DNA methylation loci in placenta associated with birthweight and expression of genes relevant for early development and adult diseases

Fasil Tekola-Ayele, Xuehuo Zeng, Marion Ouidir, Tsegaselassie Workalemahu, Cuilin Zhang, Fabien Delahaye, Ronald Wapner, Fasil Tekola-Ayele, Xuehuo Zeng, Marion Ouidir, Tsegaselassie Workalemahu, Cuilin Zhang, Fabien Delahaye, Ronald Wapner

Abstract

Background: Birthweight marks an important milestone of health across the lifespan, including cardiometabolic disease risk in later life. The placenta, a transient organ at the maternal-fetal interface, regulates fetal growth. Identifying genetic loci where DNA methylation in placenta is associated with birthweight can unravel genomic pathways that are dysregulated in aberrant fetal growth and cardiometabolic diseases in later life.

Results: We performed placental epigenome-wide association study (EWAS) of birthweight in an ethnic diverse cohort of pregnant women (n = 301). Methylation at 15 cytosine-(phosphate)-guanine sites (CpGs) was associated with birthweight (false discovery rate (FDR) < 0.05). Methylation at four (26.7%) CpG sites was associated with placental transcript levels of 15 genes (FDR < 0.05), including genes known to be associated with adult lipid traits, inflammation and oxidative stress. Increased methylation at cg06155341 was associated with higher birthweight and lower FOSL1 expression, and lower FOSL1 expression was correlated with higher birthweight. Given the role of the FOSL1 transcription factor in regulating developmental processes at the maternal-fetal interface, epigenetic mechanisms at this locus may regulate fetal development. We demonstrated trans-tissue portability of methylation at four genes (MLLT1, PDE9A, ASAP2, and SLC20A2) implicated in birthweight by a previous study in cord blood. We also found that methylation changes known to be related to maternal underweight, preeclampsia and adult type 2 diabetes were associated with lower birthweight in placenta.

Conclusion: We identified novel placental DNA methylation changes associated with birthweight. Placental epigenetic mechanisms may underlie dysregulated fetal development and early origins of adult cardiometabolic diseases.

Clinical trial registration: ClinicalTrials.gov, NCT00912132.

Keywords: Birthweight; DNA methylation; Developmental origins of health and disease (DOHaD); Expression quantitative trait methylation (eQTM); Fetal growth; Placenta; Transcriptomics.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Distributions of associations between CpG sites in placenta and birthweight. a Manhattan plot. b Volcano plot. Horizontal red line marks FDR 0.05; red dots denote CpGs significantly associated with birthweight
Fig. 2
Fig. 2
Correlations between birthweight and placental expression of genes associated DNA methylation levels of birthweight-associated CpG sites. aFOSL1. bUBASH3A
Fig. 3
Fig. 3
Triangular relationship between DNA methylation at cg06155341, expression of FOSL1, and birthweight
Fig. 4
Fig. 4
Overlap in CpGs significantly associated with birthweight in placenta and adult traits. A Non-cardiometabolic adult diseases. B Maternal perinatal cardiometabolic conditions. C Cardiometabolic traits in adults. Gene and CpG names in red represent DNA methylation changes associated with increased risk of adult disease as well as lower birthweight. Gene and CpG names in blue represent DNA methylation changes associated with increased risk of adult disease and higher birthweight. SLE stands for systemic lupus erythematosus

