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Impact of meropenem on Klebsiella pneumoniae metabolism.

ABSTRACT: The aim of this study was to analyze the metabolome of several Klebsiella pneumoniae strains characterized by different resistance patterns. A total of 59 bacterial strains (27 carbapenemase-negative and 32 carbapenemase-positive) were included and their metabolic features were assessed in basal conditions. Moreover, 8 isolates (4 wild-type and 4 KPC-producers) were randomly selected to evaluate the impact of sub-lethal concentrations of meropenem on bacterial metabolism. The metabolomic analysis was performed by 1H-NMR spectroscopy both on filtered supernatants and cell lysates. A total of 40 and 20 molecules were quantified in the intracellular and the extracellular metabolome, respectively. While in basal conditions only five metabolites showed significant differences between carbapenemase-positive and negative strains, the use of meropenem had a profound impact on the whole bacterial metabolism. In the intracellular compartment, a reduction of different overflow metabolites and organic acids (e.g. formate, acetate, isobutyrate) was noticed, whereas, in the extracellular metabolome, the levels of several organic acids (e.g. succinate, acetate, formate, lactate) and amino acids (aspartate, threonine, lysine, alanine) were modified by meropenem stimulation. Interestingly, carbapenemase-positive and negative strains reacted differently to meropenem in terms of number and type of perturbed metabolites. In wild-type strains, meropenem had great impact on the metabolic pathways related to methane metabolism and alanine, aspartate and glutamate metabolism, whereas in KPC-producers the effect was predominant on pyruvate metabolism. The knowledge about the bacterial metabolic profiles could help to set up innovative diagnostic methods and new antimicrobial strategies to fight the global crisis against carbapenemase-positive K. pneumoniae.

SUBMITTER: Foschi C

PROVIDER: S-EPMC6237392 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Impact of meropenem on Klebsiella pneumoniae metabolism.

Foschi Claudio C Salvo Melissa M Laghi Luca L Zhu Chenglin C Ambretti Simone S Marangoni Antonella A Re Maria Carla MC

PloS one 20181115 11

The aim of this study was to analyze the metabolome of several Klebsiella pneumoniae strains characterized by different resistance patterns. A total of 59 bacterial strains (27 carbapenemase-negative and 32 carbapenemase-positive) were included and their metabolic features were assessed in basal conditions. Moreover, 8 isolates (4 wild-type and 4 KPC-producers) were randomly selected to evaluate the impact of sub-lethal concentrations of meropenem on bacterial metabolism. The metabolomic analysi ...[more]

PMID: 30440048

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Project description:Carbapenem-resistant Enterobacteriaceae (CRE) are resistant to most antibiotics, making CRE infections extremely difficult to treat with available agents. Klebsiella pneumoniae carbapenemases (KPC-2 and KPC-3) are predominant carbapenemases in CRE in the United States. Nacubactam is a bridged diazabicyclooctane (DBO) β-lactamase inhibitor that inactivates class A and C β-lactamases and exhibits intrinsic antibiotic and β-lactam "enhancer" activity against Enterobacteriaceae In this study, we examined a collection of meropenem-resistant K. pneumoniae isolates carrying blaKPC-2 or blaKPC-3; meropenem-nacubactam restored susceptibility. Upon testing isogenic Escherichia coli strains producing KPC-2 variants with single-residue substitutions at important Ambler class A positions (K73, S130, R164, E166, N170, D179, K234, E276, etc.), the K234R variant increased the meropenem-nacubactam MIC compared to that for the strain producing KPC-2, without increasing the meropenem MIC. Correspondingly, nacubactam inhibited KPC-2 (apparent Ki [Ki app] = 31 ± 3 μM) more efficiently than the K234R variant (Ki app = 270 ± 27 μM) and displayed a faster acylation rate (k2/K), which was 5,815 ± 582 M-1 s-1 for KPC-2 versus 247 ± 25 M-1 s-1 for the K234R variant. Unlike avibactam, timed mass spectrometry revealed an intact sulfate on nacubactam and a novel peak (+337 Da) with the K234R variant. Molecular modeling of the K234R variant showed significant catalytic residue (i.e., S70, K73, and S130) rearrangements that likely interfere with nacubactam binding and acylation. Nacubactam's aminoethoxy tail formed unproductive interactions with the K234R variant's active site. Molecular modeling and docking observations were consistent with the results of biochemical analyses. Overall, the meropenem-nacubactam combination is effective against carbapenem-resistant K. pneumoniae Moreover, our data suggest that β-lactamase inhibition by nacubactam proceeds through an alternative mechanism compared to that for avibactam.

Project description:Minimal inhibitory concentration (MIC) is defined as the lowest concentration of an antimicrobial agent that can inhibit the visible growth of a particular microorganism after overnight incubation. Clinically, antibiotic doses for specific infections are determined according to the fraction of MIC. Therefore, credible assessment of MICs will provide a physician valuable information on the choice of therapeutic strategy. Early and precise usage of antibiotics is the key to an infection therapy. Compared with the traditional culture-based method, the approach of whole genome sequencing to identify MICs can shorten the experimental time, thereby improving clinical efficacy. Klebsiella pneumoniae is one of the most significant members of the genus Klebsiella in the Enterobacteriaceae family and also a common non-social pathogen. Meropenem is a broad-spectrum antibacterial agent of the carbapenem family, which can produce antibacterial effects of most Gram-positive and -negative bacteria. In this study, we used single-nucleotide polymorphism (SNP) information and nucleotide k-mers count based on metagenomic data to predict MICs of meropenem against K. pneumoniae. Then, features of 110 sequenced K. pneumoniae genome data were combined and modeled with XGBoost algorithm and deep neural network (DNN) algorithm to predict MICs. We first use the XGBoost classification model and the XGBoost regression model. After five runs, the average accuracy of the test set was calculated. The accuracy of using nucleotide k-mers to predict MICs of the XGBoost classification model and XGBoost regression model was 84.5 and 89.1%. The accuracy of SNP in predicting MIC was 80 and 81.8%, respectively. The results show that XGBoost regression is better than XGBoost classification in both nucleotide k-mers and SNPs to predict MICs. We further selected 40 nucleotide k-mers and 40 SNPs with the highest correlation with MIC values as features to retrain the XGBoost regression model and DNN regression model. After 100 and 1,000 runs, the results show that the accuracy of the two models was improved. The accuracy of the XGBoost regression model for k-mers, SNPs, and k-mers & SNPs was 91.1, 85.2, and 91.3%, respectively. The accuracy of the DNN regression model was 91.9, 87.1, and 91.8%, respectively. Through external verification, some of the selected features were found to be related to drug resistance.

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Impact of meropenem on Klebsiella pneumoniae metabolism.

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Impact of meropenem on Klebsiella pneumoniae metabolism.

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