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      Metabolomics-based response of Salmonella to desiccation stress and skimmed milk powder storage

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          Abstract

          The strong survival ability of Salmonella in low-moisture foods (LMFs) has been of public concern, and is considered a threat to people’s health. Recently, the development of omics technology has promoted research on the molecular mechanisms of the desiccation stress response of pathogenic bacteria. However, multiple analytical aspects related to their physiological characteristics remain unclear. We explored the physiological metabolism changes of S. enterica Enteritidis exposed to a 24 h-desiccation treatment and a subsequent 3-month desiccation storage in skimmed milk powder (SMP) with an approach of gas chromatography–mass spectrometry (GC–MS) and ultra-performance liquid chromatography-Q Exactive-mass spectrometry (UPLC-QE-MS). A total of 8,292 peaks were extracted, of which 381 were detected by GC–MS and 7,911 peaks were identified by LC–MS/MS, respectively. Through analyses of differentially expressed metabolites (DEMs) and key pathways, a total of 58 DEMs emerged from the 24 h-desiccation treatment, which exhibited the highest relevance for five metabolic pathways, involving glycine, serine, and threonine metabolism, pyrimidine metabolism, purine metabolism, vitamin B6 metabolism, and pentose phosphate pathway. After 3-month SMP storage, 120 DEMs were identified, which were related to several regulatory pathways including arginine and proline metabolism, serine and threonine metabolism, β-alanine metabolism, glycerolipid metabolism, and glycolysis. The analyses of key enzyme activities of XOD, PK, and G6PDH and ATP content provided further evidence that supported the metabolic responses such as nucleic acid degradation, glycolysis, and ATP production played an important role in Salmonella’s adaptation to desiccation stress. This study enables a better understanding of metabolomics-based responses of Salmonella at the initial stage of desiccation stress and the following long-term adaptive stage. Meanwhile, the identified discriminative metabolic pathways may serve as potentially useful targets in developing strategies for the control and prevention of desiccation-adapted Salmonella in LMFs.

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          Most cited references62

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          KEGG: kyoto encyclopedia of genes and genomes.

          M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.

            Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
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              Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.

              Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                23 February 2023
                2023
                : 14
                : 1092435
                Affiliations
                College of Biological and Pharmaceutical Science, Guangdong University of Technology , Guangzhou, China
                Author notes

                Edited by: Lin Lin, Jiangsu University, China

                Reviewed by: Monica Ponder, Virginia Tech, United States; Ján Matiašovic, Veterinary Research Institute (VRI), Czechia

                *Correspondence: Hongmei Zhang, ✉ hmzhang@ 123456gdut.edu.cn

                These authors have contributed equally to this work

                This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2023.1092435
                9996163
                36910198
                c3b60335-67fa-4f6d-8622-d6ed348d2e52
                Copyright © 2023 Li, Chen, Zeng, Zeng, Ma, Chen, Yang and Zhang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 November 2022
                : 03 February 2023
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 62, Pages: 12, Words: 8905
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 31972044, 32202186
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                salmonella,metabolomics,desiccation,lmfs,stress responses
                Microbiology & Virology
                salmonella, metabolomics, desiccation, lmfs, stress responses

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