Meta-analysis of muscle transcriptome data identifies key genes influencing intramuscular fat content in pigs.

Abstract

Objective: The content of intramuscular fat (IMF) is closely linked to meat quality, and the mechanism of IMF deposition is complex. Despite numerous transcriptomic studies on IMF, variations in sample sizes and data analysis methods have produced inconsistent gene expression patterns and results. To identify the pivotal genes influencing pig IMF content, we performed a meta-analysis on 10 pig muscle transcriptome datasets with a total of eighty samples, forty with high and forty with low IMF samples.

Methods: DESeq2 has been used to analyze the high and low IMF groups for 10 datasets each, resulting in the differentially expressed genes (DEGs) for each dataset. To identify key genes affecting IMF content, we performed a meta-analysis of the differential expression results from the 10 datasets using MetaVolcanoR. Subsequently, we conducted protein-protein interaction network analysis, Gene Ontology and Kyoto encyclopedia of genes and genomes functional enrichment analysis, and quantitative trait locus (QTL) analysis on the DEGs.

Results: The meta-analysis identified 129 DEGs, comprising 71 upregulated and 58 downregulated DEGs in the high IMF group. The DEGs exhibited enrichment in processes associated with adipocyte differentiation and fat anabolism. QTL analysis demonstrated that five DEGs, including FASN and SCD, corresponded to six QTLs associated with IMF.

Conclusion: The findings suggest that meta-analysis effectively integrates data from multiple datasets, resulting in more reliable outcomes. This approach enabled the identification of the core gene cluster comprising FASN, SCD, and PLIN1, LEP, and G0S2, which influence IMF content in pigs.