G. B. Oliveira

Longissimus dorsi muscle label-free quantitative proteomic reveals biological mechanisms associated with intramuscular fat deposition

The pathways involved in intramuscular fat (IMF) deposition in Longissimus dorsi muscle were investigated using an integrated transcriptome-assisted label-free quantitative proteomic approach by High Definition Mass Spectrometry. We quantified 1582 proteins, of which 164 were differentially abundant proteins (DAPs, p < 0.05) between animals with high (H) and low (L) genomic estimated breeding values (GEBV) for IMF content. Ingenuity pathway analysis (IPA) revealed that these DAPs were mainly involved in glycolysis metabolism, actin cytoskeleton signaling, cell-cell adherens junction and pathways for MAPK and insulin. A comparative study between transcriptomic (mRNA) and proteomic data showed 17 differentially expressed genes corresponding to DAPs, of which three genes/proteins did not agree on the direction of the fold change between groups. Moreover, we investigated microRNAs data to explain these differences in fold change direction, being able to unravel two of the three unexpected mRNA/protein relationships. Results demonstrated that changes in protein/mRNA levels of sarcomere organization, intracellular signal transduction and regulation of actin cytoskeleton, are involved in IMF deposition. These findings provide a deeper understanding of the highly complex regulatory mechanisms involved in IMF deposition in cattle and indicate target pathways for future studies. SIGNIFICANCE Intramuscular fat is the amount of fat deposited inside muscle and plays an important role in human health and meat quality attributes, influencing energy metabolism of skeletal muscle, as well as, tenderness, flavor, and juiciness of beef. We performed for the first time the utilization of integrated transcriptome-assisted label-free quantitative proteomic approach using High Definition Mass Spectrometry for characterization of the changes in the proteomic profile of the Longissimus dorsi muscle associated with intramuscular fat deposition in cattle. Furthermore, we compared the muscle proteome with the muscle transcriptome (mRNA and microRNAs), obtained by RNA-sequencing, to better understand the relationship between expression of mRNAs and proteins and to unravel essential biological mechanisms involved in bovine skeletal muscle IMF deposition.

Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle

BackgroundThe amount of intramuscular fat can influence the sensory characteristics and nutritional value of beef, thus the selection of animals with adequate fat deposition is important to the consumer. There is growing knowledge about the genes and pathways that control the biological processes involved in fat deposition in muscle. MicroRNAs (miRNAs) belong to a well-conserved class of non-coding small RNAs that modulate gene expression across a range of biological functions in animal development and physiology. The aim of this study was to identify differentially expressed (DE) miRNAs, regulatory candidate genes and co-expression networks related to intramuscular fat (IMF) deposition. To achieve this, we used mRNA and miRNA expression data from the Longissimus dorsi muscle of 30 Nelore steers with high (H) and low (L) genomic estimated breeding values (GEBV) for IMF deposition.ResultsDifferential miRNA expression analysis between animals with extreme GEBV values for IMF identified six DE miRNAs (FDR 10%). Functional annotation of the target genes for these microRNAs indicated that the PPARs signaling pathway is involved with IMF deposition. Candidate regulatory genes such as SDHAF4, FBXO17, ALDOA and PKM were identified by partial correlation with information theory (PCIT), phenotypic impact factor (PIF) and regulatory impact factor (RIF) co-expression approaches from integrated miRNA-mRNA expression data. Two DE miRNAs (FDR 10%), bta-miR-143 and bta-miR-146b, which were upregulated in the Low IMF group, were correlated with regulatory candidate genes, which were functionally enriched for fatty acid oxidation GO terms. Co-expression patterns obtained by weighted correlation network analysis (WGCNA), which showed possible interaction and regulation between mRNAs and miRNAs, identified several modules related to immune system function, protein metabolism, energy metabolism and glucose catabolism according to in silico analysis performed herein.ConclusionIn this study, several genes and miRNAs were identified as candidate regulators of IMF by analyzing DE miRNAs using two different miRNA-mRNA co-expression network methods. This study contributes to the understanding of potential regulatory mechanisms of gene signaling networks involved in fat deposition processes measured in muscle. Glucose metabolism and inflammation processes were the main pathways found in silico to influence intramuscular fat deposition in beef cattle in the integrative mRNA-miRNA co-expression analysis.

Additional file 13: Table S17. of Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle

List of miRNA modules negatively correlated with mRNA modules and the GO terms associated with them in High IMF (H) and Low IMF (L) groups. The table contains the module’s name, the correlation value, p-value of correlation, number of miRNAs and mRNAs in each module and the GO term enriched for each module. (XLSX 11 kb)

Data from proteomic analysis of bovine Longissimus dorsi muscle associated with intramuscular fat content

The proteomic data presented in this article are associated with the research article entitled “Longissimus dorsi muscle label-free quantitative proteomic reveals biological mechanisms associated with intramuscular fat deposition” published in Journal of Proteomics [1]. In this article, we characterized the proteomic profile of bovine Longissimus dorsi muscle from Nelore steers and identified differentially abundant proteins associated with the intramuscular fat (IMF) content. An integrated transcriptome-assisted label-free quantitative proteomic approach by High Definition Mass Spectrometry (HDMSE) was employed to identify and quantify the proteins. A functional enrichment analysis using the differentially abundant proteins list was performed to understand the biological processes involved in IMF deposition. Moreover, to explore and clarify the biological mechanisms that influence IMF content, the mRNA data for the same trait from Cesar and collaborators [2] obtained by RNA-sequencing technology was compared with proteomic data. The mRNA data is deposited in the European Nucleotide Archive (ENA) repository (EMBL-EBI), under accession PRJEB13188.

Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits

BackgroundIntegration of high throughput DNA genotyping and RNA-sequencing data allows for the identification of genomic regions that control gene expression, known as expression quantitative trait loci (eQTL), on a whole genome scale. Intramuscular fat (IMF) content and carcass composition play important roles in metabolic and physiological processes in mammals because they influence insulin sensitivity and consequently prevalence of metabolic diseases such as obesity and type 2 diabetes. However, limited information is available on the genetic variants and mechanisms associated with IMF deposition in mammals. Thus, our hypothesis was that eQTL analyses could identify putative regulatory regions and transcription factors (TFs) associated with intramuscular fat (IMF) content traits.ResultsWe performed an integrative eQTL study in skeletal muscle to identify putative regulatory regions and factors associated with intramuscular fat content traits. Data obtained from skeletal muscle samples of 192 animals was used for association analysis between 461,466 SNPs and the transcription level of 11,808 genes. This yielded 1268 cis- and 10,334 trans-eQTLs, among which we identified nine hotspot regions that each affected the expression of > 119 genes. These putative regulatory regions overlapped with previously identified QTLs for IMF content. Three of the hotspots respectively harbored the transcription factors USF1, EGR4 and RUNX1T1, which are known to play important roles in lipid metabolism. From co-expression network analysis, we further identified modules significantly correlated with IMF content and associated with relevant processes such as fatty acid metabolism, carbohydrate metabolism and lipid metabolism.ConclusionThis study provides novel insights into the link between genotype and IMF content as evident from the expression level. It thereby identifies genomic regions of particular importance and associated regulatory factors. These new findings provide new knowledge about the biological processes associated with genetic variants and mechanisms associated with IMF deposition in mammals.