Investigation of Metabolome Underlying the Biological Mechanisms of Acute Heat Stressed Granulosa Cells

Abstract

Heat stress affects granulosa cells and the ovarian follicular microenvironment, ultimately resulting in poor oocyte developmental competence. This study aims to investigate the metabo-lomics response of bovine granulosa cells (bGCs) to in vitro acute heat stress of 43 °C. Heat stress triggers oxidative stress-mediated apoptosis in cultured bGCs. Heat-stressed bGCs exhibited a time-dependent recovery of proliferation potential by 48 h. A total of 119 metabolites were identified through LC-MS/MS-based metabolomics of the spent culture media, out of which, 37 metabolites were determined as differentially involved in metabolic pathways related to bioenergetics support mechanisms and the physical adaptations of bGCs. Multiple analyses of metabolome data identified choline, citric acid, 3-hydroxy-3-methylglutaric acid, glutamine, and glycocyamine as being upregulated, while galactosamine, AICAR, ciliatine, 16-hydroxyhexadecanoic acid, lysine, succinic acid, uridine, xanthine, and uraconic acid were the important downregulated metabolites in acute heat stress. These differential metabolites were implicated in various important metabolic pathways directed towards bioenergetics support mechanisms including glycerophospholipid metabolism, the citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, and serine, threonine, and tyrosine metabolism. Our study presents important metabolites and metabolic pathways involved in the adaptation of bGCs to acute heat stress in vitro.

Keywords: bioenergetics; granulosa cells; heat stress; metabolites; metabolomics; pathways.

Figure 1

Results of principal component analysis (PCA) are presented for the metabolite sets in three replicates each of the control (red) and heat stress (green) groups in negative ion mode (A) and positive ion mode (B) of LC–MS/MS analysis. Likewise, the results of orthogonal partial least squares discrimination analysis (OPLS-DA) score for both ion modes’ (C,D) metabolites are given.

Figure 2

Pearson’s correlation heatmap along with clustering patterns among the 37 differential metabolites with a variable importance in the projection (VIP) score of more than 1.

Figure 3

Biomarkers and performance evaluation of metabolites obtained through a receiver operator characteristic (ROC) analysis along with the regulation status of each metabolite. ROC curves with area under curve (AUC) are plotted with sensitivity values on the y-axis and the corresponding false positive rate on the x-axis. Upper panel (A) represents significant upregulated metabolites while the lower panel (B) shows the significant downregulated metabolites.

Figure 4

Enrichment analysis (A) and pathway analysis (B) showing the biological processes of differential metabolite involvement in heat-stressed bGCs. In the enrichment analysis (A), the length of the bar along the x-axis shows the enrichment ratio among particular metabolite sets, while the color bar represents the p-value. The pathway analysis (B) shows the pathways’ impact on the x-axis and the p-value on the y-axis, while the color bar legend shows the raw p-value distribution and the dot size shows the metabolite hit ratios in the given pathways.