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Identification of epigenetic interactions between microRNA and DNA methylation associated with polycystic ovarian syndrome

Journal of Human Genetics volume 66, pages 123–137 (2021)Cite this article

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Abstract

Aberration in microRNA expression or DNA methylation is a causal factor for polycystic ovarian syndrome. However, the epigenetic interactions between miRNA and DNA methylation remain unexplored in PCOS. We conducted a novel integrated analysis of RNA-seq, miRNA-seq, and methylated DNA-binding domain sequencing on ovarian granulosa cells to reveal the epigenetic interactions involved in the pathogenesis of PCOS. We identified 830 genes and 30 miRNAs that were expressed differently in PCOS, and seven miRNAs negatively regulated target mRNA expression. 130 miRNAs’ promoters were significantly differently methylated, while 13 were associated with miRNA expression. Furthermore, the hypermethylation of miR-429, miR-141-3p, and miR-126-3p′ promoter was found related to miRNA expression suppression and therefore their corresponding genes upregulation, including XIAP, BRD3, MAPK14, and SLC7A5. Our findings provide a novel insight in PCOS. The consequential reversal of genes silencing may participate in PCOS pathogenesis and served as potential molecular targets for PCOS.

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References

  1. Escobar-Morreale HF. Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment. Nat Rev Endocrinol. 2018;14:270–84.

    Article  Google Scholar 

  2. Azziz R, et al. The prevalence and features of the polycystic ovary syndrome in an unselected population. J Clin Endocrinol Metab. 2004;89:2745–9.

    Article  CAS  Google Scholar 

  3. Norman RJ, Dewailly D, Legro RS, Hickey TE. Polycystic ovary syndrome. Lancet. 2007;370:685–97.

    Article  CAS  Google Scholar 

  4. Matsuda F, Inoue N, Manabe N, Ohkura S. Follicular growth and atresia in mammalian ovaries: regulation by survival and death of granulosa cells. J Reprod Dev. 2012;58:44–50.

    Article  CAS  Google Scholar 

  5. Das M, et al. Granulosa cell survival and proliferation are altered in polycystic ovary syndrome. J Clin Endocrinol Metab. 2008;93:881–7.

    Article  CAS  Google Scholar 

  6. Li S, Zhu D, Duan H, Tan Q. The epigenomics of polycystic ovarian syndrome: from pathogenesis to clinical manifestations. Gynecol Endocrinol. 2016;32:942–6.

    Article  CAS  Google Scholar 

  7. Holubekova V, et al. Epigenetic regulation by DNA methylation and miRNA molecules in cancer. Future Oncol. 2017;13:2217–22.

    Article  CAS  Google Scholar 

  8. Yu Y, et al. Genome-wide screen of ovary-specific DNA methylation in polycystic ovary syndrome. Fertil Steril. 2015;104:145–53.

    Article  CAS  Google Scholar 

  9. Kokosar M, et al. Epigenetic and transcriptional alterations in human adipose tissue of polycystic ovary syndrome. Sci Rep. 2016;6:1–18.

  10. Xu J, et al. Comprehensive analysis of genome-wide DNA methylation across human polycystic ovary syndrome ovary granulosa cell. Oncotarget. 2016;7:27899–909.

    Article  Google Scholar 

  11. Tu J, Cheung AH, Chan CL & Chan W. The role of microRNAs in ovarian granulosa cells in health and disease. Front Endocrinol. 2019;10:174.

  12. Chen B, Xu P, Wang J, Zhang C. The role of MiRNA in polycystic ovary syndrome (PCOS). Gene. 2019;706:91–6.

    Article  CAS  Google Scholar 

  13. Chhabra R. miRNA and methylation: a multifaceted liaison. Chembiochem. 2015;16:195–203.

    Article  CAS  Google Scholar 

  14. Wang S, Wu W, Claret FX. Mutual regulation of microRNAs and DNA methylation in human cancers. Epigenetics. 2017;12:187–97.

    Article  Google Scholar 

  15. Group, T.R.E.A. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod. 2004;19:41–7.

    Article  Google Scholar 

  16. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15.

    Article  CAS  Google Scholar 

  17. Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11:1650–67.

    Article  CAS  Google Scholar 

  18. Love MI, Huber W & Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

  19. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26:139–40.

    Article  Google Scholar 

  20. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9.

    Article  CAS  Google Scholar 

  21. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    Article  CAS  Google Scholar 

  22. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47:D155–D162.

    Article  CAS  Google Scholar 

  23. Chou C, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46:D296–D302.

    Article  CAS  Google Scholar 

  24. Yu G, Wang L, Han Y, He Q. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J Integr Biol. 2012;16:284–7.

    Article  CAS  Google Scholar 

  25. Szklarczyk D, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–D452.

    Article  CAS  Google Scholar 

  26. Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    Article  CAS  Google Scholar 

  27. Lienhard M, Grimm C, Morkel M, Herwig R, Chavez L. MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics. 2014;30:284–6.

