Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties

Abstract

Background: Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes.

Results: We applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits.

Conclusions: Porcine muscle co-expression transcript networks that correlated to post mortem traits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

Figure 1

Correlation matrix of module eigengene values obtained for mRNAs and phenotypes. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of gene co-expression. Each of the modules was labelled with a unique color as an identifier. Twenty-two modules were identified; each module eigengene was tested for correlation with meat and carcass traits. Within each cell, upper values are correlation coefficients between module eigengene and the traits; lower values are the corresponding p-values.

Figure 2

Correlation matrix of module eigengene values obtained for miRNAs and phenotypes. Weighted gene co-expression network analysis (WGCNA) groups miRNA into modules based on patterns of their co-expression. Each of the modules was labelled with a unique color as an identifier. Nine modules were identified; each module eigengene was tested for correlation with meat and carcass traits. Within each cell, upper values are correlation coefficients between module eigengene and the traits; lower values are the correspondent p-value.

Figure 3

Regulatory network of negatively-correlated mRNAs and miRNAs. Genes in modules dark-turquoise, red, black, and tan that were significantly associated with meat quality and were negatively correlated with various miRNAs as indicated by the arrows. Colors of symbols of mRNA encoded proteins indicate the assignment to the respective module (grey = black).

Figure 4

Regulatory network of negatively-correlated miRNAs and mRNAs. miRNAs in modules blue and purple that were significantly associated with meat quality and were negatively correlated with various mRNAs as indicated by the arrows. Colors of symbols of miRNAs indicate the assignment to the respective module.