Nutritional epigenomics 9780128168431, 0128168439

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Translational Epigenetics Series Trygve O. Tollefsbol, Series Editor Transgenerational Epigenetics Edited by Trygve O. Tollefsbol, 2014 Personalized Epigenetics Edited by Trygve O. Tollefsbol, 2015 Epigenetic Technological Applications Edited by Y. George Zheng, 2015 Epigenetic Cancer Therapy Edited by Steven G. Gray, 2015 DNA Methylation and Complex Human Disease By Michel Neidhart, 2015 Epigenomics in Health and Disease Edited by Mario F. Fraga and Agustin F. F Ferna´ndez, 2015 Epigenetic Gene Expression and Regulation Edited by Suming Huang, Michael Litt and C. Ann Blakey, 2015 Epigenetic Biomarkers and Diagnostics Edited by Jose Luis Garcı´a-Gime´nez, 2015

Neuropsychiatric Disorders and Epigenetics Edited by Dag H. Yasui, Jacob Peedicayil and Dennis R. Grayson, 2016 Polycomb Group Proteins Edited by Vincenzo Pirrotta, 2016 Epigenetics and Systems Biology Edited by Leonie Ringrose, 2017 Cancer and Noncoding RNAs Edited by Jayprokas Chakrabarti and Sanga Mitra, 2017 Nuclear Architecture and Dynamics Edited by Christophe Lavelle and Jean-Marc Victor, 2017 Epigenetic Mechanisms in Cancer Edited by Sabita Saldanha, 2017 Epigenetics of Aging and Longevity Edited by Alexey Moskalev and Alexander M. Vaiserman, 2017

Drug Discovery in Cancer Epigenetics Edited by Gerda Egger and Paola Barbara Arimondo, 2015

The Epigenetics of Autoimmunity Edited by Rongxin Zhang, 2018 Epigenetics in Human Disease, Second Edition Edited by Trygve O. Tollefsbol, 2018

Medical Epigenetics Edited by Trygve O. Tollefsbol, 2016 Chromatin Signaling and Diseases Edited by Olivier Binda and Martin FernandezZapico, 2016

Epigenetics of Chronic Pain Edited by Guang Bai and Ke Ren, 2018 Epigenetics of Cancer Prevention Edited by Anupam Bishayee and Deepak Bhatia, 2018

Genome Stability Edited by Igor Kovalchuk and Olga Kovalchuk, 2016 Chromatin Regulation and Dynamics Edited by Anita Go¨ndo¨r, 2016

Computational Epigenetics and Diseases Edited by Loo Keat Wei, 2019

Translational Epigenetics Volume 14

Nutritional Epigenomics

Edited by

Bradley S. Ferguson University of Nevada Reno, Reno, NV, United States

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-816843-1 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Andre Wolff Acquisition Editor: Peter Linsley Editorial Project Manager: Megan Ashdown Production Project Manager: Poulouse Joseph Cover Designer: Mark Rogers Typeset by TNQ Technologies

Contributors Sean T. Anderson Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Olivia S. Anderson University of Michigan School of Public Health, Ann Arbor, MI, USA Aneta Balcerczyk Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland Tasnim H. Beacon Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada Megan Beetch Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada Marta Biesiekierska Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland Daniela Caporossi University of Rome “Foro Italico”, Department of Movement, Human and Health Sciences, Rome, Italy Christopher G. Chapman Center for Endoscopic Research and Therapeutics (CERT), Department of Medicine, University of Chicago Medicine, Chicago, IL, USA Rachael C. Crew School of Human Sciences, The University of Western Australia, Australia James R. Davie Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada Ivan Dimauro University of Rome “Foro Italico”, Department of Movement, Human and Health Sciences, Rome, Italy Luciano DiTacchio Department of Pharmacology, Toxicology, and Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA Levi Evans University of Nevada Reno, Department of Nutrition, Reno, NV, USA; University of Nevada Reno, Center for Cardiovascular Research, Reno, NV, USA; University of Nevada Reno, Department of Environmental Sciences, Reno, NV, USA

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Contributors

Bradley S. Ferguson University of Nevada Reno, Department of Nutrition, Reno, NV, USA; University of Nevada Reno, Center for Cardiovascular Research, Reno, NV, USA Beverley Greenwood-Van Meerveld Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Oklahoma City VA Medical Center, Oklahoma City, OK, USA; Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Subash C. Gupta Department of Biochemistry, Institute of Science, Banaras Hindu University, Varanasi, India Sadaf Harandi-Zadeh Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada Iara Cassandra V. Ibay Midwestern University, Downers Grove, IL, USA Allison Isabelli Midwestern University, Downers Grove, IL, USA Sanzida Jahan Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada Ali Jawaid Laboratory of Neuroepigenetics, Medical Faculty of the University of Zu¨rich and Department of Health Science and Technology of the ETH Zu¨rich, Brain Research Institute, Zu¨rich Neuroscience Center, Zu¨rich, Switzerland Anthony C. Johnson Oklahoma City VA Medical Center, Oklahoma City, OK, USA Robert H. Lane Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, USA Ho-Sun Lee Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea Jiao Li State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, China Tijs Louwies Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Amber V. Majnik Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, USA

Contributors

Isabelle M. Mansuy Laboratory of Neuroepigenetics, Medical Faculty of the University of Zu¨rich and Department of Health Science and Technology of the ETH Zu¨rich, Brain Research Institute, Zu¨rich Neuroscience Center, Zu¨rich, Switzerland Peter J. Mark School of Human Sciences, The University of Western Australia, Australia Kristina Martinez-Guryn Midwestern University, Downers Grove, IL, USA Maria M. Mihaylova Department of Biological Chemistry & Pharmacology, The Ohio State University, Columbus, OH, USA Belinda L. Needham Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA Matthew A. Odenwald Department of Medicine, University of Chicago Medicine, Chicago, IL, USA Albert Orock Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Fadumo D.S. Osman Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada Maria Paola Paronetto University of Rome “Foro Italico”, Department of Movement, Human and Health Sciences, Rome, Italy Georgios K. Paschos Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Yong Peng State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, China Luciano Pirola Carmen Laboratory; INSERM U1060; Lyon-1 University, South Lyon Medical Faculty; Oullins, France Elesa Poteres Midwestern University, Downers Grove, IL, USA Vipin Rai Department of Biochemistry, Institute of Science, Banaras Hindu University, Varanasi, India

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Contributors

David H. Rehkopf Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA Samo Ribari c Institute of Pathophysiology, Medical Faculty, University of Ljubljana, Ljubljana, Slovenia Samantha Romanick University of Nevada Reno, Department of Nutrition, Reno, NV, USA; University of Nevada Reno, Center for Cardiovascular Research, Reno, NV, USA; University of Nevada Reno, Department of Pharmacology, Reno, NV, USA Karilyn E. Sant San Diego State University School of Public Health, San Diego, CA, USA Jeffrey L. Segar University of Iowa, Carver College of Medicine, Stead Family Department of Pediatrics, Iowa City, IA, USA Zahra Sepehri Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada Kate Shen Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada Ken Shinmura Department of General Medicine, Hyogo College of Medicine, Nishinomiya, Japan Kamla Kant Shukla Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India Barbara Stefanska Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada Matthew S. Stratton Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA Gretchen van Steenwyk Laboratory of Neuroepigenetics, Medical Faculty of the University of Zu¨rich and Department of Health Science and Technology of the ETH Zu¨rich, Brain Research Institute, Zu¨rich Neuroscience Center, Zu¨rich, Switzerland Sumit S. Verma Department of Biochemistry, Institute of Science, Banaras Hindu University, Varanasi, India Varvara Vialichka Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland

Contributors

Brendan J. Waddell School of Human Sciences, The University of Western Australia, Australia Xiangru Xu Max Planck Institute for Biology of Ageing, Cologne, Germany; Department of Anesthesiology, Yale University School of Medicine, New Haven, CT, USA Menghong Yan CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, PR China Tian Yuan Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Cynthia A. Zahnow The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA Tong Zhou Department of Physiology and Cell Biology, The University of Nevada, Reno School of Medicine, Reno, NV, USA Lei Zhu State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, China

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About the editor Dr. Bradley S. Ferguson is an Assistant Professor in the Department of Nutrition at the University of Nevada, Reno. Dr. Ferguson received his Ph.D. in Nutrition from the University of North Carolina at Greensboro prior to his postdoctoral training in the School of Medicine, Division of Cardiology at the University of Colorado Anschutz Medical Campus. Dr. Ferguson’s lab is focused on understanding the role for lysine acetylation and acylation in the epigenetic regulation of cardiovascular and skeletal muscle function. With support from the Dennis Meiss & Janet Ralston Fund for Nutri-Epigenetic Research, Dr. Ferguson’s group has highlighted the important roles for food bioactives as regulators of histone acetylation and acylation in cardiac muscle gene expression; mechanistically linking the health-benefits of fruits and vegetables to the regulation of the cardiac epigenome.

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CHAPTER

Introduction to nutritional epigenomics

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Samantha Romanicka, b, c, Bradley S. Fergusona, b University of Nevada Reno, Department of Nutrition, Reno, NV, USAa; University of Nevada Reno, Center for Cardiovascular Research, Reno, NV, USAb; University of Nevada Reno, Department of Pharmacology, Reno, NV, USAc

1. Introduction The Human Genome Project, an international collaborative project to determine the nucleotide sequence of human DNA and the mapping of 30,000 genes, was launched in 1990 and completed on April 14, 2003 (The Human Genome Project Completion: Frequently Asked Questions, 2010). The Human Genome Project remains the world’s largest collaborative biological research project. For many years’ prior to this significant achievement, the scientific community believed that sequencing of the human genome would provide many answers to health and disease [1,2]. However, years after completion of the project, our questions still remain unanswered. The Human Genome project provided us with the foundation of human genetics, but lacked understanding of the diverse architectural structures that can be built upon it. These unique structures are built atop of the DNA sequence and do not need to alter the sequence itself to change gene expression. The study of these structures is known as epigenetics, literally defined as ‘above genetics’, and these “structures” entail chemical or post-translational modifications added to histone proteins or the DNA sequence that regulate how the genome is expressed. Ever wonder how a cell with the same genomic make up could differentiate into the extensive array of cells within an organism? The answer is epigenomics, where the complete set of epigenetic modifications on the genome of an organism is studied. Environmental factors play a monumental role in the field of epigenomics. One major environmental factor that humans are exposed to from preconception to death is diet. The field of nutritional epigenomics, nutriepigenomics, studies how diet impacts human health through epigenetic mechanisms. Previous studies suggest that during embryogenesis, global genomic alterations are erased. However, after implantation of the embryo, epigenetic marks begin to be reestablished [3]. Over time, environmental factors can cause modifications to occur on top of the genome, which regulates gene silencing or activation [4,5]. Genome regulation is mediated by DNA methylation, histone modification, and non-coding RNAs or RNA-based modifications, which contribute to changes in gene expression independent of changes to the DNA code. Epigenetic marks may be global or tissue-specific, and can also be inherited from parent to offspring through the germline, in essence, inheritable transmission of a phenotype can Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00001-1 Copyright © 2019 Elsevier Inc. All rights reserved.

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Chapter 1 Introduction to nutritional epigenomics

FIG. 1.1 Diet and dietary metabolites impact the epigenome in manner that can promote health or disease. Credit: Samantha Romanick.

occur in the absence of the causative stimuli [3,4]. Past studies have linked epigenetics with diseases such as cancer, however, recent studies include depression [6], obesity [7], cardiovascular disease, metabolic disease, and more [3,5]. It is important to note that diet, has been implicated in the regulation of these epigenetic adaptations and is considered a key contributor of human health and disease (Fig. 1.1).

2. Epigenetic regulators 2.1 DNA methylation DNA methylation is vital to the germ cell due to its characteristic totipotency. DNA methylation regulates gene expression; therefore, preexisting epigenetic marks of egg and sperm must be cleared

2. Epigenetic regulators

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and reestablished, or reprogrammed, during germ cell specification to obtain totipotency. DNA methylation is regulated by DNA methyltransferases (DNMT), which include DNMT 1, DNMT 3a, and DNMT 3b, where DNMT1 is predominant in mammals [5]. DNA methylation is the process of transferring a methyl (CH3) group to the 5’ carbon on the nitrogenous base cytosine in a DNA sequence by DNMTs and S-adenosyl methionine (SAM) as a methyl group donor. Usually, the cytosine is adjacent to a guanine residue, which comprises of what is known as a CpG island. There are more than 30 million locations of these dinucleotide segments found in the human genome [4]. Transcriptional repression caused by DNA methylation is dependent upon location of the methylation site. For example, if the methylated CpG island is present in a gene promoter region, transcriptional repression will occur due to binding inhibition of transcription factors; however, if the CpG island is localized to a gene exon region, transcriptional activation may occur [4,8]. Histone epigenetics describe histone tail methylation and acetylation, which result in chromatin remodeling. Histone tail methylation may result in transcriptional repression as a result of chromatin condensation, or transcriptional activation by cause of chromatin relaxation. Conversely, histone tail acetylation causes transcriptional activation and deacetylation causes transcriptional repression. Studies have shown that methylation or demethylation of some genes may contribute to certain cardiac diseases such as coronary artery disease, hypertension, and heart failure [5,8,9].

2.2 Histone modification Previously mentioned, chromatin remodeling includes methylation and acetylation of histone tails. Histone proteins H2A, H2B, H3, and H4 make up an octamer, a dimer of each histone protein is present, within the core of the nucleosome of chromatin. Each histone protein contains an amino tail, which is the site where post-translational modifications such as methylation and acetylation occur, however, phosphorylation, sumoylation, ubiquitination, and other modifications can also occur [4,5,8]. Post-translational modifications of histone tails result in conformational changes in chromatin, which cause either an increase or decrease in DNA-histone affinity [8]. An increase in DNA-histone affinity condenses chromatin, usually observed as heterochromatin, resulting in transcriptional repression, whereas a decrease in affinity will result in chromatin relaxation, usually found in euchromatin, and ultimately transcriptional activation. Histone methylation is regulated by two enzymes, histone methyltransferases (HMTs) and histone demethylases (HDMs), where HMTs mediate methylation and HDMs mediate demethylation. Histone methylation usually occurs on arginine, lysine, and histidine residues; however, the magnitude of methylation and location of methylation determines either transcriptional activation or repression [5]. The roles of histone acetylation and deacetylation have been well described. Similar to histone methylation, histone acetylation is regulated by two groups of enzymes, histone acetyltransferases (HATs) and histone deacetylases (HDACs) [5]. Acetylation is the process of transferring an acetyl group to the ε-amine group of the amino acid lysine on histone tails by HATs and acetyl coenzyme A as an acetyl group donor. Conversely, HDACs remove this acetyl group via hydrolysis reaction [10]. Both HATs and HDACs have been implicated in many diseases including cancer [11], cardiovascular disease [3,5], obesity [7] and diabetes [12,13]. Fig. 1.2 shows a schematic representation that summarizes DNA methylation and histone modifications.

FIG. 1.2 Principal mechanisms for DNA methylation and histone modifications that regulate chromatin remodeling. DNA methylation occurs when a methyl (CH3) group is transferred to a cytosine residue on the DNA sequence by a DNA methyltransferase (DMNT). DNA methylation is present in both condensed and relaxed chromatin, transcriptional repression and activation is determined by methylation site location and magnitude of methylation. Post-translational modifications such as methylation or acetylation occur on histone tails resulting in either transcriptional repression or activation. Histone tail methylation usually results in transcription repression; whereas, histone tail acetylation results in transcriptional activation. Histone methylation is mediated by histone methyltransferases (HMTs) and histone demethylases (HDMs) and histone acetylation is mediated by histone acetyltransferases (HATs) and histone deacetylases (HDACs). Credit: Samantha Romanick.

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2.3 RNA based modifications The last component of epigenetic regulation to be discussed in this chapter involves non-coding RNAs, which include: small nucleolar RNAs, ribosomal RNAs, long regulatory non-coding RNAs (lncRNAs), microRNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs) [5]. This is a limited list of regulatory RNAs, in fact, the repertoire of regulatory RNAs has increased exponentially since their discovery in the late 1990s [14]. These regulatory RNAs have a vast range of tissue-specificity and functions. For example, piRNAs are restricted to germline gene regulation, while lncRNAs, RNAs greater than 200 nucleotides in length, are found in most tissues and regulate gene expression, as well as differentiation and function of innate and adaptive cell types of the immune system [14,15]. Small regulatory miRNAs are one of the most well-studied non-coding RNAs. miRNAs are transcribed in the nucleus where the primary transcript, or pri-miRNA, is folded into a hairpin structure that is targeted and cleaved by the DROSHA complex, resulting in a small hairpin precursor miRNA, or pre-miRNA. The pre-miRNA is subsequently transported from the nucleus to the cytoplasm with the assistance of exportin-5 (XPO5). The pre-miRNA unites with the Dicer complex where it is further processed to a mature miRNA. The RNA-inducing silencing complex (RISC) produces the negatively regulating miRNA masterpiece [5,8,16]. miRNAs serve an important role in post-transcriptional gene regulation, where the RISC-miRNA complex targets and degrades mRNA sequences, thereby regulating protein synthesis [5,14]. Studies have shown that miRNAs play an important role in the post-transcriptional regulation of many diseases including cardiovascular disease [3,5], neurodegeneration, obesity [7] and diabetes [12]. miRNA dysregulation has also been shown to associate with cancer and many miRNAs have been suggested as biomarkers for different cancer types [17,18]. While miRNAs are well-studied, recent research has outlined the role for other small, non-coding RNAs in health and disease.

3. Transgenerational inheritance The epigenome adapts to environmental factors, such as diet, across the lifespan of an organism. Increased epigenetic modifications occur on the genome during cell differentiation in the prenatal period; therefore, diet has a major influence in organismal development. The Developmental Origins of Disease Paradigm, also known as the Thrifty Phenotype Hypothesis, explains the risk of postnatal disease determined by the interaction of the fetal environment to the fetal genome, or developmental plasticity [19]. Essentially, maternal stress, birth weight, fetal undernutrition, and maternal obesity are environmental factors that can affect the fetal epigenome and result in disease throughout the lifespan [19]. Offspring of malnourished mothers during pregnancy have been shown to develop diseases such as diabetes and obesity later in life [20]. Furthermore, in several studies, low birth weight has been linked to the development of impaired glucose tolerance and type 2 diabetes [21e23], as well as dysglycemia [24]. Likewise, offspring of obese mothers or mothers consuming a high-fat diet during pregnancy have been shown to develop obesity. Increasing rates of childhood and infant obesity is correlated to the steady incline of overweight and obese reproductive-age women [20]. Maternal obesity increases fetal adiposity, which predisposes the fetus for obesity later in life. Altogether, the epigenomic marks obtained from the fetal environment can also be passed down to the next generation, also known as transgenerational inheritance.

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Chapter 1 Introduction to nutritional epigenomics

During fertilization epigenetic regulatory patterns are normally erased; however, under malnourished conditions removal of these epigenetic patterns is impaired, resulting in transgenerational effects. This is clearly shown in a maternal rat model of protein restriction during pregnancy, in which the F1 and F2 offspring presented metabolic dysfunction. Of note, the F2 offspring had metabolic dysfunction despite normal maternal nutrition for the F1 pregnant mother [25,26]. Metabolic dysfunction was also observed in the F3 offspring, however, this was dependent on the length and severity of protein restriction in the F0 mother [27,28]. In addition, F1 and F2 offspring had high systolic blood pressure and low nephron numbers when conceived from an F0 mother on a protein restricted diet during pregnancy [28]. This would be expected to contribute to hypertension later in life. These events, were in part, regulated changes in the germ cell DNA methylome, specifically at hepatic gene promoters in the F1 and F2 male offspring [29]. While these events demonstrate transgenerational epigenetic inheritance via the mother, it should be noted that recent evidence has also implicated paternal transmission as a key regulator of offspring phenotype, in which diet impacts paternal non-coding RNAs during spermatogenesis that controls offspring metabolic health [29].

4. Summary Altogether, nutriepigenomics is the study of how diet impacts such epigenetic events, DNA methylation, histone modification and non-coding RNAs, across the genome of an organism in the regulation of health and disease. Future knowledge gained from the field of nutriepigenomics may allow for personalized nutrition therapy for the prevention and/or treatment of human disease. Currently, pregnant mothers take dietary supplements for choline and folate to prevent fetal development of neurological disorders. Furthermore, dietary omega-3 poly unsaturated fatty acid supplementation during pregnancy prevents demethylation caused by the negative effects of cigarette smoking [30]. Understanding how the epigenome responds to nutritional interventions are key to preventing disease and promoting healthy generations to come. However, our understanding of diet-epigenome interactions is in its infancy, and future studies involving basic and clinical science will be necessary to better understand this relationship. This book will explore our current understanding of the epigenome in the regulation of cancer, metabolic and neurologic disease. In addition, this book will explore emerging evidence that implicates socio-environmental interactions that control epigenomic signatures related to human health and disease as well as how diet impacts future offspring development and health. Lastly, this book will dive into our current understanding for diet-epigenome regulation in disease prevention and address emerging evidence that implicates the gut microbiota in diet-epigenomic regulation.

References [1] Collins FS, Mansoura MK. The human genome project. Revealing the shared inheritance of all humankind. Cancer 2001:221e5. [2] Olson MV. The human genome project. Proc Natl Acad Sci USA 1993:4338e44. [3] Loche E, Ozanne SE. Early nutrition, epigenetics, and cardiovascular disease. Curr Opin Lipidol 2016: 449e58.

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[4] Mazzio EA, Soliman KFA. Basic concepts of epigenetics impact of environmental signals on gene expression. Epigenetics 2012:119e30. [5] Duygu B, Poels EM, da Costa Martins PA. Genetics and epigenetics of arrhythmia and heart failure. Front Genet 2013;219. [6] Lockwood LE, Su S, Youssef NA. The role of epigenetics in depression and suicide: a platform for geneenvironment interactions. Psychiatr Res 2015:235e42. [7] Cordero P, Li J, Oben JA. Epigenetics of obesity: beyond the genome sequence. Curr Opin Clin Nutr Metab Care 2015:361e6. [8] Udali S, Guarini P, Moruzzi S, Choi SW, Friso S. Cardiovascular epigenetics: from DNA methylation to microRNAs. Mol Aspect Med 2013:883e901. [9] Movassagh M, Vujic A, Foo R. Genome-wide DNA methylation in human heart failure. Epigenomics 2011: 103e9. [10] Yang XJ, Seto E. HATs and HDACs: from structure, function and regulation to novel strategies for therapy and prevention. Oncogene 2007;26:5310e8. [11] Benton CB, Fiskus W, Bhalla KN. Targeting histone acetylation: readers and writers in leukemia and cancer. Cancer J (United States) 2017:286e91. [12] Khullar M, Cheema BS, Raut SK. Emerging evidence of epigenetic modifications in vascular complication of diabetes. Front Endocrinol 2017;237. [13] Schu¨tz LF, Park MH, Choudhury M. HDACs in diabetes: a new era of epigenetic drug. Nutr Ther Interv Diabetes Metab Syndr 2018:475e86. [14] Taft RJ, Pang KC, Mercer TR, Dinger M, Mattick JS. Non-coding RNAs: regulators of disease. J Pathol 2010:126e39. [15] Chen YG, Satpathy AT, Chang HY. Gene regulation in the immune system by long noncoding RNAs. Nat Immunol 2017:962. [16] He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 2004:522. [17] Wong NW, Chen Y, Chen S, Wang X. OncomiR: an online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics 2017:713e5. [18] Chatterjee N, Rana S, Espinosa-Diez C, Anand S. MicroRNAs in cancer: challenges and opportunities in early detection, disease monitoring, and therapeutic agents. Curr Pathobiol Rep 2017:35e42. [19] Gluckman PD, Hanson MA. Developmental origins of disease paradigm: a mechanistic and evolutionary perspective. Pediatr Res 2004:311. [20] Simmons R. Epigenetics and maternal nutrition: nature v. nurture. Proc Nutr Soc 2011:73e81. [21] Jornayvaz FR, Vollenweider P, Bochud M, Mooser V, Waeber G, Marques-Vidal P. Low birth weight leads to obesity, diabetes and increased leptin levels in adults: the CoLaus study. Cardiovasc Diabetol 2016:73. [22] Li Y, Ley SH, Tobias DK, Chiuve SE, VanderWeele TJ, Rich-Edwards JW, et al. Birth weight and later life adherence to unhealthy lifestyles in predicting type 2 diabetes: prospective cohort study. BMJ 2015:h3672. [23] Jensen CB, Storgaard H, Dela F, Holst JJ, Madsbad S, Vaag AA. Early differential defects of insulin secretion and action in 19-year-old Caucasian men who had low birth weight. Diabetes 2002:1271e80. [24] Morrison KM, Ramsingh L, Gunn E, Streiner D, Van Lieshout R, Boyle M, et al. Cardiometabolic health in adults born premature with extremely low birth weight. Pediatrics 2016:e20160515. [25] Martin JF, Johnston CS, Han CT, Benyshek DC. Nutritional origins of insulin resistance: a rat model for diabetes-prone human populations. J Nutr 2000:741e4. [26] Zambrano E, Martı´nez-Samayoa PM, Bautista CJ, Dea´s M, Guille´n L, Rodrı´guez-Gonza´lez GL, et al. Sex differences in transgenerational alterations of growth and metabolism in progeny (F2) of female offspring (F1) of rats fed a low protein diet during pregnancy and lactation. J Physiol 2005:225e36. [27] Harrison M, Langley-Evans SC. Intergenerational programming of impaired nephrogenesis and hypertension in rats following maternal protein restriction during pregnancy. Br J Nutr 2008:1020e30.

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[28] Benyshek DC, Johnston CS, Martin JF. Glucose metabolism is altered in the adequately-nourished grandoffspring (F3generation) of rats malnourished during gestation and perinatal life. Diabetologia 2006: 1117e9. [29] Burdge GC, Slater-Jefferies J, Torrens C, Phillips ES, Hanson MA, Lillycrop KA. Dietary protein restriction of pregnant rats in the F0 generation induces altered methylation of hepatic gene promoters in the adult male offspring in the F1 and F2 generations. Br J Nutr 2007:435e9. [30] Lee HS, Barraza-Villarreal A, Hernandez-Vargas H, Sly PD, Biessy C, Ramakrishnan U, et al. Modulation of DNA methylation states and infant immune system by dietary supplementation with v-3 PUFA during pregnancy in an intervention study. Am J Clin Nutr 2013:480e7.

CHAPTER

DNA methylation and chromatin modifications

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Zahra Sepehri, Tasnim H. Beacon, Fadumo D.S. Osman, Sanzida Jahan, James R. Davie Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada

1. Epigenetics and chromatin organization Epigenetic refers to a variety of processes that have heritable effects on gene expression programs without changes in DNA sequence [1,2]. Key players in epigenetic control are DNA methylation and histone modifications which, in concert with transcription factors, chromatin remodeling complexes, nuclear architecture and non-coding RNAs, define the chromatin structure of a gene and its transcriptional activity. Cellular differentiation is initiated and maintained by epigenetic mechanisms. Although epigenetic marks are established early during development and differentiation, adaptations occur throughout life in response to intrinsic (e.g. oncogene expression) and environmental stimuli (e.g. diet) and may lead to late life disease (e.g. cancer). Thus, the life of an individual is not only defined by his/her genome, but also by his/her numerous epigenomes, with different epigenomes being generated through development, not only during fetal development but also during the plastic phase of early childhood, and existing in different cell types. Moreover, epigenomes react to environmental influence including maternal care, diet, exposure to toxins and xenobiotics. Epigenetic responses to environmental stimuli may have long-term consequences, even affecting future generations. The task ahead of us, to decipher all normal epigenomes and dysfunctional epigenomes leading to the vast array of diseases and cancers is colossal. The basic repeating structural unit in chromatin is the nucleosome. The nucleosome consists of a histone octamer, arranged as an (H3eH4)2 tetramer and two H2A-H2B dimers, around which DNA is wrapped. The H1 histones bind to the DNA, the linker DNA that join the nucleosomes together [3]. The core histones (H2A, H2B, H3, H4) have a similar structure with a basic N terminal domain, a globular domain organized by the histone fold, and a C terminal tail. The N terminal tails emanate from the nucleosome in all directions and interact with linker DNA, nearby nucleosomes or with other proteins [4]. The histones undergo numerous reversible post-translational modifications (PTMs), including acetylation, methylation and phosphorylation [5]. Histone PTMs are added and removed from specific sites by a variety of enzymes [6]. Some histone PTMs (active marks) are associated with transcribed chromatin regions, while others (repressive marks) are present in silent regions. Histone acetylation and H3K4me3 are active gene marks, whereas H3K27me3 is a repressive mark. Histone PTMs function to alter chromatin structure and/or provide a “code” for recruitment or occlusion of Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00002-3 Copyright © 2019 Elsevier Inc. All rights reserved.

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nonhistone chromosomal proteins to chromatin. The nucleosomal DNA may also be methylated (five methyl cytosine) [7]. Thus, the nucleosome serves multiple roles in regulating gene expression, a structural role and a role as a signaling module [8,9]. Adding to this complexity, most histones have variants, products of different genes [10]. Vertebrate histone variants are classified as replication-independent and replication-dependent. For example, H3.1 and H3.2 are replication-dependent, while H3.3 is replication-independent. Being synthesized throughout most of the cell cycle, the H3.3 variant is available to re-build nucleosomes displaced by transcription factors and the transcription machinery [11]. H3.3 is enriched in PTMs that are associated with transcriptional activity (for example, S28ph, K9ac, K14ac, K4me3) [12e14]. Mutations in H3.3 are associated with several cancers (e.g. pediatric glioblastoma) [15]. The structure and composition of a nucleosome depend to a certain extent on the DNA sequence associated with the core histones. A nucleosome with a DNA sequence that has binding sites for pioneer transcription factors and located in a tissue-specific enhancer may have an accessible configuration [16]. A pioneer transcription factor such as FoxA1 can bind to its binding site in a nucleosome context and in a repressive heterochromatin environment [17]. Nucleosomes associated with non-methylated CpG islands may be rich in H3K4me3. SETD1A/B, which catalyzes trimethylation of H3K4, has among its subunits, CXXC1 (also known as CFP1), a CpG island binding protein [18]. A nucleosome that has H3K4me3 attracts multiple lysine acetyltransferases (KATs) like HBO1 and SAGA which contain subunits that bind to K4me3. Thus, a nucleosome with H3K4me3 will have much different acetylation dynamics than a nucleosome next to it that does not have this modification [18e20]. Nucleosome structure is also radically changed during the process of transcription, which generates atypical nucleosomes known as U-shaped nucleosomes [21,22]. The open, U-shaped nucleosome will retain this altered configuration as long as its acetylation status remains high [23].

1.1 Three-dimensional structure of chromatin The organization of the genome plays an important role in the regulation of gene expression (Fig. 2.1). With the advancement of high-resolution techniques such as 3C and 3C based strategies (e.g. 3C, 4C, 5C, ChIA-PET and Hi-C analysis) [24], the 3D structure of chromatin has become an important model to address questions such as how the conformation of this structure controls interactions of regulatory elements, transcriptional activities and what factors/key players are involved in the maintenance of such conformation. High-resolution mapping of nucleosomes has allowed us to study misregulation of nucleosome positioning at the basic level of compaction of chromatin [25]. Chromatin is further organized into functional territories based on their state of condensation into euchromatin and heterochromatin. A higher level of compaction is required to fit the DNA and interacting proteins within the nucleus. As research advances to understand the underlying mechanism of how chromatin organization impacts gene regulation, major efforts have been made to understand the organization of these epigenetic compartments, map their interaction and determine their spatial arrangement [26]. Chromatin has different levels of spatial organization ranging from the nucleosome to domains. Current literature subcategorizes these spatial scales into three levels: large, intermediate and small. Compartmentalization and chromatin interactions are confirmed mainly by microscopic observations and by the Chromosome Conformation Capture (3C) technique. Microscopy is used to detect fluorescent probes used to track the spatial location of whole chromosomes and the relative positions of locus of interest, while 3C methods quantifies the interaction of two distal segments of the DNA

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FIG. 2.1 Organization of Chromatin in the Interphase Nucleus. The chromosome territories in the interphase nucleus are composed of chromatin compartments (A/B). The A-compartments are active and are associated with the active marks and their catalyzing enzymes (KMTs, KDMS, KATs and HDACs). The B compartments are found near the nuclear lamina and contain the repressed mark and associated enzymes (PcG and JmJD). The compartments are further organized into TADs each individually regulating the genes and regulatory elements within the insulating loop. The TAD boundaries are the regions where the CTCF and cohesin complexes bind. Credit: James R. Davie.

based on ligation events that takes place between the chromatin fragments after the steps of fixation and digestion. The proximity relative to one another is then used to determine their genomic location [24]. These organizations are commonly termed A/B compartments, topologically associating domains (TADs), and chromatin loops [27] (Fig. 2.1). Segregating the nuclear organization into different scales has facilitated the study of individual chromatin components correlating their dimensional interaction and overarching function in gene regulation [28]. 3C techniques have been promising in providing insight into the 3D arrangement, while the Hi- C technique appreciates and quantifies the interactions leading to average chromosome conformations. Initially the compartments were defined as closed and open chromatin compartment. The Hi-C mapping was performed at 1 Mb and as low as 100 kb and was validated with 3D-Fish to confirm the proximity [27]. With the advent of higher resolution, the structural domains were classified into 2 major compartments in the interphase stage: A compartment and B compartment (Fig. 2.1). The A compartment houses active chromatin (H3K36me3), while the B compartment is associated with

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inactive chromatin (H3K27me3). The active state is defined by transcriptional activity, associated histone modifications, and accessible DNA. Active chromatin domains are distinguished by their increased sensitivity to DNase I and association with highly acetylated histones [29e31]. Note that DNase I hypersensitive sites differ from that of DNase I sensitive regions. DNase I hypersensitivity marks regulatory elements in the genome that are typically nucleosome depleted and detected by techniques such as FAIRE and ATAC [29,32e34]. DNase I sensitivity encompasses much broader chromatin regions and correlates with histone acetylation status [35,36]. A/B compartments differ in chromatin composition and histone modifications, which identifies the compartment as active and inactive, respectively. The compartments stay constant in the genome throughout development [37]. However, changes to their associated histone modifications, DNAse I sensitivity or composition can lead to a change in their compartmentalization [38]. A pioneer transcription factor has the potential to convert a B to an A compartment [17]. The A compartment is organized into an inner ring-shaped structure, while the B-compartment is associated with the nuclear lamina and the edges of nucleoli within the nucleus. Single-cell Hi-C studies reveal the presence of these compartments in the only interphase cell. While chromosomes attain different conformations in different cells, the A/B compartments spatially segregate both within chromosomes and globally within nuclei [39,40]. At a more intermediate scale, self-interacting domains are seen which typically range in size from tens of kilobases to a few megabases usually comprising of a small number (e.g., 1e10) of genes. These interacting DNA sequences are commonly termed as ‘Topologically Associating Domains’ (TADs) but also are seen to be defined as sub-TADs, ‘contact domains’ and ‘insulated neighbourhoods’ based on their interaction and regulatory functions [37] (Fig. 2.1). TADs play an important role in domain organization. Experiments involving duplication, deletion or inversion of TADs or TAD boundaries have caused physiological malformations. Therefore, disruption of TADs can have significant effects on gene expression resulting in pathogenic phenotypes, indicating its importance in maintaining genomic architecture [41]. TADs function independently of the compartments [42]. The TAD boundaries interact more frequently with each other compared to the rest of the domain and result in a large fraction of peak (38%) when captured with Hi-C. This peak marks the binding of the CTCF and the cohesin complex [43]. The loop domain formed between CTCF and cohesin binding sites buffers the genes from the influence of enhancers residing in different domains [41,44]. The CTCF motifs of the insulating loop are always oppositely positioned marking the boundaries of the loops [39]. These boundaries are sensitive to DNAse I digestion, which indicates the low density of nucleosomes [45]. Therefore, altering the level of the nucleosome remodeling protein BRG1 was seen to change the density of nucleosomes at the boundary and affect boundary strength as well as CTCF binding [46]. While the loss of cohesin and CTCF was shown to have no effect on the compartmentalization of chromatin and the associated histone marks, it resulted in loss of TADs. TADs are segregated from each other by the formation of an insulating loop, which prevents the enhancer from one TAD to control the transcription of a gene in another TAD leading to insulated neighborhood where the gene and its regulatory elements are unaffected from external influence [47]. One exciting feature of the loop is its dynamic nature. It is observed to be present transiently while in a constant state of forming and collapsing resulting in an unstable structure [48]. One of the successful models describing the insulating loop formation is the ‘loop extrusion model’ where a loop extrusion factor (LEF) would bind to the chromatin and will initiate loop formation with the help of the boundary factors. The loop structure can incorporate additional LEF causing the formation of a secondary loop. The loop

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eventually extends to reach the TAD boundaries and are temporarily stabilized [49]. The loop will then disrupt shortly following the dissociation of the LEF and boundary factors. This model is useful to predict the role of CTCF at the TAD boundaries while setting the stage to hypothesize models to understand the underlying mechanism of their function in maintaining the conformation of the 3D structure of the chromatin. To understand how the large and intermediate level structures complement each other, experiments have been designed to explore how altering one structure affected the other. While the loss of CTCF and cohesin-associated factors (Rad21 and Nipbl) led to the disruption of the TADs and loops, deletion of WAPL (cohesin antagonist) had the opposite effect. Meanwhile loss of CTCF and cohesin-associated factors had no effect on the A/B compartment structures, indicating TADs and A/B compartments to be independent structures [50]. Another important factor was the contribution of these structures in different species. Experiments have shown that mammals show strong CTCF/cohesin loop anchors at TAD boundaries [50], whereas in Drosophila CTCF occupancy at the TAD boundaries are comparatively less and are not usually in inverted orientation [28]. These structures can be concluded as species-dependent for the factors involved in domain formation. Although the variation in abundance of CTCF and cohesin has led to different theories in their role in gene regulation, different theories have been speculated on their function leading up to the 3D organization of the chromatin. On a small-scale chromatin organization, variations to the 3C methods have been applied to detect enhancer-promoter interaction, which functions to regulate transcription. To map the interactions of the regulatory region, techniques such as OCEAN-C have been a helpful tool to identify hubs of open chromatin regions (HOCIs) and their regulatory functions. The cells are fixed, digested, ligated, and sonicated to desired fragment sizes. Following the phenol-chloroform extraction, the biotinylated DNA is analyzed for sequencing. As the technique combines FAIRE-seq and Hi-C, it eliminates the limitations of the techniques by improving the background and only captures the peaks representing open chromatin interaction. Important HOCI interactions include promoter-enhancer, promoterpromoter, and enhancer-enhancer interactions. These interactions are key to study the changes in open chromatin conformations as well as the regulation of the transcription of genes localized in proximal and distal domains [51]. Overall, studying chromatin in different scales has greatly advanced our understanding of the 3D architecture of chromatin by defining compartments, TADs and loops. Studying the interactions of the regulatory elements provides a topological basis to correlate open chromatin conformations to transcriptional regulation [52]. Knowledge of CTCF and cohesin factors along with the individual chromatin structures have laid the foundation of the mammalian 3D genome to explore conformational changes and identify novel epigenetic regulators mediating gene expression.

2. DNA methylation DNA methylation is a crucial epigenetic modification found in the genome of various organisms. It is involved in development, differentiation, tissue-specific gene expression and cellular function. Moreover, it plays a fundamental role in epigenetic reprogramming, X-chromosome inactivation and genomic imprinting. Recently, an interesting discovery of the role of DNA methylation in individual CpG sites as an epigenetic age-predictor in both human and mice was made [53,54]. Despite the availability of such tools for predicting biological age, which is quite different from chronological age, a majority of people are not willing to examine CpG DNA methylation for age prediction.

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However, this information is clinically relevant as a potential biomarker and in determining better therapeutic options for individuals according to their biological age. In mammals, methylation involves the addition of a methyl group at position five of the cytosine ring in the CpG dinucleotides by DNA methyltransferase enzymes (DNMTs) [55,56]. There are two classes of methyltransferases: the de novo methyltransferase (DNMT3a and DNMT3b) and the maintenance methyltransferase (DNMT1). The de novo methyltransferases establish the methylation mark onto the unmethylated CpG region whereas DNMT1 maintains the methylation through addition of methyl groups to hemi-methylated regions during replication. DNMT3l is a DNMT-related protein which binds H3 at lysine 4 (H3K4) and helps in guiding DNMT3a and DNMT3b to their target DNA region [57,58]. In addition to the enzymatic activity of DNMTs, these enzymes can contribute to gene inactivation through modifying the chromatin by binding to both histone deacetylase (HDAC) and lysine methyltransferases (SUV39h1/2 and G9; enzymes which methylate H3K9) [59]. Five-methylcytosine (5-mC) is predominantly found at CpG-rich sites but there are exceptions [60]. For example, human neurons and embryonic stem cells show intragenic methylation in a non-CpG context [61]. CpG islands, located usually at the 5’ end of genes, are generally unmethylated, and their methylation is more often restricted to genes which require stabilization for long-term silenced state such as imprinted genes and genes exclusively expressed in germ cells rather than somatic cells. The function of methylation varies with different genomic contexts [62,63] (Fig. 2.2). For example, methylation in the gene bodies supports gene expression and does not prevent transcriptional elongation. Recently, intragenic DNA methylation in vertebrates was shown to prevent cryptic transcription initiation within the coding region of expressed genes [64]. The intragenic methylation is catalyzed by DNMT3b, which is recruited to the gene body by H3K36me3 (catalyzed by SetD2) (Fig. 2.2). This mechanism involving crosstalk between SetD2, H3K36me3, DNMT3b and 5-mC is mediated by elongating RNA polymerase II [64]. By contrast, DNA methylation of promoter regions and transcription start sites blocks initiation and has been linked to gene silencing, either directly through blocking the binding of transcription factors [65] or indirectly through the involvement of methyl-CpG-binding proteins, which in turn recruit transcriptional repression and chromatin remodeling complexes [66] (Fig. 2.2). The methyl CpG-binding protein (MBP) family is responsible for identifying methylation including MBD1, MBD2, MBD4, MeCP2 and Kaiso. All the MBPs contain a methyl-CpG binding domain (MBD) that specifically recognizes methylated DNA [67] except for Kaiso, which depends on its zinc-finger (ZF) domain in the C terminus for methyl- CpG recognition [68]. Moreover, these proteins can function as methylation-dependent transcriptional repressors. Interestingly, not all transcription-factor binding is blocked by methylation as some factors like SP1 can bind strongly to methyl-CpG sequences and mediate gene expression through the passive removal (replication and enzyme independent demethylation) of 5-mC [69,70]. In the context of repetitive DNA sequences, methylation plays a repressive role by silencing transposable elements [71]. Similar effects are observed in methylated regulatory elements where the enhancer’s activity is reduced [72] and the binding of CTCFs is blocked [73]. However, for some enhancers, cytosine methylation is involved in maintaining an active enhancer state [74]. Also to note, several transcription factors, like SP1, bind to methylated DNA [75]. Overall, as the interpretation of 5-mC changes in a specific genomic and cellular context from another, its impact has gone beyond gene expression control to include genome stability and splicing regulation [61].

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FIG. 2.2 The Role of DNA Methylation in Gene Expression. (A) The CpG islands (CGI) within the promoter region and transcriptional start site (TSS) are normally unmethylated in active genes. The DNA methyltransferases (DNMT) are recruited by H3K36me3 to the gene body. Intragenic methylation (me) is positively correlated with active transcription and does not block the elongation process, but spurious transcription initiation is inhibited. (B) In inactive genes, methylation of promotors inhibits transcription initiation. The addition of methyl group is coupled with repressive chromatin modifications by DNMT-linked lysine methyltransferase (KMT) and histone deacetylase (HDAC). Additionally, methyl-binding domain (MBD) proteins recognize methylated regions and recruit chromatin remodeling corepressor complexes. Credit: James R. Davie.

2.1 DNA demethylation There are different pathways used in different tissues for the removal of 5-mC [76]. In mammals, 5-mC is passively demethylated during replication and cell division, while active demethylation occurs in cycling and non-replicating cells like neurons. In the oxidation pathway, 5-mC is oxidized by TET enzymes to 5-hmC which can be further oxidized to 5-formlycytosine (5-fC) and 5-carboxylcytosine (5-caC). 5-fC and 5-caC are cleaved by thymine DNA glycosylase (TDG) resulting in the involvement of the base excision repair (BER) machinery. On the other hand, the deamination pathway involves first the deamination of 5-mC by the activation-induced cytidine deaminase (AID)/apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) family producing a T:G mismatch identified by TDG which in turn creates an abasic site to be repaired by the BER machinery [76].

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In 2009, the first member of the ten-eleven translocation family of methylcytosine dioxygenases (TET1) was discovered to be responsible for 5-mC oxidation and demethylation to 5-hydroxymethylcytosine (5-hmC) [77]. The TET family of enzymes is characterized as iron- and oxoglutarate-dependent enzymes with their respective binding domains. In contrast to DNMT1, the CXXC zinc-finger DNA-binding domain of TET1 and TET3 has the ability to bind both methylated and hydroxymethylated DNA regions [78]. Five-hmC levels in adult tissues are moderately low ranging between >0.1 % and w0.7% with the highest level in the tissues of the central nervous system [79]. Five-hmC does more than its role as a demethylation intermediate, and TET enzymes are more than their catalytic function. Hydroxymethylation is enriched in euchromatic regions within the gene bodies, TSS and upstream of TSS. Gene expression can be controlled either negatively or positively by 5-hmC and TETs. The presence of 5-hmC in gene bodies prevents the binding of the DNA methylation machinery and MBPs allowing for the activation of genes [80e82]. However, TET enzymes can play another role in silencing genes by their interaction with SIN3A co-repressor complex [83]. Furthermore, the presence of 5-hmC in the case of bivalent genes (transcriptionally poised genes) facilitates the recruitment and binding of polycomb repressive complex 2 (PRC2), which catalyzes the trimethylation of H3 at lysine 27, thus maintaining the repressed state of these genes [84].

2.2 Mitochondrial DNA methylation Unlike nuclear DNA, mitochondrial DNA (mtDNA) has a 16,569-base-pair circular structure and lacks histones. It consists of a heavy (H) strand and a light (L) strand, encoding 37 genes: 13 oxidative phosphorylation-related protein-encoding genes, 22 transfer RNAs and 2 ribosomal RNAs [85]. MtDNA is believed to be modified by both 5-mC and 5-hmC, however, the presence and function of such epigenetic modifications are still highly debated. In 2011, a mitochondrially targeted isoform of DNMT1 (mtDNMT1) was discovered and accounts for 1e2% of total DNMT1 transcripts. Hypoxiaresponsive transcription factors such as peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC1a) and nuclear respiratory factor 1 (NRF1) are responsible for regulating the mtDNMT1 expression [86]. Moreover, other DNMTs and TET enzymes were identified to be present in the mitochondria in a tissue-type-dependent manner. For example, the localization of DNMT3A in excitable tissues such as skeletal and heart muscles [87]. DNA methylation and hydroxymethylation patterns are observed across the mitochondrial genome, particularly their main location in the D-loop region where all the major promoters are located. Interestingly, more non-CpG compared to CpG is methylated in the D-loop region [88]. To date, the findings supporting the functional and regulatory roles of mtDNA methylation are based on associations without confirmatory evidence on its mechanism of action. The changes in mitochondrial gene transcription as a result of mtDNMT1 upregulation are not random, but rather genespecific leading to the activation of ND1 and repression of ND6 [86]. Transcription of mtDNA is dependent on mainly three factors: mitochondrial transcription factor B2 (TFB2M), mitochondrial transcription factor A (TFAM) in addition to mitochondrial RNA polymerase (POLRMT). Methylation might influence the ability of TFAM to bind the promoter regions directly or by the recruitment of proteins that add post-transitional modifications to TFAM [89]. Further work is required to fully understand and determine the detailed mechanism as well as to identify the players regulating mitochondrial DNA methylation.

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3. Histone modifications and their distribution in the genome Histones (H1, H2A, H2B, H3, H4) are basic proteins subject to a multitude of PTMs, which are mostly reversible [5,90]. Histone PTMs can affect almost all genomic events such as transcription, replication, recombination, DNA repair, kinetochore, chromatin remodeling and centromere formation [91]. Together with histone modifying enzymes and proteins that recognize specific histone PTMs, which are categorized as “Reader,” “Writer,” “Eraser,” “Effector” and “Presenter”, histone PTMs can regulate the transcriptional state [92,93]. Changes in histone PTMs can alter the active chromatin state into an inactive state and vice versa. Histone crosstalk is defined as a combination of histone PTMs that can code for transcriptional activation or repression in a context-dependent manner [94,95]. Histone PTM crosstalk can occur either cis or trans, involving events on the same histone tail or nearby histone tail within the same or neighboring nucleosome [96]. For example, it was demonstrated that serine 10 phosphorylation on H3 enhances the GCN5 mediated acetylation of H3 at lysine 14 [97]. Histone crosstalk can be initiated by preventing the nearby histone PTM. Indeed, H3 asymmetric di-methylation (H3R2me2a) was shown to prevent the MLL mediated formation of di- and tri-methylation of H3 lysine 4 (H3K4me3/H3K4me2) [98]. Interestingly, the presence of H3K4me3 prevents protein arginine methyltransferase (PRMT) 6-mediated H3R2me2a [98]. The advancement in the technology with tools such as chromatin immunoprecipitation (ChIP) and ChIP-sequencing (ChIP-seq) enables one to determine the crosstalk between different writers and readers or effector molecules. Lysine acetylation (all histones), lysine and arginine methylation (H3, H4, and H2B), serine and threonine phosphorylation (all histones), lysine ubiquitination (H2A, H2B), ADP- ribosylation at glutamine (H1) and sumoylation are some of the well-known histone PTMs [5]. Several of the histone marks are exclusively associated with active chromatin state (H3K4me3, H3K27ac), while others are with the inactive chromatin state (H3K27me3) [96].

3.1 Histone acetylation Histone acetylation was first reported by Vincent Allfrey and his group in 1964. Allfrey’s group described the dynamic and rapid histone acetylation using nuclei isolated from calf thymus [99]. Following on this study, they later reported that histone acetylation occurs on ε-amino lysine residue, and they also identified histone deacetylase (HDAC) activity in the nuclei. In 1978, both Dr. Davie and Dr. Allfrey reported for the very first time that n- butyrate acts as an HDAC inhibitor [100,101]. Hyperacetylation of H3 and H4 and to a lesser extent H2A and H2B were observed upon sodium butyrate treatment in the cell line investigated [100]. DNA sequences associated with the hyperacetylated histones showed increased DNase I sensitivity in HeLa and chicken erythrocyte cells [101]. The first report of a direct link between histone acetylation and transcriptionally active chromatin came from the study by Dr. Crane Robinson’s group using chicken erythrocytes [102]. In this study, the ChIP assay was used for the first time to demonstrate that acetylated histones are associated with transcriptionally active DNA sequences [102]. The relationship between histone acetylation and transcription became established after the discovery of KATs were co-activators [103]. KATs are categorized into four different groups; GCN5, MYST (SAS/MOZ), P300/CBP and SRC/p160 nuclear receptor coactivator family [104]. Acetylation of histone and non-histone proteins is catalyzed by KATs [105]. Dynamic and reversible histone acetylation is catalyzed by KATs and HDACs. The rate of histone acetylation can vary across the genomic regions with some regions having a faster rate of dynamic acetylation while some have slower or none [104].

