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Utilizing the food-pathogen metabolome to putatively identify biomarkers for the detection of shiga toxin-producing E. Coli (STEC) from spinach

Jadhav, Snehal R., Shah, Rohan M., Karpe, Avinash V., Barlow, Robert S., McMillan, Kate E., Colgrave, Michelle L. and Beale, David J. 2021, Utilizing the food-pathogen metabolome to putatively identify biomarkers for the detection of shiga toxin-producing E. Coli (STEC) from spinach, Metabolites, vol. 11, no. 2, Special Issue: Development of Metabolomics Technologies and Their Applications in Food Science, pp. 1-17, doi: 10.3390/metabo11020067.

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Title Utilizing the food-pathogen metabolome to putatively identify biomarkers for the detection of shiga toxin-producing E. Coli (STEC) from spinach
Formatted title  Utilizing the food-pathogen metabolome to putatively identify biomarkers for the detection of shiga toxin-producing E. coli (STEC) from spinach
Author(s) Jadhav, Snehal R.ORCID iD for Jadhav, Snehal R. orcid.org/0000-0002-8331-275X
Shah, Rohan M.
Karpe, Avinash V.
Barlow, Robert S.
McMillan, Kate E.
Colgrave, Michelle L.
Beale, David J.
Journal name Metabolites
Volume number 11
Issue number 2
Season Special Issue: Development of Metabolomics Technologies and Their Applications in Food Science
Start page 1
End page 17
Total pages 17
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021
ISSN 2218-1989
Summary Shiga toxigenic E. coli (STEC) are an important cause of foodborne disease globally with many outbreaks linked to the consumption of contaminated foods such as leafy greens. Existing methods for STEC detection and isolation are time-consuming. Rapid methods may assist in preventing contaminated products from reaching consumers. This proof-of-concept study aimed to determine if a metabolomics approach could be used to detect STEC contamination in spinach. Using untargeted metabolic profiling, the bacterial pellets and supernatants arising from bacterial and inoculated spinach enrichments were investigated for the presence of unique metabolites that enabled categorization of three E. coli risk groups. A total of 109 and 471 metabolite features were identified in bacterial and inoculated spinach enrichments, respectively. Supervised OPLS-DA analysis demonstrated clear discrimination between bacterial enrichments containing different risk groups. Further analysis of the spinach enrichments determined that pathogen risk groups 1 and 2 could be easily discriminated from the other groups, though some clustering of risk groups 1 and 2 was observed, likely representing their genomic similarity. Biomarker discovery identified metabolites that were significantly associated with risk groups and may be appropriate targets for potential biosensor development. This study has confirmed that metabolomics can be used to identify the presence of pathogenic E. coli likely to be implicated in human disease
Language eng
DOI 10.3390/metabo11020067
Indigenous content off
Field of Research 0301 Analytical Chemistry
0601 Biochemistry and Cell Biology
1103 Clinical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30148754

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.