Developmental Human Behavioral Epigenetics: Principles, Methods, Evidence, and Future Directions (Translational Epigenetics Volume 23) [1 ed.] 0128192623, 9780128192627, 9780128192634

Table of contents :
Front-Matter_2021_Developmental-Human-Behavioral-Epigenetics
Front matter
Copyright_2021_Developmental-Human-Behavioral-Epigenetics
Copyright
Contributors_2021_Developmental-Human-Behavioral-Epigenetics
Contributors
Keeping-complexity-in-mind_2021_Developmental-Human-Behavioral-Epigenetics
Keeping complexity in mind
References
Chapter-1---Principles-of-epigenetics-and_2021_Developmental-Human-Behaviora
Principles of epigenetics and DNA methylation
Definition of epigenetics
Types of epigenetic modifications
DNA cytosine methylation
DNA cytosine hydroxymethylation
DNA adenosine methylation
RNA modifications
Histone modifications
RNAs
Environmental effects, cell metabolism and epigenetics
Epigenetic memory
Environmental effects
Cell metabolism, diet and microbiota
Methods for methylation analysis
Chromatin immunoprecipitation
Nucleosome positioning
DNA cytosine methylation analysis
Locus-specific DNA methylation analysis
Whole methylome analysis
Selected genome-wide methylation analysis
Epigenotyping arrays
Direct readout of DNA methylation
DNA methylation analysis of cell-free circulating DNA
Single-cell epigenomics
References
Chapter-2---From-animal-to-human-epi_2021_Developmental-Human-Behavioral-Epi
From animal to human epigenetics
A rat story: Behavioral epigenetics beginnings
Post-natal maternal environment shapes the epigenome and adult behavioral phenotypes of the offspring
Environmental stimuli delivered to parents trigger processes to transmit information to offspring
Permissive environments
Environmental enrichment
Maternal enrichment during pregnancy and/or lactation
Pre-reproductive parental enrichment
Aversive environments
Aversive environment: Animal studies
Epigenetic perturbation can be passed along
Combining human and non-human animal research
References
Chapter-3---An-overview-of-developmental-b_2021_Developmental-Human-Behavior
An overview of developmental behavioral genetics
Background/history
Behavioral genetic methodology
Key interpretative issues
Key results from twin and adoption studies
Gene-environment interplay
Gene-environment correlation
Passive rGE
Active rGE
Evocative rGE
Gene-environment interaction
Genomic approaches to behavioral genetics
Concluding remarks
References
Chapter-4---Prenatal-exposures-and-behavioral-ep_2021_Developmental-Human-Be
Prenatal exposures and behavioral epigenetics in human infants and children
Early environmental programming
Biological embedding
Fetal DNA methylation after exposure to prenatal stress
Does DNA methylation at birth predict postnatal outcomes?
Further directions
References
Chapter-5---Applying-behavioral-epigenetic-princi_2021_Developmental-Human-B
Applying behavioral epigenetic principles to preterm birth and early stress exposure
Introduction
Background
Epigenetic regulation by early adverse experiences
Epigenetic regulation by early protective experiences
Preterm birth and NICU-related early adverse experiences
Preterm birth and NICU-related early protective experiences
A rationale for preterm behavioral epigenetics
State of the art of PBE research
Epigenetic effects of prenatal conditions
Epigenetic profile of preterm infants/children
Epigenetic effects of NICU-related stress
Developmental outcomes of epigenetic alterations in preterm infants/children
Future directions
Clinical implications
References
Chapter-6---Long-term-epigenetic-effects-o_2021_Developmental-Human-Behavior
Long-term epigenetic effects of parental caregiving
The special case of human parenting in the ELA spectrum
Gene- (parenting-)environment interactions and epigenetics
Parental care and offspring DNA methylation
Association between parenting and DNA methylation of genes involved in the HPA axis
Parenting and DNA methylation of genes relevant for other stress-related psychobiological systems
Oxytocin
Serotonin
Neurotrophins
Genes of the immune system
Gene networks and epigenetic variation
Epigenetics and attachment theory
Challenges and opportunities for epigenetic studies on parenting
Outlook
Ethics
Conceptional
Statistical-analytical
References
Chapter-7---Intergenerational-transmission-of-s_2021_Developmental-Human-Beh
Intergenerational transmission of stress-related epigenetic regulation
A brief history of inheritance of acquired characteristics
Lamarckism
Charles Darwin and pangenesis
Weismann barrier
Neo-Darwinism and future directions
Epigenetic mechanisms
DNA methylation
Methylation life cycle and reprogramming
The Agouti mouse
Intergenerational, transgenerational inheritance of stress in humans and animals
Intergenerational inheritance vs transgenerational inheritance
Intergenerational epigenetic inheritance of stress in humans
Intergenerational transmission of stress in animal models
Mechanisms of epigenetic inheritance
Mechanisms of epigenetic heredity
Future directions, conclusions
Summary: What we know
Intervention and prevention
References
Chapter-8---The-role-of-protective-caregiving-in_2021_Developmental-Human-Be
The role of protective caregiving in epigenetic regulation in human infants
Introduction
Maternal caregiving and DNA methylation
The specific role of maternal physical contact
Open questions and challenges in human epigenetics research
Future perspectives
Conclusions
Acknowledgments
References
Chapter-9---Embedding-early-experiences-into-brain_2021_Developmental-Human-
Embedding early experiences into brain function: Perspectives from behavioral epigenetics
Perspectives
Acknowledgments
References
Index_2021_Developmental-Human-Behavioral-Epigenetics
Index

Citation preview

Developmental Human Behavioral Epigenetics

Translational Epigenetics Series Trygve 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-Gim
enez, 2015 Drug Discovery in Cancer Epigenetics Edited by Gerda Egger and Paola Barbara Arimondo, 2015 Medical Epigenetics Edited by Trygve O. Tollefsbol, 2016 Chromatin Signaling and Diseases Edited by Olivier Binda and Martin Fernandez-Zapico, 2016 Genome Stability Edited by Igor Kovalchuk and Olga Kovalchuk, 2016 Chromatin Regulation and Dynamics Edited by Anita G€ ond€or, 2016 Neuropsychiatric Disorders and Epigenetics Edited by Dag H. Yasui, Jacob Peedicayil and Dennis R. Grayson, 2016

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 The Epigenetics of Autoimmunity Edited by Rongxin Zhang, 2018 Epigenetics in Human Disease, Second Edition Edited by Trygve O. Tollefsbol, 2018 Epigenetics of Chronic Pain Edited by Guang Bai and Ke Ren, 2018 Epigenetics of Cancer Prevention Edited by Anupam Bishayee and Deepak Bhatia, 2018 Computational Epigenetics and Diseases Edited by Loo Keat Wei, 2019 Pharmacoepigenetics Edited by Ramo´n Cacabelos, 2019 Epigenetics and Regeneration Edited by Daniela Palacios, 2019 Chromatin Signaling and Neurological Disorders Edited by Olivier Binda, 2019 Transgenerational Epigenetics, Second Edition Edited by Trygve Tollefsbol, 2019 Nutritional Epigenomics Edited by Bradley Ferguson, 2019 Prognostic Epigenetics Edited by Shilpy Sharma, 2019 Epigenetics of the Immune System Edited by Dieter Kabelitz, 2020 Stem Cell Epigenetics Edited by Eran Meshorer and Giuseppe Testa, 2020

Polycomb Group Proteins Edited by Vincenzo Pirrotta, 2016

Epigenetics Methods Edited by Trygve Tollefsbol, 2020

Epigenetics and Systems Biology Edited by Leonie Ringrose, 2017

Histone Modifications in Therapy Edited by Pedro Castelo-Branco and Carmen Jeronimo, 2020

Cancer and Noncoding RNAs Edited by Jayprokas Chakrabarti and Sanga Mitra, 2017

Environmental Epigenetics in Toxicology and Public Health Edited by Rebecca Fry, 2020

Translational Epigenetics

Developmental Human Behavioral Epigenetics Principles, Methods, Evidence, and Future Directions Volume 23

Series Editor

Trygve Tollefsbol Comprehensive Cancer Center, Comprehensive Center for Healthy Aging, University of Alabama at Birmingham, AL, USA

Edited by

Livio Provenzi Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, Pavia, Italy

Rosario Montirosso 0-3 Center for the at-Risk Infant, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy

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 © 2021 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 978-0-12-819262-7 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Andre Gerhard Wolff Acquisitions Editor: Peter B. Linsley Editorial Project Manager: Megan Ashdown Production Project Manager: Maria Bernard Cover Designer: Miles Hitchen Typeset by SPi Global, India

Contributors Chloe Austerberry University College London, London, United Kingdom Erica Berretta IRCCS Santa Lucia Foundation, Rome, Italy e-Anne Bouvette-Turcot Andre Department of Psychology, McGill University; Batshaw Youth and Family Center, Montreal, QC, Canada Francesca Cirulli Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanita`, Rome, Italy Nicholas Collins Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States Debora Cutuli IRCCS Santa Lucia Foundation, Rome, Italy Lourdes Fan˜ana´s Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona; Centro de Investigacio´n Biomedica en Red en Salud Mental (CIBERSAM), Madrid, Spain Pasco Fearon University College London, London, United Kingdom Roberto Giorda Scientific Institute IRCCS ‘‘E. Medea”, Molecular Biology Laboratory, Bosisio Parini, LC, Italy Elena Guida 0-3 Center for the at-Risk Infant, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy Shantala A. Hari Dass National Centre for Biological Sciences – Tata Institute of Fundamental Research, Bangalore, India Richard Hunter Developmental Brain Sciences Program; Department of Psychology, University of Massachusetts Boston, Boston, MA, United States Daniela Laricchiuta IRCCS Santa Lucia Foundation, Rome, Italy Eleonora Mascheroni 0-3 Center for the at-Risk Infant, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy

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Contributors

Maria Meier Department of Psychology, Division of Clinical Neuropsychology, University of Constance, Constance, Germany Rosario Montirosso 0-3 Center for the at-Risk Infant, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy Helena Palma-Gudiel Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona; Centro de Investigacio´n Biomedica en Red en Salud Mental (CIBERSAM), Madrid, Spain Laura Petrosini IRCCS Santa Lucia Foundation, Rome, Italy Livio Provenzi Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, Pavia, Italy Tania L. Roth Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States Ed Tronick Developmental Brain Sciences Program; Department of Psychology, University of Massachusetts Boston, Boston, MA, United States Eva Unternaehrer Department of Psychology, Division of Clinical Neuropsychology, University of Constance, Constance, Germany; Child and Adolescent Research Department, Psychiatric University Hospitals Basel (UPK), Basel, Switzerland

Keeping complexity in mind Ed Tronick and Richard Hunter Developmental Brain Sciences Program, University of Massachusetts Boston, Boston, MA, United States Department of Psychology, University of Massachusetts Boston, Boston, MA, United States

Prologue for Livio Provenzi & Rosario Montirosso (eds.) Developmental Human Behavioral Epigenetics, ELSEVIER, San Diego It is thrilling to see what we are discovering about molecular epigenetics. The research presented in this volume is fundamental. It is exciting to be able to examine the molecular epigenetics of the interaction of genes and environment leading to the behavioral phenotype. It is an instantiated reality that earlier developmental biologists could only dream about. Take a moment to realize that we are integrating molecular genetic processes and micro- and macro-environmental stimuli into a characterization of how an organism functions in the world; its behavioral phenotype. It is breathtaking. That said, borrowing a phrase from politics, that enthusiasm can be hard to maintain in the face of nuance and cautions, we want to introduce some significant caveats to the state of the field while struggling to maintain the excitement of discovery. We hope not to dim our enthusiasm. Rather, we want to introduce some cautions that actually present critical new challenges that have emerged because of our success. As we overcome them, the field will move forward. Additionally, we suggest a conceptual model, the buffertransducer model (BTM). It is a model we are enthusiastic about. It situates molecular epigenetics in a dynamic and complex view of the forces and developmental processes operating to produce the phenotype. Our goal is not to convince you that the BTM is correct (though, of course it is!). We present it here as an illustration of the kind of complex thinking we need to engage in to understand epigenetic effects in a larger developmental context. Certainly, we need to search always for a better way to do our research, but we do not know what that better way is, although it certainly is not a singular way. Part of what we are calling for is a more humble stance in relation to how we talk about and conceptualize epigenetic processes. It is a way of speaking that does not focus on making epigenetics preeminent and singularly sovereign in generating the phenotype. Indeed, epigenetic mechanisms have always been conceptually embedded between genotype and environment. It is a way of speaking—thinking—that fully recognizes the complexity of what we are trying to understand and a way that constantly reminds of us of what we do not know. Thus while to go about our work simplification may be—actually is—necessary, our conceptualizations and our operationalizations have to keep the complexity in mind so that we will not miss things and will increase the likelihood that we will grapple with and uncover the phenomenon of significance. Briefly, let us suggest some caution in our thinking about molecular epigenetics, ideas developed more fully elsewhere (Lester et al., 2011; Tronick & Hunter, 2016). Our view is that there are a host of biological, physiological, nervous system and brain systems, and psychological and environmental factors that dynamically interact over time shaping the phenotype. Moreover, the phenotype in and of itself affects the interplay of these factors and itself. Complicated! A functional view of epigenetics might see it as a mechanism for quick adaptation by the organism (as opposed to the species) to the environment.

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But even this simple definition requires caution. “Quick” is undefined and its metric (seconds, hours, days, or even years) is likely to change depending on the factors involved and the context. Moreover, other mechanisms, such as learning, may serve the same function and be indistinguishable from an epigenetic change (e.g., Miller & Sweatt, 2007). Thus, the extent to which molecular mechanisms are, or are not, involved has to be determined, and more than one process may be simultaneously operating to produce a particular outcome. Furthermore, the organism’s “preference” for using one or another mechanism or a combination of mechanisms remains unstudied. In infants for example, leaning to avert gaze from a stressor is likely to utilize less biological capital than engaging genetic mechanisms for adapting to the stress, but is it preferentially employed? In what circumstances? We are vague about when we use the term adaptation. The term is invoked most often when genes are involved, which already is a problematic use of the term since the adaptation referred to is often not more than a speculation, a “just so story.” What do we mean by adaptation when we are talking about epigenetic processes? Certainly some of them are not adaptive in an evolutionary sense, a sense of the term which we seldom examine empirically. Those problems aside, what changes are adaptive in the short run? One need only to consider the myriad of epigenetic changes in psychopathologies to recognize that many changes are unlikely to be adaptive in any obvious sense, particularly when the context that created them is less well defined than the molecular marks we are tempted to give our whole attention to. A similar vagueness in conceptualization of our actual empirical research—the hands-on work— characterizes the description of the terms environment or context. Their vagueness may be even more concerning than that for adaptation. To say that the environment is typically undercharacterized is to grossly undercharacterize its description, especially while studying epigenetic processes. After all the brilliance of epigenetics is its integration of molecular processes and environmental events. While the molecular work is stunningly elegant, the work on the environment is crude. In many studies, a standard protocol or procedure may be utilized, but its details are unspecified. For example, a rodent behavioral task might not control for or report light levels, despite this simple, easily reportable environmental factor’s long-documented effects on behavior. With that example in mind, it takes little effort to see how much more complex factors, such as life history, might be underreported or inadequately described. Nevertheless, it is these sorts of contextual details that are thought to be responsible for generating molecular epigenetic changes. Licking rat pups by the dam does not capture what is going on, such as how many licks, how hard, how long, and in what context (home cage or open space) and portion of a diurnal cycle. Yet for any invocation of epigenetic processes, these details matter; they are half of any epigenetic formulation. They are the “whats” that make for epigenetic changes. Specifying them is needed, and demanded. Furthermore, to highlight one of the unspecified “whats” is the near total lack of concern for the state of the organism. In what state—distress, sleep, wakefulness, or hunger—is the pup? Does licking really have the same effect on a sleeping pup as it would on the one in distress, on a pup that is nursing or alert? Indeed, developmental psychologists know that state changes how an organism (human infant) reacts to the same stimulus. Neuroendocrinologists recognize that internal states dictate complex, statedependent changes in physiology that can both encode epigenetic changes and be influenced by previous experience. Thus, the organismic state along with the stimulus/context needs careful specification. A way to put this issue is that the environmental phenotype is indeterminate; it is underspecified. The very fact that we do not have a specific term of art for “environmental phenotype” is a mark of our

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conceptual blind spot in this regard. However, it is not even that simple. There are issues related to the time frame. After the experimental procedure is completed, the organism is put into a totally different—which we also leave unspecified and unobserved—environment. But back to time. In different studies, time periods vary wildly. Yet again, from an epigenetic framework, we must believe that those unobserved “whats” over those varying time periods lead to epigenetic changes. Epigenetic changes are not suspended just because those periods of time away from the experimental procedure are not the researchers’ focus. Furthermore, there are two other related issues of concern about time. A counterargument to the issue of time is that the experimental and controls are put into the same environment for the same amount of time and so they have the same exposure. This argument ignores the fact that the organisms are not the same. One organism has had a change induced by the experimental condition; its conspecific has not. As such, they will react to the environment in different ways and likely come out of it with different epigenetic changes induced by the environment. Second, making matters even more indeterminate is that in many studies, these out-of-sight time periods are long enough that they go over developmental transitions and sensitive periods. Thus again, the organism that is brought back for evaluation is not the organism that was initially studied. They have changed in ways related to development, and developmental changes are most often qualitative. A significant way they have changed is that how and which epigenetic changes occur in each of them may now be different than the way the changes occurred earlier when each was in the same but different state of developmental organization. A start to come at these issues of duration of exposures and developmental transitions would be to do time courses and dose-response studies for environmental factors as one does in pharmacology. However, complexity is added by the need to include specification of life history, biological sex, and social arrangements intersecting with the temporal factors—not easy, but at least a conceptually tractable one. Underlying these issues is another, perhaps insidious issue. The concept is that the initially induced epigenetic change is stable—fixed. The belief leads to a number of false conclusions, which fly in the face of how we think about epigenetic changes and developmental processes. One is that the epigenetic change does not have any additional effect once it has occurred. Such a view is simply, well, silly. Even if the particular change is fixed and unchanging for one system, it has epigenetic effects and other kinds of effects on other systems. Another issue is what accounts for the stability. Outside of cell biology and biochemistry, typically the view is that the change is in and of itself fixed and stable. An alternative is that stability is maintained and perhaps even created anew by the ongoing environment. Epigenetic changes induced by stress may be maintained by the organism remaining in a stressful environment. They may also be maintained by a change in the organization of the organism that was generated by the epigenetic change initially induced by the stressor. For example, an individual experiencing a trauma that induces epigenetic changes in their cortisol receptors may maintain the change, even amplify it because their physiology now is in and of itself is stressing. A more radical perspective is that a “stable” epigenetic change is actually created and re-created anew by the ongoing environment and/or by organismic changes. Indeed, this view is more in line with the actual behavior of molecular epigenetic marks than the received view: most molecular epigenetic marks are maintained by a balance of writer and eraser enzymes acting in concert. The metaphor of a printed mark is misleading, the letters of the codes of life are not written on paper but on water. Epigenetics is fundamentally dynamic in nature and reflects the constant interplay between the organism and the environment across multiple time scales. Much of what we know about epigenetic changes, especially in humans, is from studies of models of abnormal processes, such as toxic exposures, deprivation, or experimental paradigms. These studies

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are, without doubt, revealing, but they may not characterize the typical operation of epigenetic processes. From the perspective of a dynamic system, while some animals exist in what may be thought of as outlier locales in the species’ typical state space, how they are functioning may not characterize the operation of their more typical conspecifics. They are almost by definition atypical. Thus, they may be poor models of typical processes, but critically, we can only know what is typical if we know what the typical processes look like. And looping back the typical is only typical in particular environments. This issue is particularly acute with regard to work on the behavior of laboratory animals, which are customarily kept in environments that deviate so profoundly from their natural contexts so as to be—in some cases, frankly—pathogenic, and they are our controls. Furthermore, there is an emphasis on adverse events or trauma in our studies of epigenetic changes. However, the induction of changes is more than likely related to quotidian processes rather than extreme events. Certainly, extreme events can generate epigenetic changes, but it is unlikely that those changes are related to the processes of epigenetic changes induced by variations in typical species-specific events, such as caretaking (DiCorcia & Tronick, 2011). We utilize the two models (see Figs. 1 and 2) to guide our work on humans and animals—neither is meant to be definitive. They are simply illustrative of the complexity of the phenomenon we are trying to understand. Fig. 1 presents the buffer-transducer model (BTM), and Fig. 2 presents its operation over time. Even in its complexity, the reader needs to be cognizant that both are simplifications. Here, we focus on its application to humans. The BTM operates on the microtemporal process of the continuous and ongoing engagement of the organism and the environment for gaining resources for the maintenance of its organization and for its growth and development. The BTM conceptualizes the caretaker-

FIG. 1 The buffer-transducer model: All the factors in the model dynamically interact to affect the development of the phenotype.

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FIG. 2 Ongoing outcomes become causal. Effects of resource enhancing and depleting factors accumulate, cascade, and over time become causal.

infant mutual engagement as a system for regulating the infant’s acquisition of resources, energy, and information, as a final common pathway either buffering or transducing the effects of different factors or events that affect the organism, resulting in its behavioral phenotype. As such, with the centrality of the caretaker-offspring pathway, the BTM is more likely appropriate for altricial animals, such as humans. Though the different factors in the model and the outcomes (see Fig. 1) are from different domains - such as physiology, psychology, sociology, epigenetics - all of them are considered either resource-depleting or resource-enhancing factors. Over time, they all interact and affect the phenotype. They are environmental, cultural, genetic, epigenetic, physiological, psychological, and relational or regulatory. Their interactions result in the quality of the organisms’ outcome. And even that outcome, be it good or bad, acts as a depleting or enhancing factor as it interplays with the other factors. In a cascade of resource depletion, for instance, low maternal education (e.g., less than high school) and associated factors (e.g., poverty) deplete resources because such mothers are likely to have poorer self- and infant-regulatory capacities, leading to a stressed infant. Epigenetic changes are induced in the glucocorticoid receptor, and the immune system is weakened. Other physiologic systems become dysregulated or distorted. As a consequence, the infant’s health and behavior are compromised, and in turn it becomes more difficult for the caretaker to manage the infant. These consequential outcomes across multiple systems become causal, such that there is self-amplification. The cascade leads to further compromise of the phenotype, increasing derangement of its organismic systems and even of its environment. What is most important to see is that what unites these different components into a dynamic complex system is the interplay of different components over time and the central role of the infant–caretaker dyad resource regulating system. It is also worth noting that what we describe here can also be conceptualized as a process of adaptation to an adverse environment, which, in turn, calls on us to attend to the fact that adaptation and socially or personally desirable outcomes are not the same

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thing. Furthermore, it calls us to the fact that the outcomes we are studying here, whether molecular or sociodemographic, are affected by the structure of the environment, which is poorly specified and the organism’s engagement with it. It should be clear that these dynamic models are far more complex and demanding than could be enacted in a single study or even a host of studies. Certainly, we have not done it even in our own studies, in which we have looked at epigenetic changes in relation to caretaking and other factors longitudinally. At best, in any study, we only gain traction on some components and processes of the models, and it is already simplified. However, the reason for presenting the model is for it to serve as a cautionary note. While the epigenetic studies in this volume are formidable, we must not let them lead us into simplified thinking. Simplification is necessary, but so is keeping the dynamic complexity in mind, which will make it far more likely that we will grapple with and uncover a phenomenon of significance. While being thrilled with what we are finding out about molecular epigenetics, we need to remove our blinkers, or at least acknowledge we are wearing them. One of the vistas those blinkers were blocking is our view of the environment as a part of the dynamic structure of the organism itself. This realization calls us to reach higher and take more time to think about and study environmental factors as elements of organismal development, particularly how environments interact with each other and with the organism over time. We recognize that we cannot do complete empirical justice to the dynamic nature of the systems we are studying. But what we can do is keep them in mind and speak of their complexity and limitations, even if it is to only remind ourselves of the unstudied complexity, as well as the complexity we could study, but have thus far set aside.

References DiCorcia, J., & Tronick, E. (2011). Quotidian resilience: Exploring mechanisms that drive resilience from a perspective of everyday stress and coping. Neuroscience and Biobehavioral Reviews, 35, 1593–1602. Lester, B., Tronick, E., Nestler, E., Abel, T., Kosofsky, B., Kuzawa, C., … Wood, M. (2011). Behavioral epigenetics. Annals of the New York Academy of Sciences, 1226, 14–33. Miller, C. A., & Sweatt, J. D. (2007). Covalent modification of DNA regulates memory formation. Neuron, 53, 857–869. Tronick, E., & Hunter, R. G. (2016). Waddington, dynamic systems, and epigenetics. Frontiers in Behavioral Neuroscience, 10, 107. https://doi.org/10.3389/fnbeh.2016.00107.

