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Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis

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Abstract

Diminished ovarian reserve (DOR) is one of the reasons for infertility that not only affects both older and young women. Ovarian reserve assessment can be used as a new prognostic tool for infertility treatment decision making. Here, up- and down-regulated gene expression profiles of granulosa cells were analysed to generate a putative interaction map of the involved genes. In addition, gene ontology (GO) analysis was used to get insight intol the biological processes and molecular functions of involved proteins in DOR. Eleven up-regulated genes and nine down-regulated genes were identified and assessed by constructing interaction networks based on their biological processes. PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3 were the key nodes in the up-regulated networks, while the IGF2, AMH, GREM, and FOXC1 proteins were key in the down-regulated networks. MIRN101-1, MIRN153-1 and MIRN194-1 inhibited the expression of SOCS2, while CSH1 and BMP2 positively regulated IGF1 and IGF2. Ossification, ovarian follicle development, vasculogenesis, sequence-specific DNA binding transcription factor activity, and golgi apparatus are the major differential groups between up-regulated and down-regulated genes in DOR. Meta-analysis of publicly available transcriptomic data highlighted the high coexpression of CTGF, connective tissue growth factor, with the other key regulators of DOR. CTGF is involved in organ senescence and focal adhesion pathway according to GO analysis. These findings provide a comprehensive system biology based insight into the aetiology of DOR through network and gene ontology analyses.

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References

  1. Ahn HW, Morin RD, Zhao H, Harris RA, Coarfa C, Chen ZJ, Milosavljevic A, Marra MA, Rajkovic A (2010) MicroRNA transcriptome in the newborn mouse ovaries determined by massive parallel sequencing. Mol Hum Reprod 16:463–471

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Alanazi IO, Ebrahimie E (2016) Computational systems biology approach predicts regulators and targets of microRNAs and their genomic hotspots in apoptosis process. Mol Biotechnol 58:460–479

    Article  CAS  PubMed  Google Scholar 

  3. Albertini DF, Combelles CM, Benecchi E, Carabatsos MJ (2001) Cellular basis for paracrine regulation of ovarian follicle development. Reproduction 121:647–653

    Article  CAS  PubMed  Google Scholar 

  4. Alisoltani A, Fallahi H, Ebrahimi M, Ebrahimi M, Ebrahimie E (2014) Prediction of potential cancer-risk regions based on transcriptome data: towards a Comprehensive View. PLoS One 9:e96320

    Article  PubMed  PubMed Central  Google Scholar 

  5. Antczak M, Van Blerkom J (1997) Oocyte influences on early development: the regulatory proteins leptin and STAT3 are polarized in mouse and human oocytes and differentially distributed within the cells of the preimplantation stage embryo. Mol Hum Reprod 3:1067–1086

    Article  CAS  PubMed  Google Scholar 

  6. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25:25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Assou S, Al-edani T, Haouzi D, Philippe N, Lecellier CH, Piquemal D, Commes T, Ait-Ahmed O, Dechaud H, Hamamah S (2013) MicroRNAs: new candidates for the regulation of the human cumulus-oocyte complex. Hum Reprod 28:3038–3049

    Article  CAS  PubMed  Google Scholar 

  8. Carabatsos MJ, Elvin J, Matzuk MM, Albertini DF (1998) Characterization of oocyte and follicle development in growth differentiation factor-9-deficient mice. Dev Biol 204:373–384

    Article  CAS  PubMed  Google Scholar 

  9. Ebrahimie E, Nurollah Z, Ebrahimi M, Hemmatzadeh F, Ignjatovic J (2015) Unique ability of pandemic influenza to downregulate the genes involved in neuronal disorders. Mol Biol Rep 42:1377–1390

    Article  CAS  PubMed  Google Scholar 

  10. Ebrahimie M, Esmaeili F, Cheraghi S, Houshmand F, Shabani L, Ebrahimie E (2014) Efficient and simple production of insulin-producing cells from embryonal carcinoma stem cells using mouse neonate pancreas extract, as a natural inducer. PLoS One 9:e90885

    Article  PubMed  PubMed Central  Google Scholar 

  11. Elvin JA, Clark AT, Wang P, Wolfman NM, Matzuk MM (1999) Paracrine actions of growth differentiation factor-9 in the mammalian ovary. Mol Endocrinol 13:1035–1048

    Article  CAS  PubMed  Google Scholar 

  12. Fruzangohar M, Ebrahimie E, Adelson DL (2014) Application of global transcriptome data in gene ontology classification and construction of a gene ontology interaction network. bioRxiv. doi:10.1101/004911

