Identification of new progestogen-associated networks in mammalian ovulation using bioinformatics

Abstract

Background: Progesterone plays an essential role in mammalian ovulation. Although much is known about this process, the gene networks involved in ovulation have yet to be established. When analyze the mechanisms of ovulation, we often need to determine key genes or pathways to investigate the reproduction features. However, traditional experimental methods have a number of limitations.

Results: Data, in this study, were acquired from GSE41836 and GSE54584 which provided different samples. They were analyzed with the GEO2R and 546 differentially expressed genes were obtained from two data sets using bioinformatics (absolute log₂ FC > 1, P < 0.05). This study identified four genes (PGR, RELN, PDE10A and PLA2G4A) by protein-protein interaction networks and pathway analysis, and their functional enrichments were associated with ovulation. Then, the top 25 statistical pathway enrichments related to hCG treatment were analyzed. Furthermore, gene network analysis identified certain interconnected genes and pathways involved in progestogenic mechanisms, including progesterone-mediated oocyte maturation, the MAPK signaling pathway, the GnRH signaling pathway and focal adhesion, etc. Moreover, we explored the four target gene pathways. q-PCR analysis following hCG and RU486 treatments confirmed the certain novel progestogenic-associated genes (GNAI1, PRKCA, CAV1, EGFR, RHOA, ZYX, VCL, GRB2 and RAP1A).

Conclusions: The results suggested four key genes, nine predicted genes and eight pathways to be involved in progestogenic networks. These networks provide important regulatory genes and signaling pathways which are involved in ovulation. This study provides a fundamental basis for subsequent functional studies to investigate the regulation of mammalian ovulation.

Keywords: Bioinformatics; Mammalian ovulation; Progesterone; Progestogenic-associated.

Fig. 1

Pathway enrichment analysis. The top 25 statistics pathways identified by pathway enrichment analysis of the 546 DEGs, considering the involved gene numbers and P-vaule

Fig. 2

Gene expression analysis with hCG and RU treatment in preovulatory follicles from GSE54584. All validated genes were significantly differentially expressed in hCG- and RU486-treated ovarian. a and c were hCG-treated group, and b and d were RU486-treated group. Nodes in red represent the genes with expression level above the mean and nodes in green represent the genes with expression below the mean. The intensity of the pseudo-color reflects cross parts. e, Differentially expressed genes showed by visualized analysis with p-value < 0.05 and absolute log₂ FC > 1

Fig. 3

Visualized progesterone-related genes network. Nodes in different colors represent different target genes or pathways. Purple sexangles represent node genes (MAPK, SRC, PTK2, ITGA, CAPN2, GPCR, ADCY1) and predicted target genes (GNAI1, PRKCA, CAV1, EGFR, RHOA, ZYX, VCL, GRB2 and RAP1A) in this study; yellow color ellipses represent genes between networks; and red color rectangles reflect pathways involved in progestogenic networks

Fig. 4

Validation of progesterone-related predicted genes by q-PCR analysis. The two groups of rats were treated by PMSG and hCG or PMSG, hCG and RU486. Total RNA of COCs was extracted for q-PCR analysis of the genes expression. a-b: PGR and RELN expression; c-k: expression of the nine genes (GNAI1, PRKCA, CAV1, EGFR, RHOA, ZYX, VCL, GRB2 and RAP1A, respectively). Different asterisks indicate statistically significant differences (*, P < 0.05; **, P < 0.01). Error bars represent S.D.