Molecular Genomic Study of Inhibin Molecule Production through Granulosa Cell Gene Expression in Inhibin-Deficient Mice

Abstract

Inhibin is a molecule that belongs to peptide hormones and is excreted through pituitary gonadotropins stimulation action on the granulosa cells of the ovaries. However, the differential regulation of inhibin and follicle-stimulating hormone (FSH) on granulosa cell tumor growth in mice inhibin-deficient females is not yet well understood. The objective of this study was to evaluate the role of inhibin and FSH on the granulosa cells of ovarian follicles at the premature antral stage. This study stimulated immature wild-type (WT) and Inhibin-α knockout (Inha−/−) female mice with human chorionic gonadotropin (hCG) and examined hCG-induced gene expression changes in granulosa cells. Also, screening of differentially expressed genes (DEGs) was performed in the two groups under study. In addition, related modules to external traits and key gene drivers were determined through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm. The results identified a number of 1074 and 931 DEGs and 343 overlapping DEGs (ODEGs) were shared in the two groups. Some 341 ODEGs had high relevance and consistent expression direction, with a significant correlation coefficient (r2 = 0.9145). Additionally, the gene co-expression network of selected 153 genes showed 122 nodes enriched to 21 GO biological processes (BP) and reproduction and 3 genes related to genomic pathways. By using principal component analysis (PCA), the 14 genes in the regulatory network were fixed and the cumulative proportion of fitted top three principal components was 94.64%. In conclusion, this study revealed the novelty of using ODEGs for investigating the inhibin and FSH hormone pathways that might open the way toward gene therapy for granulosa cell tumors. Also, these genes could be used as biomarkers for tracking the changes in inhibin and FSH hormone from the changes in the nutrition pattern.

Elucidating ascorbate and aldarate metabolism pathway characteristics via integration of untargeted metabolomics and transcriptomics of the kidney of high-fat diet-fed obese mice

“…Talpur et al . [ 21 ] demonstrated, using a weighted Gene Co-Expression Network Analysis (WGCNA) algorithm that relevant modules and key gene drivers associated with external traits can be determined, further identifying differentially expressed genes (DEGs). Integrating transcriptomics and metabolomics data allows for normalization and statistical analyses across different biomolecular levels, facilitating the exploration of molecular relationships between these levels.…”

“…Farmanullah et al [20] used advanced bioinformatics methods such as hierarchical clustering, enrichment analysis, active site prediction, and functional domain identification through transcriptomics to analyze important genes that could be used as biomarkers and therapeutic targets for mastitis. Talpur et al [21] demonstrated, using a weighted Gene Co-Expression Network Analysis (WGCNA) algorithm that relevant modules and key gene drivers associated with external traits can be determined, further identifying differentially expressed genes (DEGs). Integrating transcriptomics and metabolomics data allows for normalization and statistical analyses across different biomolecular levels, facilitating the exploration of molecular relationships between these levels.…”