CHAPTER

Principles of epigenetics and DNA methylation

1 Roberto Giorda

Scientific Institute IRCCS ‘‘E. Medea”, Molecular Biology Laboratory, Bosisio Parini, LC, Italy

Definition of epigenetics Waddington (Waddington, 1957) originally introduced the word “epigenetics” (derived from the word “epigenesis”) as “a suitable name for the branch of biology which studies the causal interactions between genes and their products which bring phenotype into being” ( Jablonka & Lamb, 2006). This definition implied that translating the genetic blueprint into a functional organism requires a control system whose mode of action is over and above, or in addition to, the classical genotype. At the present day, epigenetics is a very wide field of study covering virtually all aspects of biology, ranging from morphogenesis to transgenerational epigenetic inheritance. In the last decades, the distinction between genetic and epigenetic control systems has been associated with specific biochemical processes. DNA methylation and histone modifications, as well as noncoding RNAs and chromatin remodeling, are now considered as epigenetic mechanisms (Halfmann & Lindquist, 2010). It is worth noting that only DNA methylation is a direct modification of the DNA molecule, thus clearly distinguished from other epigenetic mechanisms, such as chromatin modification and non-coding RNAs, which are associated with, but separate from, DNA. For over 50 years DNA methylation, or more specifically cytosine methylation, has been studied extensively in mammals, initially as a mechanism for gene silencing via hyper-methylation of promoters associated with the CpG islands and later as a genome-wide modification. The existence of a multicellular organism depends upon the transformation of a static genetic code into function. The genetic information contained within the zygote is translated into a series of complex cellular signals that guide development. As each cell differentiates, unique transcriptional profiles are established and functional roles are specified. This complex transition from genotype to phenotype, termed epigenesis, results from interactions between the underlying genetic sequence, chemical modifications on DNA and chromatin, pre-existing pools of RNA and proteins, and environmental cues. Yet, how do these layers of information contribute to establishing a phenotype? Foremost, the DNA sequence itself interacts with the transcriptional machinery to produce the multitude of proteins necessary for a functioning organism. The human genome, for instance, contains approximately 21,000 protein-coding genes whose sequences are transcribed to generate our proteome (Clamp et al., 2007). Gene transcription occurs when RNA polymerase II interacts with these genomic regions to produce a transcript. This process is directed by numerous functional elements, such as enhancers and promoters, associated with the coding regions that interact with various activators and transcription factors to Developmental Human Behavioral Epigenetics. https://doi.org/10.1016/B978-0-12-819262-7.00001-5 # 2021 Elsevier Inc. All rights reserved.

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Chapter 1 Principles of epigenetics and DNA methylation

facilitate the assembly of the pre-initiation complex and subsequent transcription. Importantly, to generate the full repertoire of proteins required to build a fully functioning organism, each gene must be expressed in a precise spatiotemporal pattern. In order to achieve this, numerous control mechanisms have evolved to tightly regulate transcription. These mechanisms range from modulating transcription initiation and elongation, which can alter whether a gene is expressed and how a transcript is processed, to post-translational processes that can, for instance, fine-tune the level of a transcript through the degradation of an mRNA product. In addition, epigenetic control systems have evolved to coordinate the action of thousands of genes and to provide an interface between the genome and environment. Each of these layers represents an important mechanism through which gene expression can be organized. Epigenetics encompasses mechanisms and processes involved in facilitating transcriptional changes via various covalent modifications made to DNA itself or the histone proteins around which the DNA is wrapped. These post-translational modifications include methylation, acetylation, phosphorylation, ubiquitination, among many others. The manner in which these modifications can influence transcription is complex, and a given mark may lead to both gene activation and repression, depending on its location and the genomic context ( Jones, 2012; Kouzarides, 2007). Importantly, none of these modifications works in isolation. Instead, they interact to produce a unique epigenetic cellular “signature.” This signature, named epigenome, describes all epigenetic modifications found across the genome. For each cell type within an organism, and across different developmental stages and disease states, the epigenome will vary (Bernstein, Meissner, & Lander, 2007). Human epigenomic maps are being assembled through the analysis of patterns of DNA methylation, histone modifications, chromatin accessibility and RNA expression across diverse cell lineages (Bernstein et al., 2010; Kundaje et al., 2015). These large-scale endeavors are bringing a greater understanding of how the epigenome contributes to cell specification and development and how its alterations contribute to disease and phenotypic variation. However, defining the exact role that a given epigenetic modification plays in directing transcriptional changes remains a challenge. This difficulty will be discussed later in the context of DNA methylation.

Types of epigenetic modifications DNA cytosine methylation In all vertebrates, cytosine nucleotides in DNA can be modified by the addition of a methyl group to their 5-carbon. This modification typically occurs at cytosines in CpG dinucleotides and is prevalent in mammalian genomes where up to 80% of cytosines in the CpG context are methylated (Smith & Meissner, 2013). An extensive system of proteins in the cell write the methylation pattern on the DNA through de novo methylation (DNMT3A and DNMT3B) or removal of methyl groups (TET1, TET2, and TET3), and a set of factors faithfully copy methylation patterns during DNA replication (DNMT1 and UHRF1). In addition to these writing tools, cells contain many protein factors that read DNA methylation, such as MeCP2 (Lewis et al., 1992), and translate its annotation into functional information (Bergman & Cedar, 2013). It has been shown that DNA methylation plays a key role in processes such as X-inactivation, genomic imprinting and transposon silencing by repressing expression. This repression is accomplished

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by a system of methyl-group recognition proteins that recruit factors specifically programmed to generate a closed chromatin structure, thus making the gene less accessible to the transcription machinery (Domcke et al., 2015; Keshet, Lieman-Hurwitz, & Cedar, 1986). DNA methylation plays a seminal part in setting up the basic gene expression infrastructure of the entire organism by repressing sequences that are not meant to be expressed in the cell, including nontissue-relevant genes and various harmful viral sequences present in the genome. Through a process called reprogramming, methylation patterns derived from the gametes are erased before implantation of the embryo and a new methylation profile is established in each individual (Smith et al., 2012, 2014). The resetting process takes place in two distinct stages: First, during implantation almost the entire genome becomes methylated de novo, apart from a group of promoter sequences (CpG islands) that are specifically recognized and protected from this modification and therefore remain unmethylated (Cedar & Bergman, 2012). Although this event only occurs once, the resulting bimodal methylation pattern (Straussman et al., 2009) is maintained in all subsequent embryonic cell divisions through a simple semiconservative mechanism. The second stage in resetting methylation patterns involves introducing discrete changes in the basal landscape, a process that occurs in coordination with cell-lineage differentiation and organogenesis. Some genes unmethylated from the time of implantation undergo de novo methylation after being turned off at a specific stage of development or in a particular cell type (Straussman et al., 2009). In parallel, many promoters and other key regulatory regions become demethylated during tissue-specific differentiation (Hon et al., 2013; Ziller, 2013) Once the new methylation state is established, it generates a tissue-specific chromatin template that is extremely stable and maintained for the rest of the organism’s life. De novo methylation events always appear to be secondary to the activity of a repressor complex that initially silenced a gene and then recruited the methylation machinery. This secondary methylation event locks the decision in place by preventing any possibility of gene reactivation in subsequent cell generations (Epsztejn-Litman, 2008; Feldman et al., 2006). Thus, rather than being a strictly dynamic mechanism for regulating gene expression, DNA methylation can serve as a long-term memory of previous gene expression decisions that were mediated by transcriptional factors that might no longer be present in the cell. However, considerable variation in the distribution of methylation across organisms and additional evidence that methylation associates with active transcription indicates that the relationship between DNA methylation and transcription is complex and not fully understood. It has recently been shown in twin pairs (Van Baak et al., 2018) and unrelated individuals from several ethnic groups (Gunasekara et al., 2019) that systemic, tissue-independent, interindividual epigenetic variations occur at selected loci. Some of these variations may not be associated with underlying genetic variations and may be consider metastable epialleles.

DNA cytosine hydroxymethylation Although 5-hydoxymethylation has been known since several decades, it has recently attracted much attention, as it constitutes an intermediate in the active DNA demethylation process and is thought to play an active role in the regulation of gene expression. Furthermore, altered patterns of hydroxymethylation have been found in different diseases, notably cancer and neurodegenerative diseases (Delatte, Deplus, & Fuks, 2014; Wang, Tang, Lai, & Zhang, 2014). In general, the total levels of

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5-hydroxymethylcytosine observed across genomes are approximately tenfold lower compared to 5-methylcytosine, although large variations between tissues exist (Ruzov et al., 2011).

DNA adenosine methylation N6-methyl-20 -deoxyadenosine (m6dA) is the most abundant DNA modification in prokaryotes and regulates replication, transcription and transposition in bacteria (Iyer, Zhang, & Aravind, 2015). Until recently, m6dA had only been detected in unicellular eukaryotes (Hattman, 2005; Hattman, Kenny, Berger, & Pratt, 1978). However, thanks to recent technological improvements (Luo et al., 2016; Yao et al., 2017), it has now been shown to accumulate in a variety of eukaryotic genomes (Fu et al., 2015; Ma et al., 2019; Zhang et al., 2015). These observations have led to the speculation that m6dA may play an important role in the regulation of gene expression. Several recent studies have extended these findings with the demonstration that m6dA is not only present in the mammalian genome (Bredy et al., 2007; Liu et al., 2016; Usheva & Shenk, 1994; Yao et al., 2017), but is also highly dynamic and negatively correlates with LINE retrotransposon activity in both embryonic stem cells and in the brain of adult C57/Bl6 mice following exposure to chronic stress (Wu et al., 2016; Yao et al., 2017). Furthermore, it was recently shown that learning-induced accumulation of m6dA in post-mitotic neurons in mouse is associated with an increase in gene expression and is required for the formation of fear extinction memory (Li et al., 2019).

RNA modifications The epigenetic effect of RNA modifications easily rivals that of modifications of proteins and DNA in terms of research activity and breakthrough results (Frye et al., 2016). Consequently, the term “epitranscriptomics” was coined (Saletore et al., 2012; Schwartz, 2016; Witkin et al., 2015) to comprise post-transcriptional alterations that do not affect the RNA sequence itself (Liu & Pan, 2015; Roundtree & He, 2016). The dynamic nature of modifications in many non-coding RNAs and mRNAs gave rise to the idea that a previously undetectable code residing in these nucleic acids outside their sequence is ready to be deciphered (Helm & Motorin, 2017). The richest source of modifications, with up to 25% of its nucleotides modified, is transfer RNA, which also features the largest chemical variety and complexity in its modifications (Liu & Pan, 2015; Roundtree & He, 2016). Modifications range from simple methylations to sophisticated multistep transformations, including the incorporation of a range of low-molecular-weight metabolites from other pathways. Ribosomal RNA and messenger RNA feature a more limited range of modifications. The effect of these findings was compounded by the discovery of RNA demethylation activity, which introduced the possibility of dynamic RNA modifications and further emphasized their potential for regulation of gene expression.

Histone modifications In human cell nuclei, genomic DNA is packed into nucleosomes. Approximately 150 bp of DNA are wrapped around a histone octamer, consisting of two copies of “core” histones (H2A, H2B, H3 and H4), to form a single nucleosome (Luger, Dechassa, & Tremethick, 2012). Recent studies have revealed that histones function both positively and negatively in the regulation of gene expression

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(The ENCODE Project Consortium, 2012). This process is mainly governed by post-translational modifications on specific amino acid residues on histones. Within a nucleosome, the H3–H4 tetramer in particular forms a tight complex with DNA (Kimura, 2005). This stable tetramer allows posttranslational histone modifications to become inheritable epigenetic marks, much like direct modifications on DNA. All four core histones have long flexible N-terminal tails that project from their central structured domains. Most, but not all, of the amino acids that are subjected to modification reside on these tails, where they are presumably more accessible to “reader” proteins (Yun, 2011). Modifications on histones include acetylation, methylation and ubiquitination on lysine, methylation and citrullination on arginine, and phosphorylation on serine, threonine and tyrosine (Bannister & Kouzarides, 2011; Greer & Shi, 2012). In particular, acetylation and methylation on specific lysine residues are important for epigenetic gene regulation. In general, histone acetylation is associated with transcriptional activation, and deacetylation with transcriptional repression. Methylation of histones can positively or negatively affect transcription (Kimura, 2013).

RNAs In many organisms, intergenic or antisense transcription gives rise to different classes of small RNAs and long non-coding RNAs (lncRNAs) that have emerged as key regulators of chromatin structure in eukaryotic cells (Cech & Steitz, 2014; Moazed, 2009). In addition to their roles in RNA degradation and translational repression, small RNAs modify chromatin and target gene expression via RNA interference (RNAi) pathways (Hammond, 2001; Reinhart, 2002; Volpe, 2002). In many instances, nuclear RNAi pathways mediate histone or DNA methylation events that repress transcription. RNA also regulates chromatin modifications and structure through pathways that do not involve RNAi; some lncRNAs, and even some mRNAs, seem to contain signals that recruit chromatin-modifying complexes independently of small RNAs (Rinn & Chang, 2012). The formation of RNA scaffolds is a unifying mechanism by which small RNAs and lncRNAs modify chromatin structure and silence transcription. These silencing mechanisms represent powerful RNA surveillance systems that are able to detect and silence inappropriate transcription events, and provide a memory of these events via self-reinforcing epigenetic loops (Holoch & Moazed, 2015).

Environmental effects, cell metabolism and epigenetics Epigenetic memory During the course of development, epigenetic mechanisms establish stable gene expression patterns to ensure proper differentiation, but they also allow organisms to adapt to environmental changes. Previous experiences can affect the future responsiveness of an organism to a stimulus over long time scales and even over generations. Dynamic epigenetic regulation is often described as epigenetic memory: a heritable change in gene expression or behavior induced by a previous stimulus (D’Urso & Brickner, 2014). The stimulus can be either developmental or environmental. Memory occurs by multiple mechanisms, but often requires chromatin-based changes such as DNA methylation, histone modifications or incorporation of variant histones (Suganuma & Workman, 2011). Different types of epigenetic memory utilize related mechanisms over different time scales (Bantignies, 2003; Jablonka & Raz, 2009; Kiefer, 2007; Youngson & Whitelaw, 2008). Cellular

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memory deals with mitotically inheritable transcriptional states established during development in response to developmental cues. In the developing Drosophila embryo, expression of homeotic genes is established by transient expression of the segmentation transcription factors. After these factors turn over, the expression pattern of the homeotic genes and many other genes is maintained through many cell divisions by the action of epigenetic regulators such as Trithorax, which catalyzes methylation of histone H3, lysine 4 to promote sustained expression, and Polycomb, which catalyzes methylation of histone H3, lysine 27 to promote stable repression. Transcriptional memory involves mitotically heritable changes in the responsiveness of organisms to environmental stimuli based on their previous experiences. This mechanism, requiring changes in chromatin structure and a physical interaction with nuclear pore proteins, allows cells to mount a more rapid or robust transcriptional response to an environmental challenge that they have previously experienced (Brickner et al., 2007; Gialitakis, Arampatzi, Makatounakis, & Papamatheakis, 2010). For example, the interferon gamma (IFN-γ)-induced class II major histocompatibility gene HLA-DRA is much more rapidly and robustly induced if cells have previously been exposed to IFN-γ (Gialitakis et al., 2010). This response persists for at least four mitotic generations and is associated with dimethylation of H3K4 in the HLA-DRA promoter. Of the 650 genes that are induced by IFN-γ, 250 exhibit faster or stronger activation upon subsequent treatment with IFN-γ (Light et al., 2013). Transgenerational memory affects meiotically heritable changes in gene expression and physiology of organisms in response to experiences in the previous generations, despite the dramatic global changes in chromatin structure and transcription associated with meiosis, gametogenesis and embryogenesis. For example, in Drosophila, the transcription factor dATF-2 binds to heterochromatin and is required for H3K9 methylation (Seong, Li, Shimizu, Nakamura, & Ishii, 2011). Upon heat or osmotic shock, stress-activated protein kinases phosphorylate dATF-2 and the protein is released from heterochromatin, leading to loss of H3K9me2 and increased transcription (Seong et al., 2011). The change in localization of dATF-2 is transmitted to the next generation (Livingstone, Patel, & Jones, 1995; Maekawa, Jin, & Ishii, 2010). If the stress is applied over multiple generations, the effect persists over several additional generations before gradually returning to the original state (Seong et al., 2011).

Environmental effects Development can be regulated by environmental conditions. For example, flowering in certain plants requires previous exposure to cold, a phenomenon called vernalization. During this process, plants become competent to flower only after prolonged exposure to the winter cold, an epigenetic change that ensures that flowering occurs under favorable conditions in spring (Kim, Doyle, Sung, & Amasino, 2009; Ream, Woods, & Amasino, 2012). In Arabidopsis thaliana, flowering is controlled by the expression of the FLOWERING LOCUS C (FLC) gene, which encodes a transcription repressor that prevents flowering. In the autumn, FLC expression is high. Extended exposure to cold represses FLC transcription; FLC mRNA gradually decreases during winter and stays low as temperatures rise in the spring (Sung et al., 2006). Extended cold induces the VERNALIZATION INSENSITIVE 3 (VIN3) gene, which interacts with the Polycomb homologue VERNALIZATION 2 (VRN2) to promote methylation of H3K27 at the FLC locus and reduce its expression (Sung et al., 2006; Sung & Amasino, 2004). Methylation of H3K9 and H3 de-acetylation are also required for full repression of FLC (Sung et al., 2006). This process also involves the noncoding RNAs COLDAIR and COOLAIR (Heo & Sung, 2011; Swiezewski, Liu, Magusin, & Dean, 2009; Turck & Coupland, 2011). Thus,

Environmental effects, cell metabolism and epigenetics

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environmental cues can influence developmental timing through a mechanism involving chromatin modification. The repression is mitotically stable through a large number of cell divisions in the absence of inducing signals creating a new epigenetic state (Kim et al., 2009; Ream et al., 2012). In mammals, environmental factors experienced in one generation can affect the behavior of unborn offspring. For example, environmental stresses such as high exposure to predators reduce maternal care in female rats, as measured by licking/grooming and arched-back nursing (LG-ABN) (Francis, 1999). Pups reared under conditions of low maternal protection and LG-ABN are more fearful and more sensitive to environmental stresses. These pups exhibit less LG-ABN with their offspring than normal pups, even in the absence of environmental stressors and this behavior is transmitted to future generations (Francis, 1999). Pups reared under low LG-ABN conditions show reduced levels of glucocorticoid receptor (GR) in the hippocampus, which controls expression of approximately 300 genes and is responsible for dampening the stress response (Weaver et al., 2004; Weaver, Meaney, & Szyf, 2006). Low LG-ABN leads to decreased binding of the transcriptional activator NGFI-A, increased DNA methylation and histone deacetylation and reduced expression of the GR gene over the first week of life (Weaver et al., 2004, 2007). These epigenetic marks persist and dictate GR expression for the rest of the animal’s life (Weaver et al., 2004, 2007). However, the phenotypic and molecular effects of stress are reversible: inhibition of histone deacetylases (HDACs) leads to decreased DNA methylation, increased expression of GR and reduced stress-sensitivity (Weaver et al., 2004). Paternal behavior and environment can also influence the physiology of the offspring through epigenetic mechanisms (Zhang, 2019). A number of studies from multiple groups have shown that injection of sperm RNAs in the zygote induces offspring phenotypes that fully or partially recapitulate the paternal environmental input, including behavioral changes, obesity and altered glucose metabolism (Chen et al., 2016; Gapp et al., 2014; Gapp et al., 2018; Grandjean et al., 2016; Zhang, 2019). The unique signature of mammalian sperm RNAs is developmentally and spatially organized and controlled by genetic and environmental factors (Chen et al., 2016; Zhang, 2019). The RNA molecules are wreathed by various modifications that may form a “sperm RNA code” to program specific offspring phenotypes during embryonic development (Zhang, 2019). This process is probably accomplished by RNA sequence-specific and/or structure-specific interactions with other RNAs or epigenetic, transcriptional and translational mechanisms in the sperm and early embryo.

Cell metabolism, diet and microbiota The substrates used to modify nucleic acids and chromatin are affected by nutrient availability and the activity of metabolic pathways. Therefore, there is an intriguing, complex connection between metabolism and epigenetics (Reid, Dai, & Locasale, 2017). More than 100 distinct covalent modifications have been identified on chromatin, DNA and RNA, many of them having substantially documented or emerging functional annotation. Addition and removal of these modifications are mostly catalyzed by enzymes whose activities are mediated by the availability of substrates, cofactors and allosteric regulators derived from metabolic pathways. These pathways may include serine-glycine-one-carbon metabolism and particularly the methionine cycle, the tricarboxylic acid cycle, β-oxidation, glycolysis, and hexosamine biosynthesis. Thus, each epigenetic modification can be affected by metabolites from multiple metabolic pathways. For instance, enzymes involved in histone and DNA methylation and demethylation can be regulated by both methionine metabolism and the tricarboxylic acid cycle, thus enabling the epigenome to respond to fluctuations of the entire metabolic network.

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Chapter 1 Principles of epigenetics and DNA methylation

Alterations in the diversity of the microbiota have been widely associated with many chronic human conditions, highlighting the fact that microbiota-derived signals act as environmental cues influencing host physiology (Brestoff & Artis, 2013; Clemente, Ursell, Parfrey, & Knight, 2012; Harakeh et al., 2016). Close association between the microbiota and the single layer of intestinal epithelial cells that line the intestine is necessary to regulate essential biological processes such as metabolism, nutrient uptake, neuronal development and angiogenesis (Cryan & Dinan, 2012; Stappenbeck, Hooper, & Gordon, 2002; Tremaroli & B€ackhed, 2012). Epigenomic modifications represent a potentially significant interface through which the microbiota can dynamically interact with the host genome. Microbiota-derived metabolites, such as short-chain fatty acids, can probably modulate host cellular processes through direct inhibition of HDACs or activation of G-protein-coupled receptors (Kim et al., 2013; Macia et al., 2015; Maslowski et al., 2009), although details of the process are still not completely understood.

Methods for methylation analysis Epigenetic phenomena are mediated by a variety of molecular mechanisms including DNA methylation, coding and noncoding RNA production and expression, location of RNA polymerases, transcription factors and other DNA-binding proteins, histone modifications, chromatin accessibility, as well as the spatial organization of the genome (Tost, 2016). The different layers of epigenetic modifications, posttranscriptional histone modifications, histone variants, and DNA methylation, are closely entwined and stabilize each other to ensure the faithful propagation of an epigenetic state over time and especially through cell division. Therefore, the best approach to epigenetic analysis is multi-pronged, as demonstrated by the ENCODE (www.encodeproject.org), modENCODE (www.modencode.org), and Roadmap (http://www.roadmapepigenomics.org/) epigenomics mapping projects.

Chromatin immunoprecipitation Chromatin immunoprecipitation (ChIP) (Gilmour & Lis, 1984) is a well-established method in cellular biology to study the specific interaction between a protein of interest and genomic DNA and it has been extensively used to identify transcription factor binding sites (Gerstein et al., 2012). Briefly, proteins are chemically cross-linked to the DNA in order to conserve the in vivo chromatin architecture. Chromatin is then extracted and randomly fragmented by sonication into 200–600 base pair fragments. DNA-protein complexes are immunoprecipitated using a specific antibody and protein A/G agarose resin. Finally, covalent cross-links are reversed by heating and DNA is purified using RNase A and proteinase K treatment. At this point, a small amount of purified DNA is available and can be subsequently analyzed by qPCR, microarrays (ChIP-on-chip), or NGS (ChIP-seq) to obtain a genome-wide picture of the DNAprotein binding events (Barski et al., 2007; Gerstein et al., 2012; Mikkelsen et al., 2007).

Nucleosome positioning Positioning of nucleosomes and remodeling of chromatin play key roles in the coordination of the correct gene expression program. Nucleosome positioning depends on (among others) the underlying DNA sequence, ATP-dependent nucleosome remodelers, DNA-binding proteins, the RNA polymerase

Methods for methylation analysis

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II transcription machinery, and their interactions. The core enhancer, promoter, and terminator regions of genes are typically depleted of nucleosomes, whereas most of the genomic DNA is occupied (Struhl & Segal, 2013). Consequently, the analysis of chromatin accessibility and nucleosome positioning is essential for the understanding of transcriptional regulation, and can be used for the identification of gene regulatory elements and for the analysis of their changes in disease. In most cases, information on nucleosome positioning is obtained by enzymatic digestion, chemical cleavage, or immunoprecipitation of chromatin, followed by next-generation sequencing of the resulting DNA fragments (Schmidl, Rendeiro, Sheffield, & Bock, 2015) or derived from chromatin accessibility profiles obtained with DNAseI-seq (He et al., 2014) or ATAC-seq (Buenrostro et al., 2013).

DNA cytosine methylation analysis As a covalent DNA-based modification that can be analyzed on DNA isolated from most biological samples, seems to be stable even under prolonged storage conditions, and is technically relatively easy to investigate, DNA methylation has been intensively studied since the 1980s and is the best-studied epigenetic mark. A large number of technologies have been developed for the study of DNA methylation, and the choice of the method largely depends on the specific research question. However, basic challenges remain, including the need for appropriate determination of the cellular composition of each sample (since each cell type is associated to its own specific DNA methylome). Another fundamental question for human studies is under which conditions and to which extent an accessible tissue such as blood, urine, or saliva can be used as a surrogate for an unavailable target organ. While technical advances have brought to the general adoption of NGS-based solutions as readout platforms for DNA methylation analysis, with the exception of the highly popular epigenomics arrays, the main approaches for the discrimination of methylation have changed very little. All current assays are based on four main principles: 1. Methylation-specific restriction endonucleases, enzymes that are inhibited by the presence of methylated cytosines in their recognition sequence (Bird & Southern, 1978) can be used, often in combination with their methylation-insensitive isoschizomers. Although methods based on methylation-specific restriction enzymes are simple and might additionally be able to distinguish between methylcytosine and its oxidative derivatives, they can only analyze CpG sites found within the recognition sequences of the enzymes used (Fazzari & Greally, 2004). 2. The methylated fraction of a genome can be enriched by precipitation with an antibody, usually bead-immobilized, specific for 5-methylcytosine following a protocol similar to chromatin immunoprecipitation and analyzed on microarrays (Weber et al., 2005) or by sequencing (Down, 2008; Feber et al., 2011). 3. Methylated DNA can be affinity purified with methyl-binding domain (MBD) proteins such as MeCP2 (Brinkman et al., 2012) or MBD2 in combination with MBD3L1 (Rauch & Pfeifer, 2005). 4. The most widely used approach consists of the chemical modification of genomic DNA with sodium bisulfite. This reaction induces hydrolytic deamination of unmethylated cytosines to uracil, while methylated cytosines are resistant to the conversion (Frommer et al., 1992; Shapiro, DeFate, & Welcher, 1974). This method converts the methylation signal into a sequence difference. After PCR and sequence analysis, the methylation status at any given position is expressed in the ratio of C (former methylated cytosine) to T (former unmethylated cytosine) and can be analyzed as

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a virtual C/T polymorphism spanning the entire allele frequency spectrum from 0% to 100%. The chemical treatment unavoidably degrades a significant amount of the input DNA, making the use of this technique problematic when a limited amount of material is available. Incomplete conversion, PCR bias due to widely different CpG content (Grunau, 2001; Warnecke et al., 1997) or clonal amplification (Zhang & Jeltsch, 2010) are additional problems that need to be taken into consideration. In addition, it should be noted that standard bisulfite conversion protocols cannot discriminate between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine. These four assay principles, depending on the requirement for resolution, coverage, quantitation, and throughput, have been combined with PCR, microarray, or sequencing-based readout technologies to generate a wealth of applications. NGS-based methods have currently replaced most other technologies because they usually require lower starting amounts of DNA, fewer amplification steps, and generate vastly higher coverage.

