epididymal adipose tissue, but minimal effects on adipogenesis in subcutaneous fat depots (Wang et al. 2013). The expansion of adipose tissue mass enhances lipid metabolism and the fatty acid flux that stimulates activation of adenine nucleotide translocase (ANT)-mediated uncoupling of respiration in adipocytes (Lee et al. 2014). This process increases oxygen consumption by adipocytes and induces relative cellular hypoxia that triggers expression of hypoxia-inducible factor 1α (HIF1α) (Pasarica et al. 2009; Lee et al. 2014). Upon the upregulation of HIF1α expression in adipocytes, the production of chemokines such as chemokine (C-C motif) ligand 2 (CCL2) and leukotriene B4 (LTB4) enhances the recruitment of immune cells into white adipose tissues, whereas loss of HIF1α impairs secretion of these chemokines from adipocytes in response to hypoxia and prevents infiltration of immune cells (Lee et al. 2014). In addition, the transcriptional function of HIF1α on

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gene expression requires the ubiquitously expressed subunit aryl hydrocarbon nuclear translocator (ARNT or HIF1β) to form a heterodimer complex (Keith et al. 2012). Interestingly, HIF2α expression serves as the negative control to counteract HIF1α in adipocytes, and loss of HIF2α in adipocytes promotes the inflammatory status of visceral fat depots in obese individuals (Jiang et al. 2011; Lee et al. 2011). Thus, the hypoxia-induced HIF1α expression in adipocytes acts as the early trigger for inflammation in white adipose tissue.

Adipose Tissue Macrophage Recruitment and the Regulatory Role of MicroRNA In response to chemokines released from the white adipose tissue, circulating monocytes/macrophages are recruited into white adipose tissues and become a major immune cell component in this niche (Weisberg et al. 2003; Lumeng et al. 2007b). The adipose tissue macrophages (ATMs) have crucial functions including the removal of dead adipocytes (Cinti et al. 2005). Indeed, the infiltrated macrophages aggregate and surround the dying adipocytes to form “crown-like structures.” In the context of obesity, ATMs can account for up to 50% of the stromal cell population in VATs and exert a profound impact on adipocyte function (Weisberg et al. 2003). Among these chemokines, CCL2 can also be produced by adipocytes and adipose tissue resident macrophages to further enhance recruitment of monocytes/macrophages into VATs (Kanda et al. 2006). Recent studies have suggested several mechanisms whereby microRNAs (miRNAs) may regulate or mediate CCL2 production and thus potentially control monocyte infiltration in response to obesity (Fig. 1). Arner et al. (2012) validated this concept in human adipocytes by demonstrating that miR-126 can directly suppress CCL2 production by binding its 30 UTR, while miR-126 expression was downregulated in obese human white adipose tissues. miR-193b is also a negative mediator of CCL2 production in adipocytes via an indirect network of transcription factors including ETS1 and MAX (Arner et al. 2012). More importantly, the negative correlation between miR-193b expression and CCL2 secretion has been confirmed in obese human tissue. A recent report from the same laboratory also demonstrated that the suppressive effect of miR-92a on CCL2 relied on silencing of SP1 expression in human adipocytes (Kulyté et al. 2014). Further, NFκB is a key regulator for CCL2 production. We recently demonstrated that miR-223 can suppress NFκB activation by directly inhibiting a proinflammatory factor Pknox1, which in turn decreases CCL2 production and the recruitment of ATMs in the obese adipose tissue (Zhuang et al. 2012).

MicroRNA-Regulated Macrophage Maturation Obesity-induced accumulation of ATM is dependent on the infiltration of monocytes from circulation into adipose tissue where these monocytes can differentiate into

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Fig. 1 Action models of microRNA regulation in adipose tissue macrophages. Adipose tissue macrophage is the largest immune population in the obese adipose tissue. Recruitment of circulating macrophages into adipose tissue can be mediated by cytokine/chemokine/adipokine secretion which is also subjected to microRNA regulation. Further, it is also likely that secreted microRNAs can act as endocrine or paracrine molecules that facilitate the recruitment of immune cells into the obese adipose tissue

macrophages. Consistently, the pool of circulating monocytes (CCR2+Ly6chi) expands in the context of obesity (Oh et al. 2012; Singer et al. 2014). Obesity amplifies the myelopoiesis of hematopoietic stem cells through TLR-/MyD88-mediated inflammatory responses, which subsequently increase the circulating monocyte reservoir (Singer et al. 2014). However, the molecular mechanisms underlying regulation of hematopoiesis in obesity are still poorly understood. miRNAs play critical roles in governing hematopoietic lineage determination and formation, including monocytopoiesis and myelopoiesis (Fig. 2). The process of hematopoiesis is accompanied by differential expression of miRNAs that exert profound regulatory roles in each stage of hematopoiesis, including lineage commitment. Early studies demonstrated a functional role of three miRNAs, miR-17-5p, miR-20a, and miR-106a, in monocytic differentiation and maturation into macrophages through suppression of acute myeloid leukemia-1 (AML1) expression (Meenhuis et al. 2011). These three miRNAs are downregulated during the differentiation of CD34+ hematopoietic progenitor cells into lineage commitment. Conversely, their target gene AML1 expression is elevated to induce the production of macrophage colony-stimulating factor receptor (M-CSFR), which ultimately drives monocytic-macrophage differentiation and maturation. Interestingly, in monocytic cells AML1 can inhibit the transcription of miRNA-17-5p-20a-106a through

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Fig. 2 MicroRNA regulation of monocytic maturation and macrophage polarization. MicroRNAs can display hematopoietic-specific expression pattern and control monocyte formation, monocytic maturation, and macrophage polarization at various developmental stages

binding its promoter region. Thus, this interaction loop between miRNA-17-5p-20a106a and AML1 controls monocytopoiesis. Transcription factors can control or regulate miRNA expression and thereby program differentiation of myeloid cells from progenitors. For example, PU.1 governs lymphomyeloid development through promotion of miRNA expression, which plays a critical role during the earliest steps of myeloid progenitor maturation. Ghani et al. (2011) showed that in myeloid progenitor cells, PU.1 can dynamically occupy the binding sites within the regulatory chromatin regions adjacent to the genomic coding loci of miR-146a which is required for the selective differentiation of adult hematopoietic stem cells into macrophages. Further, in vivo experiments showed that deficiency of PU.1 decreased the level of miR-146a and subsequently the development of acute myeloid leukemia in mice (Ghani et al. 2011). After infiltration into adipose tissues, monocytes exhibit distinct responses to local environmental cues by significantly altering their gene expression profiles that can prime the activation phenotype of differentiated macrophages. One of these responses in the local tissue environment is the alteration in miRNA expression, thereby manifesting the transition from monocyte to mature macrophage. For instance, lipopolysaccharide (LPS) can promote the expression of miR-146a and miR-155 in human monocytes, while interleukin-4 (IL-4) increases the accumulation of miR-193b and miR-222 (Eigsti et al. 2014). These miRNAs have been validated in their regulatory functions by controlling the activation of macrophages. In addition, Eigsti et al. (2014) suggest that the activation of NFκB and STAT pathways in response to environmental cues leads to the changes in miRNA abundance in monocytes after their tissue infiltration. Thus, there is compelling evidence that miRNAs regulate gene expression in response to environmental cues, and these outcomes might potently influence monocyte/macrophage behavior and function.

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Activation and Function of Adipose Tissue Macrophage and the Regulatory Role of miRNA Adipose tissue macrophages exert a profound impact on homeostasis and function of white adipose tissue through their polarization (Lackey and Olefsky 2016). The activation pattern of ATMs covers a wide spectrum of activation stages between well-defined classical (M1) and alternative (M2) activation in response to distinct environmental cues (Ivashkiv 2013). In response to the Th2 cytokines such as IL-4 and IL-13, macrophages produce anti-inflammatory cytokines through the activation of PPARγ-mediated pathways (Odegaard et al. 2007). Hence, for lean animals ATMs are preferentially driven to an M2-like phenotype and express genes including IL-10 and arginase 1, which promote tissue repair and angiogenesis. Upon stimulation of T helper type 1 (Th1) cytokines, LPS, or free fatty acids, activation of NFκB/JNK signaling pathways drives M1-like responses and production of a set of proinflammatory cytokines, such as TNFα and IL-1β (Bosisio et al. 2002; Nguyen et al. 2007). In the context of obesity, ATMs primarily exhibit an M1-like phenotype in VATs and release proinflammatory cytokines, which can impair insulin responses and induce inflammatory responses in adipocytes through a paracrine mechanism (Shi et al. 2006; Lumeng et al. 2007). In addition to the recruited monocytes/ macrophages, Lumeng et al. (2007) demonstrated that obesity induces the resident ATMs to undergo a phenotypic switch from M2-like to M1-like. The miRNA regulatory networks and key transcription factors both play critical roles in governing the activation status of ATMs in obesity (Fig. 2). Our recent findings revealed that miR-223 acts as a key mediator controlling the activation of ATMs (Ying et al. 2015). Specifically, when macrophages encounter Th2 cytokines, activation of PPARγ-mediated pathways drives the expression of miR-223. In addition, miR-223 can directly inhibit the expression of Nfat5 and Rasa1, which facilitates the development of an anti-inflammatory phenotype. In contrast, depletion of miR-223 blunts PPARγ-induced anti-inflammatory responses in macrophages. In addition to this interaction between miR-223 and PPARγ, accumulation of miR-223 in macrophages can block the activation of NFκB/JNK signaling through suppression of Pknox 1 expression, which ultimately prevents the proinflammatory responses (Zhuang et al. 2012). Our diet-induced obesity mouse studies also showed that miR-223-deficient ATMs underwent M1-like activation contributing to the pathogenesis of adipose tissue inflammation and insulin resistance in obesity. On the other hand, upon inflammatory cues, activation of JNK pathway increases miR-155 abundance, which further promotes the proinflammatory phenotype of macrophages (O’Connell et al. 2007). Conversely, JNK inhibitors prevent the induction of miR-155 expression in macrophages in response to the inflammatory stimulation. In addition, miR-155 can inhibit activation of STAT6 by repressing the expression of IL-13 receptor α1, which ultimately promotes M1 responses (Martinez-Nunez et al. 2011). miRNAs are also important mediators controlling the expression of key transcription factors and their networks, which subsequently affect the activation of macrophages. The inflammatory responses of macrophages can be induced by

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CCAAT/enhancer binding protein-σ (C/EBPσ) that is a target gene of let-7c (Banerjee et al. 2013). Ectopic expression of let-7c in M1 macrophages reduces the expression of C/EBPσ and the subsequent inflammatory response which is marked by the production of IL-12 and the cell surface marker major histocompatibility complex class II (MHC II). In addition, knockdown of let-7c in IL-4-induced macrophages results in attenuated M2 responses, suggesting a critical role of let-7c in maintaining M2 activation. In contrast, another miRNA, miR-125b, is a positive regulator for the proinflammatory activation of macrophages. For example, upon inflammatory cues, miR-125b expression is induced, and the abundance of its target gene interferon regulatory factor 4 (IRF4) is decreased (Chaudhuri et al. 2011). Given the important function of IRF4 on suppressing M1 activation, miR-125binduced reduction in IRF4 expression facilitates the proinflammatory responses of macrophages (Chaudhuri et al. 2011). The plasticity of polarization allows macrophages to rapidly switch their activation phenotype in response to microenvironmental cues. The adipose tissue resident macrophages undergo a phenotypic switch from anti-inflammatory M2-like activation to proinflammatory M1-like phenotype in obesity (Lumeng et al. 2007). In addition, weight loss leads to a shift back to M2-like activation in ATMs. This phenotypic switch of macrophages can be driven by key transcription factors such as NFκB and C/EBPσ. Banerjee et al. (2013) reported that the LPS-stimulated macrophages showed a phenotypic switch from M1 to M2 after overexpression of let-7c, through repression on key transcription factor C/EBPσ that is a key regulator for NFκB-mediated signaling pathway. Thus, under the developing conditions of obesity, miRNAs play a dynamic role in controlling the macrophage response.

Function of Adipose Tissue T Cells and the Potential Roles of miRNAs In addition to the innate immune responses that are evident in obesity, the adaptive immune cells such as T lymphocytes located in adipose tissue exert a profound impact on the metabolic function of adipose tissue. In the context of obesity, the visceral fat tissues are also characterized by a remarkable accumulation of proinflammatory Th1 cells and IFNγ-producing CD8+ T cells, whereas the population of T regulatory cells (Tregs) is diminished (Feuerer et al. 2009; Nishimura et al. 2009). Cipolletta et al. (2012) suggested that PPARγ regulates gene expression in Tregs found in adipose tissue, and PPARγ-dependent expression of forkhead box P3 (Foxp3) is required for the differentiation of Tregs. A distinguishing feature of T cells over innate cells is the exquisite specificity of the T cell receptor (TCR), and early studies showed that T cells in adipose tissue undergo selective pressure in the complementary determining region 3 (CDR3) to generate TCRs that recognize specific antigen in fat (Winer et al. 2009). This intriguing result suggests that T cell expansion in VAT during obesity might be antigen-specific, similar to what is observed in autoimmune disease. Recent studies reveal that miRNA-mediated gene regulation represents a fundamental layer of posttranscriptional control of differentiation and activation of T cells

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Adipose tissue macrophage

IFNg, IL6,...

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Iga Igb SYK

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miR-17 miR-18 1a miR-182 miR-146a

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Fig. 3 MicroRNAs regulate adipose tissue, B cell, and T cell activation. Immune cell compartments in the adipose tissue exert critical impacts on tissue homeostasis. The dynamic crosstalk among various cell components in the adipose tissue is regulated by networks involving microRNAs and their target genes, as well as secreted molecules (including cytokines, adipokines, chemokines, and others), to enable cell communication

(Fig. 3). Antigen-induced activation of TCRs and CD28-mediated costimulation play critical roles in inducing proliferation and IL-2-producing CD4+ helper T cells. Stimulation of TCR in combination with IL-2 leads to an increase in miR-182 expression through STAT5 signaling and binding the promoter region of miR-183miR-96-miR-182 locus (Stittrich et al. 2010). To facilitate the expansion of T helper cells, miR-182 directly suppresses the expression of Foxo1, which blocks cell cycle progression in resting cells (Stittrich et al. 2010). A recent report shows that miR-17 is required for inflammatory cytokine IL-6-mediated suppression on Treg differentiation (Yang et al. 2016). miR-17 can also directly reduce gene abundance of Eos that is a key co-regulatory molecule in the network mediated by key transcription factor Foxp3. This suggests a potential mechanism accounting for the decrease in Tregs in obese adipose tissue concordant with worsening inflammation. In addition, miR-181a exerts profound regulatory effects on TCR signaling during T cell development. For example, miR-181a repression with specific antisense oligonucleotides dampens TCR sensitivity and impairs thymic positive and negative selection of T cells, whereas miR-181a ectopic expression in mature T cells enhances their sensitivity to peptide antigens (Li et al. 2007). miR-181a can directly suppress the expression of phosphatases that decrease TCR signals, including protein tyrosine phosphatase type 11 (Ptpn11), Ptpn22, dual-specificity protein phosphatase 5 (Dusp5), and Dusp6, thereby lowering the threshold for TCR triggering. Another

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miRNA, miR-146a, is induced following TCR engagement and serves as an important feedback regulator of NFκB signaling that is a downstream component of the TCR signaling cascade (Yang et al. 2012). Further study revealed that miR-146a prevents the hyperresponsive inflammatory response in activated T cells through targeting NFκB signaling transducers, TNF receptor-associated factor 6 (Traf6) and IL-1 receptor-associated kinase 1 (Irak1) (Taganov et al. 2006). Zhao et al. (2011) observed that miR-146a-deficient mice spontaneously develop myeloproliferation and myeloid cell tumorigenesis due to the loss of a miR-146a-dependent feedback loop. However, there is a gap in understanding the role of miRNAs in regulating expansion and function of adipose tissue T cells in the context of obesity.

miRNA-Mediated Function in Adipose Tissue B Cells Obesity-induced accumulation of B cells is also a prominent feature of obese adipose tissues, accounting for more than 20% of stromal cells within visceral fat tissues (Winer et al. 2011). More importantly, these adipose tissue B cells exert significant functional regulation on adipose tissue in obesity. Data from Winer et al. (2011) suggest that activation of adipose tissue B cells increases immunoglobulin class switching from IgM+ IgD- or IgG+ phenotypes to proinflammatory IgG2c during obesity. This increase in pathogenic IgG antibodies directly interrupts insulin signaling in key metabolic tissues such as the adipose tissue, liver, and skeletal muscle through interactions with antigenic targets that are associated with endoplasmic reticulum stress. Given the role of B cells as professional antigen-presenting cells, B cells can induce proinflammatory responses in both CD4+ and CD8+ T cells in adipose tissues through a MHC-dependent manner, eventually exacerbating adipose tissue inflammation and systemic insulin resistance. Genetic depletion of mature B cells (B-null) in mice prevents these obesity-associated metabolic disorders. Adoptive transfer of obese adipose tissue B cells induces tissue inflammation and insulin resistance of obese B-null mice. In addition to the pathogenic IgG antibodies, other works suggest that in the context of obesity, adipose tissue B cells support the proinflammatory state and secrete proinflammatory cytokines such as IFNγ and IL-6 (DeFuria et al. 2013). This obesity-induced alteration in cytokine production by B cells increases the Th1/Th17 to Treg ratio in visceral fat depots, leading to tissue inflammation and insulin resistance. As adipose tissue B cells exhibit significant regulatory roles in the development of tissue inflammation and insulin resistance, it is critical to understand the mechanisms underlying activation of adipose tissue B cells in response to obesity. The B cell receptor (BCR)-mediated signaling cascades play essential roles in controlling B cell activation. In response to distinct microenvironmental cues, regulation of BCR complex involves gene networks orchestrated by various mediators including miRNAs. Numerous laboratories have investigated the roles of miRNAs during B cell activation under various disease conditions. Our new work, for the first time, shows that miR-150 acts as an important regulator in mediating adipose tissue functions in obesity by controlling activation of B cells and their interactions with

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other immune cells (Fig. 3) (Ying et al. 2016). miR-150-deficient B cells display significantly elevated MHC II-mediated antigen presentation upon stimulation, which subsequently enhances activation of T cells. Also, miR-150 depletion leads to increased production of the proinflammatory cytokine IFNγ that can induce proinflammatory responses by macrophages. In diet-induced obesity, results have demonstrated that B-null mice receiving adoptive transfer of miR-150KO B cells display worse insulin resistance than those that received WT B cells, and the former group was accompanied with an increase of proinflammatory T cells and macrophages in adipose tissues. Furthermore, we have identified three genes Etf1, Myb, and Elk1 as miR-150 targets, which are associated with BCR complex activation. Knockdown of these genes, by using miR-150 depletion, can prevent increased MHC II expression on B cells. Another extensively studied miRNA in mature B cell is miR-155, which has significant effects on immune responses of B cells. Mature B cells deficient in miR-155 produce less IgG1 and cytokines such as TNFα (Vigorito et al. 2007). Further, miR-155 negatively controls expression of PU.1 which is a critical transcription factor downstream of BCR signaling in mature B cells (Lu et al. 2014). However, the regulatory roles of miR-155 in adipose tissue B cells under the stress of obesity have not been elucidated.

miRNA Secretion from Adipose Tissue Although miRNAs are known to function intracellularly, there is mounting evidence that miRNAs can be secreted via a paracrine/endocrine manner under various disease conditions. Circulating extracellular miRNAs are typically carried as cargo in membrane-covered vesicles, such as exosomes and shedding microvesicles, or extracellular protein complexes including HDL and the RNA-induced silencing complex (Turchinovich et al. 2011). The extracellular miRNA profile can change in response to distinct disease states. Therefore, circulating miRNAs are potential biomarkers for the development of obesity and diabetes. Clinical studies observed decreased extracellular miR-20b, miR-21, miR-24, miR-15a, miR-126, miR-191, miR-223, miR-320, and miR-486 in circulation of type 2 diabetes patients, compared to healthy people (Zampetaki et al. 2010). In addition, miR-138, miR-15b, and miR-376a can serve as predictive biomarkers for obesity status in humans (Pescador et al. 2013). Further, recent studies suggest the change in circulating miRNAs concentration can be used to predict specific cell activities. Chen et al. (2016) found that in circulation, the exosomal miR-92a levels mirrored changes in activation of brown adipocytes. In humans, the glucose uptake of brown adipocytes was negatively correlated with the abundance of exosomal miR-92a found in circulation. In an in vitro study, Ortega et al. (2015) detected alterations in secreted miRNA profiles in both macrophages and white adipocytes in response to inflammatory stimuli. Interestingly, upon LPS stimulation, macrophages secreted more miR-155 but less miR-221, which also reflects the change in intracellular miRNA abundance in response to obesity. Therefore, the cell-specific extracellular miRNAs could provide more accurate readouts to predict disease conditions.

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Recent findings reveal that extracellular miRNAs play critical roles in cell-to-cell communication. For example, during the development of breast cancer, cancer cells secrete miR-122 carried as cargo by exosomes to target non-tumor cells in the premetastatic niche (Fong et al. 2015). This cancer cell-specific miR-122 can reduce glucose uptake and cellular metabolism of target cells by directly suppressing the expression of the glycolytic enzyme pyruvate kinase. This unique extracellular miRNA function on target cells increases nutrient availability in the premetastatic niche, prior to the migration of cancer cells. Another study also discovered a similar function of cancer cell-secreted exosomal miR-19a, which causes loss of PTEN and primes brain metastasis outgrowth (Zhang et al. 2015). However, the regulatory roles of extracellular miRNAs in the communications between immune cells and adipocyte in fat tissues during the development of obesity-associated metabolic disorders are still unclear.

Dictionary of Terms • Adipogenesis – Differentiation of preadipocytes into adipocytes. • Complementary determining region – Hypervariable regions of immunoglobulin or T cell receptors that determine their specific binding to antigens. • Endoplasmic reticulum stress – A condition in which the homeostasis of endoplasmic reticulum (ER) is disturbed, and as a result, improperly folded protein accumulates in the ER. Causes of ER stress include inflammation, viral infection, toxic substances, and increased demand on protein. • Hematopoiesis – The process that blood cells are developed from hematopoietic stem cells. • Hematopoietic stem cells – Self-renewing, multipotent progenitor cells that give rise to all the other blood cells through a series of differentiation processes. • Macrophage polarization – The process that macrophages, when induced by different environment cues, adapt a certain type of activation and perform different functions at specific time and locations. • Metabolic syndrome – A group of metabolic disorders, including obesity, high blood sugar levels, high blood pressure, and high cholesterol levels, which may lead to higher risks of developing cardiovascular diseases. • MicroRNA – Naturally occurring small noncoding RNAs containing 21~25 nucleotides, involved in posttranscriptional regulation. • Monocytopoiesis – Generation of monocytes. Monocytopoiesis is a component of myelopoiesis. • Myelopoiesis – Development of monocytes, neutrophils, dendritic cells, and other innate immune cells from myeloid progenitor cells. • Premetastatic niche – Microenvironments formed by bone marrow-derived cells which will be the further site that metastatic tumor cells locate and grow. The recruitment of bone marrow-derived cells to premetastatic niche is regulated by primary tumor cells through certain cytokines and growth factors.

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Key Facts Key facts of adipose tissue inflammation • Long-term over-intake leads to the significant hypertrophy and hyperplasia of adipose tissues, which subsequently cause tissue hypoxia. • Hypoxia triggers the onset of inflammatory responses of adipose tissues in obesity. • Obesity induces production of proinflammatory molecules from adipose tissues, including TNFα, IL1b, IL6, CCL2, and LTB4. • The obesity-induced adipose tissue inflammation exhibits chronic and low-grade features. • Proinflammatory cytokines can blunt the sensitivity of insulin receptor. Key facts of insulin resistance • The sensitivity of insulin receptor and its downstream pathways is critical to maintain glycemic homeostasis. • Obesity impairs insulin sensitivity of key metabolic tissues including adipose tissues, liver, and skeletal muscle, which eventually leads to hyperglycemia. • To compensate the decreased insulin responses, insulin secretion from pancreatic beta cells is significantly increased in obesity, which causes hyperinsulinemia. • Inflammatory activation can inhibit insulin receptor signaling. • Obesity-induced insulin resistance precedes the development of type 2 diabetes mellitus. Key facts of characteristics of immune cells in obese adipose tissues • Chemokine secretion from adipose tissues is increased in obesity. • Striking accumulation of immune cells including macrophages, Th1 cells, and B cells occurs in visceral adipose tissues in the context of obesity. • In response to obesity, adipose tissue macrophages undergo a phenotypic switch from the anti-inflammatory M2 status to the proinflammatory M1 response. • Proinflammatory macrophages and Th1 cells can secrete inflammatory cytokines such as TNFα and IFNγ that promote inflammatory status and impair insulin sensitivity of target cells. • In obesity, adipose tissue B cells can exacerbate tissue inflammation and insulin resistance through secretion of proinflammatory molecules and its antigenpresenting functions. Key facts of microRNAs • MicroRNAs are a group of highly conserved small noncoding RNAs (~22 nucleotides in length). • MicroRNAs function primarily through RNA silencing which is initiated by base pairing with their target mRNAs.

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• MicroRNA-binding sites are usually located in the 30 untranslated region of their target genes. • It is known that over 60% of human protein-coding genes contain at least one conserved miRNA-binding site, and considering that numerous non-conserved sites also exist, most protein-coding genes may be under the control of miRNAs. • Dysregulated expression of miRNAs in human and animal models is associated with various diseases and their progression, including obesity-associated metabolic syndrome.

Summary Points • Chronic low-degree inflammation and insulin resistance are two hallmarks of obesity and critically contribute to the pathogenesis of obesity-associated health risks. • miRNAs are important regulators for recruitment, activation, and function of macrophages, T cells, and B cells in adipose tissues and thus play critical roles in obesity and obesity-associated inflammation and insulin resistance. • miRNAs play critical roles in regulating macrophage differentiation from hematopoietic stem cells, monocytic differentiation, and subsequent maturation into macrophages. • miRNAs can regulate macrophage activity, differentiation, and function by either directly modulating key transcription factor expression or through interactions with transcription factor-mediated downstream pathways. • miRNAs regulate differentiation and activation of adipose tissue T cells, including Treg cells, which exert profound effects on the metabolic function of adipose tissue. However, to date, our understanding on how microRNAs regulate T cell function in obese adipose tissue demands further investigation. • Significantly increased presentation of adipose tissue B cells is an important regulator for adipose tissue function under obesity stress. • In addition to their intracellular function, miRNAs can be secreted from cells that can be used as biomarkers for obesity and diabetes and/or mediate cell-cell communication. In summary, the critical roles of miRNAs in regulating activity and function of adipose tissue macrophages, T cells, and B cells have been supported by accumulating evidence, suggesting their key involvement in obesity-associated inflammation and potentially profound impact on insulin resistance and other pathological conditions induced by the stress of obesity.

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Yangmian Yuan, Chengyu Liu, Danyang Wan, Kun Huang, and Ling Zheng

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adipocyte Formation and Its Relevance to Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetic Regulation and Adipocyte Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methylation in Adipocyte Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Methyltransferases in Adipocyte Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Demethylases in Adipocyte Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methyl-CpG Binding Proteins in Adipocyte Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Histone Methylation in Adipocyte Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H3K4 Methylation Regulatory Enzymes in Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H3K9 Methylation Regulatory Enzymes in Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H3K27 Methylation Regulatory Enzymes in Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H4K20 Methylation Regulatory Enzymes in Adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Beige Adipocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The adipose tissue has attracted great attention lately; in addition to be known as an energy store organ, it has also been shown as an endocrine organ. Dysfunction of adipose tissue plays critical roles in the pathogenesis of many

Y. Yuan · D. Wan · L. Zheng (*) College of Life Sciences, Wuhan University, Wuhan, China e-mail: [email protected]; [email protected]; [email protected] C. Liu · K. Huang (*) Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_96

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metabolic diseases including obesity, type 2 diabetes, cancer cachexia, and lipodystrophies. The increased mass of adipose tissue in obese individuals is due to hypertrophy and hyperplasia. The transcriptional cascade during adipocyte differentiation has been well defined during the past two decades, while recent studies suggest epigenetic regulation plays an important part in adipocyte differentiation. In this chapter, we focus on the regulation of DNA methylation and histone methylation in adipocyte differentiation, as well as major enzymes involved in these processes. Targeting the methylation profiles of DNA and histone to reduce adipocyte differentiation may be a potential therapeutic approach to obesity. Keywords

Adipocyte differentiation · Epigenetic regulation · DNA methylation · Histone methylation · Commitment · Adipogenesis · DNA methyltransferase · DNA demethylase · HMT · Histone demethylase List of Abbreviations

αKG 5azadC ASC-2 ASCOM BAT BMP4 C/EBP Dnmts eWAT HFD MBDs MCE MECP2 MEF NAFLD MSCs PGC-1α

PPARγ PRC2 PRDM16 PTIP TETs UCP1 WAT Zfp423

α-Ketoglutarate 5-aza-20 -Deoxycytidine Activating signal cointegrator 2 ASC-2 complex Brown adipose tissue Bone morphogenic protein 4 CCAAT-enhancer-binding protein DNA methyltransferases Epididymal white adipose tissue High fat diet Methyl-CpG binding proteins Mitotic clonal expansion Methyl-CpG binding protein-2 Mouse embryonic fibroblast Nonalcohol fatty liver disease Mesenchymal stem cells Peroxisome proliferator-activated receptor gamma coactivator-1 alpha Peroxisome proliferator-activated receptors γ Polycomb repressive complex 2 PR domain containing 16 protein Pax transactivation domain-interacting protein Ten-eleven translocation methylcytosine dioxygenases Uncoupling protein1 White adipose tissue Zinc finger protein 423

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Introduction In mammals, adipose tissue is divided into white adipose tissue (WAT) and brown adipose tissue (BAT). WAT mainly accounts for energy storage and also plays critical roles in endocrine homeostasis (Galic et al. 2010), whereas BAT is predominantly responsible for nonshivering thermogenesis (Farmer 2008). Accordingly, there are mainly two types of adipocytes, white adipocyte and brown adipocyte (Table 1, Farmer 2008). In addition to “classical” brown adipocytes, recent studies suggest that there is another type of brown fat-like adipocyte, which is interspersed in white adipose tissues in human and rodents, named as beige/brite cells or adaptive brown fat cells (Lidell et al. 2013). Accumulation of excess WAT has deleterious consequences for metabolic diseases. Obesity is a disease with continuingly increasing prevalence, independently of age group, material status, or country of origin (Lewandowska and Zielinski 2016). Fundamental alteration observed in obese subjects is WAT overgrowth, which is associated with increased incidence of obesity-related comorbidities, such as cardiovascular diseases, type 2 diabetes, or digestive tract diseases (Lewandowska and Zielinski 2016). Moreover, it is also a risk factor for certain cancers including breast cancer (Bertolini 2013). Due to the interesting correlations between BAT activation and decreased BMI, increased energy expenditure and decreased onset of diabetes, the transdifferentiation of white adipocyte and brown adipocyte has become a research hot spot (Cinti 2009). In this chapter, we discuss the epigenetic regulation, especially DNA methylation and histone methylation, in white and brown adipocyte differentiation, including the transdifferentiation between white adipocyte and brown adipocyte, which highlights certain potent targets for the therapy of fat-related diseases, such as type 2 diabetes, obesity, and nonalcohol fatty liver disease (NAFLD).

Table 1 Characteristics of white adipocyte and brown adipocyte. Brown adipocytes store less lipid and have more mitochondria than white adipocytes. A number of marker genes have been identified to distinguish brown and white adipocytes, such as leptin for white fat and uncoupling protein1 (UCP1) for brown fat. White adipocytes are located in subcutaneous and visceral depots in humans, where brown fat is located in deeper cervical, supraclavicular, and paraspinal areas (Ussar et al. 2014) Cell type White adipocyte Brown adipocyte

Morphological characters A large lipid droplet Less mitochondria Multiple lipid droplets Abundant mitochondria

Specific marker gene Leptin

UCP1

Physical distribution Subcutaneous and visceral depots Supraclavicular, paraspinal, and cervical areas

Physical function Energy storage and endocrine homeostasis Thermogenesis and endocrine homeostasis

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Adipocyte Formation and Its Relevance to Obesity To date, it is generally accepted that the progression of adipocyte differentiation (brown or white adipocyte differentiation) can be divided into two major stages, determination and adipogenesis (Rosen and MacDougald 2006). The determination phase involves commitment of pluripotent stem cells to adipogenic lineage and the subsequent differentiation into preadipocytes (Rosen and MacDougald 2006). Pluripotent mesenchymal stem cells (MSCs) can differentiate into distinct mesodermal progenitor cell lineages, including adipocytes. Thus, MSCs (such as clonal mouse embryo cell line C3H10T1/2) are commonly used model to explore regulators in this stage (Tang et al. 2004). Adipogenesis is the process from preadipocyte to mature adipocyte, including growth arrest, mitotic clonal expansion (MCE), and terminal differentiation (Fig. 1; Farmer 2006). Multipotent precursor cells isolated from adipose tissue and immortalized mouse embryonic fibroblasts (MEFs) can differentiate to primary preadipocytes to study adipogenesis when pro-adipogenic transcription factors are added (Rosen and MacDougald 2006). Moreover, cell lines such as 3T3-L1 and 3T3-F442A are usually used to study white adipocyte differentiation (Tang et al. 2004), while studies on brown adipocyte differentiation usually involve preadipocytes such as BFC-1 (Abumrad et al. 1991) and PAZ6 (Kazantzis et al. 2012) cell lines. Common reasons for white adipocyte overgrowth in humans that cause obesity are bad eating habits and dramatic reduction of physical activity. Consistently, mice fed with high fat diet (HFD) or Western diet show substantially higher body adiposity, especially epididymal white adipose tissue (eWAT) and retroperitoneal

Fig. 1 A schematic representation of experimental procedures for 3T3-L1 differentiation from preadipocytes into mature adipocytes. The initial phase is growth arrest. Upon stimulation, confluent preadipocytes immediately reenter cell cycle and after at least two cell cycles, running into the second phase called mitotic clonal expansion (MCE). Then a differentiation cocktail containing insulin, glucocorticoids (dexamethasone), and agents that elevate cAMP (isobutylmethylxanthine) and fetal bovine serum is given to induce preadipocytes undergo the terminal differentiation. At day 8, 3T3-L1 successfully differentiate into mature adipocytes characterized by lipid droplet accumulation

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white adipose tissue compared with mice receiving normal chow (Roganovic et al. 2014). Also, sugary water consumption impairs the storage capacity of eWAT, which is associated with obesity-linked NAFLD (Luo et al. 2016). The same conclusion was drawn in rats, for the adipocyte size and adiposity index are increased in both females and males after sugary water consumption (Cervantes-Rodriguez et al. 2014). Interestingly, a low-protein, high-carbohydrate diet increases 60% of uncoupling protein1 (UCP1), the marker gene responsible for heat production, and stimulates thermogenesis in the interscapular brown adipose tissue of rats (de Franca et al. 2016). However, its clinical value remains unknown. Besides the effects of diets on adipocyte formation, the numerous transcription factors may explain majority of the underlying mechanism of adipocyte genes expression during adipocyte formation. Zinc finger protein 423 (Zfp423) is the key transcription factor committing cells to the white adipogenic lineage, through suppressing the thermogenic gene program (Shao et al. 2016). Zinc finger protein B-cell lymphoma 6 is also a positive transcriptional regulator of white preadipose commitment (Hu et al. 2016). Early B-cell factor-2 promotes beige fat cell commitment by activating a thermogenic and antiobesity program (Stine et al. 2016). PR domain containing 16 protein (PRDM16) not only commits progenitors to the brown adipogenic lineage but is also required for maintaining brown adipocyte identity (Seale et al. 2008). Adipogenesis is a complex process orchestrated by a cascade of sequential transcription regulators. The most important positive regulators for white adipocyte differentiation are peroxisome proliferator-activated receptors γ (PPARγ) and CCAAT-enhancer-binding proteins (C/EBPs), which are early response transcription factors (Rosen and MacDougald 2006). PPARγ coactivator-1 alpha (PGC-1α) plays a pivotal role in the concerted regulation of UCP1 gene transcription and acts as positive transcription factor in brown adipogenesis (Lelliott et al. 2006). Furthermore, other transcription factors including members of the Wnt signaling pathway such as Wnt1, Wnt6, Wnt10a, and Wnt10b are well-known negative regulators in white and brown adipogenesis. These transcription factors involved in the cascade of adipogenesis have been extensively reviewed in other places (Farmer 2006; Rosen and MacDougald 2006) (Fig. 2). The present chapter focuses on the epigenetic regulation, especially the DNA methylation and histone methylation, on adipocyte differentiation.

Epigenetic Regulation and Adipocyte Differentiation In addition to transcription factors mentioned above, epigenetic regulations during adipocyte differentiation have also become a hot research area. Epigenetic regulation refers to regulating gene expression heritably without changes in DNA sequences, including DNA methylation and post-translational modification of histones and microRNA (Wolffe and Matzke 1999). Numerous researches have reported that epigenetic regulation plays critical roles in the regulation of white and brown adipocyte differentiation and transdifferentiation of WAT to BAT.

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Fig. 2 Major transcription factors that involved in white and brown adipocyte differentiation. The most important transcription factors for adipocyte differentiation are PPARγ and C/EBPs. C/EBPβ is rapidly expressed after induction of adipocyte differentiation, sequentially induces the expression of PPARγ and C/EBPα. PPARγ and C/EBPα then mutually induced to promote adipocyte differentiation. B-cell lymphoma 6(Bcl6) and zinc finger protein 423(Zfp423) are positive transcriptional regulators of white preadipose commitment. Early B-cell factor-2(Ebf2) and PR domain containing 16 protein (PRDM16) promote brown adipogenic commitment. Wnt10a and Wnt10b are negative transcription regulators of adipogenesis

DNA methylation is an heritable epigenetic modification involved in gene silencing, imprinting, and the suppression of retrotransposons (Bird 2002), mediated by DNA methyltransferases (Dnmts) including Dnmt1, Dnmt3a, Dnmt3b, and Dnmt3L (Wu and Zhang 2014). Dnmt1 is a maintenance methyltransferase whereas Dnmt3a and Dnmt3b are de novo methyltransferases. Dnmt3L mediates de novo methylation together with Dnmt3a but is inactive on its own (Wu and Zhang 2014). Genetic studies in mice have revealed that it is lethal to knock out any of the three enzymes: Dnmt3a, Dnmt3b, and Dnmt3L, which clearly demonstrates the importance of DNA methylation for mammalian development (Li et al. 1992; Okano et al. 1999). DNA demethylation is mainly achieved by the ten-eleven translocation methylcytosine dioxygenases (TETs), including TET1, TET2, and TET3, which decrease DNA methylation level by catalyzing 5mC to 5hmC (Wu and Zhang 2014). Importantly, the catalytic reaction of TET requires α-ketoglutarate (αKG), a key metabolite of the Krebs cycle, which links metabolism to epigenetic modifications (Wu and Zhang 2014). Vitamin C, a micronutrient known for its anti-scurvy activity in humans, is

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also implicated in activation of TET activity (Wu and Zhang 2014). Histone posttranslational modifications mainly include methylation and acetylation. Histone methylation is dynamically regulated by site-specific methyltransferases and demethylases. MLL family, which contains SET1A, SET1B, MLL1, MLL2, MLL3, and MLL4, is a group of histone methyltransferases (HMTs) on H3K4 (Ruthenburg et al. 2007). The methyltransfases that specifically methylate H3K9 belong to SET domain family, including euchromatic histone-lysine N-methyltransferase 2 (EHMT2), also known as G9a, SETDB1, PRDM2, SUV39H1, and SUV39H2 (Krishnan et al. 2011). The polycomb repressive complex 2 (PRC2) is the predominant H3K27 methyltransferase in mammalian cells (Juan et al. 2017). KMT5A is the only known lysine methyltransferase that monomethylates H4K20 in vivo (Milite et al. 2016). Insights into the enzymes and proteins involved in the epigenetic regulation of adipocyte differentiation are critical in the battle against obesity and its related metabolic disorders. Since the role of histone acetylation and microRNAs in adipocyte differentiation has been shown in other chapters of this book, here we mainly discuss DNA methylation and histone methylation, as well as the involved enzymes that regulate these modifications.

DNA Methylation in Adipocyte Differentiation Epigenetic mechanisms such as DNA methylation are essential in tissue growth and remodeling. Recent studies indicate that DNA methylation is critical for several cell differentiation processes, including adipocyte differentiation (Torres-Andrade et al. 2014). It was demonstrated that the differentiation of 3T3-L1 cells was associated with genome-wide epigenetic changes of demethylation/methylation status (Sakamoto et al. 2008). To explore the role of DNA methylation in adipocyte differentiation, a DNA methylation inhibitor, 5-aza-20 -deoxycytidine (5azadC), is widely used. Decreased proliferation and adipocyte differentiation have been demonstrated in MSCs derived from bone marrow and adipose tissue treated with 5azadC (Zych et al. 2013), demonstrating that DNA methylation is essential for adipocyte determination. Furthermore, 5azadC can activate the expression of bone morphogenic protein 4 (BMP4), which contributes to adipocyte lineage commitment of C3H10T1/2 cells while inhibits myogenesis (Bowers et al. 2006). When it comes to the stage of adipogenesis, DNA methylation may promote or inhibit it in a phase-dependent manner. Recently, it was reported that 5azadC markedly suppressed adipogenesis at early stages of 3T3-L1 differentiation by upregulation of Wnt10a but enhanced lipogenic program of WAT differentiation later due to unmethylated Srebp1c promoter (Yang et al. 2016). Furthermore, the expression levels of key regulators of adipose formation, such as PPARγ, C/EBPβ, Glut4, and leptin, are also regulated by DNA methylation in 3T3-L1 preadipocyte differentiation. All of them are hypermethylated and repressed in preadipocytes and hypomethylated after differentiation (Li et al. 2010). Recently, several in vivo studies also suggested that DNA methylation plays significant role in the regulation of adipogenesis. Clinical investigation

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has demonstrated that dexamethasone-induced osteoporosis, the most severe side effect of long-term glucocorticoid therapy for acute lymphoblastic leukemia, is accompanied by increased adipose tissue area (Li et al. 2013). Impaired binding of Dnmt 3a/3b to C/EBPα promoter contributes to the upregulation of C/EBPα expression, whereas knockdown of C/EBPα rescues the effect on differentiation balance between osteoblast and adipocyte in dexamethasone-induced osteoporotic mouse model, implying a potent therapeutic target for long-term dexamethasone therapy induced osteoporosis (Li et al. 2013). Nevertheless, the impact of DNA methylation on adipogenesis can pass to the offspring of maternal obesity. Employing an overfeeding-based rat model, maternal obesity alters genome-scale DNA methylation of male offspring. DNA hypomethylation contributes to the expression of Zfp423 and C/EBPβ, thus increases adipose tissue formation propensity in the offspring (Borengasser et al. 2013). Under multigenerational HFD-fed stress, the “feed-forward cycle” increases the adiposity of female mice, with the most severe phenotype found in the F2 generation (Ding et al. 2014). However, the causal relationship between diet-induced obesity and precise regulation of DNA methylation associated with nutritional balance still awaits elucidation. Further insights on the mechanism of DNA methylation, especially the roles of essential enzymes in this process, and the target genes in adipocyte differentiation, shall help discovering solutions for human diseases, such as obesity and type 2 diabetes.

DNA Methyltransferases in Adipocyte Differentiation Cooperation of different DNA methyltransferases, demethylases, and MBDs ensure organized development of adipose tissue as well as body weight balance. The expression of Dnmt1 in adipose tissue is positively correlated with BMI (Kim et al. 2015; Sharma et al. 2015). The same results are gained in either adipocytes from HFD-fed or db/db mice (Kim et al. 2015). It has been reported that dietary or genetic perturbation may modulate miR-148a-3p expression, thus inversely modulating Dnmt1 levels in adipose tissue (Sharma et al. 2015).To explain the role of Dnmt1 in adipose differentiation, in vitro study has shown that Dnmt1 gene expression is induced early in 3T3-L1 adipocyte differentiation during MCE and is critical for the maintenance of DNA and histone H3K9 methylation patterns during this period (Londono Gentile et al. 2013). Later in differentiation, ATP-citrate lyase (ACL)–mediated suppression of DNMT1 led to early induction of adipocyte-specific genes, such as Glut4, Fabp4, adiponectin, and adipsin, as well as early upregulation of PPARγ (Londono Gentile et al. 2013). Expression levels of Dnmt3a and Dnmt3b were also markedly upregulated in the adipose tissue of obese mice (Xia et al. 2014). In 3T3-L1 preadipocytes, Dnmt3a was also significantly elevated at both the mRNA and protein level during the contact inhibition stage (Guo et al. 2016). Transgenic mice overexpressing Dnmt3a in adipose tissue showed no difference in body weight (Kamei et al. 2010) but higher levels of inflammatory cytokines, such as tumor necrosis factor-alpha, with high-fat diet challenge, suggesting that Dnmt3a may

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contribute to obesity-related inflammation (Kamei et al. 2010). DNMT3L has been shown to participate in DNA methylation via cooperation with other DNMTs (Wu and Zhang 2014); however, it was recently found that DNMT3L affects the methylation of thymine DNA glycosylase promoter independently of DNMT1 and DNMT3b in cancer cells (Kim et al. 2010). Whether it can independently regulate DNA methylation in adipocytes is unknown.

DNA Demethylases in Adipocyte Differentiation TETs can be recruited to adipogenesis-related regulatory regions to catalyze the oxidation of 5mC to 5hmC, which appears to be part of and perhaps plays an essential role in adipogenesis. TETs catalyze region-specific demethylation of PPAR response elements and activate the expression of adipocyte-specific genes by identifying poly(ADP-ribosyl)ated (PARylated) PPARγ co-activator complex (Fujiki et al. 2013). During early brown adipogenesis, the cellular levels of αKG, a key metabolite required for TET-mediated DNA demethylation of the Prdm16 promoter, are profoundly increased, which may rescue obesity-induced suppression of brown adipogenesis (Yang et al. 2016). It has been observed that the increased intake of Vc is negatively associated with the occurrence of obesity-related mechanisms in human and animals (Garcia-Diaz et al. 2014). Moreover, supplementing Vc to the adipogenic differentiation cocktail enables a robust and efficient differentiation of mouse ESCs to mature adipocytes (Cuaranta-Monroy et al. 2014) (see Table 2, Fig. 3).

Table 2 DNA methyltransferases and demethylases regulate adipocyte differentiation. DNA methyltransferase 1, DNA methyltransferase 3a, and DNA methyltransferase 3b all methylate the promoter of critical adipogenetic transcriptional regulators. Decreased expression of these enzymes promotes 3T3-L1 terminal differentiation. It has been reported that TETs upregulate brown adipocytes formation. Up: upregulation; down: downregulation. C/EBP CCAAT-enhancer-binding protein b, Glut4 glucose transporter type 4, PPARg peroxisome proliferator-activated receptors g, Prdm16 PR domain containing 16 protein, TETs ten-eleven translocation methylcytosine Type DNA methyltransferases

DNA demethylases

Enzyme Dnmt1

Target gene PPARγ Glut4

Dnmt3a Dnmt3b TETs

C/EBPβ PPARγ PPARγ Prdm16

Biological function White fat adipogenesis (down)

Model 3T3-L1

White fat adipogenesis (down)

3T3-L1

Brown fat adipogenesis (up) Brown fat commitment (up)

Mice

Reference (Londono Gentile et al. 2013) (Kamei et al. 2010) (Garcia-Diaz et al. 2014) (CuarantaMonroy et al. 2014)

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Fig. 3 Major DNA methyltransferases and demethylases that involved in white adipocyte adipogenesis. Dnmt1 is downregulated by miR-148-3p and decreased methylation on PPARγ promotes adipogenesis. Both Dnmt3a and Dnmt3b inhibit the expression of C/EBPβ and PPARγ. TETs demethylase PPARγ and promote adipocyte differentiation

Methyl-CpG Binding Proteins in Adipocyte Differentiation The family of methyl-CpG binding protein and the role of MBDs in gene expression will be well discussed in another chapter of this book, there is no need to explain it anymore. Evidence from patients carrying methyl-CpG binding protein-2 (MECP2) mutations and transgenic mouse models demonstrates that this protein is involved in body weight control. Mecp2-null mouse exhibits a threefold increase in the amount of iWAT, accompanied by increased body weight and high leptin levels (TorresAndrade et al. 2014). In addition to MECP2, MeDIP-seq analysis of entire adipocyte genome of HFD-fed mice revealed a higher number of hypermethylated regions accompanied by increased expression of Dnmt3a and MBD3 (Parrillo et al. 2016).

Histone Methylation in Adipocyte Differentiation Recently, the effects of histone modifications to adipocyte differentiation have caught great attention. In 3T3-L1 preadipocytes, the promoters of adipogenic genes like adiponectin (apM1), Glut4, Gpd1, and Leptin are enriched in H3K4 dimethylation before expression (Musri et al. 2006). The beginning of apM1 transcription coincides with H3 promoter hyperacetylation and H3K4 trimethylation. The same histone modification pattern found in mouse primary preadipocytes and adipocytes suggests the methylation level of H3K4 is closely associated with adipocyte differentiation and adipose tissue formation (Musri et al. 2006). In this

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chapter, we focus on histone methylation as well as enzymes that regulate these modifications in adipocyte differentiation.

H3K4 Methylation Regulatory Enzymes in Adipogenesis It has been reported that activating signal cointegrator-2 (ASC-2), a transcriptional coactivator for PPARγ-mediated adipogenesis, can associate with MLL3 or MLL4 in a complex named ASCOM (ASC-2 complex) (Lee et al. 2008). To elucidate the physiological role of ASCOM, a homozygose mouse line MLL3Δ/Δ was established, in which mutant MLL3 can still be involved in ASCOM but is inactive. These mice showed significantly decreased amount of WAT associated with improved insulin sensitivity and increased energy expenditure; furthermore, ASC-2, MLL3, and MLL4 were recruited to the PPARγ-activated aP2 gene during adipogenesis, and PPARγ was shown to interact directly with the purified ASCOM. Moreover, although H3K4 methylation of aP2 is induced in WT MEFs, it is not induced in ASC-2/ MEFs and only partially induced in MLL3Δ/Δ MEFs (Lee et al. 2008). These results suggest that ASCOM-MLL3 and ASCOM-MLL4 are likely to function as a crucial but redundant H3K4MT complex, which plays an important role in PPARγ-dependent adipogenesis. Another H3K4 methylation regulator, Pax transactivation domain-interacting protein (PTIP), a component of a HMT complex consisting of MLL3, MLL4, has been demonstrated to play an important role in adipogenesis (Cho et al. 2007). Deletion of PTIP in brown adipose tissue significantly reduces tissue weight and the expression of UCP1 and PGC-1α (Cho et al. 2009). Furthermore, PTIP/ MEFs and preadipocytes all show striking defects in adipogenesis due to impaired enrichment of H3K4 trimethylation and RNA polymerase II on promoters of PPARγ and C/EBPα (Cho et al. 2009). Lysine-specific histone demethylase 1A(LSD1) is not only a specific H3K4 demethylase but also has been shown to demethylate mono- and dimethylated H3K4 in vitro (Musri et al. 2010). Knockdown of LSD1 in 3T3-L1 decreases H3K4 dimethylation and increases H3K9 dimethylation at the promoter of C/EBPα and resulted in markedly decreased differentiation of 3T3-L1 preadipocytes (Musri et al. 2010).

H3K9 Methylation Regulatory Enzymes in Adipogenesis Besides LDS1, a great number of researches have reported that H3K9 methylation has a significant role in the differentiation of adipocyte. The repressive marker H3K9 dimethylation is decreased on C/EBPα in 3T3-L1 preadipocytes during adipogenesis (Wang et al. 2013). G9a, a H3K9me2 methyltransferase, has caught great attention recently. Adipose-specific G9a KO (G9aflox/flox;aP2-Cre) mice showed increased weight of WAT and BAT, as well as increased expression of adipogenic genes (Wang et al. 2013). Furthermore, removal of H3K9me2 by G9a deletion in 3T3-L1 preadipocytes and brown preadipocytes significantly enhanced binding of C/EBPβ to PPARγ2 promoter, which directly activates PPARγ2 expression in the early phase of

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adipogenesis. Interestingly, G9a represses PPARγ expression not only in an HMT activity-dependent manner but also facilitates Wnt10a expression independent of its enzymatic activity (Wang et al. 2013). SETDB1 is another H3K9me2 methyltransferase that acts as an anti-adipogenic factor. It has been found that it could be activated by Wnt5a and repressing PPARγ function through histone H3K9 methylation (Takada et al. 2007). Knockdown of SETDB1 in 3T3-L1 preadipocytes decreased H3K9 dimethylation on the C/EBPα promoter and favored adipocyte differentiation (Musri et al. 2010). Recently, it was shown that SET domain bifurcated 1 (SETDB1) could be immediately recruited to downstream of transcription start sites marked with H3K4me3 to establish the H3K4/H3K9me3 bivalent chromatin signature in embryonic stem cells, keeping Pol II paused and maintaining C/EBPα and PPARγ expressed at low levels but poised for activation when differentiation is required (Matsumura et al. 2015). Furthermore, H3K9 demethylases have also been shown to play important part in adipogenesis. A recent research demonstrates that Jhdm2a demethylates H3K9 dimethylation on the promoter of Ucp1 and facilitates the recruitment of PPARγ, RXRα, and their coactivator PGC-1α (Tateishi et al. 2009); with Jhdm2a is related with fat storage, glucose, and energy expenditure (Tateishi et al. 2009). Overexpression of Jmjd2c in 3T3-L1 adipocyte inhibits adipocyte differentiation through binding with HDAC1 and then represses PPARγ activity (Lizcano et al. 2011). Recently, it was shown that PHF2, a Jmjdcontaining protein that also known to demethylate the histone H3K9, can interact with C/EBPα and demethylate H3K9 dimethylation in the promoters of C/EBPα targeted adipogenic genes (Okuno et al. 2013). In addition, PHF2 null mice exhibit less adipose tissue, reduced adipocyte numbers, and impaired adipogenesis in stromal vascular cells (Okuno et al. 2013). The same results have been confirmed in PHF2 knockdown cells, which also showed reduced lipid accumulation compared to control cells (Lee et al. 2014).