  13. Fruzangohar M, Ebrahimie E, Ogunniyi AD, Mahdi LK, Paton JC, Adelson DL (2013) Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria. PLoS One 8:e58759

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gebhardt KM, Feil DK, Dunning KR, Lane M, Russell DL (2011) Human cumulus cell gene expression as a biomarker of pregnancy outcome after single embryo transfer. Fertil Steril 96(e2):47–52

    Article  CAS  PubMed  Google Scholar 

  15. Gershon E, Hourvitz A, Reikhav S, Maman E, Dekel N (2007) Low expression of COX-2, reduced cumulus expansion, and impaired ovulation in SULT1E1-deficient mice. FASEB J 21:1893–1901

    Article  CAS  PubMed  Google Scholar 

  16. Greenseid K, Jindal S, Hurwitz J, Santoro N, Pal L (2011) Differential granulosa cell gene expression in young women with diminished ovarian reserve. Reprod Sci 18:892–899

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hizaki H, Segi E, Sugimoto Y, Hirose M, Saji T, Ushikubi F, Matsuoka T, Noda Y, Tanaka T, Yoshida N, Narumiya S, Ichikawa A (1999) Abortive expansion of the cumulus and impaired fertility in mice lacking the prostaglandin E receptor subtype EP(2). Proc Natl Acad Sci USA 96:10501–10506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Horvat S, Medrano JF (2001) Lack of Socs2 expression causes the high-growth phenotype in mice. Genomics 72:209–212

    Article  CAS  PubMed  Google Scholar 

  19. Jindal S, Greenseid K, Berger D, Santoro N, Pal L (2012) Impaired gremlin 1 (GREM1) expression in cumulus cells in young women with diminished ovarian reserve (DOR). J Assist Reprod Genet 29:159–162

    Article  PubMed  Google Scholar 

  20. Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucl Acids Res 28:27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lasala C, Carre-Eusebe D, Picard JY, Rey R (2004) Subcellular and molecular mechanisms regulating anti-Mullerian hormone gene expression in mammalian and nonmammalian species. DNA Cell Biol 23:572–585

    Article  CAS  PubMed  Google Scholar 

  22. May-Panloup P, Ferre-L’Hotellier V, Moriniere C, Marcaillou C, Lemerle S, Malinge MC, Coutolleau A, Lucas N, Reynier P, Descamps P, Guardiola P (2012) Molecular characterization of corona radiata cells from patients with diminished ovarian reserve using microarray and microfluidic-based gene expression profiling. Hum Reprod 27:829–843

    Article  CAS  PubMed  Google Scholar 

  23. Metcalf D, Greenhalgh CJ, Viney E, Willson TA, Starr R, Nicola NA, Hilton DJ, Alexander WS (2000) Gigantism in mice lacking suppressor of cytokine signalling-2. Nature 405:1069–1073

    Article  CAS  PubMed  Google Scholar 

  24. Nikitin A, Egorov S, Daraselia N, Mazo I (2003) Pathway studio—the analysis and navigation of molecular networks. Bioinformatics 19:2155–2157

    Article  CAS  PubMed  Google Scholar 

  25. Novichkova S, Egorov S, Daraselia N (2003) MedScan, a natural language processing engine for MEDLINE abstracts. Bioinformatics 19:1699–1706

    Article  CAS  PubMed  Google Scholar 

  26. Obayashi T, Kinoshita K (2009) Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression. DNA Res 16:249–260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Okamura Y, Aoki Y, Obayashi T, Tadaka S, Ito S, Narise T, Kinoshita K (2014) COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucl Acids Res 43:D82–D86

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pangas SA, Li X, Robertson EJ, Matzuk MM (2006) Premature luteinization and cumulus cell defects in ovarian-specific Smad4 knockout mice. Mol Endocrinol 20:1406–1422

    Article  CAS  PubMed  Google Scholar 

  29. Roudebush WE, Kivens WJ, Mattke JM (2008) Biomarkers of Ovarian Reserve. Biomark Insights 3:259–268

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Shoemaker BA, Panchenko AR (2007) Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput Biol 3:e43

    Article  PubMed  PubMed Central  Google Scholar 

  31. Skiadas CC, Duan S, Correll M, Rubio R, Karaca N, Ginsburg ES, Quackenbush J, Racowsky C (2012) Ovarian reserve status in young women is associated with altered gene expression in membrana granulosa cells. Mol Hum Reprod 18:362–371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stanton JL, Bascand M, Fisher L, Quinn M, Macgregor A, Green DP (2002) Gene expression profiling of human GV oocytes: an analysis of a profile obtained by Serial Analysis of Gene Expression (SAGE). J Reprod Immunol 53:193–201

