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Prenatal androgen exposure and transgenerational susceptibility to polycystic ovary syndrome

Abstract

How obesity and elevated androgen levels in women with polycystic ovary syndrome (PCOS) affect their offspring is unclear. In a Swedish nationwide register-based cohort and a clinical case–control study from Chile, we found that daughters of mothers with PCOS were more likely to be diagnosed with PCOS. Furthermore, female mice (F0) with PCOS-like traits induced by late-gestation injection of dihydrotestosterone, with and without obesity, produced female F1–F3 offspring with PCOS-like reproductive and metabolic phenotypes. Sequencing of single metaphase II oocytes from F1–F3 offspring revealed common and unique altered gene expression across all generations. Notably, four genes were also differentially expressed in serum samples from daughters in the case–control study and unrelated women with PCOS. Our findings provide evidence of transgenerational effects in female offspring of mothers with PCOS and identify possible candidate genes for the prediction of a PCOS phenotype in future generations.

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Fig. 1: Risk of having a PCOS diagnosis and clinical characteristics of daughters of women with PCOS.
Fig. 2: Maternal obesity and prenatal androgen exposure cause embryonic defects and transgenerational increased anogenital distance in mice.
Fig. 3: Prenatal androgen exposure causes a transgenerational increase in body weight and fat mass in adult female offspring.
Fig. 4: Maternal obesity and prenatal androgen exposure cause transgenerational impaired energy balance.
Fig. 5: scRNA-seq analysis of MII oocytes across F1–F3 generations.
Fig. 6: Transcriptional characteristics of human adipose tissue, mouse MII oocytes and human serum from daughters of women with PCOS and daughters of women in a case–control study.

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Data availability

Raw data of F1–F3 females and clinical characteristics of daughters are available through Dryad: https://doi.org/10.5061/dryad.jwstqjq4m. All raw and analyzed scRNA-seq data of mouse MII oocytes from F1–F3 females are available at the Gene Expression Omnibus database via accession number GSE133100.

For the Swedish register-based cohort, original data are held by the Swedish National Board of Health and Welfare and Statistics Sweden, and because of Swedish data privacy laws we cannot make the data publicly available. Any researcher can access the data by obtaining an ethical approval from a regional ethical review board and thereafter asking the Swedish National Board of Health and Welfare and Statistics Sweden for the original data. However, aggregated data used in the analysis of this study are available from the authors upon reasonable request and with approved data sharing and data processing agreements in line with the General Data Protection Regulation. Further use of these data must be authorized by the local ethics committee regarding the merit of the project involved. A detailed description of the unrelated case–control study, including global gene expression analyses in subcutaneous adipose tissue, has previously been published28. Source data for Figs. 1–4 and 6 and Extended Data Figs. 1–7 are presented with this paper.

References

  1. March, W. A. et al. The prevalence of polycystic ovary syndrome in a community sample assessed under contrasting diagnostic criteria. Hum. Reprod. 25, 544–551 (2010).

    Article  PubMed  Google Scholar 

  2. Dumesic, D. A. et al. Scientific statement on the diagnostic criteria, epidemiology, pathophysiology, and molecular genetics of polycystic ovary syndrome. Endocr. Rev. 36, 487–525 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Azziz, R. PCOS in 2015: new insights into the genetics of polycystic ovary syndrome. Nat. Rev. Endocrinol. 12, 183 (2016).

