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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.
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1 Department of Evolutionary Genetics and Molecular Biology, Federal University of São Carlos, São Carlos, Brazil (GRID:grid.411247.5) (ISNI:0000 0001 2163 588X)
2 School of Chemistry and Molecular Biosciences, Faculty of Sciences, The University of Queensland, Brisbane, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537)
3 Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia (GRID:grid.1016.6)
4 Department of Agroindustry, Food and Nutrition, University of São Paulo/ESALQ, Piracicaba, Brazil (GRID:grid.11899.38) (ISNI:0000 0004 1937 0722)
5 Department of Statistics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, Brazil (GRID:grid.411180.d) (ISNI:0000 0004 0643 7932)
6 Department of Animal Science, Iowa State University, Ames, USA (GRID:grid.34421.30) (ISNI:0000 0004 1936 7312)
7 Department of Animal Science, University of São Paulo/ESALQ, Piracicaba, Brazil (GRID:grid.11899.38) (ISNI:0000 0004 1937 0722)
8 Bioinformatic Multi-user Laboratory, Embrapa Informática Agropecuária, Campinas, Brazil (GRID:grid.460200.0) (ISNI:0000 0004 0541 873X)
9 Bahia Federal Institute of Education, Science and Technology, Ilhéus, Brazil (GRID:grid.454342.0)
10 Embrapa Pecuária Sudeste, São Carlos, Brazil (GRID:grid.460200.0) (ISNI:0000 0004 0541 873X)