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Histone acetylation can prevent H1-mediated salt insolubility, facilitating solubility of the region at physiological salt concentration [35,106,107].

3.2 Histone lysine methylation Lysine and arginine located in the histone N-terminal tails are methylated by lysine methyltransferases (KMTs) or PRMTs [5]. Mono-, di- or tri-methylation of lysine and mono- or di-methylation of arginine can be distinguished as active or repressive chromatin marks [108,109]. H3K4me1 along with H3K27ac typically marks active enhancers. H3K27me3 is a strong repressive mark catalyzed by the polycomb complex [110]. With our current knowledge of the histone PTMs associated with regulatory elements such active enhancers, poised enhancers, upstream promoters and silenced genes, we can now read histone PTM tracks and have a good idea of the function of the associated DNA sequence [111]. H3K4me3 is an active mark found in the upstream promoter region and strongly positioned after the first exon of transcribed genes [112]. The mitotic inheritance (memory) of the transcriptional state of genes is dependent upon H3K4me3 [113]. Bryan Turner’s group noted that the H3K4me3 regions on mitotic chromosomes coincided with CpG islands, which are found at the promoter and 50 regions of genes [18,114]. H3K4me3 has several roles in the regulation of gene expression. H3K4me3 located in the 50 coding region of expressed genes interacts with the proteins involved in pre-mRNA splicing (reviewed in Ref. [18]). Although most genes have H3K4me3 limited to the promoter region and the 50 region of the gene body, a small subset of genes have broad H3K4me3 regions (called H3K4me3 buffer domains). The function of H3K4me3 buffer domain is to ensure transcriptional consistency of the gene, that is, the same transcription rate in each cell [113,115]. Of note, these genes with the H3K4me3 buffer domains have a role in cell identity [115]. The top 5% broadest H3K4me3 regions in a given cell type were most enriched for genes that had important functions for that specific cell type (for example, genes involved in muscle function in skeletal muscle or stem cell regulators in embryonic stem cells) [115]. In normal but not cancer cells, tumor suppressor genes have broad H3K4me3 peaks [116]. Importantly genes coding for transcription factors, which were critical for cell identity or cell fate, have H3K4me3 buffer domains in the relevant cell type/tissue. Through the identification of genes with the H3K4me3 buffer domains, Anne Brunet and colleagues found genes coding for transcription factors and non-coding RNA that were novel regulators of neural progenitor cells [115]. Knocking down the genes with the broadest H3K4me3 domains resulted in decreased neural progenitor cell proliferation and decreased neurogenesis. Lysine and arginine methylation of histones can serve either as a binding site or occlude the binding of other modifiers to the site and thereby play a crucial role in histone PTM mediated signaling events. Due to the existing signaling event, aberrant binding of the modifying enzymes can lead to diseased state as observed for several cancers [117e119]. EZ, SET1, SET2, SMYD, SUV39, SUV4-20, RIZ are among the major family of lysine methyltransferases [120]. S-adenosyl methionine (SAM) serves as methyl donor and co-factor for both KMTs and PRMTs [121].

3.3 PRMTs and histone arginine methylation Eleven mammalian PRMTs have been discovered and reported to date. Mono and dimethylation of arginine are catalyzed by three classes of PRMT enzymes. PRMTs catalyze arginine methylation by

3. Histone modifications and their distribution in the genome

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using molecule of S-adenosyl-L-methionine (AdoMet) to form asymmetric (u-NG,NG-dimethylarginine or ADMA) or symmetric (u-NG,N0 G-dimethylarginine or SDMA) or monomethylarginines (MMA). Type I PRMTs, which asymmetrically dimethylate arginine, include PRMT1, 3, 4, 6 and 8, with PRMT1 accounting for most of the type I PRMT activity. PRMT5 and 9 are involved in symmetric dimethylation of arginine and belong to type II, while PRMT7, a type III methyltransferase, contributes to monomethylation of arginine [122]. Therefore, PRMTs have a wide range of substrate specificity. Arginine methylation of proteins and histones catalyzed by PRMTs can be either symmetrical or asymmetrical, and they are categorized based on this chemical feature [123]. Similar to lysine methylation, arginine methylation can contribute to activate or repress the chromatin state in a contextdependent manner [124]. H4R3me2a, an active chromatin mark, is catalyzed by PRMT1, whereas PRMT5 is responsible for the symmetric dimethylation of H4R3 (repressive mark). PRMT6 is the major methyltransferase responsible for the genesis of H3R2me2a (repressive mark) in vivo. H3R2me2s, formed by PRMT5, recruits WDR5, which is a subunit of several co-activator SET1/MLL complexes that produce H3K4me3 (an active mark) [125,126]. Other histone arginine methylations are listed below. H3R17me2a: This arginine modification is catalyzed by PRMT4/CARM1 and is associated with transcriptional activation. This mark is located at the upstream promoter of several genes [119,127,128]. Moreover, it was shown that prior acetylation of H3K18 and K23 promotes H3R17 methylation and activation of estrogen-responsive gene TFF1 [129,130]. H3R26me2a: PRMT4/CARM1 produces H3R26me2a, which is a less studied mark. Similar to H3R17me2a, this mark was also found associated with the upstream promoter region of target genes [131]. H3R2me2a: PRMT6 produces H3R2me2a. This mark has an intriguing feature as it can antagonize H3K4me3 through blocking the binding of WDR5 to the site [132]. This mark is associated with repressive chromatin and found in pericentromeric regions [133]. H3R2me2s: This mark is associated with transcriptionally active chromatin regions and is generated by PRMT5. H3R2me2s recruits WDR5, which is found in complexes such as MLL, SET1A, and SET1B, which catalyze H3K4me3. Therefore, H3R2me2s is located with H3K4me3 [134]. H4R3me2a and H2AR3me2a: PRMT1 is involved in the generation of these two modifications. H4R3me2a was found associated with the upstream promoter regions of several genes [135e138]. H4R3me2a was reported to facilitate the subsequent acetylation of H4 at Lys 8 and 12 and of H3 at Lys 9 and 14 by p300, and therefore H4R3me2a can be considered as an active mark that recruits the KATs, p300 and PCAF [135,139]. Acetylation of H4 Lys 5 results in reduced arginine methylation by PRMT1 but increased activity by PRMT5 [140,141]. Highly acetylated H4 that has H4K5ac is strongly detrimental to PRMT1 mediated H4R3 methylation. In contrast, acetylation of H4 at K16 is a better substrate for PRMT1 than unmodified H4 [141]. This is an interesting observation as H4K16ac is involved in decondensing chromatin [142]. H4R3me2s and H2AR3me2s: PRMT5 generates the repressive marks, H4R3me2s and H2AR3me2s. H4R3me2s was found associated with the upstream promoter region of several silenced genes and with imprinting control regions [143,144]. H3R8me2s: This is a repressive chromatin mark generated by PRMT5 [143]. Prior acetylation was shown to prevent H3R8me2s [145].

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3.4 Arginine demethylation Protein arginine deiminase (PAD) family of enzymes catalyze citrullination from the amino acid arginine. To date, PADs, PAD1-4 and PAD6, have been identified [146]. PADs replace the ketamine (¼NH) group of arginine to keto group (¼ O), thereby resulting in no net charge from the positively charged arginine. This change in charge due to citrullination alters the structure and function of the protein as well affect the binding of protein interacting partners [147]. It was reported that both PAD2 and PAD4 could catalyze citrullination on a histone tail, albeit it is PAD4 which is involved in citrullination of monomethyl arginine [148,149]. Symmetric and asymmetric mono and dimethylation of arginine H3/H4 were reported to be catalyzed by Jumonji domain-containing 6 protein (JMJD6) [150]. However, later it was shown that JMJD6 is involved only in the demethylation of mono- and di-methyl H4 arginine residues [151]. Although JMJD6 has been reported as a candidate for demethylation of arginine, there is still a lack of sufficient biochemical evidence for that. Moreover, demethylation of H3R2 has not yet been detected.

4. Metabolism and epigenetics Metabolism is a set of life-saving chemical modifications in the cells [152]. Every cell has a set of responses to changes in its environment. These responses depend on various factors such as cell type and duration of changes, to name a few. Overall, nutrition as an environmental input modulates cell metabolism, and in turn cell metabolism regulates epigenetic processes [153]. More precisely, the dynamic range of physiological concentrations of commensurate intermediates in metabolism will characterize the “kinetics” of crosstalk between metabolism and chromatin [154] (Fig. 2.3).

4.1 Metabolism and chromatin modifications Enzymes that are responsible for histone and DNA modifications need metabolites such as acetyl or methyl groups to do their enzymatic reactions specifically and efficiently. Availability of these metabolites and their localization in the cell play major roles in the activity of the enzymes regulating DNA and histone modifications. For example, efficiency of KATs depends on local subcellular acetylCoA concentration, which is produced during catabolism and anabolism [152,153]. In a nutrient favorable situation, that is, there is a lot of acetyl-CoA precursors entailing glucose, galactose or ethanol, adenosine triphosphate citrate lyase will convert glucose derived citrate into acetyl-CoA in the nucleus [152]. On the other hand, class III HDACs (the sirtuins) deacetylate histones and non-histone proteins dependent on the concentration of available local nicotinamide adenine dinucleotide (NADþ). The Sirtuin family of HDACs (SIRTs) “sense” the positive effects of caloric restriction on physiology of the cell and are a key controller in mitochondrial energy metabolism, senescence of the cell, inflammation and most importantly tumorigenesis [153]. Yet, they are NAD dependent [152]. When fasting happens, available NADþ and activity of SIRT1 are high. In contrast when energy resources are high, NADþ would be converted to NADH and consequently its concentration would decrease. In aging and diabetes, NAD levels significantly decrease [152]. Actually, SIRTs are the only HDACs that are dependent on an endogenous metabolite such as NAD [153]. Although SIRT6 has high affinity to bind to NAD, its dependency to NAD is less. SIRT6 specifically deacetylates pericentric

4. Metabolism and epigenetics

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FIG. 2.3 Interactions Among Environment, Cell Metabolism and Epigenetics. There are dynamic interactions among environmental inputs (such as nutrition, O2), cell metabolism pathways and epigenetic changes. NADþ, involved in redox reactions as electron carrier, is a coenzyme for class III HDACs (sirtuins). Acetyl CoA, which is an oxidizing reagent that conveys carbon atoms in TCA cycle, is a coenzyme for KATs. bOHB, ketone body in starvation and exercise, is an endogenous HDAC inhibitor. SAM, donor of methyl group, is coenzyme for DNMTs and HMTs. Credit: James R. Davie.

heterochromatin H3K18ac [155]. SIRT6 also deacetylates H3K9ac and H3K56ac resulting in repression of transcription of hypoxic inducible factor (HIF)-a driven glycolytic gene [152]. Another interesting possibility about links between nutrition and histone PTMs is that the ketone body b-hydroxybutyrate e product of breakage of fatty acids due to starvation or long time caloric restriction and consequently activation of fatty acid b oxidation - acts as an endogenous HDAC inhibitor [152,153,156,157]. This would lead to increased H3 acetylation (Lys9 and Lys14), and transcription of genes that are controlled with FOXO3a. Other studies confirmed that caloric restriction e mostly low carbohydrate diets-would lead to ketogenesis and this is associated with class I HDACs (yeast, Caenorhabditis elegans, Drosophila) in the extension of life span along with protection against reactive oxygen species (ROS). It seems that this metabolite controlled HDAC is a process that is involved in lots of cell events and not just longevity [153]. SAM is a donor of the methyl group for both DNA and histone methylation. Reduction of threonine, a metabolic precursor for the genesis of SAM, results in reduced levels of H3K4me2 and H3K4me3 but not H3K4me1 [158]. It is important to note that loss of SAM will reduce the activities of the KMTs, PRMTs and DNMTs but unless there is an event removing the histone or DNA modification, the modification will appear unaffected. TET enzymes need substrates and cofactors such as a-ketoglutarate (a-KG), Fe2þ and O2 for stepwise oxidation reactions [152,159]. More interestingly, a-KG, which is normally derived from

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Chapter 2 DNA methylation and chromatin modifications

either glucose or glutamine anabolism [160], plays a key role in the TCA cycle, amino acid synthesis and nitrogen transport. However, in any situation in which high levels of the inhibitor of F2- and a-KG-dependent dioxygenase are present as a result of dysregulative mutations, certain types of cancer would occur [161].

4.2 Hypoxia-induced epigenetic changes Hypoxia is defined as inadequate available oxygen (O2) for the demands of target tissues. Demethylation enzymes such as LSD1, JHDMs, JMJDs, JARIDs, UTX, and TETs need oxygen as a substrate and/or cofactor [154]. When tissue metabolism exceeds metabolic supplies, adaptation and maladaptation processes will start. These processes occur to prevent cell death. In the presence of enough O2, cells oxidize glucose to provide adenosine triphosphate (ATP) as the source of energy. In hypoxia, glucose is converted to lactate. This switch from oxidative to glycolytic metabolism is mediated by hypoxiainducible factor 1 (HIF-1) [162]. Hypoxia might occur as chronic, intermittent, and acute. Adaptive responses to chronic hypoxia are due to binding of HIF-1 to a hypoxia-response element (HRE) located near the target gene. This HIF-1-dependent gene regulation is inherently sensitive to cytosine methylation by DNA methyltransferases. The changes in DNA methylation alter gene expression patterns, in some cases irreversibly and last long after hypoxia is resolved [163]. Living in low barometric pressure at high altitude such as in the mountains is one of the best examples of chronic exposure to hypoxia [164]. Preeclampsia is a pathologic situation of chronic hypoxia and changes in gene expression due to disruption in DNA methylation [165]. In the tumor context, hypoxia is responsible for about half of DNA hypermethylation in solid tumors [166,167] and triggers initiating tumor occurrence in cells through epigenetic changes [168,169]. In cancer cells, there is often a lower expression of TET. However, the low oxygen levels in hypoxia also reduce the activity of the TET enzymes. Together, the low protein levels and reduced activity of the TET enzymes may alter DNA methylation and expression of some genes [167,168,170e173]. Sub-acute intermittent hypoxic exposure may result in reversible epigenetic adaptations, while chronic hypoxia leads to irreversible epigenetic adaptations [164]. One example of long-term intermittent hypoxia is obstructive sleep apnea (OSA). OSA is a common clinical problem that affects one in 5 (approximately 20%) healthy individuals and 40%e70% of obese people [174] and even children [175]. Long-term chronic intermediate hypoxia increased DNA methylation and down-regulation of anti-oxidant genes, leading to persistent cardiorespiratory abnormalities [176,177]. The expression of the anti-oxidant genes was restored after decitabine treatment. This treatment also normalized cardiorespiratory function [176]. There are fewer studies about short-term acute nonlethal hypoxia situation. A study on hypoxia induced epigenetic reprogramming in hippocampal neurons showed that acute hypoxia causes upregulation in the genes more than downregulation. In addition, promoters and CpG islands remained hypomethylated several days after hypoxic exposure. It supports the idea that whole genome methylation reprogramming is correlated with gene expression days after sub-lethal hypoxic stress [178].

5. Concluding remarks There is still much to learn about the plethora of histone PTMs and how these modifications crosstalk with each other and with DNA modifications. Several of the histone PTMs play a role in altering

Acknowledgments

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chromatin structure (such as H4K16ac); however, many do not. The latter serve important roles in attracting or repelling the binding of non-histone proteins and/or chromatin modifying enzymes (the readers). There is much to be worked out in order to understand how the readers recognize the histone and DNA modifications and what the roles of the reader once bound are. Metabolism plays a major role in regulating histone and DNA modifications involved in epigenetic processes [179]. Metabolism is impacted in many disease states particularly in cancer [180,181]. The mitochondria dynamic network “communicates” with the nucleus in the regulation of metabolism and epigenetic processes; there is much to be learned about this relationship and how it is altered in disease. Each cell may have up to 10,000 mitochondria and each mitochondrion may have up to ten circular mitochondrial DNAs. How do mitochondria numbers and network play into the dynamic web of events involving mitochondria, nucleus, metabolism and epigenetic processes? How does exercise fit into this picture? Clearly, the external (diet, environment in which we live) and internal environments (metabolic enzymes, hypoxia) will impact epigenetic programming and could potentially have long term, transgenerational consequences. As we will all find out one day, our metabolism changes as we age. NAD levels drop [182] and as a consequence, epigenetic processes do not perform with the same efficiency as they did when we were younger. With the decline of NAD, acetylation of histone and non-histone proteins may increase due to reduced activity of the sirtuins. The increased acetylation of histone and non-histone proteins will have consequences on their activities and cellular levels. In cases where the acetylated sites are also ubiquitinated, the acetylation event may prevent turnover of the protein by the proteasome [183]. For example, SIRT1 deacetylates acetylated HDAC1 [184,185]. Acetylated HDAC1 is not active and trans-represses the activity of HDAC2. Acetylation of p53 increases the stability and activity of this tumor suppressor by suppressing ubiquitination. HDAC1 and SIRT1 deacetylate acetylated p53, regulating its activity and levels. Loss of SIRT1 results in the accumulation of acetylated HDAC1 and acetylated p53 [184]. The genomic levels of the replication-independent histone variants, such as H3.3 increase with age and with the elevated levels of H3.3 comes an increase in the PTMs associated with this variant [186]. With old age, DNA methylation of the genome declines [187]. Neurological disorders, which escalate with age, are a consequence of deregulation in the epigenetic processes. As many players in these epigenetic processes are druggable, refinement of our drug arsenal may counter neural decline during normal aging [188]. As our understanding of the mechanisms regulating metabolism and epigenetic processes matures, we will realize the importance of our lifestyle choices such as diet and regular exercise in healthy living and healthy aging.

Acknowledgments This work is supported by an Environments, Genes and Chronic Disease Canadian Institutes for Health Research Team Grant (144626), a Natural Sciences and Engineering Research Council of Canada Grant (RGPIN-201705927), and a CancerCare Manitoba Foundation Grant (761020234). Ms Beacon and Dr. Sepehri were funded by the Graduate Enhancement of Tri- Council Stipends (GETS) through the University of Manitoba. Ms Osman was funded by CancerCare Manitoba/Children’s Hospital Research Institute of Manitoba Master’s Studentship. Dr. Sepehri thanks the Zabol University of Medical Sciences for the opportunity to study abroad in Dr. Davie’s research group.

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CHAPTER

Small non-coding RNAs as epigenetic regulators

3 Tong Zhou

Department of Physiology and Cell Biology, The University of Nevada, Reno School of Medicine, Reno, NV, USA

1. Introduction With the development of high-throughput transcriptomic technology, it has been revealed that, in eukaryotes, only 1%e2% of transcripts carry protein-coding potential, while the majority of the genome is transcribed to produce non-coding transcripts [1]. Small non-coding RNAs are a class of non-coding transcripts that are 15e200 nucleotides in length [2] and the diversity of small non-coding RNAs evolutionarily increases along with the biological complexity of species [3]. Small non-coding RNAs are often involved in regulating gene expression at the transcriptional and/or posttranscriptional levels. In this chapter, we will first introduce three types of well-studied canonical small non-coding RNAs, i.e. micro RNA (miRNA), small interfering RNA (siRNA), and Piwiinteracting RNA (piRNA), and briefly depict their roles in epigenetic regulation. Second, we will focus on two classes of non-canonical small non-coding RNAs, tRNA-derived small RNA (tsRNA) and rRNA-derived small RNA (rsRNA), which are markedly underinvestigated but miraculously involved in transgenerational epigenetic inheritance. Finally, we will review the high-throughput approaches to profile small non-coding RNAs in biomedical research and introduce a state-of-theart open-source framework to analyze both canonical and non-canonical small non-coding RNAs.

2. Canonical small non-coding RNAs 2.1 Micro RNAs (miRNAs) miRNAs are a family of small non-coding RNAs (about 22 nucleotides in length) found in almost all metazoan and some viruses [4e6]. To date, more than 1900 miRNAs have been identified experimentally or computationally in humans according to the miRBase (www.mirbase.org) definition. miRNAs have been found to play regulatory roles in gene expression via base-pairing with complementary sequences within mRNAs. Plant miRNAs are usually complementary to coding sequences, while in animals, miRNAs are generally complementary to three prime untranslated regions (30 -UTRs) [7]. Plant miRNAs usually have perfect or near-perfect base-pairing with target mRNAs, which results in gene silencing through cleavage of the mRNA strands [8]. In animals, the miRNA-mRNA base-pairing is usually imperfect, which means miRNAs and their target mRNAs are only partially Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00003-5 Copyright © 2019 Elsevier Inc. All rights reserved.

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complementary by using as little as 6e8 nucleotides located at the 50 end of the miRNA (so called “seed region”) [9e11]. Usually, this kind of imperfect match-ups are not enough to trigger cleavage of the target mRNAs [12] and miRNA-induced translational repression is thought to be more prevalent in animals than in plants [5]. A given miRNA may target hundreds of different mRNAs, meanwhile a given mRNA may be targeted by different miRNAs [12,13]. The seed regions within miRNAs are the most critical factor determining the target mRNAs and most mammalian mRNAs have conserved targets of miRNAs [14]. For example, in humans, more than 45,000 miRNA target regions within 30 -UTRs are conserved above background levels and more than 60% of protein-coding genes have undergone negative selection to maintain base-pairing with miRNAs [14]. Mutations within miRNA seed regions potentially lead to inherited diseases. For example, mutations in the seed region of human miR-96 are reported to induce nonsyndromic progressive hearing loss [15]; mutations in the seed region of human miR-184 may cause familial keratoconus with cataract [16]. Dysregulation of miRNA is also implicated in human diseases, including cancers [17], cardiovascular diseases [18], neurological diseases [19], pulmonary diseases [20], and so on. For example, a set of up- and down-regulated miRNAs, which disrupt suppression of the oncogene PLAG1, was found to be associated with chronic lymphocytic leukemia [21]. However, most current studies on the relationships between miRNAs and human diseases have heavily relied on animal models. No doubt, the knowledge gained from animal studies can guide future translational research and clinical investigations, the interpretation of animal data, however, needs to be cautious as not all observations from animal models are relevant to humans. Therefore, translating the current animal studies to human subjects would provide direct evidence to the regulatory role of miRNAs in the development of human diseases. Epigenetic change is defined as the heritable alterations in gene expression that do not represent changes in DNA sequence. According to this generalized definition, miRNA-induced mRNA degradation and translational repression should be considered as one type of epigenetic machinery at the post-transcriptional level. However, how miRNAs are directly involved in epigenetic regulation in terms of DNA methylation and histone modification is still controversial. Fortunately, several lines of evidence may help us understand the epigenetic role of miRNAs regarding this specialized definition of “epigenetics”. Firstly, miRNAs are key players in regulating DNA methylation machinery [22]. Fabbri et al. found that up-regulation of the miR-29 family in human lung triggered down-regulation of global DNA methylation and DNA methyltransferases (DNMTs), including DNA methyltransferase-3A (DNMT3A) and DNA methyltransferase-3B (DNMT3B) [23]. Similar results were observed by Garzon et al. in acute myeloid leukemia cancer cell lines [24]. Secondly, miRNAs may contribute to aberrant histone modification [25]. For example, miR-10a could affect trimethylation of histone 3 lysine 27 (H3K27me3) in both MCF7 and MDA-MB-231 cells [26]. It was also observed that histone deacetylase 4 (HDAC4) was a target of miR-140 in mouse embryonic cartilage [27]. Despite these findings, we are still at the beginning step to gain a clear picture regarding the epigenetic roles of miRNAs.

2.2 Small interfering RNAs micro RNAs (siRNAs) siRNAs are a class of double-stranded small RNAs with 19e25 base-pairs in length, which are produced by cleavage of longer double-stranded RNAs [28,29]. siRNAs were reported to be generated during transgene- and virus-induced silencing in plants [30], as well as originating from centromeres,

2. Canonical small non-coding RNAs

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transposons, and other tandem repeats [31]. More recently, endogenous siRNAs have been identified in animals, such as flies and mice [32e34]. Similar to miRNAs, siRNAs can induce post-transcriptional gene silencing through interfering with the expression of specific genes with complementary nucleotides. Due to the nature that a given gene can be knocked down by a particular synthetic siRNA with a complementary sequence, siRNAs have become an important tool for investigating gene function. More interestingly, several studies have shown that siRNAs are also involved in transcriptional gene silencing [35,36]. In fission yeast Schizosaccharomyces pombe, the RNA-induced transcriptional silencing (RITS) complex, which contains at least one chromatin-binding module (chromodomain), can establish a physical connection between siRNAs and heterochromatin by targeting a nascent RNA and forming a self-sustaining feedback machinery coupling siRNA production to chromatin modification [35,37]. The binding of RITS to chromatin initiates sequence specific heterochromatin formation, which consequently results in heritable silencing of transcription. In the plant Arabidopsis thaliana, siRNAs were reported to be involved in RNA-directed DNA and histone H3 lysine 9 (H3K9) methylation [35,36]. Particular plant genomic loci can be targeted by 24-nucleotide heterochromatic siRNAs, which mediate DNA and/or H3K9 methylation of the target genomic sequences and in turn regulate the transcription of the corresponding loci [36].

2.3 Piwi-interacting RNAs (piRNAs) piRNAs are a large class of endogenous small non-coding RNAs in both vertebrates and invertebrates with 24e31 nucleotides in length, which bind to Piwi-proteins and in turn form RNA-protein complexes [38]. piRNAs are distributed throughout the genome in highly conserved clusters, which only show up at a limited number of loci [39]. Thus far, over 173 million unique piRNA sequences have been discovered from 21 species according to piRBase (www.regulatoryrna.org/ database/piRNA/) definition [40]. In mammals, piRNAs have only been detected in testes [41] and ovaries [42], and in invertebrates, piRNAs have been found in both the male and female germ cells [43,44]. The mechanism of piRNA biogenesis remains unclear and appears to be different among species. piRNAs show a strong strand bias and may be derived from long single-stranded precursor molecules [38]. In flies, a so called “Ping Pong” mechanism was proposed: upon primary piRNAs recognizing their complementary targets and triggering the recruitment of Piwi-proteins, the secondary piRNAs are sequentially produced through the cleavage of the transcript of the primary piRNA [45]. However, this piRNA “Ping Pong” pathway does not appear to exist in worms [44]. Similar to miRNAs and siRNAs, the complexes of piRNAs and Piwi-proteins are potentially involved in post-transcriptional gene silencing [46], particularly the silencing of retrotransposons, which is due, in part, to the fact that the most piRNAs are antisense to retrotransposon sequences [47]. It has been reported that piRNA-induced retrotransposon silencing is essential for mammalian embryo development [48] and spermatogenesis [49]. piRNAs may also be involved in maternally inherited epigenetic regulation in flies [50]. Using Drosophila P-element and I-element transposon-mediated hybrid dysgenesis models, Brennecke et al. demonstrated a markedly different composition of piRNAs targeting each element in daughter flies, depending on their parents of origin [50]. Such difference persists from fertilization through adulthood, which suggests that maternally derived piRNAs are important for mounting an effective silencing response [50].

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Chapter 3 Small non-coding RNAs as epigenetic regulators

3. Non-canonical small non-coding RNAs 3.1 tRNA-derived small RNAs (tsRNAs) The development of small non-coding RNA-sequencing (sncRNA-seq) technology has led to the discovery of a novel class of small non-coding RNAs: tRNA-derived small RNAs (tsRNAs, or tRFs) [51]. It is well-known that tRNAs are highly modified and structured, which are a critical component of the biological synthesis of new proteins in accordance with the genetic code. tsRNAs are the products of the fragmentation of tRNAs at different loci [52]. tsRNAs exist in a wide range of species with evolutionary conservation, which is potentially due to their highly conserved precursors, tRNAs [51]. More intriguingly, tsRNAs have been found to be abundant and dynamically regulated in unicellular organisms, such as protozoa, in which canonical small RNAs, e.g. miRNAs, siRNAs, and piRNAs, are entirely absent [53,54]. This fact suggests that tsRNAs are among the most ancient classes of small RNAs involved in intra-/inter-cellular communications [55]. tsRNAs exhibit an unexpected complexity, which may be due to the RNA modifications inherited from tRNAs and the RNA interaction potential endowed by RNA modifications and novel structures [52,56]. Recently, emerging evidence highlighted the involvement of tsRNAs in mammalian biological processes, including tumorigenesis, stress response, stem cell differentiation, and more excitingly, transgenerational epigenetic inheritance [51,56e58]. Although the detailed mechanism how tsRNAs are involved in transgenerational epigenetic inheritance remains unclear, tsRNAs have been found to be enriched in mammalian sperm and serum, as well as in various types of extracellular vesicles. In 2012, by analyzing sncRNA-seq data from mouse mature sperm, it was serendipitously found that mouse mature sperm contained a unique subset of tsRNAs, which were mainly derived from 50 tRNA halves with sizes ranging from 29 to 34 nucleotides [59]. These mouse sperm tsRNAs showed a dramatic increase at late-/post-spermatogenesis during epididymis maturation, suggesting the programmed tRNA cleavage and/or selective concentrating mechanisms at these stages [59]. Also, it was found that these tsRNAs were enriched in sperm head and thus could be delivered into oocytes during fertilization [59]. Followed by the discovery of tsRNAs in sperm, the same group, along with the others, further detected tsRNAs in the serum among a wide range of vertebrates, including fish, amphibian, reptile, avian, murine, non-human primate, and human [60,61]. It was also observed that serum tsRNAs were sensitive to pathological conditions, e.g. active infection, in mice, monkey and human [60]. More intriguingly, it was found that tsRNAs extracted from serum are more stable compared with chemically synthetic tsRNAs with identical nucleotide sequences, which strongly suggests that RNA modifications in tsRNAs could contribute to their stabilization in serum and thus represent another layer of information [60]. Recently, it was revealed that sperm tsRNAs carrying RNA modification information potentially act as epigenetic mediator and contribute to transgenerational inheritance of paternally acquired traits [62,63]. In fact, there is increasing evidence suggesting that certain paternal traits acquired in response to ancestral environmental exposures, such as toxicant contact, mental stresses, and dietary habits, can be “memorized” in the sperm and consequently inherited by the offspring. However, the underlying epigenetic mechanism remains unclear. By injecting various RNAs into normal zygotes, it was revealed that both expression level and RNA modifications in sperm tsRNAs can be affected by paternal high-fat diet, which suggests that sperm tsRNAs, along with their RNA modifications, represent a carrier of paternal epigenetic information that contributes to intergenerational inheritance of diet-induced metabolic disorder [57]. A similar discovery was made

4. High-throughput approaches to profile small non-coding RNAs

41

by Dr. Oliver Rando’s group, where protein restriction in mice was found to affect tsRNAs in mature sperm [64] and that the tsRNAs were transferred from extracellular vesicles of the caput epididymis [64,65]. To understand the regulator of sperm tsRNAs that related to transmission of paternally acquired epigenetic information, Dr. Qi Chen’s group investigated the role of DNA methyltransferase-2 (Dnmt2) in mouse sperm RNA modification. Due to the close affinities in sequence and structure to authentic DNA cytosine methyltransferases, DNMT2 was previously thought to function as a nuclear DNA methyltransferase until Goll et al. demonstrated, in 2006, that DNMT2 does not methylate DNA but instead methylates aspartic acid tRNA and that DNMT2 specifically methylates cytosine 38 in the anticodon loop [66]. It was further revealed that the function of DNMT2 is highly conserved among human, mouse, A. thaliana, and fly [66] and that deletion of DNMT2 facilitates the cleavage of tRNA into tsRNAs [62,67]. Dr. Chen and his colleagues recently demonstrated that mouse Dnmt2 shapes the sperm RNA “coding signature” by regulating tRNA modifications and tsRNA biogenesis, as well as by affecting tsRNAs’ secondary structures and biological properties, which sheds a light upon the role of sperm tsRNA in transgenerational epigenetic inheritance and expands our understanding regarding the “information capacity” mediated by sperm RNA modifications and their involvement as novel layers of paternal hereditary information beyond DNA [68].

3.2 rRNA-derived small RNAs (rsRNAs) Parallel with tsRNAs, the recently discovered rsRNAs were also found to exist in multiple tissues and cell types. The dynamic expression of rsRNAs has been correlated with diabetes, and potentially involved in the regulation of metabolism [69]. A more recent observation revealed that rsRNAs are possibly associated with inflammation [70]. Interestingly, there is evidence showing that similar to tsRNAs, rsRNAs are also involved in transgenerational epigenetic inheritance. It was found that Dnmt2 deletion changes the mouse sperm rsRNA expression landscape, which might in turn alter the sperm RNA “coding signature” that is needed for paternal epigenetic memory [68]. In addition, our recently developed bioinformatical tool, SPORTS1.0 (small RNA annotation pipeline optimized for rRNA- and tRNA-derived small RNAs), has enabled us to detect a far more complicated small RNA landscape, revealing a tissue-specific dynamic regulation of rsRNAs and potential implication in physiological and pathological conditions [71]. For example, our latest investigation suggests that rsRNAs are potentially implicated in lung cancer pathogenesis and the abundance of rsRNAs are significantly increased in lung tumor tissues compared with normal tissues (Fig. 3.1).

4. High-throughput approaches to profile small non-coding RNAs A decade ago, DNA microarray was one of the most popular high-throughput approaches to profile genome-wide small non-coding RNA expression patterns. A DNA microarray (also known as biochip) is a collection of microscopic DNA spots, so called probes or probe sets, attached to a solid surface, which is a well-established technology to measure the expression level of thousands of RNA species within a particular sample or to genotype multiple regions of a genome. Today, there are still various commercial microarray platforms available in the market place to profile non-coding RNAs (particularly to profile whole-genome miRNA expression), such as ThermoFisher Scientific (Affymetrix) GeneChip miRNA Arrays, Agilent SurePrint miRNA Microarrays, Exiqon miRCURY miRNA Arrays,

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Chapter 3 Small non-coding RNAs as epigenetic regulators

FIG. 3.1 Comparison of rsRNA Abundance Between Normal and Tumor Lung Tissues. We investigated a sncRNA-seq dataset in lung cancer. Paired normal and tumor human tissues were included in the comparison. We found that, all the rsRNAs, except the ones derived from 28S- rRNA, are significantly up-regulated in tumor tissues compared with normal tissues (paired t-test: P < 0.05). RPM: reads per million clean reads. Credit: Tong Zhou.

and so on. Although microarray platforms have a decent track record spanning over two decades, Next Generation Sequencing (NGS) technology, particularly sncRNA-seq, has provided a new path for high-throughput small non-coding RNA analysis. For sncRNA-seq, small non-coding RNAs are first isolated through size selection based on the desired size range and then converted to cDNAs. The cDNAs are next used as the input for an NGS library preparation and sequencing. Compared with microarray, sncRNA-seq is more sensitive and has a wider dynamic range, which provides a better quantification for lowly and highly expressed molecules. More importantly, sncRNA-seq bypasses the “design bias” that microarrays suffer from. Usually, microarrays only return readouts for the RNAs for which probes/probe sests have been designed. Consequently, microarrays can only detect the RNA molecules known a priori. In contrast, sncRNA-Seq allows for the analysis of novel non-coding RNAs, e.g. tsRNAs and rsRNAs, without any a priori knowledge. Despite the technical advantages, sncRNASeq has several downsides: i) sncRNA-Seq are generally considered more complicated to use with more labor-intensive sample preparation than microarray; ii) the size of sncRNA-seq raw data is much larger than that of microarray, which raises the concern about data storage; iii) sncRNA-seq data analysis requires more computer resources, such as CPU and RAM, as well as extensive computational and bioinformatical skills. How to analyze raw sncRNA-seq data with optimized setup is always a hot topic in the field of bioinformatics and computational epigenetics. To date, there are already a number of computational pipelines and software, which are specifically designed for profiling small non-coding RNAs from sncRNA-seq raw data, such as sRNAnalyzer [72], UEA sRNA Workbench [73], sRNAtoolbox [74], ShortStack [75], and DARIO [76]. Although several computational tools aiming on tsRNAs have been developed, including tRex [77], tRF2Cancer [78], and tDRmapper [79], most of the currently existing small non-coding RNAs profiling tools focus on miRNAs and/or piRNAs, and to our knowledge, there is no specialized tools optimized for analyzing tsRNAs and rsRNAs, along with the other small noncoding RNAs, from sncRNA-seq data. Recently, we developed a state-of-the-art computational framework, SPORTS1.0, which not only comprehensively annotates and profiles canonical small noncoding RNAs such as miRNAs and piRNAs, but also is optimized for the analysis of tsRNAs and rsRNAs from sncRNA-seq data [71]. In addition, SPORTS1.0 is able to predict potential RNA

4. High-throughput approaches to profile small non-coding RNAs

43

modification sites based on nucleotide mismatches within small non-coding RNAs [71]. As an example of SPORTS1.0 output, Fig. 3.2 demonstrates the landscape of small non-coding RNAs in human stomach mucosa layer, which is far more complicated than we previously observed. As we observe, although miRNAs are dominant, there are still a considerable amount of tsRNAs and rsRNAs

miRNA rsRNA unanno UMG

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rRNA MG (100%) rRNA UMG (0%)

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FIG. 3.2 Exemplary Profiling of Small Non-coding RNAs in Human Gastric Mucosa using SPORTS1.0. RPM, reads per million clean reads; unanno, unannotated; MG, matching genome; UMG, unmatching genome. Credit: Tong Zhou.

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Chapter 3 Small non-coding RNAs as epigenetic regulators

in gastric mucosa (Fig. 3.2). In our opinion, SPORTS1.0 may bring fresh insights into the dynamic regulations of tsRNAs and rsRNAs across different tissues/cell types and set the platform for many exciting discoveries in the field of small non-coding RNAs.

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CHAPTER

The impact of race and ethnicity in the social epigenomic regulation of disease

4

David H. Rehkopfa, Belinda L. Needhamb Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USAa; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USAb

1. Introduction There are substantial differences in health by race and ethnicity in the United States, and eliminating these inequalities is one of the goals of Healthy People 2020, the 10 year national science-based objectives established by the U.S. Department of Health and Human Services (https://www. healthypeople.gov/). While substantial progress has been made toward this goal - for example, death rates for middle aged African Americans have decreased relative to the overall population from 1999 to 2011 [1] - substantial inequalities remain. Given the life course inequalities in environments that are experienced by racial and ethnic groups in the United States, combined with increasingly compelling evidence for the role of epigenetics in chronic disease risk, a research question has emerged regarding the role of epigenetics underlying these disparities. This question, as we will discuss in this chapter, revolves around two alternative framings of the question that have been investigated in the literature. First, are there epigenetic differences between racial/ethnic groups that are on the pathway between environmental exposures and health inequalities? To put this question another way, are epigenetic differences sentinel markers for environmental inequalities, where the environment includes physical and social environments experienced in utero and across the life course? A second research agenda asks the more proximate biomedical question: do racial and ethnic differences in DNA methylation cause health inequalities? Since the majority of studies leave race and ethnicity undefined and the environment unmeasured, this perspective is, by default, frequently interpreted using biomedical theory to attribute inherent biological or genetic differences as the explanation for epigenetic differences observed between racial and ethnic groups. In this chapter we argue that the environmentally informed question and the more proximate biomedical question should not be considered separately, but given that there are both genetic and environmental influences on epigenetic modifications, they should be considered within a single integrated conceptual and empirical framework when possible. What is particularly critical in this integrated framework is that race and ethnicity should be clearly indicated to be social categories that result in a large range of differential exposures to the environment across the life course, and that continental ancestry be considered as a biological influence. Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00004-7 Copyright © 2019 Elsevier Inc. All rights reserved.

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There are several important impacts of answering this research question. In our view, most important among these is whether DNA methylation changes can be used as a proximate outcome for evaluating the relevance of environmental exposures for contributing to racial and ethnic disparities in health. While disease and mortality are of ultimate interest, because of the long latency period between environmental exposures and these outcomes, it can be difficult to make the link between environments and health in studies of a limited sample size. That is, because the exposure may only impact a particular disease or cause of mortality to a small degree, a very large sample size would be needed to detect any impact on mortality. If DNA methylation is a mediating factor between these exposures and health outcomes, it may allow more precise and more immediate detection of damaging environmental impacts. We emphasize, however, that this premise is not inevitable, nor is it likely to be fully understood in the near future. That is, the relative importance of DNA methylation as mediator between the environment and health has not yet been established. However, its potential importance motivates a first set of descriptive studies that appropriately evaluate the potential payoff of this approach, along with the continued development of literature on the relationships between DNA methylation with mortality, clinical disease and well-being outcomes.

2. What do we mean when we talk about race and ethnicity? It is often the case that the more a word is commonly used outside of scientific discourse, the more problematic its use within science. That is because such words are often used by scientists without knowledge of their being defined in such a way that they can be used precisely within science. Such is the case for “race” and “ethnicity,” with which the often confused usage is compounded by, in the case of “race,” a history premised on subjugation and control. Chavez Cameron and Macias Wycoff refer to this as the “folk taxonomies” of race, something which leads to its use in destructive ways [2]. Add to this the fact that there is debate within science on what these terms mean, it is no wonder there have been difficulties in understanding how it should be applied in research on epigenetics, which unlike genotype - which is primarily fixed at birth, is influenced by both genetics and the environment in dynamic ways across the life course. In addition, race and ethnicity are often correlated with continental ancestry, a term that while once only under the purview of population geneticists, is becoming culturally relevant on its own through the increasingly common practice of “genetic ancestry” testing. Clearly defining these terms, even while such definitions differ depending on discipline and time, allows us to describe why their use is critical in studying the social epigenomic regulation of disease. First, it is important to clarify what the differences are between the concepts of “race” and “ethnicity” such that we can have precisely defined concepts for scientific investigation, even if these terms are used more loosely in non-scientific conversation. While science allows for disagreement and debate about evidence and concepts, this will ultimately be benefited by clarity in the use of scientific language. “Ethnicity” is defined as membership in a group, which thus inherently defines an in and out group, and includes self-identity of the individual in group membership, but also in how they are recognized by others based or how others see them classified. It is defined by Smedley and Smedley as “clusters of people who have common culture traits that they distinguish from those of other people. People who share a common language, geographic locale or place of origin, religion, sense of history, traditions, values, beliefs, food habits, and so forth, are perceived, and view themselves as constituting, an ethnic group” [3]. They further emphasize that these cultural characteristics and traits are learned

3. How is genotype related to race and ethnicity?

53

and passed between generations [3]. What may cause some confusion in the definition of ethnicity is that official U.S. census statistics take a more narrow view of ethnicity than this definition would encompass. Only one ethnic group is currently recognized by the U.S. government. Hispanic or Latino ethnicity is defined as “Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race” [4]. These ethnicity questions, however, have only been asked in the U.S. census since 1980, and current census ethnic categories are limited as compared to the number of ethnicities that people in the United States commonly identify themselves as (e.g. Jewish, Han Chinese, Berber). In contrast, the term “race” is used to refer to groups of people defined by physical characteristics, such as hair texture and skin color. The US government currently recognizes six racial groups (Black or African American; White; Asian; American Indian or Alaska Native; Native Hawaiian or Other Pacific Islander; and some other race). Racial groups are defined by the Census Bureau roughly in terms of continental ancestry: Africa for Black or African American; Europe, the Middle East, or North Africa for White; North America, South America, or Central America for American Indian or Alaska Native; the Far East, Southeast Asia, or India for Asian; and Hawaii, Guam, Samoa, or the Pacific Islands for Native Hawaiian or Other Pacific Islander. Since there is an overlap with the historically dynamic social categories being used as defining race and continental ancestry, these terms have often been conflated, with many scientists using self-reported race as a proxy for continental ancestry. The debate over the social or biological nature of race is likely to continue, but as the human genome becomes more clearly understood, this work has clarified the genetic diversity within racial categories, clarifying that it is incorrect to use race as a biological construct [5]. While some have argued that race should be abandoned as a construct, it remains a powerful social identifier that structures exposures of individuals outside of their own autonomy and self-definition. That is, to the extent that race remains a determinant of social standing, it acts as a fundamental way that societies define what rights an individual has. As long as rights differ based on the way race is perceived by others outside of the individual in countries like the United States, race will remain a critical social construct that causes differences in disease and health. The measurement of race and ethnicity to evaluate health disparities is valuable given the historical structural discrimination in the United States. In countries like France, where race is not measured in official statistics, health disparities by race remain generally invisible in routinely collected data.

3. How is genotype related to race and ethnicity? Despite efforts to establish scientifically valid racial classification schemes, genetically discrete racial (or ethnic) categories do not exist. Recent work using autosomal microsatellite loci of individuals across 52 populations showed that 93e95% of genetic variation is within populations, while major racial and ethnic population groups constitute 3%e5% of genetic variation [6]. Thus rather than being domains that capture the majority of genetic diversity, race and ethnicity are instead categories that are generated through a process of social interaction and vary across place and time. The U.S. census provides an illustration of this, with racial categories changing across the decades. The identification of race also changed. Prior to 1960, the interviewer identified the race of individuals in the U.S. census, while from 1960 forward race is self-reported. Further illustrating the social nature of race, individuals may change how they identify their own race over time [7].

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Even as there have been changes over time, the fundamental basis for the contemporary racial stratification system in the United States was established during the colonial era to ensure social, economic and ideological advantage for people of European descent and to disadvantage others, in particular people of African and Native American descent [8]. Thus racial and ethnic groups in the US differ much more on environment than on genotype. This does not mean, however, that there are no genetic differences between fluid and historically contingent racial and ethnic groups. Throughout most of human history, large-scale migration was limited. People tended to live in or near the place where they were born. Thus, geographic constraints on reproductive pairings, combined with genetic drift, gave rise to some spatial patterning of genetic variation. Due to this process, genetic structure does have difference depending on geographic ancestry and the historical influence of the environment at those locations. The small amount of genetic variation that is correlated with current racial and ethnic classifications in the United States is related to the history of human migration, from the Great Rift Valley in Eastern Africa, to Europe, about 60,000 years ago. Because migration events often involved smaller populations, certain genotypes tended to prevail, and these subsets continued to remain more similar over time. As such, areas that were more recently populated across the last 60,000 years will tend to be less genetically diverse. But in order to clarify the difference between the actual causes of genetic diversity, it is more appropriate to specify that the genetic diversity we observe is due to “continental ancestry” rather than to “race.” It is also important, when thinking about the extent to which this migration pattern and continental ancestry may impact genetic differences that are structured by this process, to consider the power of genetic drift. That is, that over time the random distribution of genotypes in offspring tends to even out the systematic variation that occurred due to continental ancestry. The current state of the evidence is that often cited traits like sickle cell genotype and genes related to skin tone remain unique and rare examples of genetic differences that have persisted due to continental ancestry, even though the contributions to further traits may be discovered.

4. Differences in the analysis of race and ethnicity in genetic research as compared to epigenetic research As a newer field of inquiry, population level examination of the role of epigenetics in health and disease has not come to an agreement on establishing approaches of treating race and ethnicity in analyses. Thus far, most analyses have patterned their approach on how race and ethnicity have been utilized in the analysis of genome wide association studies (GWAS). Depending on human migration over time across continents, populations that have more recently migrated have a greater correlation of their genetic sequences. Since geographic location has over generations been associated with physical adaptations to those environments at the population level, there is thus also some correlation between overall genetic structure and physical appearance, the latter by which individuals have been classified by race. For GWAS analyses focused on identifying single gene associations with phenotype or disease outcomes, these overall correlations of genetic structure are something that needs to be statistically controlled for in analyses. What is referred to in genetic analysis as “population stratification” is analogous to the epidemiologic principle of confounding. Epidemiologists use directed acyclic graphs

4. Differences in the analysis of race and ethnicity

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FIG. 4.1 Heuristic directed acyclic graph for studies of genotype. Credit: David Rehkopf and Belinda Needham.

(DAGs) to depict these types of relationships, and we present a figure showing the way that population stratification acts as a confounder in GWAS analyses (Fig. 4.1). The larger arrow between continental ancestry and race denotes that there is a very strong association between these factors to the extent that these variables can be considered to be collinear (note however, that this is not a standard DAG annotation, it is used here as collinearity between variables is relevant for the statistical modeling approach). Collinearity occurs when two variables are an exact or near linear combination of one another, although the statistical challenges of collinearity are in part dependent on sample size. While the basic principles can be applied to other epigenetic outcomes instead of DNA methylation, we focus on DNA methylation as a particular epigenetic change as most human population based studies have focused on this due to cost and tractability in larger samples. There are a few factors that are necessary to consider, as illustrated by DAGs, for the appropriate control of confounding variables [9e11]. DAGs are not an empirical quantitative presentation of data, rather a way of depicting hypothesized causal relationships between variables based on prior common knowledge from the scientific literature. They offer an approach to clearly and concisely expressing assumptions about the causal ordering of variables. A unidirectional arrow indicates a hypothesized causal relationship between two variables, only in the direction of the arrow. To be clear, these are not relationships that can be established by statistical correlations, which do not allow for a determination of directionality, but rather a hypothesized direction of causation based on the literature and scientific theory of the particular system. There are three main factors that need to be considered in a DAG in order to guide the appropriate statistical approach to address confounding in observational data. First, it is necessary to statistically control for factors that are prior common causes of an exposure of interest and an outcome of interest. In the case of GWAS studies, where the interest is in the exposure of genotype, typically measured by a single nucleotide polymorphism (SNP), and the outcome is a phenotype, it is critical to control for continental ancestry. This is usually accomplished by (1) examining associations in a homogenous racial/ethnic group and (2) controlling for principal components (PCs) of overall genetic structure in the sample, which are strongly associated with continental ancestry. A second important rule of statistical control that can be guided by the DAG is to not control for variables on the causal pathway between exposure and outcome. Since there are no variables illustrated in Fig. 4.1 between genotype and phenotype, this is not an issue. However, for example, if a SNP had an impact on a more proximal phenotype, it would not be appropriate to control for that variable. For example, if the phenotype was

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cardiovascular disease, it would result in inaccurate coefficients of association for the coefficient of the SNP if there was statistical control for dietary factors or blood pressure. A final third consideration from a DAG is to not condition (i.e. statistically control) on a collider, which is any factor that is caused by the exposure and the outcome. For example, in a GWAS for blood pressure it would not be appropriate to collect a sample of only individuals with cardiovascular disease (which is equivalent to statistically controlling for cardiovascular disease), as this could induce a spurious correlation between genes that cause both cardiovascular disease and the phenotype of interest, blood pressure. Based on these criteria, for the GWAS analysis between a genotype and phenotype as shown in Fig. 4.1, analyses should control for continental ancestry. One common way of operationalizing continental ancestry is through the calculation of PCs calculated from across the genome. This is a completely data driven approach, utilizing information on how closely genotypes are similar within individuals, with each individual having a value for each of a set number of PCs. When examining purely genetic impacts on phenotypes, this is a useful approach to control for confounding. Since continental ancestry is associated with skin pigmentation, and this in turn is associated with race and environmental exposures in the United States, controlling for PCs also helps to block confounding from the environment for exposures that differ by race and ethnicity. Note that based on this DAG, it is not necessary to control for race as a social construct, or environment, as these do not impact the genes of interest. However, use of PCs or other measures capturing continental ancestry do not always apply in the same way to studies of DNA methylation, because of a different hypothesized causal structure between genes, the environment, DNA methylation and phenotypes. We present another DAG showing the general nature of these relationships (Fig. 4.2). Again, the larger arrow between continental ancestry and race is written as an indication that there is a very strong relationship between these factors, such that they can be considered highly collinear. This is not the case between continental ancestry and genes, which have a much broader divergence. A key point is that continental ancestry is a broad genetic signal that correlates with self-identified race, but does not capture the nuanced signal of specific genes that impact both DNA methylation and disease. What can we infer from the DAG based rules of confounding when comparing the DAG for the study of how genotype is related to phenotype (for example, a health outcome) (Fig. 4.1) and how DNA methylation is related to a phenotype (Fig. 4.2)? First, in Fig. 4.1, controlling for continental

FIG. 4.2 Heuristic directed acyclic graph for studies of DNA methylation. Credit: David Rehkopf and Belinda Needham.