H3K27 Methylation Regulatory Enzymes in Adipogenesis The PRC2 mediated H3K27me2/me3 through its catalytic subunit EZH2 have been interchangeably associated with gene repression (Juan et al. 2017). In 2013, it was reported that H3K27me3 was lower in the Zfp423 promoter of maternal obesity fetal tissue, accompanied by reduced binding of EZH2 (Borengasser et al. 2013). These results represent a novel pathway by which maternal obesity appears to regulate adipogenesis in the offspring. In 3T3-L1 preadipocytes, the expression of Wnt family including Wnt1, Wnt6, and Wnt10b is inhibited during adipogenesis due to H3K27 trimethylation (Wang et al. 2010). Deletion of EZH2 eliminates H3K27 trimethylation on Wnt promoters and restores Wnt expression, which leads to activation of Wnt/β-catenin signaling and inhibition of adipogenesis (Wang et al. 2010). Tetratricopeptide repeat on chromosome X (UTX) decreases the methylation level of H3K27 (Agger et al. 2007). Inactivation of UTX downregulates the expression of brown fat specific genes, while overexpression does the opposite. UTX

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positively regulates brown adipocyte thermogenic program through coordinated control of demethylating H3K27me3 and acetylating H3K27, switching the transcriptional repressive state to the transcriptional active state at the promoters of UCP1 and PGC1α (Zha et al. 2015). Understanding the role of UTX shall provide insights for new therapeutic approaches against obesity (Table 2).

H4K20 Methylation Regulatory Enzymes in Adipogenesis As the only known lysine methyltransferase that monomethylates H4K20, KMT5A is upregulated by PPARγ during adipogenesis. Furthermore, induction of SETD8 positively regulates the expression of PPARγ and its targets through H4K20 monomethylation with a feedback loop and thereby promotes adipogenesis. Consistently, knockdown of Setd8 suppressed adipogenesis (Wakabayashi et al. 2009). Except histone H4, lysine residues of nonhistone proteins, including p53, are also monomethylated by SETD8 (Milite et al. 2016). As a consequence, the methyltransferase activity of SETD8 is implicated in many essential cellular processes including DNA replication, DNA damage response, transcription modulation, and cell cycle regulation, and is associated with multiple cancers, such as prostate cancer, gastric cancer, and breast cancer (Milite et al. 2016). However, little is known about its role in adipocyte differentiation (Table 3).

Conclusion and Future Directions Physiologically, adipocyte differentiation is under a precise control of a vast number of transcription factors and coregulators. Recent studies have provided a comprehensive view of the epigenetic machinery operating during adipogenesis. However, many details of the epigenetic regulation remain elusive: (1) The maintenance DNA methyltransferase Dnmt1 is induced early in 3T3-L1 adipocyte differentiation during MCE period (Xia et al. 2014), how about its function in terminal differentiation and whether other de novo methyltransferases function similarly to Dnmt1 or cooperate with Dnmt1 is still unknown. Techniques like high-throughput sequencing can be used to draw exquisite map of dynamic DNA methylation landscapes during adipogenesis. (2) By using committed preadipocyte cell lines like 3T3-L1, the epigenetic regulation in terminal differentiation of white adipocyte is relatively easy and has been extensively investigated. However, much is unknown about the regulation cascade of adipocyte lineage commitment due to the lack of appropriate cell model to distinguish the determination of pluripotent cell to white and brown preadipocytes. (3) Recently, transdifferentiating white adipocyte into brown adipocyte has caught much attention as a potential therapy direction for obesity and other metabolic disorders, such as type 2 diabetes. The role that epigenetic regulation plays in brown adipocyte differentiation and transdifferentiation awaits further investigation. (4) Most of our knowledge about adipogenesis comes from in vitro study of fibroblasts or preadipocytes, and there are little data on depot-specific

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Table 3 Histone methyltransferases and demethylases regulate adipocyte differentiation. Histone sitespecific methyltransferases and demethylases that modify the histone methylation level of critical adipogenetic transcriptional regulators. Up: upregulation; down: downregulation. C/EBPa CCAATenhancer-binding protein a, PTIP Pax transactivation domain-interacting protein, LSD1 lysine-specific histone demethylase 1A, SETDB1 SET domain bifurcated 1, JHDM2a Jumonji domain-containing histone demethylase 2a, PHF2 plant homeodomain finger protein 2, EZH2 enhancer of zeste homolog 2, UTX lysine-specific demethylase 6A; UCP1 uncoupling protein1 Specific site Enzyme H3K4 MLL family PTIP

Effect H3K4me3 methyltransferase PPARγ H3K4me3 methyltransferase C/EBPα H3K4me3 demethylase C/EBPα H3K9me2 demethylase PPARγ H3K9me2 methyltransferase

Biological function White adipocyte adipogenesis (up) Brown adipocyte adipogenesis (up) White adipocyte adipogenesis (up) White adipocyte adipogenesis (up) White adipocyte adipogenesis (down)

SETDB1 C/EBPα H3K9me2 methyltransferase Jhdm2a Ucp1 H3K9me2 demethylase Jhdm3 PPARγ H3K9me2 methyltransferase PHF2 C/EBPα H3K9me2 demethylase H3K27 Ezh2 Wnt H3K27me3 signal methyltransferase UTX Ucp1 H3K27me2/3 demethylase

White adipocyte adipogenesis (down) Brown adipocyte adipogenesis (up) White adipocyte adipogenesis (down) White adipocyte adipogenesis (down) White adipocyte adipogenesis (up) Brown adipocyte adipogenesis (up)

LSD1 H3K9

LSD1 G9a

Targets PPARγ

Model Mice BAT 3T3-L1 3T3-L1 Mice 3T3-L1 3T3-L1 Mice 3T3-L1 Mice 3T3-L1 Mice

Reference (Lee et al. 2008) (Cho et al. 2009) (Musri et al. 2010) (Musri et al. 2010) (Wang et al. 2013) (Takada et al. 2007) (Tateishi et al. 2009) (Lizcano et al. 2011) (Okuno et al. 2013) (Wang et al. 2010) (Zha et al. 2015)

aspects of differentiation. How epigenetic modification impacts adipogenesis in vivo is largely unknown. More animal studies are required to verify the in vivo regulation mechanisms. In the future, new targets of metabolic diseases, including obesity and type 2 diabetes, may be identified through investigating epigenetic regulation in adipogenesis.

Key Facts of Beige Adipocytes • Beige adipocytes show thermogenic capacities similar to brown adipocytes. • Uncoupling protein1 is also found in beige adipocytes. • In humans, the presence of beige adipocytes is correlated with a lean, metabolically healthy phenotype. • Beige adipocytes can be induced in 3T3-L1 cells.

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• Peroxisome proliferator-activated receptor α agonist induces beige cell formation in subcutaneous white adipose tissue from diet-induced male obese mice. • Implantation of human beige cells improves metabolic homeostasis in mice.

Dictionary of Terms • Adipocyte determination – A phase during which pluripotent stem cells differentiate to adipogenic lineage rather than other lineages such as myogenic lineage. • Preadipocytes – A kind of cell that has been determined to adipogenic lineage but is not fully functional. It can be induced to mature adipocyte with differentiation cocktail. • Adipogenesis – The process from preadipocytes, which have been determined to adipogenic lineage, to mature adipocytes. • De novo methyltransferases – Methyltransferases which methylate previously unmethylated CpG sequences, such as DNA methyltransferase 3a and DNA methyltransferase 3b. • Maintenance methyltransferase – Methyltransferases which methylate preexisting methylation marks onto the new strand during replication, such as DNA methyltransferase 1.

Summary Points • This chapter focuses on the regulation of DNA methylation and histone methylation in adipocyte differentiation. • High fat diets can not only affect the parental adipose formation but also influence the health of offspring because heritable epigenetics regulation. • The DNA methylation level of the promoters of transcription factors associated with adipogenesis and the histone methylation level during adipocyte differentiation are dynamically changed. • The transdifferentiation of white adipocyte and brown adipocyte has become a research hot spot. • Insights into the enzymes and proteins involved in the epigenetic regulation of adipocyte differentiation are critical in the battle against obesity and its related metabolic disorders.

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Nutritional Programming of Metabolic Syndrome: Role of Nutrients in Shaping the Epigenetics

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Sonal Patel, Arpankumar Choksi, Richa Pant, Aftab Alam, and Samit Chattopadhyay

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetics in Metabolic Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutritional Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In Utero Programming of Metabolic Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Maternal Nutrition on Fetal Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transgenerational Effects of Maternal Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Paternal Nutrition on Epigenetics of Offspring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Different Nutrients on Metabolic Reprogramming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutrition and Metabolic Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Therapeutics for Metabolic Syndrome: Epigenetic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Paternal Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Early Life Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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S. Patel · A. Choksi · R. Pant · A. Alam Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India e-mail: [email protected]; [email protected]; [email protected]; [email protected] S. Chattopadhyay (*) Indian Institute of Chemical Biology, Kolkata, West Bengal, India Chromatin and Disease Biology Lab (# 08), National Centre for Cell Science, NCCS Complex, Savitribai Phule Pune University Campus, Pune, Maharashtra, India e-mail: [email protected]; [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_42

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Abstract

Increased prevalence of metabolic syndrome like obesity, heart diseases, and diabetes is an emerging public health problem. Susceptibility to such diseases has always been attributed to environmental and genetic factors which certainly play a pivotal role but cannot be the sole causal factor leading to metabolic syndrome. Epigenetics – a mediator between genetics and environment – is emerging as a potential candidate to explain the increase in the prevalence of such metabolic diseases. Changes in the epigenetic landscape marked by DNA methylation, histone methylation, and acetylation can lead to obesity, insulin resistance, diabetes, and vascular dysfunction in both animals and humans. Nutritional programming during early stages of life can manipulate the metabolism and the physiology of the organism. This is where the importance of optimal maternal nutrition comes into play. Both maternal under- and overnutrition have the potential to adversely affect the etiology of metabolic disorders in the developing fetus by changing the epigenetic marks. Various macronutrients and micronutrients in the maternal diet have also been shown to be exhibiting specific effect on the future health of the offspring. Though the role of epigenetics in fetal programming of metabolic syndrome is constantly being well understood, research on the therapeutic aspect is still in its infancy. Interventions and manipulation of dietary supplementation which potentially can make changes in the epigenetic marks can be the future therapeutic targets for chronic metabolic syndrome. Keywords

Metabolic syndrome · Obesity · Epigenetic transgenerational inheritance · Nutritional genetics · Transgenerational effects · Fetal nutrition · Macro- and micronutrients · Therapeutics List of Abbreviations

11β-HSD1 Agtr1b AMPK CEBPB G6Pase GHSR GLUT4 GR HAT HDAC IGF2R IGFBP3 IQ IUGR LINE-1 LXRα NAD

11β-hydroxysteroid dehydrogenase type 1 Angiotensin II receptor, type 1b 50 AMP-activated protein kinase CCAAT/enhancer-binding protein beta Glucose 6-phosphatase Growth hormone secretagogue receptor Glucose transporter type 4 Glucocorticoid receptor Histone acetyltransferase Histone deacetylase Insulin-like growth factor 2 receptor Insulin-like growth factor-binding protein-3 Intelligence quotient Intrauterine growth restriction Long interspersed nuclear element-1 Liver X receptor alpha Nicotinamide adenine dinucleotide

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NOS3 Pdx1 PEPCK PGC-1α PPARα ROS SIRT1 TCA ZFP423 ZFP57 RXRA

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Nitric oxide synthase Pancreatic and duodenal homeobox 1 Phosphoenolpyruvate carboxykinase PPAR gamma coactivator -1 alpha Peroxisome proliferator-activated receptor alpha Reactive oxygen species Sirtuin 1 Tricarboxylic acid Zinc finger protein 423 Zinc finger protein 57 Retinoid X receptor alpha

Introduction Metabolic syndrome encompasses a cluster of biochemical and physiological abnormalities associated with the risk of heart disease and other health problems, such as diabetes and stroke. Metabolic risk factors include abdominal obesity, high triglyceride level, low HDL cholesterol level, high blood pressure, and insulin resistance. Large-scale clinical research has improved the traditional therapy for particular symptoms of metabolic syndrome, but the study regarding etiology of this syndrome still needs a boost. Susceptibility to develop these metabolic health problems varies greatly among individuals. Estimated role of heredity in this variation is in the range of 40–70%. However, a large portion of heritability of metabolic abnormalities is still unaccountable. The process of identifying genes involved in various human diseases has become easier with the advent of whole genome projects (Altshuler et al. 2008). However, pinpointing a particular gene for multifactorial diseases such as metabolic syndrome is very difficult. Fully understanding the physiological aspects of the available results and their clinical application is a Herculean task, and genetic mapping is just the tip of the iceberg. Nutritional genetics which is a combination of nutrigenomics and nutrigenetics has helped us understand the mechanisms of dietrelated diseases (Lovegrove and Gitau 2008). Apart from dietary differences, environmental factors such as stress, lower physical activity, calorie-rich diet, lifestyle changes, etc., also are known to play a significant role in disease progression. Risk of various common diseases varies from one individual to another, and many emergent evidences propose that this variation is governed by interaction among genes and numerous environmental factors. These environmental factors have long been known to induce changes in the genome by virtue of mutations. But, mutations in the DNA sequence cannot be solely responsible for evolutionary changes and disease development. Recently, investigators in this field are turning their attention to decoding the role of epigenetic changes such as DNA methylation, chromatin remodeling, miRNA regulation, and diverse activities of noncoding RNAs in the etiology of metabolic syndrome. Hence, an individual’s epigenetic transgenerational inheritance (Skinner et al. 2010) may be the key to missing link between genetic variants and environmental effect leading to better and individualistic treatment options.

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Epigenetics in Metabolic Syndrome Gene expression is not only dependent on the underlying DNA sequence but can also vary based on DNA and histone modifications, a mechanism giving insights into environmental influences on epigenetic marks and hence altering gene expression and disease susceptibility (Delcuve et al. 2009). These epigenetic alterations on critical genes are known to gradually accumulate and lead to the increase in the prevalence of various chronic disorders including those related to defects in metabolism (Wang et al. 2013; Barros and Offenbacher 2009). These varying epigenetic marks not only pose the individual to higher risk of developing a certain health condition but can also be heritable across the generation (Morgan et al. 1999; Cropley et al. 2006). Epigenetic modulation of gene expression can be mediated by three molecular mechanisms: DNA methylation, histone modification, and noncoding RNAs. DNA methylation considered to be more stable epigenetic mark is known to be transmitted through DNA replication and cell division (Bird 2002). Hypermethylation of CpG islands results into transcriptional repression, whereas hypomethylation results into transcriptional activation (Bird 1986; Reik and Dean 2001). Histone modifications include acetylation, methylation, ubiquitination, SUMOylation, and phosphorylation which individually or combinatorially can either facilitate or inhibit the entry of various transcription factors which results in activation or repression of gene expression (Turner 2000). Specific epigenetic modifications of the genes that control enzymes of various metabolic pathways have been shown to be linked to early onset of diseases in the offspring (Patel et al. 2015). Environmental factors can change epigenetic features and increase the susceptibility to many chronic diseases, including metabolic syndrome (Portha et al. 2014; Sun et al. 2013).

Nutritional Programming Early life is the most crucial and decisive stage for the developmental plasticity of an organism. This period provides an organism enough flexibility to develop in different ways depending on the surroundings. Therefore, any event that occurs during this setting can manipulate the metabolism and physiology of the organism in a long run (Duque-Guimara~es and Ozanne 2013). This process is known as “fetal programming” or “developmental programming.” The general idea of this concept is that developmental insults, during pre- or postnatal stage, can have long-term consequences on the health of that individual. This concept was originally proposed in “the Barker hypothesis” or “fetal and infant origins of adult disease” (Reynolds and Caton 2012). Human epidemiological studies have provided the support for this concept by showing a strong correlation between smaller size or low body weight at birth and during infancy with increased likelihood of developing pathologies (coronary heart diseases, type II diabetes, hypertension, immune dysfunction) as adults (Gluckman

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Fig. 1 Effect of nutritional programming on the health of the offspring. Pictorial representation of the effect of maternal and paternal nutrition (over- and under-) on the epigenetic landscape leading to implications on the health of the offspring

et al. 2008). Lately, it has also been documented that altered nutrient availability to fetus also forms a significant link between intrauterine environment and later diseases. When the nutrient demand exceeds placental supply, it leads to fetal undernutrition. Undernourished fetus responds by undergoing catabolism, i.e., it utilizes its own substrates to generate energy. Therefore, the result of prolonged undernutrition is slowing of growth. This slowing of growth may result in disproportionate organ size, for example, undernutrition in late gestation may lead to reduced renal growth (Barker 1998). Moreover, excessive nutrient supply can also have adverse effects. For example, maternal hyperglycemia may result in fetal hyperinsulinemia and fat deposition. This is an example of “metabolic imprinting” (Hillier et al. 2007). Hence, the effect of subsequent exposure to environment during infancy, childhood, or adulthood may be influenced by the past experiences of the fetus and may add to the risk of the disease (Gluckman et al. 2008) (Fig. 1).

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In Utero Programming of Metabolic Diseases Important observations correlating birth weight with cardiovascular disorders led researchers to assume that there can be a link between in utero fetal programming and adult onset of metabolic diseases (Barker 1990). Both undernutrition and overnutrition during the period of gestation are related to increased vulnerability of late onset of metabolic diseases like diabetes mellitus and obesity (Keating and El-Osta 2015). During prenatal and early postnatal development of an organism, chromatin marks are established which can respond to changes in nutrient availability, metabolites, and other environmental cues. These epigenetic variations have a strong influence on metabolic expression profile of the offspring (Keating and El-Osta 2015).

Effect of Maternal Nutrition on Fetal Growth Pregnant woman’s blood circulation provides all the essential nutrients and oxygen to the fetus through the placenta (Ji et al. 2016). Mature placenta is formed after 4 weeks of implantation. Before placentation, uterine secretion is known to provide nutrients, and the surrounding milieu supplies the oxygen to the developing embryo (Bazer et al. 2011). These secretions are rich in glucose, amino acids, and other nutrients which play an important role in cell signaling and metabolic pathways which are essential for protein synthesis, cell growth, and cytoskeletal remodeling of the conceptus (embryo/fetus and associated membranes). Any deficiency of nutrients during this period of growth can lead to severe abnormalities in the development of conceptus (Wang et al. 2012). Since the placenta is the major organ of transport, it develops extensive vasculature which causes the uterine and umbilical blood flow to increase extensively during pregnancy. Because the nutrient supply and gas exchange to the fetus are solely dependent on the placenta, the vasculature in the placenta holds utmost importance (Reynolds and Caton 2012). The uptake of both micro- and macronutrients by the uterus is more in pregnant woman as compared to nonpregnant woman, so restricted placental blood supply or any defect in vasculature may contribute to intrauterine growth restriction (IUGR) during mammalian pregnancies (Wang et al. 2012).

Impact of Maternal Undernutrition on Fetus Metabolism During pregnancy, mother competes with her fetus for dietary intake. Undernutrition in pregnant woman resulting from insufficient food supply places the mother and her fetus at risk. It can also be a consequence of early pregnancy (i.e., teenage pregnancy) or closely spaced pregnancies (King 2003). Severe malnutrition in gestational woman can lead to IUGR in mammals and can have adverse consequences on the growth of offspring (Ji et al. 2016) as it can result in the development of metabolic syndrome in the offspring through epigenetic changes. Since the growth of fetus is known to occur mostly in the late gestation period, the effects of undernutrition of pregnant woman like stunted growth are more pronounced in the

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third trimester than the second trimester (Wu et al. 2008). Epidemiological evidences indicate that undernutrition or malnutrition of pregnant women is associated with the development of diabetes and obesity in the offspring. Inadequate protein levels in the diet of women during pregnancy may result in skewed levels of liver X receptor-α (LXRα) histone promoter region H3 (K9, 14) acetylation. This leads to silencing of LXRα which in turn increases the expression of its target gene 11β-HSD1 (11βhydroxysteroid dehydrogenase type 1), reduces inactive corticosteroids to their active form, and hence increases glucose production and G6Pase (glucose 6-phosphatase, a key enzyme in gluconeogenic pathway). This eventually results in defects in glucose tolerance leading to early onset of diabetes in offspring (Vo et al. 2013).

Impact of Maternal Overnutrition on Fetus Metabolism Obesity in women of reproductive age is becoming a prevalent problem worldwide. Obesity in women during pregnancy has been burdened with threats to both mother and baby. Adverse consequences associated with maternal obesity include gestational diabetes, miscarriage, and hypertensive diseases. Children born to obese mothers are predisposed to insulin resistance and type 2 diabetes when they reach maturity. Maternal obesity confers an additional risk of developing congenital cardiovascular diseases and neural tube defects in the child (Templeton 2014). Obese mothers also tend to transmit an obesogenic and diabetogenic trait from one generation to the next. Recent studies in mice indicate that intake of high-calorie diet during pregnancy promotes deacetylation and methylation in the promoter regions of few genes like adiponectin and leptin at H3K9 and H4K20 positions, respectively, and hence rise in the levels of leptin in circulation and reduction in the expression of adiponectin in white adipose tissues of the offspring (Masuyama and Hiramatsu 2012). Moreover, adipocyte differentiation is also known to increase as a result of maternal obesity during fetal development. This occurs by inhibition of DNA and histone (H3K27me3) methylation in the Zfp423 (zinc finger protein 423) promoter and upregulating the expression of Zfp423, which is a known transcription factor required for initiation of adipogenic commitment (Yang et al. 2013).

Transgenerational Effects of Maternal Nutrition Transgenerational epigenetic effects refer to the transfer of physiological and behavioral information across the generations. This involves the modifications in the chromosomes that pass from one generation to another without any environmental exposure or genetic manipulation. It is referred to as non-Mendelian inheritance by many evolutionary biologists because it includes all those processes that have evolved to attain nongenetic determination of phenotype (Youngson and Whitelaw 2008). Reports have suggested the involvement of transgenerational effects of maternal malnutrition in the subsequent generations. For example, diet restriction in different subjects like in young boys (7–11 years) or in female fetuses for 7–12 weeks is seen to be related to increase in prevalence of metabolic conditions in them in future as adults and in their coming generations (Wang et al. 2012).

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Fig. 2 Effect of maternal health and nutritional status on offspring. Environment and epigenetics of the mother in the peri-conceptual period and during gestation and of the newborn during postnatal period can be influenced by many factors as shown in the figure

In 1993, Wu and Berecek reported for the first time that reduction in blood pressure induced by environmental manipulations could be transferred to the next generation (Wu and Berecek 1993). Later on Lindpaintner, proposed that certain epigenetic modifications such as DNA methylation might be the cause of this heritability (Lindpaintner 1993). Fifteen years later, independent research groups reported that maternal protein restriction can lead to hypertension in the next generation of rat offsprings (Torrens and Poston 2008, Harrison and Langley-Evans 2009). In general, there are two important developmental stages of germ cells where epigenetic encoding occurs: preimplantation embryos (precursor for oocyte) and primordial germ cells (spermatozoa precursor). So far, it is concluded that induction of adultonset disease can be different in a variety of diseases (Kunes et al. 2015). Maternal health can hence alter the prognosis of metabolic syndrome in the fetus in many different ways which is summarized in Fig. 2.

Effect of Paternal Nutrition on Epigenetics of Offspring In mammals, maternal investment during prenatal and postnatal stage of the offspring and the uncommonness of biparental care have largely focused the research on parental care to mother-infant interaction only. Nevertheless among different species, paternal care, however limited, is implicated. Studies indicate that prepubertal food

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habit of father and exposure to drugs and toxins (e.g., early onset of paternal smoking, alcoholism, chewing of beetle nuts, etc.) can induce effects on male phenotype that can be inherited by succeeding generations. Additionally, increasing age of fathers is also related to schizophrenia, autism, and early onset of bipolar disorder and reduced IQ of offspring (Curley et al. 2011). Hence, there could be a possibility for paternal effects to be perpetuated transgenerationally. This complex relationship between paternal, maternal, and offspring phenotype can result in an intriguing approach to study behavioral aspects of epigenetics. Appended below in Table 1 are the nutrients whose presence or absence in the specific window of development epigenetically modifies specific genes leading to development of metabolic disorders in the offspring.

Role of Different Nutrients on Metabolic Reprogramming Nutrition and Metabolic Disorders Nutrition is very critical for overall health of individual. Both undernutrition and overnutrition have serious impact on long-term health status and life expectancy. Intrauterine nutrition has major influence on health of offspring in later life. Maternal malnutrition as discussed earlier may result in developmental problems which may lead to metabolic disorders such as insulin resistance, diabetes, hypertension, and cardiovascular diseases in offspring. Glucose and protein metabolism are very important for proper fetal development that lay the foundation of future adult health. Moreover, fetal fatty acid deficiency may also lead to poor vascular health in adulthood. In addition to the macronutrients, deficiency of many micronutrients such as various vitamins and minerals may also contribute to various metabolic and vascular diseases. Excessive fetal exposure to saturated fatty acids and carbohydrates may contribute to cardiometabolic disorders in later life (Liang et al. 2010). Malnutrition and its impact on fetal programming of the metabolic syndrome can be divided into two major classes: (a) macronutrient malnutrition and (b) micronutrient malnutrition.

Macronutrient Malnutrition Malnutrition of macronutrients such as protein, carbohydrate, and fat may perpetually modify the homeostasis in the fetus, predisposing them to severe chronic diseases, like diabetes, cardiovascular anomalies, and cancer (Miese-Looy et al. 2008). Protein Malnutrition Dietary proteins encompass a variety of nutritional and biological importance. Apart from their nutritional value, they are also important in the regulation of growth and development (Jahan-mihan et al. 2011). Physiological functions of various proteins depend upon physicochemical properties of the underlying amino acid sequences (Jahan-mihan et al. 2011).

Free fatty acid

High-fat diet

Vitamin B2, alcohol, smoking

Fiber, protein, fat

Undernutrition (50% calorie restriction during the last week of gestation) Undernutrition (uteroplacental insufficiency)

Vitamin B12 and folate

Folic acid

3

4

5

6

7

9

10

8

Undernutrition (lowprotein diet)

Nutrient Folic acid

2

Sr. No. 1

Prenatal

Peri-conception

In utero

In utero

Dietary intake in the last 3 months of pregnancy Days 67–130 of gestation

Maternal overnutrition

Adult

In utero

Period of dietary intake Prenatal

IGFBP3, LINE-1 GR

Pdx1

GLUT4

IGF2R, H19

ZAC1

GHSR

PGC-1α

CEBPB, PEPCK

Gene PPARα

Hypomethylation

Deacetylation and dimethylation (N-tails of H3) Loss of H3K4me3 Increase in H3K9me2 DNA methylation

Hypermethylation of CpG islands

Methylation

Cytosine hypermethylation (in non-CpG islands) CpG hypermethylation in promoter region

Histone (H3, H4) acetylation

Epigenetic modification Hypomethylation

Transient neonatal diabetes mellitus type 1 Hypertension

Transient neonatal diabetes mellitus (growth retardation and diabetes) Methylation levels of IGF2R and H19 were higher in fetuses Diminished pancreatic island insulin production in offspring T2DM in adult offspring following IUGR

Obesity, T2DM, alteration in GH pathway

Fat deposition, insulin insensitivity, diabetes, high blood pressure Insulin resistance, T2DM

Health outcomes in offspring Dyslipidemia

Wistar rats

Human

Rat

Rat

Sheep

C57BL/ 6:12nine hybrid mice Humans

Human

Species Wistar rats Rat

McKay et al. (2012) Lillycrop et al. (2005)

Park et al. (2008)

Raychaudhuri et al. (2008)

Lan et al. (2013)

Azzi et al. (2014)

Dunn and Bale (2009)

Barrès et al. (2009)

References Lillycrop et al. (2005) Zheng et al. (2011)

Table 1 Health of offspring and nutrients. Effect of various nutrients and their time of intake on the health of the offspring and their mechanism of action including the genes affected and epigenetic mechanism

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The importance of proteins in fetal growth and development during pregnancy and early life has been well documented. There have been many studies identifying health outcome of protein-enriched as well as protein-deficit maternal diets in various animal models (Langley-Evans et al. 1994; Rees et al. 2000). Maternal diet with both low and high protein content can influence the offspring’s health by affecting body weight, blood pressure, and metabolism. Low-protein-containing maternal diets increase blood pressure (Langley-Evans et al. 1994; Rees et al. 2000) and adiposity (Sasaki et al. 1982). One of the studies in rats suggests that deficiency of maternal protein intake during pregnancy leads to hypermethylation of DNA in the liver of fetuses which makes them prone to glucose intolerance and hypertension during adulthood (Rees et al. 2000). Another study involving rat model advocates predisposition of progeny to hypertension in later life due to maternal protein restriction via altered expression of the agtr1b gene by change in promoter DNA methylation (Bogdarina et al. 2007). Maternal diets with low protein content in rats lead to reduced β-cell mass at birth and reduced insulin production in adult life (Petrik et al. 1999). In addition, fetal rats exposed to lowprotein diet were having increased hepatic triglycerides and fat synthesis along with reduced serum insulin which lead to obesity and insulin resistance (Maloney et al. 2003). Studies related to protein restriction in maternal diet of rats suggest that maternal low-protein diet may involve in programming food preferences. Rats exposed to protein restriction in maternal diet prefer high-fat foods in later life (Bellinger et al. 2004).

Carbohydrate Malnutrition Carbohydrates are the main source of energy for most of the living organisms. Almost all dietary carbohydrates can be converted into glucose which can easily be utilized as the energy source for a wide range of physiological activities. Proper consumption of carbohydrates during pregnancy is very important for proper fetal development and future health. The fetal brain is dependent on glucose as source of energy. To meet the requirements of fetus and placenta during later stages of gestation, maternal glucose production increases which prove the importance of carbohydrates in fetal growth and development (Butte 2000). Various studies in both animal and human suggest that maternal diet with lowand high-carbohydrate leads to various complications and metabolic disorders in offspring (Levin 2006; Sedová et al. 2007; Lenders et al. 1997). One human study related to lower maternal carbohydrate intake during early phase of pregnancy was linked with childhood obesity due to changes in epigenetic promoter methylation of RXRA and endothelial NOS3 genes (Godfrey et al. 2011). Offspring of rats fed with high-sucrose diet during pregnancy were reported to develop complications related to metabolic disorders in later life (Sedová et al. 2007). Energy restriction during pregnancy in dams has been reported to produce offspring with low birth weights, higher food intake during early life, and obesity in later life (Levin 2006). This indicates that exposure to higher levels of glucose in the uterus leads to metabolic syndrome in adult life.

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Lipid and Fat Malnutrition Lipids are essential for the formation of cell membrane and various hormones. Moreover, they are important for proper development of various organs such as the brain, eyes, and liver. So intake of lipids including sterols, phospholipids, and triglycerides is very important during fetal development as well as during early infancy (Innis and Friesen 2008). Fat is another source of energy after carbohydrates. Moreover, fat is a source of essential fatty acids and choline which are important for proper brain development and production of various hormones (Connor 2000). For proper fetal development, lipid and fat are important component of maternal diet. However, higher fat consumption has been linked to obesity and cardiovascular diseases in offspring during adult life (Liang et al. 2009). According to few reports on human patients, maternal obesity itself is directly associated with increased risk of obesity in offspring (Whitaker 2004). Human studies related to intake of fat-rich diet during pregnancy and its link to obesity and other metabolic disorders in offspring are limited in number. However, there are several animal studies related to intake of high-fat maternal diet and its association with obesity and metabolic disorders in offspring during adulthood. One of such study on mice exposed to fat-rich diet during pregnancy and postnatal period has been reported to develop obesity, hyperglycemia, insulin resistance, and hypertension during adulthood (Liang et al. 2009). Animal study involving maternal high-fat diet during pregnancy has been associated with increased body weight and altered energy homeostasis due to changed expression of leptin receptor in offspring (Chen et al. 2008). According to recent animal study, maternal high-fat diet leads to altered metabolic programming in fetal liver due to epigenetic regulation of SIRT1 and PPARα predisposing offspring to obesity (Borengasser et al. 2014).

Micronutrient Malnutrition Along with macronutrients, many micronutrients such as vitamins and minerals are very important for human health. Moreover, role of vitamins and minerals in regulation of development and growth is well studied. Adequate intake of various vitamins and minerals has been recommended for pregnant women for proper fetal development and growth. The recommended intake of various vitamins and minerals has been discussed in Table 2 (FAO/WHO 2002). Various animal studies related to dietary restriction of multiple vitamins or minerals during pregnancy have been linked to increased body fat and triglyceride levels in offspring (Venu et al. 2004a, b). Vitamin A Vitamin A is one of the fat-soluble vitamins. Beta-carotene is a precursor for vitamin A synthesis. Vitamin A and beta-carotene are essential for fetal development as they are involved in growth, vision, protein synthesis, and cell differentiation. However, vitamin A is very important for fetal development; excessive intake of vitamin A is prohibited during pregnancy. Higher intake of vitamin A during pregnancy has been

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Table 2 Recommended micronutrient intake for expecting mothers. Recommended maternal intake of vitamins and minerals during pregnancy and lactation (FAO/WHO 2002) Sr. No. 1.

Vitamin/mineral Vitamin A (retinol)

2.

Vitamin B1 (thiamine)

3.

Vitamin B2 (riboflavin)

4.

Vitamin B3 (niacin)

5.

Vitamin B5 (pantothenic acid)

6.

Vitamin B6 (pyridoxine and pyridoxal)

7.

Vitamin B7 (biotin)

8.

Vitamin B9 (folate)

9.

Vitamin B12 (cobalamins)

10.

Vitamin C

11.

Vitamin D

12.

Vitamin E

13.

Vitamin K

Group Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women

Recommended safe intake (mg/day) 0.5 0.8 0.85 1.1 1.4 1.5 1.1 1.4 1.6 14.0 18.0 17.0 5.0 6.0 7.0 1.3–1.5 1.9 2.0 0.03 0.03 0.035 0.4 0.6 0.5 0.0024 0.0026 0.0028 45 55 70 0.005 0.005 0.005 7.5 7.5 7.5 0.055 0.055 0.055 (continued)

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Table 2 (continued) Sr. No. 14.

Vitamin/mineral Calcium

15.

Iodine

16.

Iron

17.

Selenium

18.

Zinc

Group Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women Women Pregnant women Lactating women

Recommended safe intake (mg/day) 700–1,000 1,000–1,300 1,000–1,300 0.15 0.2 0.2 19–59 30–70 20–60 0.026 0.026–0.028 0.035–0.042 3.0–9.8 3.4–20.0 5.8–14.4

correlated with high risk of birth defects in offspring (Miller et al. 1998). Intrauterine vitamin A deficiency has been associated with various metabolic defects in offspring including hypertension (Lelievre-Pegorier et al. 1998) and glucose intolerance (Matthews et al. 2004).

Vitamin B Complex Vitamin B comprises of a group of water-soluble vitamins that have an important role in metabolism. They contain eight chemically distinct vitamins: thiamine (Vitamin B1), riboflavin (Vitamin B2), niacin and nicotinic acid (Vitamin B3), pantothenic acid (Vitamin B5), pyridoxine and pyridoxal (Vitamin B6), biotin (Vitamin B7), folic acid (Vitamin B9), and cobalamins (Vitamin B12). During pregnancy increased intake of B vitamins is recommended especially folate and vitamin B12 which are important for proper fetal development (Molloy et al. 2008). Folate is important for nucleic acid synthesis and cellular division (Bolesta and Szostak-Wegierek 2009). Deficiency of folate and vitamin B12 during pregnancy may lead to birth defects and preterm delivery (Molloy et al. 2008). One of the studies involving human subjects suggests that inadequate maternal folate supply along with deficiency of vitamin B12 has been related to increase in adiposity and insulin resistance (Yajnik and Deshmukh 2008). One of the reports involving sheep as model system shows maternal restriction of folate, vitamin B12, and methionine during periconceptional period leads to insulin resistance, increased blood pressure, and obesity in offspring during adult life due to altered DNA methylation of CpG islands in the fetal liver (Sinclair et al. 2007). These reports establish the role of folate and vitamin B12 in shaping the metabolic health of the offspring.

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Vitamin C Vitamin C acts as an antioxidant and protects cells and tissues from detrimental effects of free radicals and reactive oxygen species (ROS). Vitamin C is a watersoluble vitamin which guards the cell against aqueous peroxyl radicals and scavenges free radicals (Frei et al. 1989). Vitamin C has been reported to improve umbilical blood flow leading to higher exposure to oxygen in fetus (Thakor et al. 2010). One of the studies has shown positive correlation between maternal intakes of vitamin C in early pregnancy with birth weight (Mathews et al. 1999). However, in another study no association between maternal vitamin C intake during pregnancy and birth weight has been found (Neggers et al. 1997). These reports show that maternal malnutrition of vitamin C has implications on metabolic health of offspring, but its exact role is still ambiguous, and future studies should be focused on this aspect of research. Vitamin D Vitamin D is a lipid-soluble vitamin and a prohormone. It is known to play an imperative role in regulation of calcium and phosphate homeostasis, bone remodeling, and muscle function (Weaver 2007). Vitamin D deficiency during pregnancy may result in impaired bone development and fetal growth in the offspring (Mahon et al. 2010; Pasco et al. 2008). Vitamin D has been reported to affect lipolysis and adipogenesis in humans via regulation of calcium signaling in adipocytes (Kong and Li 2006). In adult humans, vitamin D deficiency has been reported as one of the risk factors for obesity development (Martini and Wood 2006). Moreover, there has been a report suggesting maternal vitamin D deficiency during pregnancy may result in insulin resistance (Lapillonne 2010). Vitamin E Vitamin E is a fat-soluble vitamin which has antioxidant activity. Vitamin E prevents lipid peroxidation and inhibits free radical chain reactions (McCay 1985). Vitamin E and vitamin C act synergistically, as vitamin C helps in regeneration and maintenance of vitamin E level (Packer et al. 1979). There has been a report showing positive associations between vitamin E intake in pregnant female and birth weight of offspring (Scholl et al. 2006). Additionally, another report suggests the effect of increased intake of vitamin E during late pregnancy and increased birth weight. However, there are several contradicting reports showing no association of vitamin E intake during early or late pregnancy and birth weight of offspring (Mathews et al. 1999). These suggest that more concrete studies should be conducted to further investigate the role of vitamin E during pregnancy in determination of offspring birth weight. Minerals There are several minerals which are important for maintaining various physiological activities of the cell. Some of the minerals including calcium, zinc, iron, magnesium, copper, iodine, and selenium are important during fetal development, and proper intake has been recommended during pregnancy (Darnton-Hill and Uzonna 2015). Out of these minerals, maternal dietary intake of few has been

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associated with vascular and metabolic disorders in offspring. One of the animal studies involving rat model suggests that insufficient intake of calcium during pregnancy may have important role in programming of hypertension during adulthood (Bergel and Belizan 2002). Moreover, low calcium intake during pregnancy has been correlated with elevated insulin resistance and adiposity in the offspring (Venu et al. 2004b). In humans, maternal calcium supplementation is seen to be associated with decrease in blood pressure of the progeny (Hiller et al. 2007). Dietary restriction of iron during pregnancy in rat has been linked to changes in renal morphology (Lisle et al. 2003), and another study of iron restriction in maternal diet has been associated with hypertension in offspring (Ingelfinger 2004). Moreover, intrauterine magnesium restriction has been associated with insulin resistance, impaired glucose tolerance, and higher adiposity (Venu et al. 2005). Dietary restriction of zinc has been linked with elevated arterial blood pressure, kidney lesions (Tomat et al. 2008), decreased insulin sensitivity, and increased weight gain in adulthood (Jou et al. 2010).

Metabolites and Their Role in Epigenetic Regulation Maternal malnutrition of macronutrients and micronutrients leads to critical changes in fetal development and programming which makes offspring more vulnerable to various metabolic disorders including obesity and diabetes. The fetal development and programming is influenced by cellular metabolites formed from these macroand micronutrients. Different metabolites affect production and activity of epigenetic regulators which ultimately leads to alteration of factors related to various metabolic pathways. Major cellular metabolites and their influence on various epigenetic factors related to metabolic pathways have been discussed in Table 3.

Therapeutics for Metabolic Syndrome: Epigenetic Approach Various studies have clearly established a distinct role of epigenetics in early life programming ultimately shaping an individual’s future health and susceptibility to metabolic syndrome. Despite these preliminary approaches to better understand the role of epigenetics in metabolic reprogramming, there is a dearth in research on therapeutic approaches to treat the same. Manipulation of an individual’s epigenome in this context could act as potential therapy for treating chronic metabolic diseases including oxidative stress, obesity, insulin resistance, diabetes, and vascular dysfunction. Potential approaches in therapeutics include: 1. Inhibition of changes in specific epigenetic modifications responsible for higher susceptibility to diseases – using molecules which can tamper modification of DNA methylation, histone deacetylation, and/or microRNA expression. 2. Changes in epigenetic patterns can ultimately alter an entire metabolic pathway, and these epigenetically disturbed metabolic pathways can serve as potential targets for treatment of diseases.

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3. Supplementation of diet with better intake of needed amino acids, vitamins, and phytochemicals that would strengthen an organism’s metabolism and prevent prognosis of metabolic syndrome. Table 3 Role of different metabolites in modulation of epigenetics and their specific functions. Metabolites can modulate epigenetics as shown by examples in the table above. The epigenetic mechanism, specific functions, and the pathways which produce these metabolites have also been included

Cellular metabolite NAD+

Epigenetic function Coenzyme for HDACs (important for activities of sirtuin histone deacetylases)

Acetyl CoA

Donor for HATmediated histone acetylation Substrate for histone GlcNAcylation by OGT

NAcetylglucosamine

α-Ketoglutarate

S-Adenosyl methionine

Cofactor for histone demethylation reaction by JumonjiC domain containing HDM (JHDM) and DNA demethylation reaction by TET Substrate for HMT and DNMT

AMPK

Phosphorylates histones

β-Hydroxybutylate

Endogenous and specific inhibitor of class I HDACs

Metabolic pathway/ function Glycolysis

TCA cycle

Hexosamine pathway, required for protein glycosylation TCA cycle

Biosynthesis De novo: tryptophan Salvage: extracellular nicotinic acid intracellular NAD+ decomposition products Citrate

End product of hexosamine biosynthetic pathway

References Sporty et al. (2009), Lu and Thompson (2012)

Lu and Thompson (2012) Lu and Thompson (2012)

Glutamate

Lu and Thompson (2012)

Lipid metabolism, polyamine biosynthesis

Methionine and ATP

Fatty acid oxidation, lipid and glucose metabolism (metabolic sensor) Ketogenesis, alternative energy source

ATP/AMP ratio

Mato et al. (1997), Lu and Thompson (2012) Lu and Thompson (2012)

Acetyl-CoA by β-OHB dehydrogenase

Dedkova and Blatter (2014)

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Fig. 3 Plausible therapeutic approaches to treat metabolic disorders. A schematic representation of the prognosis of metabolic syndrome and the steps that hold the potential to be targeted for prevention or treatment of the same

Extensive work is needed in this direction with different animal models to better understand the efficacy and off-target effects of such therapeutic approaches to prevent or treat the metabolic abnormalities. Better understanding of epigenetics in metabolism in the context of both mother and fetus is crucial to develop effective therapeutic strategies for prevention and treatment of metabolic anomalies. Potential therapeutic approaches taking epigenetics of metabolism into account have been summarized in Fig. 3.

Dictionary of Terms • Developmental programming – It refers to the set of events that occur during pregnancy or neonatal stages of an individual which can manipulate the long-term physiology and metabolism of an organism. • Metabolic imprinting – It refers to the epigenetic programming of metabolism during prenatal or neonatal stages of life that can have detrimental consequences

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on health of an individual. Overnutrition or undernutrition of pregnant woman may result in serious penalty on the health of the fetus or offspring. For example, gestational diabetes may result in fetal hyperinsulinemia and abnormal fat deposition in offspring. • Intrauterine growth restriction – It is a condition where growth of the fetus is retarded (fetus is smaller than it should be). Delayed growth puts the baby at the risk of various diseases during prenatal or neonatal stages. • Metabolic disorders – These are the genetic conditions that result in problems in metabolism. It can also be defined as inherited gene anomaly that can result in enzyme deficiency. These diseases are also called inborn errors of metabolism. • Insulin resistance – It is a metabolic syndrome where cells fail to respond to insulin (pancreatic hormone) despite of its presence in the bloodstream. This does not let the entry of glucose from bloodstream to the cells and increases the individual’s risk of diabetes and early onset of heart diseases.

Key Facts Key Facts of Paternal Effects • A paternal effect is described as male caregiving nature in species where biparental or entirely paternal care is seen (e.g., marmosets, prairie voles, giant water bug, sea horses, glass frogs, etc.). • It can also be defined as inheritance of genes through patriline. • Epidemiological studies have established that nutritional status of fathers and grandfathers can exert metabolic transgenerational effects on their sons and grandsons. • Diet restriction prior to mating can also alter the metabolic profile of offspring. • Exposure of father to drugs, toxins, or other chemicals (smoking, alcohol) is related to behavioral and developmental impairment in offsprings. • Increasing age of father has also been found to be related to elevated risks of schizophrenia, autism, and bipolar disorders in their children.

Key Facts of Early Life Nutrition • Intrauterine nutrition has main influence on health and development of offspring later in life. • Both under- and overnutrition of pregnant woman can have harmful effect on health of the offspring. • Maternal malnutrition can result in developmental problems which may result in metabolic disorders later in life of offspring. • Since the gaseous exchange and nutrient supply to the fetus are through the placenta, any defect in its vasculature may result in intrauterine growth restriction of the fetus.

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• Malnutrition of macronutrients or micronutrients in gravid woman can alter the homeostasis of fetus, predisposing them to chronic metabolic disorders.

Summary Points • Altered nutrient availability to fetus forms a considerable relation between intrauterine environment and later life diseases in the offspring. • Since nutrient allocation to the fetus is primarily through the placenta, it is believed that the placenta governs the lifelong health of an individual. • Undernutrition of fetus results in slowing of growth and disproportionate organ size, while overnutrition is responsible for many metabolic diseases. • Malnutrition of fetus in the womb and subsequent environmental exposure during infancy, childhood, adolescence, and adulthood add to the risk of disease(s). • Children born to obese mothers tend to inherit obesogenic and diabetogenic trait which is transmitted from one generation to another. • Under- or overnutrition during gestation affects the epigenetic landscape of the offspring leading to improper functioning of certain genes and therefore diseases in early or later lives. • In addition to maternal investment, paternal care is also observed in certain species where food habit of father and exposure to drugs, toxins, and chemicals can induce the male phenotype which can be transmitted over generations. • Malnutrition of both macro- and micronutrients can have an impact on metabolic programming of the offspring. • Dietary restriction of different nutrients can have different health consequences. • Some of the minerals (Ca, Zn, Fe, Mg, Se, etc.) are important for fetal development, and their proper intake is recommended during pregnancy. • Use of molecules which can alter the modification of DNA methylation, histone deacetylation, and microRNA expression may act as potential therapeutic for the treatment of metabolic diseases. • Changes in epigenetic patterns can ultimately alter the entire metabolic pathway which can serve as potential therapy for treating various diseases.

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Juan Francisco Codocedo and Nibaldo C. Inestrosa

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutritional Modulation of miRNAs and Their Role in MetS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetics of miRNAs and Their Role in MetS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parental Inheritance of MetS and the Role of miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Metabolic syndrome (MetS) corresponds to a cluster of several risk factors that increase the risk of other health problems, such as cardiovascular disease and diabetes. The combinatorial nature of MetS makes its etiology complex as it is determined by the interplay of both genetic and environmental factors like nutrition or physical activity. Accordingly, intricate regulatory networks have J. F. Codocedo CARE UC Biomedical Research Center, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile e-mail: [email protected] N. C. Inestrosa (*) CARE UC Biomedical Research Center, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia Centro de Excelencia en Biomedicina de Magallanes (CEBIMA), Universidad de Magallanes, Punta Arenas, Chile e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_97

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evolved to respond to changes in environmental conditions and physiological stress. In the search for key molecular pathways that could elucidate the complex physiopathology of MetS, as well as serve as therapeutic tools, microRNAs (miRNAs) have emerged as attractive molecules, given their role as important components of complex gene regulatory networks. MiRNAs typically control the expression of their target genes by imperfect base pairing to the 30 untranslated regions (30 UTR) of their messenger RNAs (mRNAs) targets. Currently, several aspects of the miRNA biogenic process are known in detail, as well as the translational repression mechanisms exerted by miRNAs on their target mRNAs. The number of studies associating miRNAs with the metabolic risk factors of MetS is increasing; however, few studies directly relate miRNAs to a well-defined model of MetS. There is no doubt that miRNAs play an important role in the development of individual components of MetS; however, our understanding of their function during the different combinatorial modalities of MetS is poor. In this chapter, we review several of the studies investigating the relationship between miRNA dysfunction and MetS. We discuss the role of nutrition and genetic in the modulation of miRNAs activities and how our dietary behavior can have profound consequences in the metabolic health of our progeny. Keywords

Metabolic syndrome · High-fat diet · Maternal obesity · Paternal obesity · Diabetes · Nutrition · Metabolism · MicroRNAs · Epigenetics · Transgenerational inheritance · Genetic burden List of Abbreviations

30 UTR HFD messenger RNAs MetS miRISC miRNAs MRE NAFLD SNPs T2DM

30 untranslated region High-fat diet mRNAs Metabolic syndrome miRNA-induced silencing complex microRNAs miRNA recognition element Nonalcoholic fatty liver disease Single nucleotide polymorphisms Type-2 diabetes mellitus

Introduction Metabolic syndrome (MetS), also known as “plurimetabolic syndrome,” “syndrome X,” “deadly quartet,” “insulin resistance syndrome,” and “dysmetabolic syndrome,” corresponds to a cluster of risk factors that increases the risk of other health problems, such as cardiovascular disease and diabetes. The variety of names that the syndrome has been given throughout its history is a reflection of the difficulties

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that physicians and scientists have had in agreeing on a unique definition, diagnosis and treatment. Currently, individuals with three out of five metabolic conditions – abdominal obesity, hypertriglyceridemia, low levels of high-density lipoprotein (HDL), hypertension, and impaired fasting glucose – are diagnosed with MetS, and their chances of suffering a stroke or developing diabetes are significantly higher than those if only one of these risk factors is present (Alberti et al. 2009; Grundy et al. 2005). Additionally, several other metabolic disorders, such as liver fat accumulation, have been associated with MetS (Boyraz et al. 2014). The combinatorial nature of the syndrome makes its etiology complex as it is determined by the interplay of both genetic and environmental factors (Miyamoto et al. 2009; Ye et al. 2013). In the search for key molecular pathways that could elucidate the complex physiopathology of MetS, as well as serve as therapeutic tools, microRNAs (miRNAs) have emerged as attractive molecules, given their role as important components of complex gene regulatory networks (Yousef et al. 2014). miRNAs are small endogenous noncoding RNAs; in their mature form (approximately 22 nt), they are loaded onto a protein complex called the miRNA-induced silencing complex (miRISC) and direct the sequence-specific binding of the complex to target messenger RNAs (mRNAs), repressing their translation (Bartel 2009). Because the miRNA–mRNA binding site is very short (8–14 nt), each miRNA has the potential to regulate many target genes, and one gene may be targeted by several miRNAs (Bartel 2009). Additionally, miRNAs have different functional modalities that provide another layer of complexity to the miRNA-mediated effects (Codocedo et al. 2016). For example, the short recognition elements (miRNA recognition element, MRE) may occur in many transcripts that participate in the same pathway, indicating that a single miRNA could affect a whole pathway. Several reports have shown that a change in a single miRNA-target interaction can simultaneously affect multiple other miRNA-target interactions and modify physiological phenotypes (Hanin et al. 2014). Furthermore, the biogenesis of miRNAs is a complex multistep process that is modulated by several environmental factors, including nutrition, to generate homeostatic responses (Codocedo and Inestrosa 2016). The complex biology of miRNAs is hence compatible with a key role in metabolic functions. Thus, analysis of their deregulation in MetS patients and animal models could help to develop better therapeutic strategies to improve the quality of life of the increasing population of MetS patients. The number of studies associating miRNAs with the metabolic risk factors of MetS is increasing; however, few studies directly relate miRNAs to a well-defined model of MetS. In this chapter, we review several of the studies investigating the relationship between miRNA dysfunction and MetS. We consider evidence that describes the role of the environment in the form of nutrition, as well as the genetic component in the form of mutation in metabolic miRNAs as well as their targets. Finally, we discuss the interaction between both components and their consequences in the offspring of progenitors affected by MetS, in a mechanism that could explain the epidemic increase in obesity, diabetes, and MetS.

