Abstract
Mineral contents in bovine muscle can affect meat quality, growth, health, and reproductive traits. To better understand the genetic basis of this phenotype in Nelore (Bos indicus) cattle, we analysed genome-wide mRNA and miRNA expression data from 114 muscle samples. The analysis implemented a new application for two complementary algorithms: the partial correlation and information theory (PCIT) and the regulatory impact factor (RIF), in which we included the estimated genomic breeding values (GEBVs) for the phenotypes additionally to the expression levels, originally proposed for these methods. We used PCIT to determine putative regulatory relationships based on significant associations between gene expression and GEBVs for each mineral amount. Then, RIF was adopted to determine the regulatory impact of genes and miRNAs expression over the GEBVs for the mineral amounts. We also investigated over-represented pathways, as well as pieces of evidences from previous studies carried in the same population and in the literature, to determine regulatory genes for the mineral amounts. For example, NOX1 expression level was positively correlated to Zinc and has been described as Zinc-regulated in humans. Based on our approach, we were able to identify genes, miRNAs and pathways not yet described as underlying mineral amount. The results support the hypothesis that extracellular matrix interactions are the core regulator of mineral amount in muscle cells. Putative regulators described here add information to this hypothesis, expanding the knowledge on molecular relationships between gene expression and minerals.
Highlights
Besides nutritional quality, mineral amount affects meat quality in many ways
We propose a new application of the partial correlation and information theory (PCIT) algorithm, designed originally for deriving gene co-expression networks through the identification of significant associations between expression profiles[15], which was applied for deriving correlation networks within a matrix of mRNAs, miRNAs and genomic breeding values (GEBVs) for the phenotypes
Recognizing the results of both PCIT analyses, PCIT general and PCIT miRNA, we identified a total of 242 genes and 35 miRNAs with expression values correlated to at least one mineral GEBV
Summary
Mineral amount affects meat quality in many ways. For example, the tenderization process of the skeletal muscle is driven by the action of the calcium-dependent protease calpain[1,2,3,4]. A co-expression network approach allows to identify genome-wide genes with similar expression patterns related to specific phenotypes or conditions In this methodology, traits are usually integrated into the analysis in a condition-dependent network, by a previous selection of genes or sample clusters related to the trait before the analysis[12]. It is challenging to pinpoint the direction of interactions or the regulation, as co-expression networks do not provide this information a priori[12] To overcome these limitations, we propose a new application of the partial correlation and information theory (PCIT) algorithm, designed originally for deriving gene co-expression networks through the identification of significant associations between expression profiles[15], which was applied for deriving correlation networks within a matrix of mRNAs, miRNAs and GEBVs for the phenotypes. We describe how this information was used to predict the regulatory impact of genes and miRNAs expression over the mineral amount in Nelore muscle
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