5. Recommendations for considering continental ancestry

57

ancestry as a confounder is warranted by the DAG. In Fig. 4.2, controlling for continental ancestry as a confounder depends on the relationship being analyzed. If, for example, the focus is on examining the association between DNA methylation and phenotype, it would be appropriate to control for continental ancestry, as it is a prior common cause. In fact, controlling for all prior common causes including environmental factors and behaviors as well as genotype is warranted, but not simultaneously controlling for continental ancestry and self-reported race. Similarly, if examining the association of environmental factors and behaviors in association with DNA methylation, it is appropriate to control for continental ancestry, as has been previously described [12]. In contrast, when studying the association between race and DNA methylation, the question becomes more complex. At face value, continental ancestry is a prior common cause of the exposure and outcome, so would be appropriate to control. However, the error in this assertion is that there is an extremely strong correlation, as previously discussed, between continental ancestry and self-identified or otheridentified race, to the extent that the variables could be considered collinear. While this is an advantage in studies of genes and phenotypes as depicted in Fig. 4.1, because it allows for controlling for environmental confounding, in the case of studying racial differences in DNA methylation or other epigenetic factors, it leads to the question being intractable because of the strong correlation between continental ancestry and race. A second rule that can be derived from this DAG is that when interested in questions of associations of DNA methylation with race, it is not appropriate to adjust for factors on the causal pathway, which include environments and health behaviors. An important further insight into appropriate statistical control can be derived from the structure of the DAG. While not appropriate to control for continental ancestry, it is appropriate to control for specific genotypes when examining the impact of DNA methylation on a phenotype of interest. Our recommendation is thus based on both rules for covariate control from the DAG and from the strong degree of collinearity between continental ancestry and self-reported race. Correlated variables are a common issue in statistical analyses, and omitting one of these variables from the model is one approach. The decision between which variable to remove depends on the study question. Investigators should thus decide whether their study question is about either continental ancestry as a biological construct or race as a social construct in order to proceed with a statistically appropriate analysis.

5. Recommendations for considering continental ancestry for studies involving race or ethnicity and DNA methylation We make the following recommendations for when it is appropriate to control for PCs or other approaches to capture continental ancestry in studies of DNA methylation, summarized in Table 4.1. When examining the impact of genotype on DNA methylation, it is appropriate to control for PCs that capture continental ancestry. The causal structure of this relationship is similar to that of GWAS studies, where the close correlation between continental ancestry and self-reported race results in effectively controlling for differences in racial and ethnically impacted environmental factors. It is also reasonable to control for PCs capturing continental ancestry when examining the impacts of environmental factors on DNA methylation, as continental ancestry may confound these associations, and race and ethnicity are often also confounders of these associations. However, because self-reported race and ethnicity are domains that capture environmental differences, we recommend in studies of this type to control for self-reported race and ethnicity instead of continental ancestry.

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Table 4.1 Recommendations for when to control for Continental Ancestry in studies of DNA methylation. Study objective

Statistical control for continental ancestry

Identify the impact of genotype on DNA methylation Identify the impact of the environment on DNA methylation

Yes Acceptable, but recommended to control for self-reported race and ethnicity instead No No

Identify the impact of race or ethnicity on DNA methylation Identify mediators of the impact of race or ethnicity on DNA methylation Credit: David Rehkopf and Belinda Needham.

In contrast, it is not appropriate to control for PCs for continental ancestry when examining the impact of race or ethnicity on DNA methylation. It is also not appropriate to control for continental ancestry when examining behavioral or environmental mediation models. The reason that this is often confusing is that based on standard rules of controlling for variables continental ancestry fits all three characteristics of a confounding variable: it is a prior common cause of exposure and outcome, it is not on the causal pathway between the exposure and the outcome, and it is not caused by the exposure and the outcome. However, because it is highly collinear with self-reported race, it should not be controlled for. Because it is not possible for statistical models to separate these constructs, other information must be utilized in order to differentiate the causal nature of each.

6. Current state of studies on the role of race and ethnicity in the epigenomic regulation of disease Table 4.2 summarizes some of the findings of recent studies examining differences in DNA methylation by race and ethnicity, with the majority of these studies comparing African Americans and nonHispanic whites. In addition to summarizing the results of these studies, we focus on how these studies meet our recommendations for (1) specification of race and ethnicity as social categories, and continental ancestry as a biological construct, and (2) whether continental ancestry was appropriately controlled for. Determining differences in methylation between African Americans and whites was the specific primary goal of most of these studies, while for a few studies it was one of many demographic factors examined. For example, Nielsen et al. examined an effect modification question e whether methylation of an opioid receptor gene differed by race and ethnicity, finding that there were differences [17]. This was the only study that we identified in the literature that gave a definition of race or ethnicity as being based on the ethnic/cultural background of the subjects, with the strong implication that the authors considered this a social rather than biological category [17]. Most of the studies were correct in not controlling for continental ancestry in their analyses, and some did mention that it was a limitation of their approach [18]. In this study that appropriately controlled for continental ancestry, Devaney et al. found different levels of prostate cancer related DNA methylation in African Americans as compared to whites [18]. In terms of attribution of environmental or genetic impacts, one study was

Table 4.2 Studies examining the impact of race and ethnicity in the United States on DNA methylation.

Race and sample size

Study objective

Method

Right colon and rectum

Bisulfite pyrosequencing

325 white, 22 African American, 22 Hispanic, 19 other

“In the present study, we examined the association among demographic, lifestyle, dietary, and genetic factors .

Umbilical cord blood

Human methylation 27 BeadChip

107 African Americans; 94 whites

“Our goal in this paper was to use a cohort of African American and Caucasian newborns to determine whether the differences in DNA methylation observed in adulthood are also present at birth.”

Attribution of racial or ethnic differences

Yes (no control for ancestry)

Not defined

No

Not defined

“There are several possible explanations for our findings, including lifestyle factors, genetic protection or predisposition to methylation and polymorphisms in the genes studied themselves.” “Whereas the potential effects of differences between the races in maternal diet or maternal metabolism during pregnancy cannot be excluded, the presence of DNA methylation differences at birth would suggest that those differences observed later in life were not acquired only postnatal, but were an initial feature of the epigenome."

Author and year [13]

[14]

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Definition of race or ethnicity

6. Current state of studies on the role of race and ethnicity

Sample source

Correct control for continental ancestry based on study objective

Continued

Table 4.2 Studies examining the impact of race and ethnicity in the United States on DNA methylation.dcont’d

Study objective

Prostate tumor

Real-time methylationsensitive PCR

47 AfricanAmericans; 64 whites

Prostate

Bisulfite pyrosequencing

18 African Americans; 13 whites

“Here, we studied whether there are differences in DNA methylation of three genes, GSTP1, CD44, and E-cadherin in prostate cancers from US Blacks and Whites in order to try to elucidate potential molecular mechanisms that may contribute to the increased risk of prostate cancer among black men.” “In this study, we sought to investigate DNA methylation changes in prostate tissue samples from African

Definition of race or ethnicity

Attribution of racial or ethnic differences

Yes (no control for ancestry)

Not defined

“Further, CD44 hypermethylation was highly correlated with tumor grade, with 52% of highgrade or poorly differentiated tumors having CD44 hypermethylation compared to it being found in only 10% of welldifferentiated tumors. E-cadherin was not hypermethylated in any of the tumor samples examined.”

[15]

Yes (no control for ancestry)

Not defined

“ . ethnic differences in the methylation pattern of these genes may contribute to the disparity associated with PCa. The genetic

[16]

Author and year

Chapter 4 The impact of race and ethnicity

Method

Race and sample size

60

Sample source

Correct control for continental ancestry based on study objective

Bisulfite pyrosequencing

198 African Americans; 203 Hispanics

mechanism(s) underlying the differing prevalence of methylation in these two groups is an area of active investigation in our laboratory.”

Yes (no control for ancestry)

“Ethnicity was based on the ethnic/cultural background of the subjects, their parents, grandparents, and greatgrandparents.”

“This study demonstrates ethnic diversity of DNA methylation globally and at specific CpG sites. This finding has implications for future studies on the role of DNA methylation. Population stratification has been found to produce falsepositive and falsenegative findings in genetic studies.”

[17]

61

Continued

6. Current state of studies on the role of race and ethnicity

Peripheral blood

American (AA) men in comparison with Caucasian (Cau) men to identify methylated genes that could be potentially useful as ‘ethnicsensitive’ biomarkers for the detection of PCa.” “. examining in subjects of two ethnicities, AfricanAmericans and Hispanics, methylation levels at 16 CpG sites in the OPRM1 promoter region.”

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Table 4.2 Studies examining the impact of race and ethnicity in the United States on DNA methylation.dcont’d

Prostate tissue

Method

Race and sample size

Study objective

Illumina 450K methylation platform

10 African Americans; 11 whites

“We compared the genome-wide DNA methylation pattern in normal and PCa tissue samples from AA and Cau men (focusing on gene promoter associated regions) and correlated with gene expression in PCa samples from AA and Cau men.”

Credit: David Rehkopf and Belinda Needham.

Definition of race or ethnicity

Attribution of racial or ethnic differences

Yes

Not defined

“ . we were able to determine that racial differences in methylation were greatest in AA compared to Cau .. Our future test will use genetic ancestry informative markers as proxy in the AA samples to determine whether genetic differences are correlated with the higher methylation among AAs.”

Author and year [18]

Chapter 4 The impact of race and ethnicity

Sample source

Correct control for continental ancestry based on study objective

7. Alternative approaches to examining race and ethnicity

63

clear in specifying that there were several possible reasons for the differences, including behavioral factors [13], providing an excellent example of how studies should appropriately interpret findings. One study [14] attempted to isolate the impacts of genetics on racial and ethnic differences by looking at differences in DNA methylation early in the life course, when the authors assumed that environmental influences would have had less influence. Given the large literature on in utero influences on DNA methylation, it is likely that this may greatly overestimate genetic as compared to environmental differences.

7. Alternative approaches to examining race and ethnicity and genetics in relation to DNA methylation Our prior discussion has been focused on the appropriate aspects of analysis necessary for examining race, ethnicity and DNA methylation. We concluded that in most cases it is not appropriate to control for continental ancestry in these analyses. Thus while it is not possible through standard study designs and approaches to separate the influence of continental ancestry and self-reported race and ethnicity, there are some other novel promising approaches for studies focused on this question. A first promising approach is to examine differences in DNA methylation within racial and ethnic groups that are from more admixed populations, where there is not such a high correlation between continental ancestry and self-identified race. An excellent study utilizing this approach examined the relative contribution of continental ancestry and ethnicity among Hispanics, finding key contributions of self-identified race to previously identified DNA methylation patterns associated with environmental and social exposures [19]. This approach offers perhaps the best opportunity to disentangle the relative contributions of each of these factors to DNA methylation. However, as this approach is only possible for admixed populations, other approaches are necessary for reducing genetic confounding in other racial and ethnic group comparisons. A second approach is controlling for more specific genetic differences that arise due to differences in continental ancestry through controlling for methylation quantitative trait loci (mQTLs). While the current evidence demonstrates that the racial and ethnic differences in epigenetics that are related to disease are likely due to environmental factors, there may be a small number of factors related to differences in continental ancestry, and these could confound some associations. While we have discussed previously that the strong correlations between continental ancestry and self-identified race and ethnicity make simultaneous statistical control generally inappropriate, individual SNPs that impact DNA methylation are not so highly correlated. Thus, for analyses of DNA methylation, controlling for mQTLs can more specifically control for genetic differences that influence DNA methylation at particular sites of interest. A third potential approach that will require more development is the use of alternative methods or larger datasets that allow for addressing the collinearity between continental ancestry and selfidentified race. While this has not yet been developed, simulation models have suggested that in theory high levels of correlation between variables primarily impact only the standard error of estimates, not the precision [20]. Thus, with increasingly larger datasets this may be possible. Importantly, these datasets will need to be representative of the target population or collider bias may result in biased estimates.

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8. Summary Studying the impact of race and ethnicity on epigenetics is an endeavor that should be focused on examining the biological pathways through which environments contribute to disease differences between groups, such as African-Americans and non-Hispanic whites. In contrast, studies of genetics should focus on continental ancestry as a confounding factor for examination of the influence of genotypes on epigenetics. Our recommendations are that all future research in this literature clearly specify race and ethnicity as social categories and specify whether the focus of the study is on these constructs or, rather, on continental ancestry or genetics. We demonstrate that due to the high levels of collinearity between self-identified race and continental ancestry, studies of race and ethnicity should not statistically control for continental ancestry, while studies of the environment should control for self-identified race and ethnicity, and studies of genetic impacts on DNA methylation should control for continental ancestry as the most important confounders. Without a clear theoretical premise, quantitative studies of race and ethnicity and epigenomic regulation of disease are at risk of conflating the influences of continental ancestry and the environment. This work was supported by NIH R01MD011724 (MPIs: Needham and Rehkopf).

References [1] Woolf SH, Chapman DA, Buchanich JM, Bobby KJ, Zimmerman EB, Blackburn SM. Changes in midlife death rates across racial and ethnic groups in the United States: systematic analysis of vital statistics. BMJ 2018;362:k3096. [2] Cameron SC, Wycoff SM. The destructive nature of the term race: growing beyond a false paradigm. J Couns Dev 1998;76(3):277e85. [3] Smedley A, Smedley BD. Race as biology is fiction, racism as a social problem is real: anthropological and historical perspectives on the social construction of race. Am Psychol 2005;60(1):16. [4] Humes KR, Jones NA, Ramirez RR. Overview of race and Hispanic origin: 2010. 2011. [5] Royal CD, Dunston GM. Changing the paradigm from’race’to human genome variation. Nat Genet 2004;36. [6] Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, et al. Genetic structure of human populations. Science 2002;298(5602):2381e5. [7] Saperstein A, Penner AM. Racial fluidity and inequality in the United States. Am J Sociol 2012;118(3): 676e727. [8] Malat J. Expanding research on the racial disparity in medical treatment with ideas from sociology. Health 2006;10(3):303e21. [9] Rothman KJ, Greenland S, Lash TL. Modern epidemiology. Lippincott Williams & Wilkins; 2012. [10] Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003;14(3):300e6. [11] Glymour MM. Using causal diagrams to understand common problems in social epidemiology. Methods in social epidemiology 2006:393e428. [12] Barfield RT, Almli LM, Kilaru V, Smith AK, Mercer KB, Duncan R, et al. Accounting for population stratification in DNA methylation studies. Genet Epidemiol 2014;38(3):231e41. [13] Wallace K, Grau MV, Levine AJ, Shen L, Hamdan R, Chen X, et al. Association between folate levels and CpG Island hypermethylation in normal colorectal mucosa. Cancer Prev Res 2010;3(12):1552e64. [14] Adkins RM, Krushkal J, Tylavsky FA, Thomas F. Racial differences in gene-specific DNA methylation levels are present at birth. Birth Defects Res Part A Clin Mol Teratol 2011;91(8):728e36.

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[15] Woodson K, Hayes R, Wideroff L, Villaruz L, Tangrea J. Hypermethylation of GSTP1, CD44, and E-cadherin genes in prostate cancer among US Blacks and Whites. Prostate 2003;55(3):199e205. [16] Kwabi-Addo B, Wang S, Chung W, Jelinek J, Patierno SR, Wang B-D, et al. Identification of differentially methylated genes in normal prostate tissues from African American and Caucasian men. Clin Cancer Res 2010. 1078-0432. CCR-09-3342. [17] Nielsen DA, Hamon S, Yuferov V, Jackson C, Ho A, Ott J, et al. Ethnic diversity of DNA methylation in the OPRM1 promoter region in lymphocytes of heroin addicts. Hum Genet 2010;127(6):639e49. [18] Devaney JM, Wang S, Furbert-Harris P, Apprey V, Ittmann M, Wang B-D, et al. Genome-wide differentially methylated genes in prostate cancer tissues from African-American and Caucasian men. Epigenetics 2015; 10(4):319e28. [19] Galanter JM, Gignoux CR, Oh SS, Torgerson D, Pino-Yanes M, Thakur N, et al. Methylation analysis reveals fundamental differences between ethnicity and genetic ancestry. BioRxiv 2016. 036822. [20] Schisterman EF, Perkins NJ, Mumford SL, Ahrens KA, Mitchell EM. Collinearity and causal diagramsea lesson on the importance of model specification. Epidemiology 2017;28(1):47e53.

CHAPTER

The epigenomic impact of methylation in metabolic dysfunction and cancer

5 Cynthia A. Zahnow

The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA

1. Introduction The majority of cells in an organism share the same DNA, but cells differing from each other in regard to form and function generate unique gene expression profiles to produce the proteins needed for their specialized actions. The molecular mechanisms responsible for generating these varied expression profiles and phenotypes are referred to as epigenetics. The term ‘‘epigenetics’’ refers to heritable changes in gene expression and cellular phenotype that do not involve changes to the underlying genetic code. Epigenetic changes include DNA methylation, histone modifications and nucleosome positioning that alter chromatin structure and are considered to be “in addition to” the DNA backbone. These acquired epigenetic marks can be influenced by environmental cues, can be maintained for the duration of the cell’s life and can be inherited over multiple generations [1]. Complex interplay exists between the epigenetic mechanisms that lead to observed gene expression and often involve the sum of their interactions with each other, the chromatin and the cellular microenvironment. Changes in nutrient availability and the emergence of mutations in metabolic enzymes or oncogenes that alter metabolism can act as a metabolic switch to alter DNA methylation, chromatin remodeling and consequently, changes in gene expression. An important question; however, is how the epigenetic machinery, which is located in the nucleus and associated with chromatin, is regulated by metabolic enzymes in the cytoplasm. Emerging evidence suggests that metabolism may directly regulate S-adenosylmethionine (SAM) and nicotinamide adenine dinucleotide (NAD) levels, and ten-eleven translocation (TET) protein activity in the nucleus, which in turn can alter epigenetic profiles and consequent gene expression. In this chapter we will focus on four questions: (1) What is DNA methylation and its role in regulating gene expression; (2) Which enzymes, substrates and cofactors are important for DNA methylation and how are these influenced by metabolism, (3) How does the loss or gain of DNA methylation impact metabolism; (4) How do mutations in tumor suppressors alter DNA methylation and regulate metabolism.

2. DNA methylation and its role in regulating gene expression In normal cells, the promoters of transcriptionally active genes are typically hypomethylated, whereas promoter hypermethylation in cancer cells can lead to gene silencing by affecting the Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00005-9 Copyright © 2019 Elsevier Inc. All rights reserved.

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binding of DNA binding proteins and/or altering the accessibility of the DNA to transcriptional factors [2]. Global DNA hypomethylation and promoter hypermethylation are a hallmark of the cancer genome [2e5]. Promoter hypermethylation is associated with the silencing or inactivation of genes that play a suppressive role in tumorigenesis. Aberrant DNA methylation and changes in gene expression profiles can lead to alterations in cell fate as observed by the de-differentiation of stem cells and consequent generation of more primitive cancer stem cells in some tissues. DNA methylation occurs on cytosines that precede a guanosine in the DNA sequence and are known as CpG dinucleotides. CpGs are often found clustered in gene promoters as short CpG-rich regions, called CpG-islands. CpG-rich gene promoters are usually non-methylated in normal cells, but are hypermethylated in a small percentage of genes in malignant cells (Fig. 5.1). Gene promoter hypermethylation can silence gene expression via the recruitment of methyl-CpG-binding domain (MBD) proteins, which in turn recruit histone modifying and chromatin eremodeling complexes to methylated sites to serve as transcriptional co-repressors [6]. DNA methylation and chromatin remodeling can also prevent access of DNA binding proteins to their DNA target genes. DNA

FIG. 5.1 DNA Methylation Patterns in Chromatin Differ in Normal Versus Cancer Cells. DNA methylation occurs on cytosines that precede a guanosine (CpGs). Regions with a high frequency (>55%e60%) of CpGs, or a cluster of 200 or more CpGs are referred to as islands and are often associated with gene promoters. In normal cells, CpG islands are unmethylated, (white circles) and gene transcription remains active, but in cancer cells, CpG islands can be aberrantly methylated (black circles) and gene transcription suppressed. In normal cells, the CpGs that lie outside of promoter islands in the body of the gene or in non-coding regions are often methylated, while the reverse is true in cancer cells. Pharmacologically, CpG Methylation can be removed with the use of DNA Methyltransferase (DNMT) inhibitors such as Azacytidine or Decitabine. Figure adapted from Herman JG, Baylin SB. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med 2003;349(21):2042e54. Credit: Cynthia Zahnow.

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methylation does not occur exclusively at CpG islands but also in regions of lower CpG density that are near CpG islands, in enhancer regions, or in the gene body. Gene body methylation does not repress transcription, but instead is associated with gene expression [7]. Lastly, DNA methylation is also quite commonly found in repetitive elements, to prevent reactivation of endogenous retroviral sequences and other sequences that can disrupt chromosomal integrity [8]. Silencing of tumor suppressor genes via DNA methylation has been proposed as a ‘‘second hit’’ for cellular transformation, equivalent to mutations or translocations [2]. In fact, it has been suggested that more genes are disrupted in cancer cells by epigenetic modifications than by genetic modifications [2]. The reasons for this may be due to the higher error rate in maintaining DNA methylation in comparison to DNA replication. DNA synthesis is very accurate with 1 error for every 107e108 bases copied [9]; whereas, the maintenance of DNA methylation has an accuracy rate of 96% with 1 of every 25 methylated sites copied incorrectly [10]. Epigenetic patterns can thus drift randomly over time, creating diversity and a competitive advantage for cell survival and growth.

2.1 Enzymes and substrates important in the regulation of DNA methylation DNA methylation is dynamic, and is regulated by multiple enzymes that control the balance between the addition or removal of methyl groups. Methylation occurs via DNA methyltransferases (DNMTs) with S-adenosylmethionine (SAM) serving as the methyl donor while demethylation is controlled by the TET family of hydroxylases that convert 5-methylcytosine to an unmodified cytosine (Fig. 5.2). The enzymes and inhibitors are described below, followed by their interactions with cellular metabolism.

FIG. 5.2 DNA Methylation is Regulated by Multiple Enzymes That Control the Balance Between the Addition or Removal of Methyl Groups. Methylation occurs and is maintained via a family of three DNA methyltransferases (DNMTs) with S-adenosylmethionine (SAM) serving as the methyl donor. Demethylation is controlled by the TET methylcytosine dioxygenase family, composed of TET1, TET2, and TET3, that catalyze the oxidation of 5-methylcytosine (5 mC) to 5-hydroxymethylcytosine (5hmC) by hydrolyzing a-KG in a Fe2þ-dependent reaction [11,12]. TET activity, and DNA demethylation can be inhibited by 2- hydroxyglutarate (2-HG) as well as TCA cycle intermediates fumarate and succinate, which are downstream of a-KG [13]. Credit: Cynthia Zahnow.

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2.1.1 DNMTs-(Methylation of DNA) In mammalian cells, five members of the DNA methyltransferases have been identified: DNMT1, DNMT2, DNMT3a, DNMT3b, and DNMT3L, but only DNMT1, DNMT3a and DNMT3b have methyltransferase activity and can catalyze the transfer of a methyl group from S-adenosylmethionine (SAM) to cytosine [14]. DNMT2 is involved in RNA methylation [15,16] while DNMT3L establishes maternal genomic imprinting [17]. DNMT1 is the predominant methyltransferase and is important for the maintenance of post-replicative DNA methylation. DNMT3A and DNMT3B are known as de novo methyltransferases and have a binding preference for unmethylated DNA. They play a role in the establishment of methylation patterns during development [18]. Numerous mechanisms have evolved to ensure that methylation in the genome is generated on both strands of the DNA. DNMT1’s affinity to newly synthesized DNA is increased by its interaction with the DNA polymerase processing factor proliferating cell nuclear antigen (PCNA), ensuring localization to the replication fork [19]. Similarly, the SRA domain of UHFR1 or UHFR2 (ubiquitin-like containing PHD and RING finger domains (1) binds newly replicated hemi-methylated DNA and recruits DNMT1 to the DNA replication foci, accurately copying the methylation pattern of the template DNA strand to the newly synthesized strand, and thus preserving DNA methylation throughout cell division [18,20,21]. Thus, the majority of DNA methylation in dividing cells is maintained by DNMT1 together with PCNA, UHFR1 and UHFR2. Cooperation between DNMT1 and DNMT3a and DNMT3b, has been observed and it is believed that 3a and 3b, which are anchored to nucleosomes, repair the errors made by DNMT1 after DNA synthesis [18,22]. In this way, DNMT3a and DNMT3b may also be involved in the maintenance of DNA methylation [18]. Further evidence suggests that the DNMT3a and 3b enzymes readily methylate a DNA substrate containing 5hmC, the first oxidative product in the active demethylation of 5-methylcytosine (5 mC) [23]. Although DNMT1 is primarily a maintenance DNA methyltransferase, it can also contribute to de novo methylation in human cancer cells [24] and its loss can lead to genomic instability [25,26]. For the purposes of this chapter, DNA methylation will refer to methylation in the promoter or enhancer region of genes.

2.1.2 SAM- (methyl donor) and links with metabolism S-adenosylmethionine (SAM) is a shared co-substrate for the methylation of DNA, RNA, metabolites, lipids and proteins, but is the sole source of methyl groups for DNMTs. SAM is generated by the combined actions of the methionine and folate cycle in combination with Vitamin B metabolism [27] and is synthesized from methionine and ATP by methionine adenosyltransferase (MAT) (Fig. 5.3) In fact, it has been reported that up to 50% of the daily intake of methionine is converted to SAM [28]. The donation of a methyl group from SAM during methylation releases S-adenosylhomocysteine (SAH), which is a potent inhibitor of DNMTs (Fig. 5.3). SAH is normally maintained at low levels via hydrolysis to homocysteine (Hcy) [29]. Serum levels of SAM and SAH change with diet and this ratio is an important regulator of the degree of methylation in tumors and acts as a biosensor of the cellular metabolic state [30,31]. SAH can be further recycled to methionine by methionine synthase via the transfer of a methyl group from 5-methyl tetrahydrofolate (5-methyl-THF) or via betainehomocysteine methyltransferase (BHMT) with betaine as the methyl donor (Fig. 5.3). Taken together, DNA methylation can be regulated via one carbon metabolism (serine, glycine, threonine), metabolites and cofactors such as (methionine, folate, vitamin B6, B12) that elevate SAM leading to DNA hypermethylation and aberrant gene silencing (Fig. 5.3).

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FIG. 5.3 Metabolic Regulation of SAM. S-adenosylmethionine (SAM) is the sole source of methyl groups for DNMTs and methylation of DNA. SAM is generated by the combined actions of the methionine and folate cycle in combination with Vitamin B metabolism [27] and is synthesized from methionine and ATP by methionine adenosyltransferase (MAT). The donation of a methyl group from SAM during methylation releases S-adenosylhomocysteine (SAH), which is a potent inhibitor of DNMTs. SAH can be further recycled to methionine by methionine synthase via the transfer of a methyl group from 5-methyl tetrahydrofolate (5-methyl-THF) or via betaine-homocysteine methyltransferase (BHMT) with betaine as the methyl donor. Taken together, DNA methylation can be regulated via one carbon metabolism (serine, glycine, threonine), metabolites and cofactors such as (methionine, folate, vitamin B6, B12) that elevate SAM leading to DNA hypermethylation and aberrant gene silencing (Fig. 5.3). Credit: Cynthia Zahnow.

Folate is essential for the regeneration of SAM. Studies in colorectal cancer cell lines grown without sufficient folate demonstrate impaired ability to form colonospheres and decreased expression of DNMTs and DNA methylation [32]. In another example of co-factors and their regulation of DNA methylation, the overexpression of amino acid transporters in some cancer cells can lead to an increase in the uptake of methionine [33] and associated changes to DNA methylation. Signal transduction pathways such as LKB1-AMPK-mTOR, are also part of a bio-sensing metabolic mechanism that couples nutrition availability with regulation of SAM levels and are discussed further in Section 4.1. In conclusion, alterations in SAM/SAH and the folate or one-carbon cycle directly provide crosstalk between cellular metabolism and the epigenome to regulate epigenetic homeostasis and normal cell behavior.

2.1.3 TET enzymes-(Demethylation) and links with metabolism Although a demethylase specific for DNA has not yet been identified, methylated cytosines are targeted for demethylation by ten-eleven translocation (TET) enzymes. Fe2þ and the Krebs cycle (TCA cycle) intermediate, a--ketoglutarate (a-KG), serve as cofactor and co-substrate, respectively [11].

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FIGURE 5.4 The Net Balance of DNA Methylation is Regulated by the DNMTs and TETs. DNMTs are critical for DNA methylation. Reduced SAM levels lead to less methylation. Inhibition of DNMT via SAH or DNMT inhibitors also lead to hypomethylation. Hypomethylation leads to open chromatin, increased gene expression and cellular differentiation. Vitamin C can activate TET to promote widespread, but relatively specific DNA hypomethylation. The TETs catalyze the successive oxidation of 5-methylcytosine (5 mC) to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5 fC) and 5-carboxylcytosine (5caC). TET activity, and DNA demethylation is inhibited by 2-hydroxyglutarate (R-2HG) as well as TCA cycle intermediates fumarate and succinate, which are downstream of a-KG [13]. Succinate or fumarate accumulate in tumors that harbor inactivating mutations in succinate dehydrogenase (SDH) or fumarate hydratase (FH) leading to inhibition of a-KG dependent enzymes, hypermethylation of genes, reduced cellular differentiation and increased proliferation [13,34]. IDH1 and 2 mutations lead to a gain of function and the conversion of a-KG to produce high levels of the oncometabolite, 2-hydroxyglutarate (R-2HG) [35]. R-2HG competitively inhibits the enzymatic activity of a-KG dependent dioxygenases, such as the TETs. Credit: Cynthia Zahnow.

The TET methylcytosine dioxygenase family, composed of TET1, TET2, and TET3, catalyze the successive oxidation of 5-methylcytosine (5 mC) to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5 fC) and 5-carboxylcytosine (5caC) by hydrolyzing a-KG in an Fe2þ-dependent reaction [11,12] (Fig. 5.4). The exact function of these oxidative cytosine bases remains unknown. All TET proteins contain a C-terminal core catalytic region that can bind Fe2þ and a-KG, also known as 2-oxoglutarate (2-OG). This region is believed to preferentially bind to CpGs, but does not interact

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with surrounding DNA bases [36,37]. TET activity, and DNA demethylation is inhibited by 2- hydroxyglutarate (2-HG) as well as TCA cycle intermediates fumarate and succinate, which are downstream of a-KG [13] (Figure 5.2, 5.4). Succinate or fumarate have been observed to accumulate in tumors harboring inactivating mutations in the enzymes succinate dehydrogenase (SDH) or fumarate hydratase (FH) leading to inhibition of a-KG dependent enzymes, reduced a-KG/succinate ratios, hypermethylation of genes, reduced cellular differentiation and increased proliferation [13,34]. In contrast, elevated a-KG/succinate ratios maintain DNA hypomethylation which are often observed in pluripotent embryonic stem cells [38]. The cytosine oxidation products are not generated symmetrically on both strands of the DNA as is observed for 5 mC, but asymmetrically [18,39] and may be more than just intermediates of DNA demethylation having been shown to function specifically in transcriptional regulation [40e42]. Chromatin regulators, transcription factors and even RNA polymerase II have been shown to bind to DNA containing 5hmC, 5 fC or 5caC [43,44] and may lead to an altered expression profiles specifically associated with hypomethylation. Loss of TET activity is associated with cancer, but it is not clear whether TET proteins always function as tumor suppressors or whether they may also have oncogenic tendencies in some tumor types [45]. For example, TET1 exhibits a clear oncogenic role in the context of genomic rearrangements such as in MLL-fusion rearranged leukemia, but loss of function mutations in these genes occur in a range of cancers [45]. TET2 is one of the most frequently mutated genes in hematological malignancies. Recently, hypoxia-induced TET1 has been demonstrated to regulate hypoxia-responsive gene expression [46] and epithelial-mesenchymal transition (EMT) by serving as a transcription coactivator [45]. TET1 also regulates glucose metabolism and hypoxia-induced EMT through increased expression of insulin induced gene 1 (INSIG1) [45]. The TET enzymes are most well-known and important for the removal of unwanted methylation and the inhibition of cellular transformation; however, the role of the TETs to prevent the initiation and maintenance of tumorigenesis remains largely unknown [12]. Interestingly, the TET enzymes can be regulated by Vitamin C (ascorbate) which is often deficient in patients with cancer [47]. Vitamin C can promote widespread, but relatively specific DNA hypomethylation by interacting with the catalytic domain of TET to promote recycling of Fe2þ and protein folding to stimulate catalytic activity [48e50]. Vitamin C regulates DNA demethylation in a large number of genes in human embryonic stem cells (hESCs) [51]. Vitamin C also enhances the generation of induced pluripotent stem cells (iPSCs) during somatic cell reprogramming, which is associated with genome-wide DNA demethylation and an increase in the TET-dependent production of 5hmC [50].

2.2 DNMT Inhibitors-(Inhibition of DNA methyl transferase) The DNMT inhibitor (DNMTI), azacytidine, is an analogue of cytidine with a nitrogen substituted for a carbon in the 5 position of the pyrimidine ring. It is currently being used to treat patients with hematologic disorders such as myelodysplastic syndrome (MDS) and chronic myelogenous leukemia (CML); it is also being tested in patients with solid tumors such as non-small cell lung cancer, breast cancer, ovarian cancer, and colon cancer [16,52]. To better understand the anti-tumorigenic actions of AZA, investigators have examined the effects of DNMTIs on metabolic parameters in cancer cells such as cholesterol and lipid homeostasis, gluconeogenesis and glycolysis. The two best studied of the nucleoside analogue-based DNA demethylating agents that are currently being used in clinical trials

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for solid tumors are Azacytidine (AZA, 5-azacytidine, 5-AZA-CR, trade name Vidaza, Celgene), an analogue of the nucleoside cytidine, and Decitabine (DAC, 5-deoxy azacytidine, 5-aza-20 -deoxycytidine-50 -triphosphate, 5-AZA-dC, 5-AZA-Cdr, trade name Dacogen, Eisai), the deoxyribose analogue of 5-azacytidine. AZA and DAC were synthesized as cytostatic agents in 1964 [53], and early clinical trials found both compounds to be too toxic for use. Sixteen years later, these drugs were shown to inhibit DNA methylation [54], which led to their development as hypomethylating, epigenetic agents. Both drugs have improved overall survival for patients with myeloid malignancies [16]. AZA was approved for clinical use in 2004 and DAC in 2006 [55]. DAC and AZA are FDA-approved for use in MDS and DAC for CML but neither yet approved in solid tumors. AZA and DAC are transported into cells by transporters such as the human equilibrative nucleoside transporter-1 and the ABC transporter. After triphosphorylation by the respective enzymes AZA is incorporated into RNA and DAC incorporates into DNA (Fig. 5.5). For DAC to incorporate into DNA, the cells must be in S phase of the cell cycle and actively dividing. The incorporated decitabine, forms a strong covalent bond with the DNMT, which leads to degradation of the enzyme [58]. As a consequence, DNMT levels are reduced and DNA methylation is lost rather than maintained with each cellular division via the passive dilution of methylated cytosines [59]. The majority of DAC can immediately incorporate into DNA, but only 10%e20% of the ribose analogue, AZA, can incorporate into DNA after it is first converted to decitabine via ribonucleotide reductase (Fig. 5.5) [56]. The remaining 80% of azacytidine incorporates into RNA [57] to affect nuclear and cytoplasmic RNA metabolism including ribosome biogenesis and protein synthesis. Thus, the dose of AZA administered experimentally is often raised to five times that of DAC to compensate for the differences in DNA incorporation [60]. Moreover, comparative analysis has demonstrated that AZA may be more potent than DAC in reducing cell viability and proliferation in acute myeloid leukemia cells [61] and these effects may reflect methylation-independent mechanisms [62].

2.2.1 DNMTIs alter cholesterol and lipid metabolism Evidence suggests that DNA methylation plays an important role in cholesterol and lipid metabolism and it has been shown that expression of glycerol-3-phospate acyltransferase 1, (GPAT1) a rate limiting enzyme of triglyceride biosynthesis in the liver is regulated by DNA promoter methylation [63]. Additional research using DNMTIs in cell culture and animal studies, have shown that AZA potently reduces the expression of key genes involved in cholesterol and lipid metabolism [64]. It is unclear however, whether these metabolic effects are related by changes in DNA methylation in regions other than the gene promoter, or are methylation-independent. Using cytotoxic concentrations of AZA, the authors demonstrated that treatment with AZA, but not DAC reduced expression of sterol regulatory element-binding protein (SREBP)-regulated genes such as proprotein convertase subtilisin/kexin type 9 (PCSK9), a gene associated with familial hypercholesterolemia and HMG-CoA reductase (HMGCR), the rate-limiting enzyme for cholesterol biosynthesis, but increased expression of the low density lipoprotein receptor (LDLR). AZA induced neutral lipid droplets in lipoproteindeficient cell culture media but could only induce phospholipid and cholesterol containing droplets in the presence of lipoproteins [64]. The mechanism involves the disruption of subcellular cholesterol homeostasis by inhibition of key genes in cholesterol and lipid metabolism [64]. These authors concluded that this effect may be mediated by inhibition of de novo pyrimidine synthesis as AZA at high cytotoxic doses inhibits orotidylate decarboxylase and uridine monophosphate synthetase, enzymes involved in pyrimidine biosynthesis and the formation of uridine monophosphate

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FIG. 5.5 Inhibitors of DNA Methyltransferase Enzymes Can Alter Metabolism. Azacytidine (AZA) and Decitabine (DAC) are transported into cells by transporters such as the human equilibrative nucleoside transporter-1 (hENT-1). After triphosphorylation by their respective enzymes AZA is incorporated into RNA and DAC incorporates into DNA. 100% of DAC can immediately incorporate into DNA, but only 10%e20% of the ribose analogue, AZA, can incorporate into DNA after it is first converted to decitabine via ribonucleotide reductase [56]. The remaining 80% of azacytidine incorporates into RNA [57] to affect nuclear and cytoplasmic RNA metabolism including ribosome biogenesis and protein synthesis. Credit: Cynthia Zahnow.

respectively. Cells treated with AZA have decreased UTP and CTP levels [64,65]. Cytidine triphosphate (CTP) is critical for glycerophospholipid biosynthesis [66] and decreased CTP levels could explain the effects of AZA on lipid homeostasis. In patients with MDS, increased SREBP signaling in the bone and peripheral blood cells is associated with a poor prognosis.

3. DNA methylation and hypoxia Solid tumors are not metabolically uniform as tumor growth rate in specific regions can outgrow the vasculature, leading to inadequate angiogenesis and perfusion. These regions are then transiently or chronically exposed to ischemia and reperfusion and suffer hypoxic, and/or hyperoxic conditions, an

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increased level of reactive oxygen species, glucose fluctuations, and acidosis. Hypoxia has been shown to regulate DNA methylation, but the resultant changes in methylation seem to vary depending on tumor type. In breast cancer cell lines it has been shown that hypoxia leads to a decrease in TET enzyme activity resulting in DNA promoter hypermethylation and repression of gene expression [67]. Moreover, the reduction in TET activity was associated with a decrease in oxygen availability and was not due to proliferative changes or decreases in the TET cofactors (a-ketoglutarate, Fe2þand vitamin C) [67]. Modest hypoxia (2%e5% O2) did not decrease TET activity. The decrease in 5hmC was observed with hypoxic oxygen concentrations found in tumors and predominantly at gene promoters of tumor suppressor genes. In another study, cell lines from metastatic colorectal carcinoma and malignant melanoma displayed a more hypomethylated phenotype after culture in severe hypoxic conditions [68]. Loss of global methylation is often observed in tumors along with increased promoter hypermethylation. Because this study did not specifically examine promoter methylation, and characterized only global methylation, it is difficult to assess the effect of hypoxia on promoter methylation and gene silencing in these cancer cells. CpG islands are often found within the repeat elements of DNA, and a similar study using U87MG glioblastoma cells cultured in 0.1% O2 demonstrated a loss of global methylation and a significant increase in SINE and reverse transcriptase coding long interspersed nuclear element (LINE) transcripts during hypoxia. They speculated that the hypoxia-induced decrease in the methylation of repeat element sequences may lead to increased genomic instability and genetic heterogeneity in tumors and lead to the development of a more aggressive clonal population [69]. Because both TET enzymes and hypoxia induced transcriptional programs are regulated by oxygen-dependent dioxygenases that require Fe2þ and a-KG, other investigators hypothesized that the TETS might regulate hypoxia response genes. They demonstrated that hypoxia results in transcriptional activation of TET1, increases in global 5-hmC levels and 5-hmC density at canonical hypoxia response genes in a HIF-1 dependent manner [46]. These findings suggest that TET1-mediated 5-hmC changes are an important epigenetic component of the hypoxic response. [46], In addition to hypoxia-induced regulation of TETs, DNMT and SAM levels can also be regulated by hypoxia. The expression levels and total activity levels of DNMT 1 and DNMT3a were found to be reduced in HCT116 colorectal cancer cells exposed to hypoxic and hypoglycemic conditions [70]. Hypoxia induces genomic DNA demethylation in hepatoma cells via activation of HIF-1a, upregulation of MAT2A and decreased levels of SAM [71].

4. Mutations in tumor suppressors aberrantly regulate metabolites and alter DNA methylation 4.1 LKB1 and LKB2 mutations Liver kinase B1 (LKB1), also known as serine/threonine kinase 11 (STK11) is a tumor suppressor that is inactivated by mutation in pancreatic and biliary cancers [72,73]. Germline mutations cause PeutzJeghers syndrome [74], which is associated with gastrointestinal polyps and carcinomas [75]. Cancers with LKB1 mutations are often more aggressive and exhibit varying sensitivity to therapy [76,77]. LKB1 activates a family of 14 kinases related to AMP-activated protein kinase (AMPK). Many of these kinases are involved in nutrient sensing and reprogramming of cell metabolism [78]. LKB1

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inactivation has been linked to metabolic changes that drive tumorigenesis and loss of LKB1 often coincides with mutations in the RAS-RAF pathway. This oncogenic cooperation is driven by mTORdependent induction of the serine-glycine-one-carbon pathway coupled to an increase of Sadenosylmethionine (SAM). Elevated DNA methyl transferases DNMT1 and DNMT3a, are observed in concert with the increase in DNA methylation as well as transcriptional silencing of retrotransposon elements [79]. In a genetically engineered mouse model of pancreatic cancer with oncogenic KRASG12D and LKB1 deletion, it has been shown that tumor cells containing both mutations were dependent on increased glucose uptake and could potentiate glycolysis. Moreover, it was shown that LKB1 limits serine metabolism and LKB1 deficiency can sensitize tumors to inhibitors of serine biosynthesis and DNA methylation. Lastly, loss of LKB1 leads to hypermethylation of repetitive elements, particularly retrotransposon repeats (LTRs, LINEs, and SINEs) as compared to non-repeat elements (promoters, enhancers, introns, shores and CpG islands). Retrotransposons can function as important modulators of gene expression depending on their placement or location within a gene. For example, hypermethylation of these elements in the body of a gene (Fig. 5.1) is associated with increased gene expression and has also been reported to modify the activity of linked promoters and RNA processing [80]. This study found that methylated retrotransposon elements were significantly enriched in the bodies of differentially expressed genes, but that promoter methylation was relatively unchanged. In summary, mutations in metabolic enzymes, such as loss of LKB1 can lead to an altered metabolic state in which glucose and glutamine-derived intermediates feed into the serine, glycine and one carbon network leading to elevated SAM and increased DNA methylation of retrotransposon sequences and that LKB1 status may be a marker for therapeutic vulnerability and DNMTI sensitivity.

4.2 IDH1 and IDH2 mutations The isocitrate dehydrogenase 1 (IDH1) and IDH2 enzymes are homodimers that catalyze the decarboxylation of isocitrate to 2-oxoglutarate (2OG, also referred to as a-ketoglutarate, a-KG) with a concomitant reduction of NADPþ to NADPH and production of CO2. The third member of this family, IDH3, is a heterotetramer that catalyzes the oxidative decarboxylation of isocitrate to a-KG using NADþ rather than NADPþ as the electron acceptor, and generates ATP via the electron transport chain. a-KG is a TCA cycle intermediate and is produced by the oxidative decarboxylation of isocitrate. It is an essential cofactor for many enzymes, including the TET methylcytosine hydroxylases. Mutations in IDH1 and IDH2 have been found in several tumor types, while IDH3 has not been found to be mutated in cancer. IDH1 and IDH2 mutations are found in colon cancer, gliomas [81,82] astrocytomas, oligodendrogliomas, acute myelogenous leukemia [35,83e86] chondrosarcoma [87], intrahepatic cholangiocarcinoma [88] and thyroid carcinoma. (Reviewed in Refs. [89,90]). The most common hot spot mutation in IDH1 is the substitution of an arginine R132 in the catalytic site or in the case of IDH2, R140 or R172. These mutations lead to a gain of function and the aberrant enzyme activity catalyzes the conversion of a-KG to produce excessively high levels of the oncometabolite, 2-hydroxyglutarate (2-HG) [35] (Fig. 5.4). 2-HG competitively inhibits the enzymatic activity of a-KG dependent dioxygenases, such as the TET family of 5-methylcytosine hydroxylases that are involved in DNA demethylation [90e93]. Elevated 2HG levels are associated with a hypermethylated DNA phenotype, which can inhibit cellular differentiation (Reviewed in Ref. [89]) and promote the increased self-renewal of stemelike progenitor cells to create a hyper-proliferative

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cellular state that is more susceptible to malignant transformation (Figures 5.1, 5.4) Because Decitabine and Azacytidine induce hypomethylation it can induce the differentiation of IDH-mutant glioma cells and has been shown to be more effective than IDH inhibitors [94]. Besides IDH1/2 mutations, deleterious mutations in succinate dehydrogenase (SDH) and fumarate hydratase (FH) lead to an increase in the metabolites succinate and fumarate respectively, which can inhibit TET-mediated DNA demethylation (Fig. 5.4) [13,38].

5. Conclusions The interplay between cellular metabolism and DNA methylation is critically important in tumorigenesis. DNA methylation is dynamic, and is controlled by two different enzyme families that are strongly regulated by metabolism. DNA methyltransferases (DNMTs) are responsible for methylation with S-adenosylmethionine (SAM) serving as the methyl donor. SAM is generated by the combined actions of the methionine and folate cycle in combination with Vitamin B metabolism [27] and is synthesized from methionine and ATP by methionine adenosyltransferase (MAT). In summary DNA methylation is regulated via one carbon metabolism (serine, glycine, threonine), metabolites and cofactors such as (methionine, folate, vitamin B6, B12) that elevate SAM leading to DNA hypermethylation and aberrant gene silencing. Demethylation is controlled by the TET family of hydroxylases that convert 5-methylcytosine to an unmodified cytosine. TET activity is stimulated by Vitamin C and inhibited by 2- hydroxyglutarate (2HG) as well as TCA cycle intermediates fumarate and succinate. The TET enzymes are also controlled by hypoxia in a tissue-specific manner, with TET inactivation observed in breast cancer cells and TET activation in colorectal, melanoma, and glioblastoma cells. Lastly, mutations in tumor suppressors can aberrantly regulate metabolites and alter DNA methylation. Loss of LKB1 can lead to an altered metabolic state in which glucose and glutaminederived intermediates feed into the serine, glycine and one carbon network leading to elevated SAM and increased DNA methylation of retrotransposon sequences and that LKB1 status may be a marker for therapeutic vulnerability and DNMTI sensitivity. IDH mutations lead to a gain of function and the production of excessively high levels of the oncometabolite, 2-hydroxyglutarate (2-HG). 2HG competitively inhibits the enzymatic activity of a-KG dependent dioxygenases, such as the TETs. TET inhibition can lead to a hypermethylated DNA phenotype, an inhibition of cellular differentiation and increase in the self-renewal of stemelike progenitor cells to create a hyper-proliferative cellular state that is more susceptible to malignant transformation.

References [1] Bird A. Perceptions of epigenetics. Nature 2007;447(7143):396e8. [2] Herman JG, Baylin SB. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med 2003;349(21):2042e54. [3] Feinberg AP, Vogelstein B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 1983;301(5895):89e92. [4] Goelz SE, Vogelstein B, Hamilton SR, Feinberg AP. Hypomethylation of DNA from benign and malignant human colon neoplasms. Science 1985;228(4696):187e90.

