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Genome-wide epigenetic signatures of childhood adversity in early life: Opportunities and challenges

Part of: DOHaD on International Women's Day 2020 A Canadian perspective on DOHaD

Published online by Cambridge University Press:  12 February 2019

Aya Sasaki  and
Stephen G. Matthews
Aya Sasaki
Affiliation:
Department of Physiology, University of Toronto, Toronto, ON, Canada
Stephen G. Matthews
Affiliation:
Department of Physiology, University of Toronto, Toronto, ON, Canada Departments of Obstetrics and Gynecology and Medicine, University of Toronto, Toronto, ON, Canada Alliance for Human Development, Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada
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Abstract

Maternal adversity and fetal glucocorticoid exposure has long-term effects on cardiovascular, metabolic and behavioral systems in offspring that can persist throughout the lifespan. These data, along with other environmental exposure data, implicate epigenetic modifications as potential mechanisms for long-term effects of maternal exposures on adverse health outcomes in offspring. Advances in microarray, sequencing and bioinformatic approaches have enabled recent studies to examine the genome-wide epigenetic response to maternal adversity. Studies of maternal exposures to xenobiotics such as arsenic and smoking have been performed at birth to examine fetal epigenomic signatures in cord blood relating to adult health outcomes. However, there have been no epigenomic studies examining these effects in animal models. On the other hand, to date, only a few studies of the effects of maternal psychosocial stress have been performed in human infants, and the majority of animal studies have examined epigenomic outcomes in adulthood. In terms of maternal exposure to excess glucocorticoids by synthetic glucocorticoid treatment, there has been no epigenetic study performed in humans and only a few studies undertaken in animal models. This review emphasizes the importance of examining biomarkers of exposure to adversity throughout development to identify individuals at risk and to target interventions. Thus, research performed at birth will be reviewed. In addition, potential subject characteristics associated with epigenetic modifications, technical considerations, the selection of target tissues and combining human studies with animal models will be discussed in relation to the design of experiments in this field of study.

Keywords

maternal stresssynthetic glucocorticoidxenobiotic
Type
Review
Information
Journal of Developmental Origins of Health and Disease , Volume 10 , Special Issue 1: Themed Issue: Canada / Indigenous Populations , February 2019 , pp. 65 - 72
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2019. 

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References

1. Gillman, MW. Developmental origins of health and disease. The New England journal of medicine. 2005; 353, 1848–1850.10.1056/NEJMe058187Google Scholar
2. Gluckman, PD, Hanson, MA. Evolution, development and timing of puberty. Trends in endocrinology and metabolism: TEM. 2006; 17, 7–12.10.1016/j.tem.2005.11.006Google Scholar
3. Barker, DJ. The developmental origins of chronic adult disease. Acta paediatrica. 2004; 93, 26–33.10.1111/j.1651-2227.2004.tb00236.xGoogle Scholar
4. Bird, A. Perceptions of epigenetics. Nature. 2007; 447, 396–398.10.1038/nature05913Google Scholar
5. Huyck, KL, Kile, ML, Mahiuddin, G, et al. Maternal arsenic exposure associated with low birth weight in Bangladesh. Journal of occupational and environmental medicine. 2007; 49, 1097–1104.10.1097/JOM.0b013e3181566ba0Google Scholar
6. Rahman, A, Vahter, M, Ekstrom, EC, et al. Association of arsenic exposure during pregnancy with fetal loss and infant death: a cohort study in Bangladesh. American journal of epidemiology. 2007; 165, 1389–1396.10.1093/aje/kwm025Google Scholar
7. Rodriguez-Barranco, M, Lacasana, M, Aguilar-Garduno, C, et al. Association of arsenic, cadmium and manganese exposure with neurodevelopment and behavioural disorders in children: a systematic review and meta-analysis. The Science of the total environment. 2013; 454-455, 562–577.Google Scholar
