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Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data.

ABSTRACT: 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.

SUBMITTER: Tan R

PROVIDER: S-EPMC8421019 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

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Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data.

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