Reveal key genes and factors affecting athletes performance in endurance sports using bioinformatic technologies

Abstract

Abstract Medium-intensity activities comprise the major proportion of many sorts of sports. The energy consumption of athletes has been a research emphasis for the purpose of improving both training efficiency and competition performance. However, the evidence based on large-scale gene screen has been rarely performed. This is a bioinformatic study revealing the key factors contributed to the metabolic difference between subjects with different endurance activity capacities. A dataset comprised of high- (HCR) and low-capacity running (LCR) rats was used. Differentially expressed genes (DEGs) were identified and analysed. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment was obtained. The DEGs' protein–protein interaction (PPI) network was built, and the enriched terms of the PPI network were also analysed. Our findings showed that the GO terms were enriched in lipid metabolism-related terms. The KEGG signalling pathway analysis enriched in the ether lipid metabolism. Plb1, Acad1, Cd2bp2, and Pla2g7 were identified as the hub genes. This study provides a theoretical foundation showing lipid metabolism plays an important role in the performance of endurance activities. Plb1, Acad1, and Pla2g7 may be the key genes involved. The training plan and diet for athletes can be designed based on above results and expecting a better competitive performance.

Keywords

• Bioinformatics
• Microarray
• Gene expression
• Aerobic activity
• Athlete