Locus-specific DNA methylation analysis Locus-specific analysis is currently used in clinical settings to analyze imprinting disorders and X-chromosome inactivation, but can also provide low-cost, high-throughput solutions for the validation of global analysis results. High-resolution melting analysis (Wojdacz, Dobrovic, & Hansen, 2008) compares the melting profile of a sample to a set of calibration standards. This technique, which does not provide DNA methylation profiles at single-nucleotide resolution, has been applied to the detection of aberrant methylation profiles in imprinting disorders (Alders et al., 2009; White, Hall, & Cross, 2007; Wojdacz, Dobrovic, & Algar, 2008), cancer (Balic et al., 2009; Gupta et al., 2014), and epidemiological studies analyzing environmental exposure (Li et al., 2015, 2016). Methylation-specific multiplexed ligation probe amplification (Nygren, 2005) has been used for the parallel analysis of up to 40 loci permitting a comprehensive analysis of DNA methylation aberrations in imprinting disorders (Dikow et al., 2007; Henkhaus et al., 2012; Priolo et al., 2008), the combined analysis of genetic and epigenetic alterations in imprinting disorders (Scott et al., 2007), as well as tumor analysis (H€ omig-H€ olzel & Savola, 2012; Serizawa et al., 2010). Briefly, two oligonucleotides with universal primer binding sites are annealed to a target region and ligated in the presence of complete target complementarity. A methylation-specific enzyme, digesting unmethylated templates and reducing the amount of ligated product, is added to the ligation reaction. Following amplification of the sample, a semiquantitative readout allowing the detection of methylation differences of 10% or more compared to the standards is performed using capillary electrophoresis. PCR amplification following methylation-specific restriction digestion is an alternative strategy that requires substantially less DNA and no prior bisulfite conversion treatment and works well as a rapid screening tool (Singer-Sam, LeBon, Tanguay, & Riggs, 1990). Multiple targets can be simultaneously analyzed by locus-specific multiplex PCR following methylation-specific restriction digest of genomic DNA (Melnikov, 2005). Additional information can be achieved by digesting the DNA with methylation-specific restriction enzymes, thus allowing to discriminate complete, partial, or absent methylation in the sequence (Yamada, 2004). Combined with microfluidic preparation of the PCR products, this method allows the analysis of a large number of target sequences from a very limited amount of starting DNA (Wielscher et al., 2015). To minimize false-positive results due to incomplete

Methods for methylation analysis

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digestion, DNA is overdigested using a combination of several restriction enzymes, which means that two or three restriction sites need to be present within the target sequence. Pyrosequencing (Harrington, Lin, Olson, & Eshleman, 2013; Ronaghi, 1998) is a quantitative realtime sequencing method that allows for the accurate measurement of methylation levels in a sequence of up to 100 bp (Dupont, 2004; Tost & Gut, 2007). Pyrosequencing is based on the presence or absence of the incorporation of a nucleotide during primer extension (Ronaghi, 1998, 2001). For DNA methylation analysis, a region of interest is amplified after bisulfite conversion with a standard PCR with one of the two primers being biotinylated. The biotinylated strand is captured on streptavidin-covered beads, the complementary strand is denatured and washed away, and a sequencing primer is annealed to the now single-stranded template before starting the pyrosequencing reaction. The limit of detection of pyrosequencing has been evaluated at around 5% for the minor allele. Mass spectrometry (MS) provides an attractive solution for nucleic acid analysis in general and DNA methylation analysis in particular, as it enables direct, rapid, and quantitative detection of DNA products measuring their molecular weight, rather than relying on an indirect readout. Liquid chromatography MS/MS is one of the most accurate methods to precisely quantify the global level of CpG methylation in samples of clinical interest (Godderis et al., 2015), while matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-(TOF)-MS) (Karas & Hillenkamp, 1988) has been the most widely used instrumental platform for the analysis of DNA methylation patterns in specific regions of interest. Although amenable to very high-throughput through a large degree of automation and highly parallel analyses, MALDI-MS does not permit genome-wide analyses. However, due to their multiplexing capabilities, the quantitative readout of the relative abundance of products, and their simple and reliable procedure, the MALDI mass spectrometry-based assays are valuable tools for the identification and validation of methylation variable positions in a genetargeted approach (Ragoussis, Elvidge, Kaur, & Colella, 2006). Methylation-specific PCR allows detection of methylated molecules in the presence of an excess of normal (usually unmethylated) DNA (Herman et al., 1996). This method allows the amplification of virtually any CpG site after bisulfite treatment with two pairs of primers for amplification that are complementary to the methylated or the unmethylated sequences. The presence or absence of an amplification product analyzed on a conventional agarose gel reveals the methylation status of the CpGs underlying the amplification primers. A variety of quantitative PCR methods using this principle is available (Cottrell, 2004; Eads, 2000). Amplicon bisulfite sequencing with the Sanger protocol, coupled with PCR amplicon cloning, had been the “gold standard” technology for DNA methylation analysis in the past, but it severely lagged behind the above-mentioned techniques in coverage, cost-efficiency, and throughput. Today amplicon bisulfite sequencing using NGS instruments has become the choice approach for the validation of genomic regions following methylome analyses and for answering hypothesis-driven research questions. In addition, due to the sequencing of clonal clusters generated in the sequencing machine, this method provides information on co-methylation patterns of individual molecules within the limits of the length of the reads (up to 600 base pairs in paired-end modus on a MiSeq). With a current output of 50 M reads for a MiSeq, yielding between 3.8 and 15 GB of sequence depending on the sequencing kit used, several tens to hundreds of target regions can be analyzed simultaneously depending on the desired coverage and number of samples analyzed in parallel. In general, PCR amplification products are prepared from bisulfite-treated DNA using a two-round amplification protocol with a first pair of target region-specific primers that contain tag sequences to

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label the created amplicons with sequences compatible for subsequent PCR amplification with fulllength Illumina p5 and p7 adaptor sequences. Molecular barcodes and full-length adaptor sequences are added in a second round of amplification after pooling all amplicons from a sample. Amplification of multiple products in parallel can also be performed using microfluidic tools such as the Fluidigm access array, capable of amplifying simultaneously 48 target regions in 48 samples starting from as little as 50 ng of DNA (Paliwal et al., 2013). In general, bisulfite sequencing using next-generation sequencers with their digital readout enables a more accurate quantification of DNA methylation levels, as they show reduced quantitation errors and lower standard deviations compared to conventional (analog) sequencing approaches (Masser, Berg, & Freeman, 2013). Sequencing depth of 1000  is sufficient for a precise measurement of the DNA methylation levels (Masser et al., 2013). Up to 96 samples with different regions of interest can currently be analyzed in parallel using conventional multiplexing strategies, such as dual indexing, and new indexing sets will further increase multiplexing capabilities. Freely available pipelines, such as Bismark (Krueger & Andrews, 2011) or BiQ Analyzer (Lutsik et al., 2011), enable convenient and standardized analysis of the sequencing results including demultiplexation of individual samples, alignment to target regions, and estimation of the degree of DNA methylation, allowing even users without particular bioinformatics expertise to analyze DNA methylation in target regions.

Whole methylome analysis Whole-genome bisulfite sequencing can be considered as the current gold standard for genome-wide identification of differentially methylated regions at single-nucleotide resolution. This technology is currently used in a number of international large-scale projects to map the methylome of various human tissues and cell types (Adams et al., 2012; Roadmap Epigenomics Consortium, 2015; Schultz et al., 2015). However, this unprecedented quantitative and spatial resolution comes at a cost, as it requires substantial sequencing power to obtain a proper and even coverage and necessitates bioinformatic expertise and resources. Although low-coverage bisulfite sequencing can yield some information about global DNA methylation alterations, it does not yield reliable locus-specific information (Popp et al., 2010). The most widely used protocol consists of fragmentation of genomic DNA, adapter ligation, bisulfite conversion, and limited amplification using adapter-specific PCR primers. An alternative protocol substitutes mechanical fragmentation with “tagmentation” using a trasposase that randomly fragments DNA and simultaneously tags fragments’ ends with adaptors that can be used for subsequent amplification (Adey & Shendure, 2012; Wang et al., 2013). Protocols performing the adaptor tagging after bisulfite treatment have been devised and shown to enable efficient library construction from as little as 125 pg of DNA (Miura, Enomoto, Dairiki, & Ito, 2012) and even single cells (Smallwood et al., 2014). Approximately 10% of the CpG dinucleotides in the mammalian genome still remain refractory to alignment of bisulfite-converted reads.

Selected genome-wide methylation analysis Whole methylome analysis covering each CpG in the genome at single-base resolution remains a complex and resource intensive endeavor when aiming for a reasonable coverage of at least 20–30 on any given CpG site and is, therefore, not yet feasible in large cohorts. Furthermore, more than half of all reads do not contain even a single CpG dinucleotide (Ziller, 2013). In addition, many CpGs will not

Methods for methylation analysis

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show variable DNA methylation under any condition. Therefore, several approaches that make use of sequence features, such as CpG density, or use antibody, protein, or chemical labeling-based methods to enrich the methylated or unmethylated fraction of the genome have been developed to concentrate on the “potentially informative” portion of the genome. Reduced representation bisulfite sequencing (RRBS) (Gu et al., 2011; Meissner, 2005; Meissner et al., 2008) is currently the most popular alternative to WGBS, as it requires significantly less sequencing, and CpG-rich regions enriched by restriction enzyme digestion are relatively well covered (Bock et al., 2010; Harris et al., 2010; Meissner et al., 2008). RRBS makes use of a methylation-insensitive restriction endonuclease with a CG-rich recognition sequence, such as MspI, which cuts between the two Cs in the CCGG target sequence, frequently found in CpG islands and promoter regions. After a size selection step, the generated DNA fragments are used for a standard library construction using methylated adaptors followed by bisulfite conversion. RRBS interrogates approximately 80% of CpG islands and 60% of promoters, corresponding to only  12% of the 28 million (M) CpGs in a human genome. Target capture is an alternative approach for large-scale methylome sequencing that uses long probes to capture bisulfite-treated DNA in regions of interest. This capture can be performed using either oligonucleotide microarrays (Hodges et al., 2009) or solution- based hybridization (Lee et al., 2011), and the capture can be performed on the bisulfite-converted fragments (Hodges et al., 2009) or prior to bisulfite conversion (Lee et al., 2011). Depending on the specific variation, the method may require large amounts of starting material, fail to capture a large enough percentage of CpG-rich regions, influence capture efficiency leading to distorted DNA methylation measurements. Affinity enrichment of methylated regions using antibodies specific for 5meC (in the context of denatured DNA) or using methyl-binding proteins with affinity for methylated native genomic DNA proved to be particularly powerful tools for comprehensive profiling of DNA methylation in complex genomes. Affinity purification of methylated DNA was first demonstrated with the methyl-binding protein MECP2 (Cross, Charlton, Nan, & Bird, 1994). Enrichment of methylated regions by immunoprecipitation of denatured genomic DNA with an antibody specific for methylated cytosine (Mukhopadhyay, 2004), followed by hybridization to either a tiling array or a CpG island array, is referred to as MeDIP (Weber et al., 2005, 2007), mDIP (Keshet et al., 2006) or mCIP (Gebhard, 2006). More recently, approaches have been developed that use higher affinity methyl-binding proteins, including multimerized MBD1 domains ( Jorgensen, 2006) and protein complexes that contain the short isoform of MBD2 (MBD2b) and MBD3l1 (the latter approach is called the methylated CpG island recovery assay (MIRA) (Rauch et al., 2008; Rauch & Pfeifer, 2005, 2009). These affinity-enrichment methods had originally been combined with array hybridization, in which the input DNA and enriched DNA are labeled with different fluorescent dyes. As with other array-based analyses, enrichment techniques are now rapidly shifting to analysis by next-generation sequencing techniques (Down et al., 2008). Affinity-based methods allow for rapid and efficient genome-wide assessment of DNA methylation, but they do not yield information on individual CpG dinucleotides and require substantial experimental or bioinformatic adjustment for varying CpG density at different regions of the genome.

Epigenotyping arrays With whole-genome bisulfite sequencing being not yet affordable at a large scale and the low resolution of antibody and methyl-binding protein enrichment of methylated DNA, epigenotyping technologies have emerged as an alternative tool for the identification of differentially methylated regions and DNA

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methylation-based biomarkers. Epigenotyping technologies, such as the Infinium Human Methylation 450 K or EPIC BeadChip (Illumina Inc., CA, USA), generate a methylation state-specific “pseudoSNP” through bisulfite conversion, thereby translating differences in DNA methylation patterns into sequence differences that can be analyzed using quantitative genotyping methods (Bibikova et al., 2009, 2011). These arrays have been widely used for large-scale high-throughput studies as they employ highly standardized protocols that can be implemented with a large degree of automation into existing genotyping pipelines. Nonetheless, these arrays analyze only a small number of the 28 million CpG sites of the human genome, and no commercial arrays for the analysis of non-human samples are currently available. As all bisulfite-based analysis techniques, they are also unable to differentiate between cytosine methylation and hydroxymethylation.

Direct readout of DNA methylation The two currently available direct readout technologies, Pacific Biosciences (PacBio) (Flusberg et al., 2010) and Oxford Nanopore, promise to sequence longer DNA molecules at single-molecule level, at lower cost and higher speed than existing methods. Both can provide information on DNA methylation, hydroxymethylation, and other DNA modifications in the same experiment. PacBio long-read sequencing has low signal-to-noise ratio for 5mC modifications (Beaulaurier et al., 2015) and requires relatively high coverage for calling modifications, making this method more suitable for small genomes. The method can also be adapted for high efficiency bisulfite sequencing (Yang & Scott, 2017). Several proof-of-concept studies on Oxford Nanopore sequencing techniques have demonstrated the feasibility to detect DNA modifications based on the electric signal characteristics when a modified DNA molecule passes through the nanopores (Laszlo et al., 2013; Schreiber et al., 2013). However, this technology did not initially yield sufficiently accurate results. The performance of 5mC/6-methyladenine prediction was recently improved using more sophisticated deep neural networks (Liu et al., 2019) achieving up to 99.9% average precision for 5mC detection.

DNA methylation analysis of cell-free circulating DNA Biomarkers capable of distinguishing subjects in a disease state from healthy individuals must be specific, sensitive, and detectable in specimens obtained through minimally invasive procedures to be clinically applicable. Disease-specific DNA molecules can be found in various body fluids, such as urine or sputum, or as circulating cell-free DNA molecules that can be isolated from the serum/plasma of cancer patients (Diaz & Bardelli, 2014; Heitzer, Ulz, & Geigl, 2015; Schwarzenbach, Hoon, & Pantel, 2011), individuals with autoimmune diseases (Chan et al., 2014), as well as individuals with other complex diseases (Lehmann-Werman et al., 2016). DNA methylation is an attractive marker for the analysis of cell-free DNA as methylation changes are widespread in many diseases, including cancer. Methylation-specific PCR (Hoon et al., 2004) and particularly methylation-specific real-time PCRbased methods such as MethyLight (Begum et al., 2011; Campan et al., 2011), HeavyMethyl (Church et al., 2014), as well as methylation-specific high-resolution melting analysis (Yang et al., 2015) have proven suitable for the detection of very low levels of aberrant methylation in circulating DNA. Next-generation sequencing approaches are becoming more and more used to identify and monitor the presence of mutations in cell-free DNA isolated from plasma (Crowley, Di Nicolantonio, Loupakis, & Bardelli, 2013; Heitzer et al., 2015; Newman et al., 2014). Hypomethylation of cell-free

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DNA isolated from plasma, as assessed by sequencing of bisulfite-treated DNA, yielded a diagnostic sensitivity of 74% and a specificity of 94% for non-metastatic hepatocellular cancer cases (Chan et al., 2013a). Copy number alterations can also be accurately deduced from low-coverage sequencing of cell-free circulating DNA (Chan et al., 2013b; Heitzer et al., 2013). As these copy number changes are retained in bisulfite-treated DNA, bisulfite sequencing can inform on both methylation status and copy number status at no additional cost (Chan et al., 2013a).

Single-cell epigenomics The recent development of single-cell epigenomics methods is beginning to allow us to address key developmental questions (Kelsey, Stegle, & Reik, 2017). Single-cell epigenome methods can identify open or closed chromatin, including nucleosome positioning (Buenrostro et al., 2015; Cusanovich et al., 2015; Guo et al., 2017; Jin et al., 2015; Pott, 2017). From these results, we can infer the probability of certain transcription factors to bind to specific DNA sequences within individual cells. Methods, such as single-cell chromatin immunoprecipitation sequencing, are being developed that allow direct transcription factor binding assays. Functional states of the genome also depend on the way DNA in each cell is organized into higher-order chromatin, which can be determined by single-cell high-throughput chromosome conformation capture (Nagano et al., 2013). Finally, various DNA modifications, such as methylation and hydroxymethylation, can be located in most areas of the genome at single-nucleotide resolution at single-cell level (Clark et al., 2018; Farlik et al., 2015; Guo et al., 2013; Mooijman, 2016; Smallwood et al., 2014; Rotem, 2015;Zhu, 2017). The techniques described above have been combined into single-cell multi-omics (Macaulay, Ponting, & Voet, 2017), which can reveal further connections. Hence, genome sequencing together with transcriptome sequencing can reveal how genetic variation is related to transcriptional variation (Dey et al., 2015; Macaulay et al., 2015). Genome-scale methylome coupled with transcriptome sequencing (Angermueller et al., 2016; Hu et al., 2016) has identified widespread associations between epigenetic marks and transcriptional heterogeneity. The latest incarnation, triple-omics, combines genome, methylome, and transcriptome assays (Clark et al., 2018; Hou et al., 2016).

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CHAPTER

From animal to human epigenetics

2

Erica Berretta, Debora Cutuli, Daniela Laricchiuta, and Laura Petrosini IRCCS Santa Lucia Foundation, Rome, Italy

A rat story: Behavioral epigenetics beginnings Human neurodevelopment is a dynamic and protracted process. It starts in the pre-natal life, driven by genetic information, and continues unfolding following region-specific pathways up to early adulthood (Gogtay et al., 2004; Koenderink & Uylings, 1995; Petanjek et al., 2011). Especially during the pre-natal and early post-natal life, the developing brain depends on and is sensitive to external inputs that shape its architecture and fine-tune neural connectivity patterns according to environmental requirements (Branchi & Cirulli, 2014; Fox, Levitt, & Nelson, 2010; Hensch, 2005; Takesian & Hensch, 2013; Teicher, Samson, Anderson, & Ohashi, 2016). Environmental inputs are therefore critical for a normative development. On the other hand, adverse conditions occurring during sensitive periods for the nervous system maturation can interact with genetic make-up and bias developmental trajectories toward maladaptive outcomes, as demonstrated by increased occurrence of psychopathology and psychiatric conditions following childhood neglect, maltreatment and abuse (Benjet, Borges, & Medina-Mora, 2010; Bick & Nelson, 2016; Cohen, Brown, & Smaile, 2001; Green et al., 2010; Kessler et al., 2010; Widom, 1999). It has been suggested that modifications of adult brain function and behavior changes induced by early experiences can be determined by changes in the epigenetic status of specific genes (Bale et al., 2010; Fraga et al., 2005; Maccari, Krugers, Morley-Fletcher, Szyf, & Brunton, 2014; Tsankova, Renthal, Kumar, & Nestler, 2007). In fact, epigenetic mechanisms, including DNA methylation, histone modifications, and non-coding RNAs regulation can be affected by various extrinsic factors, providing a molecular link between external cues and gene expression (Kang et al., 2011; Maze et al., 2014; Nord, Pattabiraman, Visel, & Rubenstein, 2015; Shibata, Gulden, & Sestan, 2015). Human studies evidence that individuals exposed to adversity in early post-natal life (Romens, McDonald, Svaren, & Pollak, 2015; Tyrka, Price, Marsit, Walters, & Carpenter, 2012; van der Knaap et al., 2014), or during pre-natal life (Mulligan, D’Errico, Stees, & Hughes, 2012; Perroud et al., 2014) exhibit altered methylation of genes involved in hypothalamic-pituitary-adrenal (HPA) axis functionality, as the NR3C1 gene coding glucocorticoid receptors (GR), a key element for the homeostasis of stress response system (Herman et al., 2016; Sapolsky, Meaney, & McEwen, 1985). In turn, altered NR3C1 methylation levels have been associated to emotional and behavioral problems, Developmental Human Behavioral Epigenetics. https://doi.org/10.1016/B978-0-12-819262-7.00002-7 # 2021 Elsevier Inc. All rights reserved.

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externalizing and internalizing symptoms (Cicchetti & Handley, 2017; Dadds, Moul, Hawes, Mendoza Diaz, & Brennan, 2015; Parade et al., 2016; Perroud et al., 2014; van der Knaap, van Oort, Verhulst, Oldehinkel, & Riese, 2015). Further, reduced levels of NR3C1 messenger RNA (mRNA) and mRNA transcripts, as well as increased cytosine methylation of the NR3C1 promoter were found in suicide victims with a history of childhood abuse compared to suicide victims without childhood trauma and controls (McGowan et al., 2009). Finally, longitudinal studies on very preterm infant admitted to neonatal intensive care unit, and thus subjected to pain-related stress and maternal separation, evidence an altered serotonin transporter gene (SLC6A4) methylation status, predictive of enhanced socio-emotional stress reactivity and associated with less-than-optimal score at Personal-Social scale of Griffith Mental Development Scales at 12 months of age (Fumagalli et al., 2018; Montirosso et al., 2016; Provenzi, Guida, & Montirosso, 2018). Taken together, these findings corroborate an association between environmental experiences, epigenetic modifications and behavioral outcomes. Nevertheless, the cascade of biochemical events through which the environment is embedded in the individual biology, affecting physiology and behavior remains unclear. In addition, not all individuals exposed to early life adversity develop health issues, psychopathology or psychiatric disorders (Collishaw et al., 2007; Yehuda & LeDoux, 2007). Though, the genetic make-up, epigenetic characteristics, and risk and protective factors that render individuals differently sensitive to environmental influences are not yet understood (Belsky et al., 2009; Belsky & Pluess, 2009; Branchi, 2011). Several aspects hamper the possibility to draw firm conclusions from human studies. Retrospective designs rely on indirect information about the conditions of the participant, and even when information is available or directly collected within prospective studies, it is virtually impossible to disentangle the contribution of multiple factors occurring in pre-natal and post-natal life on specific physiological and behavioral outcomes. Moreover, both retrospective and prospective human studies depend on availability and access to appropriate tissues for epigenetic analysis and are based primarily on saliva, blood and buccal cells samples. Nonetheless, epigenetic patterns appear to be tissue and gene specific (Forest et al., 2018; Smith et al., 2015) and there is little consensus on how much changes observed in peripheral tissues may correlates each other and resemble changes in nervous tissue (Di Sante et al., 2018; Thompson et al., 2013; Walton et al., 2016). Animal models have strongly stimulated and complemented human studies (Phillips & Roth, 2019) (Box 1). Indeed, the animal models allow to prospectively manipulate the onset, quality, duration and predictability of environmental exposures under controlled conditions and to evaluate immediate, long-term and trans-generational consequences on candidate gene expression and behavior. Laboratory animals can be exposed to aversive or permissive environments at different developmental time points and both genomic and non-genomic inheritance can be systematically investigated (Bohacek & Mansuy, 2015, 2017; Francis, Diorio, Liu, & Meaney, 1999; Jirtle & Skinner, 2007; Mitchell et al., 2016; Richards, 2006). In addition, since epigenetic reactions are bidirectional and potentially reversible (Cervoni & Szyf, 2001; Roth, Denu, & Allis, 2001) the causal relationship between different epigenetic identities and behavioral outcomes can be addressed in animal models by administering specific molecular compounds able to promote or inhibit epigenetic mechanisms as DNA methylation (Keller, Doherty, & Roth, 2018, 2019; Weaver et al., 2004, 2005; Weaver, Meaney, & Szyf, 2006).