    Article  PubMed  Google Scholar 

  4. Stener-Victorin, E. et al. Are there any sensitive and specific sex steroid markers for polycystic ovary syndrome? J. Clin. Endocrinol. Metab. 95, 810–819 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. O’Reilly, M. W. et al. AKR1C3-mediated adipose androgen generation drives lipotoxicity in women with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 102, 3327–3339 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Luotola, K., Piltonen, T. T., Puurunen, J., Morin-Papunen, L. C. & Tapanainen, J. S. Testosterone is associated with insulin resistance index independently of adiposity in women with polycystic ovary syndrome. Gynecol. Endocrinol. 34, 40–44 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. Barrett, E. S. et al. Anogenital distance in newborn daughters of women with polycystic ovary syndrome indicates fetal testosterone exposure. J. Dev. Orig. Health Dis. 9, 307–314 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Homburg, R., Gudi, A., Shah, A. & Layton, A, M. A novel method to demonstrate that pregnant women with polycystic ovary syndrome hyper-expose their fetus to androgens as a possible stepping stone for the developmental theory of PCOS. A pilot study. Reprod. Biol. Endocrinol. 15, 61 (2017).

  9. Hanson, M. A. & Gluckman, P. D. Early developmental conditioning of later health and disease: physiology or pathophysiology? Physiol. Rev. 94, 1027–1076 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Roland, A. V., Nunemaker, C. S., Keller, S. R. & Moenter, S. M. Prenatal androgen exposure programs metabolic dysfunction in female mice. J. Endocrinol. 207, 213–223 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hu, M. et al. Maternal testosterone exposure increases anxiety-like behavior and impacts the limbic system in the offspring. Proc. Natl Acad. Sci. USA 112, 14348–14353 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Guerrero-Bosagna, C. & Skinner, M. K. Environmentally induced epigenetic transgenerational inheritance of phenotype and disease. Mol. Cell Endocrinol. 354, 3–8 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Guerrero-Bosagna, C. et al. Epigenetic transgenerational inheritance of vinclozolin induced mouse adult onset disease and associated sperm epigenome biomarkers. Reprod. Toxicol. 34, 694–707 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Skinner, M. K. et al. Ancestral dichlorodiphenyltrichloroethane (DDT) exposure promotes epigenetic transgenerational inheritance of obesity. BMC Med. 11, 228 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Manikkam, M., Tracey, R., Guerrero-Bosagna, C. & Skinner, M. K. Dioxin (TCDD) induces epigenetic transgenerational inheritance of adult onset disease and sperm epimutations. PLoS One 7, e46249 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fornes, R., et al. Mice exposed to maternal androgen excess and diet-induced obesity have altered phosphorylation of catechol-O-methyltransferase in the placenta and fetal liver. Int. J. Obes. https://doi.org/10.1038/s41366-018-0314-8 (2019).

    Article  PubMed  CAS  Google Scholar 

  17. Saben, J. L. et al. Maternal metabolic syndrome programs mitochondrial dysfunction via germline changes across three generations. Cell Rep. 16, 1–8 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cesta, C. E., et al. Maternal polycystic ovary syndrome and risk of neuropsychiatric disorders in offspring: prenatal androgen exposure or genetic confounding? Psychol. Med. https://doi.org/10.1017/S0033291719000424 (2019).

  19. Witham, E. A., Meadows, J. D., Shojaei, S., Kauffman, A. S. & Mellon, P. L. Prenatal exposure to low levels of androgen accelerates female puberty onset and reproductive senescence in mice. Endocrinology 153, 4522–4532 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fornes, R. et al. The effect of androgen excess on maternal metabolism, placental function and fetal growth in obese dams. Sci. Rep. 7, 8066 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Chen, D. et al. The TFAP2C-regulated OCT4 naive enhancer is involved in human germline formation. Cell Rep.25, 3591–3602 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kaiser, S. et al. Reduced gene dosage of Tfap2c impairs trophoblast lineage differentiation and alters maternal blood spaces in the mouse placenta. Biol. Reprod. 93, 1–13 (2015).

    Article  CAS  Google Scholar 

  23. Sharma, N. et al. Tpbpa–Cre-mediated deletion of TFAP2C leads to deregulation of Cdkn1a, Akt1 and the ERK pathway, causing placental growth arrest. Development 143, 787–798 (2016).