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CHAPTER

The role for DNA/RNA methylation on neurocognitive dysfunctions

6 Xiangru Xua, b

a

Max Planck Institute for Biology of Ageing, Cologne, Germany ; Department of Anesthesiology, Yale University School of Medicine, New Haven, CT, USAb

1. Brain aging and cognitive dysfunction Learning, memory and cognitive dysfunction is often accompanied by mammalian brain aging. Brain aging is generally defined by the continuing deterioration in different aspects of learning, memory and cognitive performance, brain structures, and neuronal/brain functions. Learning and memory impairment in brain aging is a common feature for elder populations across the spectrum of species from invertebrates, rodents, monkeys, to humans [1]. In the developing world and advanced countries, a growing body of elder populations and their families are suffering from such age-associated learning, memory and cognitive impairments. For example, in the United States, it is anticipated that w12% of people over the age of 65 will suffer from moderate-to-severe memory defects by 2050 [2]. Cognitive dysfunction is often correlated with age-dependent weakening of synaptic functions in the hippocampus and prefrontal cortex that are crucial for memory formation and consolidation. The prefrontal cortex (PFC) in humans plays an important role in complex cognitive behaviors, decision making, personality, and the orchestration of thoughts and actions. Memory and attention deficits are common for people with PFC impairment, yet people largely attribute the age-associated memory decay to the declining functions in the hippocampus. The hippocampus is another crucial section in the brain and is closely associated with the cerebral cortex for learning, memory and cognitive functions. Two major functions of the hippocampus are to store and interpret the spatial information and to facilitate the consolidation of short-term memory into long-term memory. Hippocampus-dependent memory deficits during normal (successful) aging are distinct from the symptoms of devastating neurodegenerative disorders such as Alzheimer’s disease that profoundly impacts memory, but can occur in the absence of massive neuronal death. Instead, subtle changes in the connections and functional integrity of key hippocampal neuronal circuits appear to underlie the memory impairment in elder individuals [3]. In the context of aging, altered transcriptional regulation of genes that promote or are essential for synaptic plasticity is associated with memory impairment in aged mice/rats. Alongside a focus on negative outcomes, there is cumulative recognition that memory decline is not an inevitable consequence of aging as some older individuals maintain excellent memory abilities that match those of younger individuals across the lifespan [4]. It is therefore suggested that identifying the neurobiological mechanisms that regulate differential cognitive outcomes with age is immensely important. Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00006-0 Copyright © 2019 Elsevier Inc. All rights reserved.

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Before further discussing normal age-associated cognitive changes, it is crucial to mention methodological challenges in human cohort studies regarding normal cognitive dysfunction/brain aging [5]. Selection bias in study participants is the first real challenge; many potential study participants decline enrollment because they are either too healthy/busy or too ill, and people with limited social or financial support and functional limitations may also be less likely to be recruited into a study. This largely compromises the representative soundness in sample selection. Second, results derived from cohort differences may miscalculate the effects of aging as most studies rely on cross-sectional design to compare subjects from different age groups. For instance, a cohort that was born in the 1940’s had a very different life experience from a cohort born in the 1960’s; besides, they may also greatly differ in terms of culture, lifestyle, education, and requirements for success in life. Taken together, these practical challenges in human cognitive dysfunction/brain aging studies can potentially undermine the findings/conclusions. Model organisms, both invertebrate and vertebrate, thus turn out to be a powerful research tool to tackle the neurological mechanism of cognitive dysfunction/brain aging. The neurobiological processes underlying age-associated learning, memory and cognitive deficits include aberrant changes in gene transcription that eventually affects the resilience of the aged brain. Changes in gene expression in active neurons were thought to take place during brain aging, and analysis of regions of the hippocampus and frontal cortex by microarray has confirmed this [6,7]. The molecular mechanisms underlying these changes in gene expression and its regulation are largely unclear. Over the past decade, accumulated evidence has indicated that epigenetic mechanisms may be heavily involved in mediating age-related changes of the brain/cognitive functions. Speaking of epigenetics, the term was first created by Conrad Waddington in the 1940’s to describe the interactions between genes and their environment during development [8]. It has been evolved from a narrative term to a massively studied scientific field in the 21st century. The current meaning of epigenetics contains conformational changes in DNA and/or chromatin without altering the basic genetic code that regulate the sophisticated molecular machinery through which the spatio-temporal dynamics of gene expression are implemented. These mechanisms primarily involve DNA/RNA methylation, histone post-translational modifications and non-coding RNAs. How DNA/RNA methylation affects the cognitive dysfunction/brain aging will be discussed further with details.

2. DNA methylation, brain aging and cognitive dysfunction 2.1 DNA methylation 2.1.1 50 -methylcytosine (5 mC) DNA methylation is the most extensively studied epigenetic mark. It is evolutionarily lost in species including yeast, worm and fly but widespread in plants, rodents, primates and humans, and plays a critical role in regulating gene expression and maintaining genome stability [1,9e10]. DNA methylation involves the addition of a methyl group to the fifth carbon of a cytosine residue to form 50 -methylcytosine (5 mC) by DNA methyltransferases (Dnmts), most frequently in the context of CpG dinucleotides [1,9,10]. This biochemical process creates a relatively stable covalent modification that is traditionally understood to repress gene transcription by promoting closed chromatin states and limiting DNA accessibility to transcriptional activation machinery and/or recruiting of transcriptional repressors [1,11]. CpGs are not uniformly distributed in the genome and tend to be enriched as CpG islands, which are stretches of DNA roughly 1000 base pairs long that have a higher CpG density than

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the rest of the genome [9]. Gene promoters in the genome mostly have an associated CpG island, and, in general, DNA methylation levels at these promoter-associated islands negatively associate with gene transcription levels [9]. However, recent study suggests that the consequences of DNA methylation on transcription can vary considerably depending on a variety of factors including the CpG island locus [11]. The dynamics of genome-wide DNA methylation are regulated by Dnmts, including Dnmt1, Dnmt3a and Dnmt3b. Dnmt1 is abundant in mammalian tissues including the brain. It is responsible for the maintenance of DNA methylation patterns during DNA replication, whereas Dnmt3a and Dnmt3b perform de novo DNA methylation during development and other physiological and pathological conditions [1,11,12]. In addition, another Dnmt family member Dnmt3-Like (Dnmt3L), is homologous to Dnmt3a and Dnmt3b, but lacks a catalytic domain. Dnmt3L, however, can substantially increase activity of other Dnmt enzymes [13]. All Dnmts are intensively involved in embryonic development and their expression is reduced significantly by the time cells reach terminal differentiation. This suggests that the DNA methylation pattern is relatively stable in postmitotic cells such as neurons and cardiocytes. However, in the mature mammalian brain, postmitotic neurons still express substantial levels of Dnmts, raising the possibility that Dnmts and DNA methylation may play more crucial roles in the brain [11]. Indeed, the loss of Dnmt1 and Dnmt3a in the adult brain leads to cognitive deficits in mice [14]; in humans, mutations in Dnmt1 are associated with a form of neurodegenerative disease [15]. These studies exhibited that impairment of DNA methylation may be a fundamental mechanism in regulating mouse learning, memory and cognition. The first suggestion that DNA methylation might play an important biological role was made by Griffith and Mahler in 1969 speculating that DNA methylation could provide a basis for long term memory in the brain [16]. It has since been suggested that the presence of 5 mC in CpG island promoter regions affects the binding of transcription factors, and subsequently gene expression [17,18]. DNA methylation regulates gene expression by recruiting co-repressor complexes (e.g., histone deacetylases (HDACs) and histone methyltransferases) that can sterically block the transcriptional machinery and/or modify nucleosome structure [11]. Such complexes involve several DNA methyl-binding domain proteins (MBDs), which are required for normal cell growth and development. MBDs are expressed at higher levels in brain than in any other tissues, and many MBDs are important for normal neuronal development and function [11,12], such as methyl CpG binding protein2 (MeCP2). MeCP2 is recognized as a transcriptional repressor, and mutations in MeCP2 lead to a neurodevelopment disorder - Rett syndrome [16]. The effects of DNA methylation on gene expression are complex and may vary according to genomic location. Numerous studies suggest that methylation occurring within CpG-rich regions near the transcription start-site of a gene (i.e., CpG islands) tends to have a repressive effect on gene expression across tissues. Gene body DNA methylation is associated with a higher level of gene expression in dividing cells. However, in the murine frontal cortex, gene body methylation of non-CpG sites is negatively correlated with gene expression [19]. The presence of inconsistency in DNA methylation independent of the underlying nucleotide sequence suggests that epigenetic modifications can modulate the impact of genetic variation (i.e., genotypes) on biological processes including brain function. In order to maintain or alter genome-wide DNA methylation patterns in cells, including neurons, in response to environmental changes, both DNA methylation and demethylation have to be active. The process of DNA demethylation is much less understood. Recent studies, imply that key DNA demethylation enzymes are ten-eleven translocation (Tet) family enzymes Tet1, Tet2 and Tet3.

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Tet enzymes are involved in both global and locus-specific DNA demethylation [20e23]. Demethylation often occurs during the process of hippocampal learning, memory and adult neurogenesis, which impacts the olfactory system [24]. Tet1 mutant animals exhibit abnormal hippocampal long-term depression and impaired memory extinction. Learning and memory require neuronal activity, which can strengthen synaptic connections and weaken other synaptic connections through a process of synaptic plasticity [24].

2.1.2 50 - hydroxymethylcytosine (5hmC)

50 - hydroxymethylcytosine (5hmC) is the oxidized form of the canonical 5 mC and was identified in mammalian brain tissue and stem cells [22e25]. Tet enzymes add a hydroxyl group onto the methyl group of 5 mC to form 5hmC and initiate the complete demethylation process [22,23]. Unexpectedly, like 5 mC, 5hmC may also regulate gene expression and/or affect the function of neurons since the conversion of 5 mC to 5hmC impairs the binding of MeCP2 [26]. 5hmC accounts for w40% of modified cytosine in the brain, which is typically 5 to 10 times higher than in any other tissue, and has been implicated in DNA methylationerelated synaptic plasticity [25]. In neuronal cells, 5hmC markedly increases from the early postnatal stage to adulthood, suggesting a strong correlation between 5-hmC and neurodevelopment. Interestingly, 5-hmC is depleted on the X chromosome during postnatal neurodevelopment and aging. Functionally, 5-hmC is associated with actively transcribed genes in adult cerebellum. 5-hmCeregulated regions are dynamically changed during neurodevelopment and aging. 5-hmC is enriched throughout gene bodies in the brain, whereas 5-hmC is also present in embryonic stem cells in the bodies of active genes, although to a lesser degree than that found in the brain. The overall abundance of 5-hmC is negatively correlated with MeCP2 [27]. These findings suggest that 5hmCemediated epigenetic regulation is critical in neurodevelopment, and aging, as well as in other human neurological disorders.

2.1.3 Methods to profile the genome-wide DNA pattern Growing evidence suggested that DNA methylation regulation is critical for maintaining normal brain functions and confers an epigenetic mechanism for learning, memory and cognition [28e30]. It is thus critical to ensure that there are appropriate methods to measure the dynamics of genome-wide DNA methylation in neuronal cells/tissues. Weber M. et al. in 2005 first described a method known as methylated DNA immunoprecipitation (MeDIP)-chip, to assess genome-wide DNA methylation [31]. It consists of enriching methylated DNA fragments through an antibody against 5-methylcytosine (5 mC), and detecting the purified fraction of methylated DNA with high-throughput DNA methylation arrays (chips). MeDIP-chip (e.g., mouse and human promoter CpG arrays) can be used to map the dynamic alterations of genome-wide promoter CpG methylation in aging tissues [10]. Around the same time, Meissner et al. first reported a reduced representation bisulfite sequencing (RRBS) method to dissect the methylome of mammalian cells [32]. RRBS is based on the fact that CpG sites within the mammalian genome tend to cluster together as CpG islands (CGIs) that are usually located close to the promoters of known genes [33]. So, firstly cutting the genome into small fragments by a restriction enzyme that recognizes CpG and its flanking sequences, then most of the CGIs will be collected and sequenced with high coverage even with lower numbers of total sequencing reads (e.g., w50 million reads). RRBS has led to important findings regarding global methylation and demethylation processes during early developmental stages [34]. Lister et al. later described whole-genome bisulfite sequencing (WGBS) to map DNA methylations at single base resolution [35]. This is currently the gold standard

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for DNA methylome measurement and provides coverage for more than 90% of the approximately 28.7 million CpGs in the human genome [36]. However, it demands much higher sequencing reads, the minimum request for sequencing reads coverage is about 30X genome size. Those methods are not only good for tissue level study but also can be used for cell population interrogation of the DNA methylome. It is important to understand that the frontal brain region such as the hippocampus is not a unitary structure. Major types of hippocampus subregional neurons including cornu ammonis1 (CA1) pyramidal neurons, CA3 pyramidal neurons, and dentate gyrus (DG) granule neurons have been studied extensively, and are believed to play central roles for learning and memory and cognitive functions of the hippocampus. Hippocampal neurons are the main effectors of age-associated neurodegeneration. More specifically, CA1 and CA3 pyramidal neurons are more susceptible to neurodegenerative disorders such as Alzheimer disease, whereas granule neurons in DG are more vulnerable to age-related damage [7,37,38]. In addition, neurons from one type of population are possibly different from one to another, one is more stable and resistant to stressors while another is more vulnerable to the same stressors. Single cell transcriptome analysis has been achievable and has proven to be a powerful tool to understand the variation among the same type of cells [39]. The methods to examine the genome-wide DNA methylation at single level were also essentially desired. Guo et al. reported a methylome analysis method that enables single-cell at single-base resolution DNA methylation analysis based on reduced representation bisulfite sequencing (scRRBS) [40]. ScRRBS integrates all of the experimental processes in a single-tube reaction without including any purification steps prior to the bisulfite conversion step, since the multiple purification steps are the major problem for massive loss of DNA. This technique is sensitive and can detect the methylation status of up to 1.5 million CpG sites within the genome of an individual embryonic stem cell [41]. While Smallwood et al. described a single-cell bisulfite sequencing (scBS-seq) method, which can be applied to accurately measure DNA methylation at up to 48.4% of CpG sites [42]. In BS-seq protocols, bisulfite treatment is performed first then sequencing adaptors are ligated to fragmented DNA minimizing the DNA loss from single cell. In brief, these are all powerful tools for us to map DNA methylation at tissue, cell population and single cell level facilitating a better understanding of DNA methylation in neuronal gene regulation and thereby cognitive function.

2.2 DNA methylation and brain aging/cognitive dysfunction Studies showed that DNA methylation levels are particularly promising biomarkers of chronological aging (i.e. the calendar years that have passed since birth) and this implies a profound effect on DNA methylation levels in most human tissues and cell types [43]. In the central nervous system, DNA methylation is critical for proper postnatal neurodevelopment and undergoes age-dependent changes in the adult brain (Fig. 6.1; [27]). The overall decline in DNA methylation has been associated with cell and tissue aging including the brain for decades [1]. For example, DNA methylation in the PFC shows unique temporal patterns across life. The fastest changes occur during the prenatal period, slowing down markedly after birth and continuing to slow further with the aging process [44]. However, the effects of aging are complex, with some evidence pointing to age-related decreases in global DNA methylation, together with increased methylation at CpG islands across multiple brain regions in humans [44]. The enrichment of methylation at CpG sites tends to occur more frequently among functionally related gene transcripts,

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Methionine Homocysteine O2+ α-ketoglutarate succinate + CO 2 NH2 N O

SAM

SAH

NH2

-CH3 N H

Cytosine (C)

Dnmt

N O

NH2 CH3

N

H 5-methylcytosine (5mC)

OH

N Tet

O

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H 5-hydroxymethylcytosine (5hmC)

Gene expression

Biological function

FIG. 6.1 DNA Methylation and Demethylation Play Important Roles in Neuronal Gene Expression and Brain Function. The cytosine methylation process relies on the activity of DNA methyltransferase (Dnmt) and demethylase (Tet), plus a supply of methyl groups. SAM: S-adenosylmethionine; SAH, S-adenosylhomocysteine. Credit: Xiangru Xu.

including gene classes that regulate DNA binding and transcription factors. This age-related aggregation of methylation might contribute to transcriptional abnormalities reported in the aged brain. Consistent with this possibility, altered methylation of activity regulated cytoskeleton associated protein (Arc) DNA in the CA1 and dentate gyrus of the hippocampus in aged rats is associated with decreased Arc transcription and spatial memory impairment [45]. Evidence suggesting that DNA methylation influences differential cognitive outcomes in aging derives from a targeted study examining methylation in the promoter regions of gamma-aminobutyric acid type a receptor alpha5 subunit (Gabra5), heat shock protein family a member 5 (Hspa5), and synapsin I (Syn1) previously implicated in age-related cognitive decline in the Long-Evans rat [46]. The overall results reveal an increase in the number of methylated sites across all three genes, but only in relation to chronological age and not cognitive status. The loss of Dnmt1 and Dnmt3a in the adult brain leads to cognitive deficits in mice [28]. Transient over-expression of Dnmt3a2, a Dnmt3a isoform, in mouse hippocampus restores age-associated cognitive deficits. Moreover, inhibition of hippocampal Dnmt3a2 expression by RNAi leads to cognitive behavioral deficits in young mice [28]. Mutant Tet1 animals exhibit abnormal hippocampal long-term depression and impaired memory extinction. In humans, mutations in Dnmt1 are associated with a form of neurodegenerative disease. These results suggest that impairment of DNA methylation plays a crucial role and is a fundamental mechanism that regulates mouse learning, memory and cognition [1].

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Both developmental programming and age-dependent alterations of 5hmC occur in the mammalian brain. In hippocampus and cerebellum, 5hmC patterns may be maintained in general across life, but may be further acquired with age at specified loci [27]. In the mouse hippocampus, for example, global 5hmC content increases during aging in the absence of 5 mC decrease, suggesting that 5hmC acts as an epigenetic marker and not simply as an intermediary in DNA demethylation [47]. Besides, 5hmC levels inversely correlate with the dosage of MeCP2, a protein encoded by a gene with mutations causes Rett syndrome [27]. These findings suggest that 5-hmCemediated epigenetic regulation is critical in neurodevelopment, aging and human diseases.

3. RNA methylation, brain aging and cognitive dysfunction 3.1 RNA methylation RNA methylation such as 5-methylcytidine (m5C) and N6-methyladenosine (m6A) is a rather new theme in the last few years, though m5C and m6A both have been discovered to widely exist across the whole spectrum of animal kingdoms by recently developed transcriptome-wide sequencing approaches. It is therefore not only created a new field of research e epitranscriptome, but also revealed essential roles/functions of RNA methylation in a wide range of fundamental cellular processes. Here, we mainly review and discuss the cutting-edge knowledge/techniques of m5C RNA methylations and its impact on brain aging and cognitive dysfunctions.

3.1.1 5-Methylcytidine (m5C) Post-transcriptional modifications of RNA add complexity to RNA-mediated functions by regulating how and when a primary RNA transcript is converted into a mature RNA. There are in total around 150 known RNA modifications [48], though our knowledge about their occurrences and functions in RNA is still far more limited. The existence of methylated bases in RNA including C5-methylcytidine (m5C) had been described 50 years ago [49]. But, until only very recently, m5C was thought to be mainly restricted to the stable and highly abundant transfer RNAs (tRNAs) and ribosome RNAs (rRNAs) [50]. The latest development of novel transcriptome-wide methods to map global m5C RNA methylomes has not only restored scientific interest in the field but also contributed to a better understanding of gene expression regulation at different levels. It has become evident that post-transcriptional methylation of cytosines regulates fundamental cellular processes that are essential for normal development. The importance of tightly controlled removal of m5C on RNA is further highlighted by the link of loss-of-function mutations in methylation and demethylation enzymes to severe human diseases. RNA m5C methyltransferases belong to a large and highly conserved group of proteins, yet their RNA substrate specificity is predicted to be different [51]. Among all RNA methyltransferases Dnmt2 is the best studied, mostly for its potential function in DNA methylation. Dnmt2 shares almost all sequence and structural features of DNA methyltransferases [52]. However, it turned out that Dnmt2 plays no critical role in influencing global DNA methylation. Dnmt2-deficient mouse embryonic stem (ES) cells do not display altered genomic methylation patterns and organisms expressing only Dnmt2 as the sole candidate DNA methyltransferase gene lack genomic methylation patterns [53,54]. Dnmt2 then was identified as one of the first cytosine-5 RNA methylases in a multicellular organism [55]. At least two more enzymes NOP2/Sun RNA Methyltransferase Family Member 2 (NSun2) and member 4 (NSun4) can generate 5-methylcytidine in RNA in mammals [56,57], yet their substrate specificities are unknown.

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3.2 RNA methylation and brain aging/cognitive dysfunction NSun2 was first described in the mammalian epidermis as a transcriptional target of the protooncogene c-Myc [56]. NSun2 is up-regulated in a wide range of cancers and knockdown of NSun2 in human squamous-cell-carcinoma xenografts decreased their growth [56e58]. Interestingly, genetic mutations in the NSUN2 gene in several human cohorts have been identified and primarily linked to autosomal-recessive intellectual disability and a Dubowitz-like syndrome [59e61]. The common symptoms of the disorder include growth and mental retardation [59e61]. Similar to the human syndrome, deletion of the NSun2 ortholog in Drosophila caused severe short-term-memory deficits [60], yet it is still unknown about whether and how loss of RNA methylation is the underlying cause for the symptoms of these complex diseases.

4. Interventions and drug development targeting DNA methylation in brain aging/cognitive dysfunction Epigenetics such as DNA/RNA methylation, unlike genetics, is not only inheritable but also reversible. Strategies aimed at reversing age-associated epigenetic alterations, therefore, may lead to the development of novel therapeutic interventions that can prevent brain aging/cognitive dysfunction or alleviate symptoms of devastating, age-associated neurodegenerative diseases.

4.1 Interventions Dietary restriction (DR), without malnutrition, appears to be a promising strategy to extend the life span and counteract detrimental age-related alterations in a fashion that is evolutionarily conserved from yeast to primates and humans [62], although studies in humans are very limited and with mixed results [63]. DR of caloric intake and enhanced levels of endogenous and exogenous antioxidants are approaches that are potentially able to mitigate age-related deterioration of the brain. The beneficial effects include, in mammals, the attenuation of age-associated cognitive impairment and neurodegeneration [64]. More specifically, synaptic plasticity was shown to be enhanced by DR, as evidenced by increased long-term potentiation [65]. Besides DR, rapamycin is the first drug intervention to reliably increase mammalian lifespan by 10% or more [66]. The link between aging and disease by rapamycin treatment has been carefully discussed [67]. Interestingly, rapamycin treatment suppresses brain aging in senescence-accelerated OXYS rats [68], and also produces an improvement in cognitive functions that normally decline with age in mice [69]. The results from our study in mouse brain unexpectedly demonstrated that both DR and rapamycin can restore, at least partially, the age-related alterations in histone methylation levels [53]. This allows us to suggest a novel and beneficial epigenetic mechanism for age-interventions. The overall alterations of histone modifications in brain suggest changes of histone modification related-enzymes by these interventions. In accordance with this, a recent study showed that, independent from genotype, DR prevents the age-related increase of HDAC2 in the hippocampus, particularly in the CA3 and CA1-2 subregions. Furthermore, HDAC2 correlates positively with 5 mC while these markers were shown to co-localize in the nucleus of hippocampal cells [60]. Interestingly, in mouse cerebellar Purkinje cells, aging is associated with an increase of 5 mC and 5hmC, and these age-related increases are mitigated by DR, and the ratio between 5 mC and 5hmC decreases

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with age and DR treatment, suggesting that DR has a stronger effect on DNA methylation than DNA hydroxymethylation [70]. As discussed above, DNA methylation is implicated in age-related changes in gene expression as well as in cognition and Dnmt3a is essential for memory formation and underlying changes in neuronal and synaptic plasticity. DR indeed attenuates age-related changes in Dnmt3a in mouse hippocampus [71]. These findings enforce the notion that aging is closely connected to marked epigenetic changes, affecting multiple brain regions, and that DR is an effective means to prevent or counteract deleterious age-related epigenetic alterations. Physical exercise improves the efficiency of the capillary system and increases oxygen supply to the brain, thus enhancing metabolic activity and oxygen intake in neurons, and increases neurotrophin levels and resistance to stress. Regular exercise and an active lifestyle during adulthood have been associated with reduced risk and protective effects for mild cognitive impairment. Recent studies have examined the epigenetic impact of exercise in the brain. For example, in a rodent study, epigenetic changes in the hippocampus and cerebral cortex have been correlated with an environmental enrichment that includes voluntary exercise, which increases synaptic integrity and neuroplasticity in the brain, while improving memory, learning and stress response [72]. This study clearly indicates that a lifestyle intervention can improve cognitive functions through epigenetic mechanisms. Another study revealed that regular physical exercise induces epigenetic modifications at the dentate gyrus, which may regulate gene expression responses involved in neuroplastic and cognitive responses to stressful events. These behavioral responses to exercise were found to correlate with changes in the levels of histone H3 acetylation at lysine 14(H3K14ac) and phosphorylation of histone H3 at Ser10 (H3S10p) [73]. Aside from histones, DNA methylation is significantly increased in the hypothalamus of rats by physical exercise. Physical exercise can also increase global DNA methylation in the hippocampus, cortex, and hypothalamus and decrease expression of the Dnmt1 gene in the hippocampus and hypothalamus of rats that undergo repeated restraint stress. These findings indicate that physical exercise affects DNA methylation of the hypothalamus and might modulate epigenetic responses evoked by repeated restraint stress in the hippocampus, cortex, and hypothalamus [74]. Although the experimental data that link physical exercise or DR and epigenetics are still limited, insight into the epigenetic mechanisms involved in the brain aging process and their modulation through lifestyle interventions such as DR and physical exercise might open new avenues for the development of preventive and therapeutic strategies to treat age-related neurodegenerative diseases.

4.2 Epigenetic drug development An utmost important phase is to explore the use of epigenetic drugs as potential therapeutics for age-related brain/cognitive diseases. Unlike genetic mutations or SNPs that cannot be reversed without gene therapy, epigenetic marks that accumulate in the brain during aging are reversible and can be modulated and possibly corrected through pharmacological approaches. Several DNA methylation inhibitors, including the cytidine analogs 5-azacytidine and zebularine and nucleoside analogs that sequester Dnmt after being incorporated into DNA, are approved or are in preclinical and clinical trials for the treatment of cancer. Interestingly, Dnmt inhibitors become powerful modulators of hippocampal learning and memory when administered directly into the brains of mice and rats [75].

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5. Perspectives and challenges As an emerging field, epigenetic investigations into brain aging/cognitive dysfunction have just started to attract attention from both academics and pharmaceutical industries. It has shown promising clues in dissecting the secrets to brain aging/cognitive dysfunction. More intriguingly, interventions and drugs targeting epigenetic factors such as DNA methylation are not only providing possible cures for age-related neurological symptoms, but also assisting a mechanistic understanding of the etiology and patho-physiology of brain aging and age-related cognitive function impairment. However, piecing together the role of DNA methylation in transcriptional regulation and its impact on age-related cognitive function remains challenging.

5.1 The complexity of transcriptional regulation by DNA methylation Learning and memory are two intimately linked cognitive processes that stem from interactions between genes and the environment (experience). These cognitive functions have also been associated with changes in gene expression, and a number of synaptic plasticity associated genes have been found to enhance or impair learning and memory. Dysregulation of these synaptic plasticity genes, such as brain-derived neurotrophic factor (Bdnf), cAMP response element binding (Creb) and activity regulated cytoskeletal-associated protein (Arc) have been strongly correlated with mammalian brain aging and cognitive decline. For instance, polymorphisms in the human BDNF gene have been associated with memory and hippocampal function [76]. Bdnf-deficient mice display premature age-associated decrements [77]. Hippocampus-specific deletion of Bdnf in adult mice impairs spatial memory and extinction of aversive memories [78]. Mice with Creb deficiency have a mild cognitive impairment, and exhibit a deficit in condition-dependent learning and memory tests [79]. Expression of Arc, a neuronal activity-relevant gene, decreases with age, and this decreased expression correlates with DNA hypermethylation of its promoter [46]. It is also known that upregulation of Dnmt3a2 in hippocampus can restore age-related cognitive function, though it is not known yet how precisely Dnmt3a2 contributes to cognitive function [28]. Higher levels of Dnmt3a2 presumably result in an increase of DNA methylation of Dnmt3a2 target genes. The expression of synaptic plasticity genes like Bdnf, c-Fos and Arc are increased significantly with over-expression of Dnmt3a2. This sounds counterintuitive on the basis of the traditional view that DNA methylation is associated with transcriptional repression. However, there are reports suggesting that exonic DNA methylation may serve as a transcriptional activator that triggers gene transcription [35]. This also could be caused by methylation-associated blocking of transcriptional repressor (TF) such as neuron restrictive silencer factor (Nrst/REST), which has been reported to be involved in transcriptional repression of Bdnf. However, BDNF expression will be released after NRST/REST being inhibited by the promoter methylation [80]. This is just one example, not to mention the impact of DNA methylation on other DNA elements such as enhancers, and non-coding RNAs including long non-coding RNAs and miRNAs, which may result in regulation of neuronal gene expression [81]. Taken together, it raises additional layers of complexity for understanding the role of DNA methylation in neuronal gene expression regulation. Deep sequencing methods such as BS-DNA-methyl-seq and RNA-seq at neuronal tissue and cell level will be an effective tool to better understand this complexity.

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5.2 Elucidating the functional significance of DNA methylation Animal models as well as cutting-edge genetic manipulation approaches should be employed to determine the biological significance of DNA methylation on learning, memory and cognitive function [82]. For example, a constitutive and an inducible forebrain neuron-specific Dnmt3a2 transgenic mouse line could be generated to test if higher level Dnmt3a2 in neurons can enhance/restore mouse learning, memory and cognitive functions. In contrary, a Dnmt3a2 conditional forebrain neuron-specific knockout mouse line will also be useful to measure if Dnmt3a2 is essential for maintaining learning and cognition. Lastly, genome-wide analysis of the DNA methylation landscape and transcriptome, in parallel, in neurons under various conditions including age and expression level of Dnmt3a2 via bisulfite sequencing and RNA-seq would allow for the identification of neuronal targets of Dnmt3a2.

5.3 Interplay of DNA methylation and histone modification It is critical to understand the coordination of DNA methylation and histone modification in the regulation of neuronal gene expression and learning, memory and cognition. The epigenetic processes associated with DNA methylation/demethylation and histone modifications do not always act independently, but could closely interact to form a complex and multilayered regulatory system to dynamically fine-tune gene expression. Dnmts cooperate with histone-modifying enzymes involved in adding and/or stripping histone markers in order to impose a repressive state on a gene region [11]. For instance, there is an interesting interplay between Dnmt3a-dependent DNA methylation and Polycombgroup (PcG)-dependent H3K27me3 marks. Dnmt3a activity at non-promoter regions correlate with increased expression of neurogenic genes, by interfering with PcG binding and H3K27me3-mediated gene repression. In contrast, Dnmt3a activity at promoter regions inhibits gene expression. Additionally, Dnmt inhibitors block changes in H3 acetylation associated with memory formation. Furthermore, deficits in memory and hippocampal synaptic plasticity induced by Dnmt inhibitors can be reversed by pretreatment with an HDAC inhibitor [55]. MeCP2 binding, preferentially to fully methylated DNA, is associated with both HDAC machinery and histone methyltransferases to alter specific histone modifications [83]. Thus, DNA methylation acts in concert with histones to regulate gene expression, through interference with transcription factor binding and chromatin compaction. It is also conceivable that histone modifications influence DNA methylation patterns, indicating a bidirectional relationship between histone and DNA modifications. DNA methylation patterns are established and maintained by specific combinations of chromatin modifications. Consistent with this hypothesis, elevated histone acetylation can trigger DNA demethylation and thereby gene expression in vitro [11]. Conversely, HDACs are known to interact with Dnmts and inhibit gene expression through the induction of DNA methylation [50], whereas transcription factors that recruit histone acetyltransferases can trigger demethylation of DNA. Likewise, HDAC inhibitors are capable of inducing DNA demethylation [83]. Taken together, these results reveal a complex relationship between histone modifications and DNA methylation.

5.4 Examine RNA methylation (m5C) and its role in brain aging and cognitive dysfunction Although the precise molecular and biological functions of RNA m5C methyltransferases are still poorly understood. A noticeably high number of NSun-proteins are associated with human disease

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syndromes that include growth retardation and neurological deficits. It will be critical to dig out the specific link between human diseases and RNA methylation, since it may be explained by (1) a direct role of 5-methylcytidine in rRNA and tRNA to regulate global protein translation, or (2) roles of 5-methylcytidine in functional mRNAs and subsequently proteins. Moreover, the methods to map the epitranscriptome were mostly adapted from DNA methylation approaches, the reproducibility and reliability are still unclear, and needs careful examination [50,51].

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[43] Horvath S. DNA methylation age of human tissues and cell types. Genome Biol 2013;14(10):R115. [44] Numata S, Ye T, Hyde TM, Guitart-Navarro X, Tao R, Wininger M, et al. DNA methylation signatures in development and aging of the human prefrontal cortex. Am J Hum Genet 2012;90:260e72. [45] Penner MR, Roth TL, Chawla MK, Hoang LT, Roth ED, Lubin FD, et al. Age-related changes in Arc transcription and DNA methylation within the hippocampus. Neurobiol Aging 2011;32:2198e210. [46] Haberman RP, Quigley CK, Gallagher M. Characterization of CpG island DNA methylation of impairmentrelated genes in a rat model of cognitive aging. Epigenetics 2012;7:1008e19. [47] Chen H, Dzitoyeva S, Manev H. Effect of aging on 5-hydroxymethylcytosine in the mouse hippocampus. Restor Neurol Neurosci 2012;30:237e45. [48] Machnicka MA, et al. MODOMICS: a database of RNA modification pathways d 2013 update. Nucleic Acids Res 2013;41(Database issue):D262e7. [49] Gold M, Hurwitz J, Anders M. The enzymatic methylation of RNA and DNA, Ii. On the species specificity of the methylation enzymes. Proc Natl Acad Sci U S A 1963;50:164e9. [50] Hussain S, et al. Characterizing 5-methylcytosine in the mammalian epitranscriptome. Genome Biol 2013; 14:215. [51] Motorin Y, Lyko F, Helm M. 5-Methylcytosine in RNA: detection, enzymatic formation and biological functions. Nucleic Acids Res 2010;38:1415e30. [52] Yoder JA, Bestor TH. A candidate mammalian DNA methyltransferase related to pmt1p of fission yeast. Hum Mol Genet 1998;7:279e84. [53] Okano M, Xie S, Li E. Dnmt2 is not required for de novo and maintenance methylation of viral DNA in embryonic stem cells. Nucleic Acids Res 1998;26:2536e40. [54] Raddatz G, et al. Dnmt2-dependent methylomes lack defined DNA methylation patterns. Proc Natl Acad Sci U S A 2013;110:8627e31. [55] Goll MG, et al. Methylation of tRNAAsp by the DNA methyltransferase homolog Dnmt2. Science 2006;311: 395e8. [56] Frye M, Watt FM. The RNA methyltransferase Misu (NSun2) mediates Myc-induced proliferation and is upregulated in tumors. Curr Biol 2006;16:971e81. [57] Metodiev MD, et al. NSUN4 is a dual function mitochondrial protein required for both methylation of 12S rRNA and coordination of mitoribosomal assembly. PLoS Genet 2014;10:e1004110. [58] Frye M, et al. Genomic gain of 5p15 leads to over-expression of Misu (NSUN2) in breast cancer. Cancer Lett 2010;289:71e80. [59] Khan MA, et al. Mutation in NSUN2, which encodes an RNA methyltransferase, causes autosomal-recessive intellectual disability. Am J Hum Genet 2012;90:856e63. [60] Abbasi-Moheb L, et al. Mutations in NSUN2 cause autosomal-recessive intellectual disability. Am J Hum Genet 2012;90:847e55. [61] Martinez FJ, et al. Whole exome sequencing identifies a splicing mutation in NSUN2 as a cause of a Dubowitz-like syndrome. J Med Genet 2012;49:380e5. [62] Maalouf M, Rho JM, Mattson MP. The neuroprotective properties of calorie restriction, the ketogenic diet, and ketone bodies. Brain Res Rev 2009;59:293e315. [63] Hori N, Hirotsu I, Davis P, Carpenter D. Long-term potentiation is lost in aged rats but preserved by calorie restriction. Neuroreport 1992;3:1085e8. [64] Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, Flurkey K. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 2009;460:392e5. [65] Blagosklonny MV. Rapamycin extends life- and health span because it slows aging. Aging 2013;5:592e8. [66] Kolosova NG, Vitovtov AO, Muraleva NA, Akulov AE, Stefanova NA, Blagosklonny MV. Rapamycin suppresses brain aging in senescence-accelerated OXYS rats. Aging 2013;5:474e84.

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Histone acylation in the epigenomic regulation of insulin action and metabolic disease

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Aneta Balcerczyka, Marta Biesiekierskaa, Varvara Vialichkaa, Luciano Pirolab Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Polanda; Carmen Laboratory; INSERM U1060; Lyon-1 University, South Lyon Medical Faculty; Oullins, Franceb

1. Introduction A recent and largely accepted definition for epigenetics describes it as a stably heritable phenotype resulting from changes in a chromosome without alterations in the DNA sequence [1]. Such level of control of biological information beyond the information carried by the DNA sequence is governed by several molecular mechanisms, including DNA methylation on cytosines, multiple post-translational modifications on histones - low molecular weight proteins constituting the histone octamer around which DNA is wrapped to form the nucleosome -, and modulation of transcription via RNA-based mechanisms (transcriptional regulation via microRNAs and via methylation on messenger RNA). The four most investigated post-translational covalent histone modifications, are acetylation, methylation, phosphorylation and ubiquitination. Together, these histone PTMs contribute to the development of a compact (transcriptionally silent) or loose (transcriptionally active) chromatin structure, and control the transcriptional potential of a given gene [2]. The idea of the existence of a “histone code” has been proposed, and such a code, resulting from the overall histone PTMs pattern e and DNA methylation pattern - on a given chromosomal region, should enable the prediction of the transcriptional potential of a gene [3,4]. We can assign some predictive potential to the histone code: for example, histone acetylation mostly associates to transcriptionally active chromatin, while hypoacetylation marks silent euchromatic regions. The case for histone methylation is more nuanced, as methylation occurs both in euchromatin and heterochromatin depending on the lysine residues being modified by methylation. Methylation at lysine 9 of histone H3 (H3K9) occurs at compact chromosomal territories (centromers, telomers), on the silenced X chromosome and at inactive promoters [5]. Conversely, methylation at lysine 4 (H3K4) is associated to active genes [6]. Histone phosphorylation, as occurring on serine 10 of histone H3, controls chromosome condensation and transcriptional activation depending on the cell cycle phase [7]. Histone arginine deimination antagonises arginine methylation, adding a novel aspect to histone regulation, whereby different post-translational modifications on the same amino acid have opposite effects [8]. The regulatory interplay provided by all these different histone PTMs has led to

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the hypothesis of the “histone code” which posits that the integration of all the histone PTMs determines the transcriptional potential of chromatin [3,4]. Insulin is the main anabolic hormone of the body, controlling whole body metabolism by eliciting nutrient uptake by skeletal muscle and adipose tissue and inhibiting gluconeogenesis in liver [9]. Insulin modulates gene expression, and transcriptional alterations are observed in metabolic disease and diabetes, thus suggesting that insulin signaling and the epigenetic machinery governing transcription may be interdependent. The activity of several transcription factors has been shown to be directly regulated by insulin. Transcription factors responsive to insulin include the sterol regulatory element binding protein-1 (SREPB-1) [10], ChREBP [11] and Stat 5B [12]. On the contrary, the FOXO family transcription factors control stress response and gluconeogenesis, and their transcriptional activity is repressed by insulin via phosphorylation-mediated nuclear extrusion [13]. Transcription factors recruit RNA polymerase at gene’s promoters. However, a transcriptionally competent gene, and its physical accessibility, is determined by its epigenetic landscape, i.e. DNA methylation and histone PTMs occurring on the chromatic territory of a gene. Initial clues on the existence of a connection between epigenetic-based phenomena and control of metabolism stemmed from the observation that primary myotubes obtained from type 2 diabetic subjects retained a “diabetic” phenotype in cell culture, displaying alterations of insulin signaling and glycogen synthesis [14]. The aim of this chapter is, firstly, to introduce some basic notions of insulin signaling and of acetylation and other acylation epigenetic marks. Secondly, we will discuss the recent advances that demonstrate the existence of a functional interaction between insulin action and the building of insulin-dependent epigenetic modifications, with a particular focus on acetylation and acylation histone PTMs. Such interaction supports the idea that nutrition has a major impact on the epigenome, and provides a conceptual framework to understand the occurrence of epigenetic alterations accompanying metabolic defects such as insulin resistance or Type 2 Diabetes. Epigenetic defects associated to metabolic dysfunction can reveal valuable potential therapeutic targets to treat insulin resistance and/or Type 2 Diabetes.

2. Insulin signaling: an outlook Skeletal muscle and adipose tissue are the body’s tissues responsible for the bulk of glucose uptake and storage, in the form of glycogen or newly synthesized lipids via lipogenesis. Quantitatively, the skeletal muscle is the major glucose-disposing tissue [15]. The physiological relationship between insulin sensitivity and glucose uptake appears to be, however, predominant in the adipose tissue as compared to skeletal muscle. Indeed, fat-specific disruption of the insulin receptor gene (generating “FIRKO” mice) results in animals with striking phenotypes such as a lower fat mass, a protection against obesity and glucose intolerance and increased longevity [16,17]. On the contrary, “MIRKO” mice, harboring a muscle-specific insulin receptor knockout, have relatively minor defects in glucose disposal and glycogen deposition in muscle, suggesting that muscle contraction is sufficient to regulate the majority of glucose metabolism in muscle [18]. The intracellular signaling of insulin is initiated by the binding of the hormone to the insulin receptor (IR), a membrane-spanning tetrameric glycoprotein composed of two extracellular a-subunits and two transmembrane b-subunits [19]. The receptor undergoes activatory auto-phosphorylation on intracellular tyrosine residues. The activated IR, in turn, recruits and phosphorylates downstream

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signaling proteins, initiating intracellular signaling that controls metabolic processes, cell growth and cell survival [20]. The activated IR binds to and tyrosine phosphorylates multiple SH2-domain containing adapter proteins. Among those, the IRS (Insulin Receptor Substrate) proteins mediate most of insulin’s metabolic effects, with IRS1 and IRS2 being the major isoforms. IRS proteins contain multiple tyrosine phosphorylation sites, which transduce insulin action by generating two main signaling pathways [21]: the PI3K (phosphoinositide 3-kinase)/Akt pathway, mediating the effects of insulin on metabolism and cell survival; and the ERK/MAPK (extracellular signaleregulated kinases/Mitogen-activated protein kinases) pathway, controlling cell growth and division [22]. Insulin-regulated PI3K/Akt mediates the translocation of the glucose transporter GLUT4 to the plasma membrane, thereby allowing glucose entry into the cell. Insulin-activated Akt kinase also targets the FoxO transcription factor family. Four FOXO gene products exist in mammals: mediating the effects of insulin by up-regulating target genes involved in cell cycle arrest, apoptosis, energy metabolism, and oxidative stress resistance. Insulin is an inhibitor of FOXO-dependent transcriptional regulation. In fact, Akt-mediated phosphorylation on three phosphorylation sites on FOXO, (Thr 24, Ser 253, and Ser 316) drives nuclear extrusion of FOXO from the nucleus. FOXO activity is also regulated by lysine acetylation, with the acetyltransferase p300/CBP and the deacetylase SIRT1 controlling the acetylation balance [23]. Fig. 7.1 depicts a schematic overview of the insulin signaling pathway and FOXO nuclear translocation.

3. The structure of chromatin: general notions In the human, the genome’s 6 billion base pairs make up a 2-m thread of DNA, which is packaged within the cell nucleus that is a few microns in diameter. Hierarchical packaging of the DNA allows the containment of DNA within the nucleus. The first step of DNA compaction is the nucleosome, a globular octamer composed of dimers of the histone proteins H2A, H2B, H3 and H4. The histone octamer provides the scaffold on which 146 bp of DNA are wrapped. The binding of DNA to the histone octamer causes a sevenfold shortening of the DNA’s linear length and is the first compaction mechanism of DNA [1]. Nucleosomes are further stabilized by the external binding of histone H1. Given such high degree of hierarchical compaction, the accessibility of genetic information to allow gene transcription e or conversely the silencing of chromosome regions not requiring transcription e is controlled by the establishment of epigenetic marks on the chromatin template. Eventually, the binding of transcriptional complexes on active promoters is dictated by the DNA methylation state on CpG sequences - with methylation being transcriptionally repressive - as well as on histone PTMs - methylation, phosphorylation and acetylation e taking place mainly on the protruding N-terminal tails of H2A, H2B, H3 and H4 histones. DNA methylation and histone PTMs thus constitute an epigenetic-based way to regulate gene expression.

3.1 Histone acetylation The occurrence of an acetylated state in histones was first described in the early sixties, and soon after histone acetylation was proposed by V. Allfrey to confer a transcriptionally competent state to

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FIG. 7.1 Overview of the Insulin Signaling Pathway. The activated insulin receptor recruits IRS proteins which serve as a docking platform to the Grb2/SOS complex - activating the MAPK pathway - and the p85/p110 PI3K heterodimer, activating, via PI(3,4,5)P3, PKB and, further downstream, GLUT4 translocation at the plasma membrane to induce glucose transport. Activated PKB also phosphorylates FOXO, thus inducing its inactivation via nuclear extrusion. On the contrary, acetylated FOXO is nuclear and can exert transcriptional function. Blue circles on the signaling proteins denote phosphorylation, yellow circles denote phosphorylation on phosphoinositides, green squares on nuclear FOXO denote acetylation. Credit: Aneta Balcerczyk, Marta Biesiekierska, Varvara Vialichka and Luciano Pirola.

chromatin [24]. It was not, however, until the late 80s that the first histone acetyltransferase, from Tetrahymena, was discovered [25]. Acetylation of lysine residues on histones is an evolutionarily conserved histone PTM occurring throughout eukaryotes [26]. Lysine acetylation is a reversible modification, with acetylation occurring via the enzymatic transfer of the acetyl group from acetyl-CoA to the 3-amino group of lysine residues. The eighteen HDACs can be divided into three major classes; Zn2þ-dependent class I and II HDACs and class III NADþ-dependent HDACs, also known as sirtuins [26,27], and a more recently described class IV HDAC comprising only HDAC11 [28]. The effect of the acetyl group addition onto the histone lysine is to promote the activation of transcription, whereas the inverse process causes inactivation. However, aside from acetylation, posttranslational modifications of histones include additions of some other acyl chemical groups, such as propionyl, butyryl and butyryl-derivatives, crotonyl, succinyl, and malonyl (Fig. 7.2).

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FIG. 7.2 Histone Post-Translational Acylations of the Histone Tails of the Nucleosome. The nucleosome consists of an octamer of core histones (H2A, H2B, H3 and H4; shown as green circles) and DNA (yellow line) wrapped around them. Specific lysine (K; gray) and arginine (R; green) residues present in histone tails can undergo various post-translational modifications by the attachment of acyl groups (shown as gray and green rectangles) to their side chains. Abbreviations: H2A, H2B, H3, H4 e histones; K e lysine; R e arginine; me e methylation; cit e citrullination; bhb e b-hydroxybutyrylation; hib e 2-hydroxyisobutyrylation; pr e propionylation; bz e benzoylation; bu e butyrylation; cr e crotonylation; mal e malonylation; succ e succinylation. Credit: Aneta Balcerczyk, Marta Biesiekierska, Varvara Vialichka and Luciano Pirola.

Although, their role is not yet fully understood, the general result of these acyl modifications may control chromatin folding, structure and nuclear localization, as well as transcriptional competence [29]. The chemical structures of lysine acyl modifications are reported in Fig. 7.3.

3.2 Histone b-hydroxybutyrylation In diabetes, diabetic ketoacidosis (DKA) is a serious complication occurring when serum concentrations of ketone bodies reach high levels to compensate for the organ’s failure to utilize glucose. At the same time, ketogenesis, i.e. the production by the liver of the ketone bodies b-hydroxybutyrate and acetoacetate (AcAc), is a physiologically important process to produce an alternative metabolic source of energy during the neonatal period, starvation or prolonged physical effort [30]. b-hydroxybutyrate and AcAc are imported in extra-hepatic tissues and used to produce AcAc-CoA via the enzymatic activity of Succinyl-CoA:3-Oxoacid-CoA transferase (SCOT, coded by the nuclear gene OXCT1). In turn, AcAc-CoA is used as a substrate to generate two molecules of AcCoA. The metabolic necessity of physiological ketogenesis is highlighted by the fact that SCOT gene ablation induces early postnatal mortality in mice [31]. Ketone bodies, depending on their plasma

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FIG. 7.3 Schematic Structural Visualisation of Histone Post-Translational Acylations on Amino Acid Lysine Residues of Proteins. Substituted amine group is labeled in green, the covalent-bond acyl groups are indicated in green, whereas the black vertical lines on both sides of the lysines mimics their connections to the other amino acid residues in protein molecule. Credit: Aneta Balcerczyk, Marta Biesiekierska, Varvara Vialichka and Luciano Pirola.

concentration, are therefore a necessary nutrient or the reflection of a pathological status. The understanding of the physiological role(s) of R-b-hydroxybutyrate has taken an unexpected turn, as it was demonstrated that it acts as a histone deacetylase (HDAC) inhibitor, thereby favoring histone hyperacetylation. Such histone hyperacetylation has been associated to the anti-oxidative properties of b-hydroxybutyrate in a mouse model [32]. At approximately the same time, b-hydroxybutyrate has been proposed to act as an anti-inflammatory molecule targeting the inflammasome [33]. Therefore, b-hydroxybutyrate might potentially contribute to reverse the pro-inflammatory and pro-oxidative status of the endothelium induced by hyperglycemia. Furthermore, recent research has revealed a novel and exciting aspect in the biology of R-b-hydroxybutyrate, as R-b-hydroxybutyrylation has

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been revealed to be a novel histone PTM, associated to a permissive transcriptional state. It should be noted, however, that independent studies cast some doubt on the fact that b-hydroxybutyrate acts as an HDAC inhibitor [34].

3.3 Histone 2-hydroxyisobutyrylation The lysine amino-group modification in a form of attachment of a 2-hydroxyisobutyryl group to histone H3K8 (H3K8hib) is evolutionarily conserved in eukaryotes, from yeast to humans. 2hydroxyisobutyrylation (Khib) occurs not only on lysine residues protruding from the N-terminal histone tails, but also in core lysine residues of histone proteins, which may have an impact on inter- and intra-nucleosome interactions [35]. The attachment of a 2-hydroxyisobuturyl group takes place when cell metabolism generates an abundance of 2-hydroyisobutyryl-CoA as compared to acetyl-CoA [36]. The removal of the group is catalyzed by lysine histone deacylases Rpd3p and Hos39 in vivo [37]. Huang and co-workers showed that H3K8hib is a modification that occurs in response to stress. H3K8hib was described as a mark regulated by the available source of carbon. In turn, 2-hydroxyisobutyrylated H3K8 contributes to the control of the intracellular glucose level in cells through the regulation of chromatin state. Huang and co-workers suggested that for glucose deficiency to affect H3K8hib levels, an intact glycolysis pathway is essential for this type of modification to happen [37]. Besides taking place on histone lysine, 2-hydroxyisobutyrylation has also been shown to occur on approximately 200 proteins, and such modification is often associated with acetylation and succinylation, suggesting the existence of a cross-talk between these three acylations. Furthermore, almost all enzymes of the glycolytic pathway are 2-hydroxyisobutyrylated [37]. So far, 2-hydroxyisobutyrylation has been observed in four analyzed eukaryotic cell lines: HeLa cells, Drosophila S2 cells, Saccharomyces cerevisiae cells and mouse embryonic fibroblast cells. In human and mouse histones, 63 lysine residues were observed to contain 2-hydroxyisobutyryl group. Interestingly, 27 out of 63 of these specific lysines did not contain acetyl nor crotonyl modifications, indicating that specific lysines can undergo only a specific subset of acylation PTMs [35].

