Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-25T20:37:41.514Z Has data issue: false hasContentIssue false
  • English
  • Français

Epigenetics and DOHaD: from basics to birth and beyond

Part of: DOHaD ANZ Conference 2016

Published online by Cambridge University Press:  11 September 2017

T. Bianco-Miotto ,
J. M. Craig Open the ORCID record for J. M. Craig [Opens in a new window] ,
Y. P. Gasser ,
S. J. van Dijk  and
S. E. Ozanne
T. Bianco-Miotto*
Affiliation:
School of Agriculture, Food and Wine, Waite Research Institute & Robinson Research Institute, University of Adelaide, SA, Australia
J. M. Craig
Affiliation:
Department of Paediatrics, Murdoch Children’s Research Institute, University of Melbourne, Royal Children’s Hospital, Parkville, VIC, Australia
Y. P. Gasser
Affiliation:
Department of Paediatrics, Murdoch Children’s Research Institute, University of Melbourne, Royal Children’s Hospital, Parkville, VIC, Australia
S. J. van Dijk
Affiliation:
CSIRO Health and Biosecurity, North Ryde, NSW, Australia
S. E. Ozanne
Affiliation:
Metabolic Research Laboratories, MRC Metabolic Diseases Unit, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
*
*Address for correspondence: T. Bianco-Miotto, School of Agriculture, Food and Wine, Waite Research Institute & Robinson Research Institute, University of Adelaide, SA, 5005, Australia. (Email tina.bianco@adelaide.edu.au)
Get access Rights & Permissions [Opens in a new window]

Abstract

Developmental origins of health and disease (DOHaD) is the study of how the early life environment can impact the risk of chronic diseases from childhood to adulthood and the mechanisms involved. Epigenetic modifications such as DNA methylation, histone modifications and non-coding RNAs are involved in mediating how early life environment impacts later health. This review is a summary of the Epigenetics and DOHaD workshop held at the 2016 DOHaD Society of Australia and New Zealand Conference. Our extensive knowledge of how the early life environment impacts later risk for chronic disease would not have been possible without animal models. In this review we highlight some animal model examples that demonstrate how an adverse early life exposure results in epigenetic and gene expression changes that may contribute to increased risk of chronic disease later in life. Type 2 diabetes and cardiovascular disease are chronic diseases with an increasing incidence due to the increased number of children and adults that are obese. Epigenetic changes such as DNA methylation have been shown to be associated with metabolic health measures and potentially predict future metabolic health status. Although more difficult to elucidate in humans, recent studies suggest that DNA methylation may be one of the epigenetic mechanisms that mediates the effects of early life exposures on later life risk of obesity and obesity related diseases. Finally, we discuss the role of the microbiome and how it is a new player in developmental programming and mediating early life exposures on later risk of chronic disease.