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Nutritional Modulation of miRNAs and Their Role in MetS Dietary components affect the activity of endogenous miRNAs through different mechanisms, including modulation of critical enzymes of the miRNA biogenetic pathway or modulation of components of the miRISC (Fig. 1). Numerous studies have demonstrated the role of dietary components in miRNA modulation and their consequences in the development of metabolic syndrome (Rottiers and Näär 2012), cancer (Li et al. 2010; Parasramka et al. 2012), neurodegenerative (Codocedo et al. 2016), and anxiety disorders (Meydan et al. 2016) (Fig 1). These effects depend on the different properties of the dietary components, including their bioavailability, distribution to different organs and the effective concentration attained by nutritional uptake. Additionally, the cellular context constitutes another layer of complexity as the expression profile of miRNAs is different between organs, and tissue-specific miRNAs (e.g., MyomiR in muscle) are expressed. For example, a high-fat diet

Fig. 1 Nutritional modulation of miRNAs and their role in MetS. The miRNA biogenesis pathway produces pri-miRNA transcripts by RNA polymerase II (Pol II) from miRNA genes. Next, the Drosha microprocessor complex processes pri-miRNA transcripts into pre-miRNAs. PremiRNAs are exported from the nucleus via Exportin 5 and subsequently cleaved by Dicer, and the miRNA/miRNA* duplex is unwound via Argonaute (AGO) and loaded in a TRBP-dependent manner into the miRNA-induced silencing complex (miRISC). The binding of target mRNAs to miRNAs in RISC is followed by the inhibition of translation and/or mRNA degradation. The miRNA biogenesis pathway may be subject to regulation at different levels to control the function of miRNAs and thus gene expression. In the figure, we show examples of nutritional factors related to MetS that regulate the expression of endogenous miRNAs through different mechanisms, including modulation of critical enzymes of the miRNA biogenetic pathway or modulation of components of the miRISC (see main text for more details). Different studies have suggested that these interactions have important consequences in the development of metabolic syndrome, cancer, neurodegenerative and anxiety disorders (Modified from Codocedo et al. 2016)

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(HFD) induce changes in a specific group of liver miRNAs that are involved in the regulation of lipid metabolism and participate in the induction of various liver diseases, including nonalcoholic fatty liver disease (NAFLD) (Tessitore et al. 2016). At the same time, nutritional manipulation induces changes in a musclespecific group of miRNAs (MyomiRs) that participate in the induction of insulin resistance and decreased myogenesis (Frias et al. 2016). Several other nutritional interventions have been related to changes in miRNA levels and their possible role in the development of metabolic risk factors by affecting specific biological processes in different tissues, including liver, muscle, adipose tissues, pancreas, and brain. The mechanism by which diet regulates miRNA expression and, in consequence, contributes to the genesis of metabolic conditions is not fully understood. In both animal models and patients with MetS, altered expression levels of different miRNAs have been observed as a consequence of changes at different stages of biogenetic processes, including transcription, processing, and miRISC function. One of the best-studied transcription factors that regulate miRNA expression is the p53 tumor suppressor protein, which regulates the expression of stress-response genes, including the miR-34 family. Interestingly, the p53/miR-34 axis has been shown to be upregulated in islets of diabetic db/db mice and the beta-cell line MIN6B1. Treatment with fatty acids, such as palmitate, which is a predisposing factor for T2DM, also upregulates p53/miR-34 in the pancreatic islets of diabetic mice (Lovis et al. 2008). p53 has also been shown to participate in other clusters of MetS-related conditions, such as nonalcoholic fatty liver disease (NAFLD) (Castro et al. 2013). Additionally, p53 not only regulates miRNAs at the transcriptional level but also regulates the processing/maturation of additional miRNAs, including miR-16-1, miR-143, and miR-145. p53 was shown to interact with the DEAD-box RNA helicase p68 (also known as DDX5) and enhance its interaction with the DROSHA complex, thereby promoting miRNA maturation (Suzuki et al. 2009). This mechanism of p53 was described in the context of cancer development; however, its role in the induction of MetS has not yet been studied. Interestingly, several reports have shown that either lack of nutrients and excessive or deregulated signaling through the nutrient-sensing pathways can activate a p53 response (Hanin et al. 2014; Lee et al. 2007, 2009; Okoshi et al. 2008). Other studies have demonstrated that increased glucose metabolism stimulated by the expression of the glucose transporter GLUT1 or hexokinase also suppressed p53 activity (Zhao et al. 2008). More recently, how nutritional factors induce changes in miRNAs through epigenetic DNA modifications and their role in the development of metabolic risk factors have been investigated (Yan et al. 2016). One of the major epigenetic mechanisms is DNA methylation, which is important in insulin sensitivity (Ma et al. 2013), obesity (Ali et al. 2016; Kühnen et al. 2016), and cardiovascular diseases (Rask-Andersen et al. 2016). DNA methylation leads to a decrease in gene transcription by inhibiting the binding of transcription factors to gene promoters (Kirchner et al. 2013; Nguyen et al. 2001). Recently, a reduction in miR-9 levels was found in the livers of HFD mice and ob/ob mice. Interestingly, this report described a concomitant increase in DNA methylation at the miR-9 promoter, which could be due to enhanced accumulation of DNA methyltransferase 1 (DNMT1) at

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the miR-9-3 promoter (Yan et al. 2016). miR-9 has been associated with the development of T2DM based on evidence showing that miR-9 plays an important role in the regulation of in vitro and in vivo insulin secretion via regulation of targets such as SIRT1 (Ramachandran et al. 2011) or Onecut-2 (Oc2) (Plaisance et al. 2006). The identification of new targets of miR-9, such as FOXO1, in the livers of obese mice has uncovered new biological roles associated with T2DM, including gluconeogenesis and insulin resistance (Yan et al. 2016). Despite the important advances made in this field, several questions are still unsolved. For example, it is not clear which signaling pathways the cells use to integrate specific nutritional manipulations and induce changes in miRNA expression that contribute to the development of MetS. Additionally, most of the preclinical studies did not determine whether the animals developed comorbidities suggestive of MetS at the end of the feeding protocol. Moreover, in several studies, the miRNA evaluation was performed when the animals reached a final metabolic state, such as T2DM, NAFLD, or a cardiac condition. As MetS is considered an early stage in the metabolic deterioration (i.e., prediabetic), the altered miRNAs observed in these animals have to be evaluated with caution because they could represent changes related to the advanced stage of MetS or not be related at all.

Genetics of miRNAs and Their Role in MetS The genetic component of MetS has been examined in population studies that showed differing prevalence rates between the sexes and among ethnic groups (Sale et al. 2006; Terán-García and Bouchard 2007). These findings are supported by twin studies and an increased incidence of the MetS in individuals with a parental history of MetS (Pietiläinen et al. 2006). In mouse models, the genetic components are also evidenced by the effect of HFD on different strains. For example, B6J mice develop rapid and reproducible features of the MetS (including adipose tissue inflammation, hepatosteatosis, insulin resistance, and hyperglycemia) when exposed to a HFD (Kluth et al. 2014). In contrast, 129/Sv mice are resistant to diet-induced obesity and the development of MetS (Almind and Kahn 2004; Bezy et al. 2011; Kokkotou et al. 2005; Lin et al. 2013; Mori et al. 2010). Genome-wide analysis has identified mutations in several genes that correlate with metabolic alterations of MetS, including protein kinase C-δ (PKCδ) (Bezy et al. 2011), the solute carrier family 2 of the facilitated glucose transporter (GLUT2) (Le et al. 2013), and the catalytic α polypeptide of phosphoinositide 3-kinase (PIK3CA) (Barroso et al. 2003), which are all known to influence glucose-insulin homeostasis. Although several studies have suggested that DNA polymorphisms occurring within or close to miRNA or miRNA-binding sites may contribute to human diseases (Brendle et al. 2008; Kim et al. 2016; Zhang et al. 2016), few genetic studies have investigated miRNAs in relation to MetS. Mutations within the miRNA genes could potentially affect the processing or target selection of miRNAs by different means, including their transcription; pri-miRNA and pre-miRNA processing; and via miRNA–mRNA

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interactions (Ryan et al. 2010). These polymorphisms may be located in primiRNAs, pre-miRNAs, mature miRNA, or regulatory regions of miRNAs (Gong et al. 2014). For example, miR-124a, which plays an important role in pancreatic islet development and the regulation of insulin secretion, showed increased expression in T2DM human pancreatic islets, resulting in impaired glucose-stimulated insulin secretion (Sebastiani et al. 2015). In a case-control study of the association between genetic variations in candidate miRNA genes and T2DM susceptibility in Italians, Ciccacci et al. sequenced 13 miRNAs and found that rs531564 in pri-miR124a was significantly associated with T2DM susceptibility (Ciccacci et al. 2013). The same association was described in a Han Chinese population with T2DM, which is important considering the significant genetic differences between Caucasian and Asian populations (Li et al. 2015). Bioinformatics prediction showed that the G variant allele of rs531564 in miR-124a can change the stability of the pri-miRNA by altering the formation of a ring-shaped structure in their predicted secondary structure, increasing the efficiency of processing into the mature form (Qi et al. 2012). Thus, the increased miR-124a expression caused by the G allele could alter insulin secretion and enhance the susceptibility to the development of T2DM and MetS. However, the occurrence of single nucleotide polymorphisms (SNPs) in miRNA genes is low. Approximately, 10% of human pre-miRNAs have documented SNPs, and fewer than 1% of miRNAs have SNPs in the functional seed region (Saunders et al. 2007), which corresponds to the sequence that recognizes the miRNA binding site in the 30 UTR of the mRNA target. Genetic polymorphisms that reside in the 30 untranslated region (UTR) of miRNA target genes, which can eliminate an existing binding site, create an erroneous binding site or affect binding affinity (Fig 2), are more frequently observed. For example, the ACAA-insertion/deletion (144/140 bp) polymorphism in the 30 UTR of insulin-like growth factor II receptor gene (IGF2R) was associated with T2DM and insulin-resistant traits (Villuendas et al. 2006). Bioinformatics prediction showed that this polymorphism is located within the hsa-miR-657 and hsa-miR-453 binding sites, and luciferase reporter assays revealed that the polymorphism affected the binding affinity and the transcriptional repression mediated by hsa-miR-657. These results indicated that the ACAA-insertion/deletion polymorphism may change IGF2R expression levels at least in part by hsa-miR-657mediated regulation, contributing to the elucidation of T2DM pathogenesis (Lv et al. 2008) More recently, eight SNPs, located in seven genes linked to MetS, were selected and genotyped in a Han Chinese population with MetS. Three SNPs were found to have a statistically significant effect on MetS risk and were located in the Apolipoprotein L6 (APOL6) gene and fatty acid binding protein 2 (FABP2) gene (Ye et al. 2013). These SNPs were located in the miRNA binding site of miR-143, miR-24, and miR-132, which have important roles in insulin resistance and differentiation, proliferation and growth of adipocytes (Esau et al. 2004; Kang et al. 2013; Klöting et al. 2009). The results demonstrated that different individuals and populations possess a genetic burden, including mutations in miRNA genes, that made them either resistant or vulnerable to the development of MetS. Genetic profiling could help to develop proper diets and therapies for individuals with these predispositions.

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Fig. 2 Functional consequences of genetic polymorphisms within the 30 UTR of miRNA target gene. Genetic polymorphisms within the 30 UTR of miRNA target gene can change the normal regulation mediated by their regulatory miRNA. (a) In normal conditions, a putative miRISC X is able to repress the translation of their mRNA target through the base pairing directed by a specific miRNA. (b) Some mutations can weaken this interaction or (c) completely destroy the miRNA recognition element, increasing the translation of the protein. (d) In other rarer examples, a genetic polymorphism within the 30 UTR of miRNA target gene can destroy the original MRE for a putative miRISC X and create an erroneous binding site for a different miRNA (miRISC Y). A(n), poly A tail

Parental Inheritance of MetS and the Role of miRNAs Thus far, we have discussed the role of the environment and genetics in the modulation of miRNAs related to MetS. However, the causes of common diseases, such as the metabolic risk factors that compose MetS, are normally more complex than the independent contribution of these factors. They often involve both susceptibility genes and their interactions with the environment. The interactions between the environment and genes are mediated by epigenetic changes of the genome, and epigenetic modifications of the genome are the response to environmental challenges (Jaenisch and Bird 2003). In this sense, epigenetic mechanisms may exacerbate the epidemic of metabolic disease by first contributing to the development of MetS risk factors, such as obesity and T2DM, and then passing modifications on to the subsequent generation via intergenerational effects and/or transgenerational inheritance (Kirchner et al. 2013) (Fig. 3). Maternal consumption of a HFD during pregnancy and lactation is closely related to metabolic changes, such as hepatic

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Fig. 3 Parental inheritance of MetS and the role of miRNAs. Epidemiological studies shown that stressors we are exposed to during our lifetime might cause disease in our descendants. In murine models, a HFD induce epigenetic changes that first contributing to the development of MetS risk factors, such as obesity and T2DM in the individual exposed (somatic cells, F0 generation), and then passing modifications on to the subsequent generation via germinal cells. Epigenetic modifications involve DNA methylation, posttranslational histone modifications, and changes in miRNA levels. In pregnant dams, HFD exposure can also induce epigenetic changes in the next two generations (F1 and F2) through the fetus and its germ line (intergenerational inheritance). The effect of such multigenerational exposure in subsequent generations (F3 and beyond) would be considered a transgenerational inheritance. By contrast, intergenerational inheritance in males is limited to the F1 generation (Modified from Heard and Martienssen 2014)

lipid accumulation, insulin resistance, and increased serum cytokine levels, in offspring that persist to adulthood. This is mediated in part by deleterious effects on fetal programming, predisposing offspring to adverse outcomes, including cardiometabolic and neurodevelopmental diseases (Neri and Edlow 2016). The underlying mechanism by which the in utero environment shapes the organism is through epigenetic modifications, which involve DNA methylation; posttranslational histone modifications; and changes in miRNA levels. For example, in a mouse model of maternal HFD exposure, miRNA analysis in pup livers showed reduced expression of miR-122 and miR-370. Interestingly, miR-370 targets the 30 UTR of carnitine palmitoyl transferase 1α (Cpt1α), decreasing the rate of β

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oxidation (Benatti et al. 2014). Similar observations have been made of MyomiRs involved in intramuscular adipogenesis in fetuses of obese sheep (Yan et al. 2013), fetal hearts of obese baboon’s progeny (Maloyan et al. 2013), and several other tissues. However, it is difficult to separate maternal effects on germ cells from the direct effects of in utero exposure on offspring. Considering that fathers contribute little more than sperm to offspring, the study of environmentally induced paternal germline epigenetic effects is currently expanding and may provide an explanation for the transgenerational influence of father’s experiences on offspring development (Curley et al. 2011). Recent studies have demonstrated that paternal metabolic health at conception can impact children’s health, with obese fathers more likely to father an obese child. In a cross-sectional study including 256 children and their parents, children who had at least one parent with MetS had higher levels of obesity and insulin resistance than children with parents who did not have MetS (Pankow et al. 2004). Additional studies have shown that offspring of parents with early coronary heart disease were consistently overweight beginning in childhood. In adulthood, the offspring with a positive parental history had a higher prevalence of obesity, elevated total cholesterol and LDL-C levels, and hyperglycemia, as well as a higher coexistence of these conditions (Bao et al. 1997). In rodents, diet-induced male obesity with or without diabetes induced a worse metabolic phenotype in their offspring, with glucose intolerance in female offspring due to pancreatic islet dysfunction and white adipose tissue dysfunction or insulin resistance and obesity, with some consequences evident across two generations (Fullston et al. 2013; Lin et al. 2014; Ng et al. 2014). Interestingly, HFD alters the transcriptional profile of the testes and results in differential content of sperm canonical miRNAs in F0 males. Pathway analysis of the predicted targets of the differentially expressed miRNAs in testis and sperm of HFD F0 male mice converged on pathways crucial to male reproductive system development and function, embryo development, and insulin signaling and metabolic disorders (Fullston et al. 2013). Conversely, shortterm diet and exercise intervention in diet-induced obese founder male mice improved their metabolic health and prevented insulin resistance and large adipocytes in their female offspring, concomitant with a degree of normalization of sperm miRNA content (McPherson et al. 2015; Palmer et al. 2012). However, these studies did not exclude potential confounding variables, such as molecular factors present in seminal fluid or the maternal reproductive tract at conception (Bromfield et al. 2014), diet-induced environmental changes in utero during preimplantation and gestation (Sasson et al. 2015; Shankar et al. 2011), and milk composition during lactation (Vogt et al. 2014), among many others. To overcome this issue, a German group used in vitro fertilization and implanted the resulting embryos into healthy surrogate females to ensure exclusive inheritance via the gametes and confirmed that a parental HFD renders offspring more susceptible to developing obesity and diabetes in a sex- and parent of origin–specific mode (Huypens et al. 2016), providing strong evidence that acquired traits can be inherited. The molecular mechanisms accounting for the heritable epigenetic modifications acquired during the parent’s lifetime are still unclear. However,

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miRNA modifications as well as modifications in RNA transcripts and methylomes may play a crucial role.

Conclusion In modern societies, the consumption of highly caloric foods and sedentary lifestyle has increased the rates of obesity, T2DM, and MetS at a pace that reaches pandemic levels. Additionally, individual genetic predispositions (or resistance) could worsen (or alleviate) the pathological outcome of a dietary challenge. Different molecules and biological pathways have been proposed as the mechanism by which organisms integrate the environmental signals and stressors that result in the development of the metabolic clusters that compose the MetS. In that sense, miRNAs have emerged as attractive molecules, given their role as important components of complex gene regulatory networks. miRNAs play a critical role in the development of individual components of MetS; however, our understanding of their function during the different combinatorial modalities of MetS is poor. More detailed metabolic profiles of the animals used for metabolic manipulations are needed to determine whether the miRNA alterations occur in a context of MetS. Finally, the study of inter- and transgenerational inheritance has shown that our dietary sins are passed on to our children in the form of epigenetic modifications, such as DNA methylation, chromatin modification, and miRNAs. Biological examples have been documented of phenotypic plasticity emerging in relatively fast timescales and of frequencies that are orders of magnitude higher than can be explained by natural selection of genetics variants. The hypothesis that our nutritional experiences are coded in our epigenome could help to explain the exponential increase in obesity, T2DM, and MetS observed in the modern world.

Key Facts • In USA, nearly 35% of all adults and 50% of those aged 60 years or older were estimated to have the metabolic syndrome. • Each miRNA potentially regulates hundreds of target gene products, and it is suggested that the entire protein coding genome is regulated by miRNAs. • Recently, a rapidly growing number of miRNAs have been implicated in regulation of genes and proteins involved in the control and maintenance of metabolic homeostasis including cholesterol and lipid homeostasis, insulin signaling and glucose homeostasis, as well as cardiometabolic disorders such as obesity, NAFLD, insulin resistance, T2DM, and coronary artery disease. • Environmental factors like nutrition could regulate the expression of miRNAs through different mechanisms including the modulation of their transcription, processing or assembling in their functional complex, miRISC.

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• Change in metabolic miRNAs could be passed to next generations through intergenerational and transgenerational inheritance which may exacerbate the epidemic of MetS.

Dictionary of Terms • Pri- and pre-miRNA – MicroRNAs are transcribed via Pol II into primarymicroRNAs (pri-miRNA), which are then cleaved in the nucleus by the enzyme DROSHA. The hairpin structure formed by this cleavage is referred to as a premiRNAs (Fig. 1). • MicroRNA recognition element (MRE) – Correspond to a short sequence in the 30 UTR of the mRNA target that bind to the seed sequence in their cognate miRNA through imperfect RNA-RNA base pairing that involves not only the Watson-Crick A:U and G:C pairs but also the G:U pair. • miRNA-induced silencing complex (miRISC) – A ribonucleoprotein complex loaded with a specific miRNA that mediate translational repression of their mRNA target. The core proteins of the miRISC are Dicer, a class III RNase III, Argonaute (which bind to different classes of small noncoding RNAs, including microRNAs) and TAR RNA binding protein, a double-stranded RNA binding protein. • High-fat diet (HFD) – A diet-induced obesity model, that closely mimics the increasingly availability of the high-fat/high-density foods in modern society, which are main contributors to the obesity trend in human. • Transgenerational Inheritance – Correspond to the transmittance of epigenetic modifications (excluding DNA sequence changes) from one generation of an organism to the next one that affects the traits of offspring. In their stricter sense, the transgenerational inheritance occurs when a generation presents the trait and the epigenetic modification but never was exposed to the environmental challenges that induce such changes in their parents.

Summary Points • In modern societies, the consumption of highly caloric foods and sedentary lifestyle has increased the rates of obesity, T2DM, and MetS at a pace that reaches pandemic levels. • Metabolic syndrome corresponds to a cluster of several risk factors that increases the risk of other health problems, such as cardiovascular disease and diabetes. • miRNAs are small endogenous noncoding RNAs that typically control the expression of their target genes by imperfect base pairing to the 30 UTR of their messenger RNAs targets. • Nutritional components affect the expression and activity of endogenous miRNAs through different mechanisms, including modulation of critical enzymes of the miRNA biogenetic pathway or modulation of components of the miRISC.

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• Different individuals and populations possess a genetic burden, including mutations in miRNA genes, that made them either resistant or vulnerable to the development of MetS. • Mutations within the miRNA genes could potentially affect the processing or target selection of miRNAs by different means, including their transcription; primiRNA and pre-miRNA processing; and via miRNA–mRNA interactions. • Genetic polymorphisms that reside in the 30 UTR of miRNA target genes, which can eliminate an existing binding site, create an erroneous binding site, or affect binding affinity. • Epigenetic mechanisms may exacerbate the epidemic of MetS by first contributing to the development of MetS risk factors and then passing modifications on to the subsequent generation via parental inheritance. • The underlying mechanism by which environment shapes the organism is through epigenetic modifications, which involve DNA methylation, posttranslational histone modifications, and changes in miRNA levels. • The analysis of miRNA deregulation in MetS patients and animal models could help to develop better therapeutic strategies to improve the quality of life of the increasing population of MetS patients.

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Nur Duale, Oliwia Witczak, Gunnar Brunborg, Trine B. Haugen, and Birgitte Lindeman

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sperm DNA Methylation in Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rodent Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sperm Chromatin Structure in Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rodent Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sperm Noncoding RNAs in Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rodent Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definitions of Words and Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Paternal Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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N. Duale · G. Brunborg Department of Molecular Biology, Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway e-mail: [email protected]; [email protected] O. Witczak · T. B. Haugen Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Oslo, Norway e-mail: [email protected]; [email protected] B. Lindeman (*) Department of Toxicology and Risk, Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_53

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Abstract

Male obesity may have intergenerational and even transgenerational effects in mammals. Studies in rodents have revealed alterations in energy metabolism and disease susceptibility in offspring of obese males, pointing to sperm epigenetic modifications as probable causal factors. To date there is a paucity of studies examining obesity-related changes in the sperm epigenome, and the available epidemiological studies are limited. Upon fertilization, modifications to sperm nuclear and cytoplasmic factors, like RNAs, and to sperm chromatin are likely to influence early embryo gene expression. The available data on the sperm epigenome suggest that sperm DNA methylation status and small noncoding RNA expression patterns are susceptible to obesity-associated modifications. Very little information exists on potential diet-induced modification of sperm histones. Currently, the evidence is most convincing for the involvement of sperm RNA species from obese fathers in the modification of embryo development and offspring energy metabolism. Keywords

Obesity · Sperm · Epigenome · High-fat diet · sncRNA · miRNA · tsRNA · piRNA · DNA methylation · Sperm chromatin integrity · Histone · Protamine List of Abbreviations

CD DFI DMRs HFD m 2G m 5C miRNA piRNA RRBS SCSA sncRNA tsRNA

Control diet DNA fragmentation index Differentially methylated regions High-fat diet N2-methylguanosine 5-Methylcytidine MicroRNA PIWI-interacting RNA Reduced-representation bisulfite sequencing Sperm chromatin structure assay Small non-coding RNA tRNA-derived small RNA

Introduction Approximately 19% of the adult population across OECD countries are obese (OECD, Health at a Glance 2015). The high prevalence of human obesity is a major health concern due to its association with increased disease susceptibility. Several epidemiological studies suggest an association between obesity and reduced male fertility (Nguyen et al. 2007; Ramlau-Hansen et al. 2007). During obesity, adipocyte metabolism and the profile of adipokines secreted by adipose tissue are altered, thus modifying autocrine, paracrine, and endocrine signaling with

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implications also for spermatogenesis (Galic et al. 2010; Lim et al. 2014). Recently, there has been an increasing awareness that paternal obesity may have inter- and transgenerational effects. Epidemiological studies have shown an association between obesity in fathers and elevated BMI in offspring (Figueroa-Colon et al. 2000; Whitaker et al. 2000). In epidemiological studies, it is difficult to separate effects of genetic factors and shared environment from potential epigenetic hereditary factors. However, experimental studies in rodents suggest that paternal dietinduced obesity has significant negative effects on early embryo development and implantation (Mitchell et al. 2011; Binder et al. 2012a, b) and may lead to increased risk of obesity and altered metabolic programming in offspring (Ng et al. 2010; Fullston et al. 2013). Through epigenetic mechanisms, mammals are susceptible to changes in nutritional conditions. It is hypothesized that epigenetic modifications in sperm may influence embryo development and thus contribute to the observed paternal interand transgenerational effects. In support of the role of epigenetic modifications in paternal effects, two recent experiments using microinjection of obesity-associated sperm-derived RNAs into zygotes have shown changes in embryo gene expression and offspring metabolic phenotype (Grandjean et al. 2015; Chen et al. 2016). Furthermore, a recent cross-fostering experiment following parental pre-conceptional exposure to high-fat diet (HFD) and in vitro fertilization supports the hypothesis that sperm epigenetic changes confer an obesity-prone phenotype to offspring (Huypens et al. 2016). During spermiogenesis, the chromatin of haploid spermatids is remodeled to allow a high degree of compaction of the spermatozoal DNA. The DNA compaction process involves epigenetic chromatin modifications, guiding a successive replacement of nucleosomal histones with transition proteins and subsequently protamines. Disruption in the histone-to-protamine exchange process may be expressed as alterations in histone retention and chromatin protamine levels and is associated with both reduced fertility and increased sperm DNA fragmentation. Interestingly, both paternal nucleosomes (van der Heijden et al. 2008) and histone modifications can be transmitted to the offspring and possibly influencing gene expression in the early embryo (Carrell and Hammoud 2010). During spermatogenesis, the DNA methylation pattern changes (Rousseaux et al. 2005; Li et al. 2016). The dynamic changes of the DNA methylation pattern are important for the normal processes of spermatogenesis and fundamental for a successful pregnancy. Furthermore, epigenetic changes such as alterations in DNA methylation are associated with impairments in the histone-to-protamine transition (Schagdarsurengin and Steger 2016), suggesting that these measures are highly interconnected. In addition to DNA compaction, the extrusion of cytoplasm is critical for proper sperm maturation (Cooper 2011). However, some nuclear and cytoplasmic factors, including different RNA species, are retained and transferred to the zygote upon fertilization (Jodar et al. 2013). Lastly, it has been shown that factors in the seminal plasma may contribute to placental growth and affect fetal development (Bromfield 2014). This last aspect is outside the scope of the present review.

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In this chapter, we describe findings from human and rodent studies obtained through a PubMed database search, of all English language articles published up to August 2016, using the search words obesity or high-fat diet in combination with sperm and epigenetic, as well as selected references referred to in the publications retrieved. The studies identified that included analysis of epigenetic modifications in sperm from either obese humans or rodents, are described in the text and are listed in Table 1.

Sperm DNA Methylation in Obesity Most DNA methylation marks are erased during gametogenesis and fertilization. However, some marks, e.g., in imprinted genes, are resistant to the global methylation reprogramming in the embryo (Hackett et al. 2013; Wang et al. 2014). Imprinted genes are involved in regulation of early embryonic and fetal growth and are candidates for paternal modulation of the epigenome in offspring.

Human Studies In spite of a small sample size (10 obese vs 13 lean men), Donkin and co-workers showed a different DNA methylation profile of motile spermatozoa isolated by a swim-up procedure from obese men compared to normal weight men (Donkin et al. 2016). Using reduced representation bisulfite sequencing (RRBS), a total of 9,081unique genes were found to be differentially methylated between the two groups. The number and degree of CpG methylation level differences were higher in protamine-bound regions compared to histone-retained regions. Among the differentially methylated genes, several are involved in central control of appetite including melanocortin-4 receptor (MC4R), brain-derived neurotrophic factor (BDNF), neuropeptide Y (NPY), and cannabinoid receptor type 1 (CR1), cocaine and amphetamine regulated transcript (CART). Further, genes related to obesity and metabolism pathways, like fat mass and obesity-associated (FTO) gene, carbohydrate sulfotransferase 8 (CHST8), and SH2 binding domain-containing protein 1 (SH2B1) were also differentially methylated in obese men. The authors suggest that genes affecting energy metabolism and brain development are susceptible to germ cell epigenetic modulation in response to nutritional status. However, the potential consequences on the offspring epigenome are not known. Furthermore, in a group of six morbidly obese men, the authors investigated whether gastric bypass-induced weight reduction was associated with changes in sperm DNA methylation status. Semen samples were collected 1 week before, 1 week after, and 1 year after surgery. Already 1 week after surgery, the methylation status of 1509 genes was changed. One year after surgery, when the weight loss had stabilized and the BMI of the group had fallen from 42.6 to 33.9, 3910 genes were differentially methylated. This included several genes involved in obesity (transmembrane protein 18 (TMEM18), CHST8, SH2B1, BDNF, FTO, and MC4R). Since

sncRNA

DNA methylation sncRNA DNA methylation, imprinted genes Histone modification

Epigenetic modification DNA methylation piRNA expression Nucleosome positioning DNA methylation, imprinted genes

ChIP-sequencing analysis; H3 and H3K4me1 sncRNA sequencing

MBD-capture sequencing sncRNA sequencing Bisulfite sequencing

Bisulfite pyrosequencing

MNase sequencing

sncRNA sequencing

Method RRBS

Mouse (C57BL/6 J) HFD model (10% vs 60% fat)

No HFD-associated changes in DNA methylation of DMRs of seven imprinted genes

HFD associated with enriched H3 occupancy at GC-rich promotor in sperm. Diet-induced differential expression of H3K4me1 Diet-induced differential expression of several sperm tsRNAs and miRNAs. Several RNA modifications appeared to be sensitive to diet

Mouse (C57Bl/6 J) HFD model (10% vs 60% kcal from fat)

The spermatozoa from HFD-fed F0 and their F1 male offspring exhibited common DNA methylation pattern and some common piRNA expression changes

Model Epidemiological study 13 normal weight (20–25 kg/m2) and 10 obese men (30–40 kg/m2)

Epidemiological study. 44 normal weight (18.5 kg/ m2  BMI < 25 kg/m2) and 23 overweight/ obese men (BMI  25 kg/m2) Rat (Sprague-Dawley) HFD model (control chow vs two HFDs with 42 and 45% kcal from fat)

Main findings Difference in spermatozoal CpG methylation between obese and normal weight men Difference in expression of several piRNAs in spermatozoa between obese and normal weight men No difference in histone positioning in spermatozoa between obese and normal weight men Difference in DNA methylation patterns of multiple DMRs of imprinted genes between overweight/obese and normal weight men

Table 1 Sperm epigenetic modifications in obesity: List of human and rodent studies

Sperm Epigenome in Obesity (continued)

Chen et al. 2016

Terashima et al. 2015

de Castro Barbosa et al. 2016

Soubry et al. 2016

Reference Donkin et al. 2016

37 731

Quantitative RT-PCR

5-Methylcytosine ELISA-like assay Quantitative RT-PCR

Global DNA methylation miRNA

HFD-associated decrease in global DNA methylation in late-elongated spermatids Four miRNAs identified as differentially expressed in both the testis and epididymal sperm No HFD-induced difference in global DNA methylation in sperm Of 13 miRNAs determined to be differentially expressed in testis by RNA sequencing, eight were found to be differentially expressed also in sperm following qPCR analysis miRNA

Main findings Slight HFD-associated increase in sperm global DNA methylation level and increase in the methylation levels of satellite repeats. No observed HFD-associated change in methylation of 3 imprinted genes

Mouse (C57BL/6) HFD model (4.8% vs 22% fat) Mouse (C57BL/6) Western diet model (standard chow vs highsugar/high-fat diet)

Mouse (C57BL6) HFD model (6% vs 21% fat)

Model Rats (Sprague-Dawley) HFD model (12% vs 43–44% kcal from fat)

Binder et al. 2015 Grandjean et al. 2015

Fullston et al. 2013

Reference Youngson et al. 2015

RRBS reduced representation bisulfite sequencing, ChIP chromatin immunoprecipitation, sncRNA small noncoding RNA, MNase micrococcal nuclease, MDB methyl-CpG-binding domain, LC-MS liquid chromatography–mass spectrometry, RT-PCR reverse transcription polymerase chain reaction

Immunohistochemistry

Bisulfite pyrosequencing

Method LC-MS assay

Epigenetic modification Global DNA methylation DNA methylation, repetitive elements, DMRs Global DNA methylation miRNA

Table 1 (continued)

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significant changes in DNA methylation were observed already 1 week after surgery, the authors speculate that changes in sperm DNA methylation may occur during the last stages of sperm maturation, which is supported by the observation that DNA methyltransferases are expressed throughout human spermatogenesis (Marques et al. 2011). As part of the TIEGER (The Influence of the Environment on Gametic Epigenetic Reprogramming) male study, CpG sites in spermatozoa at 12 differentially methylated regions (DMRs) at regulatory regions of imprinted genes were examined by bisulfite pyrosequencing (Soubry et al. 2016). Twenty-three overweight/obese and 44 normal weight Caucasian men were included in the analyses. The semen samples were subjected to gradient centrifugation to select for motile spermatozoa. The study revealed changes in DNA methylation patterns at multiple DMRs in spermatozoa of overweight/obese men compared with men with normal weight. Spermatozoa of overweight or obese men had significantly lower DNA methylation levels at the maternally expressed gene 3 (MEG3), necdin (NDN), small nuclear ribonucleoprotein polypeptide N (SNRPN), and epsilon-sarcoglycan (SGCE)/paternally expressed gene 10 (PEG10). In contrast, the methylation level was higher at the MEG3-IG (intergenic) DMR and the long noncoding RNA gene H19 DMR of spermatozoa of overweight and obese men. Notably, in the group of overweight and obese men, DMRs at several of the studied genes had a tendency to be closer to the theoretically expected levels for methylation than in men of normal weight (closer to 0% methylation for maternally methylated DMRs and closer to 100% methylated for paternally DMRs). The authors speculate that the differences in DNA methylation may reflect normal epigenetic variation at the DMRs at a population level, making the phenotype flexible in response to environmental changes.

Rodent Studies De Castro Barbosa and co-workers studied how paternal HFD affected the epigenetic signature of rat spermatozoa isolated by a swim-up procedure. The study reported altered DNA methylation signatures in the spermatozoa of male rats, and their male offspring fed an HFD compared with spermatozoa from rats fed a control diet (CD) (de Castro Barbosa et al. 2016). The authors identified 18 loci that were differentially and commonly methylated in the spermatozoa of fathers and their offspring (Fig. 1) – 11 out of the 18 DMRs were hypermethylated, while seven DMRs were hypomethylated, compared to their respective controls. Several of these DMRs were located near the transcription start sites of their respective genes suggesting gene expression regulatory roles. Youngson and co-workers analyzed global DNA methylation status in the spermatozoa from rats by LC-MS assay and targeted locus-specific methylation levels of selected repetitive elements and differentially methylated regions (DMRs) by bisulfite pyrosequencing (Youngson et al. 2016). They observed that the global 5-methylC level in spermatozoal DNA from obese rats was slightly higher than in DNA from control rats. Further, they reported significant increases in the methylation levels of

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Sperm DNA methylation status 3.5 3.0 2.5

F0-rats

F1-rats

Fold Change

2.0 1.5 1.0 0.5 0.0 -0.5

Hypermethylated

Padi6

Gnl2

Pomc

D4A1F6

D4ACA2

Mfsd7

Tbrg4

Spsb4

Slc3a2

Irgq

Trpc1

Tbx3

Tmub1

Lrrc29

Csnk1e

Urm1

Artn

-1.5

Usp11

-1.0

Hypomethylated

Fig. 1 Changes in gene-specific DNA methylation in sperm from rats fed a HFD and their offspring. The data used to construct the graph are a reanalysis of supplementary data from de Castro Barbosa and co-workers (de Castro Barbosa et al. 2016) on DNA methylation levels upon HFD, at 18 loci that were differentially methylated in both fathers (F0) and their male offspring (F1), in comparison with the respective controls (CD). Fold changes of the methylation levels were calculated by dividing the level of F0/F1-HFD by the level of F0/F1-CD for each gene. Fold change >1, hypermethylated and fold change equol (IC50=3.5 μM) > genistein (IC50=15 μM) > daidzein (IC50>300 μM), whereas for ERβ binding to ERE, much lower doses of food ligands are needed in

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the order, coumestrol (IC50=0.025 μM) > genistein (IC50=0.03 μM) > daidzein (IC50=0.35 μM) > equol (IC50=0.4 μM) (Kostelac et al. 2003). These observations suggest that changes in dietary intake of ligands for NR can impact NR activation and phenotypic response. Third, NR harbor coregulatory binding domains, which function as targets for cofactors that enhance (NCoA) or repress (NCoR) transcriptional activity at target genes. Some of these cofactors possess DNA and histone-modifying properties. Therefore, their recruitment to NR brings about additional levels of regulation affecting chromatin organization. An excellent example of a NR cofactor that influences gene expression is p300, a histone acetyl transferase (HAT). In contrast, antagonist-bound NR cannot recruit coactivators; rather they recruit NR corepressors (i.e., NCoR1, NCoR2), histone deacetylase (HDAC), histone methyltransferase (HMT), and DNA methyltransferase (DNMT) modifying enzymes that reinforce transcriptional silencing (Gronemeyer et al. 2004). At the target gene, NR can act as monomers or form complexes as homodimers and heterodimers (Aranda and Pascual 2001; Gadaleta and Magnani 2014). In breast cancer biology, a classic example of NR homodimerization is the formation of an ERα/ERα complex that binds to ERE harbored in the estrogen-responsive cyclin D1 (CCND1) gene. However, some NR can form heterocomplexes with other NR. This is the case of the RXR, which interacts at target genes with the VDR and RAR, and for the AhR which forms a heterocomplex with the aromatic receptor nuclear translocator (ARNT). Also, NR can regulate gene expression through interactions with DNA-bound transcription factors rather than binding directly to DNA. In the case of the ERα, instead of binding directly to ERE, it physically interacts with DNA-bound transcription factors such as activator protein 1 (AP1), nuclear factor kB (NFkB), and specificity protein 1 (Sp1) (Gronemeyer et al. 2004).

Epigenetic Regulation Examples of epigenetic regulation include DNA methylation/demethylation at CpG islands and histone posttranslational modifications (e.g., acetylation, methylation, phosphorylation, etc.). Another important component of epigenetic control is changes in expression of noncoding RNA (ncRNA), which are not translated into proteins but influence gene expression at the transcriptional and posttranscriptional level (Baylin and Jones 2011). Unlike mutations, epigenetic modifications are reversible and play an important role in normal development of breast tissue. However, deregulation of the epigenome (i.e., the sum of all epigenetic changes) has been proposed as an “imprinting” mechanism that contributes to both hereditary and sporadic breast tumorigenesis (Baylin and Jones 2011). For example, epigenetic modifications directed by NR may contribute to loss of heterozygosity (LOH) in breast cancer gene 1 (BRCA1) mutation carriers as well as silencing of wild-type BRCA1 alleles in sporadic breast tumor patients.

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Histone Modifications In general, binding of agonists to NR favors the removal of NCoR and the recruitment of NCoA of which cAMP-response element (CRE)-binding protein (CBP) and p300 are well-known prototypes. Both CBP and p300 function as HAT and place acetylation moieties on lysine 9 of histone 3 (H3K9Ac) and lysine 20 of histone 4 (H4K20Ac). These histone acetylation changes are associated with transcriptional activation. For example, ERα activates pS2 transcription via recruitment of coactivator proteins with HAT properties (Métivier et al. 2003) including steroid receptor coactivator 1 (SRC1). The acetylation and dimethylation of H3K14 and histone 4 at arginine 3 favor the recruitment of polymerase II (Pol II) and pS2 promoter activation. Another histone modification that favors transcriptional activation by NR is methylation of H3K4 (H3K4me) (Lee et al. 2009) by the H3K4methyltransferase mixed lineage leukemia 3 (MLL3) (Yokoyama et al. 2008) and phosphorylation of H3 at serine 10 (H3Ser10P). The latter modification impedes the binding of heterochromatin protein 1 (HP1) to trimethylated histone 3 at lysine 27 (H3K27me3) and induces chromatin relaxation (also known as nucleosome opening) and transcriptional activation (Dormann et al. 2006). Elevations of H3Ser10P and H3K9Ac are modifications that usually occur during the initial stages of ERα-mediated transcription (Li et al. 2011). An example of histone modification related to breast cancer development is acetylation of H3 at lysine 23 (H3K23Ac). Increased levels of H3K23Ac have been associated with shorter overall survival of breast cancer patients with HER2-positive tumors. The latter example points to H3K23Ac as a biomarker of HER2-positive breast carcinogenesis (Ma et al. 2016). In the presence of antagonists, NR bind to cofactors that exert HMT, HDAC, histone demethylase (HDM), and phosphatase enzymatic activities leading to transcriptional repression (Rosenfeld et al. 2006). Examples of repressive cofactors include the Sin 3-member A (Sin3A) and silencing-mediator for retinoic and thyroid (SMRT) factors, which physically contact HDAC and repress transcription. Other NCoR comprises the ATP-dependent switch/sucrose non- fermentable (SWI/SNF) nucleosome factor. Both ERα and ERβ bind to mixed lineage kinase 3 (MLK3) promoter and recruit various nuclear receptor corepressors (NCoR, SMRT), leading to downregulation of MLK3 expression (Viswakarma et al. 2017). Inhibition of MLK3 kinase activity has been shown to favor survival of ER-positive breast cancer cells. Expression of NCoR is downregulated in the more aggressive breast cancer tumors, and its expression associates with that of thyroid receptor-β (TRβ). The latter induces expression of NCoR, whereas SMRT silences NCoR gene expression through HDAC3 recruitment (Hestermann and Brown 2003). The loss of NCoR results in higher levels of H3K9me3 and H3K27me3, which associate with HP1 at the NCoR promoter, thus inducing heterochromatization, i.e., a stably silenced chromatin state, at the NCoR gene. These observations have been used to formulate an epigenetic hypothesis for enhancing NCoR expression, possibly through TRβ, to prevent breast cancer (Martínez-Iglesias et al. 2016).

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Certain histone modifications such as H3K9me3, H3K27me3, and trimethylated histone 4 at lysine 20 (H4K20me3) are markers of heterochromatin (repressed state). The recruitment of HP1 to histones that are hypermethylated (i.e., H3K9me3) contributes to transcriptional repression (Hiragami-Hamada et al. 2001). A further modification of histones that brings about transcriptional silencing is demethylation by the HDM lysine-specific demethylase 1 (LSD1), which specifically demethylates H3K4me and H3K4me2. The latter usually serve as activator marks (Yokoyama et al. 2008). The biotin-ligase holocarboxylase synthetase (HLCS) (Xue et al. 2013) forms a repression complex with the eukaryotic histone methyltransferase 1 (EHMT1), NCoR, and HDAC1. The HLCS-mediated biotinylation of K161 in EHMT1 facilitates the interaction between HLCS and EHMT1 (Li et al. 2013a, b) and biotinylation of H3 and H4 (Bao et al. 2011). Although biotinylation of histones is a rare posttranslational modification, the biotin-dependent repression of repeats has been proposed as a mechanism that contributes to increasing genome stability (Chew et al. 2008). In breast cancer tissue, an important example of interactions between NR and NCoR leading to histone modifications is the one between ERα and ERβ and between PR and ERα. In normal breast tissues, the expression of ERβ is generally higher than ERα. This is reversed (i.e., ERα expression >ERβ expression) during breast tumor development. Therefore, epigenetic dysregulation of the two ER genes in favor of ERα is predicted to contribute to breast cancer. ERβ causes ESR1 (ERα) downregulation through ERβ-Sp1 protein-protein interactions and the recruitment of NCoR on the ESR1 promoter region, accompanied by hypoacetylation of H4, and dissociation of Pol II (Bartella et al. 2012). Therefore, through NCoR, ERβ downregulates epigenetically ERα (ESR1) gene expression, thus preventing ERα’s role in induction of genes (i.e., CCND1) that enhance cell growth and ultimately support breast tumor development. With respect to PR, higher levels PR-B isoform have been shown to downregulate ERα through binding of PR-B to a half-PRE site harbored in the ESR1 gene, with hypoacetylation of H4 and displacement of RNA Pol II (De Amicis et al. 2009). “Squelching” or competition for factors that acetylate histones (e.g., p300, SRC1) is a mechanism that leads to epigenetic silencing. For example, by removing p300 from ERα complexes, the activated AhR has been shown to hamper estrogenmediated transcription of BRCA1 (Hockings et al. 2006) and other genes (Ohtake et al. 2003). Another example is the repression of the VDR (25OHD31α) gene by vitamin D, which upon binding to VDR causes the removal of p300 and association of HADC with the 25OHD31a promoter. The latter epigenetic response is part of a feedback mechanism through which accumulation of vitamin D hampers its own synthesis (Murayama et al. 2004). In the case of the prostaglandin synthase 2 (PTGS2) gene encoding for cyclooxygenase 2 (COX2) (Yoon et al. 2003), transcriptional silencing by the ligand-bound peroxisome proliferator-activated receptor-γ is possible through stabilization of a complex containing NCoR and HDAC3. This hampers PTGS2 induction by the transcription factor NFκB (Ogawa et al. 2005).

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DNA Methylation Methylation of cytosine bases next to a guanine (CpG) is a DNA modification that is linked to epigenetic silencing; conversely removal of the methyl group from a CpG is commonly related to epigenetic activation (Miranda and Jones 2007). Clusters of CpG are commonly found in DNA regions proximal to transcriptional start sites. These CpG islands represent regulatory regions, whose methylation or demethylation influences negatively or positively transcriptional activity. Two types of DNMT participate in the process of CpG methylation: DNMT1 contributes to maintenance of CpG methylation signatures as DNA is replicated during cell division. DNMT1 along with methylated cytosine-binding protein 2 (MeCP2) associates with HLCS (Xue et al. 2013). DNMT3a and DNMT3b contribute to de novo CpG methylation (Niehrs 2009). Physical interaction between DNMT, HMT, and HDAC enzymes assures coordination of multiple enzymatic modifications (DNA methylation, histone methylation and deacetylation) associated with transcriptional repression. For various genes regulated by ERα and VDR, waves of CpG methylation associate with recruitment of MeCP2, SWI/SNF, DNMT1, and DNMT3a/3b. These are followed by waves of CpG demethylation (Métivier et al. 2008; Wilks et al. 1982; Thomassin et al. 2001; Kouzmenko et al. 2010). Therefore, there is a great interest to understand how NR influence gene expression at target genes through histone modifications and CpG methylation/demethylation and, ultimately, how acute and chronic exposure to food ligands influences NR activity and breast cancer development.

Food Ligands and Epigenetic Regulation by NR A working hypothesis of this chapter is that diets differing in the content of specific or combinations of food ligands for NR influence epigenetically breast tumorigenesis. This hypothesis finds support in the observation that women who reside in Asia and consume regularly soy products tend to have a lower risk of breast cancer compared to women living in North America and with lower soy intake (Chen et al. 2014). Conversely, women of Asian background acquire a high risk of breast cancer as they migrate to Western regions where the consumption of soy foods is lower. Also, markedly higher total estrogen/estrogen metabolite concentrations are found in postmenopausal Asian-American women than in Shanghai women (Moore et al. 2016). We acknowledge that dietary agents exert pleiotropic effects through non- genomic pathways, i.e., through membrane-bound receptors (e.g., HER2, VDR, ER, and others) and signaling pathways (e.g., phosphoinositide 3-kinase, PI3K). Also, we acknowledge that food ligands of NR may indirectly influence the epigenetic process by altering the expression/recruitment of NCoA and NCoR and chromatin-modifying proteins (Zempleni et al. 2013). For example, in the developing uterus, genistein, an isoflavone found in soy and ligand of the ER, induces the PI3K/AKT pathway. The latter inhibits the HMT enhancer of zeste

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homolog 2 (EZH2) through phosphorylation leading to reduced levels of H3K27me3, which is an established repressive histone mark placed by EZH2 (Greathouse et al. 2012). Therefore, the effects of genistein and other food components on gene expression may be independent (i.e., non-genomic) of their ability to physically interact with NR. Also, changes in the cellular pool of methyl and acetyl donors are expected to influence epigenetic regulation. For example, p300- mediated acetylation, and SET and MYND domain-containing 2 (SMYD2)-mediated methylation, of ERα protein enhances its transcriptional activity at estrogen-responsive genes (Zhang and Ho 2011). Therefore, dietary patterns (e.g., Mediterranean, Asian, Western, etc.) that differ in the available supply of acetyl (i.e., acetyl CoA) and methyl (i.e., methionine, choline, folate) donors for modifications of NR, histones, and DNA may exert epigenetic influences and modify endpoints of breast cancer risk. As a result, an overall understanding of interactions between dietary compounds and NR on epigenetic regulation requires consideration of both genomic (i.e., nuclear) and non-genomic signals (Zhang and Ho 2011). Importantly, NR have promiscuous binding sites for dietary ligands.

Estrogen Receptor Two isoforms of the ER have been isolated, ERα and ERβ, which share 56% protein homology. However, their respective genes (ESR1 and ESR2) are not redundant and have different expression patterns and functions. ERα is mostly found at ERE elements, whereas ERβ targets mostly AP1 sites. The ERα induces growth and survival of breast epithelial cells. Gene targets for activation by the ERα include those involved in growth factor (insulin-like growth factor-binding protein 4, IGFBP4, progesterone (PRG), cell cycle [CCND1, cyclin G2 (CCNG2), cyclin E (CCNE), c-MYC]), prostaglandin (prostaglandin E synthase, PTGES), and proteolysis (cathepsin D, CTSD) signaling (Lin et al. 2004). Conversely, the ERβ has splice variants generating multiple isoforms (full length ERβ1, β2, β4, and β5) with growth inhibitory properties (Oseni et al. 2008). ERβ opposes the actions of ERα through repression of c-MYC and activation of p27 and transforming growth factor β (Haldosén et al. 2014). The expression of ERβ is usually associated with less aggressive breast cancer, and ERβ ligands are considered to have positive prognostic effects (Tan et al. 2016). The ESRS2 gene is itself a target for epigenetic repression through promoter methylation, whereas inhibition of DNMT and HDAC activities reestablishes ERβ expression. The methylase histone-lysine N-methyltransferase SET domain 7/9 (SET7/9) methylates the ERα at lysine 302 (Subramanian et al. 2008). The methylation of ERα by SET7 stabilizes the ERα protein and is necessary for the efficient recruitment of ER-containing complexes to target genes. The agonist-bound ERα forms complexes with p300, p160, HMT, and LSD1. In contrast, ERα bound to antiestrogens (e.g., tamoxifen) recruits corepressors (i.e., NCoR1 and SMRT) and HDAC enzymes leading to repression of transcription (Fig. 1). Interestingly, expression of ERα and NCoR1 is reduced in invasive breast tumors (Kurebayashi et al. 2000) suggesting

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Fig. 1 Proposed models of epigenetic regulation of ERα by food components. Epigenetic silencing of ESR1 (ERα) via CpG methylation is reversed by luteolin and genistein.