    CAS  PubMed  Google Scholar 

  24. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kim, D. A. & Suh, E. K. Defying DNA double-strand break-induced death during prophase I meiosis by temporal TAp63ɑ phosphorylation regulation in developing mouse oocytes. Mol. Cell Biol. 34, 1460–1473 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Ribeiro, J. R., Lovasco, L. A., Vanderhyden, B. C. & Freiman, R. N. Targeting TBP-associated factors in ovarian cancer. Front. Oncol. 4, 45 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sasaki, D., Kotoh, J., Watadani, R. & Matsumoto, K. New animal models reveal that coenzyme Q2 (Coq2) and placenta-specific 8 (Plac8) are candidate genes for the onset of type 2 diabetes associated with obesity in rats. Mamm. Genome 26, 619–629 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Kokosar, M. et al. Epigenetic and transcriptional alterations in human adipose tissue of polycystic ovary syndrome. Sci. Rep. 6, 22883 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhang, Y. et al. Transcriptome landscape of human folliculogenesis reveals oocyte and granulosa cell interactions. Mol. Cell 72, 1021–1034 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Gudmundsdottir, B. et al. POGZ is required for silencing mouse embryonic β-like hemoglobin and human fetal hemoglobin expression. Cell Rep. 23, 3236–3248 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Weissenrieder, J. S., Reilly, J. E., Neighbors, J. D. & Hohl, R. J. Inhibiting geranylgeranyl diphosphate synthesis reduces nuclear androgen receptor signaling and neuroendocrine differentiation in prostate cancer cell models. Prostate 79, 21–30 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Li, C. et al. Transmembrane protein 214 (TMEM214) mediates endoplasmic reticulum stress-induced caspase 4 enzyme activation and apoptosis. J. Biol. Chem. 288, 17908–17917 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tao, W. et al. Lipid-induced muscle insulin resistance is mediated by GGPPS via modulation of the RhoA/Rho kinase signaling pathway. J. Biol. Chem. 290, 20086–20097 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gu, M. et al. RhoA phosphorylation mediated by Rho/RhoA-associated kinase pathway improves the anti-freezing potentiality of murine hatched and diapaused blastocysts. Sci. Rep. 7, 6705 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Beck, A. R., Miller, I. J., Anderson, P. & Streuli, M. RNA-binding protein TIAR is essential for primordial germ cell development. Proc. Natl Acad. Sci. USA 95, 2331–2336 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jagannath, A. et al. The CRTC1–SIK1 pathway regulates entrainment of the circadian clock. Cell 154, 1100–1111 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Doherty, D. A., Newnham, J. P., Bower, C. & Hart, R. Implications of polycystic ovary syndrome for pregnancy and for the health of offspring. Obstet. Gynecol. 125, 1397–1406 (2015).

    Article  PubMed  Google Scholar 

  38. Roos, N. et al. Risk of adverse pregnancy outcomes in women with polycystic ovary syndrome: population based cohort study. BMJ 343, d6309 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Crisosto, N. et al. Higher luteinizing hormone levels associated with antimullerian hormone in postmenarchal daughters of women with polycystic ovary syndrome. Fertil. Steril. 111, 381–388 (2018).

    Article  PubMed  CAS  Google Scholar 

  40. Torchen, L. C., Legro, R. S. & Dunaif, A. Distinctive reproductive phenotypes in peripubertal girls at risk for polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 104, 3355–3361 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Palomba, S. et al. Pervasive developmental disorders in children of hyperandrogenic women with polycystic ovary syndrome: a longitudinal case–control study. Clin. Endocrinol. 77, 898–904 (2012).

    Article  CAS  Google Scholar 

  42. Manneras-Holm, L. et al. Adipose tissue has aberrant morphology and function in PCOS: enlarged adipocytes and low serum adiponectin, but not circulating sex steroids, are strongly associated with insulin resistance. J. Clin. Endocrinol. Metab. 96, E304–E311 (2011).

