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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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Related in: MedlinePlus

Correlation matrix of module eigengene values obtained for mRNAs and phenotypes. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of gene co-expression. Each of the modules was labelled with a unique color as an identifier. Twenty-two modules were identified; each module eigengene was tested for correlation with meat and carcass traits. Within each cell, upper values are correlation coefficients between module eigengene and the traits; lower values are the corresponding p-values.
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Figure 1: Correlation matrix of module eigengene values obtained for mRNAs and phenotypes. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of gene co-expression. Each of the modules was labelled with a unique color as an identifier. Twenty-two modules were identified; each module eigengene was tested for correlation with meat and carcass traits. Within each cell, upper values are correlation coefficients between module eigengene and the traits; lower values are the corresponding p-values.

Mentions: Sets of genes (modules) with common expression patterns that were associated with particular traits were identified based on the correlation between ME and organismal phenotype. We identified five modules that significantly associated with meat quality. Modules dark-turquoise and orange were correlated positively to pH traits and negatively to drip loss (ME[dark-turquoise]: pH24MLD r = 0.34, p = 5.3 × 10-7, DL r = -0.19, p = 5.6 × 10-3; ME[orange]: pH24MLD r = 0.32, p = 3.7 × 10-6, DL r = -0.31, p = 5.8 ×10-6) (Figure 1). Module dark-turquoise (31 annotated genes) was highly enriched for genes belonging to the cluster “glucose metabolic process” (GO: 0006006) and the KEGG-pathway “insulin signaling” with an enrichment score (ES) of 2.65. Module orange (26 annotated genes) was enriched for transcripts of the functional annotation clusters “response to wounding”, “defense response” and “inflammatory response” (ES = 2.42). Modules red, black, and tan were correlated negatively to pH traits and positively to drip loss (ME[red]: pH45MLD r = -0.22, p = 1.8 × 10-3, DL r = 0.20, p = 3.9 × 10-3; ME[black]: pH45MLD r = -.23, p = 8.8 × 10-4, DL r = 0.16, p = 1.8 × 10-2; ME[tan]: pH45MLD r = -0.22, p = 1.4 × 10-3, DL r = 0.19, p = 6.9 × 10-3) (Figure 1). Further, modules red (315 annotated genes), black (436 annotated genes), and tan (154 annotated genes) were enriched for genes of the top functional annotation clusters of “mitochondrial ribosome”, “mitochondrion”, and “extracellular matrix” with ES of 10.23, 15.15, and 27.05, respectively. Only one module (ME[dark-orange]) showed association with traits related to fatness (Figure 1).


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)

Correlation matrix of module eigengene values obtained for mRNAs and phenotypes. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of gene co-expression. Each of the modules was labelled with a unique color as an identifier. Twenty-two modules were identified; each module eigengene was tested for correlation with meat and carcass traits. Within each cell, upper values are correlation coefficients between module eigengene and the traits; lower values are the corresponding p-values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Correlation matrix of module eigengene values obtained for mRNAs and phenotypes. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of gene co-expression. Each of the modules was labelled with a unique color as an identifier. Twenty-two modules were identified; each module eigengene was tested for correlation with meat and carcass traits. Within each cell, upper values are correlation coefficients between module eigengene and the traits; lower values are the corresponding p-values.
Mentions: Sets of genes (modules) with common expression patterns that were associated with particular traits were identified based on the correlation between ME and organismal phenotype. We identified five modules that significantly associated with meat quality. Modules dark-turquoise and orange were correlated positively to pH traits and negatively to drip loss (ME[dark-turquoise]: pH24MLD r = 0.34, p = 5.3 × 10-7, DL r = -0.19, p = 5.6 × 10-3; ME[orange]: pH24MLD r = 0.32, p = 3.7 × 10-6, DL r = -0.31, p = 5.8 ×10-6) (Figure 1). Module dark-turquoise (31 annotated genes) was highly enriched for genes belonging to the cluster “glucose metabolic process” (GO: 0006006) and the KEGG-pathway “insulin signaling” with an enrichment score (ES) of 2.65. Module orange (26 annotated genes) was enriched for transcripts of the functional annotation clusters “response to wounding”, “defense response” and “inflammatory response” (ES = 2.42). Modules red, black, and tan were correlated negatively to pH traits and positively to drip loss (ME[red]: pH45MLD r = -0.22, p = 1.8 × 10-3, DL r = 0.20, p = 3.9 × 10-3; ME[black]: pH45MLD r = -.23, p = 8.8 × 10-4, DL r = 0.16, p = 1.8 × 10-2; ME[tan]: pH45MLD r = -0.22, p = 1.4 × 10-3, DL r = 0.19, p = 6.9 × 10-3) (Figure 1). Further, modules red (315 annotated genes), black (436 annotated genes), and tan (154 annotated genes) were enriched for genes of the top functional annotation clusters of “mitochondrial ribosome”, “mitochondrion”, and “extracellular matrix” with ES of 10.23, 15.15, and 27.05, respectively. Only one module (ME[dark-orange]) showed association with traits related to fatness (Figure 1).

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