Comparative metabolomics shows the metabolic profiles fluctuate in multi-drug resistant Escherichia coli strains

Lin, Yongjun; Li, Wanxin; Sun, Lijun; Lin, Zhenping; Jiang, Yu-Hang; Ling, Yueming; Lin, Xiangmin

doi:10.1016/j.jprot.2019.103468

Cited by 29 publications

(24 citation statements)

References 41 publications

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“…Interestingly the metabolic profile identified enabled a clear distinction between the resistant and susceptible strains. The functional analysis described the resistant strains as more concerned in the biosynthesis of amino acids, biosynthesis of phenylpropanoids and purine metabolism [170]. Altogether, integration of the metabolomics data along with the proteomics outcomes might represent a novel approach for the investigation and prediction of the AMR traits [171].…”

Section: Latest Frontiers On Veterinary Amr Researchmentioning

confidence: 99%

Antimicrobial Resistance in Veterinary Medicine: An Overview

Palma

Tilocca

Roncada

2020

IJMS

169

100

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Antimicrobial resistance (AMR) represents one of the most important human- and animal health-threatening issues worldwide. Bacterial capability to face antimicrobial compounds is an ancient feature, enabling bacterial survival over time and the dynamic surrounding. Moreover, bacteria make use of their evolutionary machinery to adapt to the selective pressure exerted by antibiotic treatments, resulting in reduced efficacy of the therapeutic intervention against human and animal infections. The mechanisms responsible for both innate and acquired AMR are thoroughly investigated. Commonly, AMR traits are included in mobilizable genetic elements enabling the homogeneous diffusion of the AMR traits pool between the ecosystems of diverse sectors, such as human medicine, veterinary medicine, and the environment. Thus, a coordinated multisectoral approach, such as One-Health, provides a detailed comprehensive picture of the AMR onset and diffusion. Following a general revision of the molecular mechanisms responsible for both innate and acquired AMR, the present manuscript focuses on reviewing the contribution of veterinary medicine to the overall issue of AMR. The main sources of AMR amenable to veterinary medicine are described, driving the attention towards the indissoluble cross-talk existing between the diverse ecosystems and sectors and their cumulative cooperation to this warning phenomenon.

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Section: Latest Frontiers On Veterinary Amr Researchmentioning

confidence: 99%

Antimicrobial Resistance in Veterinary Medicine: An Overview

Palma

Tilocca

Roncada

2020

IJMS

169

100

View full text Add to dashboard Cite

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“…PCA has been used to reveal changes in metabolic profiles of samples under different conditions [ 11 , 20 ]. In this study, we are very interested in the changes of metabolic profiles of chilled chicken induced by the length of storage time.…”

Section: Resultsmentioning

confidence: 99%

“…The Kruskal–Wallis test is a non-parametric statistical test that evaluates whether two or more samples are drawn from the same distribution [ 18 ]. It has been widely used in analyzing differences in the abundance of metabolites [ 19 , 20 ]. In our study, a nonparametric Kruskal–Wallis test was performed to compare the relative abundance of metabolites among different groups.…”

Section: Methodsmentioning

confidence: 99%

UHPLC–MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken

Zhang

Chen

et al. 2020

Foods

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To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

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“…Besides genomic studies, transcriptomic, proteomic and, eventually, metabolomic analyses have been implemented with the aim of deciphering novel mechanisms of resistance [122] , [123] , [124] , [125] , or to detect the substrates, besides antibiotics, of known resistance determinants as multidrug efflux pumps [126] . The main drawback of these approaches is that, although the acquisition of AR usually implies changes in the level of expression of several genes/proteins, most of these changes are not associated with the mechanism of resistance, but with the fitness costs due associated with such resistance (see below).…”

Section: Genomic Approaches For the Study Of Antibiotic Resistancementioning

confidence: 99%