3.4 Histone propionylation, butyrylation, crotonylation 3.4.1 Histone propionylation

Propionylation of histones occurs when the donor for propionyl groups e propionyl-CoA e is found in a high concentration in the cell, which is dependent on the dietary availability of the SCFA propionate. Propionyl-CoA is formed by short-chain lipids and during some amino acid catabolism. This modification has been found in histones and non-histone proteins (e.g. CBP, p300 or p53) [38], where it leads to the neutralization of the positive charge of a lysine. Cheng and co-workers [39] demonstrated that p300 and CBP are able to catalyze the propionylation of p53 lysines in vitro. Generally, p300 and CBP act both as acetyl-transferases and propionyltransferases in vitro and they can also acetylate p53 in vivo. Furthermore, using co-transfection experiments in H1299 cells, it was shown that p300/CBP catalyzes p53 propionylation in vivo. Conversely, the deacetylase (and deacylase) sirtuin 1 (Sirt1) was demonstrated to be capable of inducing p53 depropionylation. These findings demonstrate that some substrates and regulatory enzymes can be

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shared in propionylation and acetylation pathways of lysine [39]. Furthermore, Sirt1 regulates b-cell function in vitro and co-activate insulin gene expression in vitro [40].

3.4.2 Histone butyrylation

Butyryl-CoA is a metabolic intermediate formed during the b-oxidation of fatty acids as well as a substrate for fatty acid elongation. This compound is a donor of butyryl groups that can be transferred to the histones and non-histone proteins in order to modify them [41]. Histones, p53, p300, and CBP are all substrates for lysine acetylation, and can also be propionylated or butyrylated. CBP and p300 are able to transfer butyryl-CoA to core histones and non-histone proteins, such as p53 [41]. Two butyrylated lysine residues (K1595 and K1597) were identified in CBP, while three butyrylated lysines (K372, K373 and K382) were found in p53, but without any propionylation. Interesting, both Kprop and Kbuty residues were also potential acceptors for acetylation. It is thought that propionylation and butyrylation have other biological functions in comparison to acetylation, but this hypothesis has not been confirmed. Chen and co-workers analyzed whether core histones may be propionylated or butyrylated in vitro with the use of either propionyl-CoA or butyrylCoA and appropriate acetyltransferases. The result was that only p300/CBP out of all tested acyltransferases were capable of propionylating or butyrylating p53 at a satisfactory level [41]. The purpose of lysine propionylation and butyrylation in epigenetic regulation of chromatin structure and function needs further investigation [42].

3.4.3 Histone crotonylation Crotonylation of histone lysine residues is dependent on intracellular concentration of crotonyl-CoA. Usually, the concentration of this metabolite is 3-fold lower in comparison to the concentration of acetyl-CoA, which means that histone crotonylation is much less abundant as compared to acetylation. This modification is carried out by p300 and it occurs broadly in core histones, activating transcription in vivo [43]. On the other hand, the removal of the crotonyl modification is mediated by three sirtuin proteins (Sirt1, Sirt2, Sirt3) in vitro and, among them, only Sirt3 has been shown to possess a significant decrotonylase activity in vivo. Sirt3 plays an important role in insulin signaling, in type 1 and type 2 diabetes, because it regulates the production of reactive oxygen species (ROS) and, in turn, insulin resistance in skeletal muscle. Deficiency of this sirtuin due to Sirt3 knockout leads to impaired insulin signaling. Furthermore, the level of Sirt3 can be modulated by feeding and fasting [44], suggesting that Sirt3 is a central sirtuin regulating the interaction between the nutritional state and the structure of chromatin. So far, little is known about how crotonylation or decrotonylation of histones modifies insulin signaling. However, histone lysine crotonylation has a clear role as an epigenetic regulator.

3.5 Histone succinilation and malonylation 3.5.1 Histone succinilation

The transfer of a succinyl group to the ε-amino lysine residue changes the lysine charge from þ1 to 1 resulting in lack of interaction with other molecules which are negatively charged (e.g. nucleotides, proteins etc.). The succinylation level is affected by the concentration of succinyl-CoA formed in different metabolic pathways. Proteins exhibiting various biological functions and involved in various processes may undergo succinylation, and this modification is common both in prokaryotic and

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eukaryotic organisms. Extensive lysine succinylation was noted in HeLa cells and mouse liver, probably because these cell lineages contain a great abundance of mitochondria, wherein succinic acid is mainly generated [45]. Generally, succinyl-CoA is formed within the mitochondria and the process of mitochondrial lysine succinylation is specifically reverted by one of the sirtuin family members e Sirt5. Sirt5 is localized in the mitochondrial matrix of liver cells, where it acts as deacylase, and in non-liver cells functioning as a desuccinylase. Sirt5 is also present in the cytosol [45]. The overlap of succinylation and acetylation on non-histone proteins was observed by Weinert and co-workers who compared the succinylated sites with acetylated sites and presented following results: 66% of E. coli, 56% of yeast, 27% of human, and 57% of mouse succinylated sites being acetylated at the same positions [45]. Xie and co-workers identified 37 histone lysine succinylation sites in the core histones of HeLa cells (13 Ksucc sites), mouse embryonic fibroblasts (7 Ksucc sites), Drosophila S2 cells (10 Ksucc sites), and Saccharomyces cerevisiae cells (7 Ksucc sites). Furthermore, 2 succinyl sites on lysine were noticed on histone H2A, two on histone H2B, another two on histone H4, and one on histone H3. Importantly, all of these sites (except H2AK13 and H2BK37) were identified within the C-terminal tail of the histones, contrary to the most widespread N-terminal PTMs [46]. Other histone residues located C-terminally can be methylated (H3K79) and acetylated (H3K56).

3.5.2 Histone malonylation Malonylation is the process of transferring a malonyl group from malonyl-CoA to histone lysine residues with the simultaneous shift of a lysine’s positive charge to a negative one. Malonylation is conserved from bacteria to humans and the removal of such acylation is performed by Sirt5 [47]. Du and co-workers identified 573 malonylation lysine sites in over 268 proteins in diabetic mice [48], however the significance of malonylation in biological systems is still poorly understood. Due to Sirt5-regulated malonylated proteins exhibiting a higher abundance in pathways such as gluconeogenesis and glycolysis, Nishida and co-workers measured the glycolytic flux in primary mouse hepatocytes to investigate the possible function of malonylation. The expression of a glucose transporter 2 (GLUT2) lacking malonyl groups turned out to be unchanged in Sirt5-knock-out primary hepatocytes, giving a suggestion that malonylation of glycolytic enzymes regulated by Sirt5 controls the glycolytic flux. In diabetic rats, the concentration of malonyl-CoA increases rapidly and hypermalonylation of liver proteins in diabetic mice shows the potential to inhibit glycolytic enzyme function [47]. At the same time, an increased level of malonyl-CoA in type 2 diabetic patients and pre-diabetic rats was observed, while malonyl-CoA decarboxylase reversed insulin resistance by decreasing the level of malonyl-CoA in the cell [48]. In comparison to acetylation or succinylation, malonylation differs in its target proteins and distribution. Nishida and co-workers demonstrated that 56% of mitochondrial Kma sites overlapped with Ksucc sites, while 44% of Kma sites were different from both Kac and Ksucc [47]. Furthermore, Xie and co-workers identified one histone lysine malonylation site in HeLa cells and two histone lysine malonylation sites in Sacharomyces cerevisiae [46].

3.6 Histone lysine benzoylation e linking nutritional additives to epigenetics Lysine benzoylation (Kbz) is one of the most recently discovered modifications of histones. The donor of benzoyl group is benzoyl-CoA, and lysine benzoylation takes place in both prokaryotic and eukaryotic organisms. In mammals, benzoyl-CoA concentration levels are regulated by sodium

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benzoate, a molecule used as a preservative in the food industry, whereas in bacteria it is formed during the degradation of aromatic substances. The removal of this histone mark is dependent on Sirt2 in vivo and in vitro [49]. Histone benzoylation has been characterized by Huang and co-workers [49]. Researchers have synthesized a peptide of the same sequence as H2B histone in vivo containing a benzoyl group at H2BK5. With the use of a pan-anti-Kbz antibody, the group detected Kbz histones of human HepG2 cells, Drosophila S2 cells, and mouse liver. Marks of lysine benzoylation were detected in core histones of the three species mentioned, implying that this type of modification is evolutionarily conserved in both insect and mammalian cells. In mammals, 22 Kbz sites were detected [49]. A schematic overview of the different acylations described here is provided in Table 7.1.

4. The interaction between insulin signaling and histone acetylation/ acylations The epigenetic response occurs in both physiological and pathological states, due to the supply of nutrients or an infection. This, in turn, leads to long-term dysregulated gene expression and is able to predispose an individual to diseases such as diabetes or hyperlipidemia [52]. Thus, metabolism can affect histones and non-histone protein modifications through the change of a local concentration of given metabolite [53]. Based on the histone acylation processes described above, we present here the evidence linking epigenetic mechanisms to the control of gene expression of enzymes involved in insulin signaling and, in beta cells, of the insulin gene itself.

4.1 The epigenetic control of insulin regulated genes As discussed above, acyl-histone PTMs are transcriptionally permissive histone post-translational modification. In addition, a further epigenetic mechanism to modulate transcription is the replacement of the canonical histones (histones H2A, H2B, H3, H4) with a histone variant. As a general rule, replacing H2A with the histone variant H2A.Z promotes transcription [54,55], and transcriptionally active chromatin is characterized by nucleosomes containing the histone variants H2A.Z and H3.3 [54]. The deposition of H2A.Z on the transcriptional start site (TSS) of insulin-regulated genes was examined in skeletal muscle cells. Nucleosomes of insulin-regulated genes were enriched in H2A.Z on chromatin surrounding the transcriptional start site. Such enrichment was observed on insulin-induced genes and correlated to insulin-stimulated histone acetylation [56].

4.2 Epigenetic alterations in type 2 diabetes and in insulin resistance Type 2 Diabetes is built upon a complex and multifactorial etiology. From a molecular point of view, alteration of multiple signaling pathways contributes to insulin resistance, dysglycaemia and triglyceridaemia, as well as to b-cell failure and dysregulation of gluconeogenesis [57]. The worldwide prevalence of Type 2 Diabetes is growing at an alarming rate. According to figures from the International Diabetes Federation, in 2015 approximately 415 million adults worldwide suffered from Type 2 Diabetes, and by 2040, 642 million people will likely be affected.

Table 7.1 Non-canonical histone acylations: targeted lysines, affected pathways and biological consequences. Histone modification

Potential molecular target

Affected signaling pathways

Biological consequences of the modification

References

2-hydroxyisobutyrylation

Lysine 8 of H3 histone (H3K8)

Glycolysis

-

[35]

Propionylation

CBP p300 p53 Lysine 14 and 23 of histone H3 (H3K14, H3K23) Lysine 5, 8, 11, and 12 of histone H4 (H4K5, H4K8, H4K11, H4K12) H3K14 H4K5, H4K8 and H4K12 Lysine 372, 373, and 382 of p53 Lysine 1595 and 1597 of CBP

Unknown

Controlling the glucose level in cells through the regulation of chromatin state Unknown

-

-

[41]

Butyrylation

Sperm cell differentiation

-

Crotonylation

Histones H2A, H2B, H3, H4

Succinylation

Lysine of H2A, H2B, H4, H3

Malonylation

Lysine 184 of GADPH

Benzoylation

Lysine 5 of H2B histone (H2BK5)

-

RNA processing Nucleic acid metabolism Chromosome organization Gene expression RNA processing Fatty acid oxidation pathway

-

Gluconeogenesis Glycolysis pathway Amino acid metabolism Fatty acid metabolism

-

Glycerophospholipid metabolism Ovarian steroidogenesis Phospholipase D pathway Serotonergic synapse pathway Insulin secretion

-

Credit: Aneta Balcerczyk, Marta Biesiekierska, Varvara Vialichka and Luciano Pirola.

-

-

-

Controlling the structure of chromatin and transcriptional activity Creating a docking site for different bromodomain-containing proteins Enhancing transcriptional activity in sperm cells Regulation of HDAC1 activity Expelling the HP1a from heterochromatin Inhibition of cell cycle progression through S-phase Chromatin succinylation as a link between the TCA cycle status and epigenetic transcriptional control Activation of transcription Controlling the glycolytic flux by glycolytic enzymes regulated by Sirt5 Potential inhibition of glycolytic enzymes function by hypermalonylated liver proteins in diabetic mice Inhibition of GAPDH enzymatic activity Regulation of PLA2G4C, ACHE, PLA2G15, and PTGS2 gene activity

[38,41]

[43]

[50,51]

[47,48]

[49]

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Genome-wide epigenetic analysis of histone PTMs in Type 2 Diabetes underscored the occurrence of epigenomic alterations [58e60]. There is a consensus over the possibility that modulation of the epigenome by lifestyle (unhealthy diet, physical inactivity, exposure to environmental pollutants or food additives) may integrate the effects of genetic predispositions and environmental factors toward the development of the disease. Acetylation profiles, and perhaps acylation profiles, of histones are among the major histone PTMs in the diabetic pathology, due to the central role of histone acetyltransferases and deacetylases in glucose and lipid metabolism. Several lines of evidence support the notion that epigenetic changes are key to the pathogenesis of diabetes. Infants exposed to intrauterine malnutrition - defined as an offspring born from mothers experiencing famine or limited food availability during pregnancy - are at high risk of Type 2 Diabetes [61,62]. During the intrauterine period, alteration of epigenetic modifications in pancreatic b-cells takes place [63,64]. Also, in utero exposure to hyperglycaemia has been shown to favor the development of diabetes and obesity in offspring [65]. Finally, modulation of b-cell function can be linked to epigenetic changes predisposing to Type 2 Diabetes. Experimentally, exposure of human pancreatic islets to palmitate induced global and specific DNA methylation alterations that resulted in coordinated changes in mRNA expression of the insulin gene and decreased insulin secretion [66].

4.3 Histone acetylation in pancreatic beta cells Histone acetylation is involved in the control of gene expression of insulin. The actively transcribed insulin gene shows hyperacetylation at lysine residues of histones H3 and H4, integrated by other transcriptionally permissive modifications such as lysine 4 methylation on histone H3. Chromatin immunoprecipitation studies against acetylation on beta cell lines, unrelated cell lines, and embryonic stem cells, detected histone H3 hyperacetylation only on the insulin promoter of mature beta cells. This hyperacetylation was mediated by p300 recruitment to the insulin gene promoter [67]. On the contrary, islet-derived precursor cells (IPCs), which can be differentiated to mature beta cells do not accrue histone acetylation on the insulin promoter. Yet, these IPCs, are poised for induction of insulin expression, as the insulin gene is lacking transcriptionally repressive H3K9 methylation, which is otherwise observed in cell types unable to differentiate into beta cells [68]. In this context, the pancreatic duodenal homeobox-1 protein (Pdx-1) is the pivotal transcription factor for the differentiation of beta cells [69]. Importantly, Pdx-1 specifically induces histone H4 acetylation and insulin gene expression in the beta lineage [70]. Interestingly, the epigenetic regulatory functions of Pdx-1 persist beyond differentiation to fine tune insulin gene expression depending on the nutritional conditions. Under hypoglycaemia, Pdx-1 recruits the histone deacetylases HDAC1 and HDAC2 to induce promoter deacetylation and transcriptional repression of the insulin gene [71]. In contrast, in the fed and hyperglycaemic state, requiring insulin, Pdx-1 recruits the histone acetyltransferase p300 to the insulin gene promoter, leading to histone hyperacetylation and insulin gene transcription [72,73].

5. Conclusions. Importance of histone acetylation/acylation in the modulation of insulin action and future challenges Insulin resistance and related metabolic disorders including Type 2 Diabetes and cardiovascular disease are dependent both on genetic predisposition and environmental factors, the latter with the

References

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potential to be mediated via epigenetic mechanisms. Interestingly, the genetic contribution to the predisposition to insulin resistance and Type 2 Diabetes contributes to a minor extent on the ability to predict the clinical development of the disease in humans [74]. Advances in epigenetics opened new points of view on deciphering the molecular mechanisms regulating insulin action and, consequently, metabolic control. It is now firmly established that regulation of gene expression by insulin is mediated by epigenetic mechanisms, with the modulation of histone acetylation/deacetylation balance though HDACs and HATs, playing a major part [75]. More recently, the canonical histone PTM consisting in the attachment of an acetyl group to lysines have been integrated by a number of chemically similar, but biologically different, acylation reactions [29]. In relation to diabetes, the associated vascular complications consequent to chronic hyperglycemia have been shown to be associated with the expression of inflammatory genes, which is dependent on histone acetylation at the promoter regions of the TNF-alpha and COX-2 genes in monocytes of both type 1 and type 2 diabetic patients [76]. More recently, dysregulation of acetylation patterns has also been observed in peripheral blood cells from participants in the DCCT (Diabetes Control and Complications Trial) - EDIC (Epidemiology of Diabetes Interventions and Complications) prospective clinical trial [52]. Notwithstanding these observations supporting the association between epigenetic alterations and diabetes, a deeper mechanistic description of the relation between insulin signaling and histone acetylation (and other acylations), and the ensuing defects in diabetes is still lacking. While the insulin-dependent modulation of histone acetylation on selected genes have been investigated, the regulation at the genome-wide level is an exciting future field of research. In spite of this lack of knowledge, some preclinical investigations in rodents suggest that inhibiting HDACs can be a therapeutic option toward insulin resistance and Type 2 Diabetes [77]. Nevertheless, clinical studies in humans will provide a definitive answer on whether pharmacological targeting of histone acylation can curb the risk of developing insulin resistance and type 2 diabetes [78]. With the availability of reliable chromatin immunoprecipitation protocols and high throughput sequencing, it is now possible to address these key questions to improve our understanding of the interaction between insulin signaling and epigenetic regulation. This will eventually pave the way to the use of epigenetic modulators as therapeutic tools against diabetes.

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[52] Miao F, Chen Z, Genuth S, Paterson A, Zhang L, Wu X, et al. Evaluating the role of epigenetic histone modifications in the metabolic memory of type 1 diabetes. Diabetes 2014;63(5):1748e62. [53] Fan J, Krautkramer KA, Feldman JL, Denu JM. Metabolic regulation of histone post-translational modifications. ACS Chem Biol 2015;10(1):95e108. [54] Jin C, Zang C, Wei G, Cui K, Peng W, Zhao K, et al. H3.3/H2A.Z double variant-containing nucleosomes mark ’nucleosome-free regions’ of active promoters and other regulatory regions. Nat Genet August 2009; 41(8):941e5. [55] Jin C, Felsenfeld G. Nucleosome stability mediated by histone variants H3.3 and H2A.Z. Genes Dev 2007; 21(12):1519e29. [56] Zerzaihi O, Chriett S, Vidal H, Pirola L. Insulin-dependent transcriptional control in L6 rat myotubes is associated with modulation of histone acetylation and accumulation of the histone variant H2A.Z in the proximity of the transcriptional start site. Biochem Cell Biol 2014;92(1):61e7. [57] Lusis AJ, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet 2008;9(11):819e30. [58] Pirola L, Balcerczyk A, Tothill RW, Haviv I, Kaspi A, Lunke S, et al. Genome-wide analysis distinguishes hyperglycemia regulated epigenetic signatures of primary vascular cells. Genome Res 2011;21(10): 1601e15. [59] Agardh E, Lundstig A, Perfilyev A, Volkov P, Freiburghaus T, Lindholm E, et al. Genome-wide analysis of DNA methylation in subjects with type 1 diabetes identifies epigenetic modifications associated with proliferative diabetic retinopathy. BMC Med 2015;13:182. [60] Kwak SH, Park KS. Recent progress in genetic and epigenetic research on type 2 diabetes. Exp Mol Med 2016;48:e220. [61] Hales CN, Barker DJ. The thrifty phenotype hypothesis. Br Med Bull 2001;60:5e20. [62] Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet 1998;351(9097):173e7. [63] Lee YY, Park KS, Pak YK, Lee HK. The role of mitochondrial DNA in the development of type 2 diabetes caused by fetal malnutrition. J Nutr Biochem 2005;16(4):195e204. [64] Park JH, Stoffers DA, Nicholls RD, Simmons RA. Development of type 2 diabetes following intrauterine growth retardation in rats is associated with progressive epigenetic silencing of Pdx1. J Clin Investig 2008; 118(6):2316e24. [65] Dabelea D, Hanson RL, Lindsay RS, Pettitt DJ, Imperatore G, Gabir MM, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000;49(12): 2208e11. [66] Hall E, Volkov P, Dayeh T, Bacos K, Ronn T, Nitert MD, et al. Effects of palmitate on genome-wide mRNA expression and DNA methylation patterns in human pancreatic islets. BMC Med 2014;12:103. [67] Chakrabarti SK, Francis J, Ziesmann SM, Garmey JC, Mirmira RG. Covalent histone modifications underlie the developmental regulation of insulin gene transcription in pancreatic beta cells. J Biol Chem 2003; 278(26):23617e23. [68] Mutskov V, Raaka BM, Felsenfeld G, Gershengorn MC. The human insulin gene displays transcriptionally active epigenetic marks in islet-derived mesenchymal precursor cells in the absence of insulin expression. Stem Cell 2007;25(12):3223e33. [69] Kaneto H, Matsuoka TA, Miyatsuka T, Kawamori D, Katakami N, Yamasaki Y, et al. PDX-1 functions as a master factor in the pancreas. Front Biosci 2008;13:6406e20. [70] Wang HW, Breslin MB, Lan MS. Pdx-1 modulates histone H4 acetylation and insulin gene expression in terminally differentiated alpha-TC-1 cells. Pancreas 2007;34(2):248e53. [71] Mosley AL, Ozcan S. The pancreatic duodenal homeobox-1 protein (Pdx-1) interacts with histone deacetylases Hdac-1 and Hdac-2 on low levels of glucose. J Biol Chem 2004;279(52):54241e7.

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CHAPTER

Cancer and non-coding RNAs

8 Yong Peng, Jiao Li, Lei Zhu

State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, China

1. Introduction Non-coding RNAs (ncRNAs) are a class of RNA molecules that are not translated into protein. Based on their length, ncRNAs can be divided into three categories: (1) ncRNAs longer than 200 nucleotides (nt), including ribosomal RNA (rRNA) and long non-coding RNA (lncRNA); (2) ncRNAs shorter than 200 nt but longer than 40 nt, such as transfer RNA (tRNA), small nucleolar RNA (snoRNA) and small nuclear ribonucleic acid RNA (snRNA); and (3) ncRNA short than 40 nt like microRNA (miRNA), piwi-interacting RNA (piRNA) and tRNA derived small RNA (tsRNA) [1,2]. Unlike linear ncRNAs mentioned above, circular RNA (circRNA) is a newly defined type of ncRNA that forms a covalently closed loop, with no 50 and 30 polar [3]. The expression of these ncRNAs is tissue and cell-type specific under physiological conditions, and they play important roles in many biological processes through regulating gene expression at both transcriptional and post-transcriptional levels [2,4]. Recently, increasing evidence demonstrate that these four ncRNAs are aberrantly expressed in different types of cancers and widely involved in tumor initiation and progression [1,2]. The hallmarks of cancer include sustained proliferative signaling, evasion of growth suppressors, enabling of replicative immortality, activation of invasion and metastasis, induction of angiogenesis, and resistance to cell death [5]. Given the abnormal expression of these ncRNAs in tumors, it is believed that their dysregulation could affect one or several hallmarks for tumor initiation and progression. Depending on their target genes, these ncRNAs could function as either oncogene or tumor suppressor under certain circumstances. Here, we will describe the diversity, biogenesis and function of miRNA, lncRNA, tsRNA and circRNA, and dissect how they are involved in the hallmarks of cancer.

2. MicroRNA MicroRNAs (miRNAs) are a class of single-stranded, short non-coding RNAs, usually 20e22 nt in length. The first miRNA, lin-4, was identified in Caenorhabditis elegans (C. elegans) and shown to regulate development through modulating the expression of the heterochronic gene lin-14 [6]. Subsequently, miRNAs are shown to be abundant and conserved across different species, indicating a general regulatory mode mediated by miRNAs [7,8]. Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00008-4 Copyright © 2019 Elsevier Inc. All rights reserved.

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2.1 miRNA biogenesis and function MiRNA biogenesis is precisely processed as the following steps: (1) miRNA gene is transcribed into primary miRNA (pri-miRNA) by RNA polymerase II; (2) The pri-miRNA is cleaved into a precursor miRNA (pre-miRNA) with a short hairpin structure. (3) Exportin-5 (XPO5) transports pre-miRNA from the nucleus to the cytoplasm with assistance of Ran/GTP complex; (4) The pre-miRNA is further processed by RNase III enzyme Dicer into mature miRNA, which is incorporated into a protein complex termed RNA-induced silencing complex (RISC). By guiding RISC to induce targeted mRNA degradation or inhibit protein translation, miRNAs play distinct roles in post-transcriptional regulation and are widely involved in cell proliferation, differentiation, apoptosis, and other biological processes [9,10]. So far, compelling evidence has revealed that miRNAs participate extensively in the development of many serious diseases, including human cancers.

2.2 Dysregulated miRNAs in human cancers The first evidence of miRNA involved in human cancer was provided by Dr. Croce’s group [11]. They found two miRNA genes, miR-15a and miR-16-1, are frequently deleted at chromosome 13q14 in chronic lymphocytic leukemia (CLL). More importantly, the deletion of miR-15a and miR-16-1 leads to CLL phenotypes, supporting the tumor suppressive role of these two miRNAs in CLL [12]. Subsequently, more and more studies show that miRNA expression is dysregulated in different types of cancers, and miRNAs could be used as potential diagnostic biomarkers or therapeutic targets for cancer.

2.3 Cause of miRNAs dysregulation in cancer Different mechanisms have been elucidated for miRNA dysregulation in cancer, including amplifications, deletions, or mutations of miRNA genes, epigenetic change and disturbed transcriptional control of miRNAs [13]. Defect in miRNA biogenesis machinery is also an important reason leading to miRNA abnormal expression. Given the fact that miRNA biogenesis is precisely guided by several endonucleases and proteins, such as Dicer and XPO5, aberrant expression of such proteins could lead to abnormal expression of miRNAs. For example, Dicer impairment is observed in certain cancers [14,15], and diminished Dicer expression down-regulates global miRNA expression in ovarian cancer [16]. XPO5 mediated pre-miRNA export is a rate-limiting step in miRNA biogenesis [17]. In hepatocellular carcinoma, XPO5 phosphorylation by ERK kinase, followed by Pin1-mediated isomerization, impairs XPO5 transport ability to retain both XPO5 and pre-miRNAs in the nucleus, leading to downregulation of global miRNA expression [18,19]. Therefore, multiple signaling pathways are involved in miRNA dysregulation, contributing to the pathogenesis and hallmarks of human malignancies.

2.4 The role of miRNAs in cancer hallmarks 2.4.1 Regulation of cell proliferation by miRNAs Cell proliferation is tightly controlled in normal cells, while cancer cells possess excessive cell proliferation due to sustaining proliferative signaling and evaded growth suppressors. Altered miRNA expression is a key determinant for excessive cell proliferation and tumorigenesis. E2F proteins are a

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class of transcription factors that function as crucial regulators of cell cycle-dependent proliferation. Overexpression of miR-17-92 cluster has been reported to shift the E2F transcriptional balance toward the proliferative network, resulting in accelerated cancer cell proliferation [20]. Cell cycle progression relies on different cyclins, cyclin-dependent kinases (Cdks) and their inhibitors such as p21Cip1 and p27Kip1, whose expression are extensively regulated by miRNAs. For example, miR-221/222, the common upregulated miRNAs in tumors, promotes cell proliferation via targeting the CDK inhibitor p27Kip1 in glioblastoma, prostate carcinoma and other tumors [21e23]. However, miR-221/222 inhibits erythroleukemic cell growth by decreasing the expression of Kit receptor [24], indicating that miRNAs exert their function as oncogenes or tumor suppressors depending on their different targets in different cell types. Besides regulating cell cycle-related proteins, miRNAs also affect cell proliferation through other signaling pathways. For instance, miR-486 is reported to inhibit cell proliferation and migration by affecting insulin growth receptor (IGF) and phosphoinositide 3-kinase (PI3K) signaling pathways in non-small-cell lung cancer [25].

2.4.2 Activation of invasion and metastasis by miRNAs

Metastasis is responsible for w90% of cancer-associated mortality [26]. Over the past decades, the epithelial-to-mesenchymal transition (EMT) has been defined as a key early step of the metastatic process. Intriguingly, a double-negative feedback loop has been elucidated between the miR-200 family and EMT inducers ZEB1/ZEB2. The commonly downregulated miR-200 family in cancers is found to suppress the expression of ZEB1 and ZEB2 [27]. In turn, ZEB1 and ZEB2 directly repress the transcription of miR-200 family [28]. TWIST and SNAIL are another two important transcription factors that facilitate EMT and metastasis by regulating the expression of certain miRNAs [29]. For example, TWIST directly binds to the promoter of the miR-10b gene to induce its transcription, and miR-10b functions as an oncogenic miRNA to promote tumor invasion and metastasis [30]. SNAI2 is reported to promote EMT by binding to the miR-203 promoter to inhibit its transcription [31]. Intriguingly, miR-203 inhibits tumor invasion by targeting SNAI2 in turn [32], which forms another double-negative feedback loop in EMT pathways and tumor metastasis.

2.4.3 Resistance to cell death by miRNAs MicroRNAs affect tumor progression by regulating apoptosis signaling pathways, such as the p53 pathway. Wild-type p53 can be antagonized by murine double minute 2 (MDM2), which is also overexpressed in many human tumors. Three miRNAs, miR-192, miR-194 and miR-215, are transcriptionally activated by p53 and then reduce MDM2 expression by directly binding to MDM2 mRNA, thereby tipping the p53/MDM2 auto regulatory loop in favor of p53 and enhancing apoptosis in multiple myeloma [33]. B-cell lymphoma 2 (Bcl2) is another central participant in cellular programs promoting survival by inhibiting cell death [34]. Overexpression of Bcl2 protein has been reported in a variety of human cancers. MiR-15a and miR-16-1 expression is inversely correlated to Bcl2 expression in CLL, and both miRNAs function as tumor suppressors to induce apoptosis by repressing Bcl2 expression [35,36]. Other miRNAs, such as miR-148a, miR-204, and miR-365, can also promote apoptosis by targeting Bcl2 [37e39]. Thus, down-regulation of such miRNAs confers cancer cell resistance to cell death.

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Inhibition of extrinsic ligand-induced death pathway is another strategy for tumor cells to evade apoptosis. MiR-21, widely overexpressed in tumors, functions as an anti-apoptotic factor not only by targeting Apaf-1, a key member of intrinsic mitochondrial apoptotic pathway, but also by downregulating Fas ligand, an important originator of extrinsic ligand-induced apoptosis [40].

2.4.4 Induction of angiogenesis by miRNAs Angiogenesis allows cells to reach and disseminate through the systemic circulation. MiR-210 is the representative angiogenesis-associated miRNA. It significantly promotes angiogenesis via activation of vascular endothelial growth factor (VEGF) signaling pathways [41] and inhibition of the antiangiogenic factor, Ephrin-A3 [42]. In contrast, other miRNAs, such as miR-519c, miR-107 and miR-20b, are viewed as the suppressors of angiogenesis by targeting HIF/VEGF signaling during cancer development [43e45].

2.5 MiRNA-targeting therapeutics Abnormal miRNA expression is well documented to be involved in tumor initiation and progression, making miRNAs as attractive diagnostic biomarker and therapeutic targets for cancer diagnosis and treatment. Although miRNA mimics and inhibitors (anti-miRs) have been shown to be potential cancer therapy in preclinical studies, much effort is still necessary to develop more efficient delivery system for these oligonucleotides [46]. Importantly, a more thorough understanding of miRNA biogenesis and function will further contribute to the development of miRNA-targeting therapeutics.

3. Long non-coding RNA 3.1 LncRNA classification, biogenesis and function LncRNAs are transcripts more than 200 nt with limited protein-coding capacity, which are distinguished from other small ncRNAs (miRNA, piRNA, tsRNA, snoRNA, snRNA) in size and messenger RNA (mRNA) in coding potential respectively [47]. According to their genomic localization relative to genes, lncRNA can be generally divided into five subtypes, including intergenic lncRNA (lincRNA), enhancer lncRNA (eRNA), intron lncRNA, sense and antisense lncRNAs that overlap other genes [48]. Similar to mRNA biogenesis processes, most lncRNAs are transcribed by RNA polymerase II (Pol II) and also undergo splicing, 50 -end capping and 30 -end polyadenylation. The expression of lncRNAs can be transcriptionally regulated by chromatin modification on their promoter regions, such as DNA methylation and histone modification [49,50] and post-transcriptionally regulated by RNA modification and miRNAs [51,52]. LncRNAs can directly interact with DNAs, RNAs or proteins to regulate gene expression at both transcriptional and post-transcriptional levels, to some extent, relying on their subcellular localization [53]. In the nucleus, lncRNAs can modulate gene expression by recruiting chromatin-modifying protein complexes to specific genomic loci to change chromatin states (such as histone modifications and DNA methylation) [54], or by interacting with splicing factors to regulate alternative splicing [55]. In the cytoplasm, lncRNA can control mRNA stability through its binding to the 3ʹ-untranslated regions (3ʹUTRs) of mRNAs [56]. In addition, cytoplasmic lncRNAs function as miRNA sponges to regulate the expression of certain genes [57]. Recently, some lncRNAs are proven to encode small peptides to exert their biological functions [58].

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3.2 The role of lncRNAs in cancer hallmarks Increasing evidence indicates that lncRNA expression is dysregulated in cancers and that they function as tumor suppressor or oncogenes during tumorigenesis. These tumor-associated lncRNAs play important roles in the hallmarks of cancer.

3.2.1 Sustained proliferative signaling bv lncRNAs lncRNAs can sustain proliferative signaling for cancer cells through regulating the expression of genes involved in cell proliferation. For example, c-MYC is a proto-oncogene encoding a transcription factor to regulate cell proliferation [59]. The lncRNA PCAT-1 (prostate cancer associated transcript 1), highly up-regulated in a subset of metastatic and high-grade localized prostate cancers, promotes cell proliferation through increasing c-Myc expression [60]. The cyclin-dependent kinase inhibitor p21 is well-known to regulate cell cycle progression at G1 and S phases [61]. Recent studies clearly demonstrate that lncRNA AB074169 (lncAB) is significantly downregulated in papillary thyroid carcinoma, and it inhibits cell proliferation via modulation of KHSRP-mediated p21 expression [62].

3.2.2 Evasion of growth suppressors by lncRNAs LncRNAs are also demonstrated to inactivate tumor suppressor functions, leading to evasion of growth suppression. For instance, the PSF protein is a tumor suppressor inhibiting transcription of several proto-oncogenes via binding to their regulatory regions [63]. Li et al. reported that five ncRNAs generated from L1PA16, HN, MALAT-1, MER11C and an annotated region can inactivate PSF’s tumor suppressive role through interacting with PSF and releasing it from human proto-oncogenes’ regulatory regions, leading to activation of proto-oncogene expression [64]. Another lncRNA called ANRIL (antisense non-coding RNA in the INK4 locus) has been shown to be up-regulated in several types of cancers, including gastric cancer, lung cancer and hepatocellular carcinoma, and can inhibit tumor suppressor gene activity in different ways [49]. For example, ANRIL interacts with SUZ12 (suppressor of zeste 12 homolog), a subunit of the polycomb repression complex 2 (PRC2) and recruits the epigenetic modification complex to repress the expression of tumor suppressor p15.

3.2.3 Enabling replicative immortality by lncRNAs The number of cell division cycles is limited by telomere length, which is shortened after each cell division [65]. Enabling replicative immortality is a trait of cancer cells with unlimited replication potential, which is contributed by the reactivation of telomerase to add telomeric repeats (TERT) at the end of chromosomes. While telomere replication can be interfered by telomeric repeat-containing RNA (TERRA) accumulated at telomeres [66]. Overexpression of H19 lncRNA in liver cancer stem cells increases the binding of TERT to the RNA component of telomerase and reduce the interplay between TERT and TERRA, subsequently enhancing the cells’ telomerase activity and extending telomere length [67].

3.2.4 Activation of invasion and metastasis by lncRNAs Cancer cells with the capabilities of invasion and metastasis usually undergo morphological changes and alter their cellecell or cellematrix interactions. EMT is a critical step for cancer metastasis, and E-cadherin (CDH1) is a key cell-to-cell adhesion molecule required for assembly of epithelial cell layers. During EMT, E-cadherin expression is dramatically decreased to enhance tumor cell migration [68].

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In bladder cancer cells, the lncRNA MALAT-1 is upregulated and contributes to increased cell migration via EMT induction. Downregulation of MALAT-1 results in a decrease of the EMTassociated ZEB1, ZEB2 and Slug levels, and an increase of E-cadherin level [69]. Another study reported that linc00460 expression is significantly upregulated in non-small cell lung cancer (NSCLC) and associated with poor prognosis for NSCLC patients. Functional experiments showed that linc00460 promotes cell migration and invasion through inducing EMT in lung cancer cells [70].

3.2.5 Induction of angiogenesis by lncRNAs Inducing angiogenesis and forming new blood vessels can supply tumor cells with nutrients and oxygen, dispose their metabolic (toxic) waste and enter the hematogenous metastatic process. VEGF is important for the formation of blood vessels, whose expression is stimulated by hypoxia inducible factor-1a (HIF-1a) under hypoxic condition [71]. It has been demonstrated that aHIF, a natural antisense lncRNA complementary to the 30 untranslated region of HIF-1a mRNA, decreases the expression of HIF1a and VEGF, thus conferring its negative role in angiogenesis [72].

3.2.6 Resistance to cell death by lncRNAs Evasion of apoptosis is another significant hallmark of cancer cells, which is believed to be regulated by lncRNAs. Cancer cells evolve various strategies to limit or avoid apoptosis, and the loss of p53 tumor suppressor function is the most common event in cancer cells. The regulatory network between lncRNAs and p53 protein is well documented, and lncRNAs may function as a regulator or effector of p53 in cancer cells [73]. For example, lncRNA MEG3, significantly down-regulated in NSCLC tissues, is a regulator for p53 protein expression. Overexpression of MEG3 decreases cell proliferation and induces apoptosis, at least in part by activation of p53 [74]. LincRNA-p21 is induced by p53, and functions as a downstream regulator of p53-induced apoptosis. Knockdown of lincRNA-p21 impacts the expression of a large number of genes involved in apoptosis and cell cycle arrest [75]. Therefore, lincRNA-p21 serves as a p53 target and plays a critical role in triggering apoptosis.

4. tRNA-derived small non-coding RNA (tsRNA) 4.1 Classification, biogenesis and function of tsRNA tsRNAs can be generally divided into 3 subclasses: 30 U tRNA-derived small RNAs (30 U tRF), tRNA-derived fragments (tRFs) and tRNA halves (tRHs or tiRF). 30 U tRFs are generated from pre-tRNA during tRNA maturation by RNase Z or ELAC2 [76e78]. tRHs, such as 50 tRHs and 30 tRHs, are produced from mature tRNAs by angiogenin (ANG) cleavage in the anticodon loop [79e81]. According to the position they generated from, tRFs can be further classified into two subclasses: 50 tRFs and 30 tRFs, which are produced by Dicer cutting in the D-loop and T-loop, respectively [82,83]. A new type of tsRNA, derived from internal mature tRNA, is defined as inter tRF (i0 tRF) with an unknown generation mechanism [84,85]. The biogenesis of tsRNAs can be regulated by different mechanisms. The expression of tRHs can be induced under specific stress, such as oxidative stress, heat shock, UV irradiation and viral infection [80,86]. In addition, several tRHs are specifically and abundantly expressed in estrogen receptor

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(ER)-positive breast cancer cells and androgen receptor (AR)-positive prostate cancer cells [87]. Up-regulation of ANG under these conditions is positively correlated with increased levels of tRHs [81,88]. RNH1, an ANG inhibitor interacting with ANG in the cytoplasm, is shown to be a negative regulator for tRH generation [80]. Tuorto et al. showed that methylation modification by DNA methyltransferase 2 (Dnmt2) and RNA methyltransferase NSun2 on the nucleotide at anticodon loop of mature tRNA can affect ANG cleavage [89]. The function of tsRNAs relies on proteins that they associate with. tRFs and tsRNAs have been shown to incorporate into AGO and/or PIWI proteins and affect target mRNA stability [83,90]. In addition, inter tRF can also affect mRNA stability through displacing HBX1 from the 30 UTR of certain mRNAs [84]. As for tRHs, they not only associate with PIWI to suppress target gene transcription, but also cooperate with the translational silencer protein YB-1 to inhibit protein translation of both capped and uncapped mRNA [81,91].

4.2 The role of tsRNAs in the hallmarks of cancer Increasing evidence demonstrates that tsRNAs are not random degradation products but functional molecules, involved in many important biological processes. The role of tsRNAs in cancer is just emerging. Several studies have shown that the expression of tsRNAs is dysregualted in cancer, which may contribute to tumor development [4].

4.2.1 3 0 U tRF

tRF-1001 is identified as one 30 U tRF produced from pre-tRNA-SerTGA by ELAC2 in the cytoplasm of prostate cancer cells and regulates cell proliferation [76]. Recently, Professor Croce’s group found that several 30 U tRFs are dysregulated in CLL, lung cancer, colon cancer, breast invasive ductal carcinoma, and ovary cancers [90,92]. Among these tsRNAs, ts-101, ts-53, ts-46 and ts-47 generated from pretRNAs of SerGCT, ThrAGT, HisGTG and ArgTCG are down-regulated in both CLL and lung cancer. Moreover, functional studies reveal that overexpression of ts-46 and ts-47 can inhibit cell proliferation in lung cancer cells, and knockout of ts-101 and ts-46 can activate the expression of cell survival related genes and suppress the expression of genes that regulate apoptosis and chromatin structure [92].

4.2.2 tRF

A 22 nt 30 tRF (CU1276), derived from mature tRNA-GlyGCC in mature B cells, is down-regulated in lymphoma cells and primary biopsies [83]. Downregulation of CU1276 activates the expression of endogenous targets including RPA1, a protein involved in DNA dynamics, such as genome replication and DNA repair. Moreover, CU1276 regulates cell proliferation and DNA damage in an RPA1dependent manner. Another study showed that a 30 tRF generated from tRNA-LeuCAG is essential for cell viability in HeLa and HCT-16 cells [93]. Knockdown of this 30 tRF induces apoptosis in rapidly dividing cells in vitro and in a patient-derived orthotopic hepatocellular carcinoma model in mice. Several hypoxia stress-induced inter tRFs, generated from tRNA-Glu, tRNA-Asp, tRNA-Gly, and tRNA-Tyr, are reported to suppress tumor metastatic progression through destabilization of multiple oncogenic transcripts in breast cancer cells by displacing their 30 untranslated regions (UTRs) from the RNA-binding protein YBX1 [84].

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4.2.3 tRH

Honda et al. identified several sex hormone-dependent tRHs, including 50 tRH-HisGUG, 30 tRHHisGUG, 50 tRH-AspGUC and 30 tRH-AspGUC, which are specifically and abundantly expressed in ER-positive breast cancer cells and AR-positive prostate cancer cells [87]. These tRHs are produced by ANG cleavage in the anticodon of mature tRNA charged with amino acid. Interestingly, only 50 tRHs enhanced cell proliferation. Another study found that the expression of 50 tRH-LeuCAG was upregulated in NSCLC tissues and NSCLC cell lines, and could promote cell proliferation [94]. In addition, 5’tRH-ValAAC is down-regulated in clear cell renal cell carcinoma (ccRCC), and its expression is inversely correlated with tumor stage and grade [95].

5. Circular RNA (circRNA) 5.1 Formation of circRNAs Unlike linear RNAs, circRNAs form a closed loop structure by covalent bond [96]. In recent years, benefiting from deep sequencing and bioinformatic analysis, a large number of circRNAs have been discovered [3]. They are distributed widely in eukaryotes and highly conserved among different species. CircRNAs are usually formed by back-splicing. According to the different source of splicing, they can be divided into exonic circRNAs, intronic circRNA, and exon-intron circRNAs or EIciRNAs. The closed loop structure enables circRNAs less susceptible to degradation by the exonuclease RNase R and therefore more stable than linear RNAs, making circRNAs as novel diagnostic biomarkers [97].

5.2 Functional mechanism of circRNAs Since the circRNA ciRS-7 (also named as CDR1as) was found to function as efficient sponge of miR-7 to regulate gene expression in neuronal tissues [98,99], increasing evidences strongly support the miRNA sponge function of circRNA. For example, a HIPK3-derived circRNA can regulate cell growth by sponging multiple miRNAs [100]. Moreover, the interaction between circRNA and miRNA is mediated by the RNA binding protein Ago2 [101]. CircRNAs can also affect its parental genes at transcriptional level, highlighting a regulatory strategy for transcriptional control via specific RNARNA interaction [102]. Recently, circRNA was found to encode protein to exert its function. For instance, circ-SHPRH can encode a peptide with 146 amino acid residues in glioblastoma, whose overexpression reduces the malignant behavior and tumorigenicity in vitro and in vivo [103].

5.3 The role of circRNAs in cancer hallmarks Dysregulated circRNAs have currently emerged in many malignant tumors, and widely function as either oncogene or tumor suppressor to affect the hallmarks of cancer. For example, circPRKCI is overexpressed in lung adenocarcinoma tissues, partly due to amplification of the 3q26.2 locus, and promotes cell proliferation and tumorigenesis of lung adenocarcinoma [104], while circ-MTO1 is significantly down-regulated in hepatocellular carcinoma, resulting in increased cell proliferation and invasion [105]. CircRNAs are also involved in apoptosis regulation. For instance, circ-Foxo3 is shown to induce stress-induced apoptosis and inhibit the growth of tumor xenografts by upregulation of the Foxo3 downstream target PUMA [106].

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As one of the most important transcription factors, c-Myc plays a pivotal role in cell proliferation and apoptosis. Its expression is commonly elevated in a variety of cancers through different mechanisms. Yang et al. demonstrate that circ-Amotl1, highly expressed in certain cancers, can directly interact with c-Myc and promote its nuclear translocation and stability, leading to increased cell proliferation and decreased cell apoptosis [107]. In turn, c-Myc can also regulate the expression of circRNAs, and these c-Myc-regulated circRNAs might impact cell proliferation through affecting Ras signaling pathway [108]. CircRNAs also play a crucial role in cell invasion and tumor metastasis. For example, circATP2B1 and ciRS-7 are reported to promote metastasis in renal cell carcinoma and triple-negative breast cancer, respectively [109,110]. Besides, circRNA is implicated in the dysregulation of autophagy. A relevant example is presented by circ-DNMT1, which acts as a promoter of cellular autophagy in breast cancer [111]. Recently, circRNAs are found to be enriched in the exosomes, the membrane vesicles of endocytic origin secreted by most cell type, and transferred from donor cells to recipient cells, thus affecting cellular functions. Moreover, the exosomal circRNAs may serve as a promising circulating biomarker for cancer diagnosis [112].

6. Conclusions In the present chapter, we describe the classification, biogenesis and function of miRNA, lncRNA, circRNA and tscnRNA, and also summarized their oncogenic or tumor suppressive roles in tumor initiation and development. Emerging evidences indicate that these ncRNAs are dysregulated in cancers and participate in different aspects of cancer hallmarks, indicating ncRNAs could be novel therapeutic targets for cancers. Moreover, ncRNAs may serve as diagnostic and prognostic biomarkers due to their unique tissue- and cancer-specific expression patterns. However, further characterization and functional study of ncRNAs, especially newly defined tsRNA and circRNA, is required for dissecting their molecular mechanisms during tumorigenesis, which could provide potential therapeutic targets for cancer treatments.

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[50] Zhang E, Han L, Yin D, He X, Hong L, Si X, et al. H3K27 acetylation activated-long non-coding RNA CCAT1 affects cell proliferation and migration by regulating SPRY4 and HOXB13 expression in esophageal squamous cell carcinoma. Nucleic Acids Res 2017;45(6):3086e101. [51] Thomson DW, Dinger ME. Endogenous microRNA sponges: evidence and controversy. Nat Rev Genet 2016;17(5):272e83. [52] Gong J, Liu C, Liu W, Xiang Y, Diao L, Guo AY, et al. LNCediting: a database for functional effects of RNA editing in lncRNAs. Nucleic Acids Res 2017;45(D1):D79e84. [53] Tang Y, Cheung BB, Atmadibrata B, Marshall GM, Dinger ME, Liu PY, et al. The regulatory role of long noncoding RNAs in cancer. Cancer Lett 2017;391:12e9. [54] Bohmdorfer G, Wierzbicki AT. Control of chromatin structure by long noncoding RNA. Trends Cell Biol 2015;25(10):623e32. [55] Gonzalez I, Munita R, Agirre E, Dittmer TA, Gysling K, Misteli T, et al. A lncRNA regulates alternative splicing via establishment of a splicing-specific chromatin signature. Nat Struct Mol Biol 2015;22(5): 370e6. [56] Gong C, Maquat LE. LncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3’ UTRs via Alu elements. Nature 2011;470(7333):284e8. [57] Zhou D, Tong S, Hacisuleyman E, Teng F, Wang X, Brown M, et al. Integrative analyses reveal a long noncoding RNA-mediated sponge regulatory network in prostate cancer. Nat Commun 2016;7:10982. [58] Raj A, Wang SH, Shim H, Harpak A, Li YI, Engelmann B, et al. Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling. Elife 2016;5. https://doi.org/10.7554/ eLife.13328. [59] Miller DM, Thomas SD, Islam A, Muench D, Sedoris K. C-Myc and cancer metabolism. Clin Cancer Res 2012;18(20):5546. [60] Prensner JR, Chen W, Han S, Iyer MK, Cao Q, Kothari V, et al. The long non-coding RNA PCAT-1 promotes prostate cancer cell proliferation through cMyc. Neoplasia 2014;16(11):900e8. [61] Abbas T, Dutta A. P21 in cancer: intricate networks and multiple activities. Nat Rev Canc 2009;9(6): 400e14. 2009. [62] Gou Q, Gao L, Nie X, Pu W, Zhu J, Wang Y, et al. Long noncoding RNA AB074169 inhibits cell proliferation via modulation of KHSRP-mediated CDKN1a expression in papillary thyroid carcinoma. Cancer Res 2018;78(15):4163e74. [63] Song X, Sui A, Garen A. Binding of mouse VL30 retrotransposon RNA to PSF protein induces genes repressed by PSF: effects on steroidogenesis and oncogenesis. Proc Natl Acad Sci U S A 2004;101(2): 621e6. [64] Li L, Feng T, Lian Y, Zhang G, Garen A, Song X. Role of human noncoding RNAs in the control of tumorigenesis. Proc Natl Acad Sci U S A 2009;106(31):12956e61. [65] Counter CM, Avilion AA, Lefeuvre CE, Stewart NG, Greider CW, Harley CB, et al. Telomere shortening associated with chromosome instability is arrested in immortal cells which express telomerase activity. EMBO J 1992;11(5):1921e9. [66] Wyatt HD, West SC, Beattie TL. InTERTpreting telomerase structure and function. Nucleic Acids Res 2010;38(17):5609e22. [67] Hu P, Zheng Q, Li H, Wu M, An J, Xin G, et al. CUDR promotes liver cancer stem cell growth through upregulating TERT and C-Myc. Oncotarget 2015;6(38):40775e98. [68] Shen XH, Qi P, Du X. Long non-coding RNAs in cancer invasion and metastasis. Mod Pathol 2014;28: 4e13. [69] Liang Y, Qi C, Wang Y, Zhou Z, Huang Y, Feng Q. Upregulated MALAT-1 contributes to bladder cancer cell migration by inducing epithelial-to-mesenchymal transition. Mol Biosyst 2012;8(9):2289e94.

