A gene coexpression network for bovine skeletal muscle inferred from microarray data

Abstract

We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlationbased method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.

Muscle-specific gene expression is underscored by differential stressor responses and coexpression changes

“…The reliable identification of stable gene coexpression networks is essential to unravel gene functions, define biological processes and find related transcription factors and their products (Prieto et al 2008). This type of networks has been reported in bovine skeletal muscle from microarray data (Hudson et al 2009;Reverter et al 2005;Reverter et al 2006). The major functional modules contained an enrichment of cell cycle, extracellular matrix, glycolisis/fast twitch muscle subunits, and mitochondrial genes.…”

“…A total of 132 out of the 206 DE genes were present in the coexpression network, accounting for 8,646 gene correlations, 22% of them significant according to Reverter et al (2006). The distribution of correlations within and between muscles is shown in Table 4.…”

Molecular cloning of the duck MyoG and MRF4 genes coding region sequence and their differential expression patterns in the breast and leg muscle during fetal development

“…Since then, the human (Strausberg et al 2002), bovine (Reverter et al 2006), swine (Te Pas et al 1999), and chicken Sun et al 2008) orthologues of these two genes have been cloned. However, the duck orthologue of these genes has not yet been found.…”

Differential expression of genes related with meat tenderness in Nellore cattle

“…The study on biological interaction networks is called systems biology (BARABÁSI;OLTVAI, 2004). The PCIT (Partial Correlation Information Theory) (REVERTER et al, 2006) is a method that shows the difference in the specific behavior or co-expression of targeted pairs of genes and the quantification of a gene differential connectivity. The PCIT finds connectors and differential magnitude of each connection, even when correlation is weak in the network.…”

“…Complementary analyses, based on the transcription factors (TF), play a fundamental regulatory role in the study on the control of gene expression DALRYMPLE, 2009b;REVERTER et al, 2006). This analysis gives the absolute co-expression correlation averaged for all genes in a given module.…”