Peptidome-Based Serotyping of The Food-Borne Pathogens Salmonella Enterica by Label-Free Mass Spectrometry

Abstract Background: Food-borne diseases caused by Salmonella enteric serovars represent a serious public health problem worldwide . More than 2500 different serovars have been reported to relate with food-borne diseases according to the classification of White-Kauffmann-Le Minor scheme by now. A quick identification for the pathogens is critical for controlling food pollution and disease spreading. Results: Here we applied a peptidomic analysis for quickly and precisely identifying serovar-specific peptide markers based on LC-MS/MS profiling of epidemiologically important Salmonella enterica subsp. enterica serovars in China. By label-free quantitative peptidomics MS identification, the 53 most variable serovar-related peptides were screened as potential peptide biomarkers, based on which a C5.0 predicted model with 4 predictor peptides was generated and a test set of 17 Salmonella enterica strains were classified with the accuracy of 94.12%. It is effective to determine the genotypic similarity among Salmonella enteric isolates according to each strain peptidome profiling, which is indicative of potential incidence even breakout of food contamination. This high-throughput strain peptidomic fingerprints are complementary to the genomic patterns by PFGE analysis for precise identification of 5 Salmonella enterica serovars including Enteritidis , Typhimurium , London, Rissen and Derby . The biological analysis showed that most of the changed peptides/proteins were enzymes related to nucleoside phosphate and energy metabolism. Conclusions: the LC-MS/MS based quantitative peptidomic dissection on Salmonella enteric serovars provides a novel insight and real-time monitoring of food-borne pathogens.