H3K27me3

NCoR1 DNMT

Luteolin Genistein

+1 ESR1

CpG methylated (Inactive)

Luteolin Genistein

HMT ERβ

GRIP1

+1 CCND1 CpG methylated C-MYC

DNMT

(Inactive)

Fig. 2 Epigenetic repression of oncogenes by ERβ. Genistein recruits ERβ to estrogen-inducible genes (i.e., CCND1, c-MYC) and represses transcription through recruitment of cofactors (GRIP1) and histone methyltransferases (HMT) such as SUV420H1.

that alterations in epigenetic regulation at the ESR1 and NCoR1 genes may contribute to the development of ER-negative and more aggressive tumors. Conversely, transcriptional activity of ERβ is usually associated with repression of ERα target genes through dominant-negative competition for NCoA and/or recruitment of NCoR complexes. For instance, genistein-bound ERβ recruits the glucocorticoid receptor interacting protein 1 (GRIP1), which in turn prevents induction of ERα-responsive promoters by estrogen (An et al. 2001). Isoflavones have 10 to 30-fold higher binding affinity for ERβ compared with ERα in the order: genistein > biochanin A > daidzein. By creating an activator function 2 (AF2) surface, binding of isoflavones to ERβ stabilizes interactions of ERβ with NCoA. Compared to ERα homodimers, heterodimers of ERβ with ERα increase recruitment of NCoR and lower transcriptional activity at target genes (Fig. 2). Studies have evaluated the in vitro and in vivo epigenetic effects of genistein on expression of ERα in ER-negative breast cancer cells. Genistein may reactivate ERresponsive genes by stimulating the recruitment of p300 (Hong et al. 2004). Silencing of p300 expression has been shown to abrogate the stimulation of ERβ by genistein (Bouchal et al. 2011). The anticancer effects of genistein have been linked to activation of BRCA1 via CpG demethylation (Bosviel et al. 2012). Genistein may mimic the stimulatory effects of estrogens on BRCA1 (Hockings et al. 2006; Romagnolo et al. 1998) by inducing the recruitment of cofactors (i.e., p300, SRC1) for the ERα and acetylation of histone 4 (H4Ac) associated with BRCA1 (Jeffy et al. 2005). The preventive actions of genistein against breast cancer have been related to reactivation of ERα expression and increased response to tamoxifen. The latter effects were enhanced by the combination of genistein plus a HDAC inhibitor and repression of DNMT3b expression (Li et al. 2012). Genistein was found to decrease DNA methylation through direct interaction with the DNMT1 catalytic domain and inhibition of DNMT1 expression (Xie et al. 2014).

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Analyses of ER transactivation kinetics in ERα-positive breast cancer cells (MCF-7, T47D) revealed that coumestrol (10 nM) and genistein (100 nM) were more effective compared to apigenin and daidzein, which required higher doses (1–10 μM) to elicit a response comparable to that of estrogen (Lecomte et al. 2017). A biphasic response was observed for apigenin. At low doses (1 μM), apigenin enhanced transcriptional activity by promoting interaction between ERα and its coactivator amplified in breast cancer 1 (ABC1) and cell growth. No growth stimulation was observed for tamoxifen (TAM) or fulvestrant-resistant MCF-7 cells. Conversely, at higher doses (>10 μM), apigenin inhibited ERα and ABC1 expression, and ERα activation, as well as repressed cell growth of MCF-7 and TAM- and fulvestrant-resistant cells (Long et al. 2008). Some of the anticancer effects of flavones (i.e., apigenin, chrysin, and luteolin) have been related to a decrease in DNMT enzyme activity and CpG demethylation. Moreover, a decrease in HMT activity and EZH2 protein expression, as well as level of H3K27me3, have been observed in histones isolated from cancer cells treated with these plant flavones (Kanwal et al. 2016). Other studies with apigenin (Tseng et al. 2017) have corroborated the hypothesis that dietary flavones exert protective effects by reversing epigenetic processes associated with breast tumor development. Combinations of epigallocatechin gallate (EGCG) and sulforaphane (SFN) were reported to be effective for epigenetic reactivation of ERα, which in turn increased TAM-dependent chemosensitivity in vitro and in vivo (Li et al. 2017). Similarly, the treatment of ERα-negative MDA-MB-231 cells with the combinations of EGCG and SFN has been shown to be effective for the reactivation of silenced p21 and klotho (KL) through active chromatin modifications involving acetylation of histones (Sinha et al. 2015). A food ligand of the ER is the grape phytoalexin resveratrol although it binds to ERα and ERβ with much lower affinities compared with estrogens (Bowers et al. 2000). Studies found that resveratrol antagonized CpG methylation of the phosphatase and tensin homolog (PTEN) gene in ERα-positive breast cancer cells (Stefanska et al. 2012). Interestingly, preclinical evidence suggests that calorie restriction (CR) may lower the risk of HER2-positive breast cancer. In a mouse model, CR decreased ERα and ERβ expression associated with increased CpG methylation of the CCCTC-binding region of ESR1 and ESR2. Increased DNMT1 in overweight and diet-induced obesity mice implied that transition to a CR diet may alleviate epigenetic insults (i.e., global hypermethylation) due to excessive caloric intake (Rossi et al. 2017).

Progesterone Receptor The PR plays an important role in breast cancer development and progression and is usually considered a marker of ERα functionality (Chuffa et al. 2017). In the cytoplasm, the complex ERα-PR-B activates the SRC/p21/RAS pathway leading to phosphorylation and activation of the PR, although it has been observed that PR

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can also be activated in the absence of ERα. The presence of both activated PR and ERα at the CCND1 promoter contributes to transcriptional activation of CCND1. Coincident nuclear localization of ERα and PR has been observed in human breast tissues (Cicatiello et al. 2004). Therefore, it is believed that combination therapies utilizing antiestrogen and antiprogestin compounds may be more beneficial than chemotherapeutic adjuvants alone. Lack of expression of PR is indicative of worse prognosis and resistance to endocrine therapy (Abdel-Hafiz and Horwitz 2015). Reduced expression of PR correlates with hypermethylation of the PRG promoter (Hansberg-Pastor et al. 2013). HDAC treatment leads to reactivation of PRG, although it is unclear whether this is a direct or indirect effect mediated by ERα. The two PR isoforms, A and B, are coexpressed in normal adult breast and bind as homo- or heterodimers, but their ratio is altered in breast cancer tissues. Specifically, higher PR-A isoform expression is correlated with poor disease-free survival after tamoxifen treatment. Methylation of PRG-A, but not PRG-B, was also found in tamoxifen-resistant cases, although the finding has not been confirmed in some studies reporting that the majority (74%) of PR-negative tumors were not methylated (Pathiraja et al. 2011). The PR forms nuclear complexes with AP1, signal transducer and activator of transcription 3 (STAT3), and HER2. This complex induces the expression of CCND1 to drive growth of breast cancer cells in vitro and in vivo. The PR interacts with STAT5 at PRE sites in the STAT5 gene in a feed-forward signaling loop (Leehy et al. 2017). Only a few studies have evaluated the epigenetic impact of PR antagonism by food components on breast cancer development. Dietary compounds that have been proposed to modify cancer risk through antagonism on the PR include luteolin, quercetin, and apigenin. For example, luteolin was found to inhibit progestindependent induction of PR-mediated transcriptional activity (Nordeen et al. 2013) (Fig. 3). Given that progestins have been shown to select for a population of drugresistant, basal-like tumor cells, more research is needed to unravel whether or not targeting of the PR with food ligands impacts on breast cancer risk (Kabos et al. 2011). Studies compared differentially methylated responsive target genes with DNA methylation changes in different clinical subtypes of breast cancer patients in The Cancer Genome Atlas (TCGA). Investigators found that withaferin A, Luteolin Withaferin A

HDM

H3K9me PR ERa +1

CCND1 CpG unmethylated (Active)

Fig. 3 Cross talk between ERα and PR at target genes. ERα and PR form complexes at target genes (i.e., CCND1) and induce transcription associated with recruitment of histone demethylases (HDM) such as JMJD2C. Antagonism on the PR by luteolin and withaferin A blocks transcription via CpG methylation.

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a plant-derived steroidal lactone commonly used in Ayurvedic medicine, silenced HER2/PRG/ESR-dependent gene expression programs to suppress aggressive breast tumor characteristics in favor of luminal breast cancer hallmarks, leading to improved therapeutic sensitivity (Szarc Vel Szic et al. 2017).

AhR Historically the function of the AhR has been investigated in the context of its role as a regulator of phase I and phase II detoxification enzymes in response to environmental xenobiotics. These include dioxins and polycyclic aromatic hydrocarbons, as well as metabolites of fatty acid metabolism such as AA and PG (Denison and Nagy 2003; Romagnolo et al. 2010). Phase I enzymes include the cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1) and subfamily B, polypeptide 1 (CYP1B1). These enzymes generate intermediates which are further metabolized by phase II enzymes such as NAD(P)H:quinine oxidoreductase and UDP- glucuronosyltransferase-1A6 (Romagnolo et al. 2010). Regulation of expression of phase I enzymes occurs through recruitment of the AhR complexes to xenobiotic response elements (XRE=50 -GCGTG-30 ). When not bound to a ligand, the AhR resides in the cytosol with heat shock protein 90 and other cofactors (e.g., p23). However, upon ligand binding, the AhR moves into the nucleus where it complexes with ARNT. Three different isoforms (ARNT1, ARNT2, and ARNT3) have been identified for ARNT. The AhR/ARNT heterocomplex then regulates transcription through direct binding to XRE. In addition, the AhR can influence transcription through direct binding to the ERα, which harbors an AhR-binding domain. Studies showed that in the absence of ligands, ER-mediated transactivation of cathepsin D (CTPSD) involved formation of a multiprotein ER/Sp1- AhR/ARNT complex. These results illustrated an example of a possible endogenous role for the AhR/ARNT in the absence of AhR agonist and associated with ER-mediated transcriptional transactivation (Wang et al. 1998). Therefore, in the absence of ligands, the AhR may cooperate with the ER in the activation of estrogen-responsive genes (i.e., CTSD) (Safe et al. 1998). Conversely, in the presence of agonists, the AhR antagonizes ER transactivation at target genes. An example of such dichotomy for AhR function and related to breast cancer is the BRCA1 gene. In the absence of ligands, the AhR cooperates with the liganded ER to induce BRCA1 transcription (Hockings et al. 2006). In contrast, when bound to ligands, the AhR associates with XRE harbored in the promoter and intervening sequence between exon 1a and exon 1B of the BRCA1 gene. This represses ERα-mediated transactivation and inhibits BRCA1 expression. Repressive epigenetic changes at the BRCA-1 gene associated with AhR activation include hypermethylation of a CpG island flanking the BRCA1 transcription start site of exon-1a; recruitment of DNMT1, DNMT3a, and DNMT3b; histone deacetylation; recruitment of HDAC1; and histone methylation (H3K9me3) (Papoutsis et al. 2010).

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Many dietary compounds bind to the AhR. These include flavones, flavonols, flavanones, isoflavones, catechins, and phytoalexins. The antagonistic IC50 activity of flavones (e.g., chrysin, baicalein, apigenin, and luteolin), flavonols (e.g., quercetin, kaempferol), phytoalexin (resveratrol), and flavanones (naringenin) against transformation of the AhR is in the low μM range (~2.0–6 μM). However, this is much lower for the flavonol galangin (0.2 μM) suggesting that foods containing this compound (Alpinia officinarum, i.e., lesser galangal, and propolis) may be effective for regulation of AhR activity. Conversely, the antagonistic activity for the AhR by isoflavones (i.e., daidzein, genistein, IC50 > 50 μM), catechins (i.e., EGCG, ~35 μM), and indoles (30 -diindolylmethane, ~50 μM) is considerably higher suggesting that the potential cancer preventative effects of these subclasses of flavonoids may not be related to antagonism at physiological doses toward the AhR. Caffeic acid inhibits transactivation and nuclear localization of the AhR, while curcumin inhibits the expression of CYP1A1 and CYP1B1 and augments the cellular levels of H4K16Ac and H3K18Ac. Corn oil, along with prolonged exposure to estrogen or overexpression of HER2, may cooperate with AhR activation in breast cancer development. In rodent models, the activation of the AhR in mammary epithelial cells was paralleled by induction of c-MYC and EZH2, repression of miR143/145, and hypermethylation of p53-binding sites on ephrin type-B receptor B3 (EPHB3) and tripartite motif-containing 6 (TRIM6) genes. These results suggested that epigenetic deregulation of p53 target genes by the AhR may contribute to disruption of cell cycle control and tumorigenesis (Locke et al. 2015). Interestingly, HER2 overexpression may induce AhR activation without exogenous ligands through signaling pathways mediated by mitogen-activated protein kinase kinase (MEK) and extracellular signal-regulated kinase 1 (ERK). In MCF10AT1 breast cancer cells overexpressing AhR, epigenetic repression of Wnt inhibitor factor 1 (WIF1) through CpG hypermethylation was reported to induce the Wnt pathway (Wu et al. 2011). In contrast, the depletion of the AhR in ERα-negative MDA-MB-231 breast cancer cells reduced the expression of factors involved in cell growth, tryptophan metabolism, multidrug resistance, and angiogenesis (Goode et al. 2014). Our laboratory has investigated the epigenetic impact of AhR activation and antagonism in breast cancer cells. The cotreatment of breast cancer MCF-7 cells with 30 -diindolylmethane (10 μM) abrogated the dioxin-induced recruitment of the AhR and histone acetylation (AcH4) to the PTGS2 promoter and induction of COX2 mRNA and protein (Degner et al. 2009). Also, we found that activation and recruitment of the AhR to the BRCA1 promoter hampered estrogen- dependent stimulation of BRCA1 transcription and protein levels. These inhibitory effects were paralleled by reduced occupancy of ERα, H4Ac, and AcH3K9. Conversely, the treatment with the AhR ligand tetra-chloro-dibenzo-dioxin was found to stimulate the association of mono- methylated-H3K9, DNMT1, and methyl-binding domain protein-2 (MBD2) with the BRCA1 promoter. The AhR-dependent repression of BRCA1 expression was reversed by small interference for the AhR and DNMT1, or pretreatment with resveratrol, which also reduced dioxin-induced DNA strand breaks (Papoutsis et al. 2010). At physiologically relevant doses (1 μM),

1054 Fig. 4 Proposed epigenetic model of BRCA1 repression by AhR. Recruitment of the AhR-ARNT heterocomplex facilitates recruitment of EZH2, methylation of histones, and repression of BRCA1 via CpG methylation.

O. I. Selmin et al. Resveratrol

EZH2 H3K9me3 ARNT DNMT

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+1 BRCA1

CpG methylated (Inactive)

resveratrol antagonized the AhR-dependent repression of BRCA1 protein, BRCA1 CpG methylation, and the association of methyl-binding protein 2 (MBD2), H3K9me3, and DNMT (1, 3a, and 3b) with the BRCA1 promoter (Papoutsis et al. 2012) (Fig. 4). The effects of resveratrol against AhR could be due to formation of resveratrol/AhR complexes with reduced binding affinity for XRE harbored in the BRCA1 gene (Papoutsis et al. 2010) as well as agonism toward the ERα (Gehm et al. 1997) and cofactors (e.g., p300) (Hardy and Tollefsbol 2011). Our in vitro human breast cancer cell studies with resveratrol were extended to in vivo rodent models of mammary tumorigenesis. The in utero activation of the AhR increased in mammary tissue of rat female offspring the number of terminal end buds and reduced BRCA1 expression coupled with increased occupancy of the Brca1 promoter by DNMT1, CpG hypermethylation of the Brca1 promoter, and higher expression of Ccnd1 and cyclin-dependent kinase 4 (Cdk4). These changes were partially overridden by in utero pre-exposure to resveratrol, which stimulated the expression of the AhR repressor and its recruitment to the Brca1 gene (Papoutsis et al. 2015). Mammary tumors induced in adult rats with the AhR antagonist DMBA had reduced BRCA1 and ERα expression, higher Brca1 promoter CpG methylation, increased expression of Ahr and its downstream target Cyp1b1, and higher proliferation markers Ccnd1 and Cdk4. In human UACC-3199 cells with hypermethylated BRCA1 gene, low BRCA1 expression was paralleled by constitutive high AhR expression, whereas the treatment with the AhR partial antagonist α-naphthoflavone rescued BRCA1 and ERα expression while enhancing preferential expression of CYP1A1 compared to CYP1B1 mRNA. Interestingly, in human triple-negative breast tumors that exhibited constitutive high expression of AhR, BRCA1 promoter CpG methylation was increased compared to luminal (LUM)-A, LUM-B, and HER2positive breast tumors (Romagnolo et al. 2015). These cumulative results support the hypothesis that epigenetic silencing of BRCA1 by the AhR may be preventable with food antagonists such as resveratrol. They also provide the molecular basis for the development of dietary strategies against ER-negative breast tumors. In keeping with this hypothesis, we recently reported that genistein exerted dose- and timedependent preventative effects against AhR-dependent downregulation of BRCA1 while reducing DNMT1 and CCND1 expression. Genistein and EGCG lowered BRCA1 CpG methylation and cell proliferation associated with increased p53 expression. In ERα-negative breast cancer UACC-3199 cells, genistein and α-naphthoflavone reduced BRCA1 and ESR1 (ERα) CpG methylation, CCND1 expression, and cell growth while inducing BRCA1 and CYP1A1 (Romagnolo et al. 2017). Animal models of AhR-induced mammary carcinogenesis showed

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that early-life activation of the AhR led to tumor development and this process was reduced by supplementation with extra-virgin olive oil (EVOO) (Manzanares et al. 2015). Compared to corn oil, EVOO increased global DNA methylation in a rodent model of mammary tumors and was protective by reducing number of tumors and improving latency. Moreover, the high corn oil diet increased CYP1A1 expression. The AhR seemed to be involved in this upregulation. Proposed mechanisms for the protective effects of EVOO include induction of apoptosis, reduction of DNA damage, and increase in expression of phase II detoxification enzymes. In vitro studies using BC JIMT-1 cells, which are resistant to several anticancer drugs, indicated that EVOO reduced mitosis and increased cellular levels of H3K18Ac. Expression of growth arrest and DNA damage 45 (GADD45), a kinase involved in histone acetylation, was also increased.

Vitamin D Receptor About 3% of the human genome is targeted by the VDR, with ~750 responsive genes (Bouillon et al. 2008). Research evidence from preclinical and epidemiological studies suggests vitamin D and the VRD exert protective effects against breast cancer (Bandera Merchan et al. 2017). This information has far-reaching impact for breast cancer prevention given the widespread vitamin D deficiency in the general population. Breast cancer patients with vitamin D deficiency tend to have a worse prognosis. Cholecalciferol (1,25(OH)2-D3) is the active form of vitamin D binding to the VDR, which forms heterodimers with RXR at VDRE harbored in target genes. At consensus VDR elements (VDRE), VDR occupies the 30 halfelement (A/GGTTCA), whereas the RXR resides on the 50 half-site (A/GGGTCA) (Haussler et al. 2013). Multiple copies and variations in base sequence of VDRE increase flexibility of action of the VDR/RXR complex at target genes and in different tissues. In addition to genes involved in bone metabolism and calcium transport, cancer-related target genes for transcriptional activation by the VDR/RXR complex include p21 and forkhead box O1 (FOXO1). Transcriptional activation steps by VDR/RXR include sequential recruitment of HAT cofactors, such as SRC1, CBP/p300, and pCAF, and factors involved in ATP-dependent chromatin remodeling, such SWI/SNF. This is the case for activation by VDR/RXR of the cytochrome P450, family 24, subfamily A, polypeptide 1 (CYP24A1) gene, which encodes a factor involved in degradation of 1,25(OH)2- D3 (Deeb et al. 2007). In contrast, the cholecalciferol (1,25(OH)2-D3)-bound VDR/XRX heterocomplex represses transcription of the cytochrome P450, family 27, subfamily B, polypeptide 1(CYP27B1) through recruitment of HDAC, Sin3A, NCoR, DNMT, and MBD (Murayama et al. 2004). The repression of CYP27B1 expression prevents 1αhydroxylation of vitamin D3 by the CYP27B1 enzyme. On the other hand, the induction of the CYP27B1 expression is triggered by the parathyroid hormone through CpG demethylation of the CYP27B1 promoter (Kim et al. 2012; Leuenberger et al. 2009). In MDA-MB231 breast cancer cells, hypermethylation of the CYP27B1 gene leads to its silencing, which can be reversed by treatment with

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5-aza-20 -deoxycytidine (5-aza). Treatment of cancer cells with 1,25(OH)2-D3 leads to phenotypic changes including cell maturation and differentiation and expression of adhesion proteins (Pendás-Franco et al. 2007). Several food VDR ligands have been identified and include omega-3 docosahexaenoic (DHA) and eicosapentaenoic acid (EPA), omega-6 linoleic acid (LA) and AA, the vitamin E derivative c-tocotrienol (Haussler et al. 2008), lithocholic acid (LCA), and curcumin (Bartik et al. 2010). The latter is a turmericderived polyphenol found in curry. LCA is produced from liver-derived chenodeoxycholic acid by the action of gut bacteria. Thus, VDR binds to these food ligands and metabolites, albeit with four orders of magnitude lower than that of 1,25(OH)2-D3. More studies are needed to examine whether adequate concentrations of these food compounds and metabolites can be reached in breast tissue to regulate the VDR at target genes. This is of particular interest for EPA and DHA, given their proposed role in prevention of breast cancer. The treatment of the triple-negative breast cancer MDA-MB-231 cells with 1,25(OH)2-D3 was found to induce differentiation associated with reduced DNA methylation of the E-cadherin (CDH1) gene (Lopes et al. 2012). The E-cadherin molecule prevents invasion and metastasis of malignant cells. The presence of CpG islands in the VDR gene suggests this gene is also subject to feedback regulation through epigenetic mechanisms. In breast tumors, 65% of CpG comprised in exon 1 of the VDR were found to be methylated compared with only 15% in normal breast tissue (15%) (Marik et al. 2010). Some of the antiestrogenic effects of 1,25(OH)2D3 in breast cancer cells may be explained by lower levels of PGE2, which induces aromatase transcription. Silencing of the VDR accelerates mammary tumor growth and metastasis. Blood levels of vitamin D lower than 27 ng/ml were associated with risk of breast cancer in postmenopausal women. The risk was abolished with levels of 1,25(OH)2-D3 equal to 35 ng/ml (Bauer et al. 2013). In ERα-positive breast cancer cells, 1,25(OH)2-D3 was reported to repress expression of COX-2 and production of PGE2, an inducer of aromatase transcription, and increase production of 15-hydroxyprostaglandin dehydrogenase, which catalyzes PGE2 degradation. Studies demonstrated that aromatase repression by 1,25(OH)2-D3 was due to direct repression of aromatase transcription via binding of VDR to VDRE harbored in the aromatase promoter II. Interestingly, combination of 1,25(OH)2-D3 with aromatase inhibitors (AI) was more effective than 1,25(OH)2D3 or AI alone suggesting potential benefits of combination therapies with vitamin D and AI against breast cancer (Krishnan et al. 2010). In MCF-7 and MDA-MB-231 breast cancer cell incubation with vitamin D, trans-RA and resveratrol, alone or in combination with nucleoside analogs, led to reactivation of PTEN (Dampf Stone et al. 2015). This event was associated with reduced DNMT activity. Genistein also reactivated PTEN. This action was linked to higher H3K9Ac and reduced expression of sirtuin 1 (SIRT1) (Pabona et al. 2013). Genome-wide investigations of gene targets for 1α,25(OH)2-D3 in breast epithelial cancer cells using RNA-Seq technology led to the identification of early transcriptional targets involved in adhesion, growth, angiogenesis, actin cytoskeleton regulation, hexose transport, inflammation and immunomodulation, apoptosis,

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endocytosis, and signaling. Growth arrest was coupled with repression of cyclindependent kinase 6 (CDK6), cyclin G2 (CCNG2), growth factors like kit ligand (KITLG), and platelet-derived growth factor C (PDGFC). Conversely, genes activated by 1α,25(OH)2-D3 included the pro-apoptotic caspase 3 (CASP3). Moreover, 1α,25(OH)2-D3 increased trimethylation of histone H3 lysine 4 (H3K4me3), a marker of active promoters, at several target genes. VDR was shown to interact with menin, which serves as a coactivator of VDR in the presence of 1α,25(OH)2-D3. Menin is part of MLL1and MLL2 complexes that possess HMT activity directed at lysine 4 of histone H3, especially H3K4me3, and is linked to ER activation (Dreijerink et al. 2006). Therefore, the bound VDR might recruit HMT to the promoter of target genes leading to accumulation of H3K4me3 (Goeman et al. 2014).

Vitamin A Receptor (RXR and RAR) Trans-retinoic acids, the bioactive derivatives of retinol or vitamin A, regulate transcription of target genes through binding to RAR, which heterodimerizes with RXR. The RAR/RXR complex binds to RA-responsive elements (RARE) often composed of two 50 -AGGTCA-30 sites arranged in a direct repeat configuration with 1–5 bp spacing. These RARE are located on the promoter region of responsive genes and recruit a cascade of NCoA and NCoR and chromatin-modifying enzymes (Tang and Gudas 2011). Levels of RA too high or too low negatively affect developmental processes and may induce tumor development. Several isoforms of both RAR and RXR have been described (α, β, and γ), and each one is transcribed from different genes. In the absence of a ligand, the RAR/RXR dimer functions as a repressor binding to corepressors such as NCoR or SMRT and other factors with HDAC activity. In the presence of RA, the affinity of RAR/RXR dimer for the NCoR complex diminishes and favors the association with NCoA with HAT activity such as p300 (Dilworth et al. 1999). In mammary gland epithelial cells, nuclear RARα is directly regulated by ERα, which in turn regulates the transcription of downstream RAR including RARβ2 (Schneider et al. 2000). Retinoic acids control the peroxisome proliferator-activator receptor β/δ (Yu et al. 2012), chicken ovalbumin upstream promoter transition factor II (COUPTFII), and protein kinase C alpha (PKCA) and phosphatidylinositol-4,5bisphosphate 3-kinase (PIK3K) (Prahalad et al. 2010). Tumorigenic cells usually become insensitive to RA signaling. It was observed both in vitro and in vivo that preventing RARα activation in mammary gland cells induced high proliferation rates and abnormal duct morphology. Breast cells, in which RARα had been silenced, developed into tumors when exposed to physiological concentrations of RA. In clinical trials, supraphysiological RA doses had opposite effects on breast cancer growth, depending on the functionality of the RARα. For example, studies found that in ERα-positive breast cancer cells (T4TD), physiological levels of RA maintained many RARα target genes in a repressed state, as indicated by histone modification marks. Conversely, higher levels of RA reversed the transcriptional block (Rossetti et al. 2016).

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RARβ2 is frequently lost or silenced in human cancers and its restoration reactivates RA- dependent growth control (Fang et al. 2015; Sirchia et al. 2002). This phenomenon is associated to retinoid resistance observed in cancer cells. RARβ2 acts as a tumor suppressor, although null mice for the gene may not have higher incidence of spontaneous cancers. Ectopic expression of RARβ in luminal breast cancer cells restores the ability of RA to cause cell cycle arrest and apoptosis. In many cancer cells, RARβ is hypermethylated (Fackler et al. 2003). Valproic acid (a HDAC inhibitor) in combination with RA and 5-aza-20 -deoxycytidine (5-aza), a DNMT inhibitor, restored the expression of RARβ in breast cancer cells (Mongan and Gudas 2005). Resistance to RA remains a big hurdle for anticancer therapies. DNA methylation of RARβ2 is low in normal breast tissue (Sun et al. 2011). Genistein was proposed to increase the risk of breast cancer in adult life through epigenetic repression of RARβ2, which mediates the antitumor actions of RA. The RARβ2 gene was reported to be hypermethylated in nipple aspirates of premenopausal women receiving ~90 mg/d genistein and with circulating levels of genistein >600 μg/L (Qin et al. 2009).

Key Facts About NR • A key function of NR is to control expression of target genes as an adaptive response to changes in steroid, environmental, and endogenous compounds. • Transcriptional regulation of a gene by NR involves an orchestrated recruitment of various components of the basal transcription machinery and cofactors. • Epigenetics by NR refers to changes in gene expression without modifications in DNA sequence (i.e., mutations, polymorphisms, etc.). • Redundancy of NR binding by food ligands may assist in the development of breast cancer prevention strategies based on combinations of foods. • Epigenetic modifications induced by nuclear receptors and the large number of factors regulating their activity play a central role in the development of breast cancer.

Dictionary of Terms • Chromatin and transcription – In the nucleus, inactive chromatin includes histones and other proteins that keep the DNA in a tight conformation, not accessible to the RNA polymerase. When the chromatin structure loosens up, due to modifications of histones and other proteins, regions of unmethylated DNA become more accessible for transcription. • Breast cancer classification – The main distinction is between hereditary and sporadic breast cancer; the former is caused by DNA mutations transmitted to offspring from parents; the latter is due to epigenetic changes affecting one or

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more genes encoding for proteins involved in biological processes related to cancer. Different types of breast cancers are also classified based on the expression of one or more proteins (e.g., estrogen and progesterone receptor) and the cellular type (e.g., luminal, basal, ductal, etc.). • Active food compounds – They are found in foods such as fruits, vegetables, fats, and oils. They act on one or more cellular processes such as proliferation, apoptosis, inflammation, etc. Their mechanisms of action are usually complex, can be dose-dependent, and cell/tissue-specific. • Breast cancer 1 or BRCA1 – People carrying mutations in the BRCA1 gene have a high risk of developing breast cancer early in life. Only 5–10% of all breast cancer cases harbor a mutated BRCA1 gene. However, the vast majority of sporadic cases have reduced or absent expression of BRCA1. Although its main function is DNA repair in all cell types, it is currently not known why its mutation or reduced expression is critical for the development of tumors of the breast and ovaries.

Summary Points • Tamoxifen, used to treat estrogen receptor-positive breast cancer, is not effective in those cases where the expression of this receptor has been lost due to epigenetic silencing. • Estrogen receptor-negative breast cancers are the most difficult to treat. • An increasing amount of data strongly suggests that it may be possible to prevent, and possibly reverse, sporadic breast tumors by inducing epigenetic modifications of genes that regulate cell growth and differentiation. • Dietary compounds found mostly in fruit and vegetables are promiscuous ligands of NR that influence gene activation and repression. Therefore, future studies should focus on understanding how different compounds alone or in combination interact to affect gene expression and cell response. • Epidemiologic studies looking at risk factors for breast cancer found that diets rich in fruits and vegetables generally reduce the risk of developing sporadic and hereditary breast tumors. • Unfortunately, it is difficult to ascertain the combined effects of dietary components, the influence of genetic polymorphism, and the time of exposure (e.g., early life vs puberty vs adult). • While available drugs have successfully used to treat ER-positive breast tumors, the search for therapies targeting ER-negative cases is still challenging. Food components alone and in combination are promising candidates in light of their ability to bind to NR and regulate ER and ER-responsive target genes. • Unlike genetic changes, epigenetic modifications are reversible. However, caution is necessary before using active foods as therapeutic agents, since they may exert even harmful effects depending on dose and time of exposure, metabolism, and interactions with other food components and drugs.

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Epigenetic Regulation of Early Nutrition on Immune System

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Lorella Paparo, Rosita Aitoro, Rita Nocerino, Carmen di Scala, Margherita Di Costanzo, Linda Cosenza, Viviana Granata, and Roberto Berni Canani

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evidences on Epigenetic Mechanisms Regulated by Early Nutrition Involved in the Development of Food Allergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New Strategy to Regulate the Epigenome in Food Allergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Dietary Fibers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Diet and gut microbiota are considered two of the most important players in regulating immune system development and function in early life. Many of these effects are mediated by epigenetic mechanisms. The “nutriepigenomics” studies the interaction of nutrients with genome and describes their effect on human health through epigenetic modifications. Dietary fiber modulates gut microbiota and influences short-chain fatty acid production with a beneficial effect on L. Paparo · R. Aitoro · R. Nocerino · C. di Scala · M. Di Costanzo · L. Cosenza · V. Granata Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] R. B. Canani (*) Department of Translational Medical Science, University of Naples “Federico II”, Naples, Italy European Laboratory for the Investigation of Food Induced Diseases (ELFID), University of Naples “Federico II”, Naples, Italy CEINGE Advanced Biotechnologies, University of Naples “Federico II”, Naples, Italy e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_54

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immune system. An increasing number of evidences on epigenetic mechanisms regulated by nutrients are involved in the development of food allergy. New possible strategies, as the choice of dietary treatment, could regulate the epigenome, influencing food allergy disease course. Keywords

Diet · DNA methylation · miRNA · Histone · Short-chain fatty acids · Food allergy List of Abbreviations

CMA DCs EHCF EoE FA GWAS HDAC LGG PA PGE2 PUFAs SCFAs Tregs TSLP

Cow’s milk allergy Dendritic cells Extensively hydrolyzed casein formula Eosinophilic esophagitis Food allergy Genome-wide association study Histone deacetylase Lactobacillus rhamnosus GG Peanut allergy Prostaglandin E2 synthesis Polyunsaturated fatty acids Short-chain fatty acids Regulatory T cells Thymic stromal lymphopoietin

Introduction The development of immune system in early life is influenced by a large number of environmental factors. Diet and gut microbiota are considered two of the most important players in regulating immune system development and function in early life. Many of these effects are mediated by epigenetic mechanisms (Paparo et al. 2014; Ho 2010). Epigenetic mechanisms, such as DNA methylation, histone modifications, and microRNAs, are responsible for heritable changes not involving alterations in DNA nucleotide sequence consisting in posttranscriptional regulation of target gene expression (Cutfield et al. 2007). These mechanisms regulate the intensity and timing of expression of specific genes, not only during embryonic and fetal development but also throughout the life course (McKay and Mathers 2011; Zeisel 2009). The “nutriepigenomics” studies the interaction of nutrients with genome and describes their effect on human health through epigenetic modifications (Tammen et al. 2013). The results of large cohort studies, together with experimental evidences, suggest a pivotal role for maternal and infant nutrition on later predisposition to many diseases, including allergy, through a modulation of epigenetic programming (Remely et al. 2015a, b). It is known that dietary fiber modulates gut microbiota and influences short-chain fatty acid (SCFA)

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production (De Filippo et al. 2010). A balanced SCFA production has a beneficial effect on immune system (Fig. 1). Among the SCFAs, butyrate has received particular attention for its multiple beneficial effects from the intestinal tracts to the peripheral tissues. Butyrate has a regulatory effect on macrophage and dendritic cell (DC) biology, supporting a strong connection between dietary fiber intake and immune response (Trompette et al. 2014). The mechanisms of action of butyrate against allergy are multiple, but many of these involve an epigenetic regulation of gene expression through the inhibition of histone deacetylase (HDAC) (Berni Canani et al. 2011). Recent evidences have emphasized the role of gut microbiota in maintaining the balance of microbial signals required to prevent or treat allergy, and there is mounting evidence that modifications in the pattern of microbial exposure (dysbiosis) early in life represent a critical factor underlying the development of allergic diseases (Arrieta et al. 2015). Food allergy (FA) is defined as an adverse immune response to food proteins. During the last decade, the pattern of FA is deeply changed. In many countries, there has been a significant increase in prevalence, persistence, and severity of clinical manifestations and a consequent negative impact on quality of life and medical care costs. It has been estimated that 1/20 of children is suffering from these conditions (Prescott et al. 2013; Ramesh 2008). The possible causes of FA become the target of intense scrutiny in recent years. During the time of immune programming, nutritional exposure, environmental factors, and microbiota and epigenetic mechanisms may play an important role in the development of FA (Fig. 2).

Evidences on Epigenetic Mechanisms Regulated by Early Nutrition Involved in the Development of Food Allergy An increasing number of studies suggest that epigenetic regulation by nutrients (folate, ω-6 and ω-3 polyunsaturated fatty acids (PUFAs), and vitamin D) plays a critical role in the development of allergy (Miles and Calder 2015; Zittermann et al. 2009). ω-6 and ω-3 PUFAs influence the IgE production through prostaglandin E2 synthesis (PGE2). PGE2 has been found to increase DNA methyltransferase expression and consequently gene-specific DNA methylation, suggesting a potential mechanism by which PUFAs regulate the immune system (Lack 2012). In a genome-wide study, demethylation of thymic stromal lymphopoietin (TSLP) promoter gene, known for its functional role in allergic diseases, was associated with high level of vitamin D in the cord blood, resulting in a higher TSLP mRNA expression. Epigenetic regulation of this gene could be involved in the vitamin D-related programming for allergy (Junge et al. 2016). It has been demonstrated that maternal supplementation of ω-3 PUFAs during pregnancy modulates global DNA methylation levels and the Th1/Th2 balance in infants. The authors found a low promoter methylation level of IFN-γ, a potent suppressor of Th2-driven allergic immune response, in the cord blood cells of pregnant women was supplemented daily with 400 mg docosahexaenoic acid (Lee et al. 2013). Robinson and Delvig (2002) demonstrated that differential DNA methylation in the regions of genes

O

Tregs

Tregs

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Blocked Th2 response

Primed immunosoppressive cells

Butyrate

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Fig. 1 Healthy diet and Lactobacillus rhamnosus GG effects on immune system. Fibers and probiotics, such as LGG, significantly increase butyrateproducing bacteria, modulating Th1/Th2 immune response and modifying allergy disease course

Intestinal epithelial cells

Inner musus layer

Out musus layer

Gut microbiota shaping

Healthy Diet (Fibers) Lactobacillus rhamnosus GG

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Fig. 2 New targets: new strategies of dietary intervention in the prevention and treatment of food allergy. A schematic representation of the potential mechanisms of action of dietary intervention in children affected by cow’s milk allergy. Dietary fibers and probiotics (LGG) shape gut microbiota and increase SCFAs (butyrate) production at intestinal level. Gut microbiota shaping and butyrate are crucial factors for food oral tolerance, and they regulate an appropriate balance between immune effectors and regulatory pathways through epigenetic mechanisms. These effects influence positively cow’s milk allergy disease course

related to T-cell differentiation and balance between Th1 and Th2, during the critical period of early life, may promote the development of allergy (Robinson and Delvig 2002). Another study analyzed genome-wide DNA methylation profile in CD4+T cell from 12 children with FA and from 12 nonallergic children at birth and at 12 months. They found alterations of DNA methylation profile in MAPK signalingassociated genes during early CD4+T-cell development that may contribute to development of FA in childhood (Martino et al. 2012). In an animal study, Song et al. (2014) demonstrated that maternal peanut allergy reduced IL-4 gene promoter methylation, leading to an increase of Th2 cytokine production in offspring of mothers with peanut allergy (PA), resulting in the development of FA. The study also found a negative correlation between IL-4 methylation and IgE production (Song et al. 2014). Hong et al. (2015) performed the first genome-wide association study (GWAS) of FA in a US cohort of children affected by three most common types of FA: PA, egg allergy, and milk allergy. They identified two genetic variants in the HLA-DR and HLA-DQ gene region that were significantly associated with PA and with differential DNA methylation levels. These evidences suggested that DNA methylation could mediate genetic susceptibility to FA (Hong et al. 2015). Alterations of methylation status in Th1-Th2 immune response (i.e., IL1RL1, IL5RA, IL4, CCL18, and STAT4) were found in children affected by cow’s milk allergy (CMA) (Hong et al. 2016). Methylation differences in other genes involved in immunological pathways and associated with allergies were found in the CMA children compared to healthy children. This study showed that hypermethylation of ZNF281 and HTRA2 and hypomethylation of DHX58 and EIF42A genes caused

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allergic inflammation (Petrus et al. 2016). Martino et al. (2015) performed genomewide DNA methylation analysis on blood mononuclear cells from 58 food-sensitized children, half of whom were positive to oral food challenges. The authors identified 96 CpG sites annotated to 73 protein-coding genes that exhibited predictive utility for discriminating symptomatic allergy from asymptomatic allergic sensitization (Martino et al. 2015). The consumption of unprocessed cow’s milk obtained directly from a farm seems to have a protective effect against allergies, through FoxP3 demethylation and activation of regulatory T cells (Treg) (Schaub et al. 2009; Lluis et al. 2014). SCFAs, in particular butyrate, regulate HDAC activity. Smith et al. (2013) showed that SCFA treatment of Ffar2+/+ mice reduced HDAC6 and HDAC9 expression and enhanced histone acetylation in Tregs. These results suggest that SCFAs via Ffar2 may affect Tregs through HDAC inhibition (Smith et al. 2013). Another study also demonstrated that butyrate is able to inhibit HDAC 9 and 6 with a subsequent demethylation of FoxP3 gene and an increase of Treg cell number (Tao et al. 2007). The deregulated expression of many miRNAs can lead to aberrant immune function (Pauley et al. 2009; Lu and Rothenberg 2013). Lu et al. (2012) found a specific “miRNA signature” of patients with eosinophilic esophagitis (EoE). In particular, analysis of the most upregulated esophageal miRNAs identified miR-21, involved in the Th2 cells polarization, as strongly correlated with esophageal eosinophil levels in the esophageal biopsy specimens and miR-146a, miR-146b, and miR-223 as the most differentially expressed miRNAs in plasma samples from EoE patients. The authors also demonstrated that this miRNA profile was largely reversible on disease remission (Lu et al. 2012). Using miR-155 / T cells generated in vitro and isolated ex vivo from airway allergy animal models, Okoye et al. showed that miR-155 was necessary for the Th2 immune response partly through the regulation of the sphingosine-1-phosphate receptor gene (S1pr1) (Okoye et al. 2014). Zech et al. (2015) provide evidence that miR-155 deficiency in DCs resulted in a reduction of IL-4, IL-5, and IL-13 cytokine production during allergic airway inflammation in a murine model (Zech et al. 2015). As miR-146a has been shown to selectively suppress Th1 differentiation (Lu et al. 2010), miR-146a upregulation could prevent the differentiation of IFN-γ-producing Th1 cells which in turn will cause unopposed Th2 cell activation (Lu et al. 2010). New evidences reported that microRNA-155, microRNA-148a, microRNA-29b, and microRNA-21 may induce thymic FoxP3+ Treg differentiation thereby preventing the development of allergy (Melnik et al. 2014).

New Strategy to Regulate the Epigenome in Food Allergy Although no therapy was approved for FA and the long-term benefits of different immunotherapies require further investigation, recent data suggest the choice of dietary treatment could influence FA disease course. Farm milk consumption has a protective effect against the development of childhood allergic disease. Indeed, farm

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milk-exposed children at age 4 years show an increased Treg cell numbers, linked to FoxP3 demethylation in peripheral blood cells, compared to nonfarm milk-exposed children (Lluis et al. 2014) . Our group observed that the treatment with extensively hydrolyzed casein formula containing the probiotic Lactobacillus rhamnosus GG (LGG) is more effective than EHCF alone in reducing the occurrence of other allergic manifestations and increases the rate of tolerance acquisition at 12, 24, and 36 months in children affected by CMA (Berni Canani et al. 2016a). In a CMA murine model, we demonstrated that dietary intervention with EHCF elicited multiple immunoregulatory activities for CMA prevention and treatment and that the presence of LGG increases the magnitude of many of these effects (Aitoro et al. 2016). We also observed that the choice of dietary treatment influenced cytokine DNA methylation profiles of the Th1/Th2 cytokine genes in children with IgE-mediated CMA. CMA children treated with EHCF + LGG, who acquired oral tolerance, showed a different Th1/Th2 cytokine gene DNA methylation profile compared to CMA patients treated with other formulas (Berni Canani et al. 2015). Another gene, involved in tolerance acquisition mechanism, is the transcription factor of Tregs, FoxP3. We analyzed the methylation status of Treg-specific demethylated region of FoxP3, and we found a different methylation profile comparing patients who acquired oral tolerance after treatment EHCF + LGG versus children treated with other formulas (Paparo et al. 2016). Recently, we explored miRNA role on FA disease course and used a next-generation sequencing-based approach. We analyzed the whole set of miRNAs related to CMA disease course, and we found the miR-193a-5p, involved in Th2 response, down-expressed in active CMA patients compared to healthy controls and in children with recent evidence of oral tolerance acquisition to cow’s milk proteins (D’Argenio et al. 2017). Melnik et al. (2014) also demonstrated that formula selection could induce a different regulation of circulating miRNAs, specifically microRNA-155, microRNA-148a, microRNA-29b, and microRNA-21, involved in allergic response (Melnik et al. 2014). Evidence obtained by our group suggests that EHCF + LGG but not EHCF alone increased an abundance of butyrate-producer bacteria strains (including Roseburia, Coprococcus, and Blautia) and these bacteria were identified in infants who acquired tolerance to cow’s milk. These findings suggest that LGG treatment contributes to acquisition of tolerance by altering the strain-level community structure of taxa with the potential to produce butyrate (Berni Canani et al. 2016b). Altogether these findings suggest that the positive action of EHCF + LGG demonstrated in these studies could derive, at least in part, from a positive modulation of gut microbiota-mediated epigenetic mechanisms (Fig. 3). Recent studies proposed an innovative strategy of therapy and prevention of FA, based on the use of antisense molecule to edit the genome of developing T cells, called hgd40/SB010, that acts against the transcriptional factors of Th2 cells, GATA3. This molecule deactivates targeted mRNAs upon specific binding without a need of accessory molecules, possessing enzymatic activity (Potaczek et al. 2016). A clinical study demonstrated that hgd40/SB010 attenuated early clinical allergic responses after allergen provocation and caused a weakening Th2 response in patients with asthma (Krug et al. 2015).

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Fig. 3 Dietary fibers and probiotics have a protective and therapeutic effect against cow’s milk allergy through an activation of the epigenetic pathways. LGG treatment contributes to CMA oral tolerance acquisition by increasing the butyrate-producing bacteria. CMA children treated with extensively hydrolyzed casein formula plus LGG, who acquired oral tolerance, show an increase of Th2 cytokine gene DNA methylation, an inhibition of histone deacetylase 6 and 9 activity, and a selective modulation of miR-193a-5p and miR-155, involved in allergic response, compared to CMA patients treated with other formulas

Conclusions Diet influence on epigenetic mechanisms might represent an innovative approach for preventive and therapeutic interventions in allergy. Despite more experimental studies with a larger sample size and longitudinal follow-ups are required to provide the role of epigenetic regulation by nutrients in the growing epidemic of allergy, the better definition of epigenetic mechanisms might be used as molecular biomarkers to quantify the different allergy-enhancing or protective exposures, to improve patient diagnosis, and to facilitate the development of novel therapies.

Dictionary of Terms • Nutriepigenomics – The nutriepigenomics studies the interaction of nutrients with genome and describes their effects on human health through epigenetic modifications.

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• miRNA signature – miRNA signature is defined as a specific profile of miRNAs that discriminate the individual subjects. • Th1-Th2 cytokines – Th1-Th2 cytokines are the hormonal messengers involved in allergic responses. They can be functionally divided into two groups: Th1-cytokines, as interferon gamma, that have a protective effect in allergic response and Th2-cytokines that include interleukins 4, 5, and 13, which are associated with the promotion of IgE and eosinophilic responses. • Treg cells – CD25+CD4+ Tregs have a pivotal role in autoimmune disease prevention by maintaining self-tolerance and suppression of allergy, asthma, and oral tolerance. Tregs’ suppressive phenotype is characterized by a stable expression of the transcription factor FoxP3 (Forkhead box P3), a main regulator of Treg development and function. • Eosinophilic esophagitis – Eosinophilic esophagitis is a non-IgE-mediated food allergy and can be associated with increased numbers of eosinophils and mast cells in the stomach, small intestine, and colon. The elimination of the offending food resolves inflammation.

Key Facts of Dietary Fibers • Dietary fibers, gut microbiota, and epigenetic are considered the most important players in regulating immune system development and function in early life. • Dietary fibers modulate gut microbiota metabolites, such as butyrate, that have multiple beneficial effects on immune system. • Butyrate has a regulatory effect on macrophage and dendritic cells, supporting a strong connection between dietary fiber intake and immune response. • Butyrate, derived by a dietary fiber, acts against allergy through the inhibition of histone deacetylase. • Butyrate inhibition of HDAC 6 and 9 induces a demethylation of FoxP3 gene, with an increase of Treg cell number, leading to oral tolerance acquisition.

Summary Points • Diet affects gut microbiota, leading to changes in bacterial metabolites (such as butyrate) that are able to promote oral tolerance through epigenetic mechanisms. • Early nutrition modulates epigenetic mechanisms involved in the development of food allergy. • Alterations of DNA methylation status in Th1-Th2 cytokine genes were found in children affected by food allergy. • Butyrate is able to inhibit histone deacetylase with a subsequent demethylation of FoxP3 gene and an increase of Treg cell number. • Modulation of expression of many miRNAs is found in allergy.

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• The treatment with EHCF + LGG reduces the occurrence of other allergic manifestations and increases the rate of tolerance acquisition in children affected by cow’s milk allergy. • CMA children treated with EHCF + LGG, who acquired oral tolerance, showed a different DNA methylation profile in Th1/Th2 cytokine and FoxP3 genes compared to CMA patients treated with other formulas. • Formula selection could induce a different regulation of miRNAs, specifically microRNA-155, microRNA-148a, microRNA-29b, microRNA-21, and microRNA-193a-5p, involved in allergic response. • Epigenetic mechanisms might be used as molecular biomarkers to quantify the different allergy-enhancing or protective exposures, to improve patient diagnosis, and to facilitate the development of novel therapies.

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miRNAs and Their Role in the Pathogenesis of Celiac Disease: A Review

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Contents Introduction: Celiac Disease Pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . miRNA and the Intestine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . miRNAs and Celiac Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mini-Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Celiac Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Celiac disease is an autoimmune disorder, mainly affecting the intestine, triggered by gluten exposure and the passage of its peptides through the gastrointestinal barrier in individuals with a specific genetic background. Although numerous studies have unraveled a lot of the steps involved in the pathogenesis of celiac disease, there are still questions that remain only partially elucidated, in particular regarding the role of noncoding RNAs. This chapter provides an overview of the pathogenetic processes and of the various cell types involved in celiac disease and focuses on the role of miRNAs as possible regulators of these events. Moreover, it analyzes the data obtained in the last few years, since some studies have demonstrated that miRNAs expression, either in the duodenal tissue or in the blood, differs in celiac disease patients compared to controls. Lastly, it discusses the possible role of plasmatic miRNAs as adjunct tools for diagnosis and follow-up of celiac patients.

D. Barisani (*) School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_124

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Keywords

Celiac disease · Gene expression · Innate immunity · Adaptive immunity · Tregs · Tight junctions · Paneth cells · NOD2 · FOXP3 · Circulating miRNAs List of Abbreviations

APC ATG7 BECN1 CD CXCL2 DCs FOXP3 HLA IEL IFNγ IL IRAK KLF4 MAD2L1 MHC miRNA NF-kB NK NOD PPR Rho SHIP1 SOCS1 STAT TG2 TLR TNF TRAF Treg UTR

Antigen-presenting cell Autophagy-related 7 Beclin 1, autophagy related Celiac disease C-X-C Motif Chemokine Ligand 2 Dendritic cells Forkhead box P3 Human leukocyte antigen Intraepithelial lymphocytes Interferon gamma Interleukin IL-1 receptor-associated kinase Kruppel-like factor 4 Mitotic Arrest Deficient 2 Like 1 Major histocompatibility complex Micro-RNA Nuclear factor kappa-light-chain-enhancer of activated B cells Natural killer Nucleotide-binding oligomerization domain-containing protein Pattern recognition receptor Ras homolog Src homology 2-containing inositol 5-phosphatase 1 Suppressor of cytokine signaling 1 Signal transducer and activator of transcription Transglutaminase 2 Toll-Like Receptor Tumor Necrosis Factor TNF receptor-associated family Regulatory T cells Untranslated region

Introduction: Celiac Disease Pathogenesis Celiac disease (CD) is an autoimmune disorder mainly affecting the small intestine, triggered and maintained by the ingestion of proteins from wheat (gluten), barley (hordein), and rye (secalin) in genetically predisposed individuals. CD has a complex genetic background, which has not been completely characterized yet. Up to now, HLA-related or non-HLA (42 different ones) loci have been identified as

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predisposing to CD, all of them implicated in the immune response, both in the intestine and in the thymus (reviewed in Withoff et al. 2016). However, the most important role belongs to HLA-related genes; they include HLA class II, as well as other five variants present in the MHC region (the former accounting for 23% and the latter for 18% of the genetic predisposition). The HLA class II region is highly polymorphic and contains the DQA and DQB loci, which encode, respectively, for the alpha and beta chain that form the class II heterodimer. The combination of HLA-DQA1*0501 and DQB1*0201 generates the HLA-DQ2.5 heterodimer, present in more than 90% of CD patients (either in cis or in trans), whereas the remaining patients carry the HLA-DQ8 heterodimer, encoded by DQA1*03 (α chain) and DQB1*0302 (β chain). Although the DQ2 heterodimer is necessary for the development of CD, it is not sufficient since HLA-DQ2 is present in ~35% of the Caucasian population, but only 2–5% of gene carriers develop celiac disease. The importance of HLA-DQ2 is explained by taking into consideration the pathogenetic mechanisms of this disorder. To activate the adaptive response, antigens are processed by antigen-presenting cells (APCs), exposed on their surface within class I or II HLA molecules, and thus recognized as antigens by T cells. In the case of celiac disease, the antigen presented to T cells is derived from gluten proteins, in particular from one of its components, i.e., gliadin. Some gliadin peptides are resistant to digestion by gastrointestinal proteases, including the 33-mer peptide (residues 57 to 89 of α-gliadin), a celiac “superantigen” with strong stimulating T-cell capacity (Shan et al. 2002) or the 31–43 peptide (from residues 31 to 43 of α-gliadin), a toxic agent for intestinal mucosa (Picarelli et al. 1999). Once gliadin peptides (in particular the 33-mer) have crossed the intestinal barrier, they are further processed in the submucosa; due to their high content in glutamine, proline, and hydrophobic amino acids, they are preferred substrates for transglutaminase 2 (TG2). After deamidation, the negatively charged gliadin peptides bind more strongly to HLA-DQ2 (or HLA-DQ8), leading to a gluten-specific CD4+ Th1 T-cell activation in the lamina propria. T cells act as central effectors of the intestinal inflammation, causing also the activation of intraepithelial lymphocytes (IELs). These processes result in crypt hyperplasia, villus atrophy, and alteration of intestinal epithelium tight junctions. Moreover, T cells can stimulate B cells to produce autoantibodies directed against deamidated gliadin peptides as well as TG2 (reviewed in Schuppan et al. 2009) (Fig. 1). In theory, this abnormal immune response should be kept under control by regulatory T cells (Tregs) present in the lamina propria and in the lymphoid structures, since their role is the suppression of the reactivity of self T cells at the peripheral level. In fact, they produce antiinflammatory cytokines, such as IL-10, and induce the apoptosis of effector T cells through the expression of molecules like CTLA-4. Tregs are characterized by the expression of FOXP3, a transcriptional factor essential for their development and function (Zheng and Rudensky 2007). Differently from what expected, FOXP3 expression is increased in CD patients, thus suggesting a reduced Treg ability, and an unbalance in intestinal immunity (Zanzi et al. 2011; Vorobjova et al. 2015; Cook et al. 2017).