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CHAPTER

Race in the social-epigenomic regulation of pre- and perinatal development

9

Robert H. Lanea, Amber V. Majnika, Jeffrey L. Segarb Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, USAa; University of Iowa, Carver College of Medicine, Stead Family Department of Pediatrics, Iowa City, IA, USAb

1. Introduction Early life exposures, occurring from preconception through early childhood, are biologically imbedded into our beings. There is substantial evidence that environmental conditions during the first 1000 days, or from conception until 2 years of age, impact health and disease later in life, including neurocognitive and behavioral development, the risk of stroke, hypertension, heart disease and metabolic disease [1e3]. The contributions of environmental factors and social forces to racial health disparities is an area of intense interest and research [4,5]. In the United States, life expectancy and other relevant health outcomes vary greatly by race, socioeconomic status, geographic location and sex. Improving the health of racial and ethnic minorities has long been a public health priority, yet despite these efforts, significant disparities persist [6,7]. For example, in 2015e6, the prevalence of hypertension among non-Hispanic Blacks (40.3%) was significantly higher than among non-Hispanic Whites (29.7%) [8]. Similarly, the prevalence of diabetes is at least 50% greater in non-Hispanic Blacks and Hispanics than in non-Hispanic Whites [9]. With regards to perinatal outcomes, the rates of stillbirth, preterm birth, low birthweight and infant mortality are all higher in the Non-Hispanic Black and Hispanic populations than Non-Hispanic, with the greatest risk in Non-Hispanic Blacks [10]. While the causes of many health conditions are complex, understanding the social and biological determinants of these conditions and other disease states are necessary if health equity is to be achieved. Appreciating that epigenetics stands as intermediary between social and biological determinants of disease moves the field forward by allowing our investigations to transition from “why” to “how”. The pivotal role that epigenetics plays should be self-evident in that epigenetics determines tissue specific gene expression, developmental ontogeny gene expression, sex determined gene expression and how well we adapt to our environment as discussed throughout this textbook. Moreover, epigenetics is a defining characteristic of eukaryotes, and epigenetics is one of only two dynamic genomes in eukaryotes (the other being the microbiome). However, it is only recently that studies have attempted to tease apart how social and environmental factors impact racial difference in epigenetic processes.

Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00009-6 Copyright © 2019 Elsevier Inc. All rights reserved.

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2. Racial disparity and environment e epigenetics (humans) Self-identified race appears to track with epigenetic marks such as DNA methylation. However, a challenge exists in differentiating the impact on chronic health issues such as obesity, socioeconomic status, perceptions of safety, physical activity and adverse childhood events. Adverse childhood events (ACEs) are traumatic experiences in a person’s life occurring before the age of 18. ACEs impact lifelong health, behaviors and opportunity. According to the “Healthiest Wisconsin 2020 Baseline and Health Disparities Report”, 44% of the African-American population of Wisconsin meet the definition of “obese” as opposed to only 27% of the White population, representing a significant chronic disease burden [11]. 39% of the AfricanAmerican population live in poverty as compared to 10% of the White population in Wisconsin [11]. The median household income is $ 27,000 for African-Americans, and the median household income is $ 53,000 for Whites [11]. 94% of White children live in neighborhoods that are felt to be safe by their parents [11]. In contrast, only 61% of African-American children live in neighborhoods that are considered safe by their parents [11]. These neighborhood concerns may affect the physical activity of these students in that 48% of White children exercise greater than 1 h for at least 5 days a week, whereas only 35% of African American children do the same [11]. Finally, 23% of AfricanAmericans in Wisconsin report four or more ACEs while only 15% of White’s do so [11]. The association of environmental, social, and personal factors with self-identified race impacts the interpretation of studies linking “epigenetic age” and race. Epigenetic age represents an estimate of biological age based on DNA methylation patterns. The concept of epigenetic age derives from seminal work by Chen et al. that analyzed data from a sample size of >13,000 individuals [12]. This sample distinguished three racial groups. The significance of an individual’s epigenetic age lies in the finding that epigenetic age predicts mortality more accurately than chronical age. Indeed, an epigenetic age that is greater than 10 years older than chronological age predicts a 50% increased risk of death. Concurrent and subsequent studies by Horvath et al. further conclude that epigenetic aging associates with sex and race, as well as inflammatory biomarkers [13,14]. These studies however do not preclude the possibility that environmental factors influence epigenetic age as well as other measures of DNA methylation. For example, epigenetic age correlates with one of the most poignant responses to environmental factors, posttraumatic stress disorder (PTSD). Verhoeven et al. revealed that combat exposed veterans with PTSD carried lower epigenetic ages than those veterans not formally diagnosed with PTSD [15]. The difference correlated significantly with the use of antidepressants and higher telomerase activity. Parenthetically, telomerase adds telomeric repeats to chromosome ends. A lack a telomerase activity results in progressive telomere shortening with cellular replication, which may lead to cellular senescence and aging. The potential that PTSD impacts measures of DNA methylation such as epigenetic age regardless of the direction is relevant to the interactions of race versus environment in the context of epigenetics. PTSD occurs more frequently in women that are dissatisfied with their neighborhood, particularly if they had been in the neighborhood for greater than five years [16]. Moreover, in children experiencing community violence exposure there is a higher incidence of PSTD [17]. Considering the demographics of the African-American population in Wisconsin, the impact of neighborhood and associated violence complicate the interpretation of how race impacts epigenetic measures such as DNA methylation.

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Other environmental factors associate with racial designation impact epigenetic measures such as DNA methylation. For example, racial discrimination impacts DNA methylation in both AfricanAmerican and Latina women. De Mendoza et al. examined the experiences of African-American women utilizing the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure (InterGEN) study [18]. The InterGEN study utilizes measures such as the Major Life Discrimination (MLD) and Race-Related Events (RES) scales. MLD associated significantly with increased DNA methylation at nine sites even after controlling for age, smoking and cell composition. In studying Latina women at gestation, twenty-four to thirty-two weeks and subsequently at four to six weeks postpartum (using Everyday Discrimination Scale), Santos et al. found that perceived discrimination decreased DNA methylation within the glucocorticoid receptor and the brain-derived neurotrophic factor genes [19]. Furthermore, the negative association was maintained between the prenatal and postnatal time points. Interestingly, perceived discrimination also decreased DNA methylation of the glucocorticoid binding protein (FKBP5) during the prenatal period, but not during the postnatal period. Additional environmental factors such as smoking, maternal nutrition and adverse childhood experiences also appear to impact epigenetic markers such as DNA methylation. A common characteristic of these factors is that there is differential epidemiological racial bias. That being said, the results of these studies are informative though somewhat disparate. For example, Park et al. evaluated the impact of smoking upon three racial groups: Whites, Native Hawaiians and Japanese. Smoking, measured though nicotine equivalents, impacted six differentially methylated DNA sites only in the Native Hawaiians [20]. Mozhui et al. evaluated the impact of maternal nutrition and racial identify through the Condition Affecting Neurocognitive Developmental and Learning in Early Childhood Study (CANDLE) [21]. Plasma levels of vitamin D and folate during pregnancy stood as surrogate markers of maternal nutrition. Using a weighted correlation analysis, these authors concluded that they defined a network of DNA methylation sites within cord blood that are modulated by ancestry and maternal vitamin D. Despite these latter findings, differentiating the impact of environment and race upon epigenetic markers such as DNA methylation presents a Gordian knot. As previously mentioned, an increased incidence of ACEs characterizes the African-American population. In a thoughtful study by Kaufman et al. that well poises the field for further studies, this investigative group studied 321 maltreated preteens and teenagers [22]. Obesity assessments were performed on a subset of this vulnerable cohort. Ten DNA methylation sites interacted with ACEs to anticipate cross-sectional measure of body mass index, and a further six DNA methylation sites exerted a significant effect in anticipating BMI. Epigenetic markers such as DNA methylation may also be informative in the arena of resilience. Young adults from backgrounds characterized by socioeconomic disparity are at risk for physical health issues, failure to complete school and conviction of criminal offenses. These latter challenges have been previously associated with poor self-control and subsequently some school curricula and social services include rating for greater self-control. Concern has arisen that while greater selfcontrol may lead to completion of school and other positive social accomplishments, that greater self-control may also negatively impact physical health due to stunting expression. Miller et al. pursued this concern by studying approximately three hundred young adults at ages seventeen to twenty annually in terms of SES, self-control, depressive symptoms, substance use, aggressive behavior and internalizing problems [23]. At age 22, this investigatory group measured the epigenetic age of these young adults. In high SES young adults, better self-control anticipated better emotional

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outcomes and lower epigenetic age. In lower SES young adults, better self-control anticipated better emotional outcomes and higher epigenetic age. These results highlight the challenge we have in untying the Gordian knot. These studies deserve further expansion and application across even more diverse environments and populations. These studies highlight the remarkable potential of epigenetics to identify, differentiate and anticipate health issues based on environment. However, even in fields that have natural boundaries such as cancer, racial differences are noted though the limited denominator of the field limits the relevant applicability of the findings [24e26]. Our ability to finely dissect the interaction between environment, self-identified race and epigenetics to anticipate health is still at its infancy. Our infant status can be clearly seen through literature searches on the topic of racial disparity and epigenetics, which still produce as many review articles as peer reviewed original science studies (irony noted). Our ability to mature in the field requires many new inputs ranging from the exponentially growing field of bioinformatics to the reductionist approach facilitated in animal studies.

3. Racial disparity and environment e epigenetics (animal) Epigenetic mechanisms stand as a defining characteristic of eukaryotic biology, and as such, are likely to be generally conserved across species, which makes the use of animal models informative when trying to focus on specific mechanisms of “how”. For example, to test the cross species effect of early life stress Dickson et al. compared microRNAs found in male sperm of humans exposed to early life stress to microRNAs found in mice sperm who were experimentally exposed to early life stress [27]. Interestingly, members of the same sperm microRNA family are reduced in both mice and humans exposed to early life stress [27]. Moreover, when stressed male mice were mated, reduced microRNAs could still be found in the offspring [27]. Studies such as this are strong reminders of how animal studies can inform human conditions. In addition to microRNAs, other epigenetic mechanisms have also been indicated in the mechanism of inheritance of early life stress. Unpredictable maternal stress and separation can influence behavioral and metabolic changes in offspring up to 4 generations later [28e30]. These changes are associated with DNA methylation changes, histone acetylation and noncoding RNA changes [28,30e32]. Interestingly, epigenetic targets have also been investigated as a potential therapeutic [32,33]. Treatment of mice following early life stress with a histone deacetylase inhibitor reversed the early life stress induced gene changes [32,33]. Interestingly, in this study a class I selective histone deacetylase inhibitor was used, which has been shown to be therapeutically beneficial in some cases [34,35]. Maternal care can influence offspring behavior much in the same way maternal diet can influence offspring weight and metabolic function. Many studies have demonstrated that maternal high fat diet increases offspring body weight and insulin resistance. In addition, the timing of high fat diet, whether during pregnancy or lactation is important to influencing offspring metabolism [36e38]. The importance of this evidence often leads to recommendations of increased healthy eating in pregnant women, however in an interesting animal study by Xu et al., the duration of the healthy diet is shown to be important [39]. The authors switched mice from a high fat diet to a normal fat diet either 1, 5 or 9 weeks prior to pregnancy. Only mice on the normal fat diet for 9 weeks prevented obesity and glucose intolerance in offspring [39].

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Similar to early life stress, the mechanism of inheritance of prenatal diet also centers around epigenetics. Huypens et al. investigated epigenetic germline inheritance of obesity and insulin resistance using in vitro fertilization [40]. The authors found an additive effect of maternal and paternal diet on female offspring weight in adulthood. Interestingly these results were not consistent with male offspring [40]. Many studies have also found that noncoding RNAs are an important mechanism of environmental inheritance [41e44]. These studies showed that tsRNAs have a distinct profile in sperm from mice consuming a low protein or high fat diet [41,43]. Interestingly, injection of these tsRNAs, testis or sperm RNA into normal zygotes resulted in altered metabolic gene expression and metabolic phenotype and preparations from healthy controls did not [41,42]. These studies are critical to our understanding and may lead to potential targets in humans.

4. Nature versus nurture The role for genetic contributions to racial disparities in health, investigated through genome-wide and candidate gene-based association studies is difficult to quantitate, particularly in human studies [45,46]. Genome-wide association studies have identified over 40 gene variants associated with increased risk of type 2 diabetes, and over 30 for obesity [47]. Similarly, well over 100 candidate genes with single nucleotide polymorphisms have been associated with increased risk for preterm birth [48]. The etiologies of these conditions are complex and genetic variants or polymorphisms within selective biological pathways likely contribute to the processes. For example, maternal infection and inflammation are known to increase the risk of preterm birth and a large number of studies in various populations have reported an association of polymorphisms for immune and inflammatory genes with preterm birth [48,49]. However, the role these genetic variants play in ethnic/racial differences in health outcomes is debated. It has been argued that human genetic variation does not naturally aggregate into racial categories, there is significant genetic admixture among U.S. populations and that in the U.S., race is largely self-described, having little to do with genetic differences between groups [50,51]. In other words, because self-described race is a poor marker of genetic differences, the consequence of genetic differences upon racial disparities is difficult to determine though human studies.

4.1 Human studies of nature versus nature Well over 100 years ago, it was argued that in the United States racial differences in health have social and environmental causes [52]. Since this time, research has implicated a large number of sociological and behavioral factors contributing to racial health disparities among differing populations [53]. Importantly, we are beginning to understand how sociological and environmental factors interplay with biology to affect the risk of chronic disease or conditions. These “factors” that combine with biology have been recognized to include a broad range of behavioral, nutritional, psychological, social, and occupational variables. Additionally, the time point(s) in which individuals experience or are exposed to these factors represent periods of vulnerability for impacting risk of chronic or other diseases. It is now clear that long term health trajectories are impacted by developmental processes that integrate behavioral, social, nutritional and environmental influences to modify gene expression, modulate physiological function, and impact tissue and organ development.

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Racial and ethnic disparities in perinatal health outcomes, including preterm birth and neonatal mortality and morbidity are long standing and well documented [54,55]. As with other health outcomes, genetic factors have been implicated, though current data do not support ethnic differences in allele or haplotype frequencies to be strong determinants of preterm birth [49]. Evidence of limited genetic contributions to racial disparities in perinatal outcomes come from studies of recent immigrants and their racial counterparts born in the US. These studies demonstrate that low birth weight and preterm birth rates for African American infants born to foreign born women are significantly lower than low birth weight and preterm rates for African American infants born to women whose families have resided in the United States for a significant period of time. Moreover, the low birth weight and preterm rates for African American infants born to foreign born women closely approximately those of Whites [56]. However, among subsequent generations born in the US, there is a shift toward lower birth rates. Thus, living in the US imparts a change in the intrauterine environments for African Americans that result in increased risk for low birth weight and preterm birth [57]. Large geographic differences in the rates of preterm birth among Black Americans further support the importance of social-environmental exposures, with limited contribution of genetic variation, in explaining the persistent disparities in preterm birth between racial categories. For example, rates of preterm birth (2013e5) for White women in Hennepin County (Minneapolis MN) and Milwaukee County (Milwaukee WI) were relatively similar, 8.3% and 8.5%, respectively, while those of Black women are highly discrepant (10.1 and 13.8%, respectively). As with other areas of health measures, key to understanding racial/ethnic disparities in perinatal health outcomes is recognizing the importance of social determinants. These factors include, but are not limited to occupational and residential environment, income, educational level, social support, and exposure to racism. Findings of studies exploring the relationships among race, socioeconomic factors and adverse birth outcomes (preterm birth) have been inconsistent. Additionally, race and socioeconomic status are related but not interchangeable, as racial differences in health exist among similar levels of socioeconomic status (5). Braverman et al. utilized a California data source enriched with socioeconomic measures and a wide range of potential confounders to assess the contribution of socioeconomic status to the racial disparity in preterm birth [58]. These investigators found similar rates of preterm birth within socioeconomically disadvantaged subgroups with no disparity between whites and blacks. In contrast, significant racial disparity existed between socioeconomically less disadvantaged groups, with White but not Black women having decreasing rates of preterm birth with advancing socioeconomic status. These findings support a role for alternative disadvantages, such as psychosocial stress affecting Black women across socioeconomic levels. Hypotheses regarding maternal stress as an important determinant of perinatal outcome address that stress may be acute, such as death of a loved one, or chronic, and may include, but is not limited to experiences of racism, poverty, homelessness, domestic violence, unemployment, discrimination, crime, feelings of anger, despair or danger [55,59]. Furthermore, these events may be preconceptional or occur during pregnancy. In a systematic review of evidence for biological and social patterning of preterm birth, Kramer and Hogue found evidence for modest association between maternal stress and racial disparities in risk for preterm birth (40 nt RNAs, from HFD and ND mouse sperm separately into normal zygotes. Surprisingly, only the offspring injected with 30e40 nt RNAs showed similar metabolic effects to those produced by injection of sperm total RNA [28]. According to previous reports, 30e40 nt RNAs are mainly tRNA-derived small RNAs in mouse mature sperm [9,53]. In a study by Sharma et al., researchers injected RNA fractions (20% in Mediterranean and Hispanic populations) or lower (six-months) of resveratrol supplementation in metabolic disease would add significant knowledge to this field. While these results express mixed outcomes, it should also be noted that trans-resveratrol is absorbed but then conjugated to sulfate or glucuronide in humans, which makes resveratrol largely bio-unavailable [69]. Similar to curcumin, increasing resveratrol bio-availability could improve its therapeutic potential. Finally, the inclusion of studies that examine combination resveratrol supplementation with standard medication may prove efficacious over using standard medication alone. Early work in rodents hints at the efficacy for combination therapy, in which rodents co-treated with resveratrol and metformin had improved glucose and insulin tolerance compared to metformin alone in high fat fed obese and diabetic mice [71]. Further examination for this combination therapy is needed.

2.3 EGCG Epigallocatechin-3-O-gallate (EGCG), a polyphenol in the catechins (flavanol) family, is the primary bioactive compound in Camellia sinensis (L.) Kuntze leaves that are used for making tea. Tea is a globally consumed beverage that has been linked to risk reductions in metabolic dysfunction and obesity-induced CVD [72]. How EGCG improves metabolic function is still an active area of investigation, yet early studies demonstrated direct actions for EGCG on adipocyte signal transduction. This interaction was mediated by EGCG binding to a 67-kDa laminin receptor (67LR) on adipocytes [73,74]. However, additional actions for EGCG on metabolic health have been noted; in particular, recent findings demonstrate an important role for EGCG as a regulator of the epigenome [75]. Quite simply, obesity develops from excess caloric intake that leads to adipocyte hypertrophy as well as adipocyte hyperplasia. Hyperplastic obesity is characterized by pre-adipocyte replication and differentiation into mature adipocytes (i.e., fat cells) [76]. EGCG has been reported to inhibit

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adipocyte differentiation, or adipogenesis [77]; this would suggest that EGCG can inhibit hyperplastic obesity. Indeed, EGCG has been reported to attenuate diet-induced obesity in mice [78]. Moreover, evidence supports an anti-obesity effect for green tea consumption [79]. A myriad of transcriptional regulators control adipocyte differentiation such as activation of PPARg [76]. Not surprisingly, EGCG has been shown to inhibit PPARg [80] as well as fat mass and obesity-associated protein (FTO) [77]. FTO was originally reported in GWAS studies to play a role in obesity [81]. FTO is a DNA and mRNA demethylase and was the first demethylase enzyme shown to demethylate N6-methyladenosine (m6A), which can impact mRNA transcription, splicing and compartmentalization [82]. FTO demethylation of m6A was shown to regulate mRNA alternative splicing that is necessary for adipogenesis [83]. EGCG has been shown to inhibit FTO expression, leading to increased m6A methylation that contributed to reduced cyclin dependent kinases necessary for mitotic clonal expansion (MCE). EGCG-mediated inhibition of MCE resulted in inhibition of adipocyte differentiation [77]. Combined, these data would suggest that tea catechins like EGCG, regulate gene expression via m6A demethylation leading to changes in alternative splicing, RNA localization and gene regulation. EGCG has also been reported to upregulate the cyclin dependent kinase inhibitors p21Cip1 and p27kip1 during adipogenesis [77]. Loss of p21 and p27 has been shown to increase fat mass, glucose intolerance and insulin insensitivity in mice [84]. Histone deacetylase inhibitors or loss of HDAC1 has been reported to upregulate p21 and p27 via increased histone acetylation of histones H3 and H4 [85,86]. Several reports have shown that EGCG targets HDACs for inhibition leading to increased histone acetylation [87e90]. It should be noted that most of these reports were in cancer cell lines. As such, it was shown that EGCG re-activated tumor suppressor genes like p21Cip1 via inhibition of HDAC activity and increased histone acetylation [88]. Similar studies have not been performed in adipocytes, and therefore it remains unknown if the upregulation of p21 and p27 with EGCG-mediated inhibition of adipogenesis also resulted from HDAC inhibition and histone hyper-acetylation. Combined, these reports suggest that EGCG regulates genome-wide changes in gene expression to attenuate adipogenesis through inhibition of HDAC activity and thus hyper-acetylation of histone proteins as well as m6A demethylation linked to alternative splicing; thus ECGC acts as an important epigenomic regulator of hyperplastic obesity. Population-based studies have linked tea consumption with a reduced risk of T2D [91e94] What role tea polyphenols play in the regulation of glucose control remains a topic of investigation. Recent findings using GWAS identified 143 risk variants associated with T2D. Moreover, integration of DNA methylation inferred that genetic variation impacts T2D via epigenetic changes in gene expression. In this large cohort of 62,892 T2D and 596,424 controls of European ancestry, GWAS and EWAS integration highlighted 3 genes (CAMK1D, TP53INP1, and ATP5G1) linked to T2D, in which enhancer-promoter interactions for CAMK1D, for instance, could be regulated by DNA methylation and/or a single-nucleotide polymorphism (SNP) in the forkhead box A1 (FOXA1)/FOXA2 DNA binding proteins [95]. In addition, others have shown that DNA hyper-methylation inhibited adiponectin gene expression, which, in turn, exacerbated obesity-induced insulin resistance [96]. Pharmacological or genetic inhibition of DNMT1 activity restored adiponectin expression and ameliorated obesity-induced glucose intolerance and insulin resistance [96]. Of note, EGCG treatment has been shown to inhibit DNMT1, leading to reduced genome-wide methylation [97e100]. However, these reports studied the role for EGCG in cancer. To date, no study has examined the role for EGCG as a DNMT1 inhibitor on insulin resistance and T2D. Despite this, these data suggest that anti-diabetic

3. Summary

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actions for tea catechins are likely mediated, in part, through global changes in DNA methylation that impacts gene expression. Similar to its roles in obesity and T2D, tea consumption is linked to lower CVD risk and CVD mortality. Many mechanisms have been proposed for the cardio-protective actions of tea or tea catechins. Of these, EGCG-mediated inhibition of HDACs and increased histone acetylation has emerged as a key regulator of cardiac function [87,101]. EGCG has been shown to inhibit classes I, IIa and IIb HDACs in cardiac tissue [87]. This inhibition has been linked to improved age-related diastolic function, in which hyper-acetylation of the cardiac troponin complex inhibitor (cTnI) promoter resulted in increased cTnI gene expression [101]. cTnI is critical for proper systolic and diastolic function, where cTnI down-regulation contributes to CVD and heart dysfunction. Class I HDACs deacetylated cTnI’s promoter region, which led to condensed chromatin, inhibition of transcription factors GATA4 and myocyte-specific enhancer factor 2c (Mef2c) binding and subsequently cTnI gene repression. EGCG inhibited HDAC activity and subsequently blocked age-related diastolic dysfunction via epigenetic regulation of cTnI gene expression [101]. In addition, EGCG has been shown to suppress cardio-pathogenic genes in order to impart cardioprotection [102]. In this report, EGCG treatment led to forkhead box O1 (FoxO1) deacetylation, which impaired FoxO1 binding to pathological gene promoter regions, repressing pathological gene expression and protecting cardiac myoblasts from hyperglycemia-induced autophagy [102]. EGCG-mediated deacetylation of FoxO1 potentially occurred via HAT inhibition. Others have reported that EGCG inhibited HAT activity in order to inhibit transcription factor acetylation that impaired transcription factor nuclear localization and/or DNA binding [103]. Together, these data would suggest that EGCG targets both HDACs and HATs for inhibition, which would allow for compartmentalized and genome-specific changes in protein (e.g. transcription factors) or histone acetylation contributing to upregulation of protective genes and downregulation of deleterious genes in the heart. Randomized control trials of EGCG supplementation/consumption in humans with metabolic dysfunction are limited [104]. In addition, it is difficult to attribute EGCG as the sole polyphenol or compound to body fat reduction in patients that consume tea, in part, because of tea’s caffeine content; caffeine intake has been linked to weight and fat loss. Nonetheless, consuming EGCG at 100e460 mg/ day in the form of tea has been shown to contribute to weight loss [105]. In agreement with findings described above, EGCG (282 mg/day) and resveratrol (80 mg/day) co-supplementation for 12 weeks attenuated adipogenesis as well as decreased gene expression of pathways related to energy metabolism, oxidative stress, inflammation and immune defense in overweight and obese patients [106]. These results cannot be credited to EGCG alone, as resveratrol was co-supplemented at 80 mg/day. Combined, studies to date suggest that EGCG supplementation or tea consumption contributes to a plethora of epigenetic adaptations (e.g. HDAC inhibition, DNA hypo-methylation) that can impact the epigenome. What remains less clear, is what role chronic tea/EGCG consumption as opposed to acute consumption plays in epigenomic regulation and ultimately human health.

3. Summary This chapter focused on three major polyphenolic compounds (curcumin, resveratrol and EGCG) as regulators of the epigenome for metabolic protection. Evidence suggests that all three compounds modulate a plethora of epigenetic adaptations contributing to global changes in transcription factor binding, alternative splicing, DNA methylation and histone acetylation, among others (Fig. 20.1).

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In combination, these adaptations lead to global changes in genome stability and regulation that impact gene and protein expression independent of genetic mutations; this highlights the emerging understanding for nutrition in the regulation of the epigenome (i.e., nutritional epigenomics). Nutritional epigenomics is an active area of study in the fields of medicine, nutrition and dietetics. Historically, diets were prescribed to treat or mitigate disease, with understanding for the role of macro- and micro-nutrients in the regulation of metabolic function, water retention, blood glucose absorption and ion regulation among others. Dietary Approaches to Stop Hypertension (the DASH diet) and the Mediterranean Diet serve as two examples of commonly prescribed diets used to improve hypertension and metabolic function in patients with CVD and T2D. However, with continued understanding for the role of macro- and micro-nutrients, including food bioactives like curcumin, resveratrol and EGCG, as regulators of the epigenome, Doctors and Dietitians of the future might prescribe foods with a personalized medicine approach to target epigenomic dysregulation. For example, a diabetic patient with low PPARg and high HDAC activity concomitant with histone hypo-acetylation might be prescribed grapes at adequate doses to accumulate resveratrol or a resveratrol supplement. However, this future remains unlikely, until scientists have a better understanding for the role of chronic versus acute dietary and food bioactive exposure on epigenetic adaptations as well as the impact for whole foods versus single food bioactives on the epigenome. In addition, bioactive compound absorption and bioavailability rates make interpretation of cell and animal data much more difficult to predict in humans. Likewise, the interphase between food-gut microbiota fermentation may impact positive actions shown at the bench. Lastly, toxicity of food bioactive supplementation is important when considering supplementation regimens similar to any drug. In conclusion, diet and its constituents impact human metabolism as well as metabolic health. Dietary bioactive compounds impart metabolic health and mitigate metabolic disease via epigenomic regulation of genome function. While current evidence suggests these dietary compounds are efficacious for preventing and treating metabolic disease, future studies examining bioactive compounds in cells, animals as well as humans are warranted to fully elucidate their epigenomic mechanisms and physiological effects.

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[81] Loos RJF, Yeo GSH. The bigger picture of FTO - the first GWAS-identified obesity gene. Nat Rev Endocrinol 2014;10:51e61. [82] Fu Y, Dominissini D, Rechavi G, He C. Gene expression regulation mediated through reversible m 6 A RNA methylation. Nat Rev Genet 2014;15:293e306. [83] Zhao X, Yang Y, Sun BF, Shi Y, Yang X, Xiao W, et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res December 21, 2014; 24(12):1403e19. [84] Naaz A. Loss of cyclin-dependent kinase inhibitors produces adipocyte hyperplasia and obesity. FASEB J December 2004;18(15):1925e7. [85] Sambucetti LC, Fischer DD, Zabludoff S, Kwon PO, Chamberlin H, Trogani N, et al. Histone deacetylase inhibition selectively alters the activity and expression of cell cycle proteins leading to specific chromatin acetylation and antiproliferative effects. J Biol Chem December 3, 1999;274(49):34940e7. [86] Lagger G, O’Carroll D, Rembold M, Khier H, Tischler J, Weitzer G, et al. Essential function of histone deacetylase 1 in proliferation control and CDK inhibitor repression. EMBO J June 3, 2002;21(11): 2672e81. [87] Godoy LD, Lucas JE, Bender AJ, Romanick SS, Ferguson BS. Targeting the epigenome: screening bioactive compounds that regulate histone deacetylase activity. Mol Nutr Food Res December 2016: 1600744. [88] Pandey M, Shukla S, Gupta S. Promoter demethylation and chromatin remodeling by green tea polyphenols leads to re-expression of GSTP1 in human prostate cancer cells. Int J Cancer June 1, 2010;126(11): 2520e33. [89] Nandakumar V, Vaid M, Katiyar SK. (-)-Epigallocatechin-3-gallate reactivates silenced tumor suppressor genes, Cip1/p21 and p16INK4a, by reducing DNA methylation and increasing histones acetylation in human skin cancer cells. Carcinogenesis April 1, 2011;32(4):537e44. [90] Li Y, Yuan Y-Y, Meeran SM, Tollefsbol TO. Synergistic epigenetic reactivation of estrogen receptor-alpha (ERalpha) by combined green tea polyphenol and histone deacetylase inhibitor in ERalpha-negative breast cancer cells. Mol Canc October 14, 2010;9(1):274. [91] Grosso G, Stepaniak U, Micek A, Topor-Mądry R, Pikhart H, Szafraniec K, et al. Association of daily coffee and tea consumption and metabolic syndrome: results from the Polish arm of the HAPIEE study. Eur J Nutr October 4, 2015;54(7):1129e37. [92] van Dieren S, Uiterwaal CSPM, van der Schouw YT, van der ADL, Boer JMA, Spijkerman A, et al. Coffee and tea consumption and risk of type 2 diabetes. Diabetologia December 1, 2009;52(12):2561e9. [93] Hamer M, Witte DR, Mosdøl A, Marmot MG, Brunner EJ. Prospective study of coffee and tea consumption in relation to risk of type 2 diabetes mellitus among men and women: the Whitehall II study. Br J Nutr November 4, 2008;100(5):1046e53. [94] Iso H, Date C, Wakai K, Fukui M, Tamakoshi A, Mori M, et al. The relationship between green tea and total caffeine intake and risk for self-reported type 2 diabetes among Japanese adults. Ann Intern Med April 18, 2006;144(8):554e62. [95] Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun December 27, 2018; 9(1):2941. [96] Kim AY, Park YJ, Pan X, Shin KC, Kwak SH, Bassas AF, et al. Obesity-induced DNA hypermethylation of the adiponectin gene mediates insulin resistance. Nat Commun December 3, 2015;6(1):7585. [97] Yiannakopoulou EC. Targeting DNA methylation with green tea catechins. Pharmacology 2015;95:111e6. [98] Jin H, Chen JX, Wang H, Lu G, Liu A, Li G, et al. NNK-induced DNA methyltransferase 1 in lung tumorigenesis in A/J mice and inhibitory effects of (-)-epigallocatechin-3-gallate. Nutr Canc January 2, 2015;67(1):167e76.

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[99] Lee WJ. Mechanisms for the inhibition of DNA methyltransferases by tea catechins and bioflavonoids. Mol Pharmacol July 18, 2005;68(4):1018e30. [100] Pal D, Sur S, Roy R, Mandal S, Kumar Panda C. Epigallocatechin gallate in combination with eugenol or amarogentin shows synergistic chemotherapeutic potential in cervical cancer cell line. J Cell Physiol August 4, 2018:825e36. [101] Pan B, Quan J, Liu L, Xu Z, Zhu J, Huang X, et al. Epigallocatechin gallate reverses cTnI-low expressioninduced age-related heart diastolic dysfunction through histone acetylation modification. J Cell Mol Med October 2017;21(10):2481e90. [102] Liu J, Tang Y, Feng Z, Hou C, Wang H, Yan J, et al. Acetylated FoxO1 mediates high-glucose induced autophagy in H9c2 cardiomyoblasts: regulation by a polyphenol -()-epigallocatechin-3-gallate. Metabolism October 2014;63(10):1314e23. [103] Choi K-C, Jung MG, Lee Y-H, Yoon JC, Kwon SH, Kang H-B, et al. Epigallocatechin-3-Gallate, a histone acetyltransferase inhibitor, inhibits EBV-induced B lymphocyte Transformation via suppression of RelA acetylation. Cancer Res January 15, 2009;69(2):583e92. [104] Eng QY, Thanikachalam PV, Ramamurthy S. Molecular understanding of Epigallocatechin gallate (EGCG) in cardiovascular and metabolic diseases. J Ethnopharmacol 2018;210:296e310. [105] Vazquez Cisneros LC, Lopez-Uriarte P, Lopez-Espinoza A, Navarro Meza M, Espinoza-Gallardo AC, Guzman Aburto MB. Effects of green tea and its epigallocatechin (EGCG) content on body weight and fat mass in humans: a systematic review. Nutr Hosp 2017;34(3):731e7. [106] Most J, Warnke I, Boekschoten MV, Jocken JWE, De P, Friedel A, et al. The effects of polyphenol supplementation on adipose tissue morphology and gene expression in overweight and obese humans. Adipocyte May 1, 2018;3945(May):1e7.

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Stilbenoids as dietary regulators of the cancer epigenome

21

Megan Beetch, Sadaf Harandi-Zadeh, Kate Shen, Barbara Stefanska Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada

1. Introduction Epigenetics is the study of changes in gene expression that do not involve changes in the underlying DNA sequence. Factors including exposure to toxic chemicals, psychological states, exercise and diets are examples of potential environmental factors that may influence epigenetic profiles and thus human health [1]. As discussed in previous chapters, epigenetic modifications, which include DNA methylation, covalent histone modifications and regulation by non-coding RNAs, play important roles in DNA accessibility and chromosomal stability [2]. Consequently, these modifications have been demonstrated to be involved in regulation of gene expression (Fig. 21.1). Due to their dynamic nature and reversibility, epigenetic modifications, particularly DNA methylation, have attracted extensive attention in terms of their outcomes in disease prevention and treatment. In mammals, DNA methylation refers to a covalent transfer of a methyl group to the fifth position of the cytosine ring in cytosine-guanosine (CpG) dinucleotides by enzymes called DNA methyltransferases (DNMTs) [2]. DNMT1 maintains already existing methylation patterns after DNA replication, whereas DNMT3A and DNMT3B are responsible for establishing de novo DNA methylation patterns predominantly during embryogenesis [3]. DNMT1 has higher affinity for hemi-methylated DNA, but has also been observed to catalyze de novo methylation in cancer [4,5]. DNMT3A has preference for unmethylated DNA, and DNMT3B has been found to target both unmethylated and hemi-methylated DNA. DNMT3A and DNMT3B require catalytically inactive DNMT3L for regulation of their activities [3]. Regions of the genome that contain a large number of CpG dinucleotides are called CpG islands and they are typically located in gene regulatory regions [1,6,7]. The pattern of DNA methylation, established through both DNA methylation and demethylation, changes over the course of biological development and abnormal epigenetic patterns have been linked to several human diseases, including cancer [8]. During carcinogenesis, alterations in DNA methylation at certain gene loci lead to changes in corresponding gene expression [9]. Tumor suppressor genes are often methylated and silenced during cancer development, while oncogenes lose methylation within their regulatory regions, including promoters and enhancers, and become actively transcribed. In addition to aberrant silencing or activation of genes, chromosomal rearrangements and genome instability caused by demethylation and activation of transposons and other repetitive sequences further contribute to carcinogenesis [10e12]. Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00021-7 Copyright © 2019 Elsevier Inc. All rights reserved.

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FIG. 21.1 Regulation of gene expression by the major components of the epigenome. The epigenome consists of interrelated mechanisms of DNA methylation, covalent histone modifications and chromatin modifying complexes (A). Regions of transcriptionally inactive chromatin are associated with histone methylation at specific lysine residues (H3K9 and H3K27), binding of silencing complexes (NuRD) and the presence of methylated DNA. On the other hand, transcriptionally active regions of the genome are associated with acetylated lysine residues, H3K4 methylation and unmethylated DNA. The epigenome also includes non-coding RNA mechanisms such as the RNA interference (RNAi) pathway associated with microRNAs (B). The RNAi pathway results in silencing of target genes through mRNA cleavage or translational repression. Credit: Barbara Stefanska.

A growing number of studies have identified roles for bioactive dietary compounds in reversal/ attenuation of tumor progression and regulation of gene expression and molecular targets implicated in carcinogenesis via epigenetic modifications [13]. Polyphenols are a vital part of the human diet, and are commonly found in a wide variety of plant-based foods and beverages, such as in vegetables, fruits, nuts, tea and red wine [14]. Estimates suggest that more than 8,000 different dietary polyphenols exist, and can be divided into ten general classes based on their basic chemical structures [15]. Of these classes, phenolic acids, flavonoids, stilbenoids, and lignans are the most abundant in the human diet [16]. Aside from general health benefits, there is also a growing body of evidence that demonstrates a

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lower incidence of cancer in certain populations that may be due to consumption of polyphenol-rich diets [17]. For example, a population in Southern Italy consumes high amounts of polyphenol-rich Annurca apple, which has been linked to a lower incidence of sporadic colorectal cancer compared to populations consuming a Western diet [18]. The observed lower cancer incidence may be partially attributable to the epigenetic modifications induced by polyphenols present in Annurca apple which have been associated with reduced cancer cell proliferation, induced cell death, and inhibition of cancer metastasis [19]. In particular, mechanistic studies on stilbenoids demonstrate their epigenetic activity in regulation of gene expression, which may play a role in cancer prevention and support of anti-cancer therapies [20e23]. The aim of this chapter is to highlight current findings on a variety of stilbenoids and their roles as dietary regulators of the cancer epigenome. Stilbenoid compounds share a common backbone structure known as stilbene, but differ in the nature and position of substituents [24]. Stilbenoids are phytoalexins, meaning they are compounds produced de novo to protect the plant from infection and toxins [25], and are naturally abundant in grapes, berries, peanuts, red wine, and some medicinal plants [26]. The most well-known stilbenoids include resveratrol (trans-3,5,40-trihydroxystilbene) and pterostilbene (trans-3,5-dimethoxy-40hydroxystilbene), but several other stilbenoids exist and have also been shown to have anti-cancer effects [26]. Many studies have reported positive results obtained from cell culture models, animal studies, and limited human clinical trials [24]. It is apparent that the efficacy of stilbenoids is diverse and may depend on their bioavailabilities and consequently on ratio of parent compound versus metabolites [27]. The reported oral bioavailability for resveratrol was 29.8% [28], 50.7% for piceatannol [29,30], and 80% for pterostilbene [31]. Higher bioavailability of pterostilbene compared to resveratrol may be due to the presence of two methoxy groups in the pterostilbene structure, which makes it more lipophilic [31]. Pterostilbene is also more metabolically stable because it has only one free hydroxyl group available for glucuronidation and sulfation, so high levels of free pterostilbene may also contribute to its higher bioavailablity and efficacy as an anti-cancer agent [32], (Table 21.1).

2. Stilbenoids as regulators of the epigenome Chemopreventive and anti-cancer effects of stilbenoid compounds are widely reported in scientific literature [33]. Studies ranging from observational to proof-of-principle to mechanistic investigations have surfaced throughout the past couple of decades, and clinical trials incorporating natural compounds have cropped up in the recent years [13,34]. Epigenetic mechanisms have been regarded as important contributors in carcinogenesis [35]. To date, several preclinical studies have evaluated the effectiveness of stilbenoids in chemoprevention and in support of anti-cancer therapies using in vitro and in vivo models with focus on epigenetics, many of which will be the focus of discussion in the following sections.

2.1 DNA methylation 2.1.1 Resveratrol and pterostilbene Genome-wide investigations have identified altered DNA methylation landscapes upon treatment with resveratrol or pterostilbene in vitro [20,36]. Other studies define stilbenoid-targeted genes or signaling pathways that are modulated by DNA methylation leading to anti-cancer effects [20,22,23,37,38] (Fig. 21.2A). Interestingly, the bulk of studies defining DNA methylation-related effects of resveratrol and pterostilbene utilize in vitro models of breast cancer.

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Table 21.1 Half-life and oral bioavailability of stilbenoid compounds in vivo.

Stilbenoid

Structure

Food sources

Half life (h)

Oral bioavailability (%)

Resveratrol [27,28,31]

Grapes, mulberries, apricots, legumes, peanuts

1.48

29.8

Pterostilbene [31]

Blueberries, vaccinium berries, almonds

1.73

80

Piceatannol [29,30]

Grapes, red wine, passion fruit, berries, white tea tree, Asian legumes, rhubarb

4.23

50.7

Credit: Barbara Stefanska.

Mirza and colleagues observed upregulation of DNA methylating enzymes (DNMTs) in breast cancer patients, prompting an investigation into changes in DNMT expression in response to natural compounds. Upon treatment with resveratrol, marked decreases in expression of DNMT1, DNMT3A, and DNMT3B, as well as other epigenetic regulators (HDAC1 and MeCP2) were detected in breast cancer cells [39]. Around that time, it was hypothesized that the anti-cancer effects exerted by resveratrol were at least partially through downregulation of DNMTs leading to hypomethylation and subsequent transcriptional activation of methylation-silenced tumor suppressor genes. This hypothesis resulted in a compilation of work assessing loci-specific DNA hypomethylation and reactivation of candidate tumor suppressor genes in response to resveratrol treatment [37,38,40]. Specifically, we showed that PTEN, APC, and RARb2 lose methyl marks in their promoters and are consequently activated in response to resveratrol [37,38]. In recent years, genome-wide technologies have more thoroughly revealed the widespread action of resveratrol on the DNA methylation landscape in cancer cells. Our group was the first to use a DNA methylation microarray to delineate bidirectional DNA methylation alterations in breast cancer cells treated with 15 mM resveratrol for 9 days. A majority of resveratrol-mediated DNA methylation changes persisted as increased methylation at loci associated with genes with oncogenic functions [20]. A smaller portion of DNA methylation changes were observed to be hypomethylated in genes

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FIG. 21.2 Examples of stilbenoid-mediated epigenetic changes contributing to anti-cancer effect. Epigenetic components are altered upon stilbenoid exposure. (A) DNA demethylation of promotors of tumor suppressor genes and hypermethylation of regulatory regions of oncogenes leads to reactivation or inhibition of corresponding gene expression, respectively. (B) Regions of condensed, transcriptionally inactive chromatin are formed by removal of acetyl groups by histone deacetylases (HDACs) which often involves binding of silencing complexes such as the nucleosome remodeling and deacetylation (NuRD) complex. Open, active chromatin is associated with acetylation at lysine residues to facilitate chromatin accessibility to transcriptional machinery. Stilbenoids inhibit HDAC and NuRD binding leading to changes in gene transcription. (C) Stilbenoids inhibit oncogenic miRNAs and activate tumor suppressive miRNAs. These miRNAs then regulate expression of target genes, leading to upregulation of tumor suppressor genes and downregulation of oncogenes and associated oncogenic pathways. Credit: Barbara Stefanska.

associated with tumor suppressive functions [20]. Further, we showed that oncogenic NOTCH signaling was a target for DNA hypermethylation upon treatment with either resveratrol or its dimethylated analog pterostilbene in breast cancer cells. Specifically, DNA methylation within an enhancer region of NOTCH co-activator, MAML2, was significantly increased upon treatment with resveratrol or pterostilbene. We found that DNMT3B was upregulated and enriched in the MAML2 enhancer in response to resveratrol. This coincided with decreased occupancy of transcription factor OCT1 at the differentially methylated site leading to subsequent downregulation of MAML2.

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Epigenetic silencing of MAML2 was associated with inhibition of NOTCH signaling activity, as evidenced by diminished expression of NOTCH target genes HEY1, HES1 and NOTCH1. These findings suggest that stilbenoid compounds may alter DNA methylation and transcriptional machinery to exert anti-cancer responses through inhibition of oncogenic signaling pathways [20]. Others have used DNA methylation microarray method to further support our observation that resveratrol exerts broad, bidirectional effects on DNA methylation patterns in breast cancer cells [36]. The significance of these findings is two-fold. First, dietary stilbenoid compounds, like resveratrol and pterostilbene, exert many subtle, broad effects on DNA methylation that may impact both tumor suppressor genes and oncogenes. This bidirectional and widespread effect is excellent for chemoprevention, as slight perturbations in DNA methylation may provide balance and maintain a healthy state. Secondly, the significance of our study on NOTCH signaling, specifically, lies in its potential to increase efficacy of existing anti-cancer drugs, reduce side effects of chemotherapeutics by allowing administration of lower doses along with natural compounds to elicit the same anti-cancer effect, and improve resistance to therapy by targeting steps in the oncogenic pathway that are potentially different from steps targeted by current drugs.

2.1.2 Response to combinatorial treatment with resveratrol and pterostilbene While resveratrol or pterostilbene treatment alone has yielded promising anti-cancer results, combinatorial treatment using resveratrol and pterostilbene together has shown further beneficial anti-cancer effects in triple negative breast cancer (TNBC) in vitro models. Combinatorial stilbenoid treatment at physiologically relevant doses of 15 mM resveratrol and 5 mM pterostilbene restored estrogen receptora (ERa) at least partially by reducing DNA methylation and reverting back to a transcriptionally active state. DNMT enzyme activity and global DNA methylation were significantly decreased upon either pterostilbene only or combination stilbenoid treatment. Most interestingly, stilbenoid-mediated reactivation of ERa led to sensitization of breast cancer cells to traditional hormonal drug therapy [22]. DNA damage response is another process influenced by combinatorial stilbenoid treatment of TNBC cells. Such a treatment downregulated all 3 catalytically active DNMTs (DNMT1, DNMT3A and DNMT3B), diminished overall DNA methylation activity, and reduced expression and activity of histone deacetylase sirtuin 1 (SIRT1), which contributed to decreased ability of cancer cells to repair DNA [23]. In addition to combinatorial stilbenoid treatment, combining resveratrol with other polyphenolic compounds present in red wine called proanthocyanidins, has yielded promising results. Resveratrol and proanthocyanidins caused synergistic anti-cancer effects on breast cancer cells by inducing apoptosis through upregulation of pro-apoptotic Bax and downregulation of anti-apoptotic Bcl-2. These changes in gene expression may be at least partially modulated by epigenetic mechanisms, as evidenced by reduction of DNMT and HDAC activities in response to combination polyphenol treatment [41].

2.1.3 Piceatannol To date, there are no published studies assessing the effects in piceatannol on DNA methylation patterns in cancer although its anti-cancer activity has been demonstrated (reviewed in Ref. [42]). Piceatannol has a similar chemical structure to resveratrol, with the exception of four hydroxyl group as opposed to resveratrol’s three hydroxyl groups, which may subject piceatannol to more intensive glucuronidation or sulfation (formation of metabolites). Piceatannol does not have methoxy groups like pterostilbene. The methoxy groups have been proposed to be a reason behind pterostilbene’s high

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bioavailability, because they contribute to lipophilicity and reduce the number of sites for modifications and formation of metabolites [32]. Interestingly, the bioavailability of piceatannol is between that of resveratrol and pterostilbene, but piceatannol has a much longer half-life than both resveratrol and pterostilbene. Because piceatannol’s structural similarity is closest to resveratrol, it is highly probable that piceatannol would exert bidirectional effects on DNA methylation profiles in cancer, modulating many genes/pathways simultaneously as observed for resveratrol. This hypothesis remains to be elucidated in future studies [20,21].

