Kohonen Artificial Neural Network and Multivariate Analysis in the Identification of Proteome Changes during Early and Long Aging of Bovine Longissimus dorsi Muscle Using SWATH Mass Spectrometry
datasetposted on 15.09.2021, 09:44 by Jessica Brandi, Elisa Robotti, Marcello Manfredi, Elettra Barberis, Emilio Marengo, Enrico Novelli, Daniela Cecconi
To study proteomic changes involved in tenderization of Longissimus dorsi, Charolais heifers and bulls muscles were sampled after early and long aging (12 or 26 days). Sensory evaluation and instrumental tenderness measurement were performed. Proteins were analyzed by gel-free proteomics. By pattern recognition (principal component analysis and Kohonen’s self-organizing maps) and classification (partial least squares-discriminant analysis) tools, 58 and 86 dysregulated proteins were detected after 12 and 26 days of aging, respectively. Tenderness was positively correlated mainly with metabolic enzymes (PYGM, PGAM2, TPI1, PGK1, and PFKM) and negatively with keratins. Downregulation in hemoglobin subunits and carbonic anhydrase 3 levels was relevant after 12 days of aging, while mimecan and collagen chains levels were reduced after 26 days of aging. Bioinformatics indicated that aging involves a prevalence of metabolic pathways after late and long periods. These findings provide a deeper understanding of changes involved in aging of beef and indicate a powerful method for future proteomics studies.
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positively correlated mainlypartial least squareslongissimus dorsi </collagen chains levels>, charolais heifersfuture proteomics studiesprincipal component analysisinstrumental tenderness measurement86 dysregulated proteins26 days ).26 daysfree proteomicsmultivariate analysisdiscriminant analysissensory evaluationproteome changespowerful methodpattern recognitionorganizing mapsmetabolic pathwaysmetabolic enzymeslong periodskohonen ’hemoglobin subunitsfindings providedeeper understandingchanges involvedbulls musclesbioinformatics indicated