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Behavioral epigenetics and the developmental origins of child mental health disorders

Published online by Cambridge University Press:  03 July 2012

B. M. Lester*
Affiliation:
Brown Center for the Study of Children at Risk at Women and Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Pediatrics, Warren Alpen Medical School of Brown University, Providence, RI, USA Department of Pediatrics, Women and Infants Hospital of Rhode Island, Providence, RI, USA
C. J. Marsit
Affiliation:
Departments of Pharmacology & Toxicology and Community & Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
E. Conradt
Affiliation:
Brown Center for the Study of Children at Risk at Women and Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Pediatrics, Women and Infants Hospital of Rhode Island, Providence, RI, USA
C. Bromer
Affiliation:
Department of Neuroscience, Brown University, Providence, RI, USA
J. F. Padbury
Affiliation:
Department of Pediatrics, Warren Alpen Medical School of Brown University, Providence, RI, USA Department of Pediatrics, Women and Infants Hospital of Rhode Island, Providence, RI, USA
*
*Address for correspondence: Dr B. Lester, Department of Pediatrics, Brown Center for Children, Women and Infants Hospital, 101 Dudley Street, Providence, RI, 02905 USA. (E-mails blester@wihri.org
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Abstract

Advances in understanding the molecular basis of behavior through epigenetic mechanisms could help explain the developmental origins of child mental health disorders. However, the application of epigenetic principles to the study of human behavior is a relatively new endeavor. In this paper we discuss the ‘Developmental Origins of Health and Disease’ including the role of fetal programming. We then review epigenetic principles related to fetal programming and the recent application of epigenetics to behavior. We focus on the neuroendocrine system and develop a simple heuristic stress-related model to illustrate how epigenetic changes in placental genes could predispose the infant to neurobehavioral profiles that interact with postnatal environmental factors potentially leading to mental health disorders. We then discuss from an ‘Evo-Devo’ perspective how some of these behaviors could also be adaptive. We suggest how elucidation of these mechanisms can help to better define risk and protective factors and populations at risk.

Information

Type
Review
Copyright
Copyright © Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2012 
Figure 0

Fig. 1 Epigenetic citations by search terms. Citation search for epigenetic articles by year of publication. The number of published articles using the terms ‘epigenetics’, ‘epigenetics and disease’ and ‘epigenetics and behavior’ indicating the recent emergence of the field of behavioral epigenetics.

Figure 1

Fig. 2 Developmental Origins Model of child mental health disorders. The model illustrates one of many pathways in which prenatal environmental stressors could impact placental gene expression altering HPA set points and the infant's responsivity to postnatal environmental conditions affecting liability to mental health disorders. Epigenetic effects on key placental genes (shaded in a) increase fetal exposure to cortisol altering the abilities of the infant to respond to the postnatal environment which includes both parenting and broader environmental conditions. The ‘Goodness of Fit’ (b) or relative match or mismatch between the infant's capacities and the kind of parenting appropriate for the infant's capacities in the context of the degree of adversity of the broader postnatal environment will determine the probability of risk for mental health disorders. This risk is maximized when the infant has restricted behavioral capacities that clash (are a mismatch) with parenting styles in the context of environmental adversity (based on Gluckman and Hanson 2005).

Figure 2

Fig. 3 Heatmap showing four classes of expression signature of the 22 imprinted genes. Gaussian recursively partitioned mixture models, a hierarchical clustering, based on imprinted gene expression identifying 4 distinct classes of expression signature. Samples are in rows, and the expression of the 22 imprinted genes examined is shown by the heatmap in column. The dendrogram depicts the splitting of the samples into 4 classes, which are separated on the heat map by red lines and labeled classes 1, 2, 3 and 4