8. Broberg, K, Ahmed, S, Engstrom, K, et al. Arsenic exposure in early pregnancy alters genome-wide DNA methylation in cord blood, particularly in boys. Journal of developmental origins of health and disease. 2014; 5, 288–298.10.1017/S2040174414000221Google Scholar
9. Kile, ML, Houseman, EA, Baccarelli, AA, et al. Effect of prenatal arsenic exposure on DNA methylation and leukocyte subpopulations in cord blood. Epigenetics. 2014; 9, 774–782.Google Scholar
10. Koestler, DC, Avissar-Whiting, M, Houseman, EA, Karagas, MR, Marsit, CJ. Differential DNA methylation in umbilical cord blood of infants exposed to low levels of arsenic in utero. Environmental health perspectives. 2013; 121, 971–977.10.1289/ehp.1205925Google Scholar
11. Rager, JE, Bailey, KA, Smeester, L, et al. Prenatal arsenic exposure and the epigenome: altered microRNAs associated with innate and adaptive immune signaling in newborn cord blood. Environmental and molecular mutagenesis. 2014; 55, 196–208.10.1002/em.21842Google Scholar
12. Houseman, EA, Accomando, WP, Koestler, DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC bioinformatics. 2012; 13, 86.10.1186/1471-2105-13-86Google Scholar
13. Waalkes, MP, Ward, JM, Liu, J, Diwan, BA. Transplacental carcinogenicity of inorganic arsenic in the drinking water: induction of hepatic, ovarian, pulmonary, and adrenal tumors in mice. Toxicology and applied pharmacology. 2003; 186, 7–17.10.1016/S0041-008X(02)00022-4Google Scholar
14. Tokar, EJ, Diwan, BA, Ward, JM, Delker, DA, Waalkes, MP. Carcinogenic effects of “whole-life” exposure to inorganic arsenic in CD1 mice. Toxicological sciences : an official journal of the Society of Toxicology. 2011; 119, 73–83.10.1093/toxsci/kfq315Google Scholar
15. Tong, VT, Jones, JR, Dietz, PM, et al. Trends in smoking before, during, and after pregnancy - Pregnancy Risk Assessment Monitoring System (PRAMS), United States, 31 sites, 2000-2005. Morbidity and mortality weekly report Surveillance summaries. 2009; 58, 1–29.Google Scholar
16. Magnus, MC, Haberg, SE, Karlstad, O, et al. Grandmother’s smoking when pregnant with the mother and asthma in the grandchild: the Norwegian Mother and Child Cohort Study. Thorax. 2015; 70, 237–243.10.1136/thoraxjnl-2014-206438Google Scholar
17. Breton, CV, Byun, HM, Wenten, M, et al. Prenatal tobacco smoke exposure affects global and gene-specific DNA methylation. American journal of respiratory and critical care medicine. 2009; 180, 462–467.10.1164/rccm.200901-0135OCGoogle Scholar
18. Guerrero-Preston, R, Goldman, LR, Brebi-Mieville, P, et al. Global DNA hypomethylation is associated with in utero exposure to cotinine and perfluorinated alkyl compounds. Epigenetics. 2010; 5, 539–546.10.4161/epi.5.6.12378Google Scholar
19. Murphy, SK, Adigun, A, Huang, Z, et al. Gender-specific methylation differences in relation to prenatal exposure to cigarette smoke. Gene. 2012; 494, 36–43.10.1016/j.gene.2011.11.062Google Scholar
20. Suter, M, Abramovici, A, Aagaard-Tillery, K. Genetic and epigenetic influences associated with intrauterine growth restriction due to in utero tobacco exposure. Pediatric endocrinology reviews : PER. 2010; 8, 94–102.Google Scholar
21. Suter, M, Ma, J, Harris, A, et al. Maternal tobacco use modestly alters correlated epigenome-wide placental DNA methylation and gene expression. Epigenetics. 2011; 6, 1284–1294.Google Scholar
22. Joubert, BR, Haberg, SE, Nilsen, RM, et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environmental health perspectives. 2012; 120, 1425–1431.10.1289/ehp.1205412Google Scholar
23. Markunas, CA, Xu, Z, Harlid, S, et al. Identification of DNA methylation changes in newborns related to maternal smoking during pregnancy. Environmental health perspectives. 2014; 122, 1147–1153.10.1289/ehp.1307892Google Scholar
24. Adkins, RM, Tylavsky, FA, Krushkal, J. Newborn umbilical cord blood DNA methylation and gene expression levels exhibit limited association with birth weight. Chemistry & biodiversity. 2012; 9, 888–899.Google Scholar