2.2 Covalent histone modifications 2.2.1 Resveratrol and pterostilbene Studies have suggested that stilbenoids can impact histone modifications, such as histone acetylation and methylation, leading to chromatin restructuring [43] (Fig. 21.2B). Acetylation of lysine residues on histone proteins, catalyzed by histone acetyltransferases (HATs), loosens chromatin structure allowing access of transcriptional machinery to promote gene expression. On the other hand, histone deacetylases (HDACs) remove acetyl groups from histone proteins tightening DNA and inhibiting transcription [44]. Methylation of histones can occur at lysine or arginine residues. Arginine methylation results in transcriptional activation while methylation at lysine residues results in either activation or repression depending on the position and number of methyl groups. Imbalance of HAT and HDAC activity leads to aberrant gene silencing/activation and contributes to carcinogenesis (reviewed in Ref. [45]). HDAC inhibitors have shown promise in reactivating silenced tumor suppressor genes in cancer, therefore agents that reduce HDAC activity are of great interest (reviewed in Ref. [45]). Docking studies showed that the chemical structure of resveratrol is such that it can block the activities of class I and class II HDACs [46]. Resveratrol-mediated inhibition of class I, II, and IV HDACs was confirmed in vitro at 50 mM and 100 mM concentrations [46]. Further, a dose-dependent decrease in cell viability in liver cancer cell lines was observed upon resveratrol treatment, which can be at least partially attributed to HDAC inhibition and subsequent increase in acetylation [46]. In several cancers, metastasis-associated protein 1 (MTA1), as part of the nucleosome remodeling and deacetylation (NuRD) complex, was shown to be implicated in stilbenoid-mediated epigenetic effects. MTA1 is part of the NuRD complex, along with HDAC1 and HDAC2, thus the NuRD complex functions as a regulator of gene transcription via deacetylation [47]. Overexpression of MTA1 in prostate cancer is associated with deregulated chromatin and aberrant gene expression, leading to promotion of tumor growth, invasion and metastasis [48]. The MTA1/NuRD complex was reported to deacetylate regulatory regions within tumor suppressors p53 and PTEN and subsequently silence their expression in cancer [49,50]. Upon resveratrol treatment, the MTA1/NuRD complex was inhibited and acetylation of p53 and PTEN followed by gene activation in prostate cancer cells was observed [51,52]. Dhar and colleagues demonstrated that pterostilbene also inhibited MTA1, thereby reducing binding of MTA1 to target genes. Specifically, decreased binding of MTA1 at tumor suppressor genes p21, p27, and E-cadherin resulted in gene upregulation [53]. In liver cancer cells treated with pterostilbene, MTA1 gene and protein expression were downregulated. In addition, HDAC1 and HDAC2 protein expression was decreased, leading to destabilization of the MTA1-HDAC-NuRD complex. A subsequent increase in acetylation of tumor suppressor p53

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was observed, followed by p53 gene activation which may contribute to anti-cancer effects of pterostilbene in vitro [54]. A similar study from the same group described the phenomenon involving the NuRD complex in increasing acetylation of tumor suppressor PTEN leading to its activation and accumulation. As a result, genes targeted by PTEN, including pro-apoptotic genes, were upregulated [55]. Together, preclinical studies of epigenetic effects of resveratrol and pterostilbene in several cancers have established the MTA1-HDAC-NuRD complex as an important target of stilbenoids. Interestingly, deacetylation associated with MTA1 can lead to either transcriptional silencing or stabilization and co-activation of target genes [47,56]. Hence, it was observed that downregulation of MTA1 can also reverse activation of oncogenes. Pterostilbene caused decreased MTA1 binding at oncogenes, such as c-Myc and Notch2, which led to destabilization and reduced oncogene expression in prostate cancer cells [53]. Moreover, an in vivo prostate cancer study determined the effectiveness of combining pterostilbene with the clinically approved HDAC inhibitor SAHA (suberoylanilide hydroxamic acid, vorinostat). Results showed that such a combination treatment significantly inhibited the NuRD complex and downregulated pro-angiogenic HIF-1a [57]. Expression of some deacetylases, such as SIRT1, has been shown to be beneficial and increase longevity [58]. For example, resveratrol treatment upregulated SIRT1 expression, a class III HDAC, in a dose-dependent manner in colorectal cancer cells. SIRT1 was found to be necessary for resveratrolmediated anti-cancer effects such as reduced cancer cell viability and suppressed migration and invasion [59]. Resveratrol-mediated SIRT1 upregulation was associated with inhibition of the pro-metastatic NFkB pathway, likely contributing to anti-cancer effects observed in response to resveratrol [58,59].

2.2.2 Response to combinatorial treatment with resveratrol and pterostilbene A time-dependent increase in histone acetylation was observed after combinatorial resveratrol and pterostilbene treatment in ERa-negative breast cancer cells. Specifically, enrichment of histone marks at the ERa promoter indicating active transcription (acetylated histone 3, acetylated histone 4, acetylated histone 3 lysine 9 (H3K9ac)) was detected after combinatorial stilbenoid treatment. The most pronounced increase in active histone marks occurred when ERa-negative breast cancer cells were treated with 5 mM pterostilbene alone or in combination with 15 mM resveratrol for 72 h [22]. The ability of natural compounds to transcriptionally reactivate ERa in ERa-negative breast cancer cells may be beneficial when designing drug schemes. Using stilbenoid compounds to restore ERa expression has potential to improve efficacy of existing hormonal therapies in breast cancers that have lost hormonal targets.

2.2.3 Piceatannol A single study involving piceatannol and histone modifiers was reported in breast cancer and colon cancer cells. Programmed cell death ligand (PD-L1) and its receptor (PD-1) have emerged as therapeutic targets for solid tumors, such that induction of PD-L1 enhances effectiveness of PD-L1 blockade therapy [60]. PD-L1 binding to PD-1 results in evasion of anti-tumor immunity, leading to reduced efficacy of anti-cancer therapy. In order to combat this, monoclonal antibodies specifically targeting PD-L1 have been used to prevent binding of PD-L1 to PD-1 and termed PD-L1 blockade therapy [61,62]. The most robust therapeutic response to the PD-L1 blockade has been found in PD-L1-positive tumors [60]. Therefore, agents that transform PD-L1-negative tumors into PD-L1-positive tumors may be applied to enable use of PD-L1 blockade therapy as an anti-cancer

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strategy. Past studies on epigenetic reactivation or upregulation of specific genes upon stilbenoid treatment, prompted exploration of piceatannol as a PD-L1 expression activator. Breast cancer and colon cancer cells were treated with piceatannol, resveratrol or a combination of both compounds. Each stilbenoid alone moderately increased expression of PD-L1. However, together piceatannol and resveratrol caused synergistic upregulation. Expression of PD-L1 in response to stilbenoid treatment was shown to require activation of NFkB, which was mediated by histone acetylation/deacetylation. Specifically, HDAC3 and HAT p300 were identified as histone modifiers involved in activation of NFkB upon combination stilbenoid treatment, leading to increased expression of PD-L1 [63]. Further investigation into the efficacy of PD-L1 blockade therapy following stilbenoid-mediated upregulation of PD-L1 is warranted. Understanding the impact of piceatannol on histone modifications is in early stages and requires further investigation, especially in the context of cancer. Considering piceatannol’s structural similarity to resveratrol and pterostilbene, it is highly probable that piceatannol may inhibit other HDACs, including HDAC1 and HDAC2, and may upregulate SIRT1, to elicit anti-cancer effects through other axes or pathways. Additionally, based on observed bioavailability of piceatannol that is between that of resveratrol and pterostilbene, piceatannol might have potential to be an effective and potent compound in chemoprevention and support of cancer therapy.

2.3 MicroRNAs 2.3.1 Resveratrol Alterations in expression of microRNAs and subsequently in expression of microRNA-target genes have been observed in cancers [64]. MicroRNAs can be classified as tumor suppressive or oncogenic based on their genomic targets [64]. Aberrant microRNA expression associated with cancer has been shown to be a target of stilbenoid treatment, by way of silencing of oncogenic microRNAs or restoring expression of tumor suppressive microRNAs, contributing to anti-cancer effects [65] (Fig. 21.2C). Resveratrol caused an apoptotic response by modulating several miRNAs associated with breast tumor suppression (miRNA-122, miRNA-542, miRNA-125b, miRNA-200c, miRNA-409) [66]. Upregulation of these miRNAs in response to resveratrol treatment led to caspase activation and reduction of anti-apoptotic Bcl-2 [66]. Another study reported that breast cancer cells treated with resveratrol or pterostilbene had elevated levels of miRNA-143, miRNA-141 and miRNA-200c, which are known to be tumor suppressive miRNAs in breast cancer [67]. Specifically, miRNA-141 may target b-catenin [68] and miRNA-200c targets a variety of cancer-promoting and metastatic genes [67,69,70]. Activation of these miRNAs upon treatment with stilbenoids resulted from transcriptional upregulation. An additional layer of stilbenoids’ effect on microRNA-related mechanisms is targeting the RNA interference (RNAi) pathway that is connected to regulation of microRNA target genes (Fig. 21.1B). Resveratrol or pterostilbene enhanced expression of Argonaute 2 (Ago2), an important regulator of the pathway that mediates cleavage or tethering of mRNA targeted by a given miRNA. Interestingly, pterostilbene caused most robust Ago2 upregulation, accompanied by more potent anticancer effects compared to resveratrol [71]. Anti-cancer effects of resveratrol in colorectal cancer cells were found to be at least partially associated with upregulation of tumor suppressive miRNA-34c and consequent silencing of its direct target stem cell factor (SCF, also known as KITLG) [72]. KITLG is a tyrosine-kinase receptor ligand that is active during cell proliferation and migration [73]. Resveratrol-mediated activation of the

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miRNA-34c-KITLG axis resulted in reduced proliferation and invasion. This action was confirmed in a colorectal tumor xenograft model where reduced tumor growth was detected upon resveratrol exposure [72]. In the same xenograft model, combination treatment of resveratrol and standard-of-care chemotherapeutic drug oxaliplatin resulted in an additive reduction of colon tumor growth [72]. In another study, the effectiveness of resveratrol in mitigating colitis-associated colon carcinogenesis was modeled using Apc(Min/þ) mice. Over 100 miRNAs exhibited >1.5-fold change in expression in response to resveratrol. In particular, miRNA-101b and miRNA-455 were validated as upregulated resveratrol targets [74]. Both microRNAs are responsible for downregulation of pro-invasive and proproliferative genes, namely COX-2 (miRNA-101b) [75] and GATA6 and RAF1 (miRNA-455) [76,77]. Most of the studies of the effects of resveratrol on miRNAs have been performed in breast and colorectal cancer. Other cancer types have been studied to a lesser extent. In bladder, pancreatic, and prostate cancer cells, inhibition of miRNA-21 by resveratrol was reported to induce apoptosis via Bcl-2 downregulation [78,79] or inhibition of the Akt pathway [80]. In acute lymphoblastic leukemia (ALL), IGFBP3 expression is inversely associated with disease. Oncogenic miRNA-196b and miRNA-1290 that target IGFBP3 are increased in ALL patients, which results in IGFBP3 to silencing. Treatment of ALL cell lines with resveratrol reduced levels of miRNA-196b and miRNA-1290 leading to elevated IGFBP3 expression and consequent anti-tumor effects [81].

2.3.2 Pterostilbene

Studies on TNBC cells showed that pterostilbene concentrations of greater than 30 mM inhibit cancer cell growth while concentrations closer to physiologically relevant levels of 10 mM primarily inhibit cancer cell invasion [82]. Investigation into regulation of microRNAs appears to deliver an explanation for anti-invasive effects of pterostilbene at 10 mM. Pterostilbene upregulated miRNA-205, which was associated with reduced Src expression in TNBC cell lines. Oncogenic Src/FAK signaling was also downregulated in TNBC-xenograft mice injected with pterostilbene at a dose of 10 mg/kg miRNA205/Src/FAK are upstream of epithelial-to-mesenchymal transition (EMT) markers (Snail, Slug, Twist, vimentin), and drive cell invasiveness [82]. Mak and colleagues reported that pterostilbene suppressed the number of breast cancer stem cells within the tumor microenvironment, which led to reduced metastatic capacity. Specifically, anti-metastatic miRNA-448 was found to be suppressed by NFkB in cancer. Treatment with pterostilbene reduced NFkB, subsequently permitting expression of miRNA-448. Pterostilbene, at a dose of 5 mg/kg, reduced cancer stem cell population in TNBCxenograft mice, which was associated with decreased NFkB, Twist1, and vimentin expression [83]. Oncogenic miRNA-17, miRNA-20a, and miRNA-106b directly target the 30 UTR of tumor suppressor PTEN and lead to transcriptional silencing of PTEN [84]. These miRNAs were decreased by resveratrol or pterostilbene treatment. Additional oncogenic miRNAs were inhibited and stronger anti-cancer effects were seen upon treatment with pterostilbene compared to resveratrol. The study further showed that pterostilbene treatment, at a dose of 50 mg/kg, slowed tumor growth, decreased miRNA-17 and miRNA-106a, and restored expression of PTEN in tumor-xenograft mice [84]. Pterostilbene also upregulated PTEN expression in liver cancer cells through a miRNA-mediated mechanism [85]. Pterostilbene treatment of liver cancer cells caused downregulation of miRNA-19a leading to transcriptional activation of PTEN, a direct target of miRNA-19a. Pterostilbene downregulated expression of miRNA-19a almost to the same extent as a synthetic miRNA-19a inhibitor. Combination treatment with pterostilbene and miRNA-19a inhibitor elicited the most robust effects on reducing miRNA-19a and upregulating PTEN expression, which consequently lead to a reduction in

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cell viability and invasion [85]. In addition, pterostilbene downregulated oncogenic miRNA-663b in endometrial cancer cells, which directly targets BCL2L14, a pro-apoptotic protein involved in p53-mediated apoptosis. Consequently, expression of BCL2L14 was increased, possibly contributing to anti-cancer effects of pterostilbene [86]. Based on the scientific evidence discussed in this section, it appears that pterostilbene exerts more potent effects on epigenetic components than resveratrol, and ultimately may be the stilbenoid compound with the most robust anti-cancer action in its natural from. Certainly, more studies are needed to confirm this assertion. There is also a need for additional genome-wide microRNA studies to establish the most important and impactful stilbenoid-mediated changes in microRNA expression to be targeted in cancer prevention and support of cancer therapy.

2.3.3 Piceatannol Few studies have assessed the effects of piceatannol on microRNAs in cancer. The limited evidence that has been reported highlights piceatannol’s role in inducing apoptotic mechanisms mediated by microRNAs [87,88]. For example, piceatannol inhibited growth of melanoma cells and induced apoptosis [87]. A miRNA microarray scan identified several differentially expressed miRNAs in piceatannol-treated melanoma cells compared to control-treated cells. Most notably, miRNA-181a was upregulated upon piceatannol treatment. Bcl-2, an anti-apoptotic protein, was found to be a direct target of miRNA-181a in melanoma cells, which may explain apoptotic effects of piceatannol [87]. A similar mechanism of induction through silencing of Bcl-2 has been described for piceatannol in colorectal cancer cells. Tumor suppressive miRNA-129 was upregulated in response to piceatannol [88]. Other mechanistic evidence was found in leukemia cells. Piceatannol was shown to suppress miRNA-183, leading to upregulation of b-TrCP, a component of E3 ubiquitin ligase that induces transcription factor Sp1 degradation. Sp1 degradation downregulated ADAM17, a protein responsible for production of TNFa which then activates NFkB. Therefore, decreased expression of ADAM17 inhibited the TNFa/NFkB oncogenic pathway, which may contribute to anti-cancer effects of piceatannol [89].

3. Conclusions and future directions Studies using cell culture and rodent models have uncovered vast health-promoting effects induced by stilbenoid compounds [24]. Many studies describe genes and pathways affected by stilbenoid treatment, as well as important roles for epigenetic mechanisms in modulating gene expression associated with cancer [13,24] (Fig. 21.3). A substantial amount of studies that assess the anti-cancer effectiveness of resveratrol and pterostilbene conclude that pterostilbene exerts greater anti-cancer action than resveratrol [22,23]. Resveratrol’s rapid metabolism could reduce its systemic effects and delivery to target organs. Nevertheless, understanding underlying mechanisms of anti-cancer actions of stilbenoid compounds is essential for translational studies that will aid in application of natural compounds in chemoprevention and support of anti-cancer schemes. As the gold standard of experimental research, randomized clinical trials are certainly needed before routine implementation of stilbenoid compounds in chemoprevention or support of anti-cancer therapy. Ideally, natural compounds should be administered at effective doses without off-target effects that can be of detriment to other tissues or systems, but should modulate multiple targets/pathways for

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FIG. 21.3 Widespread action of stilbenoids on regulation of multiple genes impacts many biological processes and signaling pathways which at least partially can explain their anti-cancer effects. Resveratrol (RSV), pterostilbene (PTS), and piceatannol (PIC) impact a wide variety of cancer-related genes and pathways through epigenetic regulation. Credit: Barbara Stefanska.

optimal anti-cancer outcome. Progression from preclinical studies to clinical trials in humans has been limited due to several factors, including bioavailability, appropriate dosage, formulation of compound, and conditions of participants [90]. A couple of human clinical trials have demonstrated that resveratrol may be more effective as a chemopreventive agent for reducing risk of developing cancer as opposed to treating existing cancer [91,92]. In already-developed tumors, fast action with single-target drugs is needed to stop cancer cell proliferation and metastasis. Multi-target, often subtle, effects of stilbenoids could be insufficient to produce immediate anti-cancer outcome. Thus, stilbenoid compounds would be most beneficial when used in cancer prevention and in support of anti-cancer therapy as suggested by findings from human clinical trials. Interestingly, gender differences have been observed regarding the efficacy of polyphenols in reducing risk of cancer in certain populations [93]. Epidemiological evidence shows that men and women have different levels of protection from cancer when following the polyphenol-rich Mediterranean diet. In fact, 4.7% of cancers in men compared to 2.4% of cancers in women would be

References

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avoided upon adherence to the Mediterranean diet [94]. Mechanisms underlying these gender-specific differences are being explored. A study by Dellinger and colleagues reported differences in stilbenoid metabolism between males and females. Specifically, females were more efficient at metabolizing resveratrol and pterostilbene to glucuronides than males [32]. These differences in stilbenoid metabolism could explain some of the variation in anti-cancer response to polyphenols. While studies to date have delivered important insights into the epigenetic mechanisms altered in response to stilbenoid compounds, the elucidation of whether stilbenoid effects on the epigenome are direct or indirect is still underway. The real mechanistic players involved in anti-cancer actions of stilbenoid compounds have not yet been identified with unwavering certainty. In fact, the topic has been slightly controversial. Several years ago, a direct interaction between resveratrol and deacetylase SIRT1 was reported but later found to be an artifact [95], highlighting the difficulty in assessing these kinds of interactions. Methods to accurately measure direct interaction between polyphenolic compounds like resveratrol and proteins/DNA need to be considered and optimized before answers to questions about direct or indirect interactions can be answered. Our group has found that transcription factors may play an important role in the epigenetic actions of stilbenoids by directing epigenetic regulators, like DNMTs, to specific loci [20]. The direct point in the epigenetic pathway where stilbenoid compounds exert their effect remains unknown. One of our group’s latest discoveries spotlights epigenetic inhibition of NOTCH oncogenic signaling in response to stilbenoids as an effective anti-cancer strategy [20]. At physiologically relevant doses, resveratrol and pterostilbene were shown to impart loci-specific changes in DNA methylation across the genome. We predict that studies like ours, delineating bidirectional changes in DNA methylation at loci-specific sites and identifying the most important, actionable targets to elicit anti-cancer action, will be vital for therapeutic schemes. As roadblocks in cancer prevention and therapy continue to arise, new and innovative ways to support anti-cancer treatments will be needed. It is likely that natural compounds, like stilbenoids, will be useful in combination with existing chemotherapeutic drugs to support existing anti-cancer strategies. Stilbenoid compounds have been shown to have the potential to (1) increase efficacy of current chemotherapeutic drugs, (2) reduce side-effects by lowering the dose of chemotherapeutic drug required to elicit the same anti-cancer effect, and (3) improve resistance to therapy by targeting different step(s) in signaling pathways than existing anti-cancer drugs. Taken together, evidence for stilbenoid compounds as regulators of the cancer epigenome is strong. If used in cancer prevention and support of cancer therapy, these compounds have the potential to contribute greatly to anti-cancer strategies.

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[84] Dhar S, Kumar A, Rimando AM, Zhang X, Levenson AS. Resveratrol and pterostilbene epigenetically restore PTEN expression by targeting oncomiRs of the miR-17 family in prostate cancer. Oncotarget 2015; 6(29):27214e26. [85] Qian YY, Liu ZS, Zhang Z, Levenson AS, Li K. Pterostilbene increases PTEN expression through the targeted downregulation of microRNA-19a in hepatocellular carcinoma. Mol Med Rep 2018;17(4): 5193e201. [86] Wang YL, Shen Y, Xu JP, Han K, Zhou Y, Yang S, et al. Pterostilbene suppresses human endometrial cancer cells in vitro by down-regulating miR-663b. Acta Pharmacol Sin 2017;38(10):1394e400. [87] Du M, Zhang Z, Gao T. Piceatannol induced apoptosis through up-regulation of microRNA-181a in melanoma cells. Biol Res 2017;50(1):36. [88] Zhang H, Jia R, Wang C, Hu T, Wang F. Piceatannol promotes apoptosis via up-regulation of microRNA-129 expression in colorectal cancer cell lines. Biochem Biophys Res Commun 2014;452(3):775e81. [89] Liu WH, Chang LS. Suppression of Akt/Foxp3-mediated miR-183 expression blocks Sp1-mediated ADAM17 expression and TNFalpha-mediated NFkappaB activation in piceatannol-treated human leukemia U937 cells. Biochem Pharmacol 2012;84(5):670e80. [90] Carter LG, D’Orazio JA, Pearson KJ. Resveratrol and cancer: focus on in vivo evidence. Endocr Relat Cancer 2014;21(3):R209e25. [91] Nguyen AV, Martinez M, Stamos MJ, Moyer MP, Planutis K, Hope C, et al. Results of a phase I pilot clinical trial examining the effect of plant-derived resveratrol and grape powder on Wnt pathway target gene expression in colonic mucosa and colon cancer. Cancer Manag Res 2009;1:25e37. [92] Zhu W, Qin W, Zhang K, Rottinghaus GE, Chen YC, Kliethermes B, et al. Trans-resveratrol alters mammary promoter hypermethylation in women at increased risk for breast cancer. Nutr Canc 2012;64(3):393e400. [93] Grosso G, Buscemi S, Galvano F, Mistretta A, Marventano S, La Vela V, et al. Mediterranean diet and cancer: epidemiological evidence and mechanism of selected aspects. BMC Surg 2013;13(Suppl. 2):S14. [94] Couto E, Boffetta P, Lagiou P, Ferrari P, Buckland G, Overvad K, et al. Mediterranean dietary pattern and cancer risk in the EPIC cohort. Br J Canc 2011;104(9):1493e9. [95] Beher D, Wu J, Cumine S, Kim KW, Lu SC, Atangan L, et al. Resveratrol is not a direct activator of SIRT1 enzyme activity. Chem Biol Drug Des 2009;74(6):619e24.

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Regulation of non-coding RNAs by phytochemicals for cancer therapy

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Sumit S. Vermaa, Vipin Raia, Kamla Kant Shuklab, Subash C. Guptaa Department of Biochemistry, Institute of Science, Banaras Hindu University, Varanasi, Indiaa; Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Indiab

1. Introduction Cancer is a common chronic disease in developed countries compared to developing countries. However, an increase in cancer incidence has recently been reported in developing countries, whereas a decline has been reported in developed countries. According to one report, cancer burden in India has more than doubled over the last 26 years [1]. By 2030, some cancer types such as breast cancer is projected to increase by >70% in low-middle income countries [2]. It is also projected that 85% of all cancer deaths will occur in low-middle income countries by 2030 [3]. The delay in accurate diagnosis, lack of awareness and changes in food habits are some of the causes for the increased rate of the cancer incidence and mortality. Mother Nature has served as a great source of therapeutics since ancient time, with one report, showing that more than 70% of drugs currently available on the market are of natural origin [4]. Plant-based products called phytochemicals have been shown to modulate multiple cancer related pathways such as 50 adenosine monophosphate-activated protein kinase (AMPK), activator protein 1 (AP-1), BCR-ABL, cyclooxygenase-2 (COX-2), epidermal growth factor receptor (EGFR), insulinlike growth factor 1 (IGF-1) receptor [IGF-1R], interleukins, mammalian target of rapamycin (mTOR), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB), nuclear factor-like 2 (NRF2), platelet-derived growth factor receptor (PDGFR), phosphoinositide-3-kinaseeprotein kinase B (PI-3-K)/AKT, peroxisome proliferator-activated receptor gamma (PPARg), Ras/Raf, signal transducer and activator of transcription (STAT), tumor necrosis factor (TNF), vascular endothelial growth factor receptor (VEGFR) and Wnt/b-catenin. Because of their ability to regulate multiple cancerrelated pathways, phytochemicals have shown promise as anti-cancer agents. Phytochemicals target cancer-related molecules either directly or indirectly through modulation of other signaling pathways. The advent of advance molecular tools suggests that the human genome contains less than 2% of protein-coding genes, whereas more than 98% are non-coding sequences. Of the non-coding sequences, more than 90% is transcribed but not translated producing a large number of non-coding RNAs. Based upon their size, non-coding RNAs are classified into long non-coding RNAs (lncRNAs) and small non-coding RNAs. The small non-coding RNAs are further classified into Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00022-9 Copyright © 2019 Elsevier Inc. All rights reserved.

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microRNAs (miRNAs), short interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs). The miRNAs (18e22 nucleotide long) and lncRNAs (200 nucleotide long) have emerged as two major classes of non-coding RNAs crucial in cancer pathogenesis. The expression profile of miRNAs and lncRNAs differ between cancer tissues and normal tissues. These non-coding RNAs can modulate multiple steps of tumor development. Whereas miRNAs are relatively well characterized, less is known about lncRNAs. Recent evidence suggest that phytochemicals exhibit anti-cancer activities partly through modulation of miRNAs and lncRNAs. The modulation of non-coding RNAs by phytochemicals can sensitize cancer cells to more traditional cancer therapeutics. In the following sections, we discuss the role of phytochemicals as regulators of miRNAs and lncRNAs for cancer prevention and treatment. The advantages and disadvantages associated with this strategy is also discussed.

2. Phytochemicals, miRNAs and cancer Phytochemicals are known to modulate numerous miRNAs in multiple cancer types. Phytochemicals can modulate both oncogenic and tumor suppressor miRNAs. The most common cancer types where phytochemicals are known to modulate miRNAs include bladder cancer, breast cancer, colorectal cancer, gall bladder cancer, head and neck cancer, lung cancer, pancreatic cancer, and prostate cancer.

2.1 Curcumin, miRNAs and cancer The role of miRNAs has been extensively investigated in breast cancer development. Although breast cancer incidence is high in developed countries, there has been a steady rise in the total incidence reported in developing countries over the last decade. The symptoms of breast cancer include the presence of fluid in the nipple, a lump on the breast, abnormal breast shape, and dimpling of the skin [5]. When the disease is spread to the distant organs, the patient can suffer from bone pain, swollen lymph nodes, and shortness of breath [6]. Bisphenol A (BPA), an endocrine disrupter is known to promote breast cancer development. In one study, BPA induced proliferation of estrogen-receptorpositive MCF-7 human breast cancer cells and triggered the transition of the cells from G1 to S phase of the cell cycle [7]. Furthermore, the oncogenic miR-19a and miR-19b were upregulated by BPA. Similarly, miR-19-related downstream proteins such as p-AKT, phosphorylated mouse double minute 2 homolog (p-MDM2), p53, phosphatase and tensin homolog (PTEN) and proliferating cell nuclear antigen (PCNA) were dysregulated by BPA. Curcumin reversed the effects of BPA in MCF-7 cells. It was concluded that curcumin modulates the miR-19/PTEN/AKT/p53 axis to reverse BPA-associated breast cancer promotion. In metastatic breast cancer cells, curcumin was also shown to regulate the expression of a series of miRNAs including miR-181b [8]. Curcumin also suppressed the expression of the proinflammatory cytokines [e.g., C-X-C motif chemokine ligand (CXCL)1 and CXCL2] and diminished the formation of prostate and breast cancer metastases [9]. Likewise, miR-181b also directly interacts and down-regulates CXCL1 and CXCL2 expression. The modulatory effects of curcumin on these two cytokines is mediated through miR-181b. Furthermore, curcumin also requires miR-181b for the suppression of proliferation, invasion and apoptosis. In cells derived from primary human breast cancers, curcumin up-regulated miR-181b and down-regulated CXCL1 and CXCL2. Overall, these results suggest that miR-181b is required for the anti-carcinogenic effects of curcumin

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against breast cancer. Lastly, curcumin upregulated the expression of miR-15a and miR-16 and downregulated B-cell lymphoma 2 (Bcl-2) expression in MCF-7 cells, which is a key protein for cell survival [10]. The suppression in Bcl-2 expression induced by curcumin was reversed by silencing miR-15a and miR-16. Overall these observations suggest that curcumin reduces the expression of Bcl-2 in MCF-7 cells through upregulation of miR-15a and miR-16. In human pancreatic cancer cells, curcumin induced miR-22 and suppressed miR-199a [11]. Curcumin-mediated changes in the expression of these miRNAs was associated with suppression in the expression of specificity protein 1 (Sp1) and estrogen receptor 1 (ESR1). ESR1 and Sp1 are now recognized as important therapeutic targets for anti-cancer drug development including pancreatic cancer [12e14]. The anti-sense oligonucleotide-mediated suppression of miR-22 was found to enhance Sp1 and ESR1 expression. These observations suggest that the modulation in miR-22 and miR-199a expression by curcumin may contribute to its anti-growth and pro-apoptotic effects. Curcumin induced apoptosis and suppressed miR-186 in A549 lung cancer cells [15]. In colorectal cancer cells, curcumin was shown to suppress proliferation, invasion, and metastasis [16]; stabilize tumor suppressor programmed cell death 4 (Pdcd4); and suppress miR-21 expression. Consistent with these observations, the progression of several cancer types is associated with the overexpression of miR-21. In some cases, curcumin has been modified to improve bio-availability. For example, difluorinated-curcumin (CDF) had improved bioavailability over non-modified curcumin in pancreatic tissues [17]. The aggressiveness of numerous cancer types is associated with increased expression of miR-21 [18e21] and suppressed expression of miR-200 [22,23] as well as decreased expression of PTEN and overexpression of membrane type-1 matrix metalloproteinase (MT1-MMP); these features are common in chemo-resistant pancreatic cancer cells (BxPC-3, MIAPaCa-2, and MIAPaCa-2-GR). In pancreatic cancer cells, the expression of miR-200 and PTEN was significantly up-regulated while that of MT1-MMP was down-regulated by the treatment with CDF [24]. CDF also suppressed the growth of human pancreatic cancer cells in association with increased expression of miRNAs (let-7a, let-b, let-c, let-d, miR-101, miR-200c, miR-26a, miR-200b, miR-146a) and decreased expression of enhancer of zeste homologue 2 (EZH2), a histone methyltransferase known to epigenetically regulate survival, proliferation and cancer stem cell (CSC) function [25]. miR-22 is a tumor suppressor known to be up-regulated by curcumin in Y79 retinoblastoma cells [26]. Furthermore, ectopic expression of miR-22 suppressed proliferation and migration of retinoblastoma cells. Curcumin also suppressed proliferation of esophageal cancer cells in association with down-regulation of Notch-1especific miR-21 and miR-34a and up-regulation of tumor suppressor let7a [27]. Wilms’ tumor 1 (WT1) is an oncogene that is constitutively expressed in most cases of human chronic myelogenous leukemia (CML) and acute myeloid leukemia (AML) [28]. WT1 is associated with poor long-term prognosis of disease [28]. In one study, curcumin up-regulated the expression of miR-15a/16-1 and suppressed expression of WT1 in leukemia cells and in primary AML cells [29]. Furthermore, gene silencing of miR-15a/16-1 reversed curcumin-mediated suppression in WT1 and promoted the growth of leukemia cells.

2.2 Genistein, miRNAs and cancer Genistein is an isoflavone that has been shown to suppress multiple steps of tumor development. Originally isolated in 1899 from Genista tinctorial, this isoflavone exhibits activities against numerous

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cancer types. Whether miR-155 is involved in the activities of genistein against metastatic breast cancer was evaluated [30]. Exposure of metastatic MDA-MB-435 and Hs578t cells to the lower physiologically relevant concentrations of genistein suppressed viability and induced apoptosis in cells. However, non-metastatic MCF-7 breast cancer cells were non-responsive to the same concentration of genistein. Consistent with these observations, genistein downregulated miR-155 while upregulating casein kinase, Forkhead box O3 (FOXO3), p27, and PTEN in both MDA-MB-435 and Hs578t cells. Again, no change in miR-155 expression was observed when MCF-7 cells were treated with genistein. The effects of genistein on the viability and apoptosis of MDA-MB-435 and Hs578t cells was reversed with miR-155 overexpression. Overall, these results suggest that downregulation of miR-155 by genistein contribute to its anti-carcinogenic effects. The modulatory effects of genistein on the expression of oncogenic miR-151 and metastasis related genes was investigated in prostate cancer [31]. An increase in the expression of miR-151 was observed in DU145 and PC3 cells as compared to those observed in RWPE-1 cells. Genistein at 25 mM suppressed the expression of miR-151 in both cell lines. Consistently, migration and cancer cell invasion was suppressed by genistein. Genistein also suppressed expression of oncogenic miR-23b-3p expression in renal carcinoma cells [32]. The expression of oncogenic miR-21 was also suppressed by the isoflavone in A-498 renal carcinoma cells and in the tumor bearing mice [33].

2.3 Honokiol, miRNAs and cancer Honokiol is a biologically active biphenolic agent isolated from the Magnolia officinalis. This biphenol suppresses the survival, proliferation, migration, invasion, and cancer stem-like cells (stemness) of urinary bladder cancer (UBC) cells [34]. Honokiol can also suppress expression of cluster of differentiation (CD) 44, EZH2, matrix metalloproteinase (MMP) 9, SRY-box 2 (SOX2), and induce the tumor suppressor miR-143. The inhibition of miR-143 or overexpression of EZH2 partially reversed honokiol-induced anti-carcinogenic activities. In a xenograft mice model bearing T24 tumor cells, honokiol suppressed tumor growth and stemness in association with dysregulation of EZH2 and miR-143 expression. Overall these observations suggest that the modulation of miR-143 by honokiol contributes to its anti-cancer activities against bladder cancer.

2.4 Other phytochemicals, miRNAs and cancer Resveratrol induced apoptosis in T24 and 5637 bladder cancer cells through down-regulation of miR-21, AKT and Bcl-2 [35]. Head and neck cancer (HNC) starts in the larynx, lip, nasal cavity, oral cavity, paranasal sinuses, pharynx or parotid glands. Environmental and lifestyle factors such as alcohol, certain strains of viruses, tobacco and UV light are known to contribute to HNC. These facts suggest that HNC is a preventable disease if diagnosed at an early stage. However, the disease often remains silent and is diagnosed at a very advanced stage. The suppression in the expression of miR23a by 10 -acetoxychavicol acetate (ACA) may contribute to its inhibitory effects on cell proliferation and induction of apoptosis in HN4 HNC cells [36]. Silibinin, the major active constituent of silymarin, inhibited the aggressiveness of head and neck squamous cell carcinomas tumor initiating cells (TICs) by up-regulation of miR-494 expression [37].

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2.5 Concluding remarks In conclusion, phytochemicals can suppress the expression of oncogenic miRNAs and up-regulate the expression of tumor-suppressive miRNAs. Whether phytochemicals modulate the expression of miRNAs in human participants remains to be explored.

3. Phytochemicals, lncRNAs and cancer During the last decade, long non-coding RNAs (lncRNAs) have emerged as crucial regulators of human chronic diseases, especially cancer. The lncRNAs regulate multiple steps of tumor development; this has been better understood thanks to approaches such as antisense oligonucleotides (ASOs) and RNAi technology that have been used for the therapeutic targeting of lncRNAs. During the last 5 years, some studies have demonstrated that lncRNAs can also be modulated by phytochemicals. The regulation of lncRNAs by phytochemicals have been demonstrated in models of colorectal cancer, breast cancer, hepatocellular cancer, gastric cancer, nasopharyngeal carcinoma, ovarian cancer, and prostate cancer.

3.1 Phytochemicals, lncRNAs and breast cancer Bharangin, a diterpenoid was found to modulate lncRNA expression in breast cancer cells [38]. The tumor suppressor lncRNAs such as growth arrest-specific 5 (GAS-5) and maternally expressed 3 (MEG-3) were induced by the diterpenoid. On the other hand, expression of H19 (oncogenic lncRNA) was suppressed by bharangin. Consistent with these observations, okadaic acid induced NF-kB activation was also suppressed by bharangin in breast cancer cells. However, the mechanistic association between modulation of lncRNAs and suppression of NF-kB activation by bharangin was not explored. The isoflavones calycosin and genistein have been reported to suppress AKT phosphorylation and HOX transcript antisense intergenic RNA (HOTAIR) expression in MCF-7 breast cancer cells [39]. Both isoflavones also suppressed proliferation and induced apoptosis in breast cancer cells. Next generation transcriptomic sequencing (RNA-Seq) and network analysis in MCF-7 and MDAMB-231 cells revealed that anacardic acid can dysregulate multiple genes including lncRNAs such as MIR22 host gene (MIR22HG) [40]. Curcumin was also shown to modulate GAS5 expression in breast cancer cells [41].

3.2 Phytochemicals, lncRNAs and bladder cancer Silibinin is a polyphenolic flavonolignan with potential against numerous cancer types. In bladder cancer cells, silibinin significantly suppressed the expression of HOTAIR and zinc finger E-boxbinding homeobox 1 (ZFAS1) without any effect on metastasis associated lung adenocarcinoma transcript 1 (MALAT1), MEG3, and GAS5 [42]. HOTAIR is linked with recurrence of bladder cancer [43] and the use of wortmannin (PI3K inhibitor) suppressed HOTAIR expression [42]. Thus, the anticarcinogenic activities of silibinin potentially result from its modulatory effects on lncRNA expression. Other phytochemicals like gambogic acid induced GAS5, a tumor suppressor lnRNA, which produced pro-apoptotic effects in bladder cancer cells [44].

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3.3 Phytochemicals, lncRNAs and other cancers In colorectal cancer DLD-1 cells, curcumin was shown to modulate promoter of CDKN1A antisense DNA damage activated RNA (PANDAR) expression and suppress proliferation [45]., while resveratrol was shown to inhibit colorectal cancer cell invasion and metastasis via regulation of MALAT1mediated Wnt/b-catenin signaling and its downstream targets [46]. Curcumin has also been shown to suppress expression of H19 and c-Myc and enhance p53 expression [47]; this inhibited proliferation and induced apoptosis in gastric cancer cells. Moreover, ectopic expression of H19 reversed the effects of curcumin on p53 expression and proliferation. Similarly, overexpression of c-Myc reversed the effects of curcumin on H19. Thus, the negative regulation of c-Myc/H19 pathway by curcumin may contribute to its anti-proliferative activities in gastric cancer cells. Curcumin has also been shown to inhibit prostate cancer stem cells by suppressing the expression of the lncRNA regulator of reprogramming (ROR) [48], while resveratrol was shown to induce expression and function of prostate cancer associated transcript 29 (PCAT29) through inhibition of IL-6/STAT3/miR-21 signaling in prostate cancer cells [49]. Genistein and sulforaphane both exhibit activity against prostate cancer, in part, through suppression in HOTAIR and [50] LINC01116 [51] expression, respectively in prostate cancer cells. Given the pleiotropic actions for curcumin it is not surprising that it has been shown to suppress expression of oncogenic lncRNAs in multiple tumor cell lines, yet shows no effect in normal cells [52]. At the same time, curcumin has been shown to up-regulate expression of lncRNAs that serve as tumor suppressors [53]. Furthermore, curcumin can 1) radio-sensitize nasopharyngeal cells for enhanced treatment, 2) reverse drug resistance in ovarian cancer [54] and pancreatic ductal adenocarcinoma (PDAC) cells [55] and 3) inhibit renal carcinoma cell migration [56]; these actions were likely mediated, in part, by regulation of lncRNAs. Similar to curcumin, resveratrol can inhibit glioma cell [57] and lung cancer cell growth [58], in part via control of lncRNAs.

3.4 Concluding remarks In conclusion, the potential of phytochemicals in modulating lncRNAs expression has provided a new molecular basis for their anti-cancer activities. Moreover, phytochemicals can sensitize cancer cells to existing drugs through modulation of lncRNAs. In addition to studies elucidating roles for phytochemicals in cancer preventative and as co-therapeutics, researchers are also examining phytochemical modifications to enhance bioavailability and efficacy; this holds true for lncRNA based therapies. For example, modification of curcumin to dendrosomal curcumin (DNC) was shown to induce tumor suppressor MEG3 in hepatocellular cancer (HCC) [59] through enhanced expression of miR-29a and miR-185. While these studies are promising, the role for phytochemicals in the regulation of lncRNAs in human subjects remains unclear and future investigations are warranted.

4. Conclusions The miRNAs and lncRNAs have emerged as crucial players of cancer pathogenesis. Both classes of non-coding RNAs can regulate multiple steps of tumor development. Thus, both miRNAs and lncRNAs represent novel targets for the development of cancer therapeutics. Phytochemicals have

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[34] Zhang Q, Zhao W, Ye C, Zhuang J, Chang C, Li Y, et al. Honokiol inhibits bladder tumor growth by suppressing EZH2/miR-143 axis. Oncotarget 2015;6(35):37335. [35] Zhou C, Ding J, Wu Y. Resveratrol induces apoptosis of bladder cancer cells via miR-21 regulation of the Akt/Bcl-2 signaling pathway. Mol Med Rep 2014;9(4):1467e73. [36] Wang H, Shen L, Li X, Sun M. MicroRNAs contribute to the anticancer effect of 1’-acetoxychavicol acetate in human head and neck squamous cell carcinoma cell line HN4. Biosc Biotech Biochem 2013;77(12): 2348e55. [37] Chang Y-C, Jan C-I, Peng C-Y, Lai Y-C, Hu F-W, Yu C-C. Activation of microRNA-494-targeting Bmi1 and ADAM10 by silibinin ablates cancer stemness and predicts favourable prognostic value in head and neck squamous cell carcinomas. Oncotarget 2015;6(27):24002. [38] Awasthee N, Rai V, Verma SS, Francis KS, Nair MS, Gupta SC. Anti-cancer activities of Bharangin against breast cancer: evidence for the role of NF-kB and lncRNAs. Biochim Biophys Acta Gen Subj 2018; 1862(12):2738e49. [39] Chen J, Lin C, Yong W, Ye Y, Huang Z. Calycosin and genistein induce apoptosis by inactivation of HOTAIR/p-Akt signaling pathway in human breast cancer MCF-7 cells. Cell Physiol Biochem 2015;35(2): 722e8. [40] Schultz DJ, Krishna A, Vittitow SL, Alizadeh-Rad N, Muluhngwi P, Rouchka EC, et al. Transcriptomic response of breast cancer cells to anacardic acid. Sci Rep 2018;8(1):8063. [41] Esmatabadi MJD, Motamedrad M, Sadeghizadeh M. Down-regulation of lncRNA, GAS5 decreases chemotherapeutic effect of dendrosomal curcumin (DNC) in breast cancer cells. Phytomedicine 2018;42: 56e65. [42] Imai-Sumida M, Chiyomaru T, Majid S, Saini S, Nip H, Dahiya R, et al. Silibinin suppresses bladder cancer through down-regulation of actin cytoskeleton and PI3K/Akt signaling pathways. Oncotarget 2017;8(54): 92032. [43] Yan T-H, Lu S-W, Huang Y-Q, Que G-B, Chen J-H, Chen Y-P, et al. Upregulation of the long noncoding RNA HOTAIR predicts recurrence in stage Ta/T1 bladder cancer. Tumor Biol 2014;35(10):10249e57. [44] Wang M, Guo C, Wang L, Luo G, Huang C, Li Y, et al. Long noncoding RNA GAS5 promotes bladder cancer cells apoptosis through inhibiting EZH2 transcription. Cell Death Dis 2018;9(2):238. [45] Chen T, Yang P, Wang H, He Z-Y. Silence of long noncoding RNA PANDAR switches low-dose curcumininduced senescence to apoptosis in colorectal cancer cells. OncoTargets Ther 2017;10:483. [46] Ji Q, Liu X, Fu X, Zhang L, Sui H, Zhou L, et al. Resveratrol inhibits invasion and metastasis of colorectal cancer cells via MALAT1 mediated Wnt/b-catenin signal pathway. PLoS One 2013;8(11):e78700. [47] Liu G, Xiang T, Wu QF, Wang WX. Curcumin suppresses the proliferation of gastric cancer cells by downregulating H19. Oncology letters 2016;12(6):5156e62. [48] Liu T, Chi H, Chen J, Chen C, Huang Y, Xi H, et al. Curcumin suppresses proliferation and in vitro invasion of human prostate cancer stem cells by ceRNA effect of miR-145 and lncRNA-ROR. Gene 2017;631:29e38. [49] Al Aameri RF, Sheth S, Alanisi EM, Borse V, Mukherjea D, Rybak LP, et al. Tonic suppression of PCAT29 by the IL-6 signaling pathway in prostate cancer: reversal by resveratrol. PLoS One 2017;12(5): e0177198. [50] Chiyomaru T, Yamamura S, Fukuhara S, Yoshino H, Kinoshita T, Majid S, et al. Genistein inhibits prostate cancer cell growth by targeting miR-34a and oncogenic HOTAIR. PLoS One 2013;8(8):e70372. [51] Beaver LM, Kuintzle R, Buchanan A, Wiley MW, Glasser ST, Wong CP, et al. Long noncoding RNAs and sulforaphane: a target for chemoprevention and suppression of prostate cancer. J Nutr Biochem 2017;42: 72e83. [52] Novak Kujundzic R, Grbesa I, Ivkic M, Katdare M, Gall-Troselj K. Curcumin downregulates H19 gene transcription in tumor cells. J Cell Biochem 2008;104(5):1781e92.

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[53] Wang Q, Fan H, Yin Z, Cai H, Shao M, Diao J, et al. Effect of curcumin on radiosensitization of CNE-2 cells and its mechanism. Zhongguo Zhong yao za zhi¼ Zhongguo zhongyao zazhi¼ China journal of Chinese materia medica 2014;39(3):507e10. [54] Zhang J, Liu J, Xu X, Li L. Curcumin suppresses cisplatin resistance development partly via modulating extracellular vesicle-mediated transfer of MEG3 and miR-214 in ovarian cancer. Cancer Chemotherapy and Pharmacology 2017;79(3):479e87. [55] Yoshida K, Toden S, Ravindranathan P, Han H, Goel A. Curcumin sensitizes pancreatic cancer cells to gemcitabine by attenuating PRC2 subunit EZH2, and the lncRNA PVT1 expression. Carcinogenesis 2017; 38(10):1036e46. [56] Pei C-S, Wu H-Y, Fan F-T, Wu Y, Shen C-S, Pan L-Q. Influence of curcumin on HOTAIR-mediated migration of human renal cell carcinoma cells. Asian Pac J Cancer Prev APJCP: Asian Pac J Cancer Prev APJCP 2014;15(10):4239e43. [57] Liu Q, Sun S, Yu W, Jiang J, Zhuo F, Qiu G, et al. Altered expression of long non-coding RNAs during genotoxic stress-induced cell death in human glioma cells. Journal of neuro-oncology 2015;122(2):283e92. [58] Yang Q, Xu E, Dai J, Liu B, Han Z, Wu J, et al. A novel long noncoding RNA AK001796 acts as an oncogene and is involved in cell growth inhibition by resveratrol in lung cancer. Toxicol Appl Pharmacol 2015;285(2): 79e88. [59] Zamani M, Sadeghizadeh M, Behmanesh M, Najafi F. Dendrosomal curcumin increases expression of the long non-coding RNA gene MEG3 via up-regulation of epi-miRs in hepatocellular cancer. Phytomedicine 2015;22(10):961e7.

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Short chain fatty acids as epigenetic and metabolic regulators of neurocognitive health and disease

23

Maria M. Mihaylovaa, Matthew S. Strattonb Department of Biological Chemistry & Pharmacology, The Ohio State University, Columbus, OH, USAa; Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USAb

1. Introduction 1.1 Short chain fatty acid e basics Short chain fatty acids (SCFAs) are comprised of a carboxyl group (COOH) bound through carboncarbon bonds to chains of 1e5 additional carbons and are designated C2 (acetic acid), C3 (propionic acid), C4 (butyric acid), C5 (valeric acid) and C6 (caproic acid) (Fig. 23.1). There are numerous options for carbon side chains in short chain fatty acids as evident in the ketone body SCFAs, Acetoacetic acid (AAA) and b-hydroxy butyrate (b-OHB), both of which contain 4 carbons. Some SCFAs are also unsaturated, containing double bonds between carbon atoms. When cellular glucose levels are low, as occurs in fasting, high fat (low carb) diets, and in some cases of diabetes, ketone bodies are produced using excess acetyl-CoA as a building block. Acetyl-CoA becomes abundant in low glucose states because the TCA cycle slows when oxaloacetate is diverted for gluconeogenesis. Ketone bodies are soluble chemicals in mammalian cells and blood and include acetone and the previously mentioned SCFAs AAA and b-OHB. Other sources of SCFAs are available directly through diet or via microbial digestion of dietary fiber in the gut [1], though there is some confusion surrounding the systemic bioavailability of short chain fatty acids acquired through the gut [2]. Concentrations of combined SCFA in the colon can reach as high as 150 mM, making them some of the most abundant bacterial metabolites in the colon. SCFAs are primarily produced by microbial fermentation of hard to digest plant polysaccharides such as inulin and pectin and are considered one of the most beneficial end products of gut microbial metabolism [3]. Unsurprisingly, they have been shown to regulate a number of processes both in epithelial and immune resident gut cells and influence gut homeostasis. Butyrate, in particular, has been shown to be a major energy source for colonocytes and germ-free mice have reduced expression of enzymes involved in the TCA cycle [4], further supporting the importance of microbial regulation of colonocyte metabolism. Colonocytes from GF animals had a 31% decrease in labeled CO2 derived from 13C-butyrate compared to conventional, control mice and addition of butyrate to germ free Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00023-0 Copyright © 2019 Elsevier Inc. All rights reserved.

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FIG. 23.1 Key short chain fatty acid molecules. C2 through C6 short chain fatty acid 2 dimensional structures are shown (A) as are structures for b-OHB, butyrate, crotonic acid and valproate which are discussed throughout the chapter (B). The pH dependent presence or absence of the carboxyl terminal H dictates the minimal difference between Butyric acid and butyrate. This relationship is similar for most short chain fatty acids (xxxic acid - > xxxate). Carnitine is produced via enzymatic processing of trimethyllysine (C). Credit: Matthew Stratton.

colonocytes rescued mitochondrial respiration. In addition, there is also clear evidence for beneficial effects of dietary short chain fatty acids on gut endothelium (reviewed in Ref. [5]). Dietary butyrate has been used in multiple rodent disease models where it has been shown to effectively inhibit cancer, diabetes, cardiovascular disease and can be cardio-protective (reviewed in Ref. [6]). Systemic effects of gut derived SCFAs occur likely, via feedback from the enteric nervous system, activity on immune cells found in the gut mucosa and portal vasculature, or through actions in the liver even if gut derived SCFAs are not found at effective doses systemically. SCFAs, including butyrate, are absorbed by the distal, which is not subjected to hepatic first pass metabolism and high fiber diets have been shown to increase circulating levels of SCFAs [7,8]. Finally, gut derived acetate from inulin fermentation was shown to be taken up in the brain where it was incorporated into glutamate, GABA, glutamine, and lactate, and exerted an appetite suppressing effect [9]. SCFAs, and in particular butyrate, has been shown to modulate excitability of enteric neurons and subsequently gastrointestinal motility [10]. Interestingly, resistant starch diet (RSD) or directly intracecal perfusion of butyrate

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increased ascending excitatory myenteric neurons containing choline acetyltransferease (ChAT), while acetate and propionate did not replicate these effects [10]. Collectively these findings suggest that SCFA have neuromodulator roles in the gut and beyond, which will be discussed in the next section.