Post-natal maternal environment shapes the epigenome

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BOX 1 Epigenomic landscape throughout several animal species Since the gene environment interactions control how all individuals (regardless their genotype) respond to environmental variations, epigenetic responses aimed at successful growth and development have been found even in plants which appear to have an environmental memory to align developmental transitions with favorable environmental conditions. A notable temperature-related example of this is vernalization, that is the transition to flowering after experiencing winter conditions (Friedrich, Faivre, B€aurle, & Schubert, 2019). Notwithstanding the reliable instances in plants, the epigenetic processes in animals (from simpler organisms to more complex species, including humans) have received increasing attention over the past years. For example, within “model organisms,” Drosophila is retained one of the most suitable experimental platform to study epigenetic alterations that increase life expectancy, and identify genes that regulate human aging, as well as to study whether specific learning experiences by parents influence the behavior of subsequent generations. Recently, evidence has been provided of how specific sensory experiences in Drosophila may bias the behavior of subsequent generations (Williams, 2016). In worms (C. elegans) (Greer et al., 2011; Lemos, Araripe, & Hartl, 2008) and flies (Drosophila) (Roussou, Savakis, Tavernarakis, & Metaxakis, 2016), specific heritable chromatin modifications are shown to induce trans-generational inheritance of longevity, through an evolutionarily conserved sex-related mechanism of lifespan regulation similar to what has been observed in humans. This implicates common mechanisms underlying lifespan extension in flies and humans. Depending on the type of stimulus, the RNA interference (RNAi) machinery and chromatin regulators differently drive inheritance. For instance, it has been demonstrated that small RNAs can mediate starvation-dependent inheritance without involvement of chromatin (Rechavi et al., 2014), whereas temperature-dependent inheritance involves the H3K9 methylation machinery (SET-25) without RNAi (Klosin, Casas, Hidalgo-Carcedo, Vavouri, & Lehner, 2017). An interesting form of environmentally induced chromatin regulation may be found in eusocial insects and particularly relevant are the molecular determinants of the division of labor in eusocial insects, as honeybees and carpenter ants. Epigenetic processes, including DNA methylation and histone post-translational modifications, play a key role in regulating caste-based behavioral plasticity and allow colonies to adapt dynamically to ecological changes. It has been recently demonstrated (Simola et al., 2016) that the colonies of carpenter ants express two worker castes, with one caste performing most scouting activities for the whole colony. Changes in histone acetylation in the brains of adult individuals belonging to this caste accentuate their foraging and scouting activities. Such behavioral changes correspond to altered transcript abundance of select neuronal genes with specific roles in synaptic transmission and olfactory learning, and to changes in histone acetylation that occur near CBP binding sites proximal to these genes. These results allow hypothesizing that the epigenetic factors might help to establish complex social interactions not only for other invertebrates, but also for vertebrate and mammalian species, in which these conserved enzymes are known to play critical roles in the regulation of behavioral plasticity as well as in learning and memory functions (Yan et al., 2014).

Here, we report the contribution of animal studies, in particular, laboratory rats (Rattus norvegicus) and mice (Mus musculus), to the field of human behavioral epigenetics. The chapter focuses on the role of maternal environment as one of the most studied vectors in inducing epigenetic modifications and enduring phenotype in the offspring. In addition, studies on the interplay among genetic polymorphisms, epigenome and aversive environments in contributing to psychiatric disorders and, as a bright note, on the effect of the exposure to permissive environment (with attention to environmental enrichment) will be reported.

Post-natal maternal environment shapes the epigenome and adult behavioral phenotypes of the offspring The mother represents the primary environment for a developing human being. She physically surrounds the embryo and the fetus during the prenatal life, and through maternal care she promotes survival, growth and protection to the newborn. Quality and predictability of maternal environment are

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critical to promote a normative cognitive and emotional development of the offspring (Baram et al., 2013; Pruessner, Champagne, Meaney, & Dagher, 2004; Sroufe, 2005). Rats have been widely used in research to study the short- and long-term consequences of altered maternal environment on neuroendocrine, cognitive, emotional and behavioral outcomes in the offspring (Knop, Joe¨ls, & van der Veen, 2017). As well as humans, rats are altricial species, thus depend on the presence of the mother for growth and survival. By birth, and even in absence of previous experience, maternal hormones render the rat mother immediately responsive to the newborn pups (Rosenblatt, Mayer, & Giordano, 1988). After parturition and placenta consumption, the mother gathers and brings the newborns close to her body, allowing heat exchange, nourishes them and licks and grooms their bodies, providing tactile stimulation and inducing micturition reflex in the pups. In turn, the maintenance of mother-pup interactions is favored by suckling patterns and vocalization from the young (Boulanger-Bertolus, Rinco´n-Cortes, Sullivan, & Mouly, 2017; Fleming, O’Day, & Kraemer, 1999; Fleming & Rosenblatt, 1974). Rat maternal care includes a constellation of behaviors whose presence, duration, quality and predictability can vary across individuals and depending on environmental conditions (Fig. 1). To note, even under standard laboratory rearing condition, rat mothers are not all equal. On the contrary, rats show spontaneous (non-experimentally induced) stable individual differences in the quality of care provided to the offspring. Maternal behaviors as licking and grooming (LG) and arched-back nursing

FIG. 1 Illustration of different maternal care behaviors in rats. (A) Nest building: When provided with appropriate material, laboratory rats can engage in the construction of nests. (B) Retrieving: Rat dam picks up the pup in her mouth to carry it to the nest. (C) Licking and grooming: The dam licks and grooms the pups providing tactile stimulation and inducing micturition and defecation reflexes. (D) Side/passive nursing: The dam lies or sleeps on her side with pups attached to her nipples. (E) Low nursing: The mother is placed over the pups while nursing them. (F) Arched-back nursing: The mother assumes a posture characterized by low/high arching of the back (crouching) and limb extensions to nurse the pups. Original drawings of A.M. Berretta.

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posture (ABN) (Fig. 1C and F) are normally distributed within the population and allow to select mothers based on different patterns of spontaneous caregiving. In particular, by selecting Long-Evans mothers on the basis of their High or Low tendency to exhibit LG-ABN behaviors (hereafter HLG and LLG dams, respectively), Micheal Meaney and colleagues have thoroughly investigated the impact of spontaneous individual differences in maternal care on the development of the offspring. When adult, the offspring born to HLG and LLG dams significantly differ from each other regarding several aspects. The offspring born from HLG dams compared to the LLG offspring, exhibit lower plasma adrenocorticotropic hormone (ACTH) and corticosterone (CORT) levels in response to restraint stress, greater hippocampal GR mRNA expression, enhanced glucocorticoid feedback sensitivity, and decreased levels of hypothalamic corticotropin-releasing hormone (CRH) mRNA (Liu et al., 1997). The HLG offspring are also less fearful at behavioral level, as indicated by shorter latency to begin eating and greater time spent eating in a novel environment compared to the LLG offspring (Caldji et al., 1998). In addition, the offspring that experienced high levels of LG during the post-natal period show enhanced spatial learning and memory in the Morris Water Maze task paralleled by enhanced hippocampal brain-derived neurotrophic factor (BDNF) mRNA and NMDA receptor subunit expression compared to the offspring of LLG mothers (Liu, Diorio, Day, Francis, & Meaney, 2000). To note, as adult, the female offspring of HLG and LLG mothers significantly differ from each other regarding their own maternal care behaviors, so that the daughters of HLG dams lick and groom more their pups compared to the daughters of LLG mothers (Champagne, 2008; Champagne, Francis, Mar, & Meaney, 2003; Francis et al., 1999). The differences observed between LLG and HLG offspring could be attributed to both genetic/prenatal and post-natal influences from mothers to pups. By using cross-fostering procedure (Box 2), that consists in removing pups from the biological dam and transferring them to a receiving dam, the role of pre- and post-natal influences can be disentangled in animal models. Using this manipulation, it has been demonstrated that the physiological and behavioral differences observed in the offspring of HLG and LLG mothers were effectively conveyed by the post-natal maternal behavior (Champagne et al., 2003; Francis et al., 1999; Weaver et al., 2004). In fact, when the offspring born to LLG dams are reared by HLG dams, they show an adult phenotype undistinguishable from the phenotype of the offspring of the latter, indicating that maternal post-natal behaviors are the major vector of transmission (Champagne et al., 2003; Francis et al., 1999). In 2004, publishing an article on Nature Neuroscience entitled “Epigenetic programming by maternal behavior”, Weaver and colleagues changed the history of behavioral epigenetics, demonstrating the causal mechanisms through which maternal care style induces stable individual differences in the offspring (Weaver et al., 2004). The authors started by the observation that the NR3C1 gene was highly methylated (at the nerve growth factor-induced protein A (NGFI-A) binding site proximal to the transcription start site) in the offspring born to LLG mothers and rarely methylated in the offspring of HLG mothers. To understand whether the NR3C1 methylation levels were dependent from the maternal care received and responsible for the phenotypic diversity observed in the offspring, the authors used multiple approaches. By assigning a limited number of pups born to LLG mothers to HLG foster dams (Fig. 2B), they observed that the pattern of GR exon 1F promoter methylation depended on the foster mother, thus, on the maternal care received by the pups postnatally. In fact, methylation levels of the offspring born to LLG mothers but reared by HLG dams were undistinguishable from those of the biological and nonfostered HLG offspring.

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BOX 2 Cross-fostering Cross-fostering (CF) consists in removing pups from one dam (biological mother) and transferring them to a receiving dam (foster mother). CF can be use with several purposes. In colony management CF is used to control for litter size (overnumerous litters are split, and pups given to other dams) and to maximize rate of breeding (under-numerous litter are assigned to other lactating dams and the biological mother can come back in the breeding pool). In research, CF has been extensively used for separating the impact of genes and pre-natal environment to post-natal maternal care influences. Biological and foster mothers are phenotypically divergent individuals that may belong to different strains (inter-strain cross-fostering) or to the same strain (intra-strain cross-fostering). If the fostered offspring develop a phenotype that resembles the phenotype of their biological mother, researchers tend to attribute that phenotypic trait to genetic influences or pre-natal effect. On the contrary, if the offspring show a phenotype consistent with that of the offspring of the foster mother, the post-natal rearing environment is given a greater role in determining the observed phenotype. In CF studies, control groups generally include non-fostered mother-pups dyad (absolute control litters or sham-adoption animals simply removed from the nest and fostered back to their biological mothers) and in-fostered group (pups are fostered to a mother with the same characteristics of the biological mother). The whole litter can be cross-fostered between mothers (Fig. 2A). However, some authors pointed out that the exchange of the whole litter can alter the natural maternal care behavior of the dam (Maccari et al., 1995). Foster a limited number of pups (limited cross-fostering, Fig. 2B) enhances the likelihood to maintain the original character of the host litter (Francis et al., 1999; McCarty & Lee, 1996). In research, CF has also been used to induce early-life adversity in pups, mimicking postnatal unstable environment. Repeated cross-fostering (RCF) consists in daily assigning the litter to different mothers during the first post-natal days of life (Di Segni et al., 2016; Dulor, Espinoza, & Bolten, 2019; Luchetti et al., 2015) or in switching mothers between their biological litters and one foster litter every day from birth until weaning (Ottinger, Denenberg, & Stephens, 1963).

FIG. 2 Illustration of cross-fostering procedures. Cross-fostering consists in removing pups from one dam (biological mother) and transferring them to a receiving dam (foster mother). (A) The entire litter is cross-fostered between mothers. (B) A limited number of pups are exchanged between litters (limited cross-fostering). Original drawings of A.M. Berretta.

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Furthermore, the authors measured methylation level of the GR exon 1F promoter at different developmental time points using sodium bisulfite mapping. The difference in the methylation status of GR gene between the offspring of HLG and LLG mothers was significant and consistent throughout young and advanced age but was not present at birth nor during embryonic development (embryonic day 20). Indeed, the epigenetic signatures emerged precisely in correspondence of the first week of post-natal life, when the behavioral differences that characterize the HLG and LLG mothers are more pronounced, further confirming the close relationship between maternal care received and offspring epigenetic profiles (Weaver et al., 2004). The authors also succeeded in testing the functional relevance of the methylation differences observed in the offspring. The HLG offspring showed a significantly greater histone H3-H9 acetylation association and a three-fold greater binding of NGFI-A protein binding to the hippocampal exon 1F GR promoter compared to the LLG offspring, indicating a greater transcriptional activity of the gene. Finally, by administering intracerebroventricular infusions of the histone deacetylase (HDAC) inhibitor trichostatin A (TSA) in LLG offspring, the authors reverted the hypermethylated status exon 1F NR3C1: when the offspring of LLG mothers were treated with TSA they showed GR hippocampal level comparable to the offspring that received high level of licking and grooming after birth. The TSA treatment was also able to decrease the basal plasma corticosterone level generally elevated in the offspring of LLG and their corticosterone response to restrain stress, bringing it to levels similar to those shown by the offspring of untreated HLG mothers (Weaver et al., 2004). These findings demonstrate that the structural modifications of the DNA can be established through environmental programming (i.e., post-natal maternal care), persist over time, result in GR expression, altered HPA axis functionality, and are potentially reversible. Maternal care can vary across individuals also depending on environmental conditions. Naturalistic paradigms of limited resources have been proved to induce fragmented and unpredictable maternal care toward the pups with lasting consequences on offspring physiology and behavior (Bolton et al., 2018; Gilles, Schultz, & Baram, 1996; Ivy, Brunson, Sandman, & Baram, 2008; Molet et al., 2016; Molet, Maras, Avishai-Eliner, & Baram, 2014; Walker et al., 2017). The experience of adverse and impoverished environmental conditions can also induce the rat mothers to take rude behavior toward pups. Stressed mothers can neglect or actively reject pups, step on them, drop them down during transport, and handle them roughly (Roth, Lubin, Funk, & Sweatt, 2009; Yan et al., 2017). The exposure of pups to abusive maternal environment has profound impact on offspring development resulting in increased methylation of BDNF DNA, decreased BDNF gene expression in the prefrontal cortex, as well as in impaired memory functions and coping strategies (Doherty, Blaze, Keller, & Roth, 2017) that can be normalized by intracerebroventricular administration of a DNA methylation inhibitor (zebularine) (Keller et al., 2018, 2019). Maternal environment can be also favored as in communal nesting condition and environmental enrichment paradigms (Branchi, Karpova, D’Andrea, Castren, & Alleva, 2011; Cutuli et al., 2015, 2019).

Environmental stimuli delivered to parents trigger processes to transmit information to offspring Although the evolutionary theories of Lamarck and Darwin suggested that, speaking at the population level, environmental factors select for particular phenotypes, the discovery that some parental responses to the environment can influence the successive generations represented a ground-breaking thought.

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During sexual reproduction, genetic material is transferred from parents to offspring. In addition, mechanisms independent of the DNA sequence, even if still dependent on the germline, contribute to transmission of acquired traits. The research of paternal and maternal effects (where the male and female are separately exposed to specific environments pre-reproductively) has provided evidence that sperm- and oocyte-borne factors responsive to environmental changes can modulate the developmental programming of the offspring by so-called epigenetic inheritance—a term that refers to the direct modification of the gametic epigenome by the environment and subsequent transmission to the next generation. In fact, the idea that the exposure to environmental stimuli may trigger processes designed to transmit information to the next generations and that environmental factors can affect germ cells and thereby alter biological processes in the offspring is fascinating, although the ultimate demonstration that epigenetic processes controlling germ cells are responsible for the transmission and expression of acquired traits in the offspring is still lacking. It is becoming increasingly apparent that ancestral, prenatal, early post-natal environmental experiences result in modifications of the phenotype later in life (Ambeskovic, Roseboom, & Metz, 2017; Franklin et al., 2010; Shachar-Dadon, Schulkin, & Leshem, 2009; Veenendaal et al., 2012; Xu, Li, Zhang, & Liu, 2016), and that external stimuli, whether they are permissive or aversive, experienced by parents may affect the behavioral and neurobiological phenotype of the offspring.

Permissive environments Forms of inter- (from F0 to F1) or trans-generational (from F0 to F2, F3, …) inheritance have been described mainly for aversive stimuli, such as chronic or early life stress that lead to increased anxiety and depressive-like behavior and altered response of the HPA axis, in the following generations (Franklin et al., 2010; Gapp et al., 2014, 2016; Rodgers, Morgan, Bronson, Revello, & Bale, 2013). Within the permissive environmental stimulations experienced by both parents but mainly by the mother there are the experience of enriched environment and communal nesting.

Environmental enrichment Environmental Enrichment (EE) is an experimental paradigm which potentiates sensorimotor, cognitive, and social stimulations experienced by animals (Rosenzweig, Bennett, & Krech, 1964) (Fig. 3). Such a paradigm mimics the human situation characterized by being exposed to complex stimulations linked to high educational level, occupational attainment, lifestyle choices, and sustained cognitive engagement requiring mental effort and constant physical activity. Such an enhancement of the environmental complexity and novelty potentially exerts neuroprotective and therapeutic effects, as demonstrated by its efficacy in enhancing neuronal plasticity, delaying the progression and/or ameliorating the severity of symptoms caused by brain lesions (Baroncelli et al., 2009; Gelfo, Mandolesi, Serra, Sorrentino, & Caltagirone, 2018; Mandolesi et al., 2017; Nithianantharajah & Hannan, 2006, 2009; Petrosini et al., 2009; Sale, Berardi, & Maffei, 2014; Sampedro-Piquero & Begega, 2017) and is thus considered a suitable strategy to reduce the risk for dementia and other cognitive diseases (Fischer, 2016; Nithianantharajah & Hannan, 2006). Starting from the pioneering indication that exposure of juvenile mice to an enriched environment enhances the hippocampal long-term potentiation properties in their offspring (Arai, Li, Hartley, &

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FIG. 3 Illustration of an enriched cage for rodents. The enriched setting provides enhanced sensory, motor, cognitive and social stimulation to the animals. Enriched cages are bigger compared to standard cages and animals are generally housed in large groups allowing social interactions. The presence of ladders, tunnels and running wheels allows motor activity and stimulates exploration. In addition, the presence of different objects, whose position is repeatedly changed within the cage, encourages recognition and manipulatory behaviors. Original drawings by A.M. Berretta.

Feig, 2009), in the last years an increasing attention has been paid to the transfer of proactive effects of enriching experiences from parents to progeny (Arai & Feig, 2011; Girbovan & Plamondon, 2013; Sale, 2018; Sale et al., 2014; Taouk & Schulkin, 2016). The role played by maternal (Arai et al., 2009; Cutuli et al., 2018; Dell & Rose, 1987; Leshem & Schulkin, 2012) or paternal (Dezsi et al., 2016; Mashoodh, Franks, Curley, & Champagne, 2012; Mychasiuk et al., 2012; Short et al., 2017; Yeshurun, Short, Bredy, Pang, & Hannan, 2017) enrichment occurring before conception (i.e., during the pre-reproductive period) or in the early post-natal phases in modifying the neurobiological and behavioral profile of the offspring has been analyzed. These studies have demonstrated that parental manipulations are able to alter the neurodevelopmental trajectories of the progeny, likely to fine-tune the

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development of specific biological systems and to enhance the match between offspring’s phenotype and environmental demand. Since early nurturing experiences may influence brain plasticity and alter epigenome (Meaney, 2010; Weaver et al., 2017), part of these studies have also evaluated if the effects of parental enrichment on offspring’s phenotype were mediated by modifications of maternal behavior (Caporali et al., 2015; Cutuli et al., 2015, 2019, 2017; Mashoodh et al., 2012; Short et al., 2017; Yeshurun et al., 2017). In fact, the interaction between mother and offspring critically impacts growth, survival, physiology, and behavior of the progeny and the variations in mother-offspring interactions represent the forecast of environmental quality for the offspring, although how these maternal effects are rendered permanent throughout the life of the offspring is not yet fully clarified. Our understanding of the adaptive processes influenced by maternal care has been promoted by human and animal studies. In humans, scattered evidence on the effects of early maternal experiences reports the beneficial consequences of:—tactile (body massage) and auditory (exposure to maternal voice) stimulations on the neurobehavioral development of preterm infants (Guzzetta et al., 2009; Picciolini et al., 2014; Webb, Heller, Benson, & Lahav, 2015);—pre-natal auditory stimulations (exposure of the fetus to music and maternal talk during pregnancy) on the reduction of autistic-like behaviors (Ruan et al., 2018);—pre-reproductive maternal enrichment (maternal educational achievement) on buffering offspring’s stress sensitivity (Swartz, Knodt, Radtke, & Hariri, 2018). On the contrary, in animals (mainly in rodents) multiple studies have investigated the impact of maternal care on the neurobiological, cognitive and emotional development of offspring. Notably, there is evidence for the association of maternal care with long-term transcriptional activation and/or repression, and thus for the impact of mother-infant interactions on epigenetic processes, such as DNA methylation, histone (perhaps also protamine) post-translational modifications, and noncoding microRNAs (Bohacek & Mansuy, 2017). The different responses to mother-infant interaction according to the moment they occur indicate the presence of windows of time or sensitive periods in which such interactions may be maximally effective in inducing neurobiological and behavioral changes. However, while the altered motherinfant interactions that follow various experimental manipulations occurring during gestational and/ or lactation periods are widely described, fewer studies have analyzed the mother-infant interactions that follow pre-reproductive parental manipulations, such as an exposure to an enriched environment occurring before conception.

Maternal enrichment during pregnancy and/or lactation The exposure of rat mothers to differently lasting periods of enrichment, ranging from the relatively short period of the gestation alone to a sensibly more long period encompassing the pre-reproductive, gestational and lactation phases, may evoke increase of pup-directed maternal behaviors, as LG and ABN, as well as reduction in the contact with the nest (Cancedda et al., 2004; Connors, Migliore, Pillsbury, Shaik, & Kentner, 2015; Dura´n-Carabali et al., 2018; Rosenfeld & Weller, 2012; Sale et al., 2004; Zuena et al., 2016). At this juncture, a crucial point arises: does the exposure of mothers to periods of enrichment affect even offspring’s phenotypes? And, if so, the changes observed in the offspring are correlated with the changes in the maternal care they received? Parental environmental experiences affect the body weight of the progeny. Namely, the exposure of female rats during pregnancy and lactation to a social colony providing enhanced physical and social stimulations influences progeny’s body weight only at birth (Sparling, Mahoney, Baker, & Bielajew,

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2010), whereas the exposure to EE of the fathers influences offspring’s body weight at adulthood (Mashoodh et al., 2012), an influence that persists until the second generation (Yeshurun et al., 2017). Thus, it is possible that the maternal and paternal effects are driven by pre-natal metabolic programming, even if it is not possible to exclude that a specific maternal behavior in the pre-weaning phase may account for the particular fitness outcome observed in the progeny of enriched fathers (Mashoodh et al., 2012). Furthermore, maternal gestational EE reduces latency of negative geotaxis (a dynamic sensorimotor reflex in which the body becomes orientated away from the force of gravity) in the offspring (Mychasiuk et al., 2012). Interestingly, parental enrichment critically influences the offspring’s cognitive performances and anxiety levels. Namely, maternal gestational EE facilitates learning and memory abilities (Kiyono, Seo, Shibagaki, & Inouye, 1985; Koo et al., 2003), and increases synaptic plasticity and hippocampal neurogenesis in the progeny (Koo et al., 2003). And yet, the maternal gestational EE modulates attentional performance according to stress levels (Cymerblit-Sabba et al., 2013) and controversially affects anxiety levels of the offspring (Cymerblit-Sabba et al., 2013; Maruoka, Kodomari, Yamauchi, Wada, & Wada, 2009; Rosenfeld & Weller, 2012). Maternal exposure to EE during pregnancy and lactation improves spatial memory performances in the Morris water maze, reduces anxiety, and prevents the hippocampal tissue loss after neonatal hypoxia-ischemia (Dura´n-Carabali et al., 2018; Sparling et al., 2010). Also the exposure of mothers to the enhancement of the only motor stimulations (physical exercise) during pregnancy (Herring et al., 2012; Lee et al., 2006; Parnpiansil, Jutapakdeegul, Chentanez, & Kotchabhakdi, 2003) or during pregnancy and lactation (Bick-Sander, Steiner, Wolf, Babu, & Kempermann, 2006) improves learning and memory abilities, protects from neurodegeneration, improves brain plasticity, and enhances hippocampal neurogenesis in the offspring. The maternal exposure to EE before and during gestation affects offspring’s developmental trajectories by modifying cognitive and emotional outcomes in a sex-specific manner with improved learning ability only in females and increased anxiety levels mainly in males (Connors et al., 2015; Zuena et al., 2016). Communal nesting (CN) consists in placing two or more pregnant females together, which give birth synchronously. The offspring born to different dams are thus combined in a single nest and share milk and maternal care, experiencing enhanced kin and non-kin peer interactions until weaning, compared to standard laboratory condition (one dam per litter) (Branchi, 2009; Branchi, D’Andrea, Santarelli, Bonsignore, & Alleva, 2011). In individuals grown under CN conditions compared to individuals reared in standard condition the BDNF epigenetic structure is modified toward a more active state (Branchi, D’Andrea, et al., 2011). Unfortunately, the effects on the adult offspring resulting from social enrichment obtained by CN are rather controversial, given that CN is reported either to reduce anxiety levels and modify oxytocin and vasopressin receptor densities or to have no effect in learning or anxiety tests (Curley, Davidson, Bateson, & Champagne, 2009; Heiderstadt, Vandenbergh, Gyekis, & Blizard, 2014).