25. Engel, SM, Joubert, BR, Wu, MC, et al. Neonatal genome-wide methylation patterns in relation to birth weight in the Norwegian Mother and Child Cohort. American journal of epidemiology. 2014; 179, 834–842.10.1093/aje/kwt433Google Scholar
26. Kupers, LK, Xu, X, Jankipersadsing, SA, et al. DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring. International journal of epidemiology. 2015; 44, 1224–1237.10.1093/ije/dyv048Google Scholar
27. Rehan, VK, Liu, J, Naeem, E, et al. Perinatal nicotine exposure induces asthma in second generation offspring. BMC medicine. 2012; 10, 129.10.1186/1741-7015-10-129Google Scholar
28. Johns, JM, Louis, TM, Becker, RF, Means, LW. Behavioral effects of prenatal exposure to nicotine in guinea pigs. Neurobehavioral toxicology and teratology. 1982; 4, 365–369.Google Scholar
29. Levin, ED, Briggs, SJ, Christopher, NC, Rose, JE. Prenatal nicotine exposure and cognitive performance in rats. Neurotoxicology and teratology. 1993; 15, 251–260.10.1016/0892-0362(93)90006-AGoogle Scholar
30. Sorenson, CA, Raskin, LA, Suh, Y. The effects of prenatal nicotine on radial-arm maze performance in rats. Pharmacology, biochemistry, and behavior. 1991; 40, 991–993.10.1016/0091-3057(91)90117-KGoogle Scholar
31. Yanai, J, Pick, CG, Rogel-Fuchs, Y, Zahalka, EA. Alterations in hippocampal cholinergic receptors and hippocampal behaviors after early exposure to nicotine. Brain research bulletin. 1992; 29, 363–368.Google Scholar
32. Zahalka, EA, Seidler, FJ, Lappi, SE, et al. Deficits in development of central cholinergic pathways caused by fetal nicotine exposure: differential effects on choline acetyltransferase activity and [3H]hemicholinium-3 binding. Neurotoxicology and teratology. 1992; 14, 375–382.10.1016/0892-0362(92)90047-EGoogle Scholar
33. Moisiadis, VG, Matthews, SG. Glucocorticoids and fetal programming part 2: Mechanisms. Nature reviews Endocrinology. 2014a; 10, 403–411.10.1038/nrendo.2014.74Google Scholar
34. Moisiadis, VG, Matthews, SG. Glucocorticoids and fetal programming part 1: Outcomes. Nature reviews Endocrinology. 2014b; 10, 391–402.Google Scholar
35. Murphy, KE, Hannah, ME, Willan, AR, et al. Multiple courses of antenatal corticosteroids for preterm birth (MACS): a randomised controlled trial. Lancet. 2008; 372, 2143–2151.Google Scholar
36. Wapner, RJ, et al. Single versus weekly courses of antenatal corticosteroids: evaluation of safety and efficacy. American journal of obstetrics and gynecology. 2006; 195, 633–642.Google Scholar
37. Wapner, RJ, Sorokin, Y, Mele, L, et al. Long-term outcomes after repeat doses of antenatal corticosteroids. The New England journal of medicine. 2007; 357, 1190–1198.Google Scholar
38. Asztalos, E, Willan, A, Murphy, K, et al. Association between gestational age at birth, antenatal corticosteroids, and outcomes at 5 years: multiple courses of antenatal corticosteroids for preterm birth study at 5 years of age (MACS-5). BMC pregnancy and childbirth. 2014; 14, 272.Google Scholar
39. Asztalos, EV, Murphy, KE, Willan, AR, et al. Multiple courses of antenatal corticosteroids for preterm birth study: outcomes in children at 5 years of age (MACS-5). JAMA pediatrics. 2013; 167, 1102–1110.Google Scholar
40. Newnham, JP, Evans, SF, Godfrey, M, et al. Maternal, but not fetal, administration of corticosteroids restricts fetal growth. The Journal of maternal-fetal medicine. 1999; 8, 81–87.Google Scholar
41. Drake, AJ, Walker, BR, Seckl, JR. Intergenerational consequences of fetal programming by in utero exposure to glucocorticoids in rats. Am J Physiol Regul Integr Comp Physiol. 2005; 288, R34–38.Google Scholar
42. Levitt, NS, Lindsay, RS, Holmes, MC, Seckl, JR. Dexamethasone in the last week of pregnancy attenuates hippocampal glucocorticoid receptor gene expression and elevates blood pressure in the adult offspring in the rat. Neuroendocrinology. 1996; 64, 412–418.Google Scholar
43. Liu, L, Li, A, Matthews, SG. Maternal glucocorticoid treatment programs HPA regulation in adult offspring: sex-specific effects. American journal of physiology Endocrinology and metabolism. 2001; 280, E729–739.Google Scholar
44. Sloboda, DM, Moss, TJ, Gurrin, LC, Newnham, JP, Challis, JR. The effect of prenatal betamethasone administration on postnatal ovine hypothalamic-pituitary-adrenal function. J Endocrinol. 2002; 172, 71–81.Google Scholar
45. Uno, H, Eisele, S, Sakai, A, et al. Neurotoxicity of glucocorticoids in the primate brain. Hormones and behavior. 1994; 28, 336–348.Google Scholar
46. Crudo, A, Petropoulos, S, Moisiadis, VG, et al. Prenatal synthetic glucocorticoid treatment changes DNA methylation states in male organ systems: multigenerational effects. Endocrinology. 2012; 153, 3269–3283.Google Scholar
47. Crudo, A, Petropoulos, S, Suderman, M, et al. Effects of antenatal synthetic glucocorticoid on glucocorticoid receptor binding, DNA methylation, and genome-wide mRNA levels in the fetal male hippocampus. Endocrinology. 2013a; 154, 4170–4181.Google Scholar
48. Crudo, A, Suderman, M, Moisiadis, VG, et al. Glucocorticoid programming of the fetal male hippocampal epigenome. Endocrinology. 2013b; 154, 1168–1180.Google Scholar
49. Hompes, T, Izzi, B, Gellens, E, et al. Investigating the influence of maternal cortisol and emotional state during pregnancy on the DNA methylation status of the glucocorticoid receptor gene (NR3C1) promoter region in cord blood. Journal of psychiatric research. 2013; 47, 880–891.Google Scholar
50. Mulligan, CJ, D’Errico, NC, Stees, J, Hughes, DA. Methylation changes at NR3C1 in newborns associate with maternal prenatal stress exposure and newborn birth weight. Epigenetics. 2012; 7, 853–857.Google Scholar
51. Oberlander, TF, Weinberg, J, Papsdorf, M, et al. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008; 3, 97–106.Google Scholar
52. Gurnot, C, Martin-Subero, I, Mah, SM, et al. Prenatal antidepressant exposure associated with CYP2E1 DNA methylation change in neonates. Epigenetics. 2015; 10, 361–372.Google Scholar
53. Non, AL, Binder, AM, Kubzansky, LD, Michels, KB. Genome-wide DNA methylation in neonates exposed to maternal depression, anxiety, or SSRI medication during pregnancy. Epigenetics. 2014; 9, 964–972.Google Scholar
54. Nemoda, Z, Massart, R, Suderman, M, et al. Maternal depression is associated with DNA methylation changes in cord blood T lymphocytes and adult hippocampi. Translational psychiatry. 2015; 5, e545.Google Scholar
55. Champagne, FA, Francis, DD, Mar, A, Meaney, MJ. Variations in maternal care in the rat as a mediating influence for the effects of environment on development. Physiology & behavior. 2003; 79, 359–371.Google Scholar
56. Francis, D, Diorio, J, Liu, D, Meaney, MJ. Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science. 1999; 286, 1155–1158.Google Scholar
57. Weaver, IC, Cervoni, N, Champagne, FA, et al. Epigenetic programming by maternal behavior. Nature neuroscience. 2004; 7, 847–854.Google Scholar
58. Weaver, IC, Meaney, MJ, Szyf, M. Maternal care effects on the hippocampal transcriptome and anxiety-mediated behaviors in the offspring that are reversible in adulthood. Proceedings of the National Academy of Sciences of the United States of America. 2006; 103, 3480–3485.Google Scholar
59. McGowan, PO, Suderman, M, Sasaki, A, et al. Broad epigenetic signature of maternal care in the brain of adult rats. PloS one. 2011; 6, e14739.Google Scholar
60. Mueller, BR, Bale, TL. Sex-specific programming of offspring emotionality after stress early in pregnancy. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2008; 28, 9055–9065.Google Scholar
61. St-Cyr, S, McGowan, PO. Programming of stress-related behavior and epigenetic neural gene regulation in mice offspring through maternal exposure to predator odor. Frontiers in behavioral neuroscience. 2015; 9, 145.Google Scholar
62. St-Cyr, S, Abuaish, S, Sivanathan, S, McGowan, PO. Maternal programming of sex-specific responses to predator odor stress in adult rats. Hormones and behavior. 2017; 94, 1–12.Google Scholar
63. Rassoulzadegan, M, Grandjean, V, Gounon, P, et al. RNA-mediated non-mendelian inheritance of an epigenetic change in the mouse. Nature. 2006; 441, 469–474.Google Scholar
