Literature DB >> 31545890

Discrimination of Escherichia coli and Shigella spp. by Nuclear Magnetic Resonance Based Metabolomic Characterization of Culture Media.

Gilles J P Rautureau1, Tony L Palama1, Isabelle Canard2, Caroline Mirande2, Sonia Chatellier2, Alex van Belkum2, Bénédicte Elena-Herrmann1,3.   

Abstract

Dysentery is a major health threat that dramatically impacts childhood morbidity and mortality in developing countries. Various pathogenic agents cause dysentery, such as Shigella spp. and Escherichia coli, which are very closely related if not identical species. Sensitive and precise detection and identification of the infectious agent is important to target the best therapeutic strategy, but the differential diagnosis of these two groups remains a challenge using conventional methods. Here, we present a nuclear magnetic resonance (NMR) based multivariate classification model employing bacterial metabolic footprints in postculture growth media with remarkable segregation capability, including the discrimination of lactose negative E. coli and Shigella spp. Our results confirm the potential of metabolomic markers in the field of bacterial identification for the distinction of even very closely related species.

Entities:  

Keywords:  E. coli; NMR; Shigella; bacterial identification; exometabolome; metabolic footprint; metabolomics

Mesh:

Substances:

Year:  2019        PMID: 31545890     DOI: 10.1021/acsinfecdis.9b00199

Source DB:  PubMed          Journal:  ACS Infect Dis        ISSN: 2373-8227            Impact factor:   5.084


  3 in total

Review 1.  Ecology, Structure, and Evolution of Shigella Phages.

Authors:  Sundharraman Subramanian; Kristin N Parent; Sarah M Doore
Journal:  Annu Rev Virol       Date:  2020-05-11       Impact factor: 10.431

Review 2.  Imaging Inflammation and Infection in the Gastrointestinal Tract.

Authors:  Alex N Frickenstein; Meredith A Jones; Bahareh Behkam; Lacey R McNally
Journal:  Int J Mol Sci       Date:  2019-12-30       Impact factor: 5.923

3.  Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles.

Authors:  Danhui Wang; Peyton Greenwood; Matthias S Klein
Journal:  Metabolites       Date:  2021-12-13
  3 in total

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