Molecular mechanisms regulating natural menopause in the female ovary: a study based on transcriptomic data - Liu, Quan; Wei, Fangqin; Wang, Jiannan; Liu, Haiyan; Zhang, Hua; Liu, Min; Liu, Kaili; Ye, Zheng (Frontiers in Endocrinology, 2023)
Natural menopause is an inevitable process and has important implications for their health. However, its molecular mechanisms are still unknown. In this study, we explored the state of molecular biology and cell biology changes in the ovary before and after perimenopause using single-cell sequencing data from the ovary and transcriptome sequencing data from ovarian tissue in the 30-59 age group of the GTEx V8 cohort (30-39: 14 individuals; 40-49: 37 individuals, 50-59: 61 individuals). Seurat was used to analyse single cell sequencing data. harmony was used to integrate the data. CytoTrace was used to infer cell differentiation trajectories. CIBERSORTX was used to assess cell infiltration scores in ovarian tissue. WGCNA was used to assess co-expression network characteristics in pre- and post-perimenopausal ovarian tissue. ClusterprofileR and Metascape were used for functional enrichment analysis of co-expression modules. MsViper was used for master regulator analysis. We identified the differentiation trajectory of follicular cells in the ovary (ARID5B+ Granulosa -> JUN+ Granulosa -> KRT18+ Granulosa -> MT-CO2+ Granulosa -> GSTA1+ Granulosa -> HMGB1+ Granulosa). In addition, the contents of terminally differentiated HMGB1+ Granulosa, GSTA1+ Granulosa in the ovaries of age 50-69 were significantly increased. The results of signaling pathway activity analysis showed that the activity of TGFb, MAPK signaling pathway gradually decreased with the progress of menopause, while the activity of p53 signaling pathway gradually increased. The results of master regulator analysis showed that transcription factors FOXR1, OTX2, MYBL2, HNF1A, and FOXN4 were significantly activated in the 30-39 age group, while GLI1, SMAD1, SMAD7, APP, and EGR1 were significantly activated in the 40-49 age group. Finally, we constructed a diagnostic model of 16 transcription factors (Logistic Regression L2) to judge ovarian status before and after perimenopause. The model can achieve average AUC=0.82, F1=0.66 in the training set (10-fold cross-validation) and average AUC=0.84, F1=0.51 in the validation set. The results of this study provide a research basis for exploring the molecular and cellular mechanisms in the natural menopause process of the ovary, and provide a theoretical basis for the health management of perimenopausal women.