    Article  CAS  PubMed  Google Scholar 

  33. Stanton JL, Green DP (2001) A set of 840 mouse oocyte genes with well-matched human homologues. Mol Hum Reprod 7:521–543

    Article  CAS  PubMed  Google Scholar 

  34. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 102:15545–15550

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Szafarowska M, Jerzak M (2013) Ovarian aging and infertility. Ginekol Pol 84:298–304

    Article  PubMed  Google Scholar 

  36. Takahashi T, Morrow JD, Wang H, Dey SK (2006) Cyclooxygenase-2-derived prostaglandin E(2) directs oocyte maturation by differentially influencing multiple signaling pathways. J Biol Chem 281:37117–37129

    Article  CAS  PubMed  Google Scholar 

  37. Thomas FH, Ethier J-F, Shimasaki S, Vanderhyden BC (2005) Follicle-stimulating hormone regulates oocyte growth by modulation of expression of oocyte and granulosa cell factors. Endocrinology 146:941–949

    Article  CAS  PubMed  Google Scholar 

  38. van Rooij IAJ, Broekmans FJM, te Velde ER, Fauser BCJM, Bancsi LFJMM, de Jong FH, Themmen APN (2002) Serum anti-Müllerian hormone levels: a novel measure of ovarian reserve. Hum Reprod 17:3065–3071

    Article  PubMed  Google Scholar 

  39. Wang T-T, Ke Z-H, Song Y, Chen L-T, Chen X-J, Feng C, Zhang D, Zhang R-J, Wu Y-T, Zhang Y, Sheng J-Z, Huang H-F (2013) Identification of a mutation in GDF9 as a novel cause of diminished ovarian reserve in young women. Hum Reprod 28:2473–2481

    Article  CAS  PubMed  Google Scholar 

  40. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim S-M, Eisenberg D (2002) DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucl Acids Res 30:303–305

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhang X, Jafari N, Barnes RB, Confino E, Milad M, Kazer RR (2005) Studies of gene expression in human cumulus cells indicate pentraxin 3 as a possible marker for oocyte quality. Fertil Steril 83:1169–1179

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Maryam Pashaiasl.

Electronic supplementary material

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11033_2016_4025_MOESM1_ESM.tif

Supplementary material 1 (TIFF 1531 kb). Protein interaction network of up- and down-regulated genes in diminished ovarian reserve (DOR). Network was constructed using one neighbourhood in the expansion algorithm. Interaction between microRNAs and the key genes in diminished ovarian reserve (DOR)

11033_2016_4025_MOESM2_ESM.xlsx

Supplementary material 2 (XLSX 8 kb)Relations between the protein and small molecules in diminished ovarian reserve (DOR).Enriched regulatory subnetworks (p < 0.05) in diminished ovarian reserve (DOR)

Supplementary material 3 (XLSX 15 kb)Relations underlying regulatory sub-networks in diminished ovarian reserve (DOR)

Supplementary material 4 (XLSX 13 kb)Enriched regulatory subnetworks (p < 0.05) in diminished ovarian reserve (DOR)

Supplementary material 5 (XLSX 263 kb)Relations underlying regulatory sub-networks in diminished ovarian reserve (DOR)

Supplementary material 6 (XLSX 23 kb). Relations underlying regulatory sub-networks in diminished ovarian reserve (DOR)

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Supplementary material 7 (XLSX 24 kb). Comparative GO analysis of up and down regulated genes in diminished ovarian reserve (DOR) in “Biological Process”

11033_2016_4025_MOESM8_ESM.xlsx

Supplementary material 8 (XLSX 11 kb). Comparative GO analysis of up and down regulated genes in diminished ovarian reserve (DOR) in “Molecular Function”

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Supplementary material 9 (XLSX 10 kb). Comparative GO analysis of up and down regulated genes in diminished ovarian reserve (DOR) in “Cellular Components”

11033_2016_4025_MOESM10_ESM.rar

Supplementary material 10 (RAR 36 kb)Meta-analysis of the key regulating genes of in diminished ovarian reserve (DOR), including, PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3, based on co-expression analysis

11033_2016_4025_MOESM11_ESM.xlsx

Supplementary material 11 (XLSX 11 kb). The shared co-expressed genes between the key regulated genes in diminished ovarian reserve (DOR)

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Supplementary material 12 (XLSX 81 kb). Regulatory network analysis of control independent dataset. Random samples were taken from whole genes and were subjected to regulatory network analysis

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Pashaiasl, M., Ebrahimi, M. & Ebrahimie, E. Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis. Mol Biol Rep 43, 923–937 (2016). https://doi.org/10.1007/s11033-016-4025-8

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  • DOI: https://doi.org/10.1007/s11033-016-4025-8

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