AUTHOR=Tan Rundong , Yu Anqi , Liu