Keywords

animal modelsdietepigeneticsmetabolic healthmicrobiome
Type
Review
Information
Journal of Developmental Origins of Health and Disease , Volume 8 , Issue 5: Themed Issue: Australian Perspectives: Outcomes from the 2016 ANZ , October 2017 , pp. 513 - 519
Check for updates
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Jablonka, E, Lamb, MJ. The changing concept of epigenetics. Ann N Y Acad Sci. 2002; 981, 82–96.Google Scholar
2. Blewitt, M, Whitelaw, E. The use of mouse models to study epigenetics. Cold Spring Harb Perspect Biol. 2013; 5, a017939.Google Scholar
3. Rosenfeld, CS. Animal models to study environmental epigenetics. Biol Reprod. 2010; 82, 473–488.Google Scholar
4. Poulsen, P, Esteller, M, Vaag, A, Fraga, MF. The epigenetic basis of twin discordance in age-related diseases. Pediatr Res. 2007; 61(5 Pt 2), 38R–42R.Google Scholar
5. Castillo-Fernandez, JE, Spector, TD, Bell, JT. Epigenetics of discordant monozygotic twins: implications for disease. Genome Med. 2014; 6, 60.Google Scholar
6. Marx, V. Epigenetics: reading the second genomic code. Nature. 2012; 491, 143–147.Google Scholar
7. Jang, HS, Shin, WJ, Lee, JE, Do, JT. CpG and non-CpG methylation in epigenetic gene regulation and brain function. Genes (Basel). 2017; 8, 148.Google Scholar
8. Zhong, X. Comparative epigenomics: a powerful tool to understand the evolution of DNA methylation. New Phytol. 2016; 210, 76–80.Google Scholar
9. Tessarz, P, Kouzarides, T. Histone core modifications regulating nucleosome structure and dynamics. Nat Rev Mol Cell Biol. 2014; 15, 703–708.Google Scholar
10. Zhang, T, Cooper, S, Brockdorff, N. The interplay of histone modifications – writers that read. EMBO Rep. 2015; 16, 1467–1481.Google Scholar
11. Du, J, Johnson, LM, Jacobsen, SE, Patel, DJ. DNA methylation pathways and their crosstalk with histone methylation. Nat Rev Mol Cell Biol. 2015; 16, 519–532.Google Scholar
12. Cora, D, Re, A, Caselle, M, Bussolino, F. MicroRNA-mediated regulatory circuits: outlook and perspectives. Phys Biol. 2017; 14, 045001.CrossRefGoogle Scholar
13. Karlsson, O, Baccarelli, AA. Environmental health and long non-coding RNAs. Curr Environ Health Rep. 2016; 3, 178–187.Google Scholar
14. Barker, DJ. The fetal and infant origins of adult disease. BMJ. 1990; 301, 1111.Google Scholar
15. Gluckman, PD, Hanson, MA, Cooper, C, Thornburg, KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008; 359, 61–73.Google Scholar
16. Barouki, R, Gluckman, PD, Grandjean, P, Hanson, M, Heindel, JJ. Developmental origins of non-communicable disease: implications for research and public health. Environ Health. 2012; 11, 42.Google Scholar
17. Lillycrop, KA, Phillips, ES, Jackson, AA, Hanson, MA, Burdge, GC. Dietary protein restriction of pregnant rats induces and folic acid supplementation prevents epigenetic modification of hepatic gene expression in the offspring. J Nutr. 2005; 135, 1382–1386.Google Scholar
18. Weaver, IC, Cervoni, N, Champagne, FA, et al. Epigenetic programming by maternal behavior. Nat Neurosci. 2004; 7, 847–854.Google Scholar
19. Park, JH, Stoffers, DA, Nicholls, RD, Simmons, RA. Development of type 2 diabetes following intrauterine growth retardation in rats is associated with progressive epigenetic silencing of Pdx1. J Clin Invest. 2008; 118, 2316–2324.Google Scholar
20. Sandovici, I, Smith, NH, Nitert, MD, et al. Maternal diet and aging alter the epigenetic control of a promoter-enhancer interaction at the Hnf4a gene in rat pancreatic islets. Proc Natl Acad Sci USA. 2011; 108, 5449–5454.Google Scholar
21. Ferland-McCollough, D, Fernandez-Twinn, DS, Cannell, IG, et al. Programming of adipose tissue miR-483-3p and GDF-3 expression by maternal diet in type 2 diabetes. Cell Death Differ. 2012; 19, 1003–1012.CrossRefGoogle ScholarPubMed
22. Maloyan, A, Muralimanoharan, S, Huffman, S, et al. Identification and comparative analyses of myocardial miRNAs involved in the fetal response to maternal obesity. Physiol Genomics. 2013; 45, 889–900.Google Scholar
23. Chambers, JC, Loh, M, Lehne, B, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015; 3, 526–534.CrossRefGoogle ScholarPubMed
24. Dayeh, T, Tuomi, T, Almgren, P, et al. DNA methylation of loci within ABCG1 and PHOSPHO1 in blood DNA is associated with future type 2 diabetes risk. Epigenetics. 2016; 11, 482–488.Google Scholar