Pre-reproductive parental enrichment Few studies have investigated the effects of pre-reproductive parental enrichment on maternal care. In particular, socially enriched females are reported to perform enhanced levels of maternal care in comparison to standard-reared females, and their female offspring (when dams) show increased frequency

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of nursing (Curley et al., 2009). The pre-reproductive EE of female rats induces maternal care modifications consisting of higher levels of licking, ABN, and nest building activities, a faster retrieving after maternal male intruder encounters, accompanied by heightened levels of BDNF in the frontal cortex (Caporali et al., 2015; Cutuli et al., 2015, 2018, 2017). As for the offspring’s phenotypic modifications, the pre-reproductive maternal EE alters the motor development of the progeny, leading to an earlier acquisition of abilities that require complex sequencing and coordination of the motor output (Caporali et al., 2014). Transgenerational beneficial effects of pre-reproductive maternal EE on mnesic performances are associated with a signaling cascade in the CA1 hippocampal region (Arai et al., 2009). Furthermore, the pre-reproductive maternal EE results in improved cognitive performances as well as increased hippocampal BDNF levels in male offspring, without changes in neurogenesis or reelin levels (Cutuli et al., 2015). Pre-reproductive maternal EE also reduces social interactions in males, but not in female adult and adolescent offspring (Cutuli et al., 2018, 2019; Leshem & Schulkin, 2012). Moreover, pre-reproductive EE is able to counteract the effects of chronic stress (social isolation) as indicated by the progeny’s higher resilient coping responses and the reduced amygdala activation in facing acute stress (Cutuli et al., 2017). Interestingly, several studies indicate that the paternal environmental experiences can modify maternal investment as well as offspring’s phenotypic plasticity even in the absence of paternal care. In fact, standard-reared female mice mated with a male enriched during the pre-reproductive period show increased frequency of pup nursing and licking during the first post-partum week. Such behavioral modifications are associated with gene expression modifications (higher levels of BDNF mRNA and lower levels of MeCP2 mRNA) in the hypothalamus of the dams (Mashoodh et al., 2012). The effects of paternal enrichment on offspring’s phenotype could be the consequence of inherited paternal epigenetic variations that lead to variations in the level of maternal care requested by the offspring. In fact, pups provide distal cues (sight, sound, tactile contact) to the mother, thus stimulating her contact with them. It can be speculated that the modulation of maternal behavior in standard-reared females mated with enriched males can be linked to a more demanding behavioral pattern of the pups. However, it has to be noted that recent studies that used protocols of enrichment based only on motor (by runningwheel) (Short et al., 2017) or cognitive and physical (Yeshurun et al., 2017) stimulations did not find any effect of the paternal enrichment on the maternal behavior, suggesting that only a long-lasting and complete set of motor, cognitive, physical and social stimulations provided to the fathers is able to potentiate maternal behavior. As for the effects of pre-reproductive paternal EE on the offspring, the existing studies mainly addressed the emotional repertoire. Namely, it has been reported that the pre-reproductive EE of adult male mice enhances hippocampal synaptic plasticity (LTP) and induces a mild memory improvement in the F1 generation, but not in the following F2 generation (Benito et al., 2018). This finding is in line with previous studies that investigated trans-generational effects in mutant mice (Rassoulzadegan et al., 2006) or in mice exposed to stressors, as altered glucocorticoid signaling (Rodgers, Morgan, Leu, & Bale, 2015), anxiety behavior (Gapp et al., 2014), or diet-induced obesity (Grandjean et al., 2015). We agree with Benito and colleagues when they advance that “nature offers to the organism a physiological system that allows for non-genetic inheritance of a cognitive benefit in situations of demand but makes sure that these phenotypes do not persist when the environmental settings change again” (Benito et al., 2018, p. 551). Notably, the modifications of the offspring’s phenotype are mediated by changes in the RNA composition in the sperm of the enriched fathers, especially through the upregulation of microRNAs 212/132. Although the precise mechanisms by which sperm RNA transmit

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EE-induced inter-generational enhancement of brain function to the adult offspring remain to be identified, it can be speculated that, since the sperm development in rodents occurs continuously, as in humans, pre-reproductive paternal enriched experiences are able to alter gene expression in the sperm of fathers, thus providing a means for the transmission of epigenetic change to the progeny. Other studies report that the male offspring of runner fathers show reduced anxiety levels and more robust fear extinction memory associated with alterations in the levels of small non-coding RNAs in sperm (Short et al., 2017). Conversely, when the component of motor enrichment is lacking, no differences in anxiety are found following pre-reproductive paternal enrichment (Yeshurun et al., 2017). As a final note, it is interesting to speculate on the potential mechanisms of inheritance involved in the transmission of the environmental influence from F0 to F1, which could then differ in the transmission from F1 to F2. Evidence is provided that paternal inter-generational inheritance from F0 to F1 is mediated by epigenetic modification including altered expression patterns of small non-coding RNAs (both microRNAs and tRNA-derived small RNAs) in the sperm (Chen et al., 2016; Gapp et al., 2014; Ng et al., 2010; Rodgers et al., 2013; Sharma et al., 2016; Short et al., 2016). There could also be environmental enrichment-induced modifications of the small RNA content in post-meiotic germ cells and this modifies maternal transcripts before zygote activation (Dadoune, 2009). Alternatively, it has been advanced a direct influence of the environment on the epigenetic profile of spermatozoa, through an alteration of microRNA content of sperm (Yeshurun et al., 2017). Recently, it has been studied the possibility of transmitting from parents to offspring and facilitating the gaining in the progeny not only of the global information related to the exposure to complex environmental stimuli but also of specific learning and memory processes related to the acquisition of a definite task together with the active epigenetic markers of those specific processes. It has been reported that pre-reproductive paternal spatial training (by Morris Water Maze) enhances offspring’s spatial cognitive performance and the hippocampal synaptic transmission (histone acetylation at the promoter of synaptotagmin 1) in both fathers and their offspring as well as in the fathers’ sperm (Zhang et al., 2017). More recently, it has been demonstrated that the pre-reproductive spatial training (by Morris Water Maze) of both parents facilitates the spatial learning and memory in their offspring. Interestingly, such a transmission occurred from fathers to their male offspring and from mothers to their female offspring. Moreover, father’s spatial training upregulates the expression of BDNF and phosphorylated ERK1/2 and increases the acetylated H3K14 in the male offspring, suggesting that the histone acetylation may underlie the upregulated protein expression and contribute to the enhancement of spatial learning and memory process in the offspring (Riyahi, Abdoli, Haghparast, & Petrosini, 2019).

Aversive environments Epigenetic elements, such as DNA methylation and hydroxymethylation, posttranslational histone modifications, and noncoding RNAs, have recently received attention as crucial contributors to phenotypic diversity and responses to disease, especially in complex disorders. Epigenetic perturbations may facilitate the process whereby life experiences (both negative and positive) alter gene expression patterns (Bjornsson, Fallin, & Feinberg, 2004; Malan-M€uller, Seedat, & Hemmings, 2014). In fact, providing a link between the environment and the transcriptome (Binder et al., 2008; Champagne, 2008; Franklin et al., 2010), epigenetic modifications may explain the inter-individual variability in

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resilience or predisposition to trauma-related diseases, such as post-traumatic stress disorders (PTSD), generalized anxiety, major depression and phobias, as well as the long-lasting effects elicited by trauma exposure (Yehuda & Bierer, 2009). People inherit a predisposition to respond to particular aversive environmental triggers, and the behavioral and biochemical consequences associated with exposure to these negative stimuli could continue to occur long after exposure to the event (Bowers & Yehuda, 2016). In this framework, epigenetic signatures are responsive to environmental factors and can be stable over long periods of time and across generations to persistently modify gene transcription. Early life events have been found to associate with the development of PTSD as well as changes in the HPA axis described in PTSD (Yehuda & LeDoux, 2007). Alterations in the HPA stress response and increased risk of suicide following childhood trauma have been reported. McGowan et al. (2009) found reduced levels of NR3C1 mRNA and mRNA transcripts, as well as increased cytosine methylation of the NR3C1 promoter in suicide victims with a history of childhood abuse compared to suicide victims without childhood trauma and controls. Individuals who suffer from child abuse have a greater risk of developing PTSD and depression later in life (MacMillan et al., 2001; Mullen, Martin, Anderson, Romans, & Herbison, 1996), and they are prone to exacerbated physiological responses to stress (Heim & Nemeroff, 2001; Weiss, Longhurst, & Mazure, 1999). In a study that investigated genome-wide promoter methylation in the hippocampus of individuals who suffered severe childhood abuse, the authors identified 362 differentially methylated promoters in abused individuals compared with controls (Labonte et al., 2012). Of these promoters, 248 were hyper-methylated and 114 were hypo-methylated. Methylation differences occurred mostly in the neuronal cellular fraction and the most significantly differentially methylated genes were those involved in cellular or neuronal plasticity. All life long, epigenetic change has been found to be a crucial component of the neuronal modifications that underlie learning and memory (Bredy et al., 2007; Chwang, O’Riordan, Levenson, & Sweatt, 2006; Miller & Sweatt, 2008), processes underlining the dysfunctional fear extinction featuring the trauma-related diseases. Only few studies have examined genome-wide DNA methylation patterns in trauma-related disorders, evidencing transmission and immune dysregulations figured prominently among the biological networks associated to maladaptive responses to trauma (Smith et al., 2011; Uddin et al., 2010). Among combat veterans, subjects with PTSD were shown to have lower peripheral blood methylation levels in the NR3C1 promoter compared with combat veterans without PTSD and increase in NR3C1 methylation levels predicted favorable PTSD response to prolonged exposure psychotherapy (Yehuda et al., 2013). Accordingly, PTSD patients had low T-cell NR3C1 methylation levels and high GR expression (Labonte, Azoulay, Yerko, Turecki, & Brunet, 2014). Methylation levels of the gene encoding interleukin-18 were increased after deployment in military service members with PTSD (Rusiecki et al., 2013). By using epigenome-wide approach, it has been demonstrated that subjects with PTSD have differential methylation level at genes involved in inflammatory processes accompanied by increased levels of interleukin-4, interleukin-2 and tumor necrosis factor α (Smith et al., 2011). Furthermore, the gene encoding FK506 binding protein 51, that decreases GR signaling and maintains homeostasis of the HPA axis, confers increased risk for PTSD, especially in the context of early adversity (Binder et al., 2008; Boscarino, Erlich, Hoffman, & Zhang, 2012; Xie et al., 2010). Not only genetic but also epigenetic regulation of the FK506 gene in response to early trauma is implicated in PTSD pathogenesis (Klengel et al., 2013).

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Aversive environment: Animal studies A key clinical feature of trauma-related disorders is the impaired fear extinction that, among other factors, results from dysregulation of HPA axis and neurotransmission in limbic-frontal brain system (Holmes & Singewald, 2013). In this framework, genetic epidemiological studies suggest that both genetic and environmental factors (and their interaction) contribute to stabilize the critical dysfunctions associated to trauma-related disorders. Evidence from animal studies highlights that histone modifications and DNA methylation contribute critically to the regulation of synaptic plasticity and learning, after the trauma exposures (Itzhak, Anderson, Kelley, & Petkov, 2012). Fear conditioning, associative learning in which organisms learn to predict aversive events, was found to be associated with DNA methylation and subsequent transcriptional repression of the protein phosphatase 1 gene, the memory suppressor gene, and transcriptional activation of gene coding reelin, a synaptic plasticity factor (Miller & Sweatt, 2008), highlighting the dynamic regulation of DNA methylation in the adult nervous system and its critical function in memory formation. In mice which displayed impaired fear extinction acquisition and extinction consolidation, persistent and context-independent rescue of deficient fear extinction was associated with enhanced expression of dopamine-related genes in the medial prefrontal cortex and amygdala (Whittle et al., 2016). Moreover, enhanced histone acetylation was observed in the promoter of the extinctionregulated dopamine D2 gene in the medial prefrontal cortex. Extinction-promoting effects of valproic acid, an anticonvulsant that acts as histone modulator and promotes GABA signaling, have been attributed to its capacity to act on the BDNF promoter to enhance BDNF gene expression in the medial prefrontal cortex (Bredy et al., 2007; Bredy & Barad, 2008). Early maltreatment of rat pups produced increased methylation of BDNF gene that resulted in persistent decreases in gene expression in the adult prefrontal cortex (Roth et al., 2009), while psychosocial stress in adulthood resulted in BDNF gene methylation increased in the dorsal hippocampus and decreased in the ventral one. Accordingly, maladaptation to traumatic stress is associated with various changes in the methylation pattern of the hippocampus (ChertkowDeutsher, Cohen, Klein, & Ben-Shachar, 2010). Together these results suggest that a traumatic event may alter DNA methylation patterns potentially associated with the stress regulation processes. Furthermore, persistent decreased DNA methylation levels in the FK506-binding protein 5 gene in brain and blood samples has been found associated with anxiety-like behavior (Lee et al., 2010), GR sensitivity and exposure to early childhood trauma (Klengel et al., 2013). The early-life traumadependent DNA demethylation in FKBP5 functional glucocorticoid response elements is a biochemical pathway resulted in an increased risk of developing stress-related psychiatric disorders during adulthood (Klengel et al., 2013). Yang et al. (2012) reported that glucorticoid-induced loss of methylation in pituitary cells can occur. Early life stress study in mice has suggested that vasopressin-induced gene hyperactivity could be also involved in the etiology of PTSD (Murgatroyd et al., 2010). A stable increase in glucocorticoids, vasopressin and depressive behavior was observed in the maternally separated mice. The behavioral outcome was reversed by administration of a vasopressin receptor antagonist. This effect was attributable to a reduction in DNA methylation of the transcription factor that increases vasopressin gene activity. Increased release of vasopressin into brain regions involved in anxiety and fear induces increased anxiety-like behaviors.

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Epigenetic perturbation can be passed along Severe stress exposure in a parent is a risk factor for a number of adverse outcomes, including psychopathology, in offspring. Stress-exposed parents may confer vulnerability via genetic-epigenetic mechanisms or via behavioral alterations stemming from the development of stress-related psychopathology (affecting their ability to parent or the childhood environment of the offspring) (Bowers & Yehuda, 2016; Yehuda & Bierer, 2009). Observations from the Dutch Famine study (examining the effects in offspring of pregnant women exposed to the stress of starvation) and others have demonstrated that parental stress may be transmitted via the gametes or the gestational uterine environment (Barker, 1990, 1998; Dias & Ressler, 2014; Franklin et al., 2010; Gapp et al., 2014; Yehuda et al., 2005). Women who develop PTSD as a result of trauma exposure during pregnancy - such as having to evacuate the World Trade Center on 9/11 give birth to affected offspring with evidence of a “trimester effect” (Yehuda et al., 2005). The greater influence of maternal exposure during the third compared with second trimester provides evidence for the relevance of in utero effects to the transmission of biological risk. Maternal PTSD and maternal prenatal stress, including psycho-social stress, maltreatment, and exposure to a terrorist attack, have been found to be associated with impaired uterine blood flow, low birth weight, and preterm birth (Berkowitz et al., 2003; Cederbaum, Putnam-Hornstein, King, Gilbert, & Needell, 2013; Christiaens, Hegadoren, & Olson, 2015; Coussons-Read et al., 2012; Glover, 1997; Lederman et al., 2004; Wadhwa, Sandman, Porto, Dunkel-Schetter, & Garite, 1993; Yonkers et al., 2014). These factors have been linked with the subsequent development of hypertension, insulin resistance, type 2 diabetes, and cardiovascular diseases in adult offspring (Barker, 1998). Holocaust survivor offspring were found to exhibit higher anxiety, lower self-esteem, and inhibition of aggression (Flory, Bierer, & Yehuda, 2011; Gangi, Talamo, & Ferracuti, 2009). Similar effects have been observed in offspring of prenatally stressed mothers, in whom anxiety in the second and third trimesters was associated with offspring depressive symptoms and behavioral/emotional problems, including conduct problems and hyperactivity/inattention (O’Connor, Heron, Golding, Beveridge, & Glover, 2002; Van den Bergh, Van Calster, Smits, Van Huffel, & Lagae, 2008). Offspring of mothers who experienced stress duration pregnancy may also experience difficulties in cognitive domains (Buss, Davis, Hobel, & Sandman, 2011; Mennes, Stiers, Lagae, & Van den Bergh, 2006; Van den Bergh, Mulder, Mennes, & Glover, 2005). Behavioral, emotional and cognitive problems may explain vulnerability of offspring to psychopathology (Huizink, van den Berg, van der Ende, & Verhulst, 2007; Roberts, Gilman, Breslau, Breslau, & Koenen, 2011). At epigenetic level, modified methylation of gene that encodes GR has been found in offspring of mothers stressed during pregnancy (Mulligan et al., 2012; Perroud et al., 2014; Radtke et al., 2011). In addition, studies have investigated the offspring methylation status and variants of the serotonin transporter gene and the association with inter-generational stress (Grabe et al., 2009; Kilpatrick et al., 2007; Taylor et al., 2006; Xie et al., 2009). In mice, Franklin et al. (2010) have investigated the trans-generational effects of early stress on behavioral traits. They found that only when maternal separation was unpredictable and combined with unpredictable maternal stress, it induced long-lasting behavioral effects in the offspring and in subsequent generations. Chronic and unpredictable maternal separation induced depressive-like behaviors as well as altered behavioral responses to aversive environments during adulthood in separated animals. The male offspring of males subjected to maternal separation also exhibited most of these behavioral

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alterations, even though they were reared normally. In addition, chronic and unpredictable maternal separation modified the DNA methylation profile (in the germline) of the separated males in promoter regions of several candidate genes, such as those coding cannabinoid type 1 receptors. Comparable changes in DNA methylation and gene expression were also evident in the brains of their offspring (Franklin et al., 2010). Although maternally mediated effects in offspring have been the subject of primary investigation, paternal stress also affects offspring (Dias & Ressler, 2014; Franklin et al., 2010; Gapp et al., 2014). Holocaust survivor offspring with paternal PTSD exhibited higher methylation of gene that encodes GR, inhibiting gene transcription (Klose & Bird, 2006). It has to be underlined that parents can model behaviors, and offspring can “learn” to react to their environments in a manner similar to their parents without necessarily invoking molecular explanations of inter-generational transmission. Furthermore, in many cases the observation in offspring of biological changes associated with trauma in the parent (potentially in the absence of offspring trauma) may be an indication of similar genetic risks in both generations, rather than an indication of intergenerational transmission of the biological effect.

Combining human and non-human animal research Development is a complex process that lasts over time, is driven by genetic information and unfolds under constant environmental influences involving stable, potentially inheritable and reversible, epigenetic modifications (Bale et al., 2010; Fraga et al., 2005; Maccari et al., 2014; Tsankova et al., 2007). Reseach on animal models in which the exposure to permissive or aversive environments can be controlled during the lifespan and genomic-and non-genomic inheritance can be systematically investigated has provided crucial insights into environmentally induced modifications in gene expression, and thus on the action mechanisms shaping phenotypic diversity (Bohacek & Mansuy, 2015, 2017; Francis et al., 1999; Jirtle & Skinner, 2007; Mitchell et al., 2016; Richards, 2006). In addition, the use of animals with known genetic backgrounds (or knock-out/knock-in animals) allows analyzing the contribution of genetic make-up to phenotypic complexity. Finally, the administration of pharmacological compounds in animal models allows studying the potential reversibility of epigenomic modifications (Keller et al., 2018, 2019; Weaver et al., 2004, 2005, 2006). However, despite the commonalities in neurodevelopmental processes in mammals, we are aware that the inter-species differences that are reflected in differences in cognition and behavior should be seriously considered in generalizing preclinical results to humans (Reilly et al., 2015; Teicher, Tomoda, & Andersen, 2006; Zhou et al., 2017). In fact, humans have a particularly long gestational developmental course compared to other species (Gogtay et al., 2004; Petanjek et al., 2011; Silbereis, Pochareddy, Zhu, Li, & Sestan, 2016; Stiles & Jernigan, 2010; Tau & Peterson, 2010) so that they appear to be very sensitive to environmental factors shaping social, cognitive and emotional capacities (Silbereis et al., 2016). And yet, the most influential epigenetic human studies are predicated on significant experimental findings that have shown that brain development and function are sculpted by very early or even ancestral experiences. It is also the case that preclinical researcher started from clinical observations to test causal hypotheses through an experimental work. This “hand and glove” approach has enabled the epigenetics to progress with surprising rapidity.

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In conclusion, without discounting the species-specific features and the respective advantages and constraints of human and animal observations, it is evident that research should adopt a multidisciplinary approach that combines both animal and human research, with the common purpose of clarify geneenvironment interactions even at molecular level accounting for personalized expression of genetic information and for vulnerability and resiliency to psychiatric diseases.

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CHAPTER

An overview of developmental behavioral genetics

3

Chloe Austerberry and Pasco Fearon University College London, London, United Kingdom

Although there are many potential applications of the science of epigenetics, a key one centres on its potential to shed light on the biological mechanisms through which environmental exposures affect physical and psychological development (Zhou, Resendiz, & Lo, 2017). Although not all epigenetic processes actually involve environmental mechanisms, elucidating the way in which the environment “gets under the skin” through the regulation of gene function is a key topic within that field and is an area of great interest in contemporary medicine and psychology (Barker & Osmond, 1986). A major challenge for this endeavor, one that is common to all domains of the human sciences, is how to cleanly identify environmental processes, when the influence of genetics is so pervasive. In that regard, it is evident that the proper study of epigenetics requires an understanding of genetics, in part because many epigenetic processes are under a degree of genetic control (Bell & Spector, 2012) and because genetic methodologies provide the most powerful tools currently available for identifying true environmental influence. In this chapter we present an overview of the field of developmental behavioral genetics, which seeks to study the role of genetic and environmental influences on individual differences in behavior and their development over time. We intend this outline not only to provide a hopefully useful introduction to developmental behavioral genetics, but also to help the reader consider how the behavioral-genetic and epigenetic literatures fit together. Findings from behavioral genetics research have largely put to rest the somewhat stale natureversus-nurture debate, by demonstrating through decades of research using a range of genetically informative designs that virtually all complex traits and behaviors are influenced by a combination of genes and the environment. Key contemporary questions in behavioral genetics instead concern the interplay of genes and the environment—how genes and the environment work together and which genetic variants and environmental influences contribute to individual differences in behavior throughout development. The current chapter reviews these key contemporary questions, provides an overview of the methodologies involved in studying them, and gives a range of illustrative examples that typify how development has been studied through the lens of behavioral genetics.

Background/history Conceptually, behavioral genetics can be traced far back in scientific history—some awareness of the heritability of behavior is implicit in the early domestication and breeding of animals and Shakespeare is said to have first introduced the nature-nurture dichotomy in the Tempest. However, behavioral Developmental Human Behavioral Epigenetics. https://doi.org/10.1016/B978-0-12-819262-7.00003-9 # 2021 Elsevier Inc. All rights reserved.

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genetics as a science really began with Sir Francis Galton (1822–1911)—an extraordinarily prolific scientist and polymath, and indeed controversial figure, who revolutionized the field of statistics and made important discoveries in numerous disciplines, of which behavioral genetics was one. Influenced by his cousin Charles Darwin’s Theory of Natural Selection, Galton was the first to research the inheritance of “mental powers,” coined the scientific use of the phrase “nature versus nurture,” and first suggested the use of twin designs to study heritability.a However, Galton also became known as the father of the eugenics movement and it was behavioral genetics’ association with this movement that came close to discrediting it as a science. While genetically informative research was on the rise in the early Twentieth Century—proceeding the rediscovery of Mendel’s Laws of heredity in 1900b—the discipline fell out of favor following the genocide in Nazi Germany and the widely condemned American eugenics movement. Understandably, behavioral scientists became extremely wary of associating themselves with genetics, and thus environmentalism prevailed for several decades. However, behavioral genetics enjoyed a gradual resurgence through the latter half of the 20th century, leading to the more nuanced position held by behavioral scientists today that acknowledges joint and combined influences from genes and the environment. This resurgence was largely thanks to developments in quantitative genetic methods such as twin and adoption designs in humans, as well as advances in animal models of behavior. More recently the Human Genome Project and developments in statistical genetics have added to our understanding of underlying genetic mechanisms and their interplay with the environment.

Behavioral genetic methodology There are many genetically informative methods employed in the field of behavioral genetics—we outline a few of the most widely used: animal studies; twin and adoption studies; and, in a later section, studies examining associations between behavioral phenotypes and genetic variants. Animal studies. Many genes implicated in core biological process have been conserved across species and are shared between humans and other animals (including, primates, rodents, fish, birds and insects). As a result, genetic studies of animal behavior have produced strong evidence of the contribution of genetics to individual differences in behavioral traits. Animal models are particularly powerful because they allow for experimentally manipulated changes to the environment and genotype that would not be possible to achieve in human studies. Manipulation or mutation of genetic variants or whole genes can be induced through methods such as selective breeding of phenotypes, gene knockout, and mutations induced by exposure to chemicals or radiation. Similarly, cross-fostering studies represent a powerful methodology for testing the causal effects of environmental processes and effectively rule out heritable genetic mechanisms. Although there is debate surrounding the extent to which animal research can be used to model human behavior, genetic studies of animal behavior have furthered our understanding of the genetic and environmental mechanisms underlying a range of complex human traits (Gewirtz & Kim, 2016; Knopik, Neiderhiser, DeFries, & Plomin, 2017), and as our ability to a Galton introduced the twin method in his1875 paper on twins. However, as discussed by Rende, Plomin, and Vandenberg (1990), Galton did not propose comparing monozygotic and dizygotic twins as in the classical twin design. The classical method appears to have been first introduced by Curtis Merriman in his 1924 report in Psychological Monographs. b Mendel’s work was separately rediscovered in the same year by three scientists who were unknown to one another—Carl Correns, Hugo DeVries and Erich von Tschermak.

