Transcriptomic profile of longissimus thoracis associated with fatty acid content in Nellore beef cattle

Abstract

The beef fatty acid (FA) profile has the potential to impact human health, and displays polygenic and complex features. This study aimed to identify the transcriptomic FA profile in the longissimus thoracis muscle in Nellore beef cattle finished in feedlot. Forty‐four young bulls were sampled to assess the beef FA profile by considering 14 phenotypes and including differentially expressed genes (DEG), co‐expressed (COE), and differentially co‐expressed genes (DCO) analyses. All samples (n = 44) were used for COE analysis, whereas 30 samples with extreme phenotypes for the beef FA profile were used for DEG and DCO. A total of 912 DEG were identified, and the polyunsaturated (n = 563) and unsaturated ω‐3 (n = 346) FA sums groups were the most frequently observed. The COE analyses identified three modules, of which the blue module (n = 1776) was correlated with eight of 14 FA phenotypes. Also, 759 DCO genes were listed, and the oleic acid (n = 358) and monounsaturated fatty acids sum (n = 120) were the most frequent. Furthermore, 243 and 13, 319 and seven, and 173 and 12 gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were enriched respectively for the DEG, COE, and DCO analyses. Combining the results, we highlight the unexplored GIPC2, ASB5, and PPP5C genes in cattle. Besides LIPE and INSIG2 genes in COE modules, the ACSL3, ECI1, DECR2, FITM1, and SDHB genes were signaled in at least two analyses. These findings contribute to understand the genetic mechanisms underlying the beef FA profile in Nellore beef cattle finished in feedlot.

Pleiotropic architectures of porcine immune and growth trait pairs revealed by a self‐product‐based transcriptome method

“…In addition to quantitative trait loci mapping and GWASs, the transcriptome method can also provide an alternative and supplementary approach to the detection of trait‐related genes via differentially expressed genes between extreme individuals (Fernandez‐Barroso et al, 2022; Schettini et al, 2022). When detecting trait‐related genes, transcriptome methods exhibit several advantages over GWAS methods, including (i) requiring a much smaller sample size, (ii) providing functional information on target genes through expression patterns and (iii) directly detecting genes rather than variants.…”