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

Ponsuksili S, Du Y, Hadlich F, Siengdee P, Murani E, Schwerin M, Wimmers K - BMC Genomics (2013)

Bottom Line: Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Research Group Functional Genome Analyses, Leibniz Institute for Farm Animal Biology, FBN, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany.

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.

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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.
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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.

Mentions: A total of 1,928 pairs of miRNAs and mRNAs that showed negative correlations at p-values ≤ 8.97 × 10-5 (FDR = 0.1) belonged to 1,073 mRNA probe sets (929 gene) and 264 miRNA probe sets (96 miRNA families). Of these, 286 pairs were assigned to modules dark-turquoise, red, black, and tan, which showed correlation with traits related to meat quality (Figure 3). However, no genes in module orange were negatively correlated with miRNA at FDR < 0.1. Only one gene (CREM) in the module dark-turquoise was negatively correlated with miR-153 (r = -0.34 p = 2.11x10-06 FDR = 0.02). In module black, 69 genes were negatively correlated with 21 miRNA families, totaling 140 miRNA-mRNA pairs. In total, 101 and 43 pairs of miRNAs and mRNAs were identified in modules red (22 miRNAs and 52 genes) and tan (17 miRNAs and 26 genes). Known genes belonging to modules dark-turquoise, red, black, and tan and their negatively-correlated miRNAs are shown in Table 5. Out of 1,928 pairs of miRNAs and mRNAs that showed negative correlations at p-values ≤ 8.97 × 10-5 (FDR = 0.1), 62 pairs were assigned to modules blue and purple, which correlated with meat quality (Figure 4). These 62 pairs of miRNAs and mRNAs belonged to 14 miRNA families and 40 genes (Table 6).


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

Ponsuksili S, Du Y, Hadlich F, Siengdee P, Murani E, Schwerin M, Wimmers K - BMC Genomics (2013)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750351&req=5

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.
Mentions: A total of 1,928 pairs of miRNAs and mRNAs that showed negative correlations at p-values ≤ 8.97 × 10-5 (FDR = 0.1) belonged to 1,073 mRNA probe sets (929 gene) and 264 miRNA probe sets (96 miRNA families). Of these, 286 pairs were assigned to modules dark-turquoise, red, black, and tan, which showed correlation with traits related to meat quality (Figure 3). However, no genes in module orange were negatively correlated with miRNA at FDR < 0.1. Only one gene (CREM) in the module dark-turquoise was negatively correlated with miR-153 (r = -0.34 p = 2.11x10-06 FDR = 0.02). In module black, 69 genes were negatively correlated with 21 miRNA families, totaling 140 miRNA-mRNA pairs. In total, 101 and 43 pairs of miRNAs and mRNAs were identified in modules red (22 miRNAs and 52 genes) and tan (17 miRNAs and 26 genes). Known genes belonging to modules dark-turquoise, red, black, and tan and their negatively-correlated miRNAs are shown in Table 5. Out of 1,928 pairs of miRNAs and mRNAs that showed negative correlations at p-values ≤ 8.97 × 10-5 (FDR = 0.1), 62 pairs were assigned to modules blue and purple, which correlated with meat quality (Figure 4). These 62 pairs of miRNAs and mRNAs belonged to 14 miRNA families and 40 genes (Table 6).

Bottom Line: Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Research Group Functional Genome Analyses, Leibniz Institute for Farm Animal Biology, FBN, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany.

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.

Show MeSH
Related in: MedlinePlus