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study genetic, epigenetic and neurobiological processes increases, animal models are likely to play an even greater role in developmental neuroscience in the coming decades. Twin and adoption studies. Twinning and adoption are two naturally occurring phenomena that provide researchers with the opportunity to robustly estimate the degree of variation in a trait that can be attributed to genetic/environmental influence and have been the mainstay of human behavioral genetics for the last 50 or more years. Neither of these methods involve any direct measurement of DNA; instead, they rely on relating patterns of phenotypic resemblance to known familial genetic relationships. How closely phenotypic resemblance appears to mirror patterns of genetic relatedness gives us a clue or estimate of the extent to which genetic factors are influencing the phenotype in question. Because of the indirect nature of this way of estimating genetic effects, these quantitative genetic methods are “black boxes” with respect to mechanisms—they identify the overall contribution of genes to a trait, but say nothing about the specific genes or genetic mechanisms involved. As we outline below, the intermediate processes, acting across development, that eventually give rise to a relationship between a complex psychological phenotype and the genome are likely extremely complex. Twin studies. The classical twin design is based on the comparison of phenotypic similarity between identical (also known as monozygotic, MZ) twins and fraternal (also known as dizygotic, DZ) twins. The logic is that, as MZ twins are genetically identical and DZ twins share on average 50% of their segregating genome, a higher degree of phenotypic similarity between MZ twins compared to DZ twins indicates genetic influence. The twin design also provides a powerful way of estimating the “pure” effect of the environment, because differences between MZ twins can only be due to environmental factors. More broadly, genetic and environmental influences on phenotypic variation between individuals in a population can be quantified using statistical models based on observed MZ and DZ twin correlations between their phenotypes. These models produce what are known as ACE estimates, which partition the phenotypic variance into the following sources: additive genetics (A, or heritability h2); the shared environment (C), and the non-shared environment (E, which also includes measurement error). It is important to stress that the two components of environmental influence (C and E) do not reflect specific types of environments in any straightforward sense; they simply describe whether unspecified environments make children in the same family similar to each other (shared environment) or different (non-shared environment). No direct measurement of the environment is involved in making this distinction, and while the shared environment may indeed involve family influences (e.g., parenting), such influences may also be experienced quite differently by siblings within the same family, and so could be estimated as non-shared environment as well. A common mistake is to assume that evidence of shared environment implies family influence (when in fact it could reflect the action of any common exposure, including in utero biological exposures) or that evidence of the non-shared environment rules out the role of the family. Additional evidence—such as direct measurement of these candidate mechanisms—is required to rule on these sorts of hypotheses. An important assumption of the twin method is the equal environments assumption, which asserts that environments are as similar for MZ twins as they are for DZ twins. If this assumption is violated due to environments being more similar for MZ than DZ twins, then estimates of genetic influence will be overestimated. A less commonly appreciated corollary is that DZ twins should not experience more dissimilar environments, which would also tend to inflate estimates of genetic influence. The method also assumes no assortative mating, because in such circumstances the DZ genetic correlation is higher than the 0.50 assumed by the statistical models, which will then underestimate genetic influences. When researchers have tested the equal environments assumption they have tended to find that it is

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not violated (Bouchard Jr. & Propping, 1993; Derks, Dolan, & Boomsma, 2006). Broadly speaking, postnatal environments appear to be just as similar for DZ twins as they are for MZ twins, bolstering confidence in findings from twin studies. However, the equal environments assumption could also be problematic if prenatal environments are systematically different for MZ and DZ twins, biasing twin estimates. One clear example where prenatal environments may be systematically different is the phenomenon of chorionicity: 66% percent of MZ twins share a chorion and placenta, whereas no DZ twins do. The influence of chorionicity can be studied by examining within-pair similarity for MZ twins who did and did not share a chorion. Evidence from such studies indicates that the influence of chorionicity tends to be small and limited to a few traits, so, as a broad generalization, the bias caused by chorionicity in twin studies is likely to be small (van Beijsterveldt et al., 2016). The twin method has been by far the most widely used tool in the field of behavioral genetics and has been instrumental in convincingly demonstrating the pervasive influence of genetics on human cognition, personality and psychopathology (Rutter, Thorpe, Greenwood, Northstone, & Golding, 2003). Although this has been a critical achievement of behavioral genetics, contemporary research has generally moved on from simply estimating heritability, to focusing on more complex mechanistic and developmental questions. These include investigating the dynamic role of genetic influence across development, the role of genetics in linking different traits together, and the two-way interplay between genes and environment and their mutual influence on development. A key early insight in the field was that environmental measures, as long as they can be meaningfully measured separately for each twin of a twin pair, can be just as easily subjected to quantitative genetic analysis as measures of behavior, and doing so makes it possible to observe how genetically-influenced characteristics of the child elicit differences in the environment. The study of so-called gene-environment correlation (rGE) has produced a wealth of important findings which challenge simple notions of one-way causation from the environment to child development. Behavioral genetics has provided a powerful set of tools to elucidate the ways in which individual, partially heritable, traits impact on the environments that children are exposed to, reversing the direction of the causal arrow typically proposed by developmental psychologists. This form of rGE is an example of a much broader phenomenon, namely that genes influence the emergence of complex traits through an enormous and complex array of indirect steps (gene transcription, protein synthesis, embryological development, and so on), many of which will involve a potentially large number of interactions with the environment. The classical twin design can also be extended to examine families of twins (McAdams et al., 2014), and used to examine the genetic causes of intergenerational resemblance. For example, as the offspring of MZ twins share as much of their DNA with their parent’s twin as with their own parent (50% on average), and the offspring of DZ twins share 25% of their DNA with their parent’s co-twin, correlations between the child of a twin and their parent’s co-twin can be used to estimate the role of genes passed from parent to child. A typical question addressed by such a study might concern the extent to which parental depression increases risk of depression in a child due to the transmission of genetic risk, versus the provision of a “depressogenic” environment. This approach has also been important in testing the extent to which smoking or psychological stress during pregnancy causes impairments in child development, or whether common genetic risk factors are responsible (D’Onofrio et al., 2008; McAdams et al., 2014). The twin design can also be extended to multivariate analyses that estimate genetic and environmental influences on the covariance between traits measured at two or more time points or between two or more phenotypes. For example, multivariate genetic analysis allows us to estimate whether the same

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genes create stability of a trait over time, or whether new genetic factors start to influence behavior at different stages of development. Similar analyses can allow us to estimate whether the association between two different traits results from common genetic or environmental factors (e.g., whether trait neuroticism and depression are linked because of common genetic liabilities, Hettema, Neale, Myers, Prescott, & Kendler, 2006). Multivariate analyses have shown that correlations between two or more traits often show substantial common underlying genetic influence (Plomin, DeFries, Knopik, & Neiderhiser, 2016). Multivariate genetic analysis can also throw important light on the mechanisms involved in rGE, for example by identifying the behavioral traits responsible for observed genetic influence on environmental measures (Tucker-Drob & Harden, 2012). Finally, twin studies are also capable of identifying what is known as gene-environment interaction (GxE). GxE is a form of gene-environment interplay in which the strength of influence of genetics varies as a function of the environment. GxE is widely assumed to play a key role in human development, largely on the basis of compelling evidence from animal studies (Cooper & Zubek, 1958; Dick, 2011). The twin method can be leveraged in several ways to estimate GxE. The first and simplest is to estimate the standard ACE parameters at different levels of an environmental measure. More complex statistical models can also estimate GxE while taking account of rGE, where the environmental moderator is non-linear and where the environmental factor is latent rather than measured (Molenaar & Dolan, 2014; Purcell, 2002). Despite the attractiveness of the GxE notion for developmentalists, and the strong evidence of its commonplace contribution to development from animal studies, demonstrating this convincingly in human populations has proved difficult. Although there may be several reasons for this, a key issue is that, by their very nature, GxE effects are dependent on the level of the environmental exposure, which may be highly variable from one population to another. For example, where a true cross-over interaction is present, a study relying on a sample that happened to be above the mean of an environmental exposure might detect increasing heritability as the environmental exposure increases, while a similar study using a sample that was below that mean could observe the reverse. GxE effects are also highly dependent on the scaling of the measurements and especially on range limitations, such as floor and ceiling effects. Sophisticated psychometric techniques are being developed to try to address these difficulties (Molenaar & Dolan, 2014), but have not yet been widely adopted. Adoption studies. Adoption is a natural experiment that creates “genetic” and “environmental” relatives. “Genetic relatives” (parents/siblings) are genetically related individuals who do not share the same family environment, e.g., adopted children and their birth parents, or genetically related siblings who are reared separately. “Environmental relatives” are genetically unrelated individuals who share a common family environment, e.g., adopted children and their adoptive parents, or genetically unrelated children who were adopted into the same home. Correlations between such relatives on developmentally relevant variables can, under certain assumptions, be used to directly estimate genetic and environmental influences respectively. The adoption design is most suited to estimating genetic and environmental influences if the children were adopted at or very close to birth, as there is less potential for environmental confounding than in later adopted children. Other key threats to the validity of the adoption design for estimating genetic effects are prenatal influences, selective placement and ongoing parental contact. Notably, while adoption practices at the turn of the 20th Century tended to include deliberate selective placement, in recent studies there is either limited evidence of selective placement, or detected effects can be largely controlled for (Horn, 1983; Leve et al., 2013; Rhea, Bricker, Wadsworth, & Corley, 2013).

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Although not limited to the adoption design, adoption studies may be particularly vulnerable to the problem of poor representativeness—birth parents and adoptive families may not be representative of the wider population. For example, samples of adoptive families are generally under-represented by those of low socioeconomic status (SES). As a result, findings from adoption studies may underestimate the importance of environments correlated with poverty and may only be generalisable to middle income families. Empirical evidence on the representativeness of adoption samples is somewhat mixed—several have found evidence for a lack of representativeness (Stoolmiller, 1999), whereas some adoption studies are broadly representative of the wider population (Leve et al., 2013). McGue et al. (2007) examined the issue of representativeness in a sample of adoptive and non-adoptive families. They found that adoptive families yielded lower variance in measures of psychopathology and SES than non-adopted families. However, this reduction in variance did not appear to markedly influence estimates of the association between family circumstances and children’s outcomes, suggesting that the lack of range in adoptive families’ circumstances may not substantially bias inferences drawn from adoption studies about the influence of the environment. Just like the twin design, correlations between two phenotypes (e.g., biological parent phenotype and child phenotype) can be used to detect genetic influences, and indeed in the adoption design the analysis is generally much simpler than the twin design—the humble correlation, for instance, provides a reasonable estimate of the genetic effect. One obvious and important difference between the twin design and the adoption design is that in the latter we are usually relating two measurements taken at different stages of the lifespan (birth parent versus child), and often using different instruments to do so. This will tend to lead the adoption method to underestimate the true heritability of a trait. In that regard, it is notable, for example, that estimates of genetic influence on antisocial behavior are considerably lower in adoption studies based on parent-offspring pairs (genetic influence 30%) compared to twin studies (genetic influence 45%), whereas sibling-based adoption studies are more consistent with the twin estimates (see Rhee & Waldman, 2002). Another important consideration when interpreting adoption studies is that statistical power is often not high, partly because large adoption samples are not easy to obtain and also because often only one birth parent is available to provide data, so that only half of the genetic effect is directly observable. A key methodological advantage of the adoption design is that it removes a major source of rGE— so-called passive rGE, which arises due to the fact that biological parents provide a child-rearing environment that is correlated with their own and their child’s genotype. The high level of correlation between genes and environments in biological families can make the detection of GxE difficult. In the adoption design, the child’s genes become effectively uncorrelated with much of the adoptive family environment, and hence adoption studies are particularly well placed for studying GxE. Just like the twin design, the adoption design can also be used to study rGE by testing for association between birth parent characteristics and measures of the adoptive family environment, such as parenting.

Key interpretative issues In outlining the twin and adoption methods above, we already touched on a number of key interpretative issues that must always be kept in mind when appraising data from quantitative genetics research. One is so critical that it warrants repeating: as black box methods for estimating the overall contribution of heritable genetic factors to complex traits, these methods say nothing about the

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underlying mechanisms involved and generally speaking they describe the net result of most likely an exceptionally large number of complex gene-environment processes unfolding at multiple levels of biological and social organization over the course of development. Finding substantial heritability does not imply simple, unmediated, genetic influence on a trait, and many genetic effects may involve substantial environmental mediation (Rutter, 2000). Secondly, the estimates of genetic influence that are obtained from quantitative genetic methods describe the current causes of population differences in a trait, and not the degree to which genetic factors are responsible for a trait in a given individual. Critically, estimates of genetic influence do not imply immutability. A commonly noted example of this is physical height, where a large proportion of the variance within a population tends to be explained by genetics, but despite this, height has increased substantially since the middle of the 19th Century (Fisher, 1919; Lettre, 2011; NCD Risk Factor Collaboration, 2016). Similar arguments apply to the study of the genetics of IQ, which has also seen considerable rises over the last 50 years, despite high heritability. Furthermore, evidence of genetic influence says little if anything about where, in the cascade of developmental events involved, one should focus intervention. The most commonly cited example to illustrate this is phenylketonuria (PKU), which is a genetic condition that leads to the inability to metabolize the amino acid phenylalanine. Untreated, PKU leads to severe damage to the central nervous system, but a comparatively simple environmental intervention— excluding phenylalanine from the diet—entirely prevents any adverse developmental effects, as long as it is introduced shortly after birth. A further, often under-appreciated, interpretative issue concerns the role of GxE. As we noted above, there are significant difficulties in human quantitative genetic studies in properly capturing GxE effects (Dick, 2011), even though most commentators agree that it is highly likely they exist and indeed are prevalent. As a result, it is helpful to be aware of the consequences of ignored GxE, when appraising studies that report genetic “main effects.” In general, in standard modeling, such as that used for twin analyses, ignored gene-by-common environment interactions will be estimated as genetic effects, whereas ignored gene-by-non-shared environment interactions will be estimated as non-shared environment effects. Ignoring GxE can lead to quite dramatic biases in effect estimates (Eaves, 1984).

Key results from twin and adoption studies As twin and adoption studies each depend on different assumptions, confidence in their findings is strengthened considerably when they converge on consistent results. Below we outline some key discoveries in developmental behavioral genetics that are supported by a substantial body of converging evidence from twin and adoption research. Heritability of complex developmental traits. Twin and adoption studies consistently show that virtually all psychological and behavioral traits are under substantial genetic influence, but none are entirely heritable. The results of these studies clearly show that individual differences in complex traits within a population generally arise through a combination of genetic and environmental influences. For example, for general cognitive ability, twin and family designs converge on a heritability estimate for cognitive ability of approximately 50% (Loehlin, 1989), for autism around 90% (Sandin et al., 2014; Tick, Bolton, Happe, Rutter, & Rijsdijk, 2016), and 74% for ADHD (Faraone & Larsson, 2019). In a recent meta-analysis of almost all published twin studies of complex traits, the heritability across all of the reported traits was 49% (Polderman et al., 2015). An obvious implication of this for those interested

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in epigenetics is that virtually any measure, including epigenetic processes such as DNA methylation, is likely to be under some degree of direct or indirect genetic control and such non-environmental mechanisms must be considered when designing and interpreting epigenetic studies (Bell & Spector, 2012). Nevertheless, the data also provide arguably the most compelling evidence for the important role played by the environment in psychological and behavioral development, as we discuss further below. Genetic influence on the stability of traits across development. Broadly speaking, genetic effects appear to be a more systematic source of individual differences in complex traits than environmental effects (Plomin, 2018), and longitudinal evidence consistently suggests that the stability of traits across development can largely be attributed to genetic stability. For example, a meta-analysis of twin and adoption studies of IQ across the lifespan found that stability in IQ over development was almost entirely due to common genetic influences. Similar findings have been replicated in relation to many other psychologically relevant traits (Plomin et al., 2016). Of course, given the increasing recognition of the importance of rGE, these results do not imply that genetically-based stability is not in part underpinned by cascading and reinforcing environmental processes correlated with a child’s genotype. Increasing heritability of IQ. A counterintuitive but now widely accepted finding is that the heritability of IQ increases across the lifespan, with a concurrent reduction of the influence of the environment that is shared by siblings (McGue, Bouchard Jr., Iacono, & Lykken, 1993; Plomin & Deary, 2015). Results from a meta-analysis of longitudinal twin and family studies from infancy to adolescence indicates that from middle childhood onwards genetic influences stabilize and the same genes appear to operate henceforth with increasing influence (Briley & Tucker-Drob, 2013). Given the stability of the genome itself, it seems somewhat paradoxical that the heritability of IQ increases across the lifespan. The most plausible explanation appears to be genetic amplification through processes of rGE. The idea is that genetic differences can become amplified across development as individuals influence, select and modify environments that are correlated with their genotype (Plomin, DeFries, & Loehlin, 1977; Scarr & McCartney, 1983). rGE will be discussed in greater detail below. Most environmental effects are non-shared. Developmental psychology has tended to draw attention to the influence of environments that are shared by family members and often assumes, implicitly, that experiences within the family will make siblings similar to each other. However, behavioral genetics research has generally found quite limited evidence for shared environmental effects on individual differences in many behavioral and psychological traits. The nonshared environment appears to be the primary source of environmental variance for the majority of complex traits (Plomin, 2011; Plomin & Daniels, 1987). In other words, the environmental influences that are most powerful in influencing individual differences in psychological traits appear to be unique to children within a family, rather than shared by siblings. Even if one takes account of the fact that the non-shared environment also captures non-systematic measurement error, these striking findings highlight the importance of unique experiences or exposures as determinants of child development. The recognition of the crucial importance of the nonshared environment has led to concerted efforts—such as the Nonshared Environment in Adolescent Development (NEAD) study (Reiss, Neiderhiser, Hetherington, & Plomin, 2000)—to identify the specific factors underlying nonshared effects. Researchers have used differences between MZ twins as a simple and powerful method for identifying environmental factors associated with discordant outcomes. For example, Caspi et al. (2004) found that higher maternal negative expressed emotion and lower warmth towards one twin at age 5 years was associated with higher antisocial behavior in that twin relative to the genetically identical co-twin. In the same sample at age

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7–8 years Arseneault et al. (2008) found that differences within MZ twin pairs in exposure to bullying were associated with differences in their internalizing behavior problems. These findings represent rather strong evidence of non-shared environmental influences, in the sense that common genetic effects can be ruled out. However, despite these positive findings, as a generalization it has been difficult to identify consistent sources of non-shared effects on child development. Researchers have hypothesized that nonshared effects may be partially a result of many experiences of small effect and may often be unpredictable, or developmentally stochastic, rather than systematic (Plomin et al., 2016; Turkheimer & Waldron, 2000). A great deal of work still remains to be done to understand how it is that siblings, even genetically identical ones, come to have such different developmental outcomes. Notwithstanding the crucial importance of the non-shared environment it would be inaccurate to say that shared environment effects are not important at all. For example, there is evidence of shared environmental influences on IQ during childhood (Briley & Tucker-Drob, 2013; Haworth et al., 2010). Haworth and colleagues combined 4 large-scale longitudinal twin studies to examine the changing contribution of genes and environment to cognitive ability from late childhood into late adolescence. Pooled analyses showed a linear increase in genetic contributions to cognitive ability from 41% in childhood (aged  9 years) to 81% in late adolescence (though note this latter estimate is higher than obtained from most meta-analytic reviews of adult IQ). At the same time, shared environment effects were significant across all ages, reducing from 33% in childhood to 16% in late adolescence. It is only in adulthood that shared environment effects on cognitive ability decline essentially to zero (Plomin et al., 2016). As we noted earlier, shared environment effects on cognitive ability may also be stronger and more persistent in low-SES populations, although the picture is complex (Tucker-Drob & Bates, 2016). A rather striking further example of shared environmental processes at work in early development can be found in the domain of child-parent attachment. Security of attachment has long been considered a key influence on childhood emotional and psychological development and has been conceptualized as resulting from variation in the quality of parental caregiving (Ainsworth, Blehar, Waters, & Wall, 1978). Attachment theorists have generally assumed that variation in attachment security is entirely environmental in origin, and strongly of the shared environmental type. As most developmental traits tend to show quite marked genetic influence and a predominance of the non-shared environment, attachment theory’s claims might be considered rather bold. Nevertheless, three twin studies, using standardized assessment of attachment security in infancy/toddlerhood, provide quite strong evidence in favor of attachment theory’s predictions. Bokhorst et al. (2003), for example, found that more than half of the variance in security of attachment was due to the shared environment, and the remainder was due to the non-shared environment. The estimate of genetic influence was effectively zero. Similar results are reported by Roisman and Fraley (2008) and O’Connor and Croft (2001). Fearon et al. (2006), using the same sample of twins as Bokhorst et al. (2003), were able to show, using multivariate genetic analysis, that common environmental variance in attachment security correlated, as predicted, with common environmental components of variance in observed maternal sensitivity. The results from these twin studies thus suggest that attachment security-insecurity might be unusually sensitive to environmental influence and specifically to the quality of caregiving. However, it is important to note that attachment is considered to be a construct of relevance right across the lifespan, so it is essential to investigate the potentially changing balance of genetic and environmental influences beyond very early childhood. In that regard, recent findings from a study of attachment in adolescence, using an instrument known as the Child Attachment Interview (Shmueli-Goetz, Target, Fonagy, & Datta, 2008), are instructive. In this comparatively large-scale twin study, Fearon, Shmueli-Goetz, Viding, Fonagy, and

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Plomin (2014) found that nearly 40% of the variance in attachment security was attributable to genetic factors, and the remainder was attributable to the non-shared environment. They found no evidence of shared environmental influence on attachment in adolescence, in marked contrast to the picture they observed in infancy. This apparent shift towards genetic and non-shared environmental influence in the move towards adolescence may reflect a pattern that is not unique to attachment, as the earlier example of cognitive ability illustrates. Indeed, Tucker-Drob, Rhemtulla, Harden, Turkheimer, and Fask (2011) report striking findings from very early cognitive development that are highly relevant to this discussion. In a sample of 750 infant twins, these authors found no evidence of genetic influence on cognitive ability at 10 months, and substantial evidence of shared environmental influence. By 2 years, the heritability of cognitive development had increased to 23%, and, most strikingly, this increase was strongest for, and indeed largely restricted to, infants in high-SES families (where nearly 50% of the variance was heritable by age 2). By contrast, in low SES families there remained little evidence of genetic influence and a consistently strong contribution from the shared environment. Another key domain where relatively consistent evidence of shared environmental influences is found is externalizing behavior problems. A number of studies have found evidence of shared environmental influence on externalizing problems in early development, and some studies report that such effects diminish with age. van der Valk, van den Oord, Verhulst, and Boomsma (2003) found that nearly 50% of the variance in externalizing problems was attributable to genetic factors, and approximately 30% to the shared environment in a sample of 7-year old twins. Hudziak et al. (2003) found a modest contribution of common environment influence on externalizing problems at age 3, which became considerably smaller by age 7 and 10 years, although Bartels et al. (2004) continued to detect shared environmental influences on externalizing problems at age 10 years in a large twin sample. Indeed, in a comprehensive meta-analysis of 490 child/adolescent twin and adoption studies, Burt (2009) found that the shared environment accounted for approximately 15% of the variance in externalizing problems and indeed did so more or less consistently for all of the major domains of psychopathology. Furthermore, consistent with the observations above, these estimates tended to decline with age, from around 23% in early childhood to 15% in adolescence. As Burt notes in her review, these are important findings, because the shared environment, unlike additive genetic effects and the non-shared environment, is not contaminated by ignored GxE or rGE. Furthermore, because measurement error is included in the denominator, the shared environmental estimate here is likely to be conservative.

Gene-environment interplay As we outlined previously, the focus of contemporary behavior genetics research has shifted from questions about whether traits are influenced by genes or the environment to questions about how genes and environments work together to influence phenotypic variation, through the study of rGE and GxE.

Gene-environment correlation rGE is a central mechanism of interest in the study of development, as it provides important insights into the dynamic interaction between inherited characteristics of the child and their psychosocial environment. Broadly speaking, rGE is said to be present when an individual’s environment is correlated with their genotype. The near-ubiquity of the phenomenon is indicated by a substantial body of

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behavioral genetics. For example, a systematic review of studies examining the heritability of 35 ostensibly environmental measures (including, parenting, family environment, and stressful life events) found a weighted heritability of 27% across each (Kendler & Baker, 2007). Three main forms of rGE have been defined in the literature: passive, active, and evocative (Plomin et al., 1977; Scarr & McCartney, 1983).

Passive rGE occurs when the same (parental) genes that influence the rearing environment also influence relevant child traits when passed to the child. Passive rGE can be detected and controlled for in adoption studies. Researchers have used a number of genetically informative designs to examine passive rGE, including comparison of biologically related families and those who are not biologically related. For example Rice, Lewis, Harold, and Thapar (2013) studied passive rGE in a sample of parents and their children who were conceived through assisted reproduction. The sample included biologically related families, as well as those who had conceived via sperm, egg or embryo donation. Comparison of associations between parent and child depressive symptoms in related and unrelated dyads provided evidence of passive rGE.

Active rGE occurs when an individual’s genetically influenced traits influence the types of environment that they select or choose, such as career or friendship group. There is good reason to believe that active rGE becomes more important beyond childhood, once individuals have a greater opportunity for active selection of their environments. For example, Tarantino et al. (2014) found substantial genetic contributions to deviant peer affiliation across adolescence (age 15–21 years), suggesting that heritable traits contributed to a young person’s tendency to seek out, or perhaps be sought out by, deviant peers. Further, Boisvert, Connolly, Vaske, Armstrong, and Boutwell (2019) used multivariate genetic analysis to show that common genetic factors underpinned the phenotypic correlation between antisocial behavior and deviant peer association. Interestingly, a study by Connolly, Schwartz, Nedelec, Beaver, and Barnes (2015) found that peer pressure encouraging of delinquent behavior showed limited genetic influence in preadolescence (suggesting little rGE) but increasing genetic influence across adolescence, consistent with the notion that active rGE starts to become a substantial driver of peer processes during this later period. Similarly, the association between peer pressure and antisocial behavior was primarily environmentally mediated at age 10–11 years, but common genes linked their interlinked growth over time.

Evocative rGE occurs when an individual’s genetically influenced characteristics evoke responses from the environment. For example, in the context of early cognitive development, initial genetic differences may systematically elicit different learning environments from parents and teachers. Evocative rGE may be the most important form of rGE in early development, when child effects on caregiving have been well established but active selection of environments is likely to be limited (Bell, 1968). Particularly strong evidence of the evocative influence of child genes on caregiving behavior comes from twin and parentoffspring adoption studies. A classic adoption study by Ge et al. (1996) found that children aged 12–18 years whose birth mothers showed high levels of externalizing problems received more negative and harsh parenting from their adoptive parents relative to those children whose birth mothers did not

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have externalizing problems. Twin and family data have also yielded quite consistent evidence of evocative rGE in adolescence. For example, there is evidence from both twin, sibling and an extended children-of-twins study to suggest that evocative rGE may explain the correlation between adolescent externalizing problems and parental negativity (Marceau et al., 2013; Pike, Mcguire, Hetherington, Reiss, & Plomin, 1996). Although less attention has been paid to rGE in the earliest stages of the lifespan, the recent Early Growth and Development Study—a prospective adoption study of early childhood—has found evidence of evocative effects on parenting in infancy and toddlerhood in relation to genetic risk for internalizing and externalizing psychopathology, low social motivation and attention-deficit hyperactivity disorder (Elam et al., 2014; Fearon et al., 2015; Harold et al., 2013; Klahr et al., 2017). Data from the EGDS study indicated, for example, that even by 9 months of age, infants of birth mothers with greater externalizing problems evoked more parent negativity in their adoptive mothers. Interestingly, this tendency was only observed in adoptive families reporting high levels of marital distress, suggesting that relationship factors may buffer or amplify the degree to which the caregiving environment is liable to being evoked by the child’s heritable traits (Fearon et al., 2014). Evoked parental negativity partially accounted for later child behavioral problems at 36 months. This way of understanding rGE—with evoked environmental responses potentially playing a causal role in the mechanisms of genetic risk transmission—is likely to be an increasingly important focus for behavioral genetic research in the coming years.

