Transcriptomic and proteomic analyses of ovarian follicles reveal the role of VLDLR in chicken follicle selection

Abstract

Background: Follicle selection in chickens refers to the process of selecting one follicle from a group of small yellow follicles (SY, 6-8 mm in diameter) for development into 12-15 mm hierarchical follicles (usually F6 follicles), which is an important process affecting laying performance in the poultry industry. Although transcriptomic analysis of chicken ovarian follicles has been reported, integrated analysis of chicken follicles for selection by using both transcriptomic and proteomic approaches is still rarely performed. In this study, we compared the proteomes and transcriptomes of SY and F6 follicles in laying hens and identified several genes involved in chicken follicle selection.

Results: Transcriptomic analysis revealed 855 differentially expressed genes (DEGs) between SY follicles and F6 follicles in laying hens, among which 202 were upregulated and 653 were downregulated. Proteomic analysis revealed 259 differentially expressed proteins (DEPs), including 175 upregulated and 84 downregulated proteins. Among the identified DEGs and DEPs, changes in the expression of seven genes, including VLDLR1, WIF1, NGFR, AMH, BMP15, GDF6 and MMP13, and nine proteins, including VLDLR, VTG1, VTG3, PSCA, APOB, APOV1, F10, ZP2 and ZP3L2, were validated. Further analysis indicated that the mRNA level of chicken VLDLR was higher in F6 follicles than in SY follicles and was also higher in granulosa cells (GCs) than in thecal cells (TCs), and it was stimulated by FSH in GCs.

Conclusions: By comparing the proteomes and transcriptomes of SY and F6 follicles in laying hens, we identified several differentially expressed proteins/genes that might play certain roles in chicken follicle selection. These data may contribute to the identification of functional genes and proteins involved in chicken follicle selection.

Keywords: Chicken; Differentially expressed genes; Differentially expressed proteins; Follicle; Proteome; Transcriptome.

Fig. 1

Transcriptome profile comparison between SY follicles (S) and F6 follicles (F). a Volcano plot of all the genes detected in six chicken follicle samples. Green spots represent downregulation, and red spots represent upregulation. b Hierarchical clustering analysis of DEGs between F and S. c GO enrichment of DEGs from the F and S transcriptomes. d KEGG signaling pathway enrichment analysis of DEGs

Fig. 2

The mRNA expression levels of genes examined by qRT-PCR. All data are presented as the mean ± SEM. *, P < 0.05

Fig. 3

TMT analysis of the DEP data for the chicken F6 and SY follicles. a Mass error distribution of all identified peptides. b The length distribution of the majority of the peptides. c Volcano plots of -log₁₀ (P value) versus log₂ (expression level) in the F6 vs SY follicles. d KEGG signal pathway enrichment analysis of the DEPs

Fig. 4

The histogram of the nine significantly abundant proteins in F6 follicles (F) vs SY follicles (S) according to PRM (P < 0.05)

Fig. 5

Association analysis (a) and Venn diagram (b) of differentially expressed genes/proteins from the TMT and DEG analyses between F6 and SY follicles in laying hens

Fig. 6

Dynamics of the expression of chicken VLDLR mRNA and the effect of follicle-stimulating hormone (FSH) treatment on the VLDLR mRNA levels in the granulosa cells (GCs) of chicken ovarian follicles. a Expression of VLDLR mRNA in different chicken tissues. b Expression levels of VLDLR in 1–2 mm follicles, 6–8 mm follicles (small yellow follicles), the fifth largest follicles (F5), the third largest follicles (F3), the largest follicles (F1), and the new postovulatory follicles (POFs). c Expression of chicken VLDLR in the granulosa cells (pre-GCs) and theca cells (pre-TCs) of prehierarchical follicles and the GCs and TCs of hierarchical follicles. d Effect of FSH on VLDLR in the GCs of prehierarchical follicles. e Effect of FSH on VLDLR in the GCs of hierarchical follicles. All data are presented as the mean ± SEM. (^abcP < 0.05; ^ABCP < 0.01)