References

    1. Gaskins RB, LaGasse LL, Liu J, Shankaran S, Lester BM, Bada HS, Bauer CR, Das A, Higgins RD, Roberts M. Small for gestational age and higher birth weight predict childhood obesity in preterm infants. Am J Perinatol. 2010;27:721–730.
    1. Wilcox AJ, Russell IT. Birthweight and perinatal mortality: II. On weight-specific mortality. Int J Epidemiol. 1983;12:319–325.
    1. Hales CN, Barker DJ, Clark PM, Cox LJ, Fall C, Osmond C, Winter PD. Fetal and infant growth and impaired glucose tolerance at age 64. BMJ. 1991;303:1019–1022.
    1. Sacks DA. Determinants of fetal growth. Curr Diab Rep. 2004;4:281–287.
    1. Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14:204–220.
    1. Nugent BM, Bale TL. The omniscient placenta: metabolic and epigenetic regulation of fetal programming. Front Neuroendocrinol. 2015;39:28–37.
    1. Kent EM, Breathnach FM, Gillan JE, McAuliffe FM, Geary MP, Daly S, Higgins JR, Dornan J, Morrison JJ, Burke G, et al. Placental cord insertion and birthweight discordance in twin pregnancies: results of the national prospective ESPRiT Study. Am J Obstet Gynecol. 2011;205(376):e371–e377.
    1. Thornburg KL, O'Tierney PF, Louey S. Review: the placenta is a programming agent for cardiovascular disease. Placenta. 2010;31(Suppl):S54–S59.
    1. Rahnama F, Shafiei F, Gluckman PD, Mitchell MD, Lobie PE. Epigenetic regulation of human trophoblastic cell migration and invasion. Endocrinology. 2006;147:5275–5283.
    1. Serman L, Vlahovic M, Sijan M, Bulic-Jakus F, Serman A, Sincic N, Matijevic R, Juric-Lekic G, Katusic A. The impact of 5-azacytidine on placental weight, glycoprotein pattern and proliferating cell nuclear antigen expression in rat placenta. Placenta. 2007;28:803–811.
    1. Fuke C, Shimabukuro M, Petronis A, Sugimoto J, Oda T, Miura K, Miyazaki T, Ogura C, Okazaki Y, Jinno Y. Age related changes in 5-methylcytosine content in human peripheral leukocytes and placentas: an HPLC-based study. Ann Hum Genet. 2004;68:196–204.
    1. Price EM, Cotton AM, Penaherrera MS, McFadden DE, Kobor MS, Robinson W. Different measures of “genome-wide” DNA methylation exhibit unique properties in placental and somatic tissues. Epigenetics. 2012;7:652–663.
    1. Novakovic B, Yuen RK, Gordon L, Penaherrera MS, Sharkey A, Moffett A, Craig JM, Robinson WP, Saffery R. Evidence for widespread changes in promoter methylation profile in human placenta in response to increasing gestational age and environmental/stochastic factors. BMC Genomics. 2011;12:529.
    1. Adkins RM, Tylavsky FA, Krushkal J. Newborn umbilical cord blood DNA methylation and gene expression levels exhibit limited association with birth weight. Chem Biodivers. 2012;9:888–899.
    1. Engel SM, Joubert BR, Wu MC, Olshan AF, Haberg SE, Ueland PM, Nystad W, Nilsen RM, Vollset SE, Peddada SD, London SJ. Neonatal genome-wide methylation patterns in relation to birth weight in the Norwegian Mother and Child Cohort. Am J Epidemiol. 2014;179:834–842.
    1. Simpkin AJ, Suderman M, Gaunt TR, Lyttleton O, McArdle WL, Ring SM, Tilling K, Davey Smith G, Relton CL. Longitudinal analysis of DNA methylation associated with birth weight and gestational age. Hum Mol Genet. 2015;24:3752–3763.
    1. Agha G, Hajj H, Rifas-Shiman SL, Just AC, Hivert MF, Burris HH, Lin X, Litonjua AA, Oken E, DeMeo DL, et al. Birth weight-for-gestational age is associated with DNA methylation at birth and in childhood. Clin Epigenetics. 2016;8:118.