    Article  CAS  PubMed  Google Scholar 

  43. Dumesic, D. A., et al. Adipose insulin resistance in normal-weight polycystic ovary syndrome women. J. Clin. Endocrinol. Metab. 104, 2171–2183 (2019).

  44. Petta, S. et al. Insulin resistance and hyperandrogenism drive steatosis and fibrosis risk in young females with PCOS. PLoS One 12, e0186136 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Aiken, C. E. & Ozanne, S. E. Transgenerational developmental programming. Hum. Reprod. Update 20, 63–75 (2014).

    Article  PubMed  Google Scholar 

  46. Crisosto, N. et al. Anti-Mullerian hormone levels in peripubertal daughters of women with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 92, 2739–2743 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Sir-Petermann, T. et al. Metabolic and reproductive features before and during puberty in daughters of women with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 94, 1923–1930 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Crisosto, N. et al. Improvement of hyperandrogenism and hyperinsulinemia during pregnancy in women with polycystic ovary syndrome: possible effect in the ovarian follicular mass of their daughters. Fertil. Steril. 97, 218–224 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Palomba, S. et al. Macroscopic and microscopic findings of the placenta in women with polycystic ovary syndrome. Hum. Reprod. 28, 2838–2847 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Maliqueo, M. et al. Placental STAT3 signaling is activated in women with polycystic ovary syndrome. Hum. Reprod. 30, 692–700 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Cavalli, G. & Heard, E. Advances in epigenetics link genetics to the environment and disease. Nature 571, 489–499 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Xavier, M. J., Roman, S. D., Aitken, R. J. & Nixon, B. Transgenerational inheritance: how impacts to the epigenetic and genetic information of parents affect offspring health. Hum. Reprod. Update 25, 518–540 (2019).

    Article  PubMed  CAS  Google Scholar 

  53. Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol.19, 327–341 (2018).

  54. Meyer, C. et al. The TIA1 RNA-binding protein family regulates EIF2AK2-mediated stress response and cell cycle progression. Mol. Cell 69, 622–635 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Dapas, M., et al. Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in polycystic ovary syndrome. J. Clin. Endocrinol. Metab. https://doi.org/10.1210/jc.2018-02496 (2019).

    Article  Google Scholar 

  56. Tee, M. K. et al. Alternative splicing of DENND1A, a PCOS candidate gene, generates variant 2. Mol. Cell. Endocrinol. 434, 25–35 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. McAllister, J. M. et al. Overexpression of a DENND1A isoform produces a polycystic ovary syndrome theca phenotype. Proc. Natl Acad. Sci. USA 111, E1519–E1527 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, F. et al. Alternative splicing of the androgen receptor in polycystic ovary syndrome. Proc. Natl Acad. Sci. USA 112, 4743–4748 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Furuhashi, M. et al. Serum FABP5 concentration is a potential biomarker for residual risk of atherosclerosis in relation to cholesterol efflux from macrophages. Sci. Rep. 7, 217 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Miyamoto, K., Uechi, A. & Saito, K. The zinc finger domain of RING finger protein 141 reveals a unique RING fold. Protein Sci. 26, 1681–1686 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Swanson, M., Sauerbrei, E. E. & Cooperberg, P. L. Medical implications of ultrasonically detected polycystic ovaries. J. Clin. Ultrasound 9, 219–222 (1981).

    Article  CAS  PubMed  Google Scholar 

  62. Manti, M., et al. Maternal androgen excess induces cardiac hypertrophy and left ventricular dysfunction in female mice offspring. Cardiovasc. Res. https://doi.org/10.1093/cvr/cvz180 (2019).

