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

Rundong Tan; Anqi Yu; Ziming Liu; Ziqi Liu; Rongfeng Jiang; Xiaoli Wang; Jialin Liu; Junhui Gao; Xinjun Wang

doi:10.3389/fmicb.2021.712886

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

Front Microbiol. 2021 Aug 23:12:712886. doi: 10.3389/fmicb.2021.712886. eCollection 2021.

Authors

Rundong Tan^{1

2}, Anqi Yu^{1

2}, Ziming Liu³, Ziqi Liu⁴, Rongfeng Jiang^{1

2}, Xiaoli Wang⁵, Jialin Liu⁵, Junhui Gao^{1

2}, Xinjun Wang⁶

Affiliations

¹ Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China.
² Shanghai Zhangjiang Institute of Medical Innovation, Shanghai, China.
³ Medical Information Engineering, Department of Medical Information, Harbin Medical University, Harbin, China.
⁴ Department of Biostatistics, School of Global Public Health, New York University, New York, NY, United States.
⁵ Department of Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
⁶ Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

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.

Keywords: Klebsiella pneumoniae; XGBoost; deep neural network; meropenem; minimum inhibitory concentration.