Systemic analysis of gene expression profiles in porcine granulosa cells during aging

Abstract

Current studies have revealed that aging is a negative factor that suppresses granulosa cell functions and causes low fertility in women. However, the difference in gene expression between normal and aging granulosa cells remains undefined. Therefore, the aim of this study was to investigate the gene expression profiles of granulosa cells during aging. Granulosa cells from young healthy porcine ovaries were aged in vitro by prolonging the culture time (for 48h). First, the extracellular ultrastructure was observed by scanning electron microscopy followed by RNA-seq and KEGG pathway analysis. The results showed that the extracellular ultrastructure was significantly altered by aging; cell membranes were rough, and cavitations were found. Moreover, the formations of filopodia were greatly reduced. RNA-seq data revealed that 3411 genes were differentially expressed during aging, of which 2193 genes were up-regulated and 1218 genes were down-regulated. KEGG pathway analysis revealed that 25 pathways including pathway in cancer, PI3K-Akt signaling pathway, focal adhesion, proteoglycans in cancer, and cAMP signaling pathway were the most changed. Moreover, several high differentially expressed genes (CEBPB, CXCL12, ANGPT2, IGFBP3, and BBOX1) were identified in aging granulosa cells, The expressions of these genes and genes associated with extracellular matrix remodeling associated genes (TIMP3, MMP2, MMP3, and CTGF), energy metabolism associated genes (SLC2A1, PPARγ) and steroidogenesis associated genes (StAR, CYP11A1 and LHCGR) were confirmed by quantitative PCR. This study identifies the differently changed pathways and their related genes, contributes to the understanding of aging in granulosa cells, and provides an important foundation for further studies.

Keywords: Gerotarget; RNA-seq; aging; gene expression; granulosa cell; porcine.

Figure 1. Extracellular ultrastructure analysis using scanning electron microscope

Ultrastructures observed at 1000×, 5000×, 10000×, and 15000×, respectively.

Figure 2. Overview of RNA sequencing data

A. The distribution of mapped reads on mRNA (5′-3′). Data in reflects the percentage of mapped reads assigned to all regions of mRNA. The location of the normalized mRNA is on the horizontal (x) axis; the percentage of reads as compared to total mapped reads for the position is on the vertical (y) axis. B. Heat map of the biological replicates. C. Reads mapped to different regions of the gene.

Figure 3. Overview of differentially expressed genes

A. The number of differentially expressed mRNAs by sample. B. FPKM distributions in each sample. C. Volcano plot of genes differentially expressed in each sample. The log2 fold change difference is represented on the x-axis and negative log of FDR is represented on the y-axis. Each point represents a gene which had detectable expression in the 6 samples. The significant up-regulated genes are plotted in red on the right side and down-regulated genes plotted in green on the left side. The no significant differentially expressed genes were shown in dark points in the middle and the bottom. D. Expression differences are shown as different colors. Red indicates up-regulated while green indicates down-regulated gene expression.

Figure 4. The differential expression of mRNA in aged GCs was analyzed by Gene Ontology (GO) annotation

A. GO analysis of differentially expressed genes in aged GCs. DEG Unigene: differentially expressed genes number in all annotation Biological Process GO term. All Unigene: Unigene number in all annotation Biological Process GO term. B. GO analysis of new detected genes.

Figure 5. Function analysis of the differentially expressed genes of aging GCs

A. KEGG pathway enrichment analysis of differentially expressed genes from aging GCs. B. Pathway enrichment analysis among DEGs. Different colors indicate different enrichment factors. The size of the plot corresponds to the number of genes. C. The numbers of up- and down-regulated genes in the most enrichment pathways.

Figure 6. Validation the interested genes identified by RNA-seq using QRT-PCR

QRT-PCR was performed to make comparisons between control and aging GCs. Results are expressed as the mean ± SEM of at least 3 independent experiments and values with different letters are significantly different (P < 0.05).