  63. Manti, M. et al. Maternal androgen excess and obesity induce sexually dimorphic anxiety-like behavior in the offspring. FASEB J. 32, 4158–4171 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Benrick, A. et al. Adiponectin protects against development of metabolic disturbances in a PCOS mouse model. Proc. Natl Acad. Sci. USA 114, E7187–E7196 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Nilsson, M. E. et al. Measurement of a comprehensive sex steroid profile in rodent serum by high-sensitive gas chromatography–tandem mass spectrometry. Endocrinology 156, 2492–2502 (2015).

    Article  CAS  PubMed  Google Scholar 

  66. Mehlem, A., Hagberg, C. E., Muhl, L., Eriksson, U. & Falkevall, A. Imaging of neutral lipids by Oil Red O for analyzing the metabolic status in health and disease. Nat. Protoc. 8, 1149–1154 (2013).

    Article  PubMed  CAS  Google Scholar 

  67. Picelli, S. E. A. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096 (2013).

    Article  CAS  PubMed  Google Scholar 

  68. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Love, M. I. et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Yu, G. et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank S. Pilström, J. Victorin and S. Edström for technical assistance during animal work; TSE Systems and the Metabolic Phenotyping Centre at the Strategic Research Program in Diabetes at the Karolinska Institutet; and the electron microscopy unit EMil at Huddinge University Hospital at the Karolinska Institutet. This work was funded by the Swedish Medical Research Council (project no. 2014-2775, 2018-02435 ESV; 2014-2870 QD; 2018-02119 MR), the Novo Nordisk Foundation (NNF17OC0026724, NNF18OC0033992 and NNF19OC0056647 ESV) and the Strategic Research Program in Diabetes at the Karolinska Institutet to E.S.-V.; the Adlerbertska Research Foundation to E.S.-V.; Karolinska Institutet KID funding to E.S.-V. and Q.D.; the Swedish Association of Medical Research and the Åke Wiberg Foundation to Q.D.; the Regional Agreement on Medical Training and Clinical Research between the Stockholm County Council and the Karolinska Institutet to E.S.-V.; the Royal Swedish Academy of Sciences (KVA, BS2015-0012) to S.R.; the National Fund for Scientific and Technological Development (FONDECYT) project no. 1071007 and 1151531 to T.S.-P.; and FONDECYT project no. 1181798 to M.M. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors

Contributions

S.R. designed the study, performed the transgenerational mouse data collection, performed molecular analyses, analyzed the data, prepared the figures and assisted in writing the manuscript. Y.P. performed and analyzed the scRNA-seq data, validated the sequencing data, performed the placenta and embryonic data analyses, prepared the figures and was involved in manuscript preparation. H.L. and H.-P.P. designed and performed the second mouse experiments with placental and embryonic data collection and analyses. M.M., R.F., Z.Z. and J.M. performed the transgenerational phenotyping of mice and analyzed the data. C.O. performed the serum sex steroid analyses with gas chromatography–mass spectrometry. E.L. performed the mouse and human molecular data analyses. N.C., M.M., B.E., A.L.d.G. and T.S.-P. provided clinical information for the case–control study, provided biological serum samples and assisted in manuscript preparation. H.L., M.A.R. and C.E.C. provided data from the Swedish nationwide register-based cohort study, analyzed these data and assisted in manuscript preparation. A.B. was involved in the study design, interpretation of the results and preparation of the manuscript. E.S.-V. and Q.D. designed the study, analyzed the data, prepared the figures and wrote the manuscript. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Qiaolin Deng or Elisabet Stener-Victorin.

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H.L. has served as a speaker for Evolan Pharma and Shire and has received research grants from Shire, all outside the scope of the submitted work. All other authors have no conflicts of interest to declare.