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Fig. 1 Mechanisms involved in the pathogenesis of celiac disease. The development of the autoimmune response, which causes celiac disease, needs the passage of gliadin peptides through the intestinal barrier either paracellularly or intracellularly. In the submucosa, peptides are deamidated by tissue transglutaminase and then processed by antigen-presenting cells and exposed on their surface within the HLA-DQ2 or HLA-DQ8 heterodimer. This presentation activates CD4+ T cells that, in turn, trigger the adaptive immune response with effector T-cell activation and autoantibody production. The autoimmune response should be kept under control by Treg activity, that normally regulates CD4+ functions. However, Tregs result insufficient in celiac disease. The passage of the gliadin peptides will also induce the activation of innate immunity, mainly mediated through IL-15 release

Innate immunity is also involved in the pathogenesis of CD. The 31–43 peptide of α-gliadin can stimulate macrophages and dendritic cells to secrete IL-15. This, in turn, leads to the activation of IELs and NK cell proliferation, IFNγ production, and epithelial damage (Maiuri et al. 2003). Other elements of innate immunity could be involved, such as pattern recognition receptors (PRRs) and Toll-like receptors (TLRs), which respond to microbial components and activate the expression of inflammatory genes (reviewed in Evavold and Kagan 2018). An increased activation of TLR4 was reported in the duodenal biopsies of celiac patients, as well as in controls, in response to another proteic component of wheat, i.e., amylase-trypsin inhibitor (Junker et al. 2012). Moreover, the activation of these receptors, via the increased expression of pro-inflammatory cytokines and chemokines, can also control the barrier function during intestinal inflammation. Other PPRs are nucleotide-binding oligomerization domain-containing proteins (NODs); NOD2, a cytosolic protein which recognizes bacterial membrane peptidoglycan and activates the NF-kB pathway, can induce the production of pro-inflammatory mediators such as

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tumor necrosis factor (TNF), interleukin 6 (IL-6), and interleukin 8 (IL-8) (reviewed in Claes et al. 2015). In turn, these cytokines can affect the permeability of the intestinal barrier.

miRNA and the Intestine miRNAs play a pivotal role in gene expression regulatory mechanisms in all tissues. Thus, miRNAs could affect the various steps necessary for the development of the autoimmune response in CD, i.e., the passage of gliadin peptides through the intestinal barrier, and the activation of the innate or adaptive immunity. miRNAs and tight junctions: miRNAs are able to affect the entire intestinal development, as demonstrated in transgenic mice in which intestinal Dicer was knocked out using the Cre-Lox technology. In fact, analysis of their intestine revealed a deepening of the crypts, neutrophil infiltrate, and alteration of intestinal permeability. This latter characteristic was associated with the reduced expression of claudin 4-positive tight junctions (McKenna et al. 2010). Further studies, performed in differentiated CaCo2 cells and in mouse intestine, identified miR-122a as a regulator of intestinal permeability via its binding to occludin 3’-UTR (Ye et al. 2011). This miRNA, however, is not the only one able to regulate occludin, since a decrease in this protein expression was associated with an increase in miR-21 level in colonic biopsies of patients with ulcerative colitis; in this case, however, the mechanism was indirect and due to miR-21 action on Rho GTPase RhoB (Yang et al. 2013). Moreover, also miR-874 can downregulate occludin and claudin 1 through its action on AQP3 (Zhi et al. 2014) and, in the intestine of diabetic mice, the overexpression of miR-429 was able to decrease occludin levels (Yu et al. 2016). Another miRNA able to affect intestinal permeability is miR-155, which is able to bind claudin 1 3’-UTR repressing its expression (Zhang et al. 2013). In addition, miR-155-5p can regulate RhoA and, in turn, ZO-1, another protein essential for the correct functioning of tight junctions (Tian et al. 2013). miR-223 targets claudin 8, and it has been suggested that it could be the intermediary between the IL-23 pathway and the increase in permeability observed in disorders such as Crohn’s disease (Wang et al. 2016) (Fig. 2). Lastly, miRNAs can also affect intestinal permeability through other mechanisms, like the activation of the NF-kB pathway, which improves the barrier function, as shown for miR-146b in a mouse model of colitis (Nata et al. 2013). miRNAs and innate immunity: The innate immune system relies primarily on the recognition of pathogen-associated molecular patterns, which trigger extracellular receptors (TLRs) or intracytoplasmic NOD-like receptors. Receptors’ activation leads to signaling cascades which include NF-κB and IFN regulatory factors (IRFs), causing an increased transcription of inflammatory cytokines such as TNFα, IL-1β, IL-6, IL-8, etc. in the former case and of type I IFN in the latter. These pathways require the activation of several intracellular proteins, including TNF receptorassociated family (TRAF)-6 and IL-1 receptor-associated kinase (IRAK)-1 and IRAK-2 (reviewed in Evavold and Kagan 2018).

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Fig. 2 miRNAs able to affect proteins involved in tight junction formation. The maintenance of the intestinal barrier requires the perfect functioning of cell-to-cell junctions, in particular tight junctions. Several proteins are involved in junction formation, and variations in their expression can alter the function of these structures. The figure depicts several miRNAs able to influence cell-tocell junctions in intestinal epithelium. aPKC, protein kinase C alpha; AKT3, AKT serine/threonine kinase 3; CDC42, cell division cycle 42; CLDN, claudin; JAM-A, also called F11R, F 11 receptor; OCLN, occludin; PAR-3 (or 6), par-3 (or 6) family cell polarity regulator; PP2A, protein phosphatase 2A; PTEN, phosphatase and tensin homolog; Rho, RAS-homolog family member; ZO-1 also called TJP1, tight junction protein-1

Immune response activation leads to the increased expression of various miRNAs; miR-146a and miR-155 are the first induced miRNAs, and, in turn, they regulate the innate immune response (Taganov et al. 2006; O’Connell et al. 2007; Wang et al. 2010). The importance of miR-146a was demonstrated in a knockout mouse model, which developed autoimmunity and an excessive pro-inflammatory response. Moreover, these mice developed myeloproliferative disorders, indicating that miR-146a regulates the proliferation in immune cells (Boldin et al. 2011). miR146a can, both in vitro and in vivo, directly target IRAK-1 and TRAF-6, as well as IRAK-2, thus acting as a negative regulator of cytokine and IFN production (Boldin et al. 2011; Park et al. 2015). Furthermore, miR-146a has a role in the induction of immune tolerance in the neonatal intestine, since its targeting IRAK-1 prevents the apoptosis of intestinal epithelial cells after bacteria exposure (Chassin et al. 2010). Another miRNA that regulates macrophages and dendritic cells (DCs) is miR155; in particular in macrophages, it is involved in their polarization and regulation of apoptosis (O’Connell et al. 2007; Wang et al. 2010; Cai et al. 2012). miR-155 can

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either increase or attenuate the inflammatory response according to the involved cell type, since it can target molecules such as suppressor of cytokine signaling 1 (SOCS1) thus increasing the production of cytokines/interferons (Wang et al. 2010), but also downregulate MYD88 and dampen TLR pathway response (Tang et al. 2010; Bandyopadhyay et al. 2014). Other miRNAs are involved in the regulation of the innate immune response, such as miR-132 and miR-212, which target IRAK-4 and can induce tolerance to TLR2 stimulation (Nahid et al. 2013). miR-21 is highly expressed in many cell types, and its transfection in a mouse macrophage cell line was able to suppress inflammatory responses, blocking NF-kB and increasing IL-10 production (Sheedy et al. 2010). miR-125a can inhibit macrophage responses by regulating macrophage differentiation or autophagy processes (Banerjee et al. 2013), whereas miR-125b can directly downregulate TNFα (Tili et al. 2007). On the contrary, miR-511 has been identified as a positive regulator of TLR4 (Tserel et al. 2011). As regards the NOD pathways, both miR-192 and miR-122 are able to downregulate NOD2 (Chen et al. 2013; Chuang et al. 2014). This interaction has been evaluated in particular in Crohn’s disease patients (Guo et al. 2015), in whom NOD2 is the strongest single genetic susceptibility locus identified up to now. miRNAs and adaptive immunity: The adaptive immunity components taking part in celiac disease pathogenesis are dendritic cells (antigen-presenting cells) and T cells, both those generating the immune response (CD4+, CD8+) or regulating it (Treg). miRNAs play a pivotal role in the processes of differentiation and maturation of all these cell types and thus can regulate intestinal adaptive immune response. The development of mature dendritic cells involves different miRNAs; miR-21 and miR-34a downregulate the protein level of Wnt1 and Jagged 1 (Notch ligand), respectively, thus permitting DC differentiation from monocytes (Hashimi et al. 2009). Interestingly, miR-155 and miR-146a are also involved in the regulation of dendritic cell function (Rodriguez et al. 2007; Karrich et al. 2013). In fact, DCs isolated from miR-155-deficient mice have an impaired ability to activate antigenspecific T cells (Rodriguez et al. 2007). miR-155 acts on arginase-2, which controls extracellular arginine availability, important for DC activation of T cells (DunandSauthier et al. 2014), as well as on SHIP1 (inositol polyphosphatase). This enzyme catalyzes the conversion of phosphatidylinositol (3,4,5)-trisphosphate to phosphatidylinositol (3,4)-bisphosphate, affecting the immune response (Lind et al. 2015). On the other hand, miR-146a acts as a negative regulator through the inhibition of NFkB pathway and of cytokine production (Karrich et al. 2013). Moreover, several other miRNAs are involved in the development of a tolerogenic phenotype, both in human and mouse dendritic cells (Zheng et al. 2012; Fordham et al. 2015; Wu et al. 2015; Naqvi et al. 2016). A specific regulation of intestinal antigen-presenting cells is due to miR-223, as demonstrated in miR-223 KO mice in which macrophages and dendritic cells show a pro-inflammatory phenotype and develop a more severe phenotype in case of DSS-induced colitis. Interestingly, the target of miR-223 is a transcription factor, C/EBPβ, that regulates the expression of different proinflammatory cytokines, such as TNF-α and IL-6 (Zhou et al. 2015).

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Several T lymphocyte classes are involved in the pathogenesis of CD, T helper (CD4+), cytotoxic T cells (CD8+), and regulatory T cells (Treg) (Schuppan et al. 2009). In turn, various miRNAs are involved in the differentiation processes leading to the generation of different phenotypes; moreover the same miRNA, according to its level of expression, can direct the differentiation toward a specific cell type. For example, miRNA-125 regulates genes such as IFN-γ, IL-2 receptor β, and IL-10 receptor α, which are involved in the differentiation of naive CD4+ T cells in humans (Allantaz et al. 2012). The reduced expression of miR-125 is associated with an effector memory CD4+ T-cell phenotype, whereas its increased expression has been observed in Treg cells (Pan et al. 2015). miRNA-146a is expressed in Treg and Th1 cells, and it targets STAT1, the transcription factor essential for Th1 differentiation (Tang et al. 2009). Decreased levels of STAT1 enable Treg cells to suppress Th1 responses, whereas miR-146a absence alters the suppressor function of Treg cells (Lu et al. 2010). miR-146a expression can also affect differentiation toward a Th1 or Th2 phenotype (Lu et al. 2010). The evolution toward a Th1 or Th2 phenotype includes, however, other miRNAs; miR-181 family leads toward Th1 development and IFN-γ production (Galicia et al. 2014), similarly to miR-155, since miR-155 deficiency shows a bias toward Th2 differentiation (reviewed in Seddiki et al. 2014). Tregs, the main mediators of adaptive tolerance, have a signature composed of five miRNAs (21, 31, 125a, 181c, and 374) that influence the expression of Tregspecific markers (Rouas et al. 2009). One of them is FOXP3, which is involved in differentiation, homeostasis, and function of Tregs. miR-21 and miR-31 affect FOXP3 expression through different mechanisms; miR-31 binds the 30 UTR of FOXP3 mRNA resulting in a decrease of its expression, whereas miR-21 indirectly causes an increase of its levels (Rouas et al. 2009) (Fig. 3).

miRNAs and Celiac Disease Although a dysregulation of the immune response represents the pathogenetic mechanism of celiac disease, and a possible miRNA involvement should be suspected, very few studies have been conducted up to now either in duodenal biopsies or plasma of CD patients. Since no previous data were available, most of the studies employed a screening approach using different types of miRNA arrays. The first published paper analyzed miRNA expression pattern in duodenal biopsies of children with CD either at diagnosis or after at least 2 years of gluten-free diet, and compared it with that observed in matched controls (Capuano et al. 2011). This work identified several differently regulated miRNAs, accounting for about 20% of the total. In particular, in active CD patients, 27 and 55 miRNAs were up- and downregulated compared to controls, whereas in GFD patients increased and decreased miRNAs were 22 and 49, respectively. Interestingly, in total, 30 miRNAs presented the same pattern of expression in biopsies obtained at diagnosis and on gluten-free diet compared to controls, 9 being upregulated and 21 downregulated. Among those upregulated, the most abundant was miR-449a; in silico analysis revealed as a possible target Notch1 and KLF4. Both these genes are involved in the mechanisms regulating proliferation

miRNAs and Their Role in the Pathogenesis of Celiac Disease: A Review

Fig. 3 miRNAs involved in the regulation of adaptive immune response. To develop an autoimmune response in celiac disease, it is necessary to process the antigen (gliadin peptides) and then present it on the surface of antigen-presenting cells (APCs) in order to activate T helper cells. However several miRNA can regulate processes in antigen-presenting cells and T cells. The figure depicts in particular miR-146a, which can have different targets according to the cell type, regulating STAT1 in T cells and the TLR2/4/Il-1b pathway in APCs. Conversely, miR-155 can have targets within APCs. AKT, AKT serine/threonine kinase 1; ERK, extracellular signal-regulated kinase; HLA, human leukocyte antigen; IRS-1, insulin receptor substrate 1; IRAK, interleukin 1 receptor-associated kinase;

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and differentiation in the intestine; Notch1 belongs to the Notch family, single transmembrane receptors that, after interaction with ligands (DLL1,3,4 and Jagged 1–2), undergo proteolytic cleavage and translocate into the nucleus where they function as transcription factors, with one of the main target being HES1. Notch1 and Hes-1 expression analysis in the duodenal biopsies of CD patients resulted significantly lower compared to controls, confirming the regulatory role of miR-449a. Moreover, KLF4 is involved in differentiation of goblet cells, and the authors detected a reduced number of these cells in the biopsies of CD patients. Other miRNAs were downregulated, in particular miR-124a, miR-189, miR-299-5p, and miR-379, which have been associated with autoimmune disorders such as Crohn’s disease (Jensen et al. 2015). However, miRNA profile could be different according to the age of the patients included in the study or to the analyzed phenotype. Moreover, differences in the starting material or in the employed methods could also affect the results. A second study analyzed miRNA expression pattern in adult celiac patients subdivided according to the phenotype at presentation, i.e., the “classical” phenotype characterized by diarrhea, or those in whom the main clinical sign was iron deficiency anemia (Vaira et al. 2014). These two categories of celiac disease patients were compared with matched controls and celiac patients on gluten-free diet. Several miRNAs were differentially regulated; the heatmap showed a similar expression profile in downregulated miRNAs in CD patients at diagnosis independently from the clinical manifestations, whereas differences in expression between the two groups of patients were observed in upregulated miRNAs. Moreover, patients on gluten-free diet had an expression profile that was only partially similar to controls. Among the 11 downregulated miRNAs in CD biopsies, there were miR-31-5p, miR-192-5p, miR-192-3p, miR-193a-5p, miR-194-5p, and miR-338-3p, whereas miR-146a-5p, miR-551a, miR-551b-5p, miR-638, and miR-1290 were detected among the 25 upregulated ones. Validation of the obtained results was performed on a different cohort and with material derived from paraffin-embedded biopsies; the qPCR analysis confirmed a significant deregulation of miR-31-5p and miR-551b-5p in “classical” patients and of miR-31-5p, miR-192-3p, miR-551b-5p, miR-638, and miR-1290 in CD patients with anemia. The authors then employed an in vitro system in which duodenal fibroblasts obtained from celiac patients and controls were exposed either to the 31–43 or to the 33-mer peptide and confirmed the regulation for miR-192-3p, miR-31-5p, and miR-1285-3p. Interestingly, only some of the miRNA identified in the two papers in children and adult biopsies showed the same pattern of expression, namely, miR-30b, miR-31, miR-192, and miR-194 that were downregulated and miR-337 and miR-432 upregulated. ä Fig. 3 (continued) JAK, Janus kinase; MEK, mitogen-activated protein kinase kinase; mTOR, mechanistic target of rapamycin kinase; NF-kB, nuclear factor kappa B; PI3K, phosphatidylinositol-4,5-bisphosphate 3-kinase; RELB, NF-kB subunit; SHIP1. inositol polyphosphate-5phosphatase D; STAT, signal-transducer and activator of transcription; TCR, T-cell receptor; TLR: Toll-like receptor; TRAF6, TNF receptor-associated factor 6

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Another paper analyzed miRNA expression pattern again in adult duodenal biopsies, but in this case celiac patients were subdivided according to the severity of the histological lesion, separating patients with subtotal atrophy (Marsh 3A-B according to Marsh-Oberhuber’s classification) from those with total atrophy (Marsh 3C) (Magni et al. 2014). Microarray analysis revealed seven miRNAs that were downregulated in biopsies of CD subjects, namely, miR-638, miR-192-5p, miR-4833p, miR-31-5p, miR-517c, miR-338-3p, and miR-197. Among these, a significant downregulation was validated by qPCR for miR-192-5p, miR-31-5p, miR-517c, miR-338-3p, and miR-197, and the decrease paralleled the severity of the histological lesion. In silico analysis revealed several possible targets; miR-192-5p could bind the mRNA of two different molecules involved in innate immunity, i.e., NOD2 and CXCL2, that in fact showed an upregulation in the biopsies of CD patients, in particular in more severe cases. Immunolocalization and laser microdissection experiments co-localized miR-192-5p and its targets in the epithelium, and transfection experiments confirmed the interaction between miR-192 and NOD2. On the other end, miR-31-5p could target Foxp3, and a significant inverse correlation was observed between the miRNA and the target mRNA. To assess whether these changes were induced by gliadin, duodenal biopsies of celiac patients on glutenfree diet were stimulated in vitro, observing changes in miRNAs and target expression that were consistent with those detected in patients at first diagnosis. Thus, this third study confirmed the downregulation of miR-192-5p, miR-31-5p, and miR-3383p; it is interesting to note that data on miR-192 are similar to those observed in inflammatory bowel diseases (IBD). In fact, miR-192 was detected as significantly downregulated in colonic mucosa of patients with ulcerative colitis (Wu et al. 2008), with a resulting upregulation of CXCL2. Moreover, a review of the literature by Kalla et al. identified an IBD expression profile which included miR-192, miR-122, miR-29, and miR-146a, with miR-192 regulating NOD2 expression (Kalla et al. 2015). The “classical” CD clinical manifestation, characterized by diarrhea and malabsorption, is usually present in children. Performing endoscopy in children could be challenging, and, in fact, recent guidelines do not require duodenal biopsies for CD diagnosis in pediatric patients if certain conditions are met (Husby et al. 2012). In children with CD, the identification of a plasmatic biomarker which parallels the degree of the histological lesion could provide a great tool for physician in the follow-up of patients, both to assess the adherence to the gluten-free diet (particularly difficult in adolescents) and to evaluate the return to normality of the intestinal mucosa. In this regard, the identification of specific plasmatic miRNAs, able to correlate with the severity of the intestinal damage, could represent an important addition to the already available serology. For this reason, Buoli Comani et al. analyzed the expression of the miRNAs already identified as differentially expressed in adult CD patients not only in the duodenal biopsies of pediatric CD patients, but also in their plasma (before and after gluten-free diet) (Buoli Comani et al. 2015). The data obtained in this study revealed the presence of a similar pattern of miRNA expression in the duodenum of pediatric and adult CD patients; however when mRNA and protein levels of the expected targets of miR-192-5p, i.e., CXCL2

Fig. 4 Selected miRNAs identified as differentially expressed in the duodenal mucosa of celiac disease patients. The depicted miRNAs have been identified as up- or down-regulated in duodenal biopsies of celiac patients in different papers and were assigned to a specific cell type either by laser

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and NOD2 were analyzed, no variation was observed. On the contrary, a significant upregulation was detected for MAD2L1, another possible target of miR-192-5p. Interestingly, this gene is not involved in the immune response but in the control of cell cycle and thus in proliferation (Skinner et al. 2008); this suggests that the same miRNA can target different genes according to the developmental stage of the intestine. This issue should be taken into consideration, especially since celiac disease can appear in children with the introduction of wheat in the diet, i.e., within the first 2 years of life, when the intestine is still undergoing maturation, in particular regarding the immune system. In pediatric biopsies, both miR-21-5p and miR-21-3p were detected as significantly upregulated in Marsh 3C patients, differently from what is observed in adults; among others, STAT3 is a target of miR-21-5p, but this transcription factor is also a main regulator of miR-21-5p expression (Han et al. 2012). In fact, in the analyzed duodenal biopsies, no inverse correlation was detected between the miRNA and its target; on the contrary, STAT3 mRNA levels were increased in Marsh 3C samples, and an increased phosphorylation of the protein was also detected. These data further confirm the idea that the miRNA network undergoes several different mechanisms of regulation and that a more integrated approach is needed. The same miRNAs evaluated in the biopsies were analyzed in plasma, both in samples obtained from patients at diagnosis and on gluten-free diet. All but one (miR-338-3p) miRNAs were detectable in plasma; the analysis of samples at diagnosis showed variations in their expression similar to those detected in duodenal biopsies for some of them, i.e., a significant downregulation for miR-192-5p and miR-31-5p and upregulation for miR-21-5p in patients with a more severe lesion. However none of these miRNAs could be defined as a marker able to evaluate the recovery of the mucosa after a gluten-free diet, since no return to normal level was observed (miR-192-5p) or a clear cutoff could not be established (miR-31-5p and miR-21-5p). A more focused approach was recently performed by other authors that analyzed genes involved in autophagy and miRNAs regulating their expression (Comincini et al. 2017). They evaluated, both in biopsies and in blood of pediatric celiac patients, the mRNA levels of ATG7 (autophagy-related 7) and BECN1 (beclin 1); the former is an enzyme necessary for the autophagic process, whereas the latter is part of the intracellular complexes that induce autophagy. The expression of these two proteins can be regulated by miR-17 and miR-30a, respectively, and thus the levels of these two miRNAs were also evaluated. Interestingly, in blood only BECN1 levels were significantly different in controls and CD patients, whereas all the analyzed miRNAs and mRNAs showed a significant difference in their level of ä Fig. 4 (continued) microdissection (miR-192-5p), in vitro culture (miR-192-3p, miR-31-5p, and miR-1285-39), or due to the localization of their target (miR-449a, miR-31-5p, and miR-338-3p). ATG7, autophagy-related 7; BECN1, beclin 1; CXCL2, C-X-C motif chemokine ligand 2; FOXP3, forkhead box P3; KLF4, Kruppel-like factor 4; NOD2, nucleotide-binding oligomerization domaincontaining 2; RUNX1, runt-related transcription factor 1

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Table 1 miRNA detected as up- or downregulated in biopsies of pediatric or adult celiac disease patients, by microarray analysis. miRNAs indicated in bold have been validated by qPCR, confirming the microarray results; miRNAs in underlined italics showed no significant difference by qPCR. *indicates the miRNA originated from the 30 arm of the hairpin; *miRNAs are usually regarded as a “minor” product

Papers Capuano et al. 2011 Only miRNAs detected as differentially expressed in patient at diagnosis and on GFD are reported

Vaira et al. 2014 Independently from the phenotype Only in patients with “classical” phenotype Only in patients with “anemia” phenotype

Upregulated in pediatric CD miR-182 miR-196a miR-330 miR-449a miR-492 miR-500 miR-503 miR-504 miR-644

Downregulated in pediatric CD miR-105 miR-124a miR-135a miR-189 miR-202 miR-219 miR-299-5p miR-323 miR-379 miR-380-5p miR-409-5p miR-412 miR-512-3p miR-566 miR-576 miR-600 miR-614 miR-616 miR-618 miR-631 miR-659

Upregulated in adult CD

Downregulated in adult CD

miR-24-2-5p miR-146a-5p miR-491-3p miR-519d miR-551b-5p miR-4300 miR-523-3p miR-642b-3p miR-2113 miR-146b-3p miR-300 miR-302a-3p miR-337-3p miR-422a miR-432-5p miR-490-3p miR-498 miR-550b-3p miR-593-3p miR-618 miR-638 miR-642a-5p/ miR-642b-5p

miR-31-5p miR-192-5p miR-194-5p miR-215 miR-451a miR-338-3p miR-30b-5p miR-138-1-3p miR-192-3p miR-193a-5p miR-664-5p

(continued)

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Table 1 (continued)

Papers

Magni et al. 2014

Upregulated in pediatric CD

Downregulated in pediatric CD

Upregulated in adult CD miR-920 miR-1270 miR-1273e miR-1285-3p miR-1290 miR-1299 miR-1304-5p miR-2355-3p miR-3135a miR-3148 miR-3183 miR-3611 miR-3654 miR-3663-5p miR-3681-5p miR-4268 miR-4303 miR-4324 miR-4329 miRPlusI137 miRPlusI320a None detected

Downregulated in adult CD

miR-31-5p miR-192-5p miR-197 miR-338-3p miR-483-3p miR-517c miR-638

expression in biopsies. It must be noted, however, that in biopsies both miRNAs and their target were downregulated, fact that prompted the author to hypothesize that the reduced miRNA levels were due to an increased release in blood through exosomes. The authors also performed a ROC curve analysis that revealed a sensitivity of 65.22% and a specificity of 74.29% for BECN1 mRNA blood levels, which is encouraging but not sufficient for a diagnostic test. The data obtained by the different papers, summarized in Fig. 4 and Table 1, support the idea that there is a discrepancy between the level of miRNAs present in the affected tissue and their circulating counterpart and underline the need for future studies including a larger number of patients and the analysis of a wider panel of miRNAs. For this reason, a possible approach would be the sequencing of circulating miRNAs in a large cohort of patients, at diagnosis and after restoration of the intestinal mucosa.

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Last but not least, it should also be reminded the role of microbiota in shaping the miRNA profile of the intestinal mucosa, fact that has been demonstrated in mice (Singh et al. 2012; Archambaud et al. 2013; Peck et al. 2017) and that could, in turn, alter intestinal permeability through the regulation of tight junction (Nakata et al. 2017). This is even more important in the pathogenesis of celiac disease, since an alteration of the microbiota and the upregulation of various miRNAs was detected in a primate model of this disorder fed with a gluten-containing diet (Mohan et al. 2016); interestingly, among upregulated miRNAs there was miR-29b, able to bind claudin 1 mRNA and reduce its protein expression, thus interfering with tight junction formation.

Conclusions miRNAs play a pivotal role in the control of gene expression in the duodenum of patients with celiac disease, affecting various cell components and pathways. In fact, they intervene both in the regulation of the immune response and of the cell cycle and may act on different targets according to the differentiation level of the intestine. Although the measurement of their levels in the blood is currently feasible, and could provide additional clinical tools (either for the diagnosis or the follow-up), a wider analysis will be necessary to choose the miRNAs that need to be evaluated and determine, if possible, the correct cutoff values.

Mini-Dictionary of Terms • Innate immunity: The first response activated by the immune system toward external epitopes. It is a non-specific response that involves neutrophils, macrophages, dendritic cells, and natural killer cells. Several cytokines are produced and released during the innate response, including interleukin 15, which has a pivotal role in celiac disease pathogenesis. • Adaptive immunity: Secondary and specific response activated by the immune system toward external epitopes, which generates also an immunological memory. It involves antigen-presenting cells, helper-, cytotoxic-, and regulatory T cells, as well as B lymphocytes. Among the cytokines that are produced and released during the innate response, interferon gamma and interleukin 17 are particularly important in celiac disease pathogenesis. • Gluten: It includes two different categories of proteins, gliadins, and glutenins. These proteins are very rich in prolines and glutamines, fact that makes them very resistant to digestion by intestinal enzymes. • Gliadins: They can be subdivided in α-, γ-, and ω-gliadins. The a-gliadin molecule contains the 31–43 peptide, activator of innate immunity, and the 33-mer peptide, which triggers the adaptive response. • Tissue transglutaminase 2 (TG2): Ubiquitous enzyme with the ability to crosslink proteins. It is present in the intestinal mucosa, where it deamidates gliadin

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peptides (in particular 33-mer) increasing their affinity to HLA class II molecules. It represents the autoantigen in celiac disease.

Key Facts of Celiac Disease • Celiac disease is an autoimmune disorder triggered by the ingestion of gluten in genetically predisposed individuals. • Celiac disease prevalence is about 1% in Caucasic populations and is more frequent in females. • To develop celiac disease, subjects must carry a specific HLA heterodimer, either DQ2 (more common) or DQ8. • The passage of gluten peptides through the intestinal barrier is essential for the development of the disease, since gluten needs to get in touch with the immune system and activate it. • Triggering of autoimmunity in celiac disease causes intestinal damage and reduced absorption of several nutrients. • Clinical manifestations can appear at weaning or later, throughout the entire adult life, and can include diarrhea, anemia, and malnutrition. • The only current therapy for celiac disease is gluten-free diet.

Summary Points • In order to have the development of celiac disease, the intestinal barrier has to be altered and the immune system activated. • miRNA can regulate tight junction through the control of expression of different proteins, such as claudin 1 (miR-29a/miR-29b, miR-375, miR-142p, miR-155) and occludin (miR-122, miR-200c, miR-429). • miR-146a and miR-155 are important for the good functioning of adaptive immunity, regulating antigen-presenting and T cells. • In celiac disease, miRNA pattern of expression changes according to the age of presentation, clinical manifestation, or severity of the histological damage. • miR-449a upregulation in the duodenal mucosa of celiac pediatric patients has been associated with a decreased expression of NOTCH1 and KLF4 and reduced number of goblet cells. • A decrease in miR-192-3p and miR-31 was observed in adult celiacs with classical manifestations, and it was confirmed in fibroblasts isolated from patients. • A significant decrease in miR-192-5p was observed in celiacs with a severe histological lesion, associated with increased levels of NOD2 and CXCL2, proteins of innate immunity. • In celiacs, a decrease in miR-31-5p and miR-338-3p was detected, whereas their respective targets, FOXP3 and RUNX1 (essential for regulatory T cell development), showed increased expression levels.

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• Also miR-17 and miR-30a were downregulated in celiac mucosa, but no inverse correlation was detected with their targets, ATG7 and BECN1, proteins involved in autophagy. • Plasmatic miRNA levels could be a good tool to monitor mucosal healing, but data are still scanty and with no perfect correlation with mucosal damage.

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Diet and Epigenetic Alteration of Renal Function

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Eva Nüsken, Kai-Dietrich Nüsken, and Jörg Dötsch

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Dietary Intake During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutrient Deficiency During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Low-Protein Diet During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . High-Salt Diet During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maternal Obesity and High-Fat Diet During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Micronutrient Deficiency During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Substance Use During Gestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Altered Dietary Intake in the Postnatal Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Early Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postnatal Calorie Restriction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postnatal High-Salt Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postnatal High-Fat Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postnatal Micronutrient Deficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular Mechanisms of Dietary Renal Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dietary Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mini-dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Nephrogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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E. Nüsken · K.-D. Nüsken Department of Pediatrics, Pediatric Nephrology, University of Cologne, Cologne, Germany e-mail: [email protected]; [email protected] J. Dötsch (*) Department of Pediatrics, University of Cologne, Cologne, Germany e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_12

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Abstract

Adequate nutrition is fundamental to ensure undisturbed renal development. Macro- and micronutrient deficiency as well as energy overload or high-salt intake during gestation may significantly impair nephrogenesis and induce susceptibility toward disease. In addition, there is growing evidence that nutrition during early postnatal life is an important modulator of adult blood pressure and kidney function. The exact renal phenotype strongly depends on the type of dietary influence and the window of exposure. Thus, reduced glomerular count, microvascular rarefaction, and increased fibrosis are possible morphological findings. On the functional level, blood pressure levels, urinary protein excretion, and glomerular filtration rate are subject to dietary influences. Mechanistically, dysregulation of renin-angiotensin-aldosterone system (RAAS) components and other vasoactive substances, oxidative stress, altered mitochondrial energy metabolism, endoplasmic reticulum stress, and inflammatory processes are key factors. The present chapter gives an overview on current knowledge of dietary programming of renal disease. Defining the adequate amount of macro- and micronutrients which is needed for optimal kidney development remains a challenge for the future. Keywords

Blood pressure · Glomerular count · Glomerular filtration rate · High-fat diet · High-salt diet · Low-protein diet · Micronutrient deficiency · Nephrogenesis · Nephron number · Proteinuria · Renin-angiotensin-aldosterone system List of Abbreviations

ACE ACH09 AGT AT1 AT2 AMPK BiP CD2AP CHOP CR eGFR ER stress GFR HFD IQ IUGR KIM-1 LCPUFAs LP mTORC1

Angiotensin-converting enzyme Grape skin extract with antioxidant properties Angiotensinogen Angiotensin II receptor type 1 Angiotensin II receptor type 2 50 adenosine monophosphate-activated protein kinase ER stress marker CD2-associated protein ER stress marker Calorie restriction Estimated glomerular filtration rate Endoplasmic reticulum stress Glomerular filtration rate High-fat diet Intelligence quotient Intrauterine growth restriction Kidney injury molecule-1, biomarker for renal proximal tubule injury Long-chain polyunsaturated fatty acids Low protein Mammalian target of rapamycin complex 1

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NaCl NCC NGAL NKCC2 PR RAAS SGA SS-31 TRPC6 US

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Sodium chloride Sodium-chloride symporter Neutrophil gelatinase-associated lipocalin, biomarker for acute kidney injury Sodium-potassium-chloride cotransporter Protein restriction Renin-angiotensin-aldosterone system Small for gestational age Substance which protects mitochondrial cristae Transient receptor potential cation channel, subfamily C, member 6 United States

Introduction Renal development is tightly regulated (Challen et al. 2005), and human nephrogenesis starts by the tenth postconceptional week (Quigley 2012). In termborn children, the number of nephrons is determined at birth, whereas in preterm infants, nephron number may still increase postnatally (Sutherland et al. 2011; Fanni et al. 2012). Epidemiologic studies have linked intrauterine and early childhood conditions to unfavorable course of glomerulopathies (Zidar et al. 1998; Sheu and Chen 2001), decreased renal function in adulthood (Hallan et al. 2008), and increased risk of end-stage renal disease (Vikse et al. 2008). Serial ultrasound measurements of the kidney in a cohort of small for gestational age (SGA) infants revealed a marked reduction of renal growth rate compared to controls between 26 and 34 weeks of gestation, which suggests that the third trimester could be a critical period of renal programming (Konje et al. 1996). Although a variety of environmental conditions may influence nephrogenesis, adequate nutrition counts among the key factors ensuring undisturbed renal development (Fanni et al. 2012). Generally, altered intake of macro- or micronutrients during critical developmental windows may lead to long-lasting effects on organ development, organ function, and susceptibility toward disease. In order to elucidate the mechanisms underlying dietary programming of renal disease, numerous animal models have been developed. Most authors have worked with rats in which kidney development is not completed before the second week of postnatal life (Neiss and Klehn 1981). The effects of dietary influences on renal outcome have been studied in a large variety of experimental settings like low-protein diet during gestation (Zeman 1968; Langley-Evans et al. 1999), calorie restriction during gestation (Bregere et al. 2010), maternal (YamadaObara et al. 2016) or postnatal high-fat diet (Aliou et al. 2016), maternal sodium overload (Cardoso et al. 2009), or postnatal hypernutrition (Boubred et al. 2007). Regarding precise molecular mechanisms, a critical role of the renin-angiotensin system has been established (Woods et al. 2001; Bogdarina et al. 2007). However, programming of renal disease is a very complex process which cannot be explained by single dysregulated genes. The following article gives an overview on current knowledge of dietary programming of renal disease (Figs. 1, 2, 3, 4, and 5).

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Fig. 1 Dietary programming of renal function. Schematic overview presenting the concept of dietary programming of renal function

Fig. 2 Important aspects of intrauterine supply. Schematic overview presenting important aspects of intrauterine supply with regard to dietary programming of renal function

Effects of Dietary Intake During Gestation Nutrient Deficiency During Gestation Over the last decades, epidemiologists have studied the effects of exposure to famine during gestation on adult health parameters. Thus, it could be shown that the prevalence of microalbuminuria was elevated in adult persons aged 48–53 years

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Fig. 3 Important aspects of postnatal supply. Schematic overview presenting important aspects of postnatal supply with regard to dietary programming of renal function

Fig. 4 Mutual interaction between kidney morphology and kidney function in the context of programming. Schematic overview presenting the mutual interaction between kidney morphology and kidney function in the context of programming

who had been exposed to the Dutch Hunger Winter 1944/1945 during midgestation (Painter et al. 2005). Women born in rural areas during the Chinese famine years of 1959–1961 also had higher levels of proteinuria in their forth decade of life compared to women born before or after the famine (Huang et al. 2014).

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Fig. 5 Mechanisms contributing to dietary programming of renal function. Schematic overview presenting mechanisms contributing to dietary programming of renal function

Male rat offspring exposed to a multideficient diet in utero showed elevated plasma volume, blood pressure, and parameters of oxidative stress in the kidney (Magalhaes et al. 2006). In a baboon model, calorie restriction (CR) during gestation reduced tubular density (Cox et al. 2006) and altered fetal expression of genes affecting renal mitochondrial energy metabolism (Pereira et al. 2015). However, there is also some evidence for a potential beneficial effect of CR during gestation. A study in sheep suggested that CR in early gestation may provide protective effects against obesity-related nephropathy in the offspring by modulating the inflammatory system (Sharkey et al. 2009).

Low-Protein Diet During Gestation The specific effect of protein restriction during gestation was tested in animal models. In 1968 already, Frances Zeman subjected rat dams to a casein restriction throughout gestation (6% vs. 24% in controls). Pups were sacrificed immediately after birth, and the kidneys were processed for histological studies. Kidneys from protein-restricted animals were characterized by fewer and less well-differentiated

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glomeruli, a greater proportion of connective tissue, and relatively fewer collecting tubules. Three decades later, Langley-Evans and colleagues studied different groups of rat offspring exposed to 9% casein restriction either throughout gestation or for single-week periods (days 0–7, 8–14, 15–22 of gestation). Controls were fed a diet containing 18% casein. At birth, nephron number was significantly reduced in offspring exposed to low-protein diet during the second and third weeks of gestation only. This indicates that the impact of protein deficiency depends on the window of exposure (Langley-Evans et al. 1999). However, low-protein (LP) diet during gestation resulted in reduced nephron numbers in most studies with the exception of protein restriction during early pregnancy only (Jones et al. 2001; Woods et al. 2001, 2004; Siddique et al. 2014). Depending on the exact model and the time point investigated, many studies also demonstrate elevated arterial blood pressure (Langley-Evans et al. 1999; Woods et al. 2004; Black et al. 2015; Lozano et al. 2015) and reduced glomerular filtration rate (Nwagwu et al. 2000; DuBois et al. 2014; Lozano et al. 2015). Microvascular rarefaction, as shown in a sheep model, may contribute to these findings (Lloyd et al. 2012). Further functional restrictions in adult LP rat offspring were demonstrated in a study evaluating the furosemide diuretic response during adulthood. The extent of diuresis was decreased in both male and female LP animals (DuBois et al. 2014) indicating altered tubular function. In addition, low-protein animals may have less regenerative capacities to recover from secondary renal injury (Plank et al. 2006).

High-Salt Diet During Gestation Human data on kidney function of children exposed to defined amounts of sodium chloride during gestation is not available. Kidney outcome in rat offspring whose mothers were exposed to water containing 1% sodium chloride throughout gestation was studied at the age of 3 months. Interestingly, only urinary protein excretion was increased in the saline group, whereas the number of nephrons, blood pressure, renal hemodynamics, and renal oxidative stress were not different between the groups. If the dams were also maintained on saline during lactation, the effect on the renal phenotype in the offspring was more prominent. These animals also had a reduced glomerular filtration rate, signs of oxidative stress, and an increase in plasma volume (Cardoso et al. 2009). In another study, both lowsodium (0.07% NaCl) and high-sodium (3%) diet throughout gestation and lactation caused reduced glomerular number. In addition, high-salt (3%) offspring developed arterial hypertension (Koleganova et al. 2011). Adult offspring exposed to 4% NaCl diet throughout gestation and lactation developed hypernatremia and elevated plasma levels of corticosterone. Blood pressure was only elevated in male offspring (Gray et al. 2013). In ewes fed a high-salt diet, kidney weight to body weight ratio was reduced in the offspring going along with changes in the renal and systemic renin-angiotensin-aldosterone system (Chadwick et al. 2009, Mao et al. 2013).

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Maternal Obesity and High-Fat Diet During Gestation Epidemiologic studies suggest that there is an association between maternal obesity and elevated systolic blood pressure in midchildhood (Perng et al. 2014). In addition, a preconceptional body mass index of greater than 30 kg/m2 was identified as a risk factor for bilateral renal aplasia/hypoplasia (Slickers et al. 2008). Consistent with these findings, rat offspring exposed to high-fat diet during gestation and weaned to normal-fat diet developed arterial hypertension. On the molecular level, permanent changes in key RAAS elements were dependent on the exact window of exposure (Guberman et al. 2013). In another study, rat offspring exposed to a lard-rich diet during gestation had reduced renal renin activity and Na+, K+ATPase enzyme activity at 180 days of age (Armitage et al. 2005). Both in utero and postnatal exposure of rats to high-fat diet induced glomerulosclerosis and tubulointerstitial fibrosis at 17 weeks of age going along with albuminuria (Jackson et al. 2012).

Micronutrient Deficiency During Gestation Small epidemiologic studies suggest that maternal vitamin A deficiency might be associated with reduced renal volume in the neonate (Goodyer et al. 2007; El-Khashab et al. 2013). This hypothesis is supported by the observation that vitamin A deprivation during rat gestation induced a nephron deficit in the offspring (Lelievre-Pegorier et al. 1998). A similar effect was demonstrated in rat models of iron and zinc deficiency during gestation. Adult offspring of iron-deficient dams had reduced glomerular density and arterial hypertension (Lisle et al. 2003). Zinc deficiency during gestation and postweaning period caused a nephron deficit, increased systolic blood pressure, and decreased glomerular filtration rate going along with signs of oxidative stress, fibrosis, and increased apoptosis. Postweaning normalization of zinc only partially normalized these findings (Tomat et al. 2008).

Substance Use During Gestation It is general knowledge that substance use during gestation can have negative influence on the health and well-being of the offspring. Although alterations in organ development and function are often considered as toxic effects in the first line, there is an overlap between toxic and programming effects during critical developmental windows. In a large human cohort study, maternal smoking showed a dose-dependent effect on fetal kidney volume (Taal et al. 2011). Continued maternal smoking during pregnancy was associated with lower eGFR in school-aged children, whereas smoking during the first trimester was only associated with a higher risk of microalbuminuria (Kooijman et al. 2015). Another epidemiological study demonstrates

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that smoking during the periconceptional period may increase the risk of bilateral renal aplasia/hypoplasia. The same study also identified binge drinking during the second month of pregnancy as a risk factor for renal agenesis/hypoplasia (Slickers et al. 2008). In animals, ethanol exposure on two occasions during rat gestation induced low nephron number and elevated arterial blood pressure in the offspring (Gray et al. 2010). Rat metanephroi cultured in the presence of ethanol showed less ureteric branching and reduced glomerular numbers. Coculture with retinoic acid ameliorated these findings (Gray et al. 2012). In sheep, repeated alcohol exposure during the second half of gestation similarly reduced nephron numbers in the offspring (Gray et al. 2008). Rat offspring exposed to 120 mg/kg of caffeine daily during gestation developed significant renal alterations indicative of glomerulosclerosis and interstitial fibrosis. In detail, the authors observed thickening of the glomerular basement membrane, expansion of the mesangium, partial tubular atrophy, and ultrastructural damage of podocytes. On the molecular level, low renal angiotensin II receptor type 2 expression was present in both fetal and adult offspring (Ao et al. 2015).

Effects of Altered Dietary Intake in the Postnatal Period Early Nutrition There is growing evidence that early nutrition as represented by early weight gain is an important modulator of adult blood pressure and kidney function. Epidemiologic data suggest that not only prenatal but also postnatal growth patterns are associated with adult blood pressure (Ben-Shlomo et al. 2008). In a large US multicenter cohort study, pronounced catch-up growth in small for gestational age (SGA) babies resulted in an increased risk of arterial hypertension. Since lack of catch-up growth was associated with an increased risk of infection and low IQ, the authors suggested that rapid catch-up growth to the 30th centile during the first months of life and slower catch-up growth up to the 50th centile might be ideal (Lei et al. 2015). Another SGA cohort study observed that nutrition with a proteinenriched formula during the first months of life was associated with increased diastolic and mean arterial blood pressure values at 6–8 years of age. In a rat model of placental insufficiency, cross-fostering the restricted offspring to control dams ameliorated both renal morphology and blood pressure in adulthood. Restricted pups raised by their own mothers developed arterial hypertension and reduced nephron number. Cross-fostering restricted pups to control mothers normalized nephron number and prevented the development of arterial hypertension (Wlodek et al. 2007). In another study, offspring exposed to LP diet prenatally and cross-fostered to foster dams receiving standard diet did not develop elevated blood pressure and presented with normal glomerular filtration rate in adulthood (Lozano et al. 2015). Conversely, offspring from standard diet dams cross-fostered to a lowprotein foster mother (NP/LP) developed arterial hypertension. On the molecular level, the latter group had increased renal NKCC2 and NCC cotransporter

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expression which was not present in a LP/LP group. Both NP/LP and LP/LP offspring had elevated plasma renin and angiotensin II levels (Siddique et al. 2014). However, control rats exposed to early hypernutrition induced by litter-size reduction developed significantly increased systolic blood pressure and proteinuria (Boubred et al. 2007) and decreased glomerular filtration rate (Alcazar et al. 2012). On the morphological level, the number of nephrons was increased, but there was enhanced glomerulosclerosis (Boubred et al. 2007). Mechanistically, this was accompanied by SOCS-3-mediated intrinsic renal leptin resistance (Alcazar et al. 2012). When litter-size reduction was performed in offspring exposed to LP diet in utero, the animals similarly developed arterial hypertension and glomerulosclerosis, but nephron number was reduced (Boubred et al. 2009).

Postnatal Calorie Restriction In a mouse model, calorie restriction (CR) mitigated the effects of renal aging. Thus, CR mice had decreased glomerular basement membrane thickening and autophagy. Interestingly, a modulation of dietary fat composition had additional beneficial effects (Calvo-Rubio et al. 2016). In another study investigating the effect of calorie restriction on renal ischemia reperfusion injury, CR showed dose-dependent protective effects. Protein restriction (PR) in addition to CR further improved renal outcome. From a mechanistic point of view, the authors provide evidence that altered activation of AMPK and mTORC1 could play a crucial role and that administration of leptin mitigates the beneficial effects of CR/PR (Robertson et al. 2015).

Postnatal High-Salt Diet In a rat study, challenge with drinking water containing 2% sodium chloride from postnatal week 12 until postnatal week 16 did not alter blood pressure or renal function in control animals. In contrast, former IUGR offspring developed sodium-dependent hypertension, reduced glomerular filtration rate, and albuminuria (Sanders et al. 2005).

Postnatal High-Fat Diet Adult mice fed a high-fat diet (HFD) for 14 weeks developed significant proteinuria. Renal gene expressions of renin, angiotensin-converting enzyme, and tubular injury markers NGAL and KIM-1 were elevated. Protein expressions of ER stress markers BiP and CHOP were also increased (Li et al. 2016). In another mouse model, longterm HFD for 28 weeks resulted in enhanced mitochondrial damage in podocytes, proximal tubules, and endothelial cells. Furthermore, infiltration with CD68+ macrophages was increased, and proinflammatory markers were upregulated. Treatment with SS-31 which protects mitochondrial cristae structure mitigated all these effects (Szeto et al. 2016). In rats, high-fat diet induced proteinuria and ultrastructural

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changes in podocyte morphology. On the molecular level, the authors observed changes in the expression of proteins relevant for podocyte function like desmin, nephrin, and CD2AP (Chen et al. 2016).

Postnatal Micronutrient Deficiency Vitamin D levels are known to be reduced in many different clinical settings. In a population-based cohort study, low plasma 25-hydroxyvitamin D levels were associated with an increased risk of developing albuminuria (Keyzer et al. 2015). However, in another population, these findings could not be confirmed (Guessous et al. 2015). In rodents, it could be shown that 1,25-vitamin D3-deficient animals develop podocyte injury (Sonneveld et al. 2016). In these mice, a slit diaphragm protein expressed by podocytes (TRPC6) was upregulated. Administration of 1,25-D3 brought TRPC6 expression back to normal and normalized podocyte morphology and urinary protein excretion (Sonneveld et al. 2013). In a lacto-vegetarian population, the role of micronutrient deficiency was studied in the context of arterial hypertension. Data analysis revealed that low intake of vitamin C, folic acid, and zinc may increase the risk of developing arterial hypertension (Chiplonkar et al. 2004).

Molecular Mechanisms of Dietary Renal Programming Regarding molecular mechanisms of dietary renal programming, there is strong evidence for a critical role of the renin-angiotensin-aldosterone system (RAAS), although study results are partly contradictory. In newborn low-protein rat pups, renal renin gene and protein expression was significantly suppressed (Woods et al. 2001). In the adrenal gland of neonatal LP rats, gene expression of the angiotensin II receptor type 1b was upregulated (Bogdarina et al. 2007). At 4 weeks of age, male LP rats responded with a greater decrease in glomerular filtration rate when challenged with a bolus of enalapril followed by an infusion of angiotensin II. In the kidney, angiotensin II receptor type I expression was increased (Sahajpal and Ashton 2003). At 4 and 13 weeks of age, plasma angiotensin-converting enzyme (ACE) activity in LP offspring was elevated going along with increased systolic blood pressure. Renin activity in these animals was normal, and plasma angiotensin II concentrations were only slightly elevated (Langley-Evans and Jackson 1995). High-salt diet (8% NaCl) during the second half of gestation in ewes induced decreased plasma angiotensin II levels in fetal offspring which normalized postnatally. Gene expressions of AGT, AT1, AT2, and ACE were elevated during fetal life and, except for AT2, decreased at postnatal day 90 (Mao et al. 2013). In another sheep study, salt-enriched diet (14% NaCl) or grazing saltbush during gestation decreased basal renin activity in the offspring. Challenged with salt, saltbush offspring still had decreased renin activity, whereas renin activity in high-salt offspring

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normalized (Chadwick et al. 2009). High-fat diet during gestation, lactation, or postweaning specifically influenced RAAS elements in adipose tissue dependent upon the timing of exposure (Guberman et al. 2013; Li et al. 2016). Treatment of human proximal tubule epithelial cells (HK2) with saturated fatty acid palmitic acid induced an increase in the cellular expression of ER stress markers as well as increased angiotensin II concentrations in cultured medium. RAAS blockade with valsartan or aliskiren protected the cells from the development of ER stress (Li et al. 2016). Caffeine exposure during gestation in rats induced downregulation of the angiotensin II receptor type 2 (Ao et al. 2015). However, not only RAAS components but also other vasoactive substances and blood pressure-regulating systems are nutrient-sensitive. Thus, maternal LP diet in rats reduced renal 11betaHSD2 expression in adult offspring (Bertram et al. 2001). Other mechanisms involved in dietary programming of renal function are oxidative stress (Magalhaes et al. 2006; Tomat et al. 2008; Costa et al. 2016), mitochondrial energy metabolism (Pereira et al. 2015; Szeto et al. 2016), ER stress (Li et al. 2016), and inflammation (Szeto et al. 2016).