25. Demerath, EW, Guan, W, Grove, ML, et al. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet. 2015; 24, 4464–4479.Google Scholar
26. Dick, KJ, Nelson, CP, Tsaprouni, L, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014; 383, 1990–1998.Google Scholar
27. Pan, H, Lin, X, Wu, Y, et al. HIF3A association with adiposity: the story begins before birth. Epigenomics. 2015; 7, 937–950.CrossRefGoogle ScholarPubMed
28. Soriano-Tarraga, C, Jimenez-Conde, J, Giralt-Steinhauer, E, et al. Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia. Hum Mol Genet. 2016; 25, 609–619.Google Scholar
29. Wang, B, Gao, W, Li, J, et al. Methylation loci associated with body mass index, waist circumference, and waist-to-hip ratio in Chinese adults: an epigenome-wide analysis. Lancet. 2016; 388(Suppl. 1), S21.Google Scholar
30. Wang, S, Song, J, Yang, Y, et al. HIF3A DNA methylation is associated with childhood obesity and ALT. PLoS One. 2015; 10, e0145944.Google Scholar
31. Wahl, S, Drong, A, Lehne, B, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017; 541, 81–86.Google Scholar
32. Relton, CL, Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012; 41, 161–176.CrossRefGoogle ScholarPubMed
33. Barres, R, Kirchner, H, Rasmussen, M, et al. Weight loss after gastric bypass surgery in human obesity remodels promoter methylation. Cell Rep. 2013; 3, 1020–1027.Google Scholar
34. Benton, MC, Johnstone, A, Eccles, D, et al. An analysis of DNA methylation in human adipose tissue reveals differential modification of obesity genes before and after gastric bypass and weight loss. Genome Biol. 2015; 16, 8.CrossRefGoogle ScholarPubMed
35. Multhaup, ML, Seldin, MM, Jaffe, AE, et al. Mouse-human experimental epigenetic analysis unmasks dietary targets and genetic liability for diabetic phenotypes. Cell Metab. 2015; 21, 138–149.Google Scholar
36. Clarke-Harris, R, Wilkin, TJ, Hosking, J, et al. PGC1alpha promoter methylation in blood at 5-7 years predicts adiposity from 9 to 14 years (EarlyBird 50). Diabetes. 2014; 63, 2528–2537.Google Scholar
37. Godfrey, KM, Sheppard, A, Gluckman, PD, et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes. 2011; 60, 1528–1534.Google Scholar
38. Sharp, GC, Lawlor, DA, Richmond, RC, et al. Maternal pre-pregnancy BMI and gestational weight gain, offspring DNA methylation and later offspring adiposity: findings from the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2015; 44, 1288–1304.Google Scholar
39. Tobi, EW, Goeman, JJ, Monajemi, R, et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun. 2014; 5, 5592.Google Scholar
40. Dominguez-Salas, P, Moore, SE, Baker, MS, et al. Maternal nutrition at conception modulates DNA methylation of human metastable epialleles. Nat Commun. 2014; 5, 3746.CrossRefGoogle ScholarPubMed
41. Rando, OJ, Simmons, RA. I’m eating for two: parental dietary effects on offspring metabolism. Cell. 2015; 161, 93–105.Google Scholar
42. Godfrey, KM, Reynolds, RM, Prescott, SL, et al. Influence of maternal obesity on the long-term health of offspring. Lancet Diabetes Endocrinol. 2017; 5, 53–64.Google Scholar
43. Donkin, I, Versteyhe, S, Ingerslev, LR, et al. Obesity and bariatric surgery drive epigenetic variation of spermatozoa in humans. Cell Metab. 2016; 23, 369–378.CrossRefGoogle ScholarPubMed
44. Kuhnen, P, Handke, D, Waterland, RA, et al. Interindividual variation in DNA methylation at a putative POMC metastable epiallele is associated with obesity. Cell Metab. 2016; 24, 502–509.Google Scholar
45. Perco, P, Wilflingseder, J, Bernthaler, A, et al. Biomarker candidates for cardiovascular disease and bone metabolism disorders in chronic kidney disease: a systems biology perspective. J Cell Mol Med. 2008; 12, 1177–1187.Google Scholar
46. Shu, L, Zhao, Y, Kurt, Z, et al. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genomics. 2016; 17, 874.Google Scholar
47. Levy, M, Thaiss, CA, Elinav, E. Metabolites: messengers between the microbiota and the immune system. Genes Dev. 2016; 30, 1589–1597.Google Scholar
48. Mischke, M, Plosch, T. The gut microbiota and their metabolites: potential implications for the host epigenome. Adv Exp Med Biol. 2016; 902, 33–44.CrossRefGoogle ScholarPubMed
49. Thorburn, AN, Macia, L, Mackay, CR. Diet, metabolites, and “western-lifestyle” inflammatory diseases. Immunity. 2014; 40, 833–842.Google Scholar