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Extended data

Extended Data Fig. 1 Effect of diet-induced obesity prior mating.

a-c, Body weight development (n = 80 per group) and food and calorie intake per day of F0-dams during the 6-weeks on the diet prior to mating. Food and calorie intake were calculated as food consumption/mouse/cage/day. d-e, Body composition (fat and lean mass) normalized to body weight (gram) (n = 40 per group). f, Blood glucose levels at different time point during the OGTT and (g) glucose area under the curve (AUC) at 0 to 90 minutes in F0-dams (n = 25 per group). h, Serum insulin levels at 0 and 15 minutes before and after the oral glucose challenge (For 0/15 minutes, F0-CD = 23/15, F0-HFHS = 13/17). Comparisons between groups was performed by two-tailed unpaired Student t-test. All data are presented as mean ± s.e.m. Number of mice are stated in bars of each group. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose.

Source data

Extended Data Fig. 2 Diet-induced maternal obesity and prenatal androgen exposure effects on litter size.

a, Pups per litter size in F1-F3 adult offspring. For litter size, there was a main effect in the androgenized + obese lineage in F1 female offspring (two-way ANOVA, Tukey’s post hoc analysis), and in the obese [F 1, 117 = 6.688, P = 0.01], and in the androgenized lineage, [F 1, 117 = 15.51, P = 0.0001]. In F2 offspring, there was a main effect in the obese lineage (two-way ANOVA, Tukey’s post hoc analysis) [F 1, 32 = 10.12, P = 0.0033]. The number of mice/group are specified in Supplementary Table 7. b, Representative images showing absorbed embryos (yellow arrows) at E12.5 in the control and obese + androgenized lineage. c, mRNA expression of germ cell markers developmental pluripotency associated 3 (Dppa3) and mouse vasa homologue (Mvh) in the gonads of F2 fetuses at E12.5 in the control (n = 14/8), androgenized (n = 3/4), obese (n = 7/5), and obese + androgenized (n = 4/3) lineages, and (two-way ANOVA, Tukey’s post hoc analysis) d, mRNA expression of germ cell marker Dppa3 and Mvh in the gonads of F2 fetus at E18.5 in the control (n = 9/10), androgenized (n = 8/10), and obese (n = 8/10) lineages (one-way ANOVA, Dunnett’s post hoc analysis). All data are presented as mean ± s.e.m. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose; Mat = maternal; GMat = grand-maternal; GGMat = great-grand maternal.

Source data

Extended Data Fig. 3 Diet-induced maternal obesity and prenatal androgen exposure effects on offspring estrus cycle changes.

a, Quantitative analysis of estrous cyclicity in F1 to F3 adult female offspring. (F1: MatCD + Veh, n = 18, MatCD + DHT, n = 15, MatHFHS + Veh, n = 14, MatHFHS + DHT, n = 12. F2: GMatCD + Veh, n = 13, GMatCD + DHT, n = 11, GMatHFHS + Veh, n = 16. F3: GGMatCD + Veh, n = 11, GGMatCD + DHT, n = 8, GGMatHFHS + Veh, n = 9). M/D, metestrus and diestrus; E, estrus; P, proestrus. For proestrus, there was a main effect in the obese lineage of F1 female offspring (two-way ANOVA, Tukey’s post hoc analysis) [F 1, 54 = 16.31, P < 0.001]. For estrus, there was a main effect in the androgenized lineage of F1 female offspring (two-way ANOVA, Tukey’s post hoc analysis) [F 1, 54 = 40.396, P < 0.001]. For metestrus/diestrus, there was a main effect in the androgenized lineage of F1 female offspring (two-way ANOVA, Tukey’s post hoc analysis) [F 1, 54 = 28.953, P < 0.001] and in the obese lineage [F 1, 54 = 6.808, P < 0.01]. There were disrupted estrus cycles in the androgenized lineage of F2 offspring (one-way ANOVA, Dunnett’s post hoc analysis) [F2, 49 = 14.19; P < 0.0001]. b, Representative estrous cyclicity assessment in 12-week old female mice for 10 consecutive days by vaginal cytology in F1-F3 adult offspring. The data are present in violin plot showing the frequency distribution curves. The median and quartiles values are shown in dotted and dashed lines respectively. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose; Mat = maternal; GMat = grand-maternal; GGMat = great-grand maternal.

