Oral Presentation Australian Society for Microbiology Annual Scientific Meeting 2017

Detection of foodborne pathogens using whole-cell proteomics and metabolomics approaches (#66)

Snehal Jadhav 1 2 , Avinash Karpe 1 3 , David Beale 3 , Konstantinos Kouremenos 2 , Enzo A Palombo 1
  1. Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne, VIC, Australia
  2. Bio21 Institute, University of Melbourne, Melbourne, Vic, Australia
  3. Land and Water, CSIRO, Brisbane, Queensland, Australia

Conventional culture-based methods of foodborne pathogen detection can be very time-consuming. The food industry requires more sensitive, rapid and cost-effective solutions for detecting pathogens from complex food matrixes such as meat and dairy. Some of the more recently developed omics-based approaches have the ability to revolutionalise microbial diagnostics. Our research explores the use of whole-cell proteomics and metabolomics approaches for detecting foodborne pathogens. Whole-cell proteomics approach (on MALDI-TOF MS platform) and untargeted metabolomics approach (on GC-MS platform) were adopted for the direct detection of pathogens from selective enrichment broths containing spiked foods, obviating the need for culturing on solid media. Meat samples spiked with as low as 10 colony-forming units of L. monocytogenes and S. enterica per mL of selective broth culture were detected using MALDI-TOF MS within 30 h and 18 h of incubation, respectively. Analysis of the same samples using GC-MS enabled discrimination of the spiked meat samples after 24 h and 18 h of incubation for L. monocytogenes and S. enterica respectively. Similar to pathogen detection, subtyping of foodborne pathogens within processing environments can often prove to be a challenge for the food industry. We investigated the use of MALDI-TOF MS approach for subtyping of L. monocytogenes isolates obtained from dairy processing environments. In this research, MALDI-TOF MS based-subtyping was found to be more rapid and also in good congruence with the gold standard PFGE technique. Overall, our research indicates that both omics-based approaches have the potential to provide more rapid solutions for the detection and subtyping of foodborne pathogens.