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A non-targeted high-coverage microbial metabolomics pretreatment method development and application in drug resistant Salmonella

Abstract

Metabolomics as an effective omics research tool is widely used in pathogenic bacteria drug resistance mechanism. The pretreatment methods affect the coverage and abundance of metabolites, leading to diversity of results. Therefore, it is necessary to develop a high-coverage and robust pretreatment method to analyze and study drug-related drug-resistant metabolites (carbohydrate, lipid, amino acid). The four essential pretreatment steps of Salmonella for GC/MS-based metabolomics study was optimized, including quenching (methanol-water and liquid nitrogen), solvent extraction (acetonitrile-isopropanol-water, methyl tert-butyl ether (MTBE) and methanol-chloroform), extraction methods (stainless-steel ball grinding and liquid nitrogen) and derivatization (normal-temperature and high-temperature derivatization). Metabolite profiling was performed to investigate metabolic differences between different methods. The optimized pretreatment method for the non-targeted metabolic profiling of drug-resistant Salmonella was as follows: liquid-nitrogen quenching, acetonitrile-isopropanol-water (3:3:2, v/v) as extracting agent, stainless-steel ball grinding for cell disruption and derivatization at 37°C. The optimized pretreatment method will obtain more types of metabolites, higher abundance of metabolites, and better repeatability and stability. The optimized methods was implemented for the metabolic profiling of the resistant and sensitive of Salmonella with serotype Derby. The method has the potential to be applied to the metabolism of other pathogenic bacteria.

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Supplementary files

Article information


Submitted
29 Nov 2019
Accepted
26 Jan 2020
First published
05 Feb 2020

Anal. Methods, 2020, Accepted Manuscript
Article type
Paper

A non-targeted high-coverage microbial metabolomics pretreatment method development and application in drug resistant Salmonella

Y. Zhang, J. Ji, M. Lan, T. Wang and X. Sun, Anal. Methods, 2020, Accepted Manuscript , DOI: 10.1039/C9AY02579K

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