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Differential gene expression analysis of human cumulus cells

  • Demiray, Sirin Bakti (Assisted Reproduction Unit, Tepecik Education and Research Hospital) ;
  • Goker, Ege Nazan Tavmergen (Department of Obstetrics and Gynecology, Ege University Faculty of Medicine) ;
  • Tavmergen, Erol (Department of Obstetrics and Gynecology, Ege University Faculty of Medicine) ;
  • Yilmaz, Ozlem (Department of Histology and Embryology, Ege University Faculty of Medicine) ;
  • Calimlioglu, Nilufer (Department of Obstetrics and Gynecology, Ege University Faculty of Medicine) ;
  • Soykam, Huseyin Okan (Epigenetiks Genetics Bioinformatics Software Inc.) ;
  • Oktem, Gulperi (Department of Histology and Embryology, Ege University Faculty of Medicine) ;
  • Sezerman, Ugur (Department of Biostatistics and Bioinformatics, Acibadem Mehmet Ali Aydinlar University, Institute of Health Sciences)
  • Received : 2018.11.16
  • Accepted : 2019.02.02
  • Published : 2019.06.30

Abstract

Objective: This study was performed to explore the possibility that each oocyte and its surrounding cumulus cells might have different genetic expression patterns that could affect human reproduction. Methods: Differential gene expression analysis was performed for 10 clusters of cumulus cells obtained from 10 cumulus-oocyte complexes from 10 patients. Same procedures related to oocyte maturation, microinjection, and microarray analyses were performed for each group of cumulus cells. Two differential gene expression analyses were performed: one for the outcome of clinical pregnancy and one for the outcome of live birth. Results: Significant genes resulting from these analyses were selected and the top 20 affected pathways in each group were analyzed. Circadian entrainment is determined to be the most affected pathway for clinical pregnancy, and proteoglycans in cancer pathway is the most affected pathway for live birth. Circadian entrainment is also amongst the 12 pathways that are found to be in top 20 affected pathways for both outcomes, and has both lowest p-value and highest number of times found count. Conclusion: Although further confirmatory studies are necessary, findings of this study suggest that these pathways, especially circadian entrainment in cumulus cells, may be essential for embryo development and pregnancy.