Source data

Extended Data Fig. 4 Diet-induced maternal obesity and prenatal androgen exposure effects on circulating sex steroids in F1-F3 adult female offspring.

Serum (a) testosterone, (b) androstenedione, and (c) Dihydrotestosterone levels measured by gas-chromatography mass spectrometry (GC-MS/MS) in F1, F2 and F3 adult offspring. d, Serum anti-Müllerian hormone (AMH) levels measured by ELISA in F1, F2, and F3 adult offspring (n = number of animals) Comparisons between groups in F1 was performed using two-way ANOVA followed by Tukey’s post hoc analysis and by using one-way ANOVA followed by Dunnett’s post hoc analysis in F2 and F3 generations. All data are presented as mean ± s.e.m. Number of mice are stated in bars of each group. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose; Mat = maternal; GMat = grand-maternal; GGMat = great-grand maternal.

Source data

Extended Data Fig. 5 Diet-induced maternal obesity and prenatal androgen exposure effects on body weight and adipose tissue and liver gene expression.

a-b, Body weight development and area under the curve (AUC) of body weight in F1, F2 and F3 adult female offspring from 3 to15 weeks of age. c, Percent lean mass normalized with body weight (grams) in F1 to F3 adult female offspring at 18-week of age. d, Subcutaneous adipose tissue mRNA expression (2-ΔΔCT) of genes involved in adipogenesis; Bmp4, Zfp423, Cebpa, Cebpb, Ppard, and Pparg of F1-F3 female offspring. (n = animals per group. F1: MatCD + Veh, n = 6, MatCD + DHT, n = 5, MatHFHS + Veh, n = 5, MatHFHS + DHT, n = 5. F2: GMatCD + Veh, n = 6, GMatCD + DHT, n = 5, GMatHFHS + Veh, n = 5, GMatHFHS + DHT, n = 1. F3: GGMatCD + Veh, n = 4, GGMatCD + DHT, n = 3, GGMatHFHS + Veh, n = 5). e, Gene expression (2-ΔΔCT) of lipid biosynthesis and free fatty acid oxidation pathways; Acaca, Fasn, Scd1, Pparg, Fitm1, Apoa1, Lxra, and Srebf1 in liver of F1-F3 female offspring. (n = animals per group. F1: MatCD + Veh, n = 9, MatCD + DHT, n = 10, MatHFHS + Veh, n = 11, MatHFHS + DHT, n = 8. F2: GMatCD + Veh, n = 10, GMatCD + DHT, n = 7, GMatHFHS + Veh, n = 10, GMatHFHS + DHT, n = 1. F3: GGMatCD + Veh, n = 9, GGMatCD + DHT, n = 8, GGMatHFHS + Veh, n = 8). F1: two-way ANOVA, Tukey’s post hoc analysis; F2 and F3: one-way ANOVA, Dunnett’s post hoc analysis. All data are presented as mean ± s.e.m. Number of mice are stated in bars of each group or in the text. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose; Mat = maternal; GMat = grand-maternal; GGMat = great-grand maternal.

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Extended Data Fig. 6 Diet-induced maternal obesity and prenatal androgen exposure effects on glucose homeostasis.