    1. Kupers LK, Monnereau C, Sharp GC, Yousefi P, Salas LA, Ghantous A, Page CM, Reese SE, Wilcox AJ, Czamara D, et al. Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight. Nat Commun. 2019;10:1893.
    1. Michels KB, Harris HR, Barault L. Birthweight, maternal weight trajectories and global DNA methylation of LINE-1 repetitive elements. PLoS One. 2011;6:e25254.
    1. Bourque DK, Avila L, Penaherrera M, von Dadelszen P, Robinson WP. Decreased placental methylation at the H19/IGF2 imprinting control region is associated with normotensive intrauterine growth restriction but not preeclampsia. Placenta. 2010;31:197–202.
    1. Dwi Putra SE, Reichetzeder C, Hasan AA, Slowinski T, Chu C, Kramer BK, Kleuser B, Hocher B. Being born large for gestational age is associated with increased global placental DNA methylation. Sci Rep. 2020;10:927.
    1. Filiberto AC, Maccani MA, Koestler D, Wilhelm-Benartzi C, Avissar-Whiting M, Banister CE, Gagne LA, Marsit CJ. Birthweight is associated with DNA promoter methylation of the glucocorticoid receptor in human placenta. Epigenetics. 2011;6:566–572.
    1. Ferreira JC, Choufani S, Grafodatskaya D, Butcher DT, Zhao C, Chitayat D, Shuman C, Kingdom J, Keating S, Weksberg R. WNT2 promoter methylation in human placenta is associated with low birthweight percentile in the neonate. Epigenetics. 2011;6:440–449.
    1. Leeuwerke M, Eilander MS, Pruis MG, Lendvai A, Erwich JJ, Scherjon SA, Plosch T, Eijsink JJ. DNA methylation and expression patterns of selected genes in first-trimester placental tissue from pregnancies with small-for-gestational-age infants at birth. Biol Reprod. 2016;94:37.
    1. Turan N, Ghalwash MF, Katari S, Coutifaris C, Obradovic Z, Sapienza C. DNA methylation differences at growth related genes correlate with birth weight: a molecular signature linked to developmental origins of adult disease? BMC Med Genet. 2012;5:10.
    1. Chen PY, Chu A, Liao WW, Rubbi L, Janzen C, Hsu FM, Thamotharan S, Ganguly A, Lam L, Montoya D, et al. Prenatal growth patterns and birthweight are associated with differential DNA methylation and gene expression of cardiometabolic risk genes in human placentas: a discovery-based approach. Reprod Sci. 2018;25:523–539.
    1. Banister CE, Koestler DC, Maccani MA, Padbury JF, Houseman EA, Marsit CJ. Infant growth restriction is associated with distinct patterns of DNA methylation in human placentas. Epigenetics. 2011;6:920–927.
    1. Grewal J, Grantz KL, Zhang C, Sciscione A, Wing DA, Grobman WA, Newman RB, Wapner R, D'Alton ME, Skupski D, et al. Cohort profile: NICHD fetal growth studies-singletons and twins. Int J Epidemiol. 2018;47:25–25l.
    1. Tekola-Ayele F, Workalemahu T, Gorfu G, Shrestha D, Tycko B, Wapner R, Zhang C, Louis GMB. Sex differences in the associations of placental epigenetic aging with fetal growth. Aging (Albany NY) 2019;11:5412–5432.
    1. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–883.
    1. Hu S, Wan J, Su Y, Song Q, Zeng Y, Nguyen HN, Shin J, Cox E, Rho HS, Woodard C, et al. DNA methylation presents distinct binding sites for human transcription factors. Elife. 2013;2:e00726.
    1. Roadmap Epigenomics C, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–330.
    1. Breeze CE, Reynolds AP, van Dongen J, Dunham I, Lazar J, Neph S, Vierstra J, Bourque G, Teschendorff AE, Stamatoyannopoulos JA, Beck S. eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data. Bioinformatics. 2019;35:4767–4769.
    1. Peng S, Deyssenroth MA, Di Narzo AF, Lambertini L, Marsit CJ, Chen J, Hao K. Expression quantitative trait loci (eQTLs) in human placentas suggest developmental origins of complex diseases. Hum Mol Genet. 2017;26:3432–3441.