Keywords

References

  1. Assidi M, Montag M, Van der Ven K, Sirard MA. Biomarkers of human oocyte developmental competence expressed in cumulus cells before ICSI: a preliminary study. J Assist Reprod Genet 2011;28:173-88. https://doi.org/10.1007/s10815-010-9491-7
  2. Balasch J. Investigation of the infertile couple: investigation of the infertile couple in the era of assisted reproductive technology: a time for reappraisal. Hum Reprod 2000;15:2251-7. https://doi.org/10.1093/humrep/15.11.2251
  3. Cillo F, Brevini TA, Antonini S, Paffoni A, Ragni G, Gandolfi F. Association between human oocyte developmental competence and expression levels of some cumulus genes. Reproduction 2007;134:645-50. https://doi.org/10.1530/REP-07-0182
  4. Bettegowda A, Patel OV, Lee KB, Park KE, Salem M, Yao J, et al. Identification of novel bovine cumulus cell molecular markers predictive of oocyte competence: functional and diagnostic implications. Biol Reprod 2008;79:301-9. https://doi.org/10.1095/biolreprod.107.067223
  5. Matzuk MM, Burns KH, Viveiros MM, Eppig JJ. Intercellular communication in the mammalian ovary: oocytes carry the conversation. Science 2002;296:2178-80. https://doi.org/10.1126/science.1071965
  6. True L, Feng Z. Immunohistochemical validation of expression microarray results. J Mol Diagn 2005;7:149-51. https://doi.org/10.1016/S1525-1578(10)60540-5
  7. Fenwick J, Platteau P, Murdoch AP, Herbert M. Time from insemination to first cleavage predicts developmental competence of human preimplantation embryos in vitro. Hum Reprod 2002;17:407-12. https://doi.org/10.1093/humrep/17.2.407
  8. Feuerstein P, Cadoret V, Dalbies-Tran R, Guerif F, Bidault R, Royere D. Gene expression in human cumulus cells: one approach to oocyte competence. Hum Reprod 2007;22:3069-77. https://doi.org/10.1093/humrep/dem336
  9. della Ragione T, Verheyen G, Papanikolaou EG, Van Landuyt L, Devroey P, Van Steirteghem A. Developmental stage on day-5 and fragmentation rate on day-3 can influence the implantation potential of top-quality blastocysts in IVF cycles with single embryo transfer. Reprod Biol Endocrinol 2007;5:2. https://doi.org/10.1186/1477-7827-5-2
  10. van Montfoort AP, Geraedts JP, Dumoulin JC, Stassen AP, Evers JL, Ayoubi TA. Differential gene expression in cumulus cells as a prognostic indicator of embryo viability: a microarray analysis. Mol Hum Reprod 2008;14:157-68. https://doi.org/10.1093/molehr/gam088
  11. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics 2010;26:2363-7. https://doi.org/10.1093/bioinformatics/btq431
  12. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47. https://doi.org/10.1093/nar/gkv007
  13. Bakir-Gungor B, Egemen E, Sezerman OU. PANOGA: a web server for identification of SNP-targeted pathways from genomewide association study data. Bioinformatics 2014;30:1287-9. https://doi.org/10.1093/bioinformatics/btt743
  14. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006;34:D535-9. https://doi.org/10.1093/nar/gkj109
  15. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504. https://doi.org/10.1101/gr.1239303
  16. Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 2002;18 Suppl 1:S233-40. https://doi.org/10.1093/bioinformatics/18.suppl_1.S233
  17. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009;25:1091-3. https://doi.org/10.1093/bioinformatics/btp101
  18. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30. https://doi.org/10.1093/nar/28.1.27
  19. Sellix MT, Menaker M. Circadian clocks in mammalian reproductive physiology: effects of the "other" biological clock on fertility. Discov Med 2011;11:273-81.
  20. Reiter RJ, Tan DX, Korkmaz A, Rosales-Corral SA. Melatonin and stable circadian rhythms optimize maternal, placental and fetal physiology. Hum Reprod Update 2014;20:293-307. https://doi.org/10.1093/humupd/dmt054
  21. Burnik Papler T, Vrtacnik Bokal E, Maver A, Kopitar AN, Lovrecic L. Transcriptomic analysis and meta-analysis of human granulosa and cumulus cells. PLoS One 2015;10:e0136473. https://doi.org/10.1371/journal.pone.0136473
  22. Huo LJ, Fan HY, Zhong ZS, Chen DY, Schatten H, Sun QY. Ubiquitin- proteasome pathway modulates mouse oocyte meiotic maturation and fertilization via regulation of MAPK cascade and cyclin B1 degradation. Mech Dev 2004;121:1275-87. https://doi.org/10.1016/j.mod.2004.05.007
  23. Yi YJ, Nagyova E, Manandhar G, Prochazka R, Sutovsky M, Park CS, et al. Proteolytic activity of the 26S proteasome is required for the meiotic resumption, germinal vesicle breakdown, and cumulus expansion of porcine cumulus-oocyte complexes matured in vitro. Biol Reprod 2008;78:115-26. https://doi.org/10.1095/biolreprod.107.061366
  24. Wayne CM, Fan HY, Cheng X, Richards JS. Follicle-stimulating hormone induces multiple signaling cascades: evidence that activation of Rous sarcoma oncogene, RAS, and the epidermal growth factor receptor are critical for granulosa cell differentiation. Mol Endocrinol 2007;21:1940-57. https://doi.org/10.1210/me.2007-0020
  25. Gloaguen P, Crepieux P, Heitzler D, Poupon A, Reiter E. Mapping the follicle-stimulating hormone-induced signaling networks. Front Endocrinol (Lausanne) 2011;2:45. https://doi.org/10.3389/fendo.2011.00045
  26. Fan HY, Liu Z, Mullany LK, Richards JS. Consequences of RAS and MAPK activation in the ovary: the good, the bad and the ugly. Mol Cell Endocrinol 2012;356:74-9. https://doi.org/10.1016/j.mce.2011.12.005
  27. Chen Y, Kong S, Tang X, Fu Y, Wang B, Zhang S, et al. Preimplantation mouse embryo is a target for opioid ligand-receptor signaling. Biol Reprod 2014;91:4. https://doi.org/10.1095/biolreprod.114.118083
  28. Yu J, Poulton J, Huang YC, Deng WM. The hippo pathway promotes Notch signaling in regulation of cell differentiation, proliferation, and oocyte polarity. PLoS One 2008;3:e1761. https://doi.org/10.1371/journal.pone.0001761
  29. Chen H, Kui C, Chan HC. Ca(2+) mobilization in cumulus cells: role in oocyte maturation and acrosome reaction. Cell Calcium 2013;53:68-75. https://doi.org/10.1016/j.ceca.2012.11.007
  30. Sanford JC, Batten BE. Endocytosis of follicle-stimulating hormone by ovarian granulosa cells: analysis of hormone processing and receptor dynamics. J Cell Physiol 1989;138:154-64. https://doi.org/10.1002/jcp.1041380121
  31. Devjak R, Fon Tacer K, Juvan P, Virant Klun I, Rozman D, Vrtacnik Bokal E. Cumulus cells gene expression profiling in terms of oocyte maturity in controlled ovarian hyperstimulation using GnRH agonist or GnRH antagonist. PLoS One 2012;7:e47106. https://doi.org/10.1371/journal.pone.0047106
  32. Watanabe M, Fukuda A, Nabekura J. The role of GABA in the regulation of GnRH neurons. Front Neurosci 2014;8:387.
  33. Huang X, Hao C, Shen X, Liu X, Shan Y, Zhang Y, et al. Differences in the transcriptional profiles of human cumulus cells isolated from MI and MII oocytes of patients with polycystic ovary syndrome. Reproduction 2013;145:597-608. https://doi.org/10.1530/REP-13-0005
  34. Luan X, Liu D, Cao Z, Luo L, Liu M, Gao M, et al. Transcriptome profiling identifies differentially expressed genes in Huoyan goose ovaries between the laying period and ceased period. PLoS One 2014;9:e113211. https://doi.org/10.1371/journal.pone.0113211
  35. Salustri A, Camaioni A, Di Giacomo M, Fulop C, Hascall VC. Hyaluronan and proteoglycans in ovarian follicles. Hum Reprod Update 1999;5:293-301. https://doi.org/10.1093/humupd/5.4.293
  36. Tiwari M, Prasad S, Tripathi A, Pandey AN, Ali I, Singh AK, et al. Apoptosis in mammalian oocytes: a review. Apoptosis 2015;20:1019-25. https://doi.org/10.1007/s10495-015-1136-y
  37. Hennebold JD. Preventing granulosa cell apoptosis through the action of a single microRNA. Biol Reprod 2010;83:165-7. https://doi.org/10.1095/biolreprod.110.086173
  38. Murdoch WJ, Lund SA. Prostaglandin-independent anovulatory mechanism of indomethacin action: inhibition of tumor necrosis factor alpha-induced sheep ovarian cell apoptosis. Biol Reprod 1999;61:1655-9. https://doi.org/10.1095/biolreprod61.6.1655
  39. Peter AT, Dhanasekaran N. Apoptosis of granulosa cells: a review on the role of MAPK-signalling modules. Reprod Domest Anim 2003;38:209-13. https://doi.org/10.1046/j.1439-0531.2003.00438.x
  40. Su YQ, Denegre JM, Wigglesworth K, Pendola FL, O'Brien MJ, Eppig JJ. Oocyte-dependent activation of mitogen-activated protein kinase (ERK1/2) in cumulus cells is required for the maturation of the mouse oocyte-cumulus cell complex. Dev Biol 2003;263:126-38. https://doi.org/10.1016/S0012-1606(03)00437-8
  41. El-Talatini MR, Taylor AH, Elson JC, Brown L, Davidson AC, Konje JC. Localisation and function of the endocannabinoid system in the human ovary. PLoS One 2009;4:e4579. https://doi.org/10.1371/journal.pone.0004579
  42. Battista N, Meccariello R, Cobellis G, Fasano S, Di Tommaso M, Pirazzi V, et al. The role of endocannabinoids in gonadal function and fertility along the evolutionary axis. Mol Cell Endocrinol 2012;355:1-14. https://doi.org/10.1016/j.mce.2012.01.014
  43. McGinnis LK, Kinsey WH. Role of focal adhesion kinase in oocytefollicle communication. Mol Reprod Dev 2015;82:90-102. https://doi.org/10.1002/mrd.22446
  44. Yodoi R, Tamba S, Morimoto K, Segi-Nishida E, Nishihara M, Ichikawa A, et al. RhoA/Rho kinase signaling in the cumulus mediates extracellular matrix assembly. Endocrinology 2009;150:3345-52. https://doi.org/10.1210/en.2008-1449
  45. Ikeda S, Yamada M. Midkine and cytoplasmic maturation of mammalian oocytes in the context of ovarian follicle physiology. Br J Pharmacol 2014;171:827-36. https://doi.org/10.1111/bph.12311
  46. Erickson GF, Shimasaki S. The spatiotemporal expression pattern of the bone morphogenetic protein family in rat ovary cell types during the estrous cycle. Reprod Biol Endocrinol 2003;1:9. https://doi.org/10.1186/1477-7827-1-9
  47. Vigone G, Merico V, Prigione A, Mulas F, Sacchi L, Gabetta M, et al. Transcriptome based identification of mouse cumulus cell markers that predict the developmental competence of their enclosed antral oocytes. BMC Genomics 2013;14:380. https://doi.org/10.1186/1471-2164-14-380
  48. Knight PG, Glister C. TGF-beta superfamily members and ovarian follicle development. Reproduction 2006;132:191-206. https://doi.org/10.1530/rep.1.01074
  49. Demiray SB, Yilmaz O, Goker EN, Tavmergen E, Calimlioglu N, Sezerman U, et al. Expression of the bone morphogenetic protein-2 (BMP2) in the human cumulus cells as a biomarker of oocytes and embryo quality. J Hum Reprod Sci 2017;10:194-200. https://doi.org/10.4103/jhrs.JHRS_21_17
  50. Hamel M, Dufort I, Robert C, Leveille MC, Leader A, Sirard MA. Identification of follicular marker genes as pregnancy predictors for human IVF: new evidence for the involvement of luteinization process. Mol Hum Reprod 2010;16:548-56. https://doi.org/10.1093/molehr/gaq051

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