Skip to content
2000
Volume 32, Issue 37
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

Metabolic Syndrome (MS) is a cluster of conditions that significantly increase the risk of infertility in women. Granulosa cells are crucial for ovarian folliculogenesis and fertility. Understanding molecular alterations in these cells can provide insights into MS-associated infertility.

Objective

This study aimed to investigate Differentially Expressed Genes (DEGs) and Proteins (DEPs) in granulosa cells from female patients with MS-associated infertility.

Methods

Transcriptome and proteome analyses were integrated to compare granulosa cells from three MS patients with infertility to three control subjects. RNA sequencing and quantitative proteomics analyses were conducted, followed by differential expression analysis, Gene Set Enrichment Analysis (GSEA), and Protein-protein Interaction (PPI) network construction. Functional enrichment of overlapping DEGs and DEPs and potential drug-protein interactions were also explored. Hub genes identified by PPI were validated quantitative Polymerase Chain Reaction (qPCR) and western blot assays.

Results

Principal Component Analysis (PCA) demonstrated a distinct separation between MS and control groups, indicating significant differences in gene and protein expression. A total of 1,046 upregulated and 23 downregulated DEGs, along with 222 upregulated and 412 downregulated DEPs, were identified in the MS group. GSEA highlighted enrichment in processes, like the cell cycle and immune response. Venn diagram revealed 71 overlapping DEGs and DEPs, mainly related to immune regulation. Key hub proteins and potential therapeutic candidates were identified, with hub genes upregulated at the mRNA level, but downregulated at the protein level in granulosa cells of MS patients.

Conclusion

The integrative analyses revealed significant molecular alterations in granulosa cells from MS patients with infertility. Identified DEGs, DEPs, and hub proteins suggested potential therapeutic targets and pathways for addressing MS-associated infertility.

Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673357582241223070335
2025-01-20
2025-11-01
Loading full text...

Full text loading...

