Transcriptome Analysis of Granulosa Cells Reveals Regulatory Mechanisms Related to Chicken Follicle Development

Abstract

In this study, we aimed to better understand the difference between the functions of the two types of granulosa cells and sought to discover more key genes involved in follicle development and follicle selection. Herein, we separately collected pre-hierarchical follicle granulosa cells (PHGCs) and preovulatory follicle granulosa cells (POGCs) for RNA extraction; the transcriptomes of the two groups were compared via RNA-seq. A total of 5273 differentially expressed genes (DEGs) were identified between the PHGCs and POGCs; 2797 genes were up-regulated and 2476 were down-regulated in the PHGCs compared with the POGCs. A qPCR analysis confirmed that the expression patterns of 16 randomly selected DEGs were highly consistent with the RNA-seq results. In the POGCs, many of the genes with the most significant increase in expression were related to steroid hormone synthesis. In addition, the genes with the most significant decline in expression, including AMH and WT1, were related to the inhibition of steroid hormone synthesis. These results suggest that steroid hormones play a key role in follicle development. Furthermore, a Gene Ontology (GO) analysis revealed that these DEGs were mainly involved in the primary metabolic process, the carbohydrate metabolic process, the cellular process, ribosomes, the cytoplasm, and intracellular processes. A Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the DEGs were mainly enriched in steroid biosynthesis, the cell cycle, ribosomes, the TGF-beta signaling pathway, focal adhesion, and so on. We also observed the morphology of the follicles at different developmental stages, and the results showed that the thickness of the granular layer of the small yellow follicles (SYFs) decreased significantly with further development. In addition, we also found that the thickness of the granulosa layer of hens over 300 days old was significantly lower than that of 200-day-old hens. In short, these data indicate that the tissue morphology and function of granulosa cells change throughout follicle development.

Keywords: follicle development; granulosa cell; steroid hormones; transcriptome.

Figure 1

Comparison of SYFs and F6 granulosa layer thickness. (a) HE morphology of small yellow follicle. (b) HE morphology of F6 follicle. (c) Comparison of granulosa layer thickness between small yellow follicle and F6 follicle. Scale bars: 20 μm (40×). G: granulosa layer, n = 5, mean ± SEM, ** means the difference is extremely significant (p < 0.01).

Figure 2

Comparison of granulosa layer thickness of small yellow follicles in hens of different ages. (a) HE morphology of small yellow follicle of 200 d layer. (b) HE morphology of small yellow follicle of 300 d layer. (c) HE morphology of small yellow follicle of 400 d layer. (d) HE morphology of small yellow follicle of 500 d layer. (e) Comparison of granulosa layer thickness in layers of different ages. Scale bars: 20 μm (40×). G: granulosa layer, n = 5, mean ± SEM, ** means the difference is extremely significant (p < 0.01).

Figure 3

Differently expressed genes between PHGCs and POGCs (PHGCs vs. POGCs). The threshold for DEGs is |log2(fold change)| > 1 and q value < 0.005.

Figure 4

Histogram of GO enrichment. Top 30 GO enrichment terms for DEGs between PHGCs and POGCs. The x-axis presents the number of DEGs (both up-regulated and down-regulated). The y-axis shows the specific GO term.

Figure 5

Histogram of GO enrichment. Top 20 GO enrichment terms for DEGs between PHGCs and POGCs. The x-axis represents the number of DEGs (both up-regulated and down-regulated). The y-axis shows the specific GO term.

Figure 6

PPI network. Each node represents a gene, and the number of edges between genes represents the number of interacting relationships.

Figure 7

qPCR verifies the results of RNA-seq. There were three samples in total, with three replicates per sample.