    1. Delahaye F, Do C, Kong Y, Ashkar R, Salas M, Tycko B, Wapner R, Hughes F. Genetic variants influence on the placenta regulatory landscape. PLoS Genet. 2018;14:e1007785.
    1. Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, Zheng J, Duggirala A, McArdle WL, Ho K, et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016;17:61.
    1. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, Parkinson H. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–D1006.
    1. Warrington NM, Beaumont RN, Horikoshi M, Day FR, Helgeland O, Laurin C, Bacelis J, Peng S, Hao K, Feenstra B, et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet. 2019;51:804–814.
    1. Li M, Zou D, Li Z, Gao R, Sang J, Zhang Y, Li R, Xia L, Zhang T, Niu G, et al. EWAS Atlas: a curated knowledgebase of epigenome-wide association studies. Nucleic Acids Res. 2019;47:D983–D988.
    1. Bisarro Dos Reis M, Barros-Filho MC, Marchi FA, Beltrami CM, Kuasne H, Pinto CAL, Ambatipudi S, Herceg Z, Kowalski LP, Rogatto SR. Prognostic classifier based on genome-wide DNA methylation profiling in well-differentiated thyroid tumors. J Clin Endocrinol Metab. 2017;102:4089–4099.
    1. Aref-Eshghi E, Schenkel LC, Ainsworth P, Lin H, Rodenhiser DI, Cutz JC, Sadikovic B. Genomic DNA methylation-derived algorithm enables accurate detection of malignant prostate tissues. Front Oncol. 2018;8:100.
    1. Imgenberg-Kreuz J, Carlsson Almlof J, Leonard D, Alexsson A, Nordmark G, Eloranta ML, Rantapaa-Dahlqvist S, Bengtsson AA, Jonsen A, Padyukov L, et al. DNA methylation mapping identifies gene regulatory effects in patients with systemic lupus erythematosus. Ann Rheum Dis. 2018;77:736–743.
    1. Chandra A, Senapati S, Roy S, Chatterjee G, Chatterjee R. Epigenome-wide DNA methylation regulates cardinal pathological features of psoriasis. Clin Epigenetics. 2018;10:108.
    1. Matsuo H, Yamamoto K, Nakaoka H, Nakayama A, Sakiyama M, Chiba T, Takahashi A, Nakamura T, Nakashima H, Takada Y, et al. Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes. Ann Rheum Dis. 2016;75:652–659.
    1. Nakayama A, Nakaoka H, Yamamoto K, Sakiyama M, Shaukat A, Toyoda Y, Okada Y, Kamatani Y, Nakamura T, Takada T, et al. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann Rheum Dis. 2017;76:869–877.
    1. Palinski W. Effect of maternal cardiovascular conditions and risk factors on offspring cardiovascular disease. Circulation. 2014;129:2066–2077.
    1. Rossen LM, Schoendorf KC. Trends in racial and ethnic disparities in infant mortality rates in the United States, 1989-2006. Am J Public Health. 2014;104:1549–1556.
    1. Mikkola K, Ritari N, Tommiska V, Salokorpi T, Lehtonen L, Tammela O, Paakkonen L, Olsen P, Korkman M, Fellman V. Neurodevelopmental outcome at 5 years of age of a national cohort of extremely low birth weight infants who were born in 1996-1997. Pediatrics. 2005;116:1391–1400.
    1. Barker DJ, Godfrey KM, Osmond C, Bull A. The relation of fetal length, ponderal index and head circumference to blood pressure and the risk of hypertension in adult life. Paediatr Perinat Epidemiol. 1992;6:35–44.
    1. Godfrey KM, Barker DJ. Fetal nutrition and adult disease. Am J Clin Nutr. 2000;71:1344S–1352S.
    1. Hu Y, O'Boyle K, Auer J, Raju S, You F, Wang P, Fikrig E, Sutton RE. Multiple UBXN family members inhibit retrovirus and lentivirus production and canonical NFkappaBeta signaling by stabilizing IkappaBalpha. PLoS Pathog. 2017;13:e1006187.