64. Rodgers, AB, Morgan, CP, Bronson, SL, Revello, S, Bale, TL. Paternal stress exposure alters sperm microRNA content and reprograms offspring HPA stress axis regulation. The. Journal of neuroscience : the official journal of the Society for Neuroscience. 2013; 33, 9003–9012.Google Scholar
65. Rodgers, AB, Morgan, CP, Leu, NA, Bale, TL. Transgenerational epigenetic programming via sperm microRNA recapitulates effects of paternal stress. Proceedings of the National Academy of Sciences of the United States of America. 2015; 112, 13699–13704.Google Scholar
66. Reinius, LE, Acevedo, N, Joerink, M, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PloS one. 2012; 7, e41361.Google Scholar
67. Bakulski, KM, Feinberg, JI, Andrews, SV, et al. DNA methylation of cord blood cell types: Applications for mixed cell birth studies. Epigenetics. 2016; 11, 354–362.Google Scholar
68. Jaffe, AE, Irizarry, RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome biology. 2014; 15, R31.Google Scholar
69. Debey, S, Schoenbeck, U, Hellmich, M, et al. Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types. The pharmacogenomics journal. 2004; 4, 193–207.Google Scholar
70. Baechler, EC, Batliwalla, FM, Karypis, G, et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes and immunity. 2004; 5, 347–353.Google Scholar
71. Guthrie, R, Susi, A. A Simple Phenylalanine Method for Detecting Phenylketonuria in Large Populations of Newborn Infants. Pediatrics. 1963; 32, 338–343.Google Scholar
72. Aberg, KA, Xie, LY, Nerella, S, et al. High quality methylome-wide investigations through next-generation sequencing of DNA from a single archived dry blood spot. Epigenetics. 2013; 8, 542–547.Google Scholar
73. Ghantous, A, Saffery, R, Cros, MP, et al. Optimized DNA extraction from neonatal dried blood spots: application in methylome profiling. BMC biotechnology. 2014; 14, 60.Google Scholar
74. Hardin, J, Finnell, RH, Wong, D, et al. Whole genome microarray analysis, from neonatal blood cards. BMC genetics. 2009; 10, 38.Google Scholar
75. Hollegaard, MV, Grauholm, J, Nielsen, R, et al. Archived neonatal dried blood spot samples can be used for accurate whole genome and exome-targeted next-generation sequencing. Molecular genetics and metabolism. 2013; 110, 65–72.Google Scholar
76. Joo, JE, Wong, EM, Baglietto, L, et al. The use of DNA from archival dried blood spots with the Infinium HumanMethylation450 array. BMC biotechnology. 2013; 13, 23.Google Scholar
77. Michels, KB, Binder, AM, Dedeurwaerder, S, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nature methods. 2013; 10, 949–955.Google Scholar
78. Plongthongkum, N, Diep, DH, Zhang, K. Advances in the profiling of DNA modifications: cytosine methylation and beyond. Nature reviews Genetics. 2014; 15, 647–661.Google Scholar
79. Chen, YA, Lemire, M, Choufani, S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013; 8, 203–209.Google Scholar
80. Dedeurwaerder, S, Defrance, M, Calonne, E, et al. Evaluation of the Infinium Methylation 450K technology. Epigenomics. 2011; 3, 771–784.Google Scholar
81. Pidsley, R, Zotenko, E, Peters, TJ, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome biology. 2016; 17, 208.Google Scholar
82. Bock, C. Analysing and interpreting DNA methylation data. Nature reviews Genetics. 2012; 13, 705–719.Google Scholar
83. Liu, Y, Siegmund, KD, Laird, PW, Berman, BP. Bis-SNP: combined DNA methylation and SNP calling for Bisulfite-seq data. Genome biology. 2012; 13, R61.Google Scholar
84. Guo, JU, Su, Y, Zhong, C, Ming, GL, Song, H. Hydroxylation of 5-methylcytosine by TET1 promotes active DNA demethylation in the adult brain. Cell. 2011a; 145, 423–434.Google Scholar
85. Guo, JU, Su, Y, Zhong, C, Ming, GL, Song, H. Emerging roles of TET proteins and 5-hydroxymethylcytosines in active DNA demethylation and beyond. Cell cycle. 2011b; 10, 2662–2668.Google Scholar
86. Booij, L, Szyf, M, Carballedo, A, et al. DNA methylation of the serotonin transporter gene in peripheral cells and stress-related changes in hippocampal volume: a study in depressed patients and healthy controls. PloS one. 2015; 10, e0119061.Google Scholar
87. Liu, XS, Wu, H, Ji, X, et al. Editing DNA Methylation in the Mammalian Genome. Cell. 2016; 167(233-247), e217.Google Scholar