50. Berding, K, Donovan, SM. Microbiome and nutrition in autism spectrum disorder: current knowledge and research needs. Nutr Rev. 2016; 74, 723–736.Google Scholar
51. Shapiro, H, Thaiss, CA, Levy, M, Elinav, E. The cross talk between microbiota and the immune system: metabolites take center stage. Curr Opin Immunol. 2014; 30, 54–62.CrossRefGoogle ScholarPubMed
52. Cortese, R, Lu, L, Yu, Y, Ruden, D, Claud, EC. Epigenome-microbiome crosstalk: a potential new paradigm influencing neonatal susceptibility to disease. Epigenetics. 2016; 11, 205–215.Google Scholar
53. Yu, DH, Gadkari, M, Zhou, Q, et al. Postnatal epigenetic regulation of intestinal stem cells requires DNA methylation and is guided by the microbiome. Genome Biol. 2015; 16, 211.Google Scholar
54. Kumar, H, Lund, R, Laiho, A, et al. Gut microbiota as an epigenetic regulator: pilot study based on whole-genome methylation analysis. mBio. 2014; 5, e02113–14.Google Scholar
55. Bierne, H, Hamon, M, Cossart, P. Epigenetics and bacterial infections. Cold Spring Harb Perspect Med. 2012; 2, a010272.CrossRefGoogle ScholarPubMed
56. Paschos, K, Allday, MJ. Epigenetic reprogramming of host genes in viral and microbial pathogenesis. Trends Microbiol. 2010; 18, 439–447.Google Scholar
57. Bonder, MJ, Kurilshikov, A, Tigchelaar, EF, et al. The effect of host genetics on the gut microbiome. Nat Genet. 2016; 48, 1407–1412.Google Scholar
58. Turpin, W, Espin-Garcia, O, Xu, W, et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat Genet. 2016; 48, 1413–1417.Google Scholar
59. Beaumont, M, Goodrich, JK, Jackson, MA, et al. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol. 2016; 17, 189.Google Scholar
60. Goodrich, JK, Waters, JL, Poole, AC, et al. Human genetics shape the gut microbiome. Cell. 2014; 159, 789–799.Google Scholar
61. Takahashi, K, Sugi, Y, Hosono, A, Kaminogawa, S. Epigenetic regulation of TLR4 gene expression in intestinal epithelial cells for the maintenance of intestinal homeostasis. J Immunol. 2009; 183, 6522–6529.CrossRefGoogle ScholarPubMed
62. Takahashi, K, Sugi, Y, Nakano, K, et al. Epigenetic control of the host gene by commensal bacteria in large intestinal epithelial cells. J Biol Chem. 2011; 286, 35755–35762.Google Scholar
63. Liu, S, da Cunha, AP, Rezende, RM, et al. The host shapes the gut microbiota via fecal microRNA. Cell Host Microbe. 2016; 19, 32–43.Google Scholar
64. Schroeder, BO, Backhed, F. Signals from the gut microbiota to distant organs in physiology and disease. Nat Med. 2016; 22, 1079–1089.Google Scholar
65. Remely, M, Haslberger, AG. The microbial epigenome in metabolic syndrome. Mol Aspects Med. 2016; 54, 71–77.CrossRefGoogle ScholarPubMed
66. Krautkramer, KA, Kreznar, JH, Romano, KA, et al. Diet-microbiota interactions mediate global epigenetic programming in multiple host tissues. Mol Cell. 2016; 64, 982–992.Google Scholar
67. Worthley, DL, Whitehall, VL, Le Leu, RK, et al. DNA methylation in the rectal mucosa is associated with crypt proliferation and fecal short-chain fatty acids. Dig Dis Sci. 2011; 56, 387–396.Google Scholar
68. Remely, M, Aumueller, E, Merold, C, et al. Effects of short chain fatty acid producing bacteria on epigenetic regulation of FFAR3 in type 2 diabetes and obesity. Gene. 2014; 537, 85–92.CrossRefGoogle ScholarPubMed
69. Lukovac, S, Belzer, C, Pellis, L, et al. Differential modulation by Akkermansia muciniphila and Faecalibacterium prausnitzii of host peripheral lipid metabolism and histone acetylation in mouse gut organoids. mBio. 2014; 5, e01438–14.Google Scholar
70. Lynch, SV, Pedersen, O. The human intestinal microbiome in health and disease. N Engl J Med. 2016; 375, 2369–2379.Google Scholar
71. Zmora, N, Zeevi, D, Korem, T, Segal, E, Elinav, E. Taking it personally: personalized utilization of the human microbiome in health and disease. Cell Host Microbe. 2016; 19, 12–20.Google Scholar
72. Ahmad, I, Nasiruddin, M, Khan, MA, et al. Nutraceuticals as chemopreventive and therapeutic agents in gut associated pathogenesis. Biochem Anal Biochem. 2016; 5, 298.Google Scholar
73. Remely, M, Ferk, F, Sterneder, S, et al. EGCG prevents high fat diet-induced changes in gut microbiota, decreases of DNA strand breaks, and changes in expression and DNA methylation of Dnmt1 and MLH1 in C57BL/6J male mice. Oxid Med Cell Longev. Epub 2017 Jan 4.Google Scholar