Dietary Interventions A better knowledge of the mechanisms and principles leading to programming of renal disease bears the opportunity to develop strategies with the aim of modifying the long-term effects of unfavorable environmental conditions (“reprogramming”). In addition, this knowledge can also be used to establish interventional approaches to influence the natural course of “nonprogrammed” pathologic conditions. Most studies on “nutritional reprogramming” of renal disease have been performed in animals. However, there are a couple of interventional studies in humans. In rural Bangladesh, the effect of early food supplementation (608 kcal/day energy and 18 g/ day of vegetable protein) and micronutrient supplementation (containing either iron and folate or multiple micronutrients) during pregnancy was studied. Adjusted analysis provided evidence that there is a small but significant positive effect of food supplementation on blood pressure at age 4.5 years. In addition, high-iron supplement was associated with a higher GFR (Hawkesworth et al. 2013). Nepalese children whose mothers had been supplemented with the daily allowance of 15 vitamins and minerals during pregnancy had a slightly lower blood pressure at age 2.5 years than controls (Vaidya et al. 2008). Maternal supplementation with folic acid or folic acid + iron + zinc reduced the risk of developing albuminuria in 6–8-year-old Nepalese children (Stewart et al. 2009). In an Argentinian study, the effect of additional 2 g calcium per day during pregnancy was studied in 5–9-year-old children. Children whose mothers had been assigned to the calcium group had lower systolic blood pressure values (Belizan et al. 1997). Calcium supplementation in a small group of 11year-old children also provided a small blood pressure-lowering effect (Gillman et al. 1995). In a small European study, supplementation of infant formula with LCPUFAs reduced blood pressure values at 6 years of age compared to standard formula-fed infants (Forsyth et al. 2003).

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In animals, most dietary interventions have been performed in the low-protein model of IUGR. Thus, it is obvious that supplementation of single amino acids might have a positive effect. Indeed, supplementation of a low-protein diet with 3% glycine during gestation prevented the development of arterial hypertension in the offspring (Jackson et al. 2002). Since oxidative stress may significantly impair nephrogenesis and adequate levels of retinoic acid are crucial for normal kidney development (Lee et al. 2012), several studies have focused on the importance of retinoic acid and other antioxidants. In one study, administration of a single dose of retinoic acid (20 mg/kg) during low-protein gestation at embryonic day 11.5 normalized nephron number in the offspring (Makrakis et al. 2007). However, postnatal treatment with retinoic acid in preterm baboons had neither beneficial nor negative effects on glomerular number or glomerular morphology (Sutherland et al. 2009). Supplementation of lazaroid, a lipid peroxidase inhibitor, throughout LP gestation prevented the development of arterial hypertension. In addition, capillary density normalized and measures of vascular function improved (Cambonie et al. 2007). Administration of ACH09, a grape skin extract with antioxidant properties, normalized nephron numbers in LP offspring (Costa et al. 2016). A further approach is the administration of food or spices with anti-inflammatory properties. Curcumin has been studied in various settings and exerts both antiinflammatory and antioxidant effects. In a rat model of postnatal kidney damage induced by unilateral ureteral obstruction, there was evidence for the activation of an antiapoptotic mechanism by increased expression of the TRADD-RIP-TRAF complex (Hashem et al. 2016). Another study could demonstrate that curcumin can alleviate diabetic nephropathy by regulating the Wnt pathway (Ho et al. 2016).

Conclusion Both macro- and micronutrient deficiency and overload during critical developmental windows may significantly impair nephrogenesis and induce susceptibility toward disease. Defining the adequate amount of macro- and micronutrients which is needed for optimal kidney development remains a challenge for the future.

Mini-dictionary of Terms • Blood Pressure – Intravascular pressure. Blood pressure is influenced by cardiac output, vascular resistance and intravascular volume. • Gestation – Pregnancy. • Glomerular count – Used to estimate nephron number. • Glomerular Filtration Rate (GFR) – Blood volume which is filtered by the kidney during a defined time unit. GFR is the most common measure of renal function. In clinical practice, creatinine or cystatin C based calculations are usually used to estimate GFR.

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• High-Fat Diet – Nutrient composition with increased percentage of fat (e.g. 3060% of calories in rodents). • High-Salt Diet – Increased dietary intake of salt. • Low-Protein Diet – Reduced intake of protein (e.g. in rodents: 6-9 g protein in 100 g diet). • Micronutrient Deficiency – Dietary lack of one or more micronutrients (e.g. vitamins, minerals) essential to an individual’s health. • Nephrogenesis – Development of the kidney. • Nephron Number – The nephron is the basic structural and functional unit of the kidney. Nephron number considerably varies between individuals. Low nephron number has been linked to an increased risk of cardiovascular and renal disease. • Postnatal(ly) – After birth. • Proteinuria – Increased excretion of proteins by the kidney resulting in elevated concentrations of proteins in the urine. • Renin-angiotensin-aldosterone system (RAAS) – Hormons (renin, angiotensinogen, angiotensin, aldosterone) interacting to regulate blood pressure, intravascular volume and plasma sodium concentration. In addition, local effects like cardiac remodeling are known.

Key Facts of Nephrogenesis • Nephrogenesis is a tightly controlled process, and small changes in gene or protein expression during critical timespans may significantly impair renal development. • Human nephrogenesis starts by the tenth postconceptional week. • The final kidney originates from two embryonic tissues: the ureteric bud forming the collecting duct system of the kidney and the mesonephros which will form the nephrons. • In humans, the number of nephrons is fixed at term, but it can vary between three hundred thousand and two millions per kidney • There are numerous causes for a reduction in nephron number including ethnicity; prematurity; placental insufficiency; maternal diets deficient in protein, iron, or vitamin A; maternal hyperglycemia; and intrauterine exposure to certain drugs (e.g., COX-2 inhibitors). • Reduced nephron number has been linked to the development of arterial hypertension and susceptibility toward renal disease in later life.

Summary Points • Adequate nutrition is fundamental to ensure undisturbed renal development. • Macro- and micronutrient deficiency as well as energy overload or high-salt intake during gestation may significantly impair nephrogenesis and induce susceptibility toward disease.

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• In addition, there is growing evidence that early nutrition is an important modulator of adult blood pressure and kidney function. • The exact renal phenotype strongly depends on the type of dietary influence and the window of exposure. • In order to elucidate the mechanisms underlying dietary programming of renal disease, numerous animal models have been developed. • Reduced glomerular count, microvascular rarefaction, or increased fibrosis are possible morphological findings. • Mechanistically, dysregulation of renin-angiotensin-aldosterone system (RAAS) components and other vasoactive substances, oxidative stress, altered mitochondrial energy metabolism, endoplasmic reticulum stress, and inflammatory processes are key factors. • On the functional level, blood pressure levels, urinary protein excretion, and glomerular filtration rate are subject to dietary influences. • In interventional studies, the effect of dietary supplementation with vitamin A, iron, folic acid, zinc, calcium, long-chain polyunsaturated fatty acids, or antioxidants has been tested and yielded some beneficial effects. • Defining the adequate amount of macro- and micronutrients which is needed for optimal kidney development remains a challenge for the future.

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Liver Diseases: Epigenetic Mechanisms, Oxidative Stress, and Use of Alpha-Lipoic Acid

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Aleksandra Uskoković, Svetlana Dinić, Jelena Arambašić Jovanović, Goran Poznanović, Melita Vidaković, and Mirjana Mihailović

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liver Diseases: The Role of Epigenetic Mechanisms and Oxidative Stress . . . . . . . . . . . . . . . . . . A Link Between Oxidative Stress and Epigenetic Modifications in Liver Pathologies . . . . . . Alpha-Lipoic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Protective Effects of Alpha-Lipoic Acid in Liver Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Liver Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Alpha-Lipoic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The liver is the central organ for lipid and glucose metabolism. Impaired homeostasis of metabolism promotes the development of nonalcoholic fatty liver disease which is recognized worldwide as the most common liver disease. It covers the entire spectrum of liver disorders, from steatosis which can progress to steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma. Nonalcoholic fatty liver disease is primarily associated with the metabolic syndrome, which is assumed to represent the hepatic manifestation of the metabolic syndrome. Besides endogenous factors such as the metabolic syndrome, obesity, hypertriglyceridemia, and diabetes, all important risk factors for the development and progression of liver injury, increased alcohol consumption, certain drugs, and

A. Uskoković (*) · S. Dinić · J. A. Jovanović · G. Poznanović · M. Vidaković · M. Mihailović Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia e-mail: [email protected]; [email protected]; [email protected]; jelena. [email protected]; [email protected]; [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_112

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environmental contaminants can also induce hepatotoxicity. Epigenetic alterations that are involved in the regulation of hepatic lipid metabolism and the oxidative stress response are important players in the development and progression of liver diseases. Concerning the vital role of oxidative stress in the etiology of liver injury, a number of studies have established the efficacy of antioxidants in the prevention and treatment of liver disease. Alpha-lipoic acid is a naturally occurring compound with a powerful in vivo antioxidant activity that can modulate the redox status of cells and the activities of proteins, thus affecting cell signaling and transcriptional responses involved in glucose and lipid metabolism. This review summarizes the effects of alpha-lipoic acid in liver pathologies related to obesity, metabolic disorders, diabetes, nonalcoholic fatty liver disease, drug toxicity, and radiation. The many beneficial effects of alpha-lipoic acid include improvement of liver transaminases, enhanced scavenging of reactive oxygen species, increased activities of antioxidant enzymes and the resulting decrease in oxidative stress and inflammatory signals, reduced DNA damage, suppression of the fibrotic process, and improved lipid metabolism. In addition, alpha-lipoic acid administration could indirectly prevent epigenetic modifications in the liver by scavenging reactive oxygen species and regulating the NAD+/ NADH ratio which is important for NAD+-dependent deacetylase sirtuin activity. Alpha-lipoic acid also mitigates the changes in DNA methylation in rat liver induced by low-density irradiation. However, the majority of alpha-lipoic acid actions have been primarily observed in in vitro and in vivo experimental studies. Translation of this biological knowledge and experimental data to human clinical use warrants further investigation. Keywords

Nonalcoholic fatty liver disease · Steatosis · Fibrosis · Alpha-lipoic acid · Drug toxicity · Reactive oxygen species · Oxidative stress · Antioxidant enzymes · DNA methylation · Sirtuins List of Abbreviations

α-SMA ALT AMPK ARE AST CAT CBP DHLA FoxO Foxo3a GCL GCLC GCLM GPx

α-smooth muscle actin Alanine aminotransferase AMP-activated protein kinase Antioxidant response element Aspartate aminotransferase Catalase CREB binding protein Dihydrolipoic acid Forkhead box O Forkhead transcription factor 3a γ-Glutamylcysteine ligase Catalytic subunit of GCL Modulatory subunit of GCL Glutathione peroxidase

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GSH GSSG HAT HDAC 4HNE HSC LA miRNA MDA MMP-2 NAFLD Nrf2 ROS SAM SIRT1 SOD SREBP-1 TGF-β1

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Glutathione Oxidized glutathione Histone acetyl transferases Histone deacetylases 4-hydroxynonenal Hepatic stellate cells Alpha-lipoic acid microRNA Malondialdehyde Matrix metalloproteinase-2 Nonalcoholic fatty liver disease Nuclear factor erythroid 2-related factor 2 Reactive oxygen species S-adenosyl methionine Sirtuin 1 Superoxide dismutase Sterol regulatory element-binding protein 1 Transforming growth factor-β1.

Introduction Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide (Bedogni et al. 2005). According to the underlying pathogenesis, NAFLD can be categorized as primary NAFLD, which is associated with the metabolic syndrome, and secondary NAFLD, which is connected to other factors such as exposure to xenobiotics, parental nutrition, acute starvation, surgical interventions, and use of certain drugs (Paschos and Paletas 2009). NAFLD covers a wide range of disorders, ranging from simple steatosis to steatohepatitis, fibrosis and cirrhosis and hepatocarcinoma (Hijona et al. 2010). Hepatic steatosis involves the intracellular accumulation of triglycerides that leads to the formation of lipid droplets in hepatocytes. When steatosis is accompanied by inflammation, the result is increased mitochondrial oxidative stress, generation of free radicals and peroxisomes, and progression to nonalcoholic steatohepatitis (Chalasani et al. 2003). The final end-stage of chronic inflammation and chronic liver diseases is fibrosis, characterized by an accumulation of the extracellular matrix rich in fibrillar collagens and scar tissue formation (Xu et al. 2012). Hepatic fibrosis is mainly characterized by cellular activation of hepatic stellate cells (HSC) that change from vitamin A storing quiescent cells to myofibroblast-like cells. The process of fibrosis can progress to cirrhosis, which manifests through liver cell injury, liver dysfunction, portal hypertension, and an increased risk of liver cancer (Fig. 1). As the progression of liver pathologies is dependent on the interplay between host genetics and environmental factors that determine the responsiveness/sensitivity of host liver cells to initial injury, it is not surprising that epigenetic mechanisms which mediate environment-gene interactions are emerging as the central element in the development of severe liver disease.

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Fig. 1 Stages of liver damage. Different exogenous and endogenous factors disrupt liver metabolism, which promotes the accumulation of triglycerides, liver enlargement, and formation of fatty liver. Epigenetic dysregulation, prolonged inflammation, and scar tissue formation lead to fibrosis, which can progress to cirrhosis, portal hypertension, liver failure, with an increased risk of hepatocellular cancer development

Liver Diseases: The Role of Epigenetic Mechanisms and Oxidative Stress Epigenetic regulation in liver diseases involves three major mechanisms: DNA methylation, noncoding RNAs of which microRNAs (miRNA) are the best characterized, and posttranslational modifications of the amino acid tails of histones (Fig. 2). These processes elicit different reactions that affect gene expression without changing the primary DNA sequence. According to literature data, the DNA methylation status of key fibrosis gene loci in liver tissue differs in patients with nonalcoholic steatohepatitis according to the degree of fibrosis intensity (Zeybel et al. 2015). Namely, genes involved in fibrogenesis, including TGFβ1 and PDGFα, are more methylated and consequently less expressed in patients with a mild degree of fibrosis intensity, whereas anti-fibrogenic genes such as PPARα and PPARδ are found to be less methylated (Zeybel et al. 2015). This study provided additional evidence for the assumption that DNA methylation is an important factor that determines the development of fibrosis and progression of steatohepatitis towards

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severe liver disease. Besides DNA methylation, alterations in intrahepatic miRNA networks have been associated with different aspects of liver disease (Bandiera et al. 2015). For example, miR-122, the most frequent miRNA in adult liver which accounts for 70% of the total liver miRNA pool and is an important player in liver physiology and disease, is downregulated in steatohepatitis. It thus correlates with disease severity and its progression from steatosis to steatohepatitis (Bandiera et al. 2015). Histone modifications also provide a link between an epigenetic process and liver disease. Histones can be acetylated and methylated on lysine and arginine, phosphorylated on serine, as well as ubiquitinated, sumoylated, and ADP-ribosylated. These modifications induce conformational changes in chromatin structure and affect gene expression either by promoting or suppressing transcription. The most prominent chromatin modification to be described in liver diseases was related to acetylation/deacetylation of different lysine amino acids in the N-terminal tails of histones H3 and H4, which is regulated by histone acetyl transferases (HATs) and histone deacetylases (HDACs) (Fig. 2). In vitro and in vivo studies suggest that HDACs are upregulated in chronic liver disease and that some HDAC inhibitors can attenuate the activation of HSCs as the initial event during liver fibrosis (Zeybel et al. 2013). Histone H3K4 and H3K9 trimethylation in genes involved in lipid metabolism is correlated with NAFLD and can contribute to the development and progression of hepatic steatosis (Jun et al. 2012).

Fig. 2 Epigenetic modifications in liver diseases. Three distinct mechanisms implicated in epigenetic modifications: DNA methylation, RNA-associated silencing, and histone modifications. DNA methylation, a major epigenetic mark, involves the covalent transfer of a methyl group to the C-5 position of cytosine. MicroRNAs (miRNAs) are small RNA molecules that negatively posttranscriptionally control their target gene expression. Histone acetylation and deacetylation are processes in which the epsilon amino group of lysine residues within the N-terminal tail of histones is acetylated and deacetylated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively, as part of gene regulation

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At the molecular level, hepatic oxidative stress has been considered as an underlying pathological mechanism responsible for the initiation and development of liver injury (Malhi and Gores 2008). The initial detrimental process in steatosis begins with the accumulation of free fatty acids which are esterified to triglycerides in the liver. Excessive accumulation of fat in hepatocytes enhances oxidative phosphorylation of free fatty acids, leading to increased reactive oxygen species (ROS) production (Duvnjak et al. 2007). Augmented generation of ROS triggers lipid peroxidation that damages hepatocyte membranes and organelles, leading to degeneration and hepatocellular necrosis (García-Monzón et al. 2000). The damage produced in mitochondria disrupts the respiratory chain reactions leading to additional ROS production in a positive feed-back loop, perpetuating the oxidative stress. Additionally, lipid peroxidation products such as malondialdehyde (MDA) and 4-hydroxynonenal (4HNE) bind to hepatocyte proteins, producing an antigenic component that initiates a harmful immune response and activating HSCs, collagen synthesis, and liver fibrosis (Duvnjak et al. 2007). Increased ROS production also stimulates the redox-sensitive transcription factor NF-κB which in turn induces the expression of proinflammatory cytokines (TNF-α, TGF-β, IL-6, IL-8). The resulting inflammation leads to further hepatic injury, activation of HSCs, synthesis of extracellular matrix proteins, and the development of liver fibrosis. Aside from steatohepatitis, in other liver diseases such as chronic viral hepatitis and alcoholic liver disease, oxidative stress is assumed to be one of the main pathogenic mechanisms responsible for the development and progression of diseases. Also, ROS production has been proposed to be an early event in hepatotoxicity induced by drugs and environmental pollutants such as heavy metals and radiation (Li et al. 2015). Important components of the cell’s defense against oxidative stress include the antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx), and the nonenzymatic molecule glutathione (GSH) (Fig. 3). Nuclear factor erythroid 2-related factor 2 (Nrf2), a major transcriptional regulator of cellular redox balance, is also an important component of the antioxidant defense system (Tang et al. 2014). Under oxidative stress, the activation of Nrf2 by phosphorylation causes its translocation into the nucleus where it interacts with the antioxidant response element (ARE), inducing the expression of antioxidant enzymes (Zhang et al. 2013) (Fig. 3). Mice with a deleted hepatic Nrf1 gene progressively developed all of the features common to human NAFLD, including steatosis, apoptosis, necrosis, hepatitis, fibrosis, and even liver cancer, thus proving additional evidence for the proposed role of ROS in steatosis progression to severe liver diseases (Xu et al. 2005).

A Link Between Oxidative Stress and Epigenetic Modifications in Liver Pathologies A recent investigation has revealed an important link between oxidative stress and the influence of redox state on epigenetic modifications (Cyr and Domann 2011). The intracellular redox status is established by several redox pairs: NAD+/NADH,

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Fig. 3 The cellular antioxidant system. Important components of the cell’s antioxidant defense include the antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), the nonenzymatic molecule glutathione (GSH), and transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) that interacts with the antioxidant response element (ARE), inducing the expression of antioxidant enzymes

NADP+/NADPH, and reduced glutathione (GSH)/oxidized glutathione (GSSG). Prolonged oxidative stress depletes the cellular GSH pool. The consequence is perturbed cysteine and methionine metabolism and disrupted conversion to S-adenosyl methionine (SAM) in the liver, which is a critical step since SAM is a methyl donor for methyltransferase reactions such as DNA methylation (Cyr and Domann 2011). Fluctuations of these intermediates can have direct effects on epigenetic signaling and can lead to changes in gene expression that have been observed during aging, in cancer, acute pancreatitis, and fatty liver disease (Fig. 4a). Oxidative stress and epigenetics are intertwined via the Sir2-like proteins (sirtuins) that provide a link between these two processes in liver pathologies. The sirtuins are a family of deacetylases that target histone and nonhistone proteins and require NAD+ as an enzymatic cofactor for their enzymatic activity (Bosch-Presegué and Vaquero 2015). Sirtuin-mediated deacetylation of the histone lysine residue is involved in direct epigenetic modulation of transcription. Aside from the histones, their targets include nonhistone proteins, and through deacetylation of these substrates, the sirtuins produce diverse biological effects in many physiological and pathophysiological conditions. In mammals, seven sirtuins (SIRT1–7) have been

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Fig. 4 The link between oxidative stress and epigenetic modifications and role of SIRT1 in the regulation of lipid homeostasis in the liver. (a) Decreases in the intracellular redox ratios of GSH/ GSSG and NAD+/NADH result in disturbances of methyltransferase and sirtuin activities. (b) SIRT1 increases fatty acid oxidation and inhibits hepatic lipogenesis through activation/inactivation of forkhead box O1 (FoxO1) and sterol regulatory element-binding protein 1 (SREBP-1)

described that are found in distinct subcellular compartments where they regulate specific biological functions via deacetylation of different target proteins (Frye 2000). Several sirtuin isoforms that are activated during redox stress modulate crucial responses and ameliorate ROS-induced pathologies providing a link between epigenetic modifications and the redox state (Webster et al. 2012). SIRT1 is the most widely characterized protein in the sirtuin family. In the context of hepatic lipid metabolism SIRT1 plays an important role in the regulation of lipid homeostasis in the liver (Fig. 4b). AMP-activated protein kinase (AMPK) increases fatty acid oxidation and inhibits hepatic lipogenesis, cholesterol synthesis, and glucose production (Viollet et al. 2009). Activation of SIRT1/AMPK signaling plays a central role in hepatic fatty acid metabolism through activation/inactivation of forkhead box O1 (FoxO1) and sterol regulatory element-binding protein 1 (SREBP-1). SIRT1 regulates fatty acid oxidation through increased deacetylation of transcription factor FoxO1, which in turn increases lipid catabolism and hydrolysis of triglycerides. SREBP-1 upregulates transcription factors for fatty acid synthesis through de novo lipogenesis. Given that SIRT1/AMPK signaling is intimately associated with FoxO1 activation and SREBP-1 inactivation, it represents a potential target in the development of therapies aimed at repressing hepatic steatosis (Viollet et al. 2009). Loss of SIRT1 in hepatocytes increases the accumulation of fatty acids and decreases fatty acid oxidation, both of which could result in the development of hepatic steatosis (Purushotham et al. 2009; Gao et al. 2011). The sirtuins are thus important for the maintenance of redox and lipid homeostasis within an epigenetic landscape in the liver.

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A better understanding of the different mechanisms that underlie liver disease and how they are linked is crucial for the development of novel therapeutic approaches aimed at preventing the onset and progression of liver diseases.

Alpha-Lipoic Acid A number of studies have established the efficacy of antioxidants in the prevention and treatment of liver disease. Alpha-lipoic acid (LA) is a naturally occurring dithiol compound synthesized from octanoic acid and cysteine through a reaction catalyzed by lipoic acid synthase in the mitochondria where it functions as a cofactor for mitochondrial enzymes in its protein-bound form. Unlike the endogenously synthesized protein-bound LA, in supplements LA is present in a free form. Aside from its essential role as a cofactor in oxidative metabolism, much of the current research on LA is focused on its biological functions as a free, non-proteinbound molecule. LA has been primarily described as a potent biological antioxidant that exerts antioxidant effects through several mechanisms. It reduces ROS levels by ROS scavenging, by stimulating the regeneration of endogenous antioxidants such as GSH, vitamins E and C, and by chelating metals (Singh and Jialal 2008) (Fig. 5a). LA and its reduced dithiol form dihydrolipoic acid (DHLA), which is formed in vivo, comprise an active redox pair since the latter molecule also possesses antioxidant and biological activity. This thiol/disulfide exchange can modulate the redox status within cells and the activities of proteins and transcription factors, affecting cell signaling and transcriptional responses, especially in terms of glucose and lipid metabolism. These effects of LA prompted research of the potential beneficial and therapeutic roles of LA as a nutritional supplement and as a therapeutic agent, especially in the treatment of diseases where oxidative stress is implicated, with particular attention focused on diabetes. Namely, it was shown that LA has the potential for application in treatments of several aspects of diabetes pathology, including glycemic control, glucose metabolism, improved insulin sensitivity, and oxidative stress (Fig. 5b). One of the most important consequences of glucose toxicity is increased ROS production (Wolff 1993). The impaired redox status due to increased formation of free radicals and decreased endogenous antioxidant defenses results in increased oxidative stress, an important contributing factor to diabetes pathology which influences the onset, progression and outcome of complications from long-term diabetes (Förstermann 2008) (Fig. 5b). Diabetesrelated complications affect different organ systems and lead to permanent pathological disorders such as nephropathy, retinopathy, neuropathy and cardiovascular disease, gastrointestinal disorders and hepatopathy (Mohamed et al. 2016). The established clinical use of LA in the treatment of diabetic polyneuropathy (Singh and Jialal 2008) should encourage experimental and clinical studies related to the use of LA as a therapeutic agent against diabetes-related complications in the liver, but also in liver diseases with other etiologies in which oxidative stress plays a significant role.

Fig. 5 Alpha-lipoic acid structure, dietary sources, and related biological activities. (a) Common dietary sources of alpha-lipoic acid are vegetables (spinach, broccoli, tomato, carrots) and meats (mainly viscera). Alpha-lipoic acid and its reduced form dihydrolipoic acid are potent natural antioxidants that scavenge free radicals, chelate transition metal ions and promote the regeneration of other antioxidants. (b) Through its antioxidant activity, alpha-lipoic acid (LA) improves insulin sensitivity and mitigates the detrimental effects of hyperglycemia-increased oxidative stress which is responsible for the development of diabetic complications

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Protective Effects of Alpha-Lipoic Acid in Liver Diseases The administration of LA to diabetic rats decreased oxidative stress in the liver, lipid peroxidation, cytotoxic signaling and DNA damage, restored the GSH:GSSG ratio as well as the activities of established markers of hepatotoxicity, the liver enzymes aspartate aminotransferase AST, and alanine aminotransferase ALT, all of which pointed to improved liver function (Dinić et al. 2013). The ability of LA to indirectly improve endogenous antioxidant mechanisms may be an even more important aspect in combating oxidative stress than its direct antioxidant actions that affect ROS production. It was reported that LA can increase endogenous ascorbate levels by inducing ascorbate uptake from blood plasma (Xu and Wells 1996). LA also increases intracellular GSH through improved cystine uptake from the plasma, which is the limiting substrate for GSH synthesis (Shay et al. 2009). The LA-mediated improvement of endogenous antioxidant defense mechanisms also includes restoration of the activities of the antioxidant enzymes SOD and CAT that are involved in ROS detoxification in the liver (Dinić et al. 2013; Sadi et al. 2008). It was noted that the levels of both CuZn-SOD and CAT proteins and their mRNAs increased in the liver of diabetic rats after LA treatment, implying that LA promoted the upregulation of transcription of both genes that was suppressed in diabetes (Fig. 6). Additionally it was hypothesized by Dinić et al. (2013) that LA prevented the diabetes-related posttranslational O-GlcNAcylation of SOD and CAT, transcription factors NF-κB, and C/EBPβ, and of upstream kinases, p38, and ERK, which are involved in the transcriptional regulation of enzyme expression. O-GlcNAcylation is a dynamic and reversible enzymatic glycosylation of proteins with O-linked N-acetylglucosamine groups which adversely affects protein functions (Parker et al. 2003; Yang et al. 2001). The decreased posttranslational O-GlcNAcylation of the major proteins involved in redox signaling pointed to a novel antioxidant effect of LA in diabetic rat liver (Dinić et al. 2013). As mentioned, Nrf2 may play an important role in the defense against oxidative stress through activation of cellular antioxidant machinery. Therefore, targeting the Nrf2 signaling pathways could be a potential therapeutic strategy for the prevention of oxidative stress-related diseases, including liver disease. Nrf2 signaling can be inhibited by NF-κB through competitive interaction with the transcriptional coactivator CREB binding protein (CBP) which is required for the transcriptional activity of Nrf2 (Liu et al. 2008) (Fig. 7). On the other hand, NF-κB p65 promotes the recruitment of co-repressor HDAC3 to ARE which deacetylates CBP and abolishes its co-activator activity (Liu et al. 2008). Furthermore, this is an epigenetic-related mechanism as the NF-κB-mediated recruitment of HDAC3 to ARE at the chromosomal level leads to local histone hypoacetylation and repression of ARE-dependent gene expression. The antioxidant activity of many natural phytochemicals could be attributed to their direct stimulatory effect on Nrf2 signaling and also to their ability to prevent NF-κB p65-mediated repression of ARE trans-activity. An important aspect of the effects of LA is related to the activation of Nrf2 and the inhibition of NF-κB activity, which results in increased Nrf-mediated antioxidant gene transcription and decreased NF-κB p65-mediated inflammatory reactions

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Fig. 6 Alpha-lipoic acid increases antioxidant enzymatic activities in diabetic liver. Increases in the enzymatic activities of CuZn superoxide dismutase (CuZnSOD) and catalase (CAT) in alpha-lipoic acid (LA)-treated diabetic rats result from LA-related transcriptional upregulation and decreased posttranslational O-GlcNAcylation of CuZnSOD and CAT

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Fig. 7 Alpha-lipoic acid in oxidative stress-induced liver injury. In oxidative stress, activated nuclear factor erythroid 2-related factor 2 (Nrf2) interacts with the antioxidant response element (ARE), inducing the expression of antioxidant enzymes. Activation of redox sensitive NF-κB generates reactive oxygen species (ROS) production and consequently increases oxidative stress and inflammation. Alpha-lipoic acid (LA) administration activates Nrf2 and inhibits NF-κB activation, preventing cell damage

(Fig. 7). Therefore, LA administration can differentially regulate antioxidant/antiinflammatory signaling pathways, rendering Nrf2 and NF-κB as potentially important targets for treating oxidative stress-induced liver injury (Monastra et al. 2016). In in vitro and in vivo experiments, LA administration exhibited a protective effect on hepatic steatosis by decreasing fatty acid synthase and increasing triglyceride hydrolysis through enhanced SIRT1 deacetylase activity of liver kinase B1 and stimulated AMPK (Yang et al. 2014). LA decreased lipid accumulation in the liver by regulating the transcriptional factors, SREBP-1 and FoxO1, and their downstream lipogenic targets via activation of the SIRT1/LKB1/AMPK pathway (Fig. 8). LA improves hepatic steatosis in mice at least in part by activating the Nrf2/ARE axis (Yang et al. 2014). In an animal model, it was shown that LA prevented hepatic steatosis by decreasing the acetylation of Forkhead transcription factor 3a (Foxo3a) through SIRT1 and SIRT3 stimulation (Valdecantos et al. 2012). As the deacetylated form of Foxo3a stimulates antioxidant defense, it was concluded that the beneficial effect of LA on hepatic steatosis could be attributed to its ability to restore the

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Fig. 8 The beneficial effects of alpha-lipoic acid in different liver injuries. Summary of the beneficial effects of alpha-lipoic acid (LA) on liver injuries in diabetes, obesity, metabolic disorders, alcohol and drug toxicity, ionizing radiation, aging, steatosis, and fibrosis. LA exerts beneficial effects through reactive oxygen species scavenging, by increasing antioxidant enzymes and glutathione levels, decreasing DNA damage, attenuating inflammatory signals and the fibrotic process, and improving lipid metabolism. Through antioxidant actions, LA also affects epigenetic modifications which could cause severe liver diseases

oxidative redox balance mediated by SIRT1 and SIRT3 activities (Valdecantos et al. 2012). Besides the LA-mediated increased expression of sirtuins, LA possesses the ability to increase the NAD+/NADH ratio. NAD+ is required as an enzymatic cofactor for sirtuin enzyme activity. All these findings regarding the effects of LA on sirtuin abundance and/or activity imply that LA could indirectly affect certain epigenetic mechanisms since sirtuins are involved in histone deacetylation; however, this direct effect needs additional proof. Different exogenous risk factors for liver disease whose hepatotoxicity is mediated by oxidative stress include increased alcohol intake, drug abuse, viral infections (hepatitis A, B and E), and exposure to ionizing radiation and environmental

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contaminants (Fig. 1). Many studies have investigated the beneficial effect of LA in various liver diseases related to alcohol, solvent and heavy metal intoxication, hepatitis, radiation-induced damage, and mushroom poisoning (Bustamante et al. 1998). In acute alcohol intoxication, the beneficial effect of LA relies on its ability to affect the NAD+/NADH ratio through cellular reduction to DHLA which opposes the effect of alcohol. Growing evidence supports the effect of alcohol on several epigenetic parameters such as DNA methylation, histone modifications, and miRNAs in the gastrointestinal tract and liver through increased ROS generation (Shukla and Lim 2013). In addition, disruption of lipid metabolism and increased inflammatory reactions are also affected by ethanol-induced histone modifications. LA administration could indirectly prevent epigenetic modifications in alcohol-induced liver damage by scavenging ROS and by regulating the NAD+/NADH ratio which is important for NAD-dependent SIRT1 deacetylase activity (Chen et al. 2012). Studies have been undertaken to investigate the treatment with LA as a potent antioxidant against drug- and toxin-induced hepatic oxidative stress (Pari and Murugavel 2004; Mansour et al. 2009; Ma et al. 2015). In CCl4-induced hepatotoxicity, LA administration was effective in restoring liver function. Since the biotransformation of CCl4 by cytochrome P450 leads to ROS production, the beneficial effect of LA relies on its ability to inhibit cytochrome P450 reductase by inducing a chemical modification of the -SH groups via a thiol-disulfide exchange reaction (Slepneva et al. 1995) (Fig. 8). Aflatoxin B1 is a naturally occurring mycotoxin and a potent teratogenic, mutagenic, hepato- and nephrotoxic agent that affects human health. Aflatoxin B1 and its metabolites deplete GSH due to the formation of high amounts of epoxides and other ROS affecting epigenetic mechanisms, including DNA methylation, histone modifications, and maturation of miRNAs (Bbosa et al. 2013). Treatment of broilers exposed to aflatoxin B1 with LA prevented hepatic oxidative stress through downregulation of the pro-inflammatory factor IL-6 mRNA and protein expression of both NF-κB p65 and inducible nitric oxide synthase (Ma et al. 2015) (Fig. 8). In addition, LA treatment decreased the MDA level and increased GPx activity and the GSH content, which was reflected as improved liver histology. All the beneficial effects of LA could indirectly prevent epigenetic modification in aflatoxin B1 and other xenobiotic-induced hepatic toxicity. Ali et al. (2014) described the antioxidant and antifibrotic potential of LA supplementation on an animal model of thioacetamide-induced liver cirrhosis, characterized by progressive fibrosis, excessive collagen deposition, and disruption of normal liver architecture. LA treatment prevented GSH depletion and decreased collagen deposition, matrix metalloproteinase-2 (MMP-2) activity, the level of transforming growth factor-β1 (TGF-β1), and α-smooth muscle actin (α-SMA) gene expression. Serological and histopathological analyses of liver function supported these findings (Fig. 8). In addition to liver drug toxicity, an impaired ability to maintain the level of GSH is one of the hallmarks of aging cells (Suh et al. 2004). The rate-controlling enzyme in GSH synthesis is γ-glutamylcysteine ligase (GCL). With aging, both the catalytic (GCLC) and modulatory (GCLM) subunits of GCL decrease. The basal and inducible expression of these GCL components is determined by the ARE. Nrf2 as the

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primary transcription factor regulating ARE-mediated gene transcription is altered in the aging liver. Suh et al. (2004) showed that LA administration increased the levels of nuclear Nrf2 in old rats and induced Nrf2 binding to the ARE, which resulted in higher levels of GCLC protein and enzymatic GCL activity in the liver. Accordingly, the age-related loss in liver GSH synthesis can be diminished by treatment with LA (Fig. 8). High doses of ionizing radiation cause genomic instability and affect epigenetic signaling, with ROS playing a crucial role because of increased oxidative stress. Maternal dietary antioxidant supplementation, including LA, mitigated the DNA methylation changes in rat liver induced by low-density irradiation, proving that oxidative stress is in part responsible for the epigenetic changes in response to irradiation (Bernal and Wendon 2013). In addition, LA effectively prevents radiation-induced fibrosis, one of the most common late complications of radiation therapy, through suppressed expression of pro-fibrotic genes as a result of inhibition of the transcriptional activity of NF-κB over histone acetyltransferase activity inhibition (Seung-Hee Ryu et al. 2016).

Conclusion Liver injury is a life-threatening complication in which oxidative stress is the central contributor to pathological mechanisms. LA, a naturally occurring dithiol compound, has gained considerable attention as an antioxidant which is capable of managing oxidative stress-related pathologies such as liver diseases with different etiologies. The perturbed redox homeostasis in liver diseases could affect epigenetic modifications, rendering the liver cells incapable of maintaining their functionality and responsiveness to challenges induced by endogenous metabolic triggers or exogenous insults, such as alcohol abuse, intake of different drugs, viral infections, irradiation, and others. In response to these influences, epigenetic mechanisms affect liver disease and progression by changing gene expression. Although antioxidant supplementation, including LA, could improve hepatic oxidative stress and mediate changes in epigenetic pathways, the detailed mechanisms of direct actions of LA on epigenetic pathways have not been established. As the majority of LA actions have been demonstrated in in vitro and in vivo studies, translation of experimental data to the clinical application of LA in humans warrants further investigation.

Dictionary of Terms • Liver injury – Encompasses liver pathologies provoked by different endogenous factors associated with diabetes, insulin resistance, obesity and metabolic disorders, as well as different exogenous insults, including alcohol and drug intoxication, viral infections, irradiation, environmental contaminants. • Stages of liver injury – Accumulation of triglycerides causes liver enlargement and fatty liver; prolonged inflammation and scar tissue formation leads to fibrosis,

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progressing to cirrhosis, liver failure, portal hypertension, and an increased risk of malignant tumor development. • Oxidative stress-related diseases – Encompasses pathologies resulting from a redox imbalance caused by increased reactive oxygen species production and decreased antioxidant defenses. • Epigenetic modification and oxidative stress cross talk – Sirtuin proteins which belong to NAD+-dependent histone and nonhistone deacetylases provide a link between epigenetic modifications and redox state in liver diseases. • Natural antioxidant compound – A compound contained in natural products of plant/animal origin, and provided as a supplement, with an antioxidant potential that can be used in management of diseases related to increased oxidative stress.

Key Facts of Liver Diseases • Liver diseases result from pathological processes in the liver provoked by endogenous insults (metabolic syndrome, obesity, hypertriglyceridemia, and diabetes) and exogenous risk factors (alcohol, certain drugs, and environmental contaminants). • One of the most common liver diseases is nonalcoholic fatty liver disease that covers the spectrum of hepatic disorders, ranging from steatosis to more severe steatohepatits, fibrosis, cirosis, hepatocellular carcinoma, and even liver-related mortality. • Drug-induced liver injury is caused by drugs herbs and toxins and comprises about 20–40% of all incidence of hepatic failure. • Susceptibility to develop most serious liver disease is determined by genetic predisposition and environmental factors. • Epigenetic mechanisms mediate gene-environment interactions and may direct liver disease progression. • Increased production of reactive oxygen species and resulting oxidative stress in the liver has been considered as one of the main pathological mechanism responsible for the initiation and progression of liver diseases.

Key Facts of Alpha-Lipoic Acid • Alpha-lipoic acid is a naturally occurring compound with a powerful in vivo antioxidant activity. • Alpha-lipoic acid scavenges reactive oxygen species, regenerates endogenous antioxidants, chelates metals, and improves the activities of antioxidant enzymes. • Alpha-lipoic acid is commonly found in foodstuffs, including vegetables (spinach, broccoli, tomato) and meats (mainly viscera). • In humans, alpha-lipoic acid is synthesized in the liver, but its synthesis decreases with age and in conditions of disturbed health, when alpha-lipoic acid needs to be supplied exogenously from diet sources or dietary supplements.

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• The beneficial effects of alpha-lipoic acid as a nutritional supplement have a promising potential in treating diseases in which oxidative stress is implicated. • According to clinical evidence, the antioxidant effects of alpha-lipoic acid have been shown to be particularly useful in treating diabetic neuropathy.

Summary Points • Disturbed redox homeostasis in liver could affect epigenetic modifications which could cause liver pathologies to progress to severe liver diseases by changing gene expression. • The antioxidant and hepatoprotective effect of alpha-lipoic acid in experimentally induced diabetes includes improvement of the activities of antioxidant enzymes, which are involved in reactive oxygen species detoxification in the liver. • The beneficial effects of alpha-lipoic acid on hepatic steatosis is the result of its ability to increase antioxidant defenses via activation of sirtuin proteins, implying that alpha-lipoic acid could indirectly affect some epigenetic mechanisms since sirtuins are involved in histone deacetylation. • In acute alcohol intoxication, the beneficial effect of alpha-lipoic acid relies on its ability to affect the NAD+/NADH ratio through its reduction to dihydrolipoic acid which opposes the effect of alcohol. • Alpha-lipoic acid administration could indirectly prevent modifications of epigenetic marks in alcohol-induced liver damage by scavenging reactive oxygen species, and through regulation of the NAD+/NADH ratio which is important for NAD+-dependent deacetylase sirtuin activity. • In hepatotoxicity induced by CCl4-poisoning, the beneficial effect of alpha-lipoic acid and its effectiveness in liver restoration could rely on its ability to inhibit cytochrome P450 reductase by inducing chemical modifications of SH-groups via the thiol-disulfide exchange reaction. • Administration of alpha-lipoic acid after aflatoxin B1-induced toxicity prevented hepatic oxidative stress by decreasing malondialdehyde level and increased glutathione peroxidase activity and the glutathione content. • Alpha-lipoic acid supplementation against thioacetamide-induced liver cirrhosis increased the glutathione content, decreased the markers of fibrosis, and improved the serological and histopathological parameters. • The age-related loss of glutathione in the liver can be alleviated by treatment with alpha-lipoic acid which increases γ-glutamylcysteine ligase activity and glutathione synthesis. • Alpha-lipoic acid effectively prevents radiation-induced fibrosis as one of the most common late complications of radiation therapy through the suppression of pro-fibrotic genes expression. Acknowledgement This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant No. 173020.

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High-Fat Diet and Maternal ObesityAssociated Epigenetic Regulation of Bone Development

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Association Between Obesity and Reduced Postnatal Bone Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetic Regulation of Bone Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ezh2 and Histone Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HFD-Induced Obesity Accelerates Osteoblastic Cell Senescence Program Through Epigenetic Activation of PPARγ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Management of Impaired Bone Development Associated with HFD-Induced Obesity . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Due to the worldwide epidemic in obesity, maternal obesity has recently seen an explosion in investigations in both animal models and humans on its effects on offspring phenotype and pathologies including diabetes, hyperlipidemia, cardiovascular disease, and cancer. Epigenetic mechanisms presumably explain how metabolic or nutritional status during intrauterine and early postnatal life impacts the risk of chronic diseases. Developmental programming and epigenetic regulation of the fetal skeletal development associated with maternal obesity and diet is understudied. The fetal and neonatal bone cells represent potential targets for developmental programming. Maternal obesity-associated epigenetically regulated events in utero contributes toward changes in the ability to attain peak bone

J.-R. Chen (*) Arkansas Children’s Nutrition Center and the Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA e-mail: [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_113

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mass and increases in risk of the adult onset of degenerative bone disorders. Recent studies in rodents showed that the embryonic/neonatal skeletal phenotype can be programmed by maternal high fat/high sugar obesity-promoting diets prior to and during pregnancy. Importantly, evidence from a human study suggested that umbilical cord mesenchymal stem cells (UC-MSCs) from obese mothers have less potential to differentiate toward osteoblast, and more potential for adipogenesis. Further research on the mechanisms connecting maternal obesity, fetal bone development, and postnatal bone formation are required. Keywords

Obesity · High fat diet · Bone development · Epigenetic · DNA methylation · Osteoblast senescence · Nutrition List of Abbreviations

AMP BB BMC BMD CBP DNMTs E15.5 ECOCs EGR1 Ezh2 GNATs H3K27 HATs HDACs HDACs HFD Hox IL-6 IRF8 IRS MSCs NEFA PA PDK-1 PI3K PIP2 PKB PPARγ PPRE Runx2

50 adenosine monophosphate-activated protein kinase Blueberry Bone mineral content Bone mineral density CREB-binding protein DNA methyltransferases Embryos on day 15.5 of gestation Embryonic calvarial osteoblastic cells Early growth response 1 Enhancer of zeste homolog 2 Gcn5-related N-acetyltransferases Lysine 27 in histone H3 Histone acetyltransferases Histone deacetylases Histone deacetylases High fat diet Homeobox Interleukin-6 Interferon regulatory factor 8 Insulin receptor substrates Mesenchymal stem cells Non-esterified free fatty acids Phenolic acids Phosphatidylinositol phosphate-dependent kinase-1 Phosphoinositide 3-kinase Phosphatidylinositol 4,5-bisphosphate Protein kinase B Peroxisome proliferator-activated receptor γ Peroxisome proliferator responsive element The runt domain-containing transcription factor

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SATB2 SAβG UC

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Special AT-rich sequence-binding protein 2 Senescence-associated β-galactosidase Umbilical cord

Introduction The number of women of childbearing age who are overweight or obese has been increasing in the last decades, and in the USA over 60% of pregnant women are either overweight or obese at initiation of pregnancy (Institute of Medicine 2009). Obesity at conception increases risk of obesity in offspring and other long-term health outcomes in both the mothers and their offspring (Levin and Govek 1998; Bayol et al. 2008; Bruce et al. 2009; Nathanielsz et al. 2007). Although genetics and inadequate physical activity are components, the majority of development of maternal obesity is highly associated with excessive caloric intake and long-term positive energy balance. Evidence from experimental models clearly demonstrates that maternal obesity or excessive consumption of a high fat diet (HFD) during pregnancy leads to developmental programming of adiposity in the offspring (Zhu et al. 2008; Barker et al. 2006). These evidences also support that maternal obesity combined with high energy Western-style high fat, high sugar obesogenic diets can have adverse effects on mothers and fetuses during pregnancy and lactation and predispose offspring to later life metabolic dysfunction (Musial et al. 2017; Maffeis and Morandi 2017). We are gaining a greater understanding that the origin of any individual metabolic pathway takes place during early life (Koletzko et al. 2017); however, how epigenetic changes induced by exogenous stimuli inheritably affect gene expression involving skeletal development remains unclear. Results from the Danish National Birth Cohort show that maternal poor-quality diet (i.e., high fat, high refined and simple sugar dietary pattern in which the most abundant food items are potatoes, French fries, white bread, pork, beef veal, mixed meat, cold meat, and dressing sauce) is associated with significantly increased risk of childhood fractures in offspring (Petersen et al. 2015), supporting the idea that offspring skeletal health is influenced by the gestational environment and maternal diet. Maternal diet altering growth of the skeleton in utero may have a persistent influence on bone cell development (Chen et al. 2012; Liang et al. 2009; Lanham et al. 2010) and on postnatal bone acquisition and remodeling. Overwhelming evidence from rodent models has shown that a HFD-induced maternal environment can permanently change fetal organ structure and system function during critical periods in utero (Dennison et al. 2013). These critical periods often coincide with intervals of rapid cell division. A few studies have described inhibition of perinatal skeletal formation by HFD-induced obesity during gestation (Chen et al. 2012; Liang et al. 2009). However, mechanistic understanding of these observed clinical and experimental results is lacking, and the effects of maternal obesity and obesity-promoting diets on fetal bone forming cell division are not clear. The specific blood-borne bioactive compounds impairing bone development in the fetus born of a HFD-

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induced obese mother are presently unknown. In this chapter, an overview of associations between maternal obesity and diet with bone development will be discussed, and potential mechanisms and regulatory factors involved will be elaborated.

Association Between Obesity and Reduced Postnatal Bone Mass Low bone mass osteopenia or osteoporosis is the most common chronic bone disease; it is similar to obesity with changed body composition. Osteoporosis has multifactorial etiologies, including genetic and environmental components. It is generally accepted that a larger body mass imposes a greater mechanical load on bone and that bone mass will increase to accommodate the greater load (Reid 2002). However, published data showed that body fat mass is negatively correlated with bone mass when the mechanical loading effect of body weight is statistically removed (Zhao et al. 2008). Moreover, a large community-based cross-sectional study demonstrated inverse associations between fat mass, serum triacylglycerol, and bone mineral content (BMC) in men, pre- and postmenopausal women, with odds ratios of 5.0–6.9 for osteoporosis risk based on hip bone mineral density (BMD) between the highest and lowest quartiles of % fat mass (Hsu et al. 2006). High adiposity has also been associated with increased risk of bone fragility fracture in children (Goulding et al. 2001; Mobley et al. 2005; Taylor et al. 2006), and studies in adolescents and young adults indicate that fat mass is not beneficial to bone at this age (Zhao et al. 2008). This is in full agreement with data on bone quality (bone strength) in weanling rats having the same body weight, but differing degrees of adiposity: these obese rats were generated using high fat feeding via total enteral nutrition (Chen et al. 2010a). Interestingly, when genetic factors were precluded, the incidence of hip fracture is much lower in countries such as China, where relative lower fat content diet is consumed compared to Western countries, where high fat diets are consumed regularly (Reinwald and Weaver 2006). The relationship between adiposity and bone mass is complex, and specific links remain to be identified. Some aspects of obesity that could impact bone include endocrine and metabolic changes associated with alterations in energy balance, including (1) insulin sensitivity, (2) secretion of hormones such as estradiol, (3) adipokines, such as leptin, (4) cytokines, such as IL-6, and (5) NEFA liberated via lipolysis from increased fat stores that are less insulin sensitive. It has been suggested that intake of excess dietary fat is principal contributor to lifestyle-related causes of insulin resistance and obesity-related diseases, including metabolic syndrome and perhaps osteoporosis (Rosen and Bouxsein 2006; Khaodhiar et al. 1999). However, to date, there are an extremely limited number of studies detailing mechanistic explanations of how high fat diet or excess calories act on bone formation. There are no extensive studies to define the mechanisms underlying the recently observed increased fracture risk in obese children and adolescents (Taylor et al. 2006), and higher prevalence of obesity among diagnosed osteoporotic patients (Premaor et al. 2010). Noticeably, bone loss induced by obesity and high fat feeding is accompanied

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by increased bone marrow adiposity and insulin resistance and in most cases associated with decreased osteocalcin secretion (Chen et al. 2006). Bone has recently been suggested to be a large endocrine organ controlling energy metabolism and glucose metabolism; evidence has been provided that the secretion of osteocalcin, particularly under-carboxylated osteocalcin, an osteoblasts-derived hormone, regulates pancreatic insulin secretion, peripheral insulin sensitivity, and energy expenditure in bone (Ferron et al. 2010). Insulin signaling in osteoblasts might be a positive regulator of postnatal bone acquisition (Fulzele et al. 2010). As for effects of maternal metabolic health and bone, it is known that NEFAs, insulin, and glucose can cross the maternal-fetal barrier into the fetal circulation. Notably, it has been discovered that fetal osteo-progenitors from both obese rodents and humans display insulin resistance and decreased glucose metabolism, illustrating that metabolic regulation of bone cell development should be considered (Chen et al. 2016). Moreover, evidence has been presented that increased NEFA levels in the blood of HFD obese rodent (rats and mice) might explain increased adipogenesis but decreased osteoblast differentiation (Chen et al. 2010a, 2012).

