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

Abstract

Objective: Intramuscular fat (IMF) content is closely linked to meat quality, and the process of IMF deposition is intricate. Despite numerous transcriptomic studies on IMF, varying sample sizes and data analysis methods have led to 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, including forty each of high and IMF samples.

Methods: We used DESeq2 to analyze the high and low IMF groups for each of the 10 datasets, identifying differentially methylated genes in 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 PPI network analysis, as well as GO and KEGG functional enrichment analysis, and quantified the trait locus (QTL) analysis on the differentially expressed genes (DEGs).

Results: The meta-analysis revealed 494 DEGs, with 321 upregulated and 173 downregulated DEGs observed in the high IMF group. These DEGs were found to be enriched 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: Our findings suggest that meta-analysis can effectively integrate data from multiple datasets to yield more reliable results. Using this approach, we identified the core gene cluster, including FASN, SCD, and PLIN1, LEP, and G0S2, that influence IMF content in pigs.