a-b, Blood glucose levels at different time point during oral glucose tolerance test (OGTT) and glucose area under the curve (AUC) at 0 to 90 minutes in F1 to F3 adult female offspring. (F1: MatCD + Veh, n = 16, MatCD + DHT, n = 15, MatHFHS + Veh, n = 11, MatHFHS + DHT, n = 12. F2: GMatCD + Veh, n = 13, GMatCD + DHT, n = 9, GMatHFHS + Veh, n = 12, GMatHFHS + DHT, n = 1. F3: GGMatCD + Veh, n = 11, GGMatCD + DHT, n = 8, GGMatHFHS + Veh, n = 9). (n = number of animals per group). c, Serum insulin levels at 0 and 15 minutes before and after oral glucose challenge. (F1: MatCD + Veh, n = 16, MatCD + DHT, n = 15, MatHFHS + Veh, n = 13, MatHFHS + DHT, n = 12. F2: GMatCD + Veh, n = 13, GMatCD + DHT, n = 10, GMatHFHS + Veh, n = 12, GMatHFHS + DHT, n = 1. F3: GGMatCD + Veh, n = 11, GGMatCD + DHT, n = 8, GGMatHFHS + Veh, n = 8). F1: two-way ANOVA, Tukey’s post hoc analysis; F2 and F3: one-way ANOVA, Dunnett’s post hoc analysis. All data are presented as mean ± s.e.m. Number of mice are stated in bars of each group. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose; Mat = maternal; GMat = grand-maternal; GGMat = great-grand maternal.

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Extended Data Fig. 7 Diet-induced maternal obesity and prenatal androgen exposure alters mitochondrial morphology in MII oocytes of F1, F2 and F3.

a, e, and i Representative transmission electron microscopy (TEM) images of F1–F3 MII oocytes (n = 5 MII oocytes per mice per group from three adult offspring). Blue arrows denote abnormal mitochondria shape and vacuoles. Yellow arrow denotes normal mitochondrial shape. b, f, and j, Average number of mitochondria per MII oocytes (n = number of animals per group). For average mitochondrial number, there was a main effect in the obese lineage in F1 female offspring (two-way ANOVA) [F 1, 210 = 12.10, P = 0.0006] and in in F3 offspring (one-way ANOVA) [F2, 115 = 3.746; P= 0.026]. c, g, and k, Mitochondrial DNA content per MII oocytes, expressed in 2-ΔCT value (n = number of animals per group). For mitochondrial DNA content, there was a main effect in the androgenized lineage in F1 female offspring (two-way ANOVA) [F 1, 52 = 8.335, P = 0.0057]. d, h, and l, Quantitative analysis of lipid droplets in MII oocytes in the four different groups (n = number of animals per group). For average lipid droplets, there was a main effect in the androgenized lineage in F1 female offspring (two-way ANOVA) [F 1, 16 = 19.98, P = 0.0004]. Comparisons between groups F1 and 2 generations were performed using two-way ANOVA followed by Tukey’s post hoc analysis and F3 generation one-way ANOVA followed by Dunnett’s post hoc analysis. Data are presented as mean ± s.e.m. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose; Mat = maternal; GMat = grand-maternal; GGMat = great-grand maternal.

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Extended Data Fig. 8 Single cell RNA sequencing of MII oocytes.

a, Principal component analysis (PCA) plot of all MII oocytes clustered according to generation indicating batch effects due to technical variability. b, PCA plot of MII oocytes of F1-F3 generations in the control, androgenized, and obese lineages after batch effect correction (c) PCA plot of MII oocytes of F1-F3 generations in control, androgenized, and obese lineages based on 641 differentially expressed genes (DEGs). Generation: F1 = first generation; F2 = second generation and F3 = third generation. Condition: CD + Veh = control lineage; CD + DHT = androgenized lineage: HFHS + Veh = obese lineage. F1 = first generation; F2 = second generation and F3 = third generation. Veh = vehicle; DHT = dihydrotestosterone; CD = control diet; HFHS = high-fat high-sucrose.

Extended Data Fig. 9 Venn diagram of orthologues genes.

Venn diagram demonstrating overlap of orthologous genes in human and mouse MII oocytes, respectively. The human data are from previous published reports: GSE107746.

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Risal, S., Pei, Y., Lu, H. et al. Prenatal androgen exposure and transgenerational susceptibility to polycystic ovary syndrome. Nat Med 25, 1894–1904 (2019). https://doi.org/10.1038/s41591-019-0666-1

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