Epigenetic Regulation of Bone Development Epigenetic regulation is known heritable posttranslational modifications of DNA or histones, while the DNA sequence itself remains unchanged (Szyf 2009). Although various physiological alterations such as development and aging are capable to influence the epigenetic processes, environmental factors (diet, drugs, smoking, etc.) and various human diseases can also change epigenetic state (Egger et al. 2004). If such epigenetic regulation happens in mesenchymal stem cells (MSCs), changes in differentiation potential toward tissue-specific functional cells can be expected (Collas 2010). In the nucleus, DNA is wrapped around octamers composed of dimeric histones H2A, H2B, H3, and H4, forming chromatin. Different modifications of their N-terminal tails, such as acetylation, methylation, phosphorylation, and ubiquitination, regulate the accessibility of DNA for transcription factors (Huidobro et al. 2013). Chromatin structure is importantly modulated by histone acetylation and methylation. Transfer of acetyl groups to amino groups of lysines by histone acetyltransferases (HATs), as well as the effect of stearic hindrance, facilitates the binding of transcription factors to DNA and consequently increases gene expression. HAT families consist of the GNAT/MYST and P300/CPB groups, basal transcription factors, and nuclear receptor cofactors (Roth et al. 2001; Jin et al. 2011). Transfer of acetyl groups to histone lysine residues seems to follow a global pattern, but acetylation on specific lysine residues is also possible (Roth et al. 2001). Histone deacetylases (HDACs) can remove the acetyl groups and have a reverse effect on transcription (Marmorstein and Roth 2001). In contrast to acetylation, methylation of histones, especially histone trimethylation, is a more complex procedure that it usually does not lead to gene activation but instead gene silencing (Bannister and Kouzarides 2011). As the epigenome is influenced by many interacting external factors, understanding its modification is thought to be one of

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the keys to comprehend diseases of multifactorial genesis such as obesity and environmental factors such as HFD. Bone mineralization within the skeletal envelope is strongly influenced by postnatal diet and physical activity, and intrauterine programming of osteoporosis was also recently speculated to be an influence (Holroyd et al. 2012). Studies from experimental animal models have indicated that maternal obesity results in gene promoter methylation at birth, and this gene promoter methylation is associated with increased adiposity, but decreased bone development, in the offspring (Rosen and Bouxsein 2006). Since both osteoblasts and adipocytes originate from the same pool of MSCs, it is likely that osteoblastogenesis is also affected by gene methylation at birth (Chen et al. 2012). Recent evidence further suggests that epigenetic altering of gene expression such as SATB2 results in changes in potential of a bone-forming cell to either differentiate or proliferate (Zhou et al. 2016) and to undergo phenotypic and metabolic changes (e.g., promotion of appearance of senescent phenotype and decreased glucose metabolism). Maternal HFD- or obesity-associated offspring phenotypic cell changes may appear greater when they are challenged by secondary stimuli (Shankar et al. 2008, 2010), such as HFD, obesity, or aging. Similarly, it has been hypothesized that maternal obesity-associated cell phenotype re-programming including increased cellular senescence signaling and decreased glucose metabolism and insulin resistance in the skeletal system may be more apparent if these cells are exposed to secondary stimulus postnatally. A specific link among maternal HFD-induced obesity, increased gene methylation in embryonic calvarial osteoblastic cells (ECOCs), and impaired bone development in animal model has been reported recently (Chen et al. 2012). ECOCs contain pluripotent cells capable of differentiating into either mature osteoblasts or adipocyte-like cells upon appropriate stimulation; therefore, they are usually referred to as either osteoblastic or adipogenic cells. Such results from mechanistic exploration may explain those observed clinical and experimental studies describing inhibition of perinatal skeletal formation by HFD consumption during gestation (Cooper et al. 2009; Liang et al. 2009). However, which specific blood-borne bioactive compounds or gene methylation changes produce impairment of bone development in the fetus born of an obese or HFD-fed mother is presently not clear. What is known is that NEFAs in maternal circulation can cross the maternal-fetal barrier into the fetal circulation. NEFAs are bioactive compounds and may be able to modify DNA methylation (Chen et al. 2012). It has been recently reported that DNA methylation in a cluster of genes involved in tissue development, such as the HoxA10 gene, is associated with maternal HFD and reduced fetal bone development (Chen et al. 2012). It is known that homeodomain-containing factor/Hox genes are involved in a wide variety of processes during embryogenesis (Hombria and Lovegrove 2003). The functional significance of these transcription factors in embryonic skeletogenesis has been welldocumented. By using knockout mouse models, Hox genes have been shown to control skeletal patterning or to be directly involved in osteoblastogenesis (Gordon et al. 2010). For example, HoxA10 may directly activate bone regulatory and phenotypic genes, such as Runx2. Most importantly, Hox status may determine

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bone-forming progenitor cell activity and turnover in adult bone (Leucht et al. 2008). In neuroblasts, cell cycle is known to be controlled by variety of factors, for example, Hox proteins may facilitate cells entering quiescent stage (Sousa-Nunes et al. 2011). On the other hand, nutritional factors may help cells to exit quiescent stage. In adipocytes, insulin signaling may be involved in both processes (Sousa-Nunes et al. 2011). If a cell does not enter quiescent stage or stays in quiescent stage for prolonged period, it bears some resemblance to the process of aging (Sousa-Nunes et al. 2011) or senescence. However, potential connections among HFD-fed maternal obesity-induced impairment of fetal skeletal development and accelerated cell senescence, insulin resistance and HoxA10 gene methylation in osteoblasts during gestation or the postnatal period need further investigation. It is possible that gestational HFD may produce changes in many different fetal genes in addition to Hox, for example those involved in insulin signaling. Specific determination of whether there is a mechanistic linkage between insulin signaling and Hox gene methylation associated with HFD-induced maternal obesity and accelerated osteoblastic cell senescence will be very interesting. Obesity during gestation has longterm effects on insulin signaling in the offspring. Previous study has demonstrated that maternal obesity at conception programs obesity, insulin and adiponectin signaling in adipose tissue in offspring (Shankar et al. 2008, 2010). The causative role of altered insulin signaling in mediating reduced skeletal formation in offspring remains unproven. MSCs and osteoblastic cells express abundant insulin receptor. Altered insulin receptor signaling in these cells may be one of several possible mechanisms mediating changes in the intra-uterine environment in response to maternal obesity (induced by HFD) on skeletal development (Fulzele et al. 2010).

Ezh2 and Histone Methylation Polycomb group protein Ezh2, one of the key regulators of development in organisms from rodents to humans, exerts its epigenetic function through regulation of histone methylation (Su et al. 2003, 2005). Specifically, it plays an essential role on trimethylation of lysine 27 in histone H3 (H3K27). Ezh2-mediated epigenetic mechanism is believed to regulate cellular signaling programs which heritably change the characteristics of the cell without altering its DNA sequence. The mechanism by which it does this is by altering the chromatin (the DNA and its associated proteins), which changes the availability of the genes to be expressed. Ezh2 also directly interacts with a group of enzymes that attach methyl groups to the DNA, the DNA methyltransferases (DNMTs), to collaborate with H3K27 to set up cellular signaling program (Taghavi and van Lohuizen 2006). Polycomb group proteins, including Ezh2, form chromatin-modifying complexes that are essential for embryonic development and stem cell renewal and are commonly investigated on deregulation on cancer (Bracken et al. 2006). The influence of Ezh2 on epigenetic regulation on HFD-induced obesity and degenerative bone disorders has also been reported. Recent evidence showed that the epigenetic activity of Ezh2 is required for skeletal patterning and development: increased Ezh2 is associated with decreased

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osteoblastogenesis and Ezh2 expression declines during terminal osteoblast differentiation and matrix production (Wei et al. 2011; Dudakovic et al. 2015; Guo et al. 2016). Ezh2 has also been shown critical for controlling hematopoietic program and growth (Danis et al. 2016), and it has been reported that Ezh2 promotes osteoclastogenesis by epigenetic silencing of the negative regulator IRF8 (interferon regulatory factor 8) (Fang et al. 2016). Data from our laboratory showed that maternal obesity activates Ezh2 expression in osteo-progenitors in both mice and humans, and over-expression Ezh2 resulted in decreased SATB2 gene expression (Chen et al. 2016). Moreover, HFD promoted Ezh2 expression in bone marrow hematopoietic cells, and this is associated with decreased IRF8 expression but increased osteoclastogenesis.

HFD-Induced Obesity Accelerates Osteoblastic Cell Senescence Program Through Epigenetic Activation of PPARg Cellular senescence is a general process of cell proliferation, and it is thought to occur in most of an organism’s cells during aging and notably within tumors (Campisi and d’Adda di Fagagna 2007; Collado et al. 2007). Interestingly, cellular senescence was also recently identified during mammalian embryonic development (Muñoz-Espín et al. 2013; Storer et al. 2013), but it disappears after E15.5. This suggests that maternal obesity-associated increased senescence signaling in fetal skeleton at E18.5 as recently observed is atypical (Fig. 1), and cellular senescence signaling can be epigenetically regulated. Although many senescence mediators have been characterized, in most rodent cells, induction of tumor suppressor genes p53, p21, and p16 is critical to the induction of senescence (Campisi 2014). Senescence-associated β-galactosidase (SAβG) activity is the most widely used

SABG Activity (OD420)

a 0.32

b *

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SABG on cryosections Fetus from control dams

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0.28 0.26 0.24 0.22 0.20 EOCCs from control dams

EOCCs from HFD dams

Fig. 1 Increased senescence signaling in EOCCs and fetal skeletal tissues from HFD obese dams. (a) SAβG activity measurement in EOCCs from control or HFD obese dams. (b) SAβG staining on cryosection of whole-mount rat embryos, white arrows are pointing blue color positively SAβG stained cells from embryonic rat femurs. *p 200 nt) and short (twofold in hormonesensitive mammary tumors and decreased the same miRNAs in normal tissue 10–50% compared to untreated control (Fig. 2). The effects of resveratrol on miRNAs appear to be dose related. Lo- but not hi-dose resveratrol increased miR10a and miR-10b expression in tumor tissue, whereas hi- but not lo-dose resveratrol increased miR-21, miR-129, miR-204, and miR-489 (Fig. 2). An important way in which miRNAs are inactivated in human cancer is through hypermethylation (Lopez-Serra and Esteller 2012). miRNAs have also been reported to regulate DNMT3b expression (Malumbres 2012). Of the six miRNAs influenced by resveratrol treatment in a rodent model of hormone-sensitive breast cancer, there was a significant (P < 0.05) inverse correlation with DNMT3b for three (129, r2 = 0.29; 204, r2 = 0.20; 489, r2 = 0.18) in normal and one (489, r2 = 0.26) in tumor tissue, suggesting their epigenetic regulation (Fig. 3) (Qin et al. 2014). The two that experienced the greatest increase with treatment in tumor tissue were miR-129 and miR-489. miR-129 is known to be aberrantly methylated and downregulated in endometrial, colon, esophageal, and stomach cancer (Suzuki et al. 2012). The inverse correlation of miRNA-129 with DNMT3b in normal but not tumor tissue may indicate that its downregulation occurs early in mammary tumor

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Fig. 2 Effect of low (lo)- and high (hi)-dose resveratrol on miRNA expression compared to control. Treatment with lo resveratrol significantly influenced miR-10a (a) and miR-10b (b) expression. There was a dose-dependent effect of resveratrol treatment on miR-21, miR-204, miR-129, and miR-489 (c–f)

development, before histopathologic evidence of malignancy. We are not aware of a report demonstrating that miR-489 is inversely correlated with DNA methylation or methyltransferase levels. The correlation in both normal and tumor tissues suggests that methylation may be important both in tumor initiation and progression.

Cardioprotection Some of the medications used to treat breast cancer, including adriamycin and trastuzumab, are cardiotoxic (Brown et al. 2015). Patients also often receive

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Fig. 3 Correlation of miRNAs with DNMT3b. ΔCt values for miR129, miR-204, and miR-489 were inversely correlated with ΔCt DNMT3b values in normal (a) and in tumor (b) tissue

radiation to the breast and/or chest wall to treat breast cancer, resulting in a portion of the heart being radiated. Resveratrol has been reported to exhibit cardioprotective effects (Fan et al. 2016), including reducing the severity of cardiac hypertrophy. One possible mechanism for this is resveratrol’s ability to downregulate miR-155 in cardiomyocytes. Breast cancer susceptibility gene (BRCA) 1 inactivation can increase expression of miR-155, contributing to cardiac hypertrophy, and resveratrol, by downregulating miR-155, activates BRCA1. miR-155 is also known to be involved in breast cancer progression (Mattiske et al. 2012). Thus, the effect of resveratrol on miR-155 appears to decrease breast cancer risk/progression and also decrease cardiac hypertrophy.

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Resveratrol Analogs to Increase Bioavailability and Efficacy Human studies evaluating the efficacy of resveratrol have been mixed. Investigators who have measured resveratrol levels in the blood or urine have pointed to the high concentration of resveratrol metabolites compared to levels of free trans-resveratrol (Gambini et al. 2015) as a possible reason for inconsistent efficacy in humans, since the free form is generally considered the most active form. Nonetheless, evidence suggests that at least some resveratrol derivatives also have an antiproliferative effect (Colin et al. 2008). Trans-resveratrol is efficiently absorbed after oral administration and rapidly metabolized by glucuronidation and sulfation in the liver (Zhu et al. 2012). In the blood, trans-resveratrol binds both to albumin and low-density lipoproteins (LDLs) which assist in its delivery to epithelial cells for uptake (Zhu et al. 2012). Once taken up, resveratrol metabolites can be cleaved or hydrolyzed back to its free (most active) form (Zhu et al. 2012). To address concerns over poor efficacy and bioavailability, various groups have synthesized resveratrol analogs and tested them for efficacy. While many investigators report that some of the synthesized resveratrol analogs demonstrated efficacy superior to free trans-resveratrol in vitro and/or in animals (Ronghe et al. 2016), none have proven sufficiently convincing to be in common clinical use.

Resveratrol in Therapy Combination Stilbene Therapy 5-Aza-20 -deoxycytidine is a demethylating agent approved to treat various blood disorders (Cheishvili et al. 2015). Its side effects, pancytopenia, nausea, and vomiting, often limit its use. The plant stilbenes resveratrol and pterostilbene have epigenetic activity and are well tolerated. Pterostilbene is found in high concentration in blueberries. It is not found in the skin of red grapes in appreciable amounts, unlike resveratrol. Pterostilbene is reported to have increased bioavailability in comparison to other stilbene compounds such as resveratrol (Mccormack and Mcfadden 2013), which may enhance its dietary and clinical benefit. A recent preclinical study evaluating the potential additive or synergistic effects of the two compounds (Kala and Tollefsbol 2016) demonstrated that the combination decreased DNMT enzyme activity and 5-methylcytosine levels in MDA-MB-157 breast cancer cells, as well as reactivation of ERα expression. After treatment, the previously ERα-negative MDA-MB-157 breast cancer cells increased their proliferation in response to estradiol, with decreased proliferation after treatment with 4-hydroxytamoxifen. Resveratrol and pterostilbene administered together at close to physiologically relevant doses resulted in synergistic growth inhibition of HCC1806 and MDA-MB-157 triple negative breast cancer cells (Kala et al. 2015). SIRT1, a type III histone

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deacetylase (HDAC), and DNA methyltransferase enzymes, were downregulated in response to treatment.

Resveratrol and Chemotherapy Resveratrol has demonstrated synergistic/additive tumor regression when administered with the chemotherapeutic agents adriamycin (Rai et al. 2016) and cisplatin (Osman et al. 2015).

Summary There is strong preclinical evidence that resveratrol suppresses tumor formation and shrinks already formed tumors. Resveratrol works primarily through epigenetic mechanisms to induce apoptosis, decrease cell proliferation, and silence gene transcription. There is in vivo evidence that these effects are selective for tumors, sparing normal cells, and that there is a dose response, supporting their specificity related to resveratrol treatment rather than a random effect. It has been suggested that resveratrol does not work in humans because of first-pass effects in the liver, where most free resveratrol (believed to be the most active form) is modified yielding less active glucuronide and sulfate metabolites. However, short-term (8 days) administration of resveratrol led to high concentrations of both free resveratrol and its glucuronide and sulfate metabolites in normal and malignant colon tissues (Patel et al. 2010). In the blood, trans-resveratrol binds both to albumin and low-density lipoproteins (LDLs) (Jannin et al. 2004) which assists in its delivery to the epithelial cell surface for cell membrane uptake (Jannin et al. 2004). Intracellularly, the glucuronide can be enzymatically cleaved (Wang et al. 2004) or sulfated trans-resveratrol hydrolyzed (Santner et al. 1984) to its free form. This intracellular “reactivation” provides rationale for the efficacy reported in animal and limited human studies that resveratrol is a molecule worth further study to prevent and/or treat breast cancer, as well as its side effects such as cardiotoxicity.

Key Facts • Resveratrol has strong preclinical evidence for efficacy to prevent and treat mammary cancer. • Resveratrol has only limited data supporting its efficacy to treat breast cancer in humans. • Resveratrol is rapidly metabolized by the liver, such that the free form of resveratrol is difficult to detect in the circulation. • There is evidence that resveratrol metabolites are cleaved or hydrolyzed intracellularly, increasing the percentage of intracellular free resveratrol.

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• Additional human studies are needed to determine the potential usefulness of the agent in the prevention and/or treatment of human breast cancer, as well to protect humans from the side effects of breast cancer treatment.

Dictionary of Terms • Methylation – alteration in the DNA of a gene which decreases the amount of RNA and protein that is produced. • Metabolism – modification of the native (free) molecule by the addition of a chemical side group, such as a glucuronide or sulfate, which often alters the function of the native molecule. • Synergy – when agents are combined, yielding more than an additive effect • Polyphenol – a compound, often found in nature, containing large multiples of the chemical structure phenol. The structures are found widely in plants and components in foods, dyes, paints, varnishes, rubbing compounds, and many other uses.

Summary Points • Resveratrol is found in two isometric forms, cis and trans, with the latter predominating. • Trans-resveratrol (resveratrol) is a polyphenol present in foods that has shown promise in tissue culture and animal studies as an anticancer agent. • Resveratrol has many non-mutational, or epigenetic, mechanisms of action demonstrating its preclinical evidence of efficacy. • There is evidence that resveratrol may have cardioprotective effects during the treatment of breast cancer. • Human studies to confirm the preclinical findings are limited. • The metabolism of free resveratrol in the liver had led some investigators to question the potential of resveratrol to prevent or treat breast cancer. • Resveratrol analogs have been synthesized to increase the bioavailability of the agent. None had been proven to have clinical usefulness. • Combination therapy of resveratrol with another natural compound, or with a synthesized compound, has been investigated and shows some promise. • More human studies are needed to determine the role of resveratrol in the prevention or treatment of human breast cancer or to mitigate the toxicity of breast cancer treatment.

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Berner C, Aumuller E, Gnauck A, Nestelberger M, Just A, Haslberger AG (2010) Epigenetic control of estrogen receptor expression and tumor suppressor genes is modulated by bioactive food compounds. Ann Nutr Metab 57:183–189 Bhat KP, Lantvit D, Christov K, Mehta RG, Moon RC, Pezzuto JM (2001) Estrogenic and antiestrogenic properties of resveratrol in mammary tumor models. Cancer Res 61:7456–7463 Brown SA, Sandhu N, Herrmann J (2015) Systems biology approaches to adverse drug effects: the example of cardio-oncology. Nat Rev Clin Oncol 12:718–731 Cheishvili D, Boureau L, Szyf M (2015) DNA demethylation and invasive cancer: implications for therapeutics. Br J Pharmacol 172:2705–2715 Chen FP, Chien MH (2014) Phytoestrogens induce differential effects on both normal and malignant human breast cells in vitro. Climacteric 17:682–691 Colin D, Lancon A, Delmas D, Lizard G, Abrossinow J, Kahn E, Jannin B, Latruffe N (2008) Antiproliferative activities of resveratrol and related compounds in human hepatocyte derived HepG2 cells are associated with biochemical cell disturbance revealed by fluorescence analyses. Biochimie 90:1674–1684 Dhar S, Hicks C, Levenson AS (2011) Resveratrol and prostate cancer: promising role for microRNAs. Mol Nutr Food Res 55:1219–1229 Fan Y, Liu L, Fang K, Huang T, Wan L, Liu Y, Zhang S, Yan D, Li G, Gao Y, Lv Y, Chen Y, Tu Y (2016) Resveratrol ameliorates cardiac hypertrophy by down-regulation of miR-155 through activation of breast cancer type 1 susceptibility protein. J Am Heart Assoc 5:e002648 Gambini J, Ingles M, Olaso G, Lopez-Grueso R, Bonet-Costa V, Gimeno-Mallench L, Mas-Bargues C, Abdelaziz KM, Gomez-Cabrera MC, Vina J, Borras C (2015) Properties of resveratrol: in vitro and in vivo studies about metabolism, bioavailability, and biological effects in animal models and humans. Oxidative Med Cell Longev 2015:837042 Hayashi SI, Eguchi H, Tanimoto K, Yoshida T, Omoto Y, Inoue A, Yoshida N, Yamaguchi Y (2003) The expression and function of estrogen receptor alpha and beta in human breast cancer and its clinical application. Endocr Relat Cancer 10:193–202 Izzotti A, Cartiglia C, Steele VE, Deflora S (2012) MicroRNAs as targets for dietary and pharmacological inhibitors of mutagenesis and carcinogenesis. Mutat Res Rev Mutat Res 751:287–303 Jang M, Cai L, Udeani GO, Slowing KV, Thomas CF, Beecher CW, Fong HH, Farnsworth NR, Kinghorn AD, Mehta RG, Moon RC, Pezzuto JM (1997) Cancer chemopreventive activity of resveratrol, a natural product derived from grapes. Science 275:218–220 Jannin B, Menzel M, Berlot JP, Delmas D, Lancon A, Latruffe N (2004) Transport of resveratrol, a cancer chemopreventive agent, to cellular targets: plasmatic protein binding and cell uptake. Biochem Pharmacol 68:1113–1118 Kala R, Tollefsbol TO (2016) A novel combinatorial epigenetic therapy using resveratrol and pterostilbene for restoring estrogen receptor-alpha (ERalpha) expression in ERalpha-negative breast cancer cells. PLoS One 11:e0155057 Kala R, Shah HN, Martin SL, Tollefsbol TO (2015) Epigenetic-based combinatorial resveratrol and pterostilbene alters DNA damage response by affecting SIRT1 and DNMT enzyme expression, including SIRT1-dependent gamma-H2AX and telomerase regulation in triple-negative breast cancer. BMC Cancer 15:672 Klose RJ, Bird AP (2006) Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci 31:89–97 Lancon A, Kaminski J, Tili E, Michaille JJ, Latruffe N (2012) Control of microRNA expression as a new way for resveratrol to deliver its beneficial effects. J Agric Food Chem 60:8783–8789 Lopez-Serra P, Esteller M (2012) DNA methylation-associated silencing of tumor-suppressor microRNAs in cancer. Oncogene 31:1609–1622 Lubecka K, Kurzava L, Flower K, Buvala H, Zhang H, Teegarden D, Camarillo I, Suderman M, Kuang S, Andrisani O, Flanagan JM, Stefanska B (2016) Stilbenoids remodel the DNA methylation patterns in breast cancer cells and inhibit oncogenic NOTCH signaling through epigenetic regulation of MAML2 transcriptional activity. Carcinogenesis 37:656–668

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PARylation, DNA (De)methylation, and Diabetes

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Melita Vidaković, Anja Tolić, Nevena Grdović, Mirunalini Ravichandran, and Tomasz P. Jurkowski

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establishment and Removal of DNA Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA (De)methylation and Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Poly(ADP-ribosyl)ation in Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Mingled Yarn: PARylation, DNA (De)methylation and Diabetes . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of DNA Methylation in Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Diabetes and diabetic complications, autoimmunity and inflammatory diseases, have recently become the focus of epigenetic therapy, since with epigenetic drugs it is possible to reverse aberrant gene expression profiles associated with the disease states. For diabetes, the therapy challenges depend on identifying the most appropriate molecular target and its influence on a relevant gene product. This chapter summarizes the current view on the interplay between ten-eleven

M. Vidaković (*) Department of Molecular Biology, Institute for Biological Research Siniša Stanković, University of Belgrade, Belgrade, Serbia e-mail: [email protected] A. Tolić · N. Grdović Institute for Biological Research, University of Belgrade, Belgrade, Serbia e-mail: [email protected]; [email protected] M. Ravichandran · T. P. Jurkowski Institute of Biochemistry, University of Stuttgart, Stuttgart, Germany e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_55

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translocation (TETs) and the poly(ADP-ribose) polymerase (PARPs) family of enzymes in regulating DNA methylation and how this interplay could be targeted to attenuate diabetes. This molecular interchange jigsaw puzzle is emerging as an important focus of research, and we can expect to see further advances in the elucidation of its role in diabetes as well as other pathologies. Moreover, the possibility for designating specific PARP-1 inhibitors as potential “EPI-drugs” for diabetes prevention/attenuation is also discussed. Understanding the epigenetic machinery and the differential roles of its components is essential for the development of targeted epigenetic therapies for diseases. Keywords

Diabetes · DNA methylation · DNA demethylation · DNMT enzymes · Epigenetic drug targets · Chromatin architecture · PARylation · PARP-1 inhibitors · TET enzymes List of Abbreviations

3AB 5caC 5fC 5hmC 5hmU 5mC BER C CpG CRISPR/Cas9 DNMTs NAD+ PARPs PARs PARylation PARG RO/NS T1D T2D TDG TETs α-KG

3-aminobenzamide 5-carboxylcytosine 5-formylcytosine 5-hydroxymethylcytosine 5-hydroxymethyluridine 5-methylcytosine base excision repair cytosine cytosine-phosphate-guanine clustered regularly interspaced short palindromic repeats/ associated protein-9 nuclease DNA methyltransferases nicotinamide adenine dinucleotide poly(ADP-ribose) polymerase family of enzymes poly(ADP-ribose) polymers poly(ADP-ribosyl)ation poly(ADP-ribose) glycohydrolases reactive oxygen/nitrogen species type 1 diabetes type 2 diabetes thymine-DNA glycosylase ten-eleven translocation family of enzymes α-ketoglutarate

Introduction Epigenetic marks represent an additional stratum of information that can, in combination with genetic information, produce a plethora of phenotypic outcomes. As a result of its reversible nature, deregulation of epigenetic processes is at the root of

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Fig. 1 Epigenetic drugs as an upgrade for the treatment of diabetes and obesity. Although there are no completely effective preventive or therapeutic strategies due to the multifactorial etiology of diabetes, the plastic nature of the epigenome represents a potential target in the design of future therapy and novel epigenetic drugs

several complex disorders, including diabetes. While these diseases cannot be effectively prevented by therapeutic strategies due to their complex etiologies and phenotypes, the epigenetic malleability of the genome renders it open to therapeutic drug targeting (Fig. 1). Thus research into new players that interact with and regulate epigenetic mechanisms presents a potential avenue for novel therapeutic approaches. As this field continues to develop, the epigenome could also become a biomarker for tracking therapeutics efficacy and toxicity of potential therapies. Diabetes is a complex metabolic disorder characterized by hyperglycemia as a consequence of either impaired insulin secretion or insulin action, which presents in two major forms: type 1 diabetes (T1D) resulting from autoimmune destruction of insulin-producing pancreatic b-cells and type 2 diabetes (T2D) which develops as a result of insensitivity of target cells to insulin that can progressively compromise b-cell function and survival. Several studies have provided comprehensive information on the DNA methylomes of human pancreatic islets, underscoring epigenetic regulation as a mechanism driving the onset and development of diabetes (Agardh et al. 2015; Dayeh et al. 2014). Consequently DNA methylation has become an acceptable diagnostic tool for both diabetes types as changes in the methylation pattern are observed even prior to disease development. While DNA methylation is the most investigated epigenetic modification, aside from the knowledge of passive DNA demethylation which has been linked to

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reduced activities or absence of DNA methyltransferases (DNMTs), the mechanism of active DNA demethylation has remained elusive for quite some time. Recently, the ten-eleven translocation (TET) enzyme family has been identified as the key initiator of active DNA demethylation through the iterative oxidation pathway of 5-methylcytosine (5mC) that leads to generation of three consecutive oxidized cytosine forms. The poly(ADP-ribose) polymerase family of enzymes (PARPs) is a focal point that links DNA methylation (via DNMTs) and demethylation (via TETs) by controlling both classes of enzymes. PARPs have the ability to catalytically add long and branched poly(ADP-ribose) polymers (PARs) using NAD+ as a substrate, to target proteins in a poly(ADP-ribosyl)ation (PARylation) reaction (Bai 2015). At present, there is only one study that has incorporated the TET-PARP interplay in diabetes-related research (Dhliwayo et al. 2014). To untangle this triangle, we first need to learn how to synchronize the enzyme activities of PARPs and TETs to ensure fine-tuned regulation of DNA (de)methylation. More stringent control of this equilibrium could attenuate the onset and progression of several complex diseases, including diabetes. This could be achieved by elucidating all the elements that maintain homeostasis of PARylation, a process determined by the balanced actions of PAR synthesizers and erasers. The fact that PARP inhibitors are included in therapeutic approaches for the prevention or reversal of diabetic complications (Burkart et al. 1999) lends additional support to further research efforts.

Establishment and Removal of DNA Methylation DNA methylation is the most extensively studied epigenetic mark. As a major regulator of transcriptional activity, it participates in the control of the expression of a variety of genes. It plays an important role in maintaining genome integrity (Dodge et al. 2005), in genomic imprinting, in X chromosome inactivation, and in the silencing of retrotransposons, repetitive elements, and tissue-specific genes (Jones and Takai 2001). As once set, DNA methylation state can be faithfully maintained during each cycle of DNA replication and cell division. In mammals, DNA methylation is the covalent addition of a methyl group to the carbon at the fifth position of cytosine (C) catalyzed by DNMTs. It is usually introduced in the context of cytosine-phosphate-guanine (CpG) dinucleotides located in regions that can differ in size and position within the gene: CpG islands, shores (regions 0–2 kb from CpG islands), shelves (regions 2–4 kb from CpG islands), and open sea (regions are isolated CpG sites with no specific position). De novo methyltransferases DNMT3a and DNMT3b are responsible for establishing de novo DNA methylation patterns (Yokochi and Robertson 2002), whereas the maintenance methyltransferase DNMT1 is responsible for renewing previously established methylation patterns after DNA replication (Hermann et al. 2004). Yet, this classical division for de novo and maintenance DNMTs seems to be more complex than initially assumed (Jeltsch and Jurkowska 2014). In addition to these enzymes, the availability of cofactors, such as S-adenosyl-L-methionine, is essential for the regulation of DNA and protein methylation (Stead et al. 2006).

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A breakthrough in DNA (de)methylation research was made with the identification of the ten-eleven translocation (TET) family of DNA dioxygenases capable of generating 5-hydroxymethylcytosine (5hmC) through catalytic oxidation of 5mC (Ito et al. 2010). The TETs (TET1/2/3 enzymes) belong to a larger family of Fe [2]/α-ketoglutarate-dependent dioxygenases. TETs use molecular oxygen for oxidative decarboxylation of α-ketoglutarate (α-KG), thereby generating a high-valent Fe (IV)-oxo intermediate capable of converting 5mC to 5hmC. TETs can also generate 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) in successive oxidation reactions (Ito et al. 2011) (Fig. 2). DNA methylation and 5mC formation within CpG islands could represent also the mutation hot spot, since the deamination of 5mC eventually leads to thymine formation, which is the primary cause of elevated mutagenesis. Since 5mC also represents a potential threat for genomic integrity, a cell needs to rely on a strictly controlled DNA demethylation mechanism that should be viewed as a part of the DNA repair process. In dividing cells, passive DNA demethylation occurs during replication and is caused by loss of DNMT1 activity, which leads to the dilution of 5mC throughout the genome (Kagiwada et al. 2013). Furthermore, TET- and replication-dependent pathways promote 5mC conversion to 5hmC (possibly further to 5fC and 5caC) which in turn dilutes methylation marks in successive DNA replications as DNMT1 does not recognize these modifications as location markers

Fig. 2 Role of TETs in DNA demethylation pathways. DNA methylation is initiated by DNMTs. The 5mC can be further oxidized by TET1/2/3 proteins to generate 5hmC, 5fC, and 5caC. All modifications can be lost through passive demethylation (green route). Active demethylation can occur by the action of TDG that can excise 5fC and 5caC, which can be further repaired to cytosine (red route) or through deamination of 5mC or 5hmC in thymine or 5hmU (orange route)

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for reintroduction of methylation (Kohli and Zhang 2013). An alternative, replication-independent, active DNA demethylation mechanism initiated by the oxidation of 5mC and further to 5fC and 5caC by TETs and finalized by base excision repair (BER), resulting in the replacement of a modified base with an unmodified C, was recently proposed (Fig. 2). One possibility is the deamination of 5hmC to 5-hydroxymethyluridine (5hmU) through the activity of AID/APOBEC enzymes, followed by BER (Guo et al. 2011). This model is controversial as there appear to be problems in replicating the results of the initial study. The other model that relies on BER has the best experimental confirmation and is generally accepted. An alternative mechanism is through direct 5caC decarboxylation; however, the enzyme catalyzing this reaction has not yet been identified. Finally, another BER-independent pathway has been proposed and incorporates the fact that DNMT3a/3b can function as DNA 5hmC-dehydroxymethylases in an oxidizing environment (Chen et al. 2012) yet not confirmed by independent study. DNA demethylation, both active and passive, is indispensable for maintaining genome integrity and for its proper functioning. TET-mediated oxidation is an important epigenetic modification which leads to the creation of new epigenetic marks, such as 5hmC, 5fC, and 5caC. Several important questions need to be answered before we can obtain a full understanding of this vital process, especially in terms of cell-fate transition. Aside from the great importance of TET enzyme activities and DNA demethylation in cellular reprograming, the role of TETs in DNA methylation fidelity, modulation of chromatin architecture, and in transcriptional priming (Hill et al. 2014) should also be underlined. The role of TETs in faithful DNA methylation is realized by their ability to neutralize aberrant de novo DNA methylation and thereby preserve the proper unmethylated state of certain genomic regions and in transcriptional regulation through the formation of 5hmC that acts as an epigenetic mark allowing for gene activation or repression in combination with certain histone marks (Hill et al. 2014).

DNA (De)methylation and Diabetes DNA methylation has been implicated in a number of biological processes and has been shown to be affected by environmental influences and aging (Christensen et al. 2009). However, much less is known about DNA methylation in diabetes. Altered DNA methylation patterns observed in hyperglycemia, oxidative stress, and inflammation have been proven to be responsible for impaired gene regulation in diabetic individuals (Rakyan et al. 2011; Toperoff et al. 2012). There are only a limited number of studies regarding epigenetic changes in target tissues from patients with diabetes. It has been confirmed that the promoters of several genes involved in glucose metabolism exhibit differential DNA methylation. These are genes encoding for glucose transporter 4, the major glucose transporter in adipose and muscle tissues (Yokomori et al. 1999), and uncoupling protein 2 (Carretero et al. 1998), which is a candidate gene for the development of T2D. DNA methylation of another candidate gene, PPARGC1A, was increased in pancreatic islets from patients

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with T2D when compared to healthy control subjects (Ling et al. 2008). Accordingly, PPARGC1A expression was lower in diabetic islets and correlates inversely with the degree of DNA methylation. A recent study also discovered that p.Ile1762Val substitution in TET2 is associated with liver PPARGC1A methylation and transcription (Pirola et al. 2015). A significant association between p.Ile1762Val TET2 and T2D came as no surprise since it was previously shown that liver expression of PPARGC1A modulates insulin resistance (Sookoian et al. 2010). In light of the above findings, it was suggested that TET2 is involved in the modulation of the PPARGC1A (de)methylation balance, possible in response to changes in the cellular environment (Pirola et al. 2015). Further, analysis of skin fibroblasts derived from T1D-affected monozygotic twins cultured in media containing high levels of glucose demonstrated differential gene expression in twin pairs in a number of genes that are involved in epigenetic regulation (Caramori et al. 2012). It has been suggested that errors in DNA methylation can lead to decreased gene responsiveness to external stimuli, thus contributing to the development of diabetes (Gallou-Kabani and Junien 2005). This assumption was confirmed by the first comprehensive attempt at DNA methylation profiling of pancreatic islets in T2D and nondiabetic individuals (Volkmar et al. 2012). The authors discovered that 276 CpG loci associated with promoters of 254 genes are differentially methylated in diabetic islets. These methylation patterns were not detected in blood cells from T2D individuals nor were they found in nondiabetic islets exposed to high glucose concentration (Volkmar et al. 2012). In a more recent study, researchers performed a genome-wide DNA methylation analysis of 479,927 CpG sites in human pancreatic islets from T2D donors (Dayeh et al. 2014). This work revealed the presence of differential DNA methylation patterns for 1649 CpG sites belonging to 853 genes in T2D patients. The authors published a detailed methylome map of the human pancreatic islets with 102 genes that showed differential DNA methylation and gene expression patterns in islets of T2D patients, demonstrating that changes in DNA methylation are responsible, at least in part, for altered insulin secretion and pathogenesis of T2D (Dayeh et al. 2014). In both of these large-scale DNA methylation profiling studies, the majority of the identified differentially methylated genes had decreased methylation and increased expression in T2D islets, while changes in global levels of DNA methylation have not been observed (Dayeh et al. 2014; Volkmar et al. 2012). Concerning T1D, a very comprehensive study in monozygotic twins has identified DNA methylation variable positions that arise very early prior to the development of overt T1D (Rakyan et al. 2011). The most recent epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs revealed enrichment of differentially variable CpG positions in T1D twins when compared with their healthy co-twins, at gene regulatory elements which, after integration with cell type-specific gene regulatory circuits, highlight the pathways involved in immune cell metabolism and the cell cycle, including mTOR signaling (Paul et al. 2016). Since a common feature of both types of diabetes is a reduction in b-cell mass, therapeutic interventions that promote the growth and survival of functional b-cell are a valid approach for diabetes treatment. It is now clear that adult pancreatic b-cells replicate to maintain normoglycemia during body growth and after oxidative

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stress damage and inflammation (Bouwens and Rooman 2005). DNA methylation is proven to be important for maintenance of b-cell identity. Extreme loss of b-cells due to severe metabolic stress can induce transdifferentiation of pancreatic a- to b-cells, suggesting that b-cells deficient in DNMT1 are converted to pancreatic a-cells. Moreover, Aristaless-related homeobox gene was identified as methylated and repressed in b-cells but hypomethylated and expressed in a-cells and DNMT1deficient b-cells (Dhawan et al. 2011). Another study has shown that adult human skin fibroblasts can be converted to insulin-secreting cells after they have been exposed to the DNA methyltransferase inhibitor 5-azacytidine (Pennarossa et al. 2013). Further research in this field also revealed that pancreatic islet dysfunction and development of diabetes in rats are associated with DNA methylation-dependent epigenetic silencing of the Pdx1 gene, which is a key transcriptional regulator of b-cell differentiation and insulin gene expression (Park et al. 2008). Cell reprogramming requires viral transfection of appropriate transcription factors and use of many growth factors which limits its therapeutic potential. Therefore, alternative approaches need to be developed to overcome this limitation. The concept of epigenetic editing could be a vital alternative cell reprogramming approach. Recently, an epigenetic editing platform based on a programmable DNA-binding domain composed of CRISPR/Cas9 fused to engineered DNA methyltransferases was developed in order to change the DNA methylation status of the selected loci in cells (Stepper et al. 2016). These novel epigenetic editing tools could be employed for targeted DNA methylation of key transcription factors responsible for maintaining a- and b-cell identity (Fig. 3). Finally, any methodological success in increasing the number of insulin-producing b-cells is a promising therapeutic avenue for curing diabetes. The crucial information obtained from these studies underlies that the profiles of differentially methylated genes determine b-cell survival, function, and adaptation to stressors and point to a tight link between DNA methylation, diabetes, and its complications. Diabetes is a complex disease with polygenic susceptibility, thereby depending on several environmental influences that through interplay between genome-epigenome factors influence its onset and further development (Fig. 4). Environmental factors that influence diabetes onset and progression have been well characterized in contrast to the details of their interaction with genetic factors and epigenetic modifications. While we are continuously acquiring knowledge about the interactions between these factors, the relationship between specific epigenetic modifications and genomic features remains poorly explained in the context of the diabetes phenotype. Thus, there is an urgent need for integrative approaches aimed at obtaining fundamental insight into how genetic and epigenetic factors underlie diabetic etiopathogenesis.

Poly(ADP-ribosyl)ation in Diabetes PARylation has been implicated in numerous processes both in normal and pathological cell states and is involved in the regulation of epigenetic mechanisms and,

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Fig. 3 The concept of epigenetic editing. The targeting device (CRISPRs/Cas9), a sequencespecific DNA-binding domain that can be redesigned to recognize desired sequences by the guided RNA, is fused to an effector domain (DNMTs or TETs) that can modify the epigenetic state of the targeted locus, leading to a long-lasting biological effect (gene repression (a) or activation (b)). Blue lollipops–cytosine; green lollipops–5mC; red lollipops–5hmC

accordingly, also in diabetes development and progression. The mammalian PARP superfamily of enzymes consists of 17 members all sharing a conserved ADP-ribosyl transferase catalytic domain, with PARP-1 and PARP-2 being the most prominent family members. PARPs have been implicated in many molecular and cellular processes, such as replication, DNA repair, genomic stability, chromatin remodeling, and transcriptional regulation (Schuhwerk et al. 2016). PARP-mediated PARylation is a reversible posttranslational modification that can be directed to both the enzymes carrying out the reaction (automodification) and to other target proteins (heteromodification). In the cell, PARP activity in both basal and stimulated states is primarily exerted by PARP-1 and to lesser extent by PARP-2 (Bai 2015). Historically, PARP-1 and PARP-2 were implicated in DNA repair as their activity is markedly stimulated by their binding to DNA strand breaks. Under basal conditions, PARP-1 activity is generally low, but excessive DNA damage or other pathophysiological conditions can induce PARP-1 hyperactivation, thus initiating an energy-consuming cycle leading to NAD+ and ATP depletion and necrosis (Ha and Snyder 1999). On the other hand, oxidative stress resulting from an overload of the endogenous enzymatic and nonenzymatic antioxidant defenses with reactive oxygen/nitrogen species (RO/NS) plays an important role in the etiology of both types of diabetes. The prooxidant action in the hyperglycemic environment causes DNA damage and subsequent PARP-1/2 activation which in turn leads to a specific type of pancreatic b-cell death due to extreme energy consumption (Grdović et al. 2014). Since PAR synthesis is generally evoked by different stress signals,

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Fig. 4 Epigenome-genome interaction as a valid approach for diabetes treatment. Future diabetes research should be directed toward achieving a solid research link between genetics, epigenetics, and nongenetic factors in order to improve the quality of life in both health and disease state

genotoxic, hormonal, or metabolic, the regulation of their formation by PARPs and their degradation by poly(ADP-ribose) glycohydrolases (PARG) are an essential aspect of the adaptive response of the cell. Transient PAR formation/degradation coordinates chromatin remodeling and transcriptional regulation, thus allowing the cell to follow the proper survival pathway. Therefore, the precise regulation of the equilibrium between PAR synthesis and degradation is of utmost importance for cell fate in diabetes (Fig. 5). Additionally, PARP-1 can also induce the production of pro-inflammatory mediators via activation of NF-κB transcription, which is especially important during the autoimmune destruction of b-cells in T1D (Ba and Garg 2011). Other complications observed in diabetic patients appear to be also connected to these pathological consequences of PARP-1 hyperactivation. In models of T1D, it was shown that mice lacking PARP-1 gene expression are resistant to pancreatic b-cell destruction and diabetes development (Burkart et al. 1999). Taking into account the aforementioned, PARP-1 inhibition has been identified as a promising

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approach for the treatment of diabetes complications (Pacher and Szabo 2007). Several PARP-1 inhibitors have been used in clinical studies in susceptible individuals as agents capable of preventing the development of insulin-dependent diabetes (Pandya et al. 2010). Up to now, several inhibitors of PARP-1 have been identified (Szabo et al. 2006). The first introduced was the competitive inhibitor of PARP-1, nicotinamide, a natural NAD+-metabolizing enzyme competitor (Khan et al. 2007). A structurally similar compound, 3-aminobenzamide (3AB), is the foremost PARP inhibitor which is still in use. Both nicotinamide and 3AB have been shown to protect against diabetes development (Masiello et al. 1990). The administration of nicotinamide, a free-radical scavenger and a weak and nonselective PARP-1/2 inhibitor, prevents the development of diabetes in a spontaneous mouse model of autoimmune diabetes (Virag and Szabo 2002). Clinical trials assessing diabetes treatment by PARP-1/2 inhibition were performed with nicotinamide and, unfortunately, revealed no significant therapeutic effects. The problem with these inhibitors is their low potency (mM range) and low specificity, along with additional effects, such as antioxidant activity (Szabo et al. 1998). Other more potent PARP-1 inhibitors have been discovered, and some, such as PJ34 and INH2BP, show promising effects in diabetes models and also exert anti-inflammatory effects (Szabo et al. 2006). PARP-1 inhibitor, PD128763, is effective at protecting islet cells from NO, ROS, and streptozotocin at concentrations 100 times lower than required for nicotinamide (Wurzer et al. 2000). In general, the involvement of PARP-1/2 proteins in the maintenance of genome integrity and major cellular functions anticipates that PARP-1 inhibition could be used in “next-generation” diabetes prevention therapy (Fig. 5). In the latest review that summarizes epigenetic drugs used for the treatment of diabetes and obesity (Arguelles et al. 2016), PARP inhibitors are not listed, and this should be corrected in any further work, since inhibitors of PARP activity due to all of the abovementioned deserve to be defined as a potential group of epigenetic drugs. Researchers should carefully contemplate on the effect they aim to induce by inhibiting PARPs. In some medical conditions, a global inhibition of PARPs could be more beneficial since it would stop any PAR synthesis and energy expenditure. Search for a selective inhibitor for a specific PARP enzyme will be more effective if the disease to be cured depends on a particular PARP molecule. Considering the importance of the equilibrium between PAR synthesis and degradation for cell fate in diabetes, aside from developing new, selective and potent PARP-1/2 inhibitors, researchers should also consider producing molecules capable of regulating PARG activity.

A Mingled Yarn: PARylation, DNA (De)methylation and Diabetes During the last decade, it has become evident that PARPs and PARylation also have a role in epigenetic mechanisms, more precisely in the regulation of DNA

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Fig. 5 (continued)

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methylation (Fig. 6). The first information linking DNA methylation and PARP-1 activity was reported in 1997, where it was pointed out that the PARylated isoform of histone H1 and/or long and branched protein-free PARs could protect genomic DNA against methylation of the CpGs (Zardo et al. 1997). In the confirmation of this result in 2008, it was indicated that automodified, PARylated PARP-1 marks and protects sequences in the genome that should remain unmethylated, thus directly pointing to a role of PARP in the epigenetic regulation of gene expression (Zampieri et al. 2009). Caiafa and coworkers revealed that cells with hyperactivated PARP-1 are characterized by widespread DNA hypomethylation (Guastafierro et al. 2008). In vitro experiments showed that DNMT1 activity is inhibited by both free and PARs bound to PARP-1, while co-immunoprecipitation results confirmed that DNMT1 interacts with PARylated PARP-1 in vivo (Reale et al. 2005). Moreover, a new molecular participant in demethylation process, CTCF, as an insulator that binds preferentially to unmethylated target DNA sequences and whose insulator function is impaired by the inhibition of its N-terminal domain by PARylation, was identified (Yu et al. 2004). PARP-1, CTCF, and DNMT1 were identified as the main players in the cross talk between PARylation and DNA methylation. There is strong evidence that CTCF can activate PARP-1, leading to increased levels of nuclear PARs. Also, overexpression of CTCF can indirectly, through the agency of PARP-1, decrease DNMT1 activity by 70%, leading to widespread hypomethylation (Guastafierro et al. 2008). Recently it was shown that PAR depletion leads to the removal of CTCF from its target sites and to its perinuclear accumulation (Guastafierro et al. 2013). The consequence of both PAR depletion and CTCF silencing is increased DNA condensation and DNA hypermethylation, which emphasizes the importance of the cross talk between CTCF and PARylation in maintenance of chromatin structure and organization. In 2014, Caiafa’s group described the involvement of PARylation in the control of DNA and histone methylation in the TET1 gene (Ciccarone et al. 2014) and next year the importance of TET1 and PARP-1 interplay (Ciccarone et al. 2015). It was suggested that TET1 has the ability to stimulate PARP-1 activity independently of DNA damage, which can promote covalent PARylation of TET1, bringing about either an increase or inhibition of TET1 activity as a result of noncovalent interaction with PAR polymers (Ciccarone et al. 2015). It was shown that PARP-1 is also involved in active demethylation in mouse primordial germ cells by upregulation of TET1 transcription (Ciccarone et al. 2012). As TET enzymes are undoubtedly initiators of DNA demethylation, the search for factors that can influence them in a direct or indirect manner continues. PARPs are proteins that are increasingly ä Fig. 5 PARP-1 in diabetes: an omnipresent molecule with great importance. PARP-1 is a key enzyme in DNA repair. PARP-1 is activated by hyperglycemia-induced RO/NS overproduction that causes DNA damage; PARP-1 overspends the energy pool of the cell, leading to cell death (apoptosis, necrosis, parthanatos); PARP-1 assumes the roles of transcriptional regulator, an important contributor to chromatin 3D reshaping and of a regulator of DNA methylation/demethylation. The role of PARP-1 in the regulation of inflammatory process is highlighted in diabetes

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Fig. 6 (continued)

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implicated in DNA demethylation (Fig. 6). As important components of the BER pathway, the inhibition of PARPs was used to confirm the involvement of BER in TET-initiated demethylation (Guo et al. 2011). Evidence suggesting that PARP-1 can also have a BER-independent role in DNA demethylation has emerged (Ciccarone et al. 2012). The same study showed that PARylation inhibition considerably reduces TET1 gene expression and moderately raises the TET3 gene expression in mouse primordial germ cells. Furthermore it was confirmed that all three TETs interact with PARP-1 (Muller et al. 2014). Additionally, it has been demonstrated that PARP-1 and TET2 are recruited to Nanog and Esrrb loci during somatic cell reprogramming and are important for downregulating 5mC modification and establishing 5hmC (Doege et al. 2012). Recent findings that TET1/2 can bind PAR polymers add yet another layer of complexity to the intricate interplay of TETs and PARP-1 in DNA demethylation (Fujiki et al. 2013). Specifically, the authors discovered that the Cys-rich domain exhibits strong affinity for binding PAR in both TET1/2, while the N-terminal region and CXXC domain of TET1 show somewhat weaker binding. In this study it was postulated that PARylation of sequence-dependent transcription factors serves to produce landmarks and docking site for TETs, directing targeted DNA demethylation (Fujiki et al. 2013). Additionally, PARylation of TET1 may lead to the formation of complexes containing PARylated and PAR-interacting proteins that participate in the regulation of DNA demethylation and transcription (Ciccarone et al. 2015). Surprisingly, no research has focused explicitly on the role of PARP-2 in the regulation of DNA (de)methylation, even though it contributes to overall PARylation levels in a cell. Finally, describing molecular machinery that is responsible for the hyperglycemia-induced DNA demethylation, diabetes is included in this already complicated interplay of PARPs and TETs in regulating DNA demethylation process (Dhliwayo et al. 2014). It was proposed that PARP-1 can stimulate TET1-initiated demethylation in diabetes that eventually leads to persistent complications. Though it was previously documented that hyperglycemia induces DNA demethylation (Williams et al. 2008), this research went further by demonstrating that for most specific CpG islands, demethylation induced by hyperglycemia persists in metabolic memory which underlies diabetic complications, even when glycemic control is reestablished (Dhliwayo et al. 2014). This study demonstrated that PARP-1 inhibition can prevent hyperglycemia-induced demethylation and restore the regenerative capacity of the fin in the zebrafish model, giving hope for new treatment possibilities (Fig. 6). ä Fig. 6 A mingled yarn: PARylation, DNA (de)methylation, and diabetes. Alterations in DNA methylation pattern are involved in different aspects of diabetes. Three enzyme families, DNMTs, TETs, and PARPs, have elevated this already complicated game to a higher level of complexity. PARP-1 is a connecting factor since it modifies chromatin structure, interacts with DNMT1 and TET1, and is capable of PARylating both enzymes and thereby influencing their activities. PARP inhibition could be the key for controlling widespread genomic hypomethylation that is observed in diabetes

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Conclusion Since diabetes is such a complex multifactorial disease, it is hard to pinpoint the precise pathways of its progression and development of diabetic complications. Nevertheless, it is important to gain as much knowledge of the possible mechanisms that underlie this widespread disease. Epigenetic marks and DNA methylation, in particular, represent a promising direction for future diabetes research since, as evinced in this chapter, a number of studies have implicated alterations in DNA methylation patterns in different aspects of diabetes. Therefore it is both important to gain knowledge from basic research examining the processes involved in the establishment, removal, and regulation of DNA methylation marks and to apply these insights in research focused on diabetes therapy. DNA methylation is an important epigenetic mark, and thus regulation of its establishment and removal is crucial for a basic understanding of epigenetic regulatory mechanisms. The discovery of TET enzymes has opened a new path in the research of mechanism of active DNA demethylation. On the other hand, PARPs are implicated in a wide variety of cellular processes, including DNA methylation. PARPs are emerging as promising regulators of TETs’ activities and possibly even in their localization in the genome. They thus represent molecules that link DNMTs and TETs and which, through fine-tuned activity, can influence DNA methylation/ demethylation process. Therefore, studying TETs/PARPs and DNMTs/PARPs interactions and the influence of PARylation on TETs or DNMTs activity can provide new important insights into DNA metabolism. Research of basic molecular mechanisms is the first important step on the long road to the clinical application of epigenetics in diseases therapy.

Key Facts of DNA Methylation in Diabetes • A common feature of diabetes is the reduction in b-cell mass; thus therapeutic interventions promoting functional b-cell growth and survival are a valid approach for diabetes treatment. • DNA methylation has been implicated in diabetes onset and development of diabetic complications and is proven to be important for maintenance of pancreatic b-cell identity. • To cure diabetes by triggering the transdifferentiation of pancreatic a- to b-cells, synthetic epigenetic editing tools could be employed for targeted DNA methylation of key transcription factors that maintain pancreatic cell phenotypes. • PARP-1 activity and PARylation have been implicated in the regulation of DNA (de)methylation by influencing TET1 and DNMT1 activities and also in diabetes development and progression. • PARP-1 inhibitors could be used as “next-generation” diabetes prevention drugs.

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Dictionary of Terms • Diabetes – a complex metabolic disorder marked by hyperglycemia due to either impaired insulin secretion or insulin action. • DNA methyltransferases (DNMTs) – epigenetic enzymes that modify DNA by adding a methyl group on the fifth position of cytosine. • Poly(ADP-ribosyl) polymerases (PARPs) – mammalian superfamily of enzymes capable of cleaving NAD+-forming poly(ADP-ribose) polymers covalently attached to themselves or other target proteins. • Ten-eleven translocation (TET) family of DNA dioxygenases – enzymes responsible for iterative oxidation of 5-methylcytosine. • Epigenetic drugs – molecules with a potential to change the activity of druggable proteins involved in any aspect of epigenetic processes.