References

  1. FahedG. AounL. Bou ZerdanM. AllamS. Bou ZerdanM. BouferraaY. AssiH.I. Metabolic syndrome: Updates on pathophysiology and management in 2021.Int. J. Mol. Sci.202223278610.3390/ijms2302078635054972
    [Google Scholar]
  2. ColléeJ. MawetM. TebacheL. NisolleM. BrichantG. Polycystic ovarian syndrome and infertility: Overview and insights of the putative treatments.Gynecol. Endocrinol.2021371086987410.1080/09513590.2021.195831034338572
    [Google Scholar]
  3. LiuS. JiaY. MengS. LuoY. YangQ. PanZ. Mechanisms of and potential medications for oxidative stress in ovarian granulosa cells: A review.Int. J. Mol. Sci.20232411920510.3390/ijms2411920537298157
    [Google Scholar]
  4. YangJ. ChenC. Hormonal changes in PCOS.J. Endocrinol.20242611e23034210.1530/JOE‑23‑034238285626
    [Google Scholar]
  5. ZengX. XieY. LiuY. LongS. MoZ. Polycystic ovarian syndrome: Correlation between hyperandrogenism, insulin resistance and obesity.Clin. Chim. Acta202050221422110.1016/j.cca.2019.11.00331733195
    [Google Scholar]
  6. ZhangQ.L. WangY. LiuJ.S. DuY.Z. Effects of hypercaloric diet-induced hyperinsulinemia and hyperlipidemia on the ovarian follicular development in mice.J. Reprod. Dev.202268317318010.1262/jrd.2021‑13235236789
    [Google Scholar]
  7. ZanjirbandM. BaharlooieM. SafaeinejadZ. Nasr-EsfahaniM.H. Transcriptomic screening to identify hub genes and drug signatures for PCOS based on RNA-Seq data in granulosa cells.Comput. Biol. Med.202315410660110.1016/j.compbiomed.2023.10660136738709
    [Google Scholar]
  8. CozzolinoM. HerraizS. TitusS. RobertsL. RomeuM. PeinadoI. ScottR.T. PellicerA. SeliE. Transcriptomic landscape of granulosa cells and peripheral blood mononuclear cells in women with PCOS compared to young poor responders and women with normal response.Hum. Reprod.20223761274128610.1093/humrep/deac06935451009
    [Google Scholar]
  9. WangC. FeiX. ZhangH. ZhouW. ChengZ. FengY. Proteomic analysis of the alterations in follicular fluid proteins during oocyte maturation in humans.Front. Endocrinol. (Lausanne)20221283069110.3389/fendo.2021.83069135185790
    [Google Scholar]
  10. GuoY. CaiL. LiuX. MaL. ZhangH. WangB. QiY. LiuJ. DiaoF. ShaJ. GuoX. Single-cell quantitative proteomic analysis of human oocyte maturation revealed high heterogeneity in in vitro matured oocytes.Mol. Cell. Proteomics202221810026710.1016/j.mcpro.2022.10026735809850
    [Google Scholar]
  11. RitchieM.E. PhipsonB. WuD. HuY. LawC.W. ShiW. SmythG.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.2015437e4710.1093/nar/gkv00725605792
    [Google Scholar]
  12. LiberzonA. SubramanianA. PinchbackR. ThorvaldsdóttirH. TamayoP. MesirovJ.P. Molecular signatures database (MSigDB) 3.0.Bioinformatics201127121739174010.1093/bioinformatics/btr26021546393
    [Google Scholar]
  13. WuT. HuE. XuS. ChenM. GuoP. DaiZ. FengT. ZhouL. TangW. ZhanL. FuX. LiuS. BoX. YuG. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.Innovation20212310014110.1016/j.xinn.2021.10014134557778
    [Google Scholar]
  14. SzklarczykD. KirschR. KoutrouliM. NastouK. MehryaryF. HachilifR. GableA.L. FangT. DonchevaN.T. PyysaloS. BorkP. JensenL.J. von MeringC. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest.Nucleic Acids Res.202351D1D638D64610.1093/nar/gkac100036370105
    [Google Scholar]
  15. ChinC.H. ChenS.H. WuH.H. HoC.W. KoM.T. LinC.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome.BMC Syst Biol.20148Suppl 4S1110.1186/1752‑0509‑8‑S4‑S1125521941
    [Google Scholar]
  16. CannonM. StevensonJ. StahlK. BasuR. CoffmanA. KiwalaS. McMichaelJ.F. KuzmaK. MorrisseyD. CottoK. MardisE.R. GriffithO.L. GriffithM. WagnerA.H. DGIdb 5.0: Rebuilding the drug–gene interaction database for precision medicine and drug discovery platforms.Nucleic Acids Res.202452D1D1227D123510.1093/nar/gkad104037953380
    [Google Scholar]
  17. LiaoB. QiX. YunC. QiaoJ. PangY. Effects of androgen excess-related metabolic disturbances on granulosa cell function and follicular development.Front. Endocrinol. (Lausanne)20221381596810.3389/fendo.2022.81596835237237
    [Google Scholar]
  18. DaiM. HongL. YinT. LiuS. Disturbed follicular microenvironment in polycystic ovary syndrome: Relationship to oocyte quality and infertility.Endocrinology20241654bqae02310.1210/endocr/bqae02338375912
    [Google Scholar]
  19. RudnickaE. SuchtaK. GrymowiczM. Calik-KsepkaA. SmolarczykK. DuszewskaA.M. SmolarczykR. MeczekalskiB. Chronic low grade inflammation in pathogenesis of PCOS.Int. J. Mol. Sci.2021227378910.3390/ijms2207378933917519
    [Google Scholar]
  20. WeberE.W. LynnR.C. SotilloE. LattinJ. XuP. MackallC.L. Pharmacologic control of CAR-T cell function using dasatinib.Blood Adv.20193571171710.1182/bloodadvances.201802872030814055
    [Google Scholar]
  21. ChambersJ.K. Epoetin alfa: Focus on inflammation and infection. Case study of the anemic patient.ANNA J.19982533533569801493
    [Google Scholar]
  22. RanaD. MandalB.M. BhattacharyyaS.N. Miscibility and phase diagrams of poly(phenyl acrylate) and poly(styrene-co-acrylonitrile) blends.Polymer (Guildf.)19933471454145910.1016/0032‑3861(93)90861‑4
    [Google Scholar]
  23. RanaD. MandalB.M. BhattacharyyaS.N. Analogue calorimetric studies of blends of poly (vinyl ester) s and polyacrylates.Macromolecules19962951579158310.1021/ma950954n
    [Google Scholar]
  24. RanaD. MandalB.M. BhattacharyyaS.N. Analogue calorimetry of polymer blends: Poly(styrene-co-acrylonitrile) and poly(phenyl acrylate) or poly(vinyl benzoate).Polymer (Guildf.)199637122439244310.1016/0032‑3861(96)85356‑0
    [Google Scholar]
  25. RanaD. BagK. BhattacharyyaS.N. MandalB.M. Miscibility of poly(styrene-co-butyl acrylate) with poly(ethyl methacrylate): Existence of both UCST and LCST.J. Polym. Sci., B, Polym. Phys.200038336937510.1002/(SICI)1099‑0488(20000201)38:3<369::AID‑POLB3>3.0.CO;2‑W
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673357582241223070335
Loading
/content/journals/cmc/10.2174/0109298673357582241223070335
Loading

Data & Media loading...


  • Article Type:
    Research Article
Keyword(s): granulosa cells; hub proteins; infertility; Metabolic syndrome; proteomics; transcriptomics
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test