    1. Meder B, Haas J, Sedaghat-Hamedani F, Kayvanpour E, Frese K, Lai A, Nietsch R, Scheiner C, Mester S, Bordalo DM, et al. Epigenome-wide association study identifies cardiac gene patterning and a novel class of biomarkers for heart failure. Circulation. 2017;136:1528–1544.
    1. Cela P, Hampl M, Shylo NA, Christopher KJ, Kavkova M, Landova M, Zikmund T, Weatherbee SD, Kaiser J, Buchtova M. Ciliopathy protein Tmem107 plays multiple roles in craniofacial development. J Dent Res. 2018;97:108–117.
    1. Norris AA, Lewis AJ, Zeitlin IJ. Changes in colonic tissue levels of inflammatory mediators in a guinea-pig model of immune colitis. Agents Actions. 1982;12:243–246.
    1. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–1283.
    1. Benjamin EJ, Dupuis J, Larson MG, Lunetta KL, Booth SL, Govindaraju DR, Kathiresan S, Keaney JF, Jr, Keyes MJ, Lin JP, et al. Genome-wide association with select biomarker traits in the Framingham Heart Study. BMC Med Genet. 2007;8(Suppl 1):S11.
    1. Eferl R, Wagner EF. AP-1: a double-edged sword in tumorigenesis. Nat Rev Cancer. 2003;3:859–868.
    1. Fleischmann A, Hafezi F, Elliott C, Reme CE, Ruther U, Wagner EF. Fra-1 replaces c-Fos-dependent functions in mice. Genes Dev. 2000;14:2695–2700.
    1. Kent LN, Rumi MA, Kubota K, Lee DS, Soares MJ. FOSL1 is integral to establishing the maternal-fetal interface. Mol Cell Biol. 2011;31:4801–4813.
    1. Renaud SJ, Kubota K, Rumi MA, Soares MJ. The FOS transcription factor family differentially controls trophoblast migration and invasion. J Biol Chem. 2014;289:5025–5039.
    1. Bohlin J, Haberg SE, Magnus P, Reese SE, Gjessing HK, Magnus MC, Parr CL, Page CM, London SJ, Nystad W. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biol. 2016;17:207.
    1. Shynlova O, Tsui P, Dorogin A, Lye SJ. Monocyte chemoattractant protein-1 (CCL-2) integrates mechanical and endocrine signals that mediate term and preterm labor. J Immunol. 2008;181:1470–1479.
    1. Condon JC, Jeyasuria P, Faust JM, Mendelson CR. Surfactant protein secreted by the maturing mouse fetal lung acts as a hormone that signals the initiation of parturition. Proc Natl Acad Sci U S A. 2004;101:4978–4983.
    1. Han Z, Mulla S, Beyene J, Liao G, McDonald SD, Knowledge Synthesis G. Maternal underweight and the risk of preterm birth and low birth weight: a systematic review and meta-analyses. Int J Epidemiol. 2011;40:65–101.
    1. Dhana K, Braun KVE, Nano J, Voortman T, Demerath EW, Guan W, Fornage M, van Meurs JBJ, Uitterlinden AG, Hofman A, et al. An epigenome-wide association study of obesity-related traits. Am J Epidemiol. 2018;187:1662–1669.
    1. Buck Louis GM, Grewal J, Albert PS, Sciscione A, Wing DA, Grobman WA, Newman RB, Wapner R, D'Alton ME, Skupski D, et al. Racial/ethnic standards for fetal growth: the NICHD Fetal Growth Studies. Am J Obstet Gynecol. 2015;213:449 e441.
    1. Buck Louis GM, Zhai S, Smarr MM, Grewal J, Zhang C, Grantz KL, Hinkle SN, Sundaram R, Lee S, Honda M, et al. Endocrine disruptors and neonatal anthropometry, NICHD Fetal Growth Studies - Singletons. Environ Int. 2018;119:515–526.
    1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:3156.
    1. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2012;29:189–196.
    1. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, Lin SM. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11:587.
    1. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417.
    1. Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, Mason CE. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13:R87.

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