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Targeted metabolic profiling rapidly differentiates Escherichia coli and Staphylococcus aureus at species and strain level
Author(s) -
Li Haorong,
Zhu Jiangjiang
Publication year - 2017
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.7949
Subject(s) - staphylococcus aureus , escherichia coli , bacteria , microbiology and biotechnology , serotype , chemistry , food science , food microbiology , biology , biochemistry , gene , genetics
Rationale Pathogenic foodborne bacteria have been associated with severe infectious disease in humans and animals worldwide. Rapid detection and screening of these foodborne pathogens are critical for our food safety. This study aimed at detecting Escherichia coli and Staphylococcus aureus , two important foodborne bacteria, at the species and strain/serovar level using a mass spectrometry (MS)‐based targeted metabolic profiling approach. Methods Ten E. coli strains (8 out of 10 were foodborne outbreak isolates) and four S. aureus strains were tested at two growth time points. A high‐performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS)‐based targeted metabolomics approach was applied for metabolic profile based bacteria detection. A total of 108 metabolites from multiple metabolic pathways were confidently detected from these bacteria. Results Our study demonstrated that with only 4 h of enrichment in the same medium, the metabolic profiles from E. coli and S. aureus showed significant difference. Furthermore, seven out of ten E. coli strains and all four tested S. aureus strains showed strain/serovar‐level differentiation at the 4‐h time point, which indicated great potential for strain level stratification in future food screening using our MS‐based targeted metabolic profiling approach. Conclusions A targeted metabolomics method was developed to demonstrate the utility of HPLC/MS/MS‐based metabolic profiling in rapidly (4 h) differentiating E. coli and S. aureus bacteria, two of the most notorious foodborne bacteria, at both the species and strain/serovar levels. The results indicated that our approach has great potential in the future for fast and specific detection of foodborne pathogenic bacteria based on their metabolic diversity.