1.2 Neurocognition and mood disorders By 2050, it is estimated that the global incidence of Alzheimer’s Disease (AD) will reach 106 million individuals [11], with all cause dementia estimated to afflict 5%e7% of individuals over the age of 60 [12]. Mild cognitive impairment has been observed in 20% of individuals over the age of 60 [13]. Clearly, neurocognitive decline is associated with aging. Chronic low-grade inflammation has also been associated with aging, termed “inflammaging” and is thought to have at least a disease accelerating effect on dementia [14]. The lifetime incidence of depression for women is near 25% and for men is near 12% [15]. Individuals suffering from depression frequently also suffer from anxiety and the lifetime prevalence of any anxiety disorder is over 30% in the US population [16]. It should be noted that dementia and mood disorders are frequently comorbid as 30% of adults with dementia showed signs of depression within a 30-day study window [17]. Another study estimates the rate of depression at over 50% for nursing home residents [18]. For comorbid anxiety and dementia, the rate of anxiety symptoms in patients with dementia can range from 72% to 30% depending on the cause of dementia [19]. While certain realities of how one perceives the world around them could help explain why individuals with dementia might be predisposed to anxiety or depression disorders, the situation is more complex. Evidence suggests that either shared etiology or physiological connections contribute to the manifestation of these frequently comorbid conditions. The presence or absence of anxiety/ depression diagnoses can differentiate the likelihood of future dementia in the elderly [20,21]. Also, many of the brain regions that display altered function in anxiety and depression are similarly affected in dementia. This concept is most accessibly illustrated in the regulation of the HypothalamicPituitary-Adrenal (HPA) axis (below and Fig. 23.2). In the HPA axis, regions in the cortex and limbic system (e.g. hippocampus and anterior cingulate cortex) negatively regulate hypothalamic and pituitary output to the adrenal gland where glucocorticoids are released to facilitate adaptive fight or flight responses. Under normal conditions, glucocorticoids produced in the adrenal gland in response to HPA axis activation act on glucocorticoid receptors (GRs) and mineralocorticoid receptors (MRs) at multiple levels. This includes in the above mentioned cortico/limbic structures, and leads to GABAergic inhibition of the hypothalamus; a classic negative feedback circuit. In patients with AD and other forms of dementia, three dimensional volumes of the cortico/limbic regions that mediate HPA axis negative feedback are reduced [22]. In the settings of chronic stress, anxiety and depression, neurons in the cortico/limbic structures that secrete GABA to the hypothalamus are subject to desensitization of GRs and MRs and glucocorticoid toxicity in response to chronically elevated glucocorticoid levels. Thus, negative feedback of the HPA axis can be lost in both dementia and chronic anxiety/depression [23]. Chronic inflammation exacerbates this condition via direct cytokine influence on neurotransmitter release and by increasing susceptibility of neurons to glucocorticoid toxicity [24]. Furthermore, reduced GABA signaling, through decreased GABA concentrations, decreased receptor expression or both, has been reported in AD (reviewed in Ref. [25]), anxiety disorders (reviewed in Ref. [26]), depression (reviewed in Ref. [27]) and is clearly

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FIG. 23.2 Loss of HPA negative feedback in Alzheimer’s disease and depression. Corticotrophin releasing hormone (CRH) is secreted from neurons residing in the paraventricular nucleus of the hypothalamus. CRH travels through the portal vasculature to corticotroph cells in the anterior pituitary which secrete adrenocorticotropic hormone (ACTH) into circulation. ACTH stimulates cells in the adrenal to produce glucocorticoids, including cortisol. Cortisol binds glucocorticoid- and mineralocorticoid receptors in the hippocampus and anterior cingulate cortex (ACC), which causes GABA mediated inhibition of CRH neurons in the hypothalamus. Chronically elevated glucocorticoid levels and other stressors can remove the inhibitory signals from the hippocampus and ACC either through receptor desensitization or neuronal atrophy. Credit: Matthew Stratton.

central to the pathogenesis of epilepsy (reviewed in Ref. [28]). Ketogenic diets can increase GABA concentrations in the brain and improve symptoms of epilepsy ([29], reviewed in Ref. [30]). Much emphasis in recent years has been placed on understanding the effects of the microbiotagut-brain axis. Interestingly, germ-free animals show heightened neuroendocrine responses to

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stimuli such as stress [31e33] and conversely, stress can alter microbial composition [34]. Global gut microbial alterations and diminished microbial diversity, in addition to immunologic changes, were observed in disease models of stress induced behavioral deficits such as chronic social defeat models [35]. Predictive pathway analysis showed a reduced abundance of pathways in fatty acid synthesis and SCFA metabolism. Very recently it was also shown that SCFA supplementation, in conjunction with chronic stress, can alleviate some of the stress induced consequences such as intestinal permeability, while not affecting expression levels of SCFA receptors FFAR2 and FFAR3 or stress induced weight changes [36]. Interestingly, many of the neurological and behavioral effects of SCFAs are observed only in male mice and it would be necessary in the future to understand the mechanism behind the observed sexual dimorphisms, although neurodevelopmental disorders such as schizophrenia and autism do have male predominance. How do SCFAs restore GABA signaling? Still an area of active research, exogenous labeled SCFAs, (Beta-hydroxybutyrate, butyrate and acetate) can be utilized by the brain as building blocks for the neurotransmitters Glutamate and GABA [9,37]. Also, expression of the Glutamic acid decarboxylase (GAD), which converts glutamate to GABA, is increased by ketogenic diet [29]. The brain also uses ketones for amino acid synthesis, thus sparing glucose for energy metabolism and neurotransmitter production [38]. Acetoacetate has also been shown to block vesicular loading of glutamate and subsequent glutamate release [39]. Collectively, these findings point toward exogenous SCFAs (particularly beOHB and Acetoacetate) and the ketogenic diet/exercise regimens as being attractive therapeutic strategies for disorders characterized by decreased GABA:Glutamate ratios in the central nervous system.

2. SCFAs e molecular mechanisms As with any molecule in a biological system, SCFAs interact with proteins or other molecules through classic biochemical interactions and are influenced by molecular shape and charge. As one would expect, the particular interactions vary depending on the specific SCFA. Uniquely, SCFAs can also serve as metabolic substrates, for instance as precursors of acetyl-CoA which is used in the Krebs cycle.

2.1 SCFAs e metabolism Though glucose is the predominant energy source for the brain [40], roughly 20% of the brain’s energy demand can be met under basal conditions through b-oxidation [41,42]. In addition, the brain clearly uses SCFAs as an energy source during fasting or in ketogenic states. Also, the human neonatal brain is predominantly fueled by fatty acid oxidation, mostly of SCFAs, which is thought to buffer the inflammatory stress responses in the brain tissue that occur during the birthing process [43]. Neurons in the brain are metabolically supported by non-neuronal cell types (e.g astrocytes, glia, and potentially endothelial). Non-neuronal cells supply neurons with lactate and neurotransmitter precursors, which can readily be modified to enter the TCA cycle [37]. It is thought that these non-neuronal populations account for the vast majority of the b-oxidation observed in the brain, though some b-oxidation has been observed in neurons or mitochondria isolated from neurons.

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Long chain fatty acids enter the mitochondria via an acyl-carnitine shuttle where fatty acids (acyl groups) are first bound to CoA and then transferred to carnitine at the outer mitochondrial membrane. Acyl-carnitine is then transported into the mitochondrial matrix (across the inner mitochondrial membrane) where the fatty acid is transferred from carnitine back to CoA, and becomes available for b-oxidation or in the case of acetyl-CoA, it enters into the TCA cycle (reviewed in Ref. [44]). This process, which is dependent on membrane transporters and multiple enzymes, is potentially dysregulated in several diseases, including heart failure and Alzheimer’s disease [45,46]. SCFAs like b-OHB can bypass the carnitine shuttle and disease linked inefficiencies in early b-oxidation pathway steps to effectively restore reducing equivalent balances in the electron transport chain for ATP generation. Very long chain and branched chain fatty acids are typically oxidized to long or medium chain fatty acids in the peroxisome. These shortened fatty acids are then shuttled to the mitochondria, via the acylcarnitine shuttle pathway discussed above, where they are metabolized to acetyl-CoA for use in the TCA cycle [47]. Acyl-CoAs molecules generated in the peroxisome can also be transported to the mitochondria via the same system. b-oxidation in the peroxisome rather than the mitochondria can be beneficial as the mitochondria is spared exposure to oxidative products generated during b-oxidation. Glucose metabolism consumes less oxygen per molecule of ATP generated than fatty acid metabolism. This difference is magnified when fatty acids are processed in the peroxisome, as O2 is used as an electron acceptor but no FADH2 or NADH þ H is fed to the mitochondria to drive the electron transport chain. Evolution of the brain’s predominant reliance on glucose metabolism is likely related to these oxygen consumption considerations [40]. In addition to SCFAs being able to bypass any disease linked inefficiencies in early b-oxidation pathway steps (including fatty acid transport into the mitochondria), SCFAs are also a substrate that can be used rapidly to feed the TCA cycle with minimal oxygen consumption or reactive oxidation generation. The peroxisome has additional functions, which are not discussed in this chapter but include fatty acid assembly, which has particular importance in the brain where it is necessary for membrane maintenance and myelination [48]; a point that may in fact be directly related to this topic and will require continued attention and effort. In b-oxidation, equal amounts of FADH2 and NADH þ H are reduced from their respective oxidized forms of FAD or NADþ per cycle. In the TCA cycle, three molecules of NADH þ H and one molecule of FADH2 are generated per cycle. NADH þ H passes its gained electrons to the electron transport chain via complex I. FADH2 generated from b-oxidation or the TCA cycle provide electron motive force through complex II of the electron transport chain. FADH2 from b-oxidation requires intermediary proteins to carry electrons to complex II, whereas TCA cycle derived FADH2 directly feeds complex II via FAD covalently bound to the complex II subunit, succinate dehydrogenase - the enzyme that catalyzes succinate to fumarate conversion in the TCA cycle. Electron flow through complex II can be a generator of reactive oxygen species (ROS) when electrons begin to flow backwards through complex I if facing an existing steep electron gradient. Abundant production of FADH2 relative to NADH þ H, as occurs with mitochondrial b-oxidation, can create large amounts of ROS. While mitochondrial ROS generation has been evolutionarily harnessed as an adaptive cell signaling mechanism [49], chronic fatty acid metabolism in the mitochondria results in ROS mediated damage if the cell lacks A) high rates of ATP synthase activity to drain the electron gradient, B) tightly regulated electron transport chain complex spatial organization (relative to TCA enzymes and b-oxidation enzymes) to avoid reverse electron flow through

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complex I, and/or C) robust ROS buffering capacity. ROS induced mitochondrial damage frequently leads to inflammation [50]. Interestingly, germ free mice show an energy deficit and diminished NADH/NAD þ ratios, ATP levels and oxidative phosphorylation in the colon, where colonocytes rely heavily on butyrate for energy [4]. Further, the authors showed that autophagy becomes activated as a compensatory catabolic mechanism to overcome energy deficit in colonocytes from germ-free mice. Although this study compared neuronal SCFA utilization and metabolism between germ-free and conventional mice, it is important to note that they did not observe energy disbalance in other metabolic tissues such as liver, heart and kidney.

2.2 SCFAs e revisiting the carnitine shuttle Carnitine has also been proposed to have an important buffering role to regulate the amount of CoA that is acyl-bound or free to engage additional substrates (e.g. Ref. [51]), and also has antioxidant properties by virtue of its ability to scavenge hydrogen peroxide and superoxide radicals (reviewed in Ref. [52]). Carnitine biosynthesis has long been thought to be generated through scavenging of trimethyllysine from degraded protein in the lysosome and subsequent enzymatic processing to carnitine. Nearly all cells in the body are able to carry out this process through the penultimate step of butyrobetaine. However, expression of the enzyme that catalyzes the final conversion to carnitine is limited to select tissues (e.g. liver, reviewed in Ref. [53]). Thus, in most cells throughout the body, the availability of carnitine to shuttle fatty acids to the mitochondrial matrix and to buffer fatty acid bound CoA is dependent on cellular uptake of carnitine via the organic cation transporter SLC22A5/OCTN2 [54]. Normally, carnitine is not limiting, however when fatty acids are overly abundant, such as in obesity, carnitine availability is reduced [52]. This is potentially due to carnitine’s role in removing fatty acids from acyl-CoA and transporting it to the blood where it can be excreted from the kidney in urine. Reduced free carnitine levels have also been reported in Alzheimer’s disease and diabetes while carnitine supplementation has been reported to have normalizing properties in diabetes and obesity [46,52,55,56]. Interestingly, carnitine is essentially, the trimethylated nitrogen of trimethyllysine attached to b-OHB. An alternate pathway for local synthesis of carnitine using b-OHB and trimethyllysine would, if identified, be another potentially beneficial mechanism for improving fatty acid handling, particularly in chronically energy rich environments. This may not be such an outlandish supposition given the ability of homocysteine to accept methyl groups from the quaternary amine during choline degradation and the open tunnel configuration of the associated methyltransferase.

2.3 SCFAs e receptor binding Short chain fatty acids have been shown to bind previously categorized orphan G-protein coupled receptors (GPCRs), now named Free Fatty Acid Receptors (FFARs) FFAR2/GPCR43 and FFAR3/ GPCR41, as well as Niacin receptor, HCA2/GPR109A (thoroughly reviewed in Ref. [57]). Work investigating FFAR2 and FFAR3, with loss of function genetic manipulation using in vivo rodent disease models, has been largely contradictory in terms of pro- or anti-inflammatory effects, with few studies addressing FFAR3. Cell culture experiments investing activation of FFAR2 and FFAR3 indicate pro-inflammatory effects [58], though there are also contradictory findings that show FFAR2 activation as eliciting anti-inflammatory and anti-ROS effects (e.g. Ref. [59]). This apparent

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contradiction is yet to be fully explained. Likely culprits may include artificial conditions of cell culture media, differential effects of specific SCFAs used, or cell-type specific effects. These contradictions could simply be an illustration of the complexity of the myriad of mechanisms involved in inflammation and our incomplete understanding of in vivo inflammation and metabolism in health and disease. Importantly, b-OHB is an inhibitor of FFAR2 and FFAR3, while other SCFAs are activators of the receptors. Perhaps some inflammation in select cell types is necessary to initiate adaptive responses, which create a protective environment. Specifically related to nervous system function, SCFAs and ketone activation has been shown to mediate the sympathetic nervous system with remarkably divergent effects of different SCFAs. The SCFA propionate promoted sympathetic activity via FFAR3, while b-OHB blocked sympathetic activity by antagonizing FFAR3 [60]. Butyrate and b-OHB binding the HCA2 receptor is potently anti-inflammatory (reviewed in Ref. [61]). In adipocytes, activity of HCA2 receptor slows lipolysis, which under normal physiological conditions would provide negative feedback to prevent ketoacidosis. HCA2 receptors are expressed in a number of cell types including monocytes, macrophages, T cells, and microglia. Ligands of HCA2 including butyrate, b-OHB, and niacin, have been shown to be anti-inflammatory and protective in human patients or rodent disease models of epilepsy, Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, Parkinson’s disease, multiple sclerosis, stroke and traumatic brain injury [61]. Despite the multiple mechanisms potentially engaged by HCA2 ligands, evidence suggests a dependence on the HCA2 receptor, at least for benefits seen in stroke and Parkinson’s models [62e64].

3. SCFAs e epigenetic regulators of disease 3.1 SCFAs e enzyme activity

Removal of acetyl modifications (and some larger acyl modifications) on histone and non-histone proteins is catalyzed by histone deacetylases (HDACs). The 18 mammalian HDACs have been divided by class based on sequence similarity, as Class I HDACs, Class IIa and Class IIb HDACs, Class III HDACs (Sirtuins), and Class IV HDACs (HDAC11) [65]. The catalytic activity of Class I, II, and IV HDACs is dependent on Znþ2. Class III HDACs/Sirtuins are NADþ dependent. This places the sirtuins in a unique position to be responsive to cellular and mitochondrial NADþ/ NADH balance, a more recently appreciated mediator of chronic disease. Sirtuin deacetylase activity is increased with increasing concentrations of NADþ, which is thought to activate adaptive responses that decrease ATP expenditure (e.g. mTOR inhibition and autophagy), while increasing ATP production (e.g. mitochondrial biosynthesis and mitochondria quality control via mitophagy). Similarly, when NADþ concentrations are low, Sirtuins are less active. NADþ concentrations are not only dependent on its reduction to NADH þ H but also by shuttling between cellular compartments (e.g. cytoplasm, nucleus, mitochondria). Short chain fatty acid metabolism may allow increased cytoplasmic NADþ concentrations (and Sirtuin activity) by minimizing the requirement to shuttle cytoplasmic NADþ to the mitochondria compared to requirements in glucose metabolism [66]. The short chain fatty acids butyrate [67] and b-OHB [68] are inhibitors of ZNþ2 dependent HDAC catalytic activity. HDAC inhibition has been robustly shown to elicit anti-inflammatory effects in cell culture experiments and in rodent models of disease. Specifically related to neurocognitive and mood disorder models, the class I HDAC inhibitor MS-275 prevented depression-like behaviors in mice

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subjected to a social defeat stress model [69]. Similar benefit is reported for the more non-specific HDAC inhibitor and SCFA, sodium butyrate [70]. Also, the SCFA valproate has proven effective in treating bipolar depression and PTSD [71,72]. HDAC inhibition has also been shown to prevent protein accumulation and memory impairment in mouse models of dementia/AD [73e75]. Some of these benefits are likely owed to increased expression of ROS protective genes including, forkhead box O3a (FOXO3a), superoxide dismutase 2 (SOD2), catalase, and metallothionein II (MTII). This pattern of gene expression appears to be conserved between Class I HDAC inhibition studies, beOHB treatment and ketogenic diets. b-OHB and HDAC inhibition also leads to increased calbindin expression in the brain, which would serve to buffer calcium influx downstream of inflammatory cytokines, and excitatory neurotransmitters [76]. HDAC inhibition has also been shown to promote DNA damage repair [77]. Finally, b-OHB has been shown to inhibit the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome in neutrophils and macrophages [78]. Collectively this points to SCFAs (specifically butyrate, valproate, b-OHB) as being potently antiinflammatory in the brain, likely through epigenetic mechanisms regulated by class I HDAC inhibition.

3.2 SCFAse acylation and gene expression Another potential effect of SCFAs, particularly b-OHB and Acetoacetate, is in the production of acyl-CoA molecules used for protein post-translational modifications. The recently identified crotonyl-lysine modification [79] on histone proteins appears to have similar effects on select gene expression as does histone lysine acetylation though with more robust changes in gene expression observed with crotonylation [80]. Whether this pattern will hold true across the genome or for regulation of non-histone protein function remains to be demonstrated. The crotonyl modification differs from the acetyl modification in size, as a crotonyl group has two additional carbon atoms, and possesses a double bond between the second and third carbon atoms in crotonate, conferring a more rigid structure. This more rigid structure of crotonyl-lysine may lead to more stable reader protein interactions. Surprisingly, given the size difference, many of the enzymes that catalyze addition or removal of acetyl groups can also do the same for crotonyl groups (e.g. Class I HDACs and P300; [80,81]). A survey of Kyoto Encyclopedia of Genes and Genomes (KEGG, www.gemone.jp/kegg/) metabolic pathways indicates that in addition to fatty acid degradation and ketosis, products and intermediates from the TCA cycle, amino acid metabolism and vitamin B6 metabolism can be processed to crotonyl-CoA (Fig. 23.3). Experimental validation is needed to confirm if alterations in these processes lead to changes in crotonyl-lysine modifications, and subsequent regulation of cellular function or health of the organism.

4. SCFAs e recent relevant studies Cryan and colleagues recently tested the ability of a SCFA cocktail (Acetate, Propionate, Butyrate) administered in drinking water to protect rodents from psychosocial stress using a thorough battery of behavioral and physiological tests. The SCFA treatment caused decreased anxiety-like and depressive-like behaviors. SCFAs also prevented stress induced anhedonia, decreased stress induced HPA axis activity and prevented stress induced intestinal leak [36].

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FIG. 23.3 Potential metabolic regulation of Crotonyl-CoA availability. A review of KEGG metabolic pathways indicates that Vitamin B6 and amino acid metabolism, fatty acid degradation, and ketosis may lead to elevated levels in Crotonyl-CoA. Credit: Matthew Stratton.

Similarly, exogenous b-OHB was shown to be anxiolytic in an elevated plus maze test. This effect was absent in the presence of an adenosine A1R receptor antagonist, suggesting that b-OHB may act through adenosine receptors to modulate neurotransmitter release [82]. Multiple reports indicate that adenosine receptors play an important role in regulating anxiogenic glutamate activity in the CNS (e.g. Refs. [83,84]) and this may be a mechanism for b-OHB efficacy in both anxiety and epilepsy (as was also investigated by the same group; [85,86]). This might offer a potentially compounded benefit when coupled with increased GABA concentration observed in ketogenic diets as previously discussed. Another recent publication shows that b-OHB treatment prevents lipopolysaccharide (LPS)-induced depression-like behaviors in rodents. Remarkably this appears to be mediated by shifting microglia to an M2 macrophage-like state and was dependent on Akt activity [87]. In a natural aging study conducted in mice, the ketogenic diet was shown to increase lifespan and corresponded with protections in memory and grip strength. The ketogenic diet was also associated with decreased mTOR activity, as well as increased histone and non-histone protein acetylation (e.g. p53) and increase expression of the antioxidant protein, manganese superoxide dismutase or MnSOD [88]. Though not specifically assessing neurological function, the ketogenic diet has also been reported to elicit many beneficial effects in diabetes patients (e.g. Ref. [89]). Although the mechanisms through which ketogenic diets improve neurological disorders are not well understood, it is hypothesized they

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mainly improve aberrant metabolic dysregulation and exert beneficial effects on neuronal plasticity. More specifically, it has been proposed that ketone bodies can increase ATP levels while maintaining low ROS levels [90], which would be beneficial for long term neuronal health and could prevent some age associated neuronal decline.

5. SCFAs e potential side effects and concerns for clinical use The preponderance of cautionary data surrounding SCFAs comes from pediatric studies where the ketogenic diet was used as an anti-epileptic treatment. Here, possible adverse events included, poor growth, kidney stones, dyslipidemia, prolonged QT interval, excessive bruising, optic neuropathy, elevated very-long-chain fatty acids, Vitamin D deficiency, trace mineral deficiencies, constipation and acid reflux (reviewed in Ref. [91]). Several children have died while on the ketogenic diet for epilepsy indications, with the cause of death being linked to the above adverse events (e.g. hemorrhagic pancreatitis potentially caused or exacerbated by dyslipidemia; [92]). The causes for mineral deficiencies are likely multifactorial. The ketogenic diet as designed for epilepsy treatment, is restrictive on food choices and could lead to lack of intake for select minerals. This is compounded by increased kidney perfusion and excretion of minerals in addition to gut absorbance of calcium being reduced on high fat diets [93]. For these patients, vitamin and mineral deficiency side effects are managed with mineral supplementation. In rodents, postnatal injection of propionic acid has led to a constellation of behaviors associated with autism [94]. Similarly, when pregnant rodents were treated with propionic acid, offspring also displayed this autism-like phenotype [95]. Increased use of this three carbon SCFA as a food preservative has been proposed as a potential reason for recently increasing rates of autism. However, comparison of propionate concentrations between autistic and neurotypical children have failed to find differences in propionate, though differences in multiple other metabolites and indicators of mitochondrial function have been observed [96].

6. Conclusion In the last ten years, the field of SCFA research has experienced tremendous growth. As with any field, expansion comes with growing pains, including contradictory findings (at times directly contradictory). En masse, high fiber containing (SCFA generating via gut microbes) and ketogenic diets appear to provide overwhelmingly positive effects, particularly in the presence of inflammatory insults or inflammation associated chronic diseases. There may be added safety to strategies that preferentially generate even number carbon SCFAs as the majority of studies that point toward negative effects of SCFAs use odd chain SCFAs (not including the side effects of ketogenic diet). These require additional enzymatic processing. In one turn of the TCA cycle, 2 carbons are removed, and then replaced with 2 carbons from acetyl-CoA. Similarly, Beta oxidation generates 2 carbons per reaction cycle in the form of acetyl-CoA. Odd number SCFAs, especially in the mitochondria, pose the issue of requiring additional processing while sponging valuable eCoA. Studies directly assessing lifelong consequences of ketogenic diets or SCFA supplementation are not available, though there are clearly potential concerns as listed above.

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Some have raised doubt regarding the wisdom of chronic ketogenic diets. Mammals have evolved the ability to fuel cells in the body from multiple energy sources. Self-restricting to predominantly one source (e.g. fat) for prolonged periods of time (years to decades) may result in a lack of metabolic flexibility. This flexibility is dictated by the enzymes, transporters, and receptors that are expressed throughout the body. If fueled by predominantly fat for decades, are the cells of the body able to turn back on the expression of genes necessary for carbohydrate metabolism? Studies observing teens being weaned off of a ketogenic diet for epilepsy treatment suggest this is a non-issue, but what about for an unsupervised 70 year old? What stress/damage or accelerated aging occurs during periods of metabolic reprogramming? These are among some of the questions that need to be addressed in the coming years. That said, we would not be here had our ancestors not been able to thrive in fasted or ketogenic states for prolonged periods of time. Reviewed above are a number of possible mechanisms that might mediate the beneficial effects seen with short chain fatty acids. It is very difficult to delineate if one mechanism is more important than another. Creating a SCFA that can be metabolized but cannot bind HDACs or GPCRs or a SCFA that cannot be metabolized but can bind HDACs and receptors, is not straightforward. Experiments using means other than SCFAs, have provided evidence that the potential metabolic, epigenetic, and GPCR mechanisms are all likely to provide benefit in multiple diseases of the brain characterized by chronic inflammation.

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CHAPTER

Diet-microbiome interactions and the regulation of the epigenome

24

Iara Cassandra V. Ibay1, Elesa Poteres1, Allison Isabelli1, Kristina Martinez-Guryn Midwestern University, Downers Grove, IL, USA

1. Introduction The gut microbiota has been implicated in the development of several life-altering diseases such as obesity, diabetes, inflammatory bowel disease, and cardiovascular diseases. However, the mechanisms that link gut microbiota composition and function to disease outcome are only beginning to be discovered. One such mechanism that may drive disease development in response to altered gut microbiota is epigenetic modification. The interaction between gut microbiota and epigenetics is further influenced by dietary-induced shifts in the gut microbiota. Collectively, these factors drive epigenetic programs that influence the development of disease. The goal of this chapter is to describe (1) the general characteristics of the gut microbiota, (2) the dietary impact on the gut microbiota, (3) the microbial impact on epigenetics, and (4) mechanisms underlying diet-microbe interactions on the host epigenome and consequences for host health.

2. Characteristics of the gut microbiota While microbes are present on virtually every part of the body, the gastrointestinal tract (GI) houses approximately 70% of our total microbial population [1]. The gastrointestinal tract is rich in nutrients and non-digestible food components making it an ideal site for bacterial colonization. It has been reported that the gut microbiota is comprised of over 35,000 bacterial species belonging to prominent phyla including: Firmicutes, Bacteroidetes, Actinobacteria, Verrucomicriobia, and Proteobacteria [2,3]. Location of phyla within the GI tract is variable as certain factors such as chemical composition and nutrient gradients help govern the location of certain bacteria [4,5]. The acidic environment, bile acid and oxygen levels, and antimicrobials present in the small intestine contribute to its lack of overall microbiota diversity. Therefore, facultative anaerobes that can withstand these conditions dominate this region of the gut [4,5], and although fewer in number, may elicit significant changes in host physiology. Abundant phyla of the small intestine include Firmicutes and Proteobacteria [5,6]. The large intestine houses most microbes found in the body due to its large surface area and more conducive conditions for bacterial growth. While Firmicutes and Bacteroidetes dominate, the large 1

These authors contributed equally.

Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00024-2 Copyright © 2019 Elsevier Inc. All rights reserved.

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intestine consists of an array of anaerobes that utilize undigested carbohydrates and resources for continual colonization [3,4]. The gut microbiome also exhibits vast differences in mucosal and luminal bacterial populations. An abundance of Bacteroidetes, Bifidobacterium, Streptococcus and many other microbes populate luminal areas, while mucosal-associated locations are sparser housing a limited amount of species such as Clostridium, Lactobacillus, Enterococcus, and Akkermansia [3]. A high level of inter-individual variability exists in gut microbiota composition and can influence the level of microbial metabolites that are generated given the presence of particular substrates [7]. While each individual’s microbiome is vastly unique, some researchers believe that all humans possess a core set of bacteria [5]. This conservation of bacteria suggests that bacteria perform necessary functional processes critical for host survival. These include protection against pathogens, bile acid deconjugation, short chain fatty acid (SCFA) production via metabolism of indigestible carbohydrates, nutrient digestion and absorption, promotion of epithelial integrity and immune function. However, gut dysbiosis, or the deleterious shift in community composition or presence of harmful pathogens, can disrupt normal physiology processes leading to disease. Gaining a greater understanding of these unique biomes and their functions within the host will provide greater opportunities for personalized medicine in the future.

3. Impact of gut microbes on epigenetics It is becoming more appreciated that the gut microbiome can influence epigenetic modifications in the host or that the host epigenome can influence gut microbiota composition [8]. Epigenetics involves alterations in gene expression that are not due to direct changes in the DNA sequence. Instead, modifications to gene expression occur through (1) post-translational modifications on amino acids on histone proteins, (2) methylation of DNA, (3) altered expression of enzymes that regulate methylation [i.e., DNA methyltransferase (DNMT) or ten-elven translocation (TET)] and acetylation [(i.e., HDACs and histone acetyltransferases (HATs)], or (4) by microRNA activity at the post-transcriptional level. Post-translational modifications (PTMs) of histones, such as methylation, acetylation, ubiquitination, phosphorylation and SUMOylation, among others contribute to the activation and repression of genes. Acetylation of histone tails typically opens up chromatin and allows for gene transcription, whereas deacetylation mediated by HDACs strengthens the association of DNA with histones and prevents gene transcription [9]. The classically studied diet-microbe-host epigenetic interaction is that of microbe-derived butyrate and inhibition of HDAC activity [10,11]. Other interactions involve microbe-mediated DNA methylation, HDAC deacetylation and methylation, as well as the influence of host epigenetics on microbial colonization [8,9,12]. It was demonstrated by Alenghat et al. [12] that deficiency in HDAC3 in intestinal epithelial cells (IECs) resulted in altered gene expression, histone acetylation, and decreased intestinal barrier function. These HDAC3-IEC deficient mice (HDAC3DIEC) were re-derived germ free and were consequently protected from dysfunction of the intestinal barrier, directly implying a microbial role in this process [12]. HDAC3DIEC mice also displayed significant alterations in gut microbiota composition in this study, suggesting that an altered host epigenome elicits pressures on microbial ecology of the gut [12].

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This phenomenon was also revealed in a study by Cortese et al. investigating both the impact of gut microbes on epigenetic modification in intestinal epithelial cells but also the impact of epigenetics on microbial colonization in a model of neonatal necrotizing enterocolitis (NEC). NEC is an inflammatory bowel disease that affects premature infants. In this study, IECs were treated with probiotic and pathogenic bacteria, which elicited DNA modifications in over 200 regions. In addition, prenatal exposure to the glucocorticoid dexamethasone triggered the altered epigenome of the host and dictated the colonization of microbiota including a slight increase in Firmicutes and slight decrease in Bacteroidetes as well as changes at lower taxonomic levels [8]. These studies highlight the cross-talk between the gut microbiota and the host epigenome [8,12].

4. Dietary impact on the gut microbiota Interactions between diet and gut microbiota have been well-established. Diet can dramatically alter gut microbial composition and function and have the potential to elicit changes in microbial phyla in as little as 24e48 h [13]. For instance, humans fed a diet rich in animal-based foods and low in fiber displayed a shift in beta diversity in fecal microbiota after only 2 days and also displayed reduced SCFA levels [14]. Similar rapid changes in the gut microbiota due to high fat diets have been reported in mice [15,16]. Another study in mice demonstrated that a diet deficient in microbe-accessible carbohydrates led to the generational loss of bacterial species. This model was proposed to represent how microbial communities may shift in human populations over time with the prolonged consumption of western-style diets low in dietary fiber [17]. Whether or not the loss of bacterial species over generations was influenced by epigenetic changes in the host has yet to be investigated in this model. It was also recently reported in our own work that HF diets can dramatically influence the gut microbiota along the length of the intestine such as increasing the abundance of Clostridiaceae, particularly in the jejunum and ileum, which influenced the level of lipid absorption in conventionalized animals [6]. This work underscores the importance of considering the site at which microbial communities are assessed in relation to the host response. Taken together, dramatic shifts in dietary intake can profoundly and rapidly impact the gut microbiota composition and function and may have long-term and functional consequences for the host. Dietary fiber, including insoluble and soluble fiber, is an essential component of a healthy diet having a number of health benefits including decreasing plasma lipid levels, providing satiety, and promoting gut regularity. Soluble fiber undergoes fermentation by gut bacteria to synthesize SCFAs including butyrate, acetate, and propionate which are important players in maintaining homeostasis within the host [18]. Although acetate and propionate have important roles for the host, benefits of butyrate are many including increased integrity of the gut epithelium, brain health, regulation of circadian rhythm, and metabolism [18]. Mechanisms include activation of g-protein coupled receptors (GPCRs), acting as an energy source for gut epithelial cells, and inhibition of HDAC activity. The latter has been associated with prevention of colon cancer [19] and Western diet-induced obesity [20]. Butyrate represents a well-studied link between gut microbiota and epigenetic regulation and will be discussed in greater detail in the next section. Other dietary components that have been found to elicit changes in the gut microbiota and host epigenetics include glucosinolates from cruciferous vegetables as well as dietary fat; these will be further discussed in the following section.

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5. Diet-microbe interactions that regulate the epigenome 5.1 Short chain fatty acids Metabolism of dietary fiber leads to the production of SCFAs that have been widely shown to influence host physiology. Butyrate reaches mM concentrations in the intestinal lumen and has a wide variety of effects on host systems. A well-defined mechanism of action of butyrate is HDAC inhibition which influences the progression of colon cancer and inflammatory bowel disease (IBD). Butyrate-mediated inhibition of HDAC activity decreases epithelial cell proliferation and induces of apoptosis [10,11,21]. Intriguingly, butyrate does not inhibit cell growth of normal colonic cells. Donohoe et al. [10] proposed that this is because butyrate is metabolized though beta oxidation in normal cells whereas cancer cells primarily undergo aerobic glycolysis. This allows for a build-up of intracellular butyrate particularly in the nucleus where it inhibits HDAC activity. Donohoe et al. [10] also found that butyrate had additional roles in epigenetic regulation by increasing histone acetylation through being metabolized into acetyl CoA and stimulating HAT activity, and thus epigenetically regulates the expression of target genes. They later showed that supplementing mice with B. fibrisolvens while being fed a high fiber diet (rich in inulin) protected them from AOM/DSS-induced tumor growth compared to control groups (Donohoe et al. 2014), presumably through increased SCFA production. Butyrate-mediated HDAC inhibition is a classic and well-known diet-microbe interaction that influences host epigenetics.

5.2 Isothiocyanates Bacteria with thioglucosidase activity metabolize glucosinolates into ITCs which have been shown to have anti-cancer effects through epigenetic modifications [21]. Foods rich in glucosinolates include cruciferous vegetables such as broccoli. It was recently demonstrated that consumption of 200 g of cooked broccoli (w2 cups) for 18 days significantly altered microbiota composition, characterized by a 9% decrease in Firmicutes and a 10% increase in Bacteroidetes, in human subjects [22]. Notably, interindividual variability in gut microbiota composition determines the level of ITC produced as demonstrated by Li et al. [7]. Here, human participants were fed a standardized meal containing 200 g cooked broccoli and stool samples were collected from those with the highest and lowest ITC excretion levels. These samples were incubated with glucoraphanin, a major glucosinolate from broccoli, in an ex vivo experiment and it was found that high-ITC excreted stool metabolized more glucoraphanin than low-ITC excreted stool. This is an important consideration, as ITCs have been shown to reduce tumor growth via altering DNA methylation and histone acetylation [21]. One particular ITC, sulforaphane, has been shown to have chemoprotective effects in colon, breast, and prostate cancer models [23e26]. For instance, sulforaphane prevents carcinogenesis in rodent models by inhibiting HDAC activity and increasing acetylation of histones in the colon [23]. Taken together, ITCs represent an important diet-microbe interaction that have direct effects on the epigenome.

5.3 Dietary fat As previously discussed, extensive research has demonstrated that diets high in fat and even the fatty acid composition of the diet can alter the composition and function of the gut microbiota. Recent work by Whitt et al. [27] showed that HDAC3 in intestinal epithelial cells may promote the development of

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obesity, as IEC-specific disruption of HDAC3 protected against obesity, glucose intolerance, and elevated plasma lipids in a murine diet-induced obesity (DIO) model [27]. Administration of butyrate led to significant weight loss in control mice but not HDAC3DIEC mice, suggesting that intact HDAC3 is necessary for the weight loss-promoting effects of butyrate. In addition, HDAC3 levels from intestinal biopsy samples correlated with patient weight. Altogether this study presents compelling evidence for diet-microbe interactions that suggest consuming a high fat diet that decreases butyrate levels may lead to increased HDAC3 activity in IECs, thereby promoting an obese phenotype [27]. Krautkramer et al. [20] showed that conventionally-raised or germ free (GF) mice conventionalized with conventionally-raised (ConvR) microbiota have significantly greater histone acetylation and methylation compared to GF animals, suggesting a direct role of microbes in regulating histone modification. A diet-microbe-host interaction was also demonstrated in this study, whereby a Western Diet high in fat and sugar decreased SCFA levels as well as decreased histone acetylation in the colon, liver, and white adipose tissue (WAT). Delivery of SCFAs to GF mice restored histone acetylation and methylation. Interestingly, RNA seq analysis revealed that the hepatic gene profile was similarly altered with SCFA supplementation in GF mice as compared to those ConvR or ConvD. This study demonstrated a direct association between host diet, microbiota-derived SCFA, and host epigenetic function [20]. Taken together, these recent reports suggest an important interaction between dietary consumption, gut microbiota, and host epigenetic programming.

6. Conclusion A wealth of literature exists demonstrating the dramatic impact of diet on gut microbiota and emerging studies have provided evidence for diet-microbe interactions that impact epigenetic modifications that ultimately have important implications for host health and disease development. Another key consideration for applying this information to personalized healthcare is that one’s microbiota dictates the types and levels of microbial byproducts generated, given a particular diet (e.g., high fiber vs. high fat), that may confer physiological benefits. This speaks to the importance of a personalized approach to healthcare that may involve the assessment of the gut microbiota to determine treatment plans or dietary recommendations for inclusion of particular foods or the use of a combination of probiotics and prebiotics until better options become available. However, this is an early field of investigation and further studies are needed to characterize diet-microbe-host interactions that could have important implications for various immune, cancer, and metabolic-related diseases through epigenetic modifications.

References [1] Sekirov I, Russell SL, Antunes CMFB. Gut microbiota in health and disease. Physiol Rev 2010;90:859e904. [2] Frank DN, St Amand AL, Feldman RA, Boedeker EC, Harpaz N, Pace NR. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci Unit States Am 2007;104(34):13780e5. [3] Jandhyala SM, Talukdar R, Subramanyam C, Vuyyuru H, Sasikala M, Reddy DN. Role of the normal gut microbiota. World J Gastroenterol 2015;21(29):8836e47.

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[4] Thursby E, Juge N. Introduction to the human gut microbiota [Internet] Biochem J 2017;474(11):1823e36. Available from: http://biochemj.org/lookup/doi/10.1042/BCJ20160510. [5] Donaldson GP, Lee SM, Mazmanian SK. Gut biogeography of the bacterial microbiota. Nat Rev Microbiol 2015;14(1):20e32. [6] Martinez-Guryn K, Hubert N, Frazier K, Urlass S, Musch MW, Ojeda P, et al. Small intestine microbiota regulate host digestive and absorptive adaptive responses to dietary lipids. Cell Host Microbe 2018;23(4): 458e469.e5. [7] Li F, Hullar MAJ, Beresford SAA, Lampe JW. Variation of glucoraphanin metabolism in vivo and ex vivo by human gut bacteria. Br J Nutr 2011;106(3):408e16. [8] Cortese R, Lu L, Yu Y, Ruden D, Claud EC. Epigenome-Microbiome crosstalk: a potential new paradigm influencing neonatal susceptibility to disease. Epigenetics 2016;11(3):205e15. [9] Alenghat T. NIH public access. Toxicol Pathol 2015;43(1):101e6. [10] Donohoe DR, Garge N, Zhang X, Sun W, O’Connell TM, Bunger MK, et al. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab [Internet] 2011;13(5): 517e26. Available from: https://doi.org/10.1016/j.cmet.2011.02.018. [11] Donohoe DR, Holley D, Collins LB, Montgomery SA, Whitmore AC, Hillhouse A, et al. A gnotobiotic mouse model demonstrates that dietary fiber protects against colorectal tumorigenesis in a microbiota- and butyrate-dependent manner. Cancer Discov 2014;4(12):1387e97. [12] Alenghat T, Osborne LC, Saenz SA, Kobuley D, Ziegler CGK, Mullican SE, et al. Histone deacetylase 3 coordinates commensal-bacteria-dependent intestinal homeostasis [Internet] Nature 2013;504(7478): 153e7. Available from: https://doi.org/10.1038/nature12687. [13] Ojeda P, Bobe A, Dolan K, Leone V, Martinez K. Nutritional modulation of gut microbiota - the impact on metabolic disease pathophysiology [Internet] J Nutr Biochem 2016;28:191e200. Available from: https://doi. org/10.1016/j.jnutbio.2015.08.013. [14] David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome [Internet] Nature 2014;505(7484):559e63. Available from: https://doi.org/10.1038/nature12820. [15] Carmody RN, Gerber GK, Luevano JM, Gatti DM, Somes L, Svenson KL, et al. Diet dominates host genotype in shaping the murine gut microbiota [Internet] Cell Host Microbe 2015;17(1):72e84. Available from: https://doi.org/10.1016/j.chom.2014.11.010. [16] Howe A, Ringus DL, Williams RJ, Choo ZN, Greenwald SM, Owens SM, et al. Divergent responses of viral and bacterial communities in the gut microbiome to dietary disturbances in mice [Internet] ISME J 2016; 10(5):1217e27. Available from: https://doi.org/10.1038/ismej.2015.183. [17] Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, Sonnenburg JL. Diet-induced extinctions in the gut microbiota compound over generations [Internet] Nature 2016;529(7585):212e5. Available from: https://doi.org/10.1038/nature16504. [18] Bourassa MW, Alim I, Bultman SJ, Ratan RR. Butyrate, neuroepigenetics and the gut microbiome: can a high fiber diet improve brain health? [Internet] Neurosci Lett 2016;625:56e63. Available from: https://doi. org/10.1016/j.neulet.2016.02.009. [19] Davie JR. Nutritional proteomics in cancer prevention. Nutr Proteomics Cancer Prev 2003;133(7 Suppl. l): 2485e93. [20] Krautkramer KA, Kreznar JH, Romano KA, Vivas EI, Barrett-Wilt GA, Rabaglia ME, et al. Diet-microbiota interactions mediate global epigenetic programming in multiple host tissues [Internet] Mol Cell 2016;64(5): 982e92. Available from: https://doi.org/10.1016/j.molcel.2016.10.025. [21] Hullar MAJ, Fu BC. Diet, the {gut} {microbiome}, and {epigenetics} [Internet] Cancer J 2014;20(3):170e5. Available from: http://content.wkhealth.com/linkback/openurl?sid¼WKPTLP:landingpage&an¼00130404201405000-00002.

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[22] Kaczmarek JL, Liu X, Charron CS, Novotny JA, Jeffery EH, Seifried HE, et al. Broccoli consumption affects the human gastrointestinal microbiota [Internet] J Nutr Biochem 2019;63:27e34. Available from: http:// www.ncbi.nlm.nih.gov/pubmed/30317146%0Ahttps://linkinghub.elsevier.com/retrieve/pii/S095528631830 3000. [23] Myzak MC. Sulforaphane inhibits histone deacetylase in vivo and suppresses tumorigenesis in Apcmin mice [Internet] FASEB J 2006;19(10):1e19. Available from: https://doi.org/10.1096/fj.05-4785fje. [24] Myzak MC, Karplus PA, Chung F, Dashwood RH. A novel mechanism of chemoprotection by sulforaphane. Cancer Res 2004;(541):5767e74. [25] Myzak MC, Hardin K, Wang R, Dashwood RH, Ho E. Sulforaphane inhibits histone deacetylase activity in BPH-1, LnCaP and PC-3 prostate epithelial cells. Carcinogenesis 2006;27(4):811e9. [26] Meeran SM, Patel SN, Tollefsbol TO. Sulforaphane causes epigenetic repression of hTERT expression in human breast cancer cell lines. PLoS One 2010;5(7). [27] Whitt J, Woo V, Lee P, Moncivaiz J, Haberman Y, Denson L, et al. Disruption of epithelial HDAC3 in intestine prevents diet-induced obesity in mice. Gastroenterology 2018;155(2):501e13.

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Matthew A. Odenwalda, Christopher G. Chapmanb Department of Medicine, University of Chicago Medicine, Chicago, IL, USAa; Center for Endoscopic Research and Therapeutics (CERT), Department of Medicine, University of Chicago Medicine, Chicago, IL, USAb

1. Introduction The intestinal microbiota is the collection of microbes e commensal, symbiotic, and pathogenic bacteria e that reside within the gastrointestinal tract. The intestine contains an enormous number of microbes with some estimates as high as 100 trillion microbes that together contain more genetic information than the human host. Despite the overwhelming number of microbes within the human intestine, the majority can be categorized into two dominant phyla, Bacteroides and Firmicutes [1]. The term dysbiosis refers to a microbial balance deviating from the normal microbiota composition and is most commonly thought of as a replacement of normal, “healthy,” or “protective” microbes with potentially harmful intestinal bacteria. The microbiome has been implicated in a number of processes that promote human health including the response to intestinal epithelial damage [2], normal fat storage [3], and intestinal angiogenesis [4]. Conversely, dysbiosis, has been correlated with a variety of disease states including autoimmune diseases such as inflammatory bowel disease, type 1 diabetes, and multiple sclerosis [5e7] as well as obesity [8,9]; vascular diseases including cardiovascular disease and strokes [10e14]; and many neoplastic processes [15]. Given the potential role of the intestinal microbiome in such a diverse array of organ systems and disease processes, understanding its composition and regulation is of great importance. In this chapter, we review the normal mechanisms of microbiome development as well as both intrinsic and extrinsic mechanisms that regulate microbiome composition with a particular focus on dietary factors effecting both host and microbiome epigenetics.

2. The microbiome composition is dynamic and modifiable 2.1 Microbiome colonization Many factors influence the composition of the intestinal microbiome, which changes over a lifetime. Infants are sterile prior to birth, but are rapidly colonized upon delivery. It is therefore not surprising that the mode of delivery affects initial microbiome composition [16]. One study sampled mother’s skin, vagina and oral mucosa prior to delivery and the neonate’s skin, oral mucosa, nasopharyngeal Nutritional Epigenomics. https://doi.org/10.1016/B978-0-12-816843-1.00025-4 Copyright © 2019 Elsevier Inc. All rights reserved.

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aspirate and meconium shortly after delivery. This study demonstrated that infants delivered vaginally were colonized with microbiomes enriched in vaginal species such as Lactobacillus. On the contrary, lack of vaginal exposure when delivered by Caesarian section resulted in microbiomes more closely related to that of their mother’s skin flora and were enriched in species such as Staphylococcus [16]. Interestingly, regardless of delivery technique, these initial exposures resulted in infant microbiomes that were similar in all sampled anatomical locations, in contrast to the adult microbiome, which has varied composition throughout the gastrointestinal tract [16,17]. This observation suggests that the infant microbiome must undergo further differentiation with subsequent environmental exposures. Similar results were reported in a separate study that sequenced stool samples from infants and showed maturation of the infant microbiome over the first year of life to more closely resemble that of their mother’s microbiome [18]. Feeding method (exclusively breast fed vs. exclusively formula fed) affected microbiome composition, but the greatest change resulted from cessation of breast feeding at 12 months of age when the microbiome composition shifted to closely resemble an adult-like microbiome that was enriched in Bacteroides, Bilophila, Roseburia, Clostridium, and Anaerostipes [18]. Regardless of delivery method, the infant microbiome is characterized by lability and low diversity [19]. This was most clearly demonstrated by a case study that characterized the development of the human microbiome over the first 2.5 years of a single infant’s life [20]. In this study, the authors collected 60 stool samples from a single newborn over 2.5 years and mapped changes in the microbiome as assessed by 16S ribosomal RNA sequencing with significant life events such as an illness, antibiotic exposure, or changes in feeding habits. While phylogenetic diversity gradually increased throughout this period, changes in diet (e.g. introduction of table food) or illnesses and subsequent antibiotic exposure were associated with abrupt taxonomic shifts [20]. Introduction of table food appeared to be the single most influential event on the development of the studied infant microbiome as consumption of table food resulted in many features of an adult microbiome, including sustained abundance of Bacteroides, elevated short chain fatty acid levels, and a more stable microbiome composition [20]. Despite the relative stability of the adult microbiome, many environmental factors continue to alter the microbiome composition throughout adulthood, most notably antibiotic exposure and diet.

2.2 Antibiotic exposure modifies the microbiome Short-term antibiotic exposure can drastically modify the composition of the gut microbiome. For example, Dethlefsen, et al. studied the stool microbiome composition of 3 healthy individuals before, during, and after a 5 day course of ciprofloxacin treatment and reported that this course of antibiotics resulted in decreased abundance of approximately one-third of the taxa as well as decreased community richness, diversity, and evenness [21]. The majority of these changes resolved four weeks post-exposure; however, several taxa did not fully recover even six months post-treatment [21]. The same group later reported that repeated exposure to short courses of ciprofloxacin also resulted in a sustained shift in microbiome composition, as the microbiome stabilized with altered composition many months post-exposure [22]. Another group tested the effect of both multiple unique antibiotics and host-targeted drugs on microbiome viability and composition [23]. Host-targeted drugs (including digoxin, sulfasalazine, tetramisole, nizatidine, and phenacetin) had limited effect on microbial community structure; however, treatment with many different antibiotics significantly affected

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microbiota composition, gene expression, and functionality [23]. Together, these studies indicate that even a short course of antibiotic exposure can have lasting effects on the intestinal microbiome composition, which can in turn effect multiple physiologic processes in the host thereby influencing organism health.

2.3 Diet-induced modification of the microbiome Empiric evidence for diet influencing the microbiome composition comes from a study that characterized the fecal bacterial 16S rRNA content from 326 children (