    Article  CAS  Google Scholar 

  28. Lizio M, et al. Update of the FANTOM web resource: expansion to provide additional transcriptome atlases. Nucleic Acids Res. 2019;47:D752–D758.

    Article  CAS  Google Scholar 

  29. Azziz R, et al. Polycystic ovary syndrome. Nat Rev Dis Primers. 2016;2:1–18.

  30. Tibbles LA, Woodgett JR. The stress-activated protein kinase pathways. Cell Mol Life Sci. 1999;55:1230–54.

    Article  CAS  Google Scholar 

  31. LiuHT Z. MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Res. 2002;12:9–18.

    Article  Google Scholar 

  32. Aydos A, et al. Identification of polycystic ovary syndrome (PCOS) specific genes in cumulus and mural granulosa cells. PLoS ONE. 2016;11:e168875.

    Article  Google Scholar 

  33. Zhao H, et al. Beneficial effects of Heqi san on rat model of polycystic ovary syndrome through the PI3K/AKT pathway. DARU. 2017;25:21.

    Article  Google Scholar 

  34. Bi X, Zhai Z, Wang S. Identification of the key pathways and genes related to polycystic ovary syndrome using bioinformatics analysis. Gen Physiol Biophys. 2019;38:205–14.

    Article  Google Scholar 

  35. Chan W, et al. MetaMirClust: Discovery of miRNA cluster patterns using a data-mining approach. Genomics. 2012;100:141–8.

    Article  CAS  Google Scholar 

  36. Ghorai A & Ghosh U. miRNA gene counts in chromosomes vary widely in a species and biogenesis of miRNA largely depends on transcription or post-transcriptional processing of coding genes. Front Genet. 2014;5:100.

  37. Dini P, et al. Kinetics of the chromosome 14 microRNA cluster ortholog and its potential role during placental development in the pregnant mare. BMC Genomics. 2018;19:954.

  38. Zhang L, et al. microRNA-141 is involved in a nasopharyngeal carcinoma-related genes network. Carcinogenesis. 2010;31:559–66.

    Article  CAS  Google Scholar 

  39. Mateescu B, et al. miR-141 and miR-200a act on ovarian tumorigenesis by controlling oxidative stress response. Nat Med. 2011;17:1627–35.

    Article  CAS  Google Scholar 

  40. Miko E, et al. miR-126 inhibits proliferation of small cell lung cancer cells by targeting SLC7A5. FEBS Lett. 2011;585:1191–6.

    Article  CAS  Google Scholar 

  41. Zhu W, et al. miR-200bc/429 cluster modulates multidrug resistance of human cancer cell lines by targeting BCL2 and XIAP. Cancer Chemoth Pharmacol. 2012;69:723–31.

    Article  CAS  Google Scholar 

  42. Ferrero H, et al. Dysregulated genes and their functional pathways in luteinized granulosa cells from PCOS patients after cabergoline treatment. Reproduction. 2018;155:373–81.

    Article  CAS  Google Scholar 

  43. Udhane SS, Flück CE. Regulation of human (adrenal) androgen biosynthesis—new insights from novel throughput technology studies. Biochem Pharmacol. 2016;102:20–33.

    Article  CAS  Google Scholar 

  44. Li D, et al. Differential expression of microRNAs in the ovaries from letrozole-induced rat model of polycystic ovary syndrome. DNA Cell Biol. 2016;35:177–83.

    Article  CAS  Google Scholar 

  45. Saraei R, et al. The role of XIAP in resistance to TNF-related apoptosis-inducing ligand (TRAIL) in Leukemia. Biomed Pharmacother. 2018;107:1010–9.

    Article  CAS  Google Scholar 

  46. Hussain AR, et al. XIAP over-expression is an independent poor prognostic marker in Middle Eastern breast cancer and can be targeted to induce efficient apoptosis. BMC Cancer. 2017;17:640.

  47. Hasuwa H, Ueda J, Ikawa M, Okabe M. MiR-200b and miR-429 function in mouse ovulation and are essential for female fertility. Science. 2013;341:71–3.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by the Natural Science Foundation of Shanghai (19ZR1476100), National Infrastructures for Translational Medicine (Shanghai) (TMSK-2020-109), Interdisciplinary Program of Medical Engineering Cross Fund (YG2019GD02, YG2019QNB23, YG2019QNA49, and YG2019QNA52) and Laboratory Innovative Research Program of Shanghai Jiao Tong University (JCZXSJB2019002).

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Authors and Affiliations

  1. School of Biomedical Engineering, Bio-ID Center, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhanrui Mao, Hui Zhao, Yulan Qin & Yani Kang

  2. Department of Obstetrics and Gynecology, Yuncheng Central Hospital, Yuncheng, 044000, Shanxi Province, China

    Ting Li & Xuesong Wang

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  1. Zhanrui Mao
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  2. Ting Li
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Correspondence to Yani Kang.

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Mao, Z., Li, T., Zhao, H. et al. Identification of epigenetic interactions between microRNA and DNA methylation associated with polycystic ovarian syndrome. J Hum Genet 66, 123–137 (2021). https://doi.org/10.1038/s10038-020-0819-6

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