Cargando…
Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data
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....
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421019/ https://www.ncbi.nlm.nih.gov/pubmed/34497594 http://dx.doi.org/10.3389/fmicb.2021.712886 |
_version_ | 1783748992245432320 |
---|---|
author | Tan, Rundong Yu, Anqi Liu, Ziming Liu, Ziqi Jiang, Rongfeng Wang, Xiaoli Liu, Jialin Gao, Junhui Wang, Xinjun |
author_facet | Tan, Rundong Yu, Anqi Liu, Ziming Liu, Ziqi Jiang, Rongfeng Wang, Xiaoli Liu, Jialin Gao, Junhui Wang, Xinjun |
author_sort | Tan, Rundong |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8421019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84210192021-09-07 Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data Tan, Rundong Yu, Anqi Liu, Ziming Liu, Ziqi Jiang, Rongfeng Wang, Xiaoli Liu, Jialin Gao, Junhui Wang, Xinjun Front Microbiol Microbiology 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. Frontiers Media S.A. 2021-08-23 /pmc/articles/PMC8421019/ /pubmed/34497594 http://dx.doi.org/10.3389/fmicb.2021.712886 Text en Copyright © 2021 Tan, Yu, Liu, Liu, Jiang, Wang, Liu, Gao and Wang. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Microbiology Tan, Rundong Yu, Anqi Liu, Ziming Liu, Ziqi Jiang, Rongfeng Wang, Xiaoli Liu, Jialin Gao, Junhui Wang, Xinjun Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data |
title | Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data |
title_full | Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data |
title_fullStr | Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data |
title_full_unstemmed | Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data |
title_short | Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data |
title_sort | prediction of minimal inhibitory concentration of meropenem against klebsiella pneumoniae using metagenomic data |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421019/ https://www.ncbi.nlm.nih.gov/pubmed/34497594 http://dx.doi.org/10.3389/fmicb.2021.712886 |
work_keys_str_mv | AT tanrundong predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT yuanqi predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT liuziming predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT liuziqi predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT jiangrongfeng predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT wangxiaoli predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT liujialin predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT gaojunhui predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata AT wangxinjun predictionofminimalinhibitoryconcentrationofmeropenemagainstklebsiellapneumoniaeusingmetagenomicdata |