Summary Points • Alterations in DNA methylation pattern are implicated in different aspects of diabetes. • Diabetes and diabetic complications have recently become the focus of epigenetic therapy. • PARP-1 serves as a pivot of the DNA methylation equilibrium in the genome. • PARP-1 has emerged as a bridging molecule regulating the activities of both TETs and DNMTs. • Efforts aimed at defining the interplay between TETs and PARPs in regulating DNA methylation are key for the development of novel diabetes attenuation strategies. • Epigenetic editing concept could be an alternative cell reprogramming approach for diabetes cure. • The epigenetic editing tools could be employed for targeted DNA methylation of key transcription factors responsible for maintaining pancreatic cell identity. • “EPI-drugs” allow reversal of aberrant gene expression associated with different disease states. • Specific PARP-1 inhibitors are viable candidates for potentially new “EPI-drugs” for diabetes prevention/attenuation. • Understanding the epigenetic machinery and differential roles of its components is essential for developing more targeted epigenetic therapy for noncancerous diseases. Acknowledgments This work was supported by the Alexander von Humboldt foundation, program for funding a Research Group Linkage (2014) and Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant No. 173020. This article is based upon work from COST Action (CM1406), supported by COST (European Cooperation in Science and Technology), participants MV and TPJ.

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Extra Virgin Olive Oil and Corn Oil and Epigenetic Patterns in Breast Cancer

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Breast Cancer and Dietary Lipids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Extra Virgin Olive Oil and Corn Oil on DMBA-Induced Carcinogenesis and Gene Expression Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Extra Virgin Olive Oil and Corn Oil on Global DNA Methylation in Mammary Gland and Experimental Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Extra Virgin Olive Oil and Corn Oil on Gene-Specific Methylation in Mammary Gland and Experimental Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Extra Virgin Olive Oil and Corn Oil on Histone Modifications in Mammary Gland and Experimental Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Influence of Olive Oil and Other Dietary Lipids on MicroRNA Expression Patterns in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetic Effects of Olive Oil Minor Compounds on Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Breast cancer is the leading neoplasia in women worldwide. Nutrition and especially dietary lipids can influence mammary carcinogenesis through multiple mechanisms. This works aims to get insight into the effects of two common oils,

R. Moral (*) · E. Escrich Multidisciplinary Group for the Study of Breast Cancer, Department of Cell Biology, Physiology and Immunology, Physiology Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain e-mail: [email protected]; [email protected]; [email protected]; gr. [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_15

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extra virgin olive oil (EVOO) and corn oil, on mammary carcinogenesis and the molecular mechanisms of such effects. The administration of a diet high in corn oil (HCO) from weaning had a clear stimulating effect on 7,12-dimethylbenz(a) anthracene-induced mammary carcinogenesis, increasing the morphological and clinical degree of tumor malignancy, while a high-EVOO diet has a weak tumor-enhancing effect. The HCO diet modified gene expression profiles in mammary gland and tumors, downregulating genes with a role in apoptosis and immune system. On the contrary, the high-EVOO diet mainly modulated genes with a role in metabolism. These effects may be a consequence of an influence on the epigenetic machinery. Thus, the high-EVOO diet increased global DNA methylation in the mammary gland, mainly around puberty, and also in experimental mammary tumors. In relation to gene-specific methylation, the HCO diet, but not the high EVOO one, increased the total DNA methyltransferase activity in mammary glands and tumors, concomitantly with the increase in Rassf1a and Timp3 promoter methylation. Both high-fat diets may influence the modification of histones (the levels of H3K4me2, H3K27me3, H4K16ac, and H4K20me3), especially in the mammary gland. Although there is little data reported at other epigenetic levels, the differential effects of the diets are likely to be also due to different modification of microRNA patterns. Considering the unspecific tumor-promoting effect of all high-fat diets, the results suggest some beneficial effect of EVOO that counteracts the deleterious influence of excessive fat intake. The EVOO minor components may have a key role in such beneficial effects modulating, at least in part, the epigenetic machinery. Keywords

Breast cancer · Mediterranean diet · High-fat diets · Extra virgin olive oil · N-6 PUFA · DMBA · Experimental mammary tumors · Mammary gland · Global DNA methylation · DNMT activity · Rassf1a · Timp3 · Histone H3 · Histone H4 List of Abbreviations

DMBA DNMT EVOO H3K4me2 H3K27me3 H4K16ac H4K20me3 HCO HDAC HEVOO LF MUFA PUFA

7,12-Dimethylbenz(a)anthracene DNA methyltransferase Extra virgin olive oil Dimethylation at lysine 4 of histone H3 Trimethylation at lysine 27 of histone H3 Acetylation at lysine 16 of histone H4 Trimethylation at lysine 20 of histone H4 High corn oil Histone deacetylase High extra virgin olive oil Low fat Monounsaturated fatty acid Polyunsaturated fatty acid

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Introduction Breast cancer is the most frequent malignant neoplasia in women worldwide with increasing incidence rates in all countries (Ferlay et al. 2015). This neoplasia is a heterogeneous and multifactorial disease, with several factors acting simultaneously and/or sequentially in all the steps of the carcinogenesis process, i.e., genetic and epigenetic, endocrine, and environmental factors. Geographical variation of incidence rates suggests an important contribution of lifestyle, especially diet and nutrition, in its etiology (WCRF/AICR 2007). The complex process by which a normal mammary cell becomes neoplastic involves profound changes in the function of a myriad of genes. Such changes are elicited by genetic and epigenetic alterations, which are the basis of the acquisition of the different capacities for cell transformation into malignant cancer (Hanahan and Weinberg 2011). In mammary carcinogenesis, there has been described disruption of epigenetic patterns at all levels, including aberrant DNA methylation, histone modifications, and microRNA profiles (Fraga et al. 2005; Veeck and Esteller 2010). The fact that epigenetic events are heritable and reversible provides a mechanistic link for the environmental influence on cell biology in health and disease. It has long been reported that dietary factors such as lipids may modify epigenetic events regulating metabolism genes (Burdge and Lillycrop 2014), but little is known about their implication in the control of genes with a role in breast cancer. This work is focused on the effects that diets rich in two commonly used oils (olive and corn oils) have on experimental mammary carcinogenesis and if such effects are accompanied by changes in the epigenetic profiles of mammary glands and tumors.

Breast Cancer and Dietary Lipids Epidemiological and experimental studies have demonstrated the influence of nutritional factors, especially dietary lipids, on the development of some neoplasias including breast cancer (Escrich et al. 2006; WCRF/AICR 2007). Although some analyses in humans have generated conflicting results, prospective cohort studies associating food patterns with breast cancer risk have shown an effect of saturated and total fat intake (Schulz et al. 2008; Sieri et al. 2008). Furthermore, experimental assays have provided evidence of the modulatory influence of lipids on the susceptibility of the mammary gland to malignant transformation, and this influence depends not only on the total amount but also on the type of dietary fat. Hence, diets rich in n-6 polyunsaturated fatty acids (PUFA) have a strong stimulating effect on mammary carcinogenesis. Saturated fats and trans-fatty acids are also stimulators. On the contrary, mainly n-3 PUFA but also gamma-linolenic acid and conjugated linoleic acid have shown antiproliferative properties on cancer cells. The influence of monounsaturated fatty acids (MUFA) still remains unclear, and studies have reported from weak promoting to protective effects on experimental mammary carcinogenesis (Escrich et al. 2006, 2011). Olive oil is rich in the n-9 MUFA oleic acid, and there is a large body of evidence of its health benefits (Quiles et al. 2006).

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Table 1 Minor components in extra virgin olive oil. Groups and classes of the representative minor components found in EVOO (detailed in Quiles et al. 2006) Non-glyceryde esters and waxes Aliphatic alcohols Volatile compounds: aldehydes, ketones, alcohols, acids, esthers, etc. Triterpene alcohols: erythrodiol, uvaol Sterols: β-sitosterol, campesterol, stigmasterol, avenasterol Hydrocarbons Squalene Carotenoids: β-carotene, lycopene Volatile hydrocarbons: phenanthrene, pyrene, fluoranthene Pigments Chlorophylls Pheophytins Lipophilic phenolics Tocopherols Tocotrienols Hydrophilic phenolics Phenolic acids: gallic, vanillic, cinnamic, caffeic, coumanic acids Phenolic alcohols: hydroxytyrosol, tyrosol, and their glucosides Secoiridoids: oleuropein and ligstroside derivatives (such as oleocanthal) Lignans: pinoresinol Flavonoids: apigenin, luteolin

Such benefits have been related to its high MUFA content but also to its many minor but highly bioactive compounds (Table 1). Olive oil is the main source of fat in the Mediterranean diet, and this dietary pattern has been traditionally linked to a protective effect on some chronic diseases such as cancer, obesity, inflammatory, and cardiovascular diseases (Sofi et al. 2010; Couto et al. 2011). Actually, prospective studies have associated the Mediterranean dietary pattern with a reduction of the breast cancer risk (Couto et al. 2011). One of the most widely used experimental models of mammary carcinogenesis is the one induced in female rats using dimethylbenz(a)anthracene (DMBA). The mammary tumors generated with this carcinogen are predominantly adenocarcinomas resembling in pathogenesis, morphology, hormone dependence, and molecular features to human breast tumors (Escrich 1987; Russo and Russo 1996; Costa et al. 2002). Using this model, it has been observed different effects of diets high in extra virgin olive oil (EVOO) or in corn oil (rich in n-6 PUFA) on clinical behavior and histopathological features of mammary tumors. The results obtained in 18 experimental series point to the conclusion that dietary fat would not modify key classification features of human breast cancer (e.g., hormone receptors, amplification of HER2). On the other hand, dietary lipids act through multiple, complex, and lipid-specific mechanisms (Fig. 1). Such mechanisms include advanced growth and sexual maturation (Moral et al. 2011), alteration of hepatic carcinogen

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detoxification (Manzanares et al. 2015), and molecular changes in mammary gland and tumor, such as in membrane composition, in the signaling pathways driving to modifications in the proliferation/apoptosis balance (Solanas et al. 2010), in differentiation (Escrich et al. 2004; Moral et al. 2008), in oxidative stress, in DNA damage (Solanas et al. 2010), and in gene expression profiles (Escrich et al. 2004; Moral et al. 2016).

Effects of Extra Virgin Olive Oil and Corn Oil on DMBA-Induced Carcinogenesis and Gene Expression Profile Dietary intervention with a high extra virgin olive oil diet (HEVOO, with 3% corn oil þ17% extra virgin olive oil -w/w-) and a high-corn oil diet (HCO, with 20% corn oil -w/w-), both containing 39.5% calories in the form of fat, affected morphological, clinical, and molecular development of DMBA-induced tumors (Table 2). Sprague-Dawley rats were fed with the experimental diets from weaning and induced with 5 mg of DMBA on 53 days of age. EVOO contained 73.7% of the MUFA oleic acid, while the corn oil contained 51.3% of the n-6 PUFA linoleic acid (Moral et al. 2011). Hence, adenocarcinomas from animals fed the HCO diet displayed anatomopathological characteristics of high malignancy (high nuclear and pattern grade, high mitotic activity, stromal reaction, necrotic areas, and overall histopathologic grade). Moreover, clinical parameters of the carcinogenesis showed an acceleration of the disease, i.e., the group fed with such n-6 PUFA diet showed the shortest latency time (earliest onset of tumor appearance), and the highest percentage of tumor-bearing animals and total mammary adenocarcinomas (Table 2, Fig. 2). In contrast, the high-EVOO diet had a weak stimulating effect. In previous experiments, animals fed the HEVOO diet showed clinical manifestations of the disease similar to those of control group or intermediate between control and HCO diet groups (Escrich et al. 2006; Moral et al. 2011, 2016; Solanas et al. 2010). Thus, while diets rich in n6 PUFA had a strong and clear stimulating effect on experimental mammary carcinogenesis, the weaker and more variable effect (depending on the studied parameter) of diets rich in EVOO is probably related to the different varieties of this oil. In any case, considering that all high-fat diets have an unspecific promoting influence on carcinogenesis due to the high content of lipids (Escrich et al. 2006), the EVOO must have some health benefits that may partly counteract the effect of the intake of high amounts of fat. One of the mechanisms by which dietary lipids may influence the susceptibility or resistance of the mammary gland to carcinogenesis is altering gene expression. In fact, the administration of HEVOO and HCO differentially modified gene expression profiles of the gland at different life stages (36, 51, 100, and 246 days of age). These ages are of interest when studying the malignant transformation and promotion of DMBA-induced breast tumors. At 36 days, just after the puberty onset, the mammary gland is actively proliferating and developing. Such active period extends

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Fig. 1 (continued)

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Table 2 Effects of experimental diets on morphological and clinical parameters of DMBAinduced mammary carcinogenesis. Histological degree of tumor malignancy was categorized using the modified Scarff-Bloom-Richardson (SBR) method (Costa et al. 2002). The group of animals fed with the high-corn oil diet (HCO) had the lowest percentage of low-degree tumors (SBR3/4/5) and the highest percentage of high-degree tumors (SBR9/10/11). Carcinogenesis parameters also indicated a more aggressive behavior in the group fed the HCO diet, since tumors appeared earlier (lowest latency time), and there were more affected animals and total number of tumors. *: p < 0.05 compared to low-fat (LF) diet group; Mann-Whitney U test Histological degree (SBR score) Low (SBR3/4/5, % of tumors) Medium (SBR6/7/8, % of tumors) High (SBR9/10/11, % of tumors) Latency time (days) Tumor-bearing animals (%, n) Tumor yield (number of tumors)

LF

HEVOO

HCO

57.4 29.8 12.8 97 80 (16/20) 46

36.5 42.3 21.1 89 75 (15/20) 58

23 44 33 71.5 100 (20/20)* 100*

to 51 days, time point of particular relevance since it is within the window of maximum susceptibility of the gland to transformation (Russo and Russo 1996), and 2 days before the chemical induction with DMBA. Around 100 days of age (i.e., 47 days after induction), carcinogenesis starts manifesting clinically in all groups. In this experimental model, and with the dose used (5 mg of DMBA), the assays are classically extended to 200–250 days. Analysis of the transcriptome profile of the mammary gland indicated a significant effect of both diets (stronger in the case of HCO) especially short after dietary intervention (36 days of age, 12–13 days on the experimental diets), while differences were smaller as intervention extends to the adaptation to chronic consumption. At different ages, both high-fat diets co-regulated genes related to metabolism. Regarding specific sequences, not commonly modified, the HCO diet downregulated genes with roles in immune system function and in apoptosis, while the HEVOO diet changed the expression of metabolism genes. In tumor tissues, there were also ä Fig. 1 Mechanisms of the differential effects of a high-EVOO diet and a high-corn oil diet on DMBA-induced mammary carcinogenesis. Animals fed with a high-EVOO diet (HEVOO, green line) showed a similar degree of tumor morphological malignancy and percentage of affected animals than the controls fed with a low-fat diet (blue line), while animals fed with a high-corn oil diet (HCO, red line) presented more aggressive morphological and clinical manifestation of the disease. Mechanisms of such differential effects included advance in growth and sexual maturation, especially by the HCO diet, alteration in liver metabolism resulting in increased carcinogen detoxification by the HEVOO diet while decreased by the HCO diet, modifications in cell membrane composition by effect of both diets, changes in signaling pathways driving to increased apoptosis by the HEVOO diet while increased proliferation by the HCO diet, decreased expression of differentiation genes by the HCO diet, increased oxidative stress by both diets, changes in monoubiquitinated-PCNA suggesting lower DNA damage by the HEVOO diet, and modifications by both diets, especially the HCO, in gene expression profiles

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a

c

Mammary gland

BA

du

c

70

60

Tumor 80

Global DNA methylation (%)

DM

in

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Global DNA methylation (%)

80

n tio

50

60

*

40

20

0 0

50

100

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200

250

72

*

72

68

68

64

64

60

*

60

36

51

Time (days)

Carcinogenesis

b

100

Tumor-bearing animals (%)

Global DNA meth. (%)

Time (days)

*

80 60 40 20 0

high extra virgin olive oil diet high corn oil diet

Tumor yield

low fat diet

(total number of tumors)

50

100

150

200

250

100

*

80 60 40 20 0 50

100

150

200

250

Fig. 2 Effects of high-EVOO and high-corn oil diets on global DNA methylation and carcinogenesis. (a) Global DNA methylation (%, median) in mammary gland at 24, 36, 51, 100, and 246 days of age. Histograms detail the values at ages around puberty (36 and 51 days). *: p < 0.1 compared to HCO (36 days) or compared to LF (51 days); Mann-Whitney U test. (b) Evolution of carcinogenesis parameters (tumor-bearing animals and tumor yield, medians) from day 74 to the end of the assay. *: p < 0.05 compared to LF and HEVOO groups; Friedman test. (c) Global DNA methylation in the experimental mammary tumors at 246 days. *: p < 0.05 compared to LF group; Mann-Whitney U test

evidence of the differential effect of these high-fat diets. Although the number of detected modified genes was low (probably due to the high variability among tumors), analysis of biological significance and functional clustering revealed similar enriched categories of genes compared to the mammary gland. Specifically the HCO diet downregulated genes associated with immune response and cell death, while the diet rich in EVOO upregulated genes with a role in proliferation and cell death (Moral et al. 2016).

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Effects of Extra Virgin Olive Oil and Corn Oil on Global DNA Methylation in Mammary Gland and Experimental Tumors The mammary gland undergoes profound remodeling through puberty, reproductive life, and aging (Russo and Russo 1996). Although there is evidence that gene expression profiles modify during physiologic mammary development and in malignant transformation, scarce data have been published in relation to the changes in the epigenetic profiles of this tissue through lifelong development. The study of the global DNA methylation in rat mammary gland showed variations at different life stages, independently of the dietary administration (Fig. 2a). Global DNA methylation decreased around puberty (from postweaning age of 23 days to pubertal age of 36 days) and also with aging (from young adult of 100 days to 246 days). Puberty is a key developmental period of the mammary gland, and the decrease in methylation at this stage may be related to the high proliferation rate and the remodeling of the tissue. Interestingly, the end of this period (around 50 days of age) is the most susceptible age to induce mammary carcinogenesis in this model (Russo and Russo 1996). Moreover, the age-related decrease in mammary global DNA methylation in adulthood is in accordance with the gradual loss of methylation with aging reported in human tissues (Sierra et al. 2015), although no data have been published in this model. In addition to the variations throughout lifetime, dietary intervention with high-fat diets modified global DNA methylation in mammary gland and tumors. Animals fed with the high-EVOO diet showed higher levels of methylated DNA, especially at puberty (close to significance compared to animals fed HCO at 36 days and compared to animals fed the low-fat diet at 51 days; Fig. 2a). As already mentioned, puberty is a critical window of susceptibility for mammary malignant transformation. Considering that global DNA hypomethylation is associated to chromosome instability, cell transformation, and tumor progression (Eden et al. 2003), the higher levels of global DNA methylation at puberty by the effect of the HEVOO diet may decrease the vulnerability of the mammary gland to chemically induced carcinogenesis. In accordance with this, clinical and histological features of tumors from the HEVOO group showed lower degree of malignancy than those from the HCO group (Fig. 2b, Table 2). The high-EVOO diet, despite being high in fat, elicited similar tumor behavior than the control low-fat diet, while the HCO clearly increased the percentage of tumor-bearing animals and tumor yield. Moreover, DNA methylation levels in tumors were also significantly increased in HEVOO group than in the control group (Fig. 2c). Since hypomethylation is considered a hallmark of cancer cells, the higher global DNA methylation in tumors from this group is also in accordance with a low degree of tumor aggressiveness. This influence of EVOO decreasing global DNA hypomethylation in tumors has been observed with two different dietary interventions: administering the HEVOO diet from weaning or feeding the rats with the low-fat diet until carcinogen induction (day 53 of age) and thereafter with the HEVOO diet (Rodríguez-Miguel et al. 2015). Thus, the data obtained in mammary glands and tumors and with different timing of exposure indicated an influence of EVOO even after the carcinogenic insult

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occurred, suggesting a beneficial effect on malignant transformation but also on tumor progression. Paradoxically, global methylation was not decreased in DMBA-induced tumor tissues in comparison with mammary glands. Although there is no published data using this model and the interpretation is unclear, these results may be related to other observations suggesting altered global methylation in experimentally induced rat mammary glands (Starlard-Davenport et al. 2010; Kutanzi and Kovalchuk 2013).

Effects of Extra Virgin Olive Oil and Corn Oil on Gene-Specific Methylation in Mammary Gland and Experimental Tumors There is a wealth of evidence on the important role of gene-specific hypermethylation on breast cancer. Environmental factors affecting tumorigenesis processes are likely to disrupt methylation patterns of tumor suppressor genes. DNA methylation is catalyzed by the highly conserved family of enzymes DNA methyltransferases (DNMT). Thus, determination of total DNMT activity in mammary glands and DMBA-induced tumors showed a significant increase in tumors in comparison with the glands, resembling the increase in activity reported in human breast cancer (Veeck and Esteller 2010). Interestingly, the high-corn oil diet significantly increased the total DNMT activity in mammary gland and tumor, when compared with the low-fat diet, but also when compared with the high-EVOO diet (Fig. 3a). This increase in DNMT activity was not a consequence of the up-modulation of mRNA levels of the distinct isoforms with catalytic activity (DNMT1, DNMT3a, DNMT3b) (Rodríguez-Miguel et al. 2015), suggesting a stimulation of the activity rather than a modulation of the protein expression. Epigenetic silencing of Ras-association domain family 1 isoform A (Rassf1a) is one of the most common molecular changes in human cancers and is considered a frequent and early event in breast cancer (Hesson et al. 2007). Rassf1a has an important role in cell cycle and apoptosis. Tissue inhibitor of metalloproteinase-3 (Timp3) avoids degradation of the extracellular matrix and is also a gene frequently hypermethylated in human breast cancer (Radpour et al. 2009). However, there is no data on the role of the silencing of these genes in DMBA-induced mammary carcinogenesis. Analysis of the methylation levels of their promoters in rat mammary glands and experimental tumors showed an increase in tumor tissues in relation to the gland for both genes, what suggests the importance of silencing these genes in experimental transformation. Interestingly, methylation levels were influenced by dietary intervention. The administration from weaning of the HCO diet significantly increased Rassf1a and Timp3 promoter methylation both in mammary gland and tumor, while the HEVOO diet only increased the promoter methylation of Rassf1a in tumors (Fig. 3b). Differences in the effects of both isocaloric high-fat diets suggest again a key role of the specific composition in fatty acids and minor compounds of corn oil and EVOO. In this sense, hydroxytyrosol and oleuropein, the most representative polyphenols in EVOO, are able to regulate by epigenetic mechanisms the expression of the tumor suppressor gene CB1 in Caco-2 cells and in rat colon

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a/

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Tumor 6

4

*

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* 2

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high corn oil diet

Fig. 3 Effects of high-EVOO and high-corn oil diets on total DNMT activity and gene-specific methylation. (a) Determination of total DNA methyltransferase (DNMT) activity (medians) in the mammary glands and tumors from animals fed the experimental diets. Values depicted are relative to the median level in the mammary gland from the control group. *: p < 0.05 compared to LF and HEVOO groups; Mann-Whitney U test. (b) Analysis by pyrosequencing of the methylation levels (%, median) of Rassf1a and Timp3 promoter methylation in mammary glands and tumors. *: p < 0.05 compared to LF group; Mann-Whitney U test

(Di Francesco et al. 2015). No data have been reported about the influence that oleic or linoleic acids may exert on gene-specific methylation in breast cancer cells. Although the methylation levels of Rassf1a and Timp3 were differentially influenced by the high-EVOO or high-corn oil diet, further gene expression analysis by real-time PCR showed little effect of such diets in mammary glands and no clear correlation between promoter methylation and gene expression (Rodríguez-Miguel et al. 2015). Since epigenetic silencing of Rassf1a and Timp3 is considered early events in the carcinogenesis multistep process, methylation levels of these specific genes long after transformation may not reflect the degree of clinical and morphological tumor malignancy. In any case, these results suggest that the high-corn oil diet had a role in carcinogenesis increasing DNMT activity, which was reflected in the higher methylation of Rassf1a and Timp3, but should also be affecting other genes. The inhibition of different genes would contribute in some step to the acquisition of the hallmarks of cancer, such as resisting cell death or evading growth

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suppressors, although once the genes are silenced they may become less important in cancer progression.

Effects of Extra Virgin Olive Oil and Corn Oil on Histone Modifications in Mammary Gland and Experimental Tumors Postraductional modifications in histones, specially affecting lysine (K) residues, have a key role in transformation. Aberrant histone modifications such as global decreases of acetylation at K16 and trimethylation at K20 of H4 are considered hallmarks of human cancer (Fraga et al. 2005). Moreover, global decrease in the methylation of H3 is frequently detected in human breast cancer, such as dimethylation at K4 and trimethylation at K27 (Greer and Shi 2012). There is some evidence that different components of dietary oils may modulate the histone-modifying machinery, thus changing the global levels of histone modifications, but limited data have been reported in breast tumor cells. As an example, in MCF7, T47D, and MDA-MB-231 cells, treatment with n-3 PUFA, but not with n-6 PUFA, downregulated EZH2. This protein is the catalytic subunit of the polycomb repressive complex 2, responsible for the trimethylation of H3K27, and is frequently dysregulated in cancer cells (Dimri et al. 2010). Moreover, several minor components of extra virgin olive oil have demonstrated inhibition of histone deacetylase (HDAC) activity in breast cancer cells (Tseng et al. 2017). In the rat DMBA-induced mammary cancer model, determination of posttranslational modifications of histone H3 (H3K4me2, H3K27me3) and histone H4 (H4K20me3, H4K16ac) in nuclear extracts showed lower levels of all four modifications in experimental tumors compared with mammary glands at 246 days of age (Fig. 4), indicating the aberrant histone modification also occurring in experimental tumorigenesis. In relation to the influence of the high-fat diets, in mammary glands a significant reduction in H3K27me3 was found by the effect of HCO diet. Although not statistically significant, alterations in H4K20me3 (increased) and H4K16ac (decreased) by the high-EVOO diet in mammary gland were observed. One of the enzymes with a role in deacetylation of H4K16 is Sirt6 (Han et al. 2015). Interestingly, oleoylethanolamide, an N-acylethanolamine derived from oleic acid, increases Sirt6 activity (RahnastoRilla et al. 2016). In the experimental mammary tumors, no clear differences have been found by the effect of the high-EVOO or HCO diets when administered from weaning (Fig. 4). However, when the EVOO diet was administered from induction onward, a decrease in the global levels of H4K20me3 was observed (RodríguezMiguel et al. 2015). Thus, the results suggested a disruption in the histone modifications pattern by influence of high-fat diets on experimental carcinogenesis. These modifications, in the context of different DNA methylation levels, could contribute to the different gene expression profiles observed in the mammary gland and tumors of the animals subjected to dietary intervention (Moral et al. 2016).

Extra Virgin Olive Oil and Corn Oil and Epigenetic Patterns in Breast. . .

Fig. 4 Effects of high-EVOO and high-corn oil diets on H3 and H4 modifications. Analysis by Western blot of the levels of dimethylation at lysine 4 and trimethylation at lysine 27 of histone H3 (H3K4me2, H3K27me3) and trimethylation at lysine 20 and acetylation at lysine 16 of histone H4 (H4K20me3, H4K16ac). For each sample the levels of each modification have been relativized to the total level of the histone (H3 or H4). For each histone modification, values depicted are relative to the median level in the mammary gland from the control group. *: p < 0.05 compared to LF group; Mann-Whitney U test

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Influence of Olive Oil and Other Dietary Lipids on MicroRNA Expression Patterns in Cancer It is likely that the effects of olive oil and other dietary lipids on breast cancer risk are also mediated by changes in microRNA expression patterns. Actually, there is evidence that EVOO modulated microRNA transcriptome in human peripheral blood mononuclear cells (D’Amore et al. 2016) and in mouse brain (Luceri et al. 2017), but there is no data regarding mammary tumors. In relation to other dietary lipids, in vitro studies have assessed the effect of docosahexaenoic acid (DHA), a long-chain n-3 PUFA. In the breast cancer cell lines MDA-MB-231 and MCF-7, DHA blocked miR-21, increasing PTEN protein levels to prevent expression of CSF-1 (Mandal et al. 2012). In the same cell lines, DHA altered exosome microRNAs, especially the levels of let-7a, miR-23b, miR-27a/b, miR-21, let-7, and miR-320b, which are known to have antiproliferative or anti-angiogenic activity (Hannafon et al. 2015). Other dietary interventions, such as caloric restriction, altered microRNA patterns in mouse breast tissues, such as the levels of miR-29c, miR-203, miR-150, and miR-30 (Ørom et al. 2012). There are also scarce data addressing the effects of olive oil and other dietary lipids in other types of cancers. As an example, the only study related to olive oil was performed with olive tree leaf extract (richer than olive oil in the phenolic-type oleuropein). This extract induced the expression of miR-153, miR-145, and miR-137 in stemlike cells of glioblastoma multiforme tumors (Tezcan et al. 2014). In addition, treatment of glioblastoma cells with different types of PUFAs (GLA, AA, and DHA) also resulted in altered expression of microRNAs (Faragó et al. 2011). On the other hand, diets rich in n-3 and in n-6 PUFA differentially modified the microRNA profile in experimental colon tumors (Davidson et al. 2009).

Epigenetic Effects of Olive Oil Minor Compounds on Breast Cancer As already mentioned, there is a wealth of evidence on the favorable properties of olive oil minor compounds, in addition to its high content in MUFA, on health. Many bioactive components have antioxidant and anti-inflammatory properties both associated with chronic diseases, including cancer (Quiles et al. 2006). In general, compounds rich in antioxidants may reduce oxidative stress levels, thus altering histone modifications and DNA methylation. In this sense, cells exposed to reactive oxygen species presented altered activity of histone demethylases and acetyltransferases as well as decreased DNA demethylase activity of the family TET (Niu et al. 2015). More specifically, in relation to the minor compounds more abundant in EVOO (terpenes, sterols, or hydrocarbons like squalene), there are no studies on epigenetic effects on breast cancer. On the other hand, other components, especially polyphenols, have demonstrated antitumor properties concomitantly with modifications of epigenetic patterns in breast cancer cells (Table 3). Caffeic acid, a component of EVOO phenolic acids, inhibited in vitro DNA methylation catalyzed by DNMT1 and decreased RARβ2 promoter methylation in MCF-7 and

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Table 3 Epigenetic effects of EVOO minor components on breast cancer cells. Polyphenols found in the hydrophilic phenolic class of EVOO minor compounds that have demonstrated an epigenetic effect on breast cancer cells Phenolic class Phenolic acids

Compound Caffeic acid

In vitro model MCF-7 and MDAMB-231 JIMT-1

Secoiridoids

Secoiridoids extract

Flavonoids

Apigenin

MDA-MB231

Luteolin

MCF-7

Epigenetic action Inhibition of DNMT1 activity

References Lee and Zhu (2006)

Hyperacetylation of H3 at lysine 18 (H3K18)

OliverasFerraros et al. (2011) Tseng et al. (2017)

Inhibition of HDAC activity, increased H3 acetylation Decreased H4 acetylation at PLK-1 promoter

Markaverich et al. (2011)

MDA-MB-231 breast cancer cells (Lee and Zhu 2006). In JIMT-1 breast cancer cells, an EVOO phenolic extract rich in secoiridoids regulated histone deacetylase (HDAC) activity, increasing the levels of acetylated H3 (Oliveras-Ferraros et al. 2011). Flavonoids such as apigenin and luteolin have also demonstrated in vitro effects on the epigenetics machinery. Apigenin induced histone H3 acetylation through inhibition of HDAC in MDA-MB-231 cells (Tseng et al. 2017), while luteolin blocked the acetylation of histone H4 associated with the promoter of the cell cycle gene PLK1 in MCF7 cells (Markaverich et al. 2011). Other polyphenols, abundant in other vegetable sources although represented in little quantities in some olive oil varieties, had strong effects on epigenetic mechanisms in breast cancer cells. That is the case of quercetin inhibiting p300 histone acetyltransferase activity in MDA-MB-231 and MCF7 cells (Xiao et al. 2011) or ellagic acid inhibiting the DNA methyltransferase activity in MCF7 cells (Paluszczak et al. 2010).

Dictionary of Terms • Mediterranean diet – This diet includes a variety of food patterns from the Mediterranean region and is characterized by the consumption of abundant and varied plant foods (fruits, vegetables, legumes, and nuts), dairy products, fish, and olive oil as the principal source of fat. • Extra virgin olive oil – The juice obtained from the first pressing of olives, through mechanical processes and without chemical substances. • Extra virgin olive oil minor compounds – Several compounds representing 1–2% of oil weight, including over 230 components some of them highly bioactive. They are predominantly present in extra virgin oil but not in refined oil. • 7,12-Dimethylbenz(a)anthracene – Polycyclic aromatic hydrocarbon that in a single dose by oral gavage is able to evoke mammary tumors in rats highly similar to human breast cancers

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• Carcinogenesis – Multistep process by which cancer is originated and includes initiation, promotion, and progression. In experimental models, clinical evolution of cancer can be monitored by different carcinogenesis parameters (latency time, percentage of tumor-bearing animals, tumor yield, or tumor volume). • Saturated fatty acid (SFA) – Fatty acid without double bonds. Foods rich in SFA are animal fat (dairy products and fatty meat) and some vegetable oils (coconut or palm kernel oil). • Monounsaturated fatty acid (MUFA) – Fatty acid with one double bond. They are mainly n-9 (omega-9 or ω-9; the double bond is the omega-9 position, i.e., the ninth bond from the end of the fatty acid). Foods rich in MUFA include vegetable oils (olive or canola oils) and nuts. • Polyunsaturated fatty acid (PUFA) – Fatty acid with more than one double bond, mainly classified in n-6 (last double bond in omega-6 position) and n-3 (last double bond in omega-3 position). Fats rich in n-6 PUFA are mainly vegetable oils (sunflower or corn oils), while the richest in n-3 PUFA are oily fish and some vegetable oils (flaxseed oil). • Trans-fatty acids – For unsaturated fatty acids (MUFA and PUFA), the acid is a cis-isomer if hydrogen atoms are on the same side of the double bond and a trans-isomer if hydrogen atoms are on opposite sides of the double bond. Transfatty acids are uncommon in natural sources but produced industrially from vegetable fats.

Key Facts of Breast Cancer • The mammary gland, unlike most organs, remains highly undifferentiated at birth. With puberty the gland develops and undergoes profound remodeling, but differentiation is only completed at the end of the first full-term pregnancy. • The windows of susceptibility are the periods in which mammary gland is especially vulnerable to environmental factors that influence breast cancer risk, such as the pubertal period. • Breast cancer is the most frequent malignancy among women worldwide, with more than 1.6 million new cases diagnosed in 2012 (25% of all newly cases in women). • In 2012 this neoplasia was the first cause of cancer death in women in less developed regions, while the second leading cause of cancer death, after lung cancer, in more developed regions. • Only a small percentage of breast cancers are linked to inherited highsusceptibility genes such as BRCA. Most cases are caused by the accumulation of spontaneous somatic mutations of DNA and epigenetic alterations. • There are clear geographical differences in incidence and prevalence rates of breast cancer as well as changes in the incidence among immigrants, suggesting an important influence of environmental and lifestyle factors.

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Summary Points • The administration of a high-corn oil diet from weaning has a clear stimulating effect on 7,12-dimethylbenz(a)anthracene-induced mammary carcinogenesis, increasing the morphological and clinical degree of tumor malignancy, while a high extra virgin olive oil (EVOO) diet has a weak tumor-enhancing effect. • The high-corn oil diet modifies gene expression profiles in mammary glands at different ages and in tumors, downregulating genes with a role in apoptosis and immune function. The high-EVOO diet mainly modulates genes with a role in metabolism. • The high-EVOO diet increases global DNA methylation in the mammary gland, mainly around puberty, which is compatible with a lower vulnerability of the gland to malignant transformation. • The high-EVOO diet increases global DNA methylation in experimental tumors, which is concordant with the low degree of tumor anatomopathological malignancy. • The high-corn oil diet, but not the high EVOO one, increases total DNA methyltransferase activity in mammary glands at 246 days of age and tumors, concomitantly with an increase in Rassf1a and Timp3 promoter methylation. • Both high-fat diets may influence the modification of histones (the levels of H3K4me2, H3K27me3, H4K16ac, and H4K20me3), especially in the mammary gland. • Independently of dietary intervention, in 7,12-dimethylbenz(a)anthraceneinduced mammary carcinogenesis, disruption of epigenetic patterns occurs similarly to human breast cancer (higher DNA methyltransferase activity and gene-specific methylation, decreased levels of key histone modifications). • Although there are scarce data reported on other mechanisms, the effects of these diets are likely to be also due to different modification on microRNA patterns. • Considering the unspecific tumor-promoting effect of all high-fat diets, there is some beneficial effect of EVOO that counteracts the deleterious influence of excessive fat intake. • The EVOO minor components may have a key role in the beneficial effects of this oil modulating, at least in part, the epigenetic machinery. Acknowledgments Research in the authors’ laboratory is funded by grants from “Plan Nacional de IþDþI” (AGL2006-07691; AGL2011-24778); “Fundación Patrimonio Comunal Olivarero (FPCO)” (FPCO2008-165.396; FPCO2013-CF611.084); “Agencia para el Aceite de Oliva del Ministerio de Medio Ambiente y de Medio Rural y Marino” (AAO2008-165.471); “Organización Interprofesional del Aceite de Oliva Español (OIAOE)” (OIP2009-CD165.646); “Departaments d’Agricultura, Alimentació i Acció Rural, i de Salut de la Generalitat de Catalunya” (GC2010165.000); and FPCO and OIAOE (FPCO-OIP2016-CF614.087). The sponsors had no role in study designs, data collection and analyses, interpretation of results, preparation of the manuscript, and the decision to submit the manuscript for publication or the writing of the manuscript. The authors are grateful to I. Costa, R. Escrich, C. Rodríguez-Miguel, M.C. Ruiz de Villa, M. Solanas, and E. Vela for their collaboration in these studies.

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References Burdge GC, Lillycrop KA (2014) Fatty acids and epigenetics. Curr Opin Clin Nutr Metab Care 17:156–161 Costa I, Solanas M, Escrich E (2002) Histopathologic characterization of mammary neoplastic lesions induced with 7,12-dimethylbenz(alpha)anthracene in the rat: a comparative analysis with human breast tumors. Arch Pathol Lab Med 126:915–927 Couto E, Boffetta P, Lagiou P et al (2011) Mediterranean dietary pattern and cancer risk in the EPIC cohort. Br J Cancer 104:1493–1499 D’Amore S, Vacca M, Cariello M et al (2016) Genes and miRNA expression signatures in peripheral blood mononuclear cells in healthy subjects and patients with metabolic syndrome after acute intake of extra virgin olive oil. Biochim Biophys Acta 1861:1671–1680 Davidson LA, Wang N, Shah MS et al (2009) n-3 polyunsaturated fatty acids modulate carcinogendirected non-coding microRNA signatures in rat colon. Carcinogenesis 30:2077–2084 Di Francesco A, Falconi A, Di Germanio C et al (2015) Extravirgin olive oil up-regulates CB1 tumor suppressor gene in human colon cancer cells and in rat colon via epigenetic mechanisms. J Nutr Biochem 26:250–258 Dimri M, Bommi PV, Sahasrabuddhe AA et al (2010) Dietary omega-3 polyunsaturated fatty acids suppress expression of EZH2 in breast cancer cells. Carcinogenesis 31:489–495 Eden A, Gaudet F, Waghmare A et al (2003) Chromosomal instability and tumors promoted by DNA hypomethylation. Science 300:455 Escrich E (1987) Validity of the DMBA-induced mammary cancer model for the study of human breast cancer. Int J Biol Markers 2:197–206 Escrich E, Moral R, García G et al (2004) Identification of novel differentially expressed genes by the effect of a high-fat n-6 diet in experimental breast cancer. Mol Carcinog 40:73–78 Escrich E, Solanas M, Moral R (2006) Olive oil, and other dietary lipids, in cancer: experimental approaches. In: Quiles JL, Ramírez-Tortosa MC, Yaqoob P (eds) Olive oil and health. CABI Publishing, Oxford, pp 317–374 Escrich E, Solanas M, Moral R et al (2011) Modulatory effects and molecular mechanisms of olive oil and other dietary lipids in breast cancer. Curr Pharm Des 17:813–830 Faragó N, Fehér LZ, Kitajka K et al (2011) MicroRNA profile of polyunsaturated fatty acid treated glioma cells reveal apoptosis-specific expression changes. Lipids Health Dis 10:173 Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359–E386 Fraga MF, Ballestar E, Villar-Garea A et al (2005) Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer. Nat Genet 37:391–400 Greer EL, Shi Y (2012) Histone methylation: a dynamic mark in health, disease and inheritance. Nat Rev Genet 13:343–357 Han L, Ge J, Zhang L et al (2015) Sirt6 depletion causes spindle defects and chromosome misalignment during meiosis of mouse oocyte. Sci Rep 5:15366 Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674 Hannafon BN, Carpenter KJ, Berry WL et al (2015) Exosome-mediated microRNA signaling from breast cancer cells is altered by the anti-angiogenesis agent docosahexaenoic acid (DHA). Mol Cancer 14:133 Hesson LB, Cooper WN, Latif F (2007) The role of RASSF1A methylation in cancer. Dis Markers 23:73–87 Kutanzi K, Kovalchuk O (2013) Exposure to estrogen and ionizing radiation causes epigenetic dysregulation, activation of mitogen-activated protein kinase pathways, and genome instability in the mammary gland of ACI rats. Cancer Biol Ther 14:564–573 Lee WJ, Zhu BT (2006) Inhibition of DNA methylation by caffeic acid and chlorogenic acid, two common catechol-containing coffee polyphenols. Carcinogenesis 27:269–277

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Luceri C, Bigagli E, Pitozzi V et al (2017) A nutrigenomics approach for the study of anti-aging interventions: olive oil phenols and the modulation of gene and microRNA expression profiles in mouse brain. Eur J Nutr 56:865–877 Mandal CC, Ghosh-Choudhury T, Dey N et al (2012) miR-21 is targeted by omega-3 polyunsaturated fatty acid to regulate breast tumor CSF-1 expression. Carcinogenesis 33:1897–1908 Manzanares MA, Solanas M, Moral R et al (2015) Dietary extra-virgin olive oil and corn oil differentially modulate the mRNA expression of xenobiotic-metabolizing enzymes in the liver and in the mammary gland in a rat chemically induced breast cancer model. Eur J Cancer Prev 24:215–222 Markaverich BM, Shoulars K, Rodriguez MA (2011) Luteolin regulation of estrogen signaling and cell cycle pathway genes in MCF-7 human breast cancer cells. Int J Biomed Sci 7:101–111 Moral R, Solanas M, Garcia G et al (2008) High corn oil and high extra virgin olive oil diets have different effects on the expression of differentiation-related genes in experimental mammary tumors. Oncol Rep 20:429–435 Moral R, Escrich R, Solanas M et al (2011) Diets high in corn oil or extra-virgin olive oil provided from weaning advance sexual maturation and differentially modify susceptibility to mammary carcinogenesis in female rats. Nutr Cancer 63:410–420 Moral R, Escrich R, Solanas M et al (2016) Diets high in corn oil or extra-virgin olive oil differentially modify the gene expression profile of the mammary gland and influence experimental breast cancer susceptibility. Eur J Nutr 55:1397–1409 Niu Y, DesMarais TL, Tong Z et al (2015) Oxidative stress alters global histone modification and DNA methylation. Free Radic Biol Med 82:22–28 Oliveras-Ferraros C, Fernández-Arroyo S, Vazquez-Martin A et al (2011) Crude phenolic extracts from extra virgin olive oil circumvent de novo breast cancer resistance to HER1/HER2-targeting drugs by inducing GADD45-sensed cellular stress, G2/M arrest and hyperacetylation of histone H3. Int J Oncol 38:1533–1547 Ørom UA, Lim MK, Savage JE et al (2012) MicroRNA-203 regulates caveolin-1 in breast tissue during caloric restriction. Cell Cycle 11:1291–1295 Paluszczak J, Krajka-Kuźniak V, Baer-Dubowska W (2010) The effect of dietary polyphenols on the epigenetic regulation of gene expression in MCF7 breast cancer cells. Toxicol Lett 192:119–125 Quiles JL, Ramírez-Tortosa MC, Yaqoob P (eds) (2006) Olive oil and health. CABI Publishing, Oxford Radpour R, Kohler C, Haghighi MM et al (2009) Methylation profiles of 22 candidate genes in breast cancer using high-throughput MALDI-TOF mass array. Oncogene 28:2969–2978 Rahnasto-Rilla M, Kokkola T, Jarho E et al (2016) N-acylethanolamines bind to SIRT6. Chembiochem 17:77–81 Rodríguez-Miguel C, Moral R, Escrich R et al (2015) The role of dietary extra virgin olive oil and corn oil on the alteration of epigenetic patterns in the rat DMBA-induced breast cancer model. PLoS One 10:e0138980 Russo IH, Russo J (1996) Mammary gland neoplasia in long-term rodent studies. Environ Health Perspect 104:938–967 Schulz M, Hoffmann K, Weikert C et al (2008) Identification of a dietary pattern characterized by high-fat food choices associated with increased risk of breast cancer: the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br J Nutr 100:942–946 Sieri S, Krogh V, Ferrari P et al (2008) Dietary fat and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr 88:1304–1312 Sierra MI, Fernández AF, Fraga MF (2015) Epigenetics of aging. Curr Genomics 16:435–440 Sofi F, Abbate R, Gensini GF et al (2010) Accruing evidence on benefits of adherence to the Mediterranean diet on health: an updated systematic review and meta-analysis. Am J Clin Nutr 92:1189–1196

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Natural Polyphenol Kaempferol and Its Epigenetic Impact on Histone Deacetylases: Focus on Human Liver Cells

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Sascha Venturelli, Christian Leischner, and Markus Burkard

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaempferol and Corresponding Glycosides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Occurrence of Kaempferol in Plants and Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacodynamics of Kaempferol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetic Activity of Kaempferol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preclinical Evaluation of Kaempferol and Role in Hepatotoxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Other Flavonols in Nutrition, Epigenetics, and Hepatotoxicity . . . . . . . . . . . . . . . . . . . . . . Kaempferol and Its Derivatives in the Context of Other Epigenetic Modifiers . . . . . . . . . . . . . . . Dictionary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Facts of Kaempferol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The flavonol kaempferol, which is found in many vegetables and fruits, is suggested to exhibit various and promising beneficial health effects in vitro and in vivo. Although there is strong evidence for health-promoting effects and good tolerability of kaempferol as common ingredient of daily nutrition, only little is known about the underlying pharmacodynamics and especially kaempferol-mediated effects on liver gene expression, enzyme levels, and phase I metabolism. Noteworthy, recent studies revealed that kaempferol is

S. Venturelli · C. Leischner · M. Burkard (*) Department of Vegetative and Clinical Physiology, Institute of Physiology, University Hospital Tuebingen, Tuebingen, Germany e-mail: [email protected]; [email protected]; christian. [email protected]; [email protected]; [email protected]; [email protected] # Springer Nature Switzerland AG 2019 V. B. Patel, V. R. Preedy (eds.), Handbook of Nutrition, Diet, and Epigenetics, https://doi.org/10.1007/978-3-319-55530-0_62

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an interesting inhibitor of histone deacetylases with high affinity toward all members of HDAC families I, II, and IV that were tested. Therefore, the epigenetic activity of kaempferol could, at least in part, be responsible for the promising health effects and remain to be intensively studied in vivo. Investigation of hepatotoxic effects and interactions with CYP450 enzymes is one of the major prerequisites for a possible clinical use of kaempferol. Therefore, preclinical evaluation of high doses of kaempferol was performed with primary human hepatocytes, which are widely used as valuable tools to predict toxic drug effects on the human liver. Additionally, an in vivo chicken embryotoxicity assay to check for embryotoxic effects yielded good tolerability of kaempferol. According to the promising preliminary results, it would be important to evaluate long-term effects of low physiological doses of kaempferol compared to interventions with high pharmacological doses in future experiments.

Keywords

Flavonoid · Flavonol · Kaempferol · Quercetin · Hepatocellular carcinoma · Primary human hepatocytes · Pan-HDAC inhibitor · Cytochrome p450 enzymes · Liver · Phase I metabolism

List of Abbreviations

B[a]P CDK1 CYP DMBA DNMT GSTP1–1 HAT HCC HDAC PHH P-PST QR SAHA SIRT SULT1A1 SULT1E1 TCDD TSA TS-PST UDP UGT

Benzo[a]pyrene Cyclin-dependent kinase 1 Cytochrome P450 7,12-Dimethylbenz[a]anthracene DNA methyl transferase Glutathione S-transferase Pi 1 peptide 1 Histone acetyl transferase Hepatocellular carcinoma Histone deacetylase Primary human hepatocyte Phenol-sulfating form of phenol sulfotransferase Quinone reductase Suberoylanilide hydroxamic acid Sirtuin Sulfotransferase family 1A Member 1 Sulfotransferase family 1E Member 1 2,3,7,8-Tetrachlorodibenzo-p-dioxin Trichostatin A Thermostable phenol sulfotransferase Uridine diphosphate UDP-glucuronyl transferase

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Introduction To date, malignant diseases are still ranked as the second most disease-related death causes worldwide. Despite recent progress in some areas of oncology, several tumor entities are still characterized by poor prognosis and especially solid tumors like the hepatocellular carcinoma (HCC) that distinguish themselves by a lack of treatment options. Long absence of any HCC-related symptoms procures a delay in diagnosing the disease. Median survival time is hence only 3–6 months. Besides alcohol abuse, nonalcoholic fatty liver disease, and diabetes mellitus, viral infections with hepatitis B or C virus are the major causes for malignant transformation of human hepatocytes (El-Serag 2012). Surgical resection is the therapeutic option of choice but only applicable for the minority of the very diffuse-growing HCCs. On the other hand, sorafenib, which is the most effective pharmacological treatment option to date, can prolong median survival only for a few months, thereby often exhibiting severe toxicity and unwanted side effects. Therefore, there is an urgent need to find and develop new treatment options and anticancer compounds. Polyphenols derived from nutrition seem to be an increasingly attractive alternative to treat different tumor entities with a favorable toxicity profile, at once. Especially, kaempferol emerged as a promising plant polyphenol with strong antitumor effects on human HCC cell lines and a distinct epigenetic activity. Its clinical use in the treatment or prevention of HCC and eventually other chronic liver diseases requires careful examination of kaempferol effects on human liver cells and their metabolic activity.

Kaempferol and Corresponding Glycosides Kaempferol (3,5,7,40 -tetrahydroxyflavone), a naturally occurring secondary plant metabolite, belongs to the flavonol (3-hydroxyflavone) subclass of flavonoids from a chemical point of view. Further flavonoid subclasses are flavan-3-ols, flavones, flavanones, isoflavones, and the positively charged anthocyanidins (Fig. 1). To date, the flavonoid family comprises more than 5000 individual compounds, and their number is still increasing. All of them share the flavan backbone as their basic chemical structure, which consists of two aromatic rings (ring A and B) linked to an oxygenated heterocyclic ring (C ring), which defines the different flavonoid subclasses by its structural variation. The flavonol kaempferol possesses a double bond at the 2–3 position and a hydroxyl group at C3. Other common flavonol derivatives with di- and tri-hydroxylated B rings are, e.g., quercetin, myricetin, fisetin, and galangin (Fig. 2). Many of these polyphenols are part of the daily diet showing distinct bioactive properties, which are suggested to provide health benefits like reduced risk of developing chronic diseases such as cardiovascular issues (Liu 2004). In nature, flavonoids often appear as O-glycosylated or esterified forms preferably at the C3 position. The corresponding aglycones arise during cooking or are prevalent in food, which was otherwise processed. More than 80 naturally occurring different sugars account for

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3' 2' 8 7

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Fig. 1 Chemical structures of the six major flavonoid subclasses. The flavonoid family is usually divided into flavones, flavonols (e.g., kaempferol), isoflavones, flavanones, flavan-3-ols, and anthocyanidins

OH OH HO

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Fig. 2 Chemical structures of kaempferol and other flavonols with epigenetic activity. The kaempferol molecule is closely related to other naturally occurring flavonols like quercetin, myricetin, and fisetin. Interestingly, it can also be metabolized, e.g., to quercetin or isorhamnetin by hepatic phase I metabolism. The close structural relationship might explain that the displayed flavonols all exhibit epigenetic activity to certain degree

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an exceptionally diversified repertoire of glycosides that results in an almost unmanageable multitude of different flavonoid derivatives when combined to the more than 5000 known flavonoid aglycones (Hollman and Arts 2000). Less frequently the C7 position and very seldom the 40 -, 30 - and 5-positions also serve as bonding sites (Herrmann 1988). The most common sugar found in flavonoid glycosides is glucose resulting in flavonoid glucosides. Other sugars that often contribute to flavonoid glycoside formation are, e.g., D-galactose, L-rhamnose, L-arabinose, D-xylose, D-apiose, or D-glucuronic acid. D-sugars generate β-glycosides, whereas sugars of the L-series result in the formation of the α-configuration (Herrmann 1988).

Occurrence of Kaempferol in Plants and Nutrition Kaempferol, measured as free aglycone after hydrolysis (Table 1), is described as a bioactive plant polyphenol at varying concentrations notably in leek (~30–31 mg kg1 fresh weight), endive (~46 mg kg1), broccoli (~60–72 mg kg1), and most abundantly in kale (~211–470 mg kg1). To a lesser extent, kaempferol is also found in French beans (