| Summary: | Food-borne diseases caused by <i>Salmonella enterica</i> of 2500 serovars represent a serious public health problem worldwide. A quick identification for the pathogen serovars is critical for controlling food pollution and disease spreading. Here, we applied a mass spectrum-based proteomic profiling for identifying five epidemiologically important <i>Salmonella enterica subsp. enterica</i> serovars (<i>Enteritidis</i>, <i>Typhimurium</i>, <i>London</i>, <i>Rissen</i> and <i>Derby</i>) in China. By label-free analysis, the 53 most variable serovar-related peptides, which were almost all enzymes related to nucleoside phosphate and energy metabolism, were screened as potential peptide biomarkers, and based on which a C5.0 predicted model for <i>Salmonella enterica</i> serotyping with four predictor peptides was generated with the accuracy of 94.12%. In comparison to the classic gene patterns by PFGE analysis, the high-throughput proteomic fingerprints were also effective to determine the genotypic similarity among <i>Salmonella enteric</i> isolates according to each strain of proteome profiling, which is indicative of the potential breakout of food contamination. Generally, the proteomic dissection on <i>Salmonella enteric</i> serovars provides a novel insight and real-time monitoring of food-borne pathogens.
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