Gene-environment interaction The last couple of decades have seen a sharp increase in the number of studies being conducted on GxE in the development of complex traits (Dick, 2011). Approaches used to examine GxE include animal research, twin and adoption studies, and molecular genetic methods. Here we focus principally on twin and adoption studies, and we return to the issue of GxE in our discussion of molecular genetic methods in the section that follows. Adoption studies. Adoption studies have been used to examine GxE by testing whether children whose birth parents exhibit particular characteristics (representing genetic influence) have different outcomes when exposed to different adoptive family environments. As we noted above, findings from such adoption studies have been somewhat mixed (Knopik et al., 2017) and several studies have failed to find evidence of GxE, in particular in relation to cognitive development (Plomin et al., 1977; Plomin, DeFries, & Fulker, 1988). However, others have found robust evidence of GxE. For example, the Early Growth and Development Study has reported evidence of GxE across numerous traits in early and middle childhood (Leve et al., 2019). This includes evidence of GxE in the development of behavior problems (Leve et al., 2009; Lipscomb et al., 2012; Roos et al., 2016), callous and unemotional traits (Hyde et al., 2016), behavioral inhibition (Natsuaki et al., 2013), attention (Brooker et al., 2011; Leve et al., 2010) and social competence (Van Ryzin et al., 2015). Twin studies. Although classical twin studies have predominantly been used to produce ACE estimates without considering GxE, an increasing number of twin studies have been used to test for GxE. For example, we mentioned already the Early Childhood Longitudinal Survey Birth Cohort study, which examined GxE in early cognitive outcomes through calculating heritability estimates in children raised in high and low SES environments (Tucker-Drob et al., 2011). In this study, genes were found to account for approximately 50% of the variance in cognitive ability at 2 years of age among children raised in high SES environments. By contrast, there was negligible heritability in the

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low SES group of children, consistent with gene-by-SES interaction. A meta-analysis by TuckerDrob and Bates (2016) found that while similar findings have been replicated in several other studies conducted in the United States, there is mixed evidence in Western Europe and Australia. One interpretation of this heterogeneity is that the zero or reversed gene-by-SES effects in Western Europe and Australia may reflect the greater availability of high-quality education in these countries. This pattern of GxE is not restricted to IQ/cognitive ability. For example, in a sample of >18 k twin pairs, Middeldorp et al. (2014) found that the heritability of behavioral problems was lower, and environmental effects larger, for children in low SES circumstances, compared to their high SES counterparts. A striking example of GxE in the domain of socio-emotional development is provided by Jaffee et al. (2005). Using a large risk-oversampled cohort (the E-Risk study), Jaffee and colleagues used the co-twin’s conduct problems as a marker for the target twin’s genetic risk for conduct problems. Using this genetic risk marker, the authors found that exposure to maltreatment substantially increased conduct problems for those at high genetic risk, but much less so for those at low genetic risk. A recent study of 6–11-year-old twins found that genetic contributions to children’s externalizing problems were greater at higher levels of maternal depressive symptomatology than at lower levels (Clark, Klump, & Burt, 2018). Similarly, in teenagers, Samek et al. (2015) found that genetic influences on externalizing disorders were strongest in the context of parent-child relationship problems. It is interesting to note that for some environmental risks or developmental domains, greater environmental risk appears to be associated with lower heritability estimates (e.g., for SES-related risk), whereas for others it is associated with higher heritability (e.g., parental depression, maltreatment). These different patterns of GxE are sometimes interpreted with respect to two plausible models of interaction. The first is the familiar diathesis-stress model, which suggests that environmental adversity may exacerbate genetic vulnerability; or, put the other way around, genetic vulnerability may only be expressed when triggered by an adverse environmental exposure. In contrast, the “bioecological interaction” model seeks to explain how genetic effects may become stronger in less adverse environmental circumstances. According to this model, enriched environmental circumstances may remove environmental constraints on individual potential, leaving mostly genetic effects to explain individual differences. In harsh or deprived conditions, environmental forces may “overwhelm” and thereby reduce genetic differences in development (Bronfenbrenner & Ceci, 1994; Burt & Klump, 2014). Although it is tempting to conclude, from the studies reviewed above, that intra-familial risk processes, like parental negativity, tend to conform to a diathesis-stress model, whereas broad contextual risks like low SES conform more to the bioecological model, the picture is probably not as simple as that. For example, Burt and Klump (2014) found that higher parent-child conflict was associated with lower genetic influences (and greater shared environmental influence) on child conduct problems in 6–10 year olds. Understanding the circumstances and mechanisms that give rise to these distinct forms of GxE across the course of development is an important goal for future behavioral genetic research. A further form of GxE that has garnered increased attention in recent years is the notion of differential susceptibility. This term captures the idea that some genotypes may confer greater plasticity, which may render an individual more susceptible to both positive and negative environmental exposures—for better, or for worse, whereas other individuals, or genotypes, may be associated with adequate levels of functioning across a wide range of environmental circumstances (Belsky, Bakermans-Kranenburg, & Van IJzendoorn, 2007). The evidence in support of this hypothesis has been reviewed in detail in Bakermans-Kranenburg and Van Ijzendoorn (2015).

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Genomic approaches to behavioral genetics Heritability estimates from twin and adoption studies represent the end result of very indirect chains of events from the genome to phenotypes—they reveal nothing about the specific genetic variants that may be influencing individual differences in complex behavior. The Human Genome Project and emerging genomic technologies have allowed researchers to efficiently genotype human DNA, and test for associations between genetic variants and behavioral phenotypes. Increasingly, behavioral genetics researchers have been attempting to uncover specific genetic mechanisms using these genomic tools. In addition to providing direct clues about biological mechanisms (through the identification of specific genes and their functions) genomic studies also provide a potentially crucial corroboration of the results of twin and adoption studies because they rely on very different assumptions. Two major approaches in the field include candidate gene studies and tests of genome-wide association. Candidate Gene Studies. Candidate gene studies are theory-driven tests of association between phenotypes and variants of genes which have been identified as related or relevant to these phenotypes, typically based on their known biological properties. By bypassing expensive and time-consuming whole-genome analysis, it is a relatively cheap and quick method. However, candidate gene studies have largely fallen out of favor due to a failure to replicate significant findings of association (Tabor, Risch, & Myers, 2002). Indeed, an enormous body of research has investigated the association between individual candidate genes, usually related to neurotransmitter function, and the development of personality and psychopathology. Despite seemingly promising early findings, large-scale studies with pre-registration of hypotheses and/or rigorous control for multiple hypothesis testing showed that few if any of these associations were reliable Several candidate genes have also been examined in analyses of GxE. For example, Caspi and colleagues (Caspi et al., 2002) identified an apparent interaction between maltreatment and a functional polymorphism in the gene encoding the neurotransmittermetabolizing enzyme monoamine oxidase, such that children who carried the gene were less likely to develop antisocial behavior problems than those who did not. In a further paper these (Caspi et al., 2003) authors examined short and long serotonin transporter alleles in the 5-HTT gene (as in animal studies by (Barr et al., 2003) and (Bennett et al., 2002) and reported an interaction between allele size and stressful life events. In their sample, individuals carrying the short 5-HTT serotonin transporter allele were more likely to experience depression in relation to stressful life events than individuals who were homozygous for the long allele. As with the candidate gene literature more broadly, findings of candidate GxE have tended not to replicate (Dick, 2011). Indeed, a meta-analysis of attempts to replicate the interaction between 5-HTT and stressful life events in relation to depression symptoms found no consistent evidence of this interaction (Risch et al., 2009). Increasingly, the field is concluding that there are very few single variant associations with complex traits that show remotely large effect sizes, either as main effects or in interaction with the environment. Such conclusions have been underpinned by large-scale genome-wide association studies. Genome-wide association studies. Genome-wide association studies (GWAS) scan very large numbers of commonly occurring genetic variants (single-nucleotide polymorphisms, SNPs) across the entire genome of many individuals and test for associations between phenotypes and sometimes millions of genetic variants. In effect, GWAS represent a blind (hypothesis-free) search of the genome for evidence of association, in contrast to candidate gene studies. Compared to candidate gene studies, GWAS require very large samples, in part due to the extremely low significance threshold required to account for the multiple statistical tests being conducted. As sample sizes have increased

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dramatically over the years, GWAS studies have successfully identified a large number of seemingly reliable (replicable) genotype-phenotype associations for many complex traits. For example, a recent GWAS of educational attainment involving over 1.1 million individuals identified more than 1000 genome-wide significant SNPs. Summing together the number of outcome-related SNPs into so-called polygenic scores allowed the researchers to explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance in independent prediction samples (Lee et al., 2018). GWAS typically find associations between behavioral phenotypes and individual SNPs that represent very small effect sizes, suggesting that—unlike single-gene disorders—complex traits are highly polygenic, being influenced by a very large number of genetic variants of small effect. GWAS rarely—if ever—recover associations between phenotypes and the “usual suspect” candidate genes (although the APOE gene in the case of Alzheimer’s Disease is an exception, see for example, Harold et al., 2009). In the last 5–10 years, large-scale GWAS, often combining data from multiple samples, have identified reliable genome-wide significant variants for a number of important developmental or psychological disorders, including autism, ADHD, depression, bipolar disorder and schizophrenia, and demonstrated the crucially important role of common genetic risk in accounting for comorbidity among many of these disorders (see Smoller et al., 2019). A surprising finding has been that GWAS studies explain only a small fraction of the heritability identified in twin and adoption studies. For example, twin studies estimate height to have a heritability of around 0.8, whereas recent GWAS studies have identified around 50 SNPs associated with height that account for just 5% of its variance in the population (Yang et al., 2010). This issue has become known as the missing heritability problem. The small proportion of heritability explained by GWAS also limits their predictive value. While some of the missing heritability gap may be explained by rare and ultra-rare variants of large effect, which may never be possible to detect using GWAS, the hope is that with improved methods (such as wholegenome sequencing) and increasing sample sizes, GWAS may soon account for a larger fraction of the heritability of behavioral phenotypes. Study of the interplay of genes with other genes, and with the environment is also likely to help explain more of the overall phenotypic variation. While GWAS have made extremely important contributions to our understanding of the genetic basis of complex developmental traits, major challenges remain in using these methods for understanding mechanisms of development. The identification of specific genomic variants gets us a little closer to informative underlying neurobiology, especially when combined with transcriptomics data and modeling based on biological pathways and interactions databases, but there remains a vertiginous gulf between indications of genetic association and mechanistic understanding of development. There are many hurdles to overcome before these genomic techniques can more substantially advance developmental science. One serious barrier is the very large sample sizes required, which currently place severe limits on how frequently waves of data can be collected and on the richness of the data that can be captured at that scale. Realistic and in-depth measurements of the environment, which we know are a crucial part of the picture from quantitative genetic studies, are currently difficult to include in GWAS due to the prodigious costs. Similar cost problems are faced if one wishes to study genetic mechanisms in brain development using neuroimaging. As polygenic risk scores improve, in terms of the range of domains of development captured and the proportion of variance they explain, it is likely that smaller scale (though still likely in the 1000s of participants), developmentally richer, longitudinal studies will become more feasible in the coming years. These methods are also amenable, at least in principle, to studying the same kinds of complex

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developmental questions that quantitative genetics has investigated over the last 30 or more years, including genetic mechanisms of continuity and change, rGE and GxE. An elegant recent example is that of Kong et al. (2018). These authors analyzed data on educational attainment using triplets of genomewide datasets from two parents and an offspring. Doing so allowed the authors to distinguish genes within the educational polygenic risk score that were and were not transmitted from either parent to the child. Statistical analyses showed that the non-transmitted alleles were associated with the offspring’s educational attainment even though the offspring never received them. This therefore indicates that the parents’ genotypes were influencing the child’s educational attainment via the environment they provided, an effect referred to as genetic nurture. This neat insight provides a novel method to study a potentially important form of what is clearly rGE. Kong et al. estimated that approximately 30% of the polygenic risk score’s association with educational attainment is actually due to environmental mechanisms of transmission. As well as highlighting this important application of genomic techniques for studying rGE, the Kong paper highlights one of several significant and not always thoroughly appreciated methodological issues in GWAS-based genetic epidemiology. The vast majority of GWAS do not take account of parental genotype, and, for all but a small number of phenotypes, we currently do not know the degree to which current association estimates are confounded by parental genotype. Genetic epidemiological studies are also quite susceptible to bias due to population stratification—where sub-strata of the population differ both in the prevalence of the phenotype of interest and in their genotypes, leading to artefactual association. Although attempts have been made to statistically control for such stratification using principal components, it is becoming clear that quite subtle ancestral differences may be common and can bias GWAS estimates (Byrne et al., 2020). This, and other forms of bias, continue to be important and active areas of methodological development within the field (Morris, Davies, Hemani, & Davey Smith, 2019).

Concluding remarks The field of behavioral genetics has made an enormously important contribution to development science, and will continue to be a key framework and set of research tools for developmentalists in the coming decades. The accumulated evidence shows persuasively just how widespread and ubiquitous genetic factors are in shaping the course of development. It highlights the critically important need to consider genetic counter-explanations of assumed environmental influences, and compels us to consider the dynamic interactions between the genome and the environment. Arguably, no understanding of environmental influences on development is possible without an understanding of genetics. Epigenetic regulation of gene function, the subject of many of the chapters in this book, provides a potentially vital way of understanding the biological “leading edge” of such gene-environment interplay, which may have the potential to dramatically advance our understanding of the dynamic mechanisms of nature and nurture.

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Prenatal exposures and behavioral epigenetics in human infants and children

4

Helena Palma-Gudiela,b and Lourdes Fan˜ana´sa,b Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, edica en Red en Salud Mental (CIBERSAM), Madrid, Spainb Barcelona, Spaina Centro de Investigacio´n Biom


Early environmental programming Human plasticity reaches its maximum potential during the prenatal period. Thus, one could consider that humans are most vulnerable to external threats during that very same stage. From an evolutionary point of view, observed deviations from normal development in response to unexpected inputs are aimed to improve short-term survival. This principle was first posited by Barker (1995) in his fetal programming hypothesis, also known as the Developmental Origins of Health and Disease (DOHaD) theory. One of the best examples to understand the principles of such a theory is the so-called “thrifty phenotype” as observed in the offspring of women exposed to malnutrition during pregnancy due to the Dutch famine (Roseboom, 2019). Intrauterine growth restricted (IUGR) and small for gestational age (SGA) infants would grow to be more prone to obesity, metabolic syndrome and type 2 diabetes; this paradoxical relationship might arise from a mismatch between prenatal and postnatal environment (Hales & Barker, 2001). Additionally, prenatal exposure to famine has been also associated with an increased risk of suffering psychiatric disorders, including schizophrenia (Wang & Zhang, 2017). Interestingly, early programming is not restricted to the prenatal period but has also been documented in response to the early postnatal care, particularly in animal models. Natural variation in maternal care of rat pups, such as licking/grooming and arched-back nursing (LG-ABN) behavior, has been associated with adult rat behavior; specifically, lower LG-ABN reared pups exhibited higher fearfulness and lower cognitive development when compared to pups reared by high LG-ABN rats (Caldji et al., 1998; Liu, Diorio, Day, Francis, & Meaney, 2000). Authors further demonstrated this long-term programming to be mediated by different maternal behaviors rather than being transmitted by genetic risk by means of several cross-fostering studies (Francis, Diorio, Liu, & Meaney, 1999). Likewise, institutionalized children are at increased risk of developing a wide range of long-term psychopathological outcomes (Humphreys et al., 2015; Sonuga-Barke et al., 2017), possibly mediated by deregulated biological mechanisms, including stress response and brain wiring (Bick et al., 2015; Kumsta et al., 2017). The observed developmental plasticity must be embedded through some kind of Developmental Human Behavioral Epigenetics. https://doi.org/10.1016/B978-0-12-819262-7.00004-0 # 2021 Elsevier Inc. All rights reserved.

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biological mechanism that acts at a different level than the unvarying genetic sequence. Epigenetic modifications are one of the main candidates to mediate such long-term programming of health and disease thanks to their plasticity and subsequent stability (Provenc¸al et al., 2019).

Biological embedding Epigenetics include any change in the chromatin conformation that affects gene expression without altering the genetic sequence itself. One of the more studied epigenetic mechanisms is DNA methylation. It consists in the addition of a methyl group to cytosine (C) residues, when those are adjacent to guanine (G) residues; these dinucleotides are known as CpG sites (where the “p” refers to the phosphate group linking both nucleotides). Intriguingly, CpG sites are scarce relative to the human genome total length, with an estimated 28 million CpG sites. Despite their infra-representation, CpG sites are found clustered in CpG islands, usually located in gene promoter regions and characterized by very low levels of DNA methylation. The exploration of DNA methylation in human studies posits several challenges (for a review see Heijmans & Mill, 2012). First, DNA methylation is tissue-specific; so much so that intraindividual differences across tissues are higher than interindividual differences when looking at the same tissue. One of the more widely explored tissues in the epigenetic field is peripheral blood due to its accessibility; however, it comprises several cell types, each of them carrying unique epigenetic marks (Houseman et al., 2012). Specifically, cord blood collection at birth is well-suited for epigenetic analysis since it can be sampled non-invasively, although it might contain unusual cell types such as nucleated red blood cells (nRBCs) that have to be accounted for due to their unique methylation pattern (de Goede, Lavoie, & Robinson, 2016). Likewise, placenta also arises as a biologically relevant tissue to be explored in the context of the perinatal period; again, the human placenta is characterized by lower levels of methylation when compared to other tissues suggesting its unique epigenetic regulation (Schroeder et al., 2013). Indeed, cord blood and placental methylation patterns are not directly comparable as both tissues differentially record prenatal exposures (Ma et al., 2019). Comparability and relevance of findings obtained when assessing peripheral samples (as opposed to brain tissue) is often called into question in the context of prenatal stress and its relationship with postnatal behavior. Conversely, downstream effects of stress are most certainly mediated by cortisol, which is a steroid hormone and thus easily diffuses through the blood brain barrier. Moreover, since DNA methylation is a dynamic modification, cross-sectional studies are not sufficient to disentangle whether observed differences are cause or consequence regarding the variable of interest. Nonetheless, methylation patterns measured at birth might overcome this limitation to a degree, as they should be informative of prior prenatal environment. One potential drawback of this study design is the inability to pinpoint the exact timing of the noxious stimulus throughout the 9 months of pregnancy. Finally, straightforward replication of findings across studies is hampered by the number of different methodologies to account for DNA methylation, including pyrosequencing, mass spectrometry, DNA immunoprecipitation and nanopore sequencing ( Jacinto, Ballestar, & Esteller, 2008; Rand et al., 2017; Tost & Gut, 2007, 2012). One of the most widely used platforms for assaying genomewide DNA methylation in hypothesis-free driven approaches is the BeadChip array by Illumina

Fetal DNA methylation after exposure to prenatal stress

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(Bibikova et al., 2011). Its later version, EPIC, contains more than 850,000 CpG probes, enriched for CpG islands of annotated genes and enhancer regions (Moran, Arribas, & Esteller, 2015). Nevertheless, it should be noted that the human genome contains approximately 28 million CpG sites thus rendering the use of commercially available arrays insufficient for capturing a complete picture of the methylomic landscape. In this regard, one study exploring the effects of prenatal stress in newborn DNA methylation by means of whole genome bisulfite sequencing (WGBS) revealed that only 5% of their findings could have been captured by means of the 450 k array (Trump et al., 2016). Additionally, different preprocessing and normalization pipelines available to obtain methylation estimates (beta values) from original methylated and unmethylated signals can also influence findings from such studies (Fortin, Triche Jr, & Hansen, 2016).

Fetal DNA methylation after exposure to prenatal stress An increasing number of studies is approaching the issue of whether prenatal stress has the ability to modify the fetal epigenome. Nonetheless, stress conceptualization has proven rather complex. Individual studies analyze individual stressors such as maternal stress during pregnancy, exposure to natural disasters and exposure to environmental pollutants and chemicals; while each of them focuses on specific factors such as exposure to intimate partner violence (IPV) or cadmium. The variability of the targeted study designs renders their findings heterogeneous. Both candidate gene and genome-wide approaches can be used to answer the question of whether prenatal stress is sufficient to alter the fetal epigenome so it can be measured at birth. Candidate gene studies have traditionally analyzed genes involved in stress response, emotion regulation and social bonding, such as NR3C1 (Palma-Gudiel, Co´rdova-Palomera, Eixarch, Deuschle, & Fan˜ana´s, 2015), SLC6A4 (Provenzi, Giorda, Beri, & Montirosso, 2016), or OXTR (Unternaehrer et al., 2016), suggesting that different types of maternal adversity can somehow reach the fetal epigenome. Nevertheless, such studies are exploratory in nature and need more research to be fully understood. Perhaps genome-wide DNA methylation studies offer a better opportunity to understand which genes and pathways are more vulnerable to intrauterine adversity. Instead of focusing on a priori selected candidate genes, genome-wide approaches allow the exploration of DNA methylation throughout the whole genome (as represented in the available arrays) in association with the exposure (or outcome) of interest. That is why they are described as “hypothesis-free”. Due to the high number of independent tests performed, big sample sizes and statistic correction for multiple testing are required. A recent meta-analysis in two independent cohorts totaling 1740 participants, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Generation R Study, revealed no genome-wide significant probes to be associated with maternal stress (Rijlaarsdam et al., 2016). Nevertheless, the methyltransferase activity pathway was enriched among the top differentially methylated CpG sites; epigenetic and genetic variation modulating epigenetic machinery function has been repeatedly highlighted as an underlying etiological factor for several complex disorders, especially those of a psychiatric nature (Co´rdova-Palomera et al., 2015; Mitjans et al., 2019). An independent study assessing the epigenetic consequences of exposure to IPV during pregnancy revealed differential methylation at a CpG site located within FKBP5 gene (Serpeloni et al., 2019), in accordance with its role in stress response regulation and programming after glucocorticoid challenges

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in neural tissue (Provenc¸al et al., 2019). Likewise, differential methylation in immune pathways was described in adolescents born to women that experienced a major ice storm either immediately before or during pregnancy, as a function of both objective hardship and maternal cognitive appraisal (Cao-Lei et al., 2016, 2014). However, postnatal stress (from birth to adolescence) was not accounted for. While the aforementioned reports focused on different psychosocial stressors as experienced by the pregnant woman, there is a myriad studies investigating the role of exposure to toxic substances during pregnancy (for a review see Palma-Gudiel, Cirera, Crispi, Eixarch, & Fan˜ana´s, 2018). Exposure to particulate air pollution during pregnancy seems more significantly associated with placental methylation than other types of prenatal stress. Likewise, several maternal disorders as experienced during pregnancy have also been hypothesized to leave their epigenetic signature in the newborn (Girchenko et al., 2017; Hogg, Blair, McFadden, von Dadelszen, & Robinson, 2013; Suarez et al., 2018). A notable mega-analysis of 11 different cohorts of pregnant women and their newborns revealed 43 Bonferroni-significant loci to be associated with hypertensive disorders of pregnancy (Kazmi et al., 2019). This study provides appealing evidence of how limitations of genome-wide DNA methylation studies can be circumvented if the sample is big enough and the exposure variable is well defined. Promising new avenues to disentangle the establishment of fetal DNA methylation signatures after exposure to prenatal stress include the exploration of fetal stress rather than solely examining maternal exposures as described above. Collaboration with obstetricians might improve our understanding of long-term programming of early life stress after exposure to adverse prenatal environment (PalmaGudiel et al., 2019). Following this approach, our group was able to identify differential methylation at EP300 gene in response to placental insufficiency/hypoxia, which might be involved in schizophrenia proneness. Although epigenetic biomarkers of prenatal stress will be meaningful on their own, they hold promise to be predictive of postnatal outcomes such as behavior and disease vulnerability. Longitudinal studies assessing DNA methylation at birth and re-assessing the children a few months or years later are required to disentangle the biological relevance of the identified prenatal stress-differential methylation marks. Such studies would also allow to measure the efficacy of parenting strategies and clinical interventions in reducing the impact of intrauterine adversity.

Does DNA methylation at birth predict postnatal outcomes? Although scarce, prospective studies including longitudinal follow-up of newborns through infancy, childhood and even adolescence allow further exploration of downstream behavioral effects of DNA methylation signatures caused by prenatal stress. Epigenetic age acceleration at birth, considered as a proxy for developmental maturity, predicted lower respiratory morbidities in newborns (Knight et al., 2018); these findings are particularly relevant in the context of the epigenetic age literature since epigenetic age deceleration has been associated with a number of adverse pregnancy conditions (Palma-Gudiel, Fan˜ana´s, Horvath, & Zannas, 2019). Paralleling the rat model of maternal care, an exploratory study described maternal stroking (i.e., tactile stimulation) to buffer the effects of prenatal stress in NR3C1 methylation (Murgatroyd, Quinn, Sharp, Pickles, & Hill, 2015), pointing to DNA methylation as a biomarker of treatment efficacy. Newborn toenail mercury concentration was associated with a high-risk neurodevelopmental profile and this association was partially mediated by EMID2 methylation (Maccani, Koestler, Lester,

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Houseman, & Armstrong, 2015), suggesting DNA methylation might act as a mediator between exposure and outcome. Similarly, genome-wide DNA methylation at birth predicted which newborns would grow up to develop early onset conduct problems (Cecil et al., 2018); in the same study, maternal smoking and alcohol use during pregnancy were associated with early-onset conduct problems differential methylation at a trend level. While GABBR1 was identified as a new candidate gene for prenatal stress embedding after exposure to maternal anxiety during pregnancy, DNA methylation at this gene was further associated with infant stress reactivity at 4 months old (Vangeel et al., 2015). All of these studies point to the mediator role of DNA methylation, bridging prenatal stress with neurodevelopment, psychopathology and stress response, respectively. Unfortunately, the majority of studies linking prenatal stress with postnatal outcomes via DNA methylation, collect biological samples for epigenetic analysis at the same time of outcome assessment. Thus, it is not possible to disentangle the epigenetic effects of prenatal stress from those arising due to the following postnatal environment. Environmental exposures other than those circumscribed to the prenatal period are rarely explored although they are major drivers of methylation variation in humans. Indeed, attachment style was recently described to impact the methylome, particularly when comparing infants with secure versus disorganized attachment styles (Garg, Chen, Nguyen, Pokhvisneva, & Chen, 2018). This study highlighted the ephrin signaling pathway as epigenetically deregulated in children with disorganized attachment. Notably, most of the differentially methylated probes in their analyses were best explained in the context of a gene-environment model, underscoring the need to develop integrated genetic-epigenetic analyses to better understand the dynamics of DNA methylation.

Further directions Despite this field is in its early stages, studies exploring newborn DNA methylation after exposure to prenatal stress have rapidly accumulated in the span of barely 5 years. A general lack of findings reaching genome-wide significance might be explained by small sample sizes, different definitions of stress itself and expected low effect sizes; all of these factors limit the statistical power of the samples assessed. However, search for differentially methylated regions rather than probes, pathway analyses, and use of different analytical tools are starting to clear up the big picture. Longitudinal prospective studies are needed to understand the downstream effects of the identified epigenetic marks on postnatal behavior, resilience and disease risk.

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CHAPTER

Applying behavioral epigenetic principles to preterm birth and early stress exposure

5

Livio Provenzia, Elena Guidab, and Rosario Montirossob Child Neurology and Psychiatry Unit, IRCCS Mondino Foundation, Pavia, Italya 0-3 Center for the at-Risk Infant, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italyb

Introduction Our capacity of understanding how early adverse events affect the developing biology of living organisms and contribute to the emergence of unique individual phenotypes has received unprecedented boost from recent advances in behavioral epigenetics (Hyman, 2009). Epigenetics refers to the process in which chemical modifications of DNA or of the structural and regulatory proteins bound to it determines heritable traits (Felsenfeld, 2014). For the sake of this chapter, we will focus on DNA methylation, a specific epigenetic mechanism that has been linked with environmental exposures to caregiving variations (Curley, Jensen, Mashoodh, & Champagne, 2011) and adverse events (Griffiths & Hunter, 2014) in both animal model studies (Meaney & Szyf, 2005) and human research (Booij, Wang, Levesque, Tremblay, & Szyf, 2013). In the last years, the epigenetic lens has been applied to the study of early adversities in preterm infants, who are born with a neurobehavioral immature profile and are precociously exposed to stressful procedures during the hospitalization in the Neonatal Intensive Care Unit (NICU) (Montirosso & Provenzi, 2015). The preterm infant model can be considered as an elective population to assess the epigenetic effects of early environmental conditions in human beings in a prospective longitudinal way. Some recent theories highlighted the potential implications and value of epigenetic studies for the study of preterm infants’ development: some of them identified risk and protective factors (Maddalena, 2013; Samra, McGrath, Wehbe, & Clapper, 2012), and in 2015 Montirosso and Provenzi developed a theoretical model to guide research in what has been called Preterm Behavioral Epigenetics (PBE).

Background Epigenetic regulation by early adverse experiences A first evidence of DNA methylation changes in relation to adverse environmental conditions has been provided by Meaney and colleagues (Meaney, 2001): they examined the effects of variations in the caregiving environment on the methylation status of the NR3C1, a specific gene which encodes for Developmental Human Behavioral Epigenetics. https://doi.org/10.1016/B978-0-12-819262-7.00005-2 # 2021 Elsevier Inc. All rights reserved.

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glucocorticoid receptors (GRs) in the brain (Weaver et al., 2004). GRs are involved in the regulation of feedback-mechanisms the hypothalamic-pituitary-adrenal (HPA) axis, a key element in determining stress reactivity in mammals (Tsigos & Chrousos, 2002). For example, lower levels of GRs in the hippocampus and elevated stress reactivity during adulthood characterized rat pups from mothers providing low quality of caregiving—e.g., less engaged in receptive nursing and in linking/grooming the offspring—(Francis & Meaney, 1999). Similar findings have been documented in association with early maternal separation in rodents: male offspring exposed to early maternal separation exhibited behavioral inhibition in maze exploration, mediated by DNA hyper-methylation of the gene encoding for the corticotropin-releasing hormone, which is key regulator of the stress response (Kember et al., 2012). Moreover, Own, Iqbal, and Patel (2013) reported that in mice pups maternal separation also associates with increased methylation of another stress-related gene, the SLC6A4, a key gene for the encoding of the serotonin transporter, that a fundamental regulator of the serotoninergic system (Lesch, 2011). Concerning human studies, similar epigenetic alterations were documented in young individuals exposed to stressful adverse experiences during the prenatal and the postnatal life (Provenzi, Giorda, Beri, & Montirosso, 2016). Particularly, both the HPA axis functioning and serotoninergic system have shown to be susceptible to epigenetic regulation: for example, prenatal exposure to maternal depression during the third trimester of pregnancy has been associated in full-term newborns with the methylation status of a specific CpG site of the NR3C1 gene, which encodes for the hippocampal GRs (Oberlander et al., 2008). Moreover, the altered methylation of NR3C1 gene was predictive of increased salivary cortisol response to routine care-related stress at 3-month-age. More recent studies highlight how exposure to parental stress during infancy and childhood associates with different methylation in 28,000 CpG sites measured from buccal epithelial cells in adolescents (Essex et al., 2013). Furthermore, even postnatal adverse exposure has an influence on methylation: the SLC6A4 methylation status of 5- to 14-aged children with a previous history of parental abuse or neglect has been compared with that of a counterpart of children without exposure to family violence (Vijayendran, Beach, Plume, Brody, & Philibert, 2012). Maltreated and neglected children showed lower methylation for CpG sites that were highly methylated in the control group, and higher methylation for CpG sites that were low-methylated in the control group. Another study compared adults with post-traumatic stress disorder and with history of early traumatic experiences to adults without post-traumatic stress disorders who did not present adverse experiences in childhood (Mehta et al., 2013). Individuals with a history of abuse showed non-overlapping areas of DNA methylation, when compared to individuals with similar history of abuse, but without anxiety disorder. On the other hand, the methylation status of the SLC6A4 gene, which encodes for the 5-HTT (i.e., serotonin transporter), is affected by maternal depression during pregnancy (Devlin, Brain, Austin, & Oberlander, 2010) and by post-natal adversities (Wang et al., 2012).

Epigenetic regulation by early protective experiences Not only adverse events have epigenetic effects on DNA methylation: indeed, animal studies also showed that DNA methylation is susceptible to caring and protective environmental conditions. When rats born from mothers characterized by low quality of caregiving are cross-fostered to mothers characterized by high quality of care they show similar levels of Nr3c1 methylation of rats born and raised by high-quality of care mothers (Hellstrom, Dhir, Diorio, & Meaney, 2012). Similarly, positive experiences for rats—such as being sensitively touched during the early post-natal period—decreased the

Background

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production of glucocorticoids during the first day of life ( Jutapakdeegul, Casalotti, Govitrapong, & Kotchabhakdi, 2003). Recent research suggests that it might be possible to inquire the epigenetic underpinnings of positive experiences also in humans. Roberts and colleagues (Roberts et al., 2014) measured the level of SLC6A4 methylation in children with anxiety disorder before and after a cognitive-behavioral psychotherapy intervention. When children improved after the intervention, significant changes of SLC6A4 methylation appeared if compared to peers without any improvement. In a retrospective study, infants from depressed mothers, compared to infants from non-depressed mothers, showed increased methylation of the NR3C1 gene (Murgatroyd, Quinn, Sharp, Pickles, & Hill, 2015). Nonetheless, the quality of early caregiving, measured as the frequency of maternal stroking, was found to reverse this effect, similarly to what has been documented in animal models (Meaney & Szyf, 2005).

Preterm birth and NICU-related early adverse experiences Preterm infants are an elective population to assess the epigenetic effects of early environmental conditions because they are hospitalized in the NICU. During this early period of development, the infant brain is extremely sensitive to environmental stimulations and the NICU is a source of enormous stress for the infant. Indeed, they are not prepared to handle the NICU stressful environment—including physical and sensorial stimulations, painful procedures and maternal separation—without the comforting and protective support of the maternal womb (Altimier & Phillips, 2013; Haumont et al., 2013). NICU physical and sensorial stimulations are hardly tolerated by neurobehavioral immature newborns (Brown, 2009; Ozawa, Sasaki, & Kanda, 2010). High-intensity lights and noise are associated with physiological and behavioral instability in preterm infants (Altuncu et al., 2009; Graven, 2004; Lee, Malakooti, & Lotas, 2005). Moreover, life-saving procedures include intubations, venipunctures, arterial insertions and surgery. Due to their immature neuro-developmental state, preterm infants have a lower threshold and higher sensitization to external perturbations, so that even routinely handling (e.g., diaper change) might be responded to with heightened physiologic response. The immature neuro-developmental state of preterm infants includes less-than-optimal reflexes and attentional skills, as well as reduced overall quality of movements, difficulties in state regulation, hypo- and/or hypertonicity (Spittle et al., 2016). Among NICU invasive procedures, skin-breaking procedures have been largely studied as a source of pain for preterm infants (Grunau, 2013). Grunau et al. (2009) underlined that skin-breaking interventions have been associated with several detrimental consequences for briefand long-term development, encompassing structural and functional alterations of brain development and dysregulation of the HPA axis stress response system (Ranger, Synnes, Vinall, & Grunau, 2014; Smith et al., 2011; Zwicker et al., 2013). Finally, the sudden separation of the preterm newborn from the mother after birth disrupts the biological and emotional caregiving bonding which generally occurs after birth (Latva, Lehtonen, Salmelin, & Tamminen, 2007) with long-lasting effects on preterm infants’ stress regulation development (M€ orelius, Nelson, & Gustafsson, 2007).

Preterm birth and NICU-related early protective experiences In order to manage the quality of life of preterm infants during the early weeks of life, NICUs have progressively adopted family-centered and developmental care (DC) strategies. The most investigated DC strategy is the facilitation of early mother-infant contact through skin-to-skin kangaroo care

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(Feldman & Eidelman, 2004; Feldman, Eidelman, Sirota, & Weller, 2002). Skin-to-skin care consists in a prompt support for precocious physical and emotional closeness, in which the infant is prone and upright on maternal chest. The preterm infant keeps the diaper, but unclothed, so that physical contact is favored. Skin-to-skin care has shown beneficial effects for the infants, including the promotion of physiologic stability (Cong et al., 2015), sleep organization (Calciolari & Montirosso, 2011), brain maturation (Scher et al., 2009), behavioral (Kiechl-Kohlendorfer et al., 2015) and emotional development (Keren, Feldman, Eidelman, Sirota, & Lester, 2003), and adaptive regulation of the HPA axis system € (M€ orelius, Ortenstrand, Theodorsson, & Frostell, 2015).

A rationale for preterm behavioral epigenetics In the light of the evidence reported above, the question is how altered patterns of DNA methylation are involved in preterm infants’ development and how they associate with early exposures to NICU-related adversity and care. The PBE model (Montirosso & Provenzi, 2015) gives an innovative framework that applies behavioral epigenetic research to the study of prematurity and the effects of NICU stay, both through adverse experiences and DC caregiver engagement. This model assumes that various environmental conditions might contribute to the developing trajectories and to the behavioral phenotype of preterm infants via epigenetic modifications (e.g., DNA methylation). These conditions include known prenatal factors associated with increased risk of preterm birth (e.g., maternal stress and depression) as well as post-natal exposures to stress (e.g., pain-related stress) and DC interventions during the NICU hospitalization. In this chapter, we will highlight the state of the art of PBE research, providing an overview of the following issues: • • • • •

epigenetic effects of prenatal conditions in prematurity, epigenetic signatures related to preterm birth status, epigenetic regulation linked with early stress exposure as well as, developmental outcomes associated with altered epigenetic markers, methodological aspects of research conducted to date in the field of PBE will be reported.

State of the art of PBE research Epigenetic effects of prenatal conditions Two studies assessed the epigenetic effects of prenatal exposure to adverse conditions in preterm infants (Liu et al., 2012; Vidal et al., 2014). Liu et al. (2012) research enrolled infants from depressed and non-depressed mothers, assessing DNA methylation at imprinted genes. Infants from mothers characterized by severe depressed mood (i.e., history of depression plus depression in pregnancy) had higher methylation of MEG3 gene—whose regions have been identified and hypothesized to affect growth and development in both the placenta and the fetus (Kagami et al., 2010; Skaar et al., 2012)—and this difference was grater within female infants as well as in infants from black women. No differences in MEG3 methylation emerged among mothers with or without severe depression as well as those with or without a preterm infant. Compared to normal birth weight infants, low-birth weight infants had 1.6%

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lower IGF2 methylation and 5.9% lower methylation at the PLAGL1 gene. Similarly, Vidal et al. (2014) reported increased methylation of the MEST gene in preterm infants exposed to maternal stress during pregnancy compared to controls from mothers without significant depressive symptoms before delivery. Despite maternal stress was not found to be associated with heightened risk of preterm birth, infants from mothers who reported higher stress during pregnancy had 2.8% increase in DNA methylation of the MEST gene, compared to control infants (Vidal et al., 2014). This effect was more robust in females, compared to male infants. The MEST gene is involved in the metabolism pathways that affect growth and maintenance of mesodermal cells (Kobayashi et al., 1997). Recent studies on animal models suggest that this gene is up-regulated in offspring exposed to stress (Takahashi, Kamei, & Ezaki, 2005). Taken together, these findings extend previous evidence on full-term infants to preterm ones, suggesting that early exposure to adverse events during the third trimester of pregnancy is capable to alter the epigenetic status of imprinted and placenta-related genes that have relevant implications for fetal development.

Epigenetic profile of preterm infants/children Five studies to date have contributed to explore the epigenetic profile associated with preterm birth in the neonatal and perinatal periods as well as during childhood. The methylation level of the imprinted gene PLAGL1 has been assessed recently (Provenzi et al., 2018). The PLAGL1 is a key gene that appears to be of specific interest for prematurity. It acts a suppressor of cell growth (Abdollahi et al., 2003), it is downregulated in placentas from intrauterine growth-restriction (Iglesias-Platas et al., 2014) and higher levels of PLAGL1 methylation have been linked with high birth weight (>45,000 kg; Liu et al., 2012). PLAGL1 gene has been found to be hypo-methylated in very preterm infants at birth with a persistent pattern of reduced methylation at NICU discharge, in comparison to full-term counterparts. Kantake, Yoshitake, Ishikawa, Araki, and Shimizu (2014) highlighted differences in the methylation rate of NR3C1. Preterm infants showed a significant increase of CpG-specific NR3C1 methylation between postnatal days 0 and 4 and after day 4 when compared to full-term infants, whose methylation remained stable across the same post-natal period. More specifically, lower methylation was found in three CpG sites and higher methylation in one CpG site of the NR3C1 gene in VPT newborns compared to FT peers at birth. Notably, lower Apgar scores at 1 and 5 min, admission in NICU, and mode of delivery emerged as significant predictors of the CpG-specific methylation increases among preterm infants. A similar increment in NR3C1 methylation was reported by Lester et al. (2015) in preterm infants. More specifically, preterm infants who showed high risk of neurobehavioral problems at discharge had doubled methylation of the NR3C1 CpG3 site compared to the counterpart with low neurobehavioral risk. By converse, lower methylation of the HSD11B2 gene at CpG3 was found to be linked with higher neurobehavioral risk in the same sample. Chau et al. (2014) assessed the epigenetic profile of children born preterm at seven years, comparing them to full-term controls. Very preterm children had significantly higher methylation of the SLC6A4 promoter (i.e., CpG sites 7-to-10). Sparrow et al. (2016) found an association between preterm birth and hypomethylation of SLC7A5 and SLC1A2. The SLC7A5 gene down-regulation is associated with impaired cell cycles (He, Zhang, & Zhao, 2016) and it has a prominent role in the thyroid hormone uptake in fetal cortex (Chan et al., 2011). The SLC1A2 gene is the principal membrane-bound transporter that clears the excitatory neurotransmitter glutamate from the extracellular space at synapses in the central

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nervous system (Sparrow et al., 2016). Both genes are involved in cell growth and they contribute to regulate the synaptic cleft (Fiorentino, Sharp, & McQuillin, 2015). Additionally, Sparrow et al. (2016) found that specific risk modulators of neurodevelopmental outcome after preterm birth (gender, chorioamnionitis and early nutritional factors) explained a modest but significant proportion of the variance in DNA methylation. Provenzi et al. (2015) assessed the methylation status of 20 CpG sites close to the promoter region of the serotonin transporter gene (SLC6A4) at birth in preterm and full-term infants and they reported no significant differences related to the birth status between these two groups. Such a conflicting pattern of findings appears to suggest that the epigenetic profile of preterm birth is complex and may involve different genes that have diverse impact in the fetal, neonatal and postnatal growth and development.

Epigenetic effects of NICU-related stress A limited number of studies assessed the effects of NICU-related stress on the methylations status of stress-related genes in preterm infants (Chau et al., 2014; Kantake et al., 2014; Provenzi et al., 2015). Kantake et al. (2014) have the merit of suggesting that the postnatal environment influences epigenetic programming in premature infants: NICU hospitalization correlated to higher DNA methylation. They documented that NR3C1 methylation increased at 11 CpG sites and decreased at one CpG site from birth to day four in the preterm newborns’ group, whereas it remained stable in term newborns across the same post-natal period. Nonetheless, no specific information about NICU-related stressful factors was provided. Provenzi et al. (2015) made a step foreward reporting that SLC6A4 methylation at specific CpG sites significantly increased from birth to NICU discharge. Importantly, the significant SLC6A4 increase was observed only in very preterm infants exposed at high levels of pain-related stress, whereas it remained stable in the counterpart exposed to low pain-related stress. This finding suggest that there might be a relative epigenetic resistance to low-level or low-intensity stress exposure and only moderate-to-high pain exposure—or repeated and chronic pain exposure—may result in an altered methylation status of the serotonin transporter gene. When very preterm have been assessed at school age (i.e., 7 years), a different effect of NICU exposure to pain-related stress was detected. Higher pain exposure during NICU stay was significantly linked with lower methylation of SLC6A4, but only in children carrying the met-homozygous genotype of the COMT val158met polymorphism (Chau et al., 2014). Although further studies are needed to deeply understand the direction of epigenetic modifications connected to NICU-related stress, these findings are consistent and suggest a specific epigenetic effect of early pain exposure which might persist during childhood.

Developmental outcomes of epigenetic alterations in preterm infants/children Another issue concerning epigenetic modifications in preterm infants is to assess how those changes are going to influence the future development of the child. Developmental outcomes associated with early epigenetic markers of adversity in preterm infants have been addressed by five studies (Chau et al., 2014; Kantake et al., 2014; Montirosso et al., 2016, 2016; Sparrow et al., 2016). Sparrow et al. (2016) showed an association between DNA methylation and white matter integrity and shape in the phenotype of preterm infant at birth. This finding indirectly supports the idea that preterm birth

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is a detrimental environmental stressor that is closely associated with long-term alterations in connectivity of neural systems. In preterm infants who were assessed for NR3C1 methylation at birth and 4 days of life, a significant increase in methylation of the CpG16 was associated with higher risk of complications during neonatal period (Kantake et al., 2014). Montirosso, Provenzi, Giorda, et al. (2016) assessed very preterm infants’ socio-emotional stress regulation at 3 months (corrected age for prematurity) and compared them with a control group of age-paired full-term controls. Socioemotional stress regulation was observed in response to acute and repeated exposures to maternal unresponsiveness (i.e., double-exposure Still-Face paradigm; Provenzi et al., 2016; Tronick, Als, Adamson, Wise, & Brazelton, 1978). In the Still-Face paradigm, infants face socio-emotional stress elicited by the maternal display of still and unexpressive face for about 2 min (Adamson & Frick, 2003; Tronick, Als, Adamson, Wise, & Brazelton, 1978). This experimental paradigm is a well-established procedure to assess socio-emotional stress in healthy full-term (Montirosso et al., 2015) and preterm (Montirosso, Borgatti, Trojan, Zanini, & Tronick, 2010) infants. Greater SLC6A4 CpG2 methylation at NICU discharge predicted poorer stress regulation in response to repeated socio-emotional stress exposure in VPT infants compared to FT infants. Moreover, in the same sample, greater SLC6A4 CpG5 methylation at NICU discharge predicted less-than-optimal temperament profile at 3 months (Montirosso, Provenzi, Fumagalli, et al., 2016). Very preterm infants with higher CpG2 methylation of the SLC6A4 gene had lower duration of orienting and approach compared to full-term infants. Chau et al. (2014) highlighted that the amount of behavioral problems reported by the mothers in very preterm 7-year-old children were significantly associated with greater SLC6A4 methylation. It is noteworthy that both in the neonatal and in the school-age studies the pattern of SLC6A4 methylation resulted in a significant association with behavioral and socio-emotional outcomes only in the preterm group, and not in the full-term one. This suggests that it is not the absolute extent of methylation, rather an environmental-driven increase in methylation, that should be regarded as a biomarker of early adversity capable of altering the developmental trajectories of behavioral and stress regulation. Taken together these findings suggest that precocious NICU-related epigenetic alterations of stressrelated genes associated with less-than-optimal developmental outcomes. As such, precocious methylation of these genes (e.g., SlC6A4, NR3C1) appears to be a potential biomarker of early adversity, which contributes to detrimental consequences for neurobehavioral and socio-emotional development later in life.

Future directions Researches of the epigenetic vestiges of prematurity are still at the beginning, but the emerging pattern of PBE studies is highly promising for both research and clinical practice. The PBE model can be useful for a better understanding of risk and prevention factors of preterm birth, to reveal pathways through which genes and environment influence stress responsiveness, to investigate the potential protective role of NICU-related DC interventions, to understand how epigenetic mechanisms might be involved among different preterm infants’ populations and to set up perspective studies looking at the effects of early NICU related stress on behavioral, emotional and neurological development. Preterm infants seems to exhibit altered methylation at both imprinted and stress-related genes and such alterations deserve future investigation. Imprinted genes function as critical growth effectors and regulators of development since they are maintained in all somatic tissues (Ideraabdullah, Vigneau, &

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Bartolomei, 2008). Altered methylation of these genes affect embryonic growth and development in the placenta. Understanding imprinted genes regulation is critical, since a significant proportion of these human genes are implicated in complex diseases (Vickers, 2014) and in the mechanisms that lead to preterm birth (Liu et al., 2012). Furthermore, a proper human imprintome map would enhance the ability to identify risk factors of preterm birth end, eventually, prevent it (Skaar et al., 2012). Epigenetic alterations of stress-related genes due to early adverse experiences have been associated to HPA and serotoninergic system modifications (Griffiths & Hunter, 2014). For this reason, NICU-related stress contributes to heightened risk for altered stress regulation capacities in preterm infants and the study of increased methylation of stress-related genes is warranted to become of specific scientific and clinical concern in this population. The epigenetic approach might help to fill the explanatory gaps between the influences of gene and environment on stress responsiveness, revealing pathways through which early adversities are embedded in the developing biology of children, and the contribute of these genes on the long-lasting programming of health and disease (Roth & Sweatt, 2011). Emerging evidence corroborates the hypothesis that preterm infants might present altered epigenetic status of imprinted and stress-related genes and that these alterations might be related partially to the early exposure to prenatal and post-natal adverse environments. Nonetheless, it is still uninvestigated the hypothesis that NICU-related protective factors (e.g., DC strategies) might exert significant buffering effects in the face of early stress-related epigenetic variations. These variations have been found to have long-lasting behavioral and neurological outcomes for short- (Montirosso, Provenzi, Fumagalli, et al., 2016; Montirosso, Provenzi, Giorda, et al., 2016) and long-term (Chau et al., 2014) development. As such, we suggest that future research should investigate the potential protective role of NICU-related DC interventions in reversing or partially reducing the methylation increased status observed in preterm infants exposed to maternal depression/stress as well as high levels of painrelated stress during NICU stay. Intriguingly, Murgatroyd et al. (2015) reported that increased maternal stroking at 5 weeks specifically reduced NR3C1 methylation in infants exposed to maternal depression. By contrast, there was no effect of maternal stroking at 9 weeks of age, highlighting the importance of timing of early intervention and caregiving support. Speculatively, one might wonder if DC strategies which are directed at supporting the early mother-infant contact and bonding in NICU by favoring physical contact exert similar benefits to preterm infants. Moreover, it should be considered that preterm infants represent a heterogeneous group, that varies with regard of a set of perinatal and medical variables (e.g., birth weight, gestational age, clinical complications, etc.). As such, preterm infants born at different gestational ages and birth weight, for instance, might present very different developmental trajectories. It appears reasonable to speculate that extremely preterm infants (