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Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs

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ABSTRACT

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.

No MeSH data available.


Chromosome-wide Manhattan plot of Sus scrofa chromosome (SSC) 18. Drip loss measured in Musculus longissimus dorsi (LD) 24 h post-mortem (p.m.); PGAM2 = phosphoglycerate mutase 2; the declaration of gene symbols (in black boxes) can be obtained from Ensembl or http://www.ncbi.nlm.nih.gov/genegenes.
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ijms-17-01426-f002: Chromosome-wide Manhattan plot of Sus scrofa chromosome (SSC) 18. Drip loss measured in Musculus longissimus dorsi (LD) 24 h post-mortem (p.m.); PGAM2 = phosphoglycerate mutase 2; the declaration of gene symbols (in black boxes) can be obtained from Ensembl or http://www.ncbi.nlm.nih.gov/genegenes.

Mentions: For drip loss, five candidate genes were identified on SSC 18 (Table 5 and Table 6). The most significant SNPs (Varmax = 8.82%; pmin ≤ 6.58 × 10−5) associated with drip loss were detected on SSC 16, but these SNPs do not fulfill the previously described conditions to detect potential candidate genes (Table 6). Distributed over four regions, SSC 18 harbors two genes for PGAM2, four genes for drip loss and one gene (LRGUK) significantly associated with drip loss and glycine. Because LRGUK is in linkage disequilibrium with EXOC4 that was associated with drip loss as well, this region ranging from 15.9 Mb to 16.1 Mb is of particular interest. From 12.2 Mb to 12.9 Mb there is a second interesting region with two candidate genes, for PGAM2 and drip loss, respectively. The Manhattan plot of SSC 18 is presented in Figure 2. Moreover, the Manhattan plots of SSC 1, 4, 6, 10, 13, 14 and 17 are shown in Figure S1.


Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs
Chromosome-wide Manhattan plot of Sus scrofa chromosome (SSC) 18. Drip loss measured in Musculus longissimus dorsi (LD) 24 h post-mortem (p.m.); PGAM2 = phosphoglycerate mutase 2; the declaration of gene symbols (in black boxes) can be obtained from Ensembl or http://www.ncbi.nlm.nih.gov/genegenes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037705&req=5

ijms-17-01426-f002: Chromosome-wide Manhattan plot of Sus scrofa chromosome (SSC) 18. Drip loss measured in Musculus longissimus dorsi (LD) 24 h post-mortem (p.m.); PGAM2 = phosphoglycerate mutase 2; the declaration of gene symbols (in black boxes) can be obtained from Ensembl or http://www.ncbi.nlm.nih.gov/genegenes.
Mentions: For drip loss, five candidate genes were identified on SSC 18 (Table 5 and Table 6). The most significant SNPs (Varmax = 8.82%; pmin ≤ 6.58 × 10−5) associated with drip loss were detected on SSC 16, but these SNPs do not fulfill the previously described conditions to detect potential candidate genes (Table 6). Distributed over four regions, SSC 18 harbors two genes for PGAM2, four genes for drip loss and one gene (LRGUK) significantly associated with drip loss and glycine. Because LRGUK is in linkage disequilibrium with EXOC4 that was associated with drip loss as well, this region ranging from 15.9 Mb to 16.1 Mb is of particular interest. From 12.2 Mb to 12.9 Mb there is a second interesting region with two candidate genes, for PGAM2 and drip loss, respectively. The Manhattan plot of SSC 18 is presented in Figure 2. Moreover, the Manhattan plots of SSC 1, 4, 6, 10, 13, 14 and 17 are shown in Figure S1.

View Article: PubMed Central - PubMed

ABSTRACT

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.

No MeSH data available.