Transcriptome profile of goat folliculogenesis reveals the interaction of oocyte and granulosa cell in correlation with different fertility population

To understand the molecular and genetic mechanisms related to the litter size in one species of two different populations (high litter size and low litter size), we performed RNA-seq for the oocytes and granulosa cells (GCs) at different developmental stages of follicle, and identified the interaction of genes from both sides of follicle (oocyte and GCs) and the ligand-receptor pairs from these two sides. Our data were very comprehensive to uncover the difference between these two populations regarding the folliculogenesis. First, we identified a set of potential genes in oocyte and GCs as the marker genes which can be used to determine the goat fertility capability and ovarian reserve ability. The data showed that GRHPR, GPR84, CYB5A and ERAL1 were highly expressed in oocyte while JUNB, SCN2A, MEGE8, ZEB2, EGR1and PRRC2A were highly expressed in GCs. We found more functional genes were expressed in oocytes and GCs in high fertility group (HL) than that in low fertility group (LL). We uncovered that ligand-receptor pairs in Notch signaling pathway and transforming growth factor-beta (TGF-beta) superfamily pathways played important roles in goat folliculogenesis for the different fertility population. Moreover, we discovered that the correlations of the gene expression in oocytes and GCs at different stages in the two populations HL and LL were different, too. All the data reflected the gene expression landscape in oocytes and GCs which was correlated well with the fertility capability.

Automated summary

The study utilized RNA-seq to uncover genetic mechanisms related to litter size in different populations of a species, identifying marker genes for fertility assessment and highlighting the roles of Notch signaling pathway and TGF-β superfamily pathways in goat folliculogenesis. The data showed differences in gene expression between high and low fertility groups, providing insights into the genetic landscape of oocytes and GCs correlated with fertility capability.