Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment

• Published in: PloS one 2016-02, Vol.11 (2), p.e0149758-e0149758
• Main Authors: Welzenbach, Julia, Neuhoff, Christiane, Looft, Christian, Schellander, Karl, Tholen, Ernst, Große-Brinkhaus, Christine
• Format: Article
• Language: English
• Subjects: Animal sciences; Animals; Bioindicators; Bioinformatics; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - metabolism; Color; Computer and Information Sciences; Correlation analysis; Energy metabolism; Energy Metabolism - physiology; Food; Glycolysis; Indicators; Lipid metabolism; Mass spectrometry; Meat; Meat quality; Medicine and Health Sciences; Metabolism; Metabolites; Metabolome; Metabolomics; Methods; Muscle, Skeletal - metabolism; Network analysis; Pentose; Pentose phosphate; Pentose phosphate pathway; pH effects; Phenotype; Phosphates; Physical Sciences; Pork; Principal components analysis; Proteomics; Quality assessment; Quality control; Red Meat - standards; Regression analysis; Research and Analysis Methods; Scientific imaging; Statistical analysis; Statistical models; Statistics; Studies; Swine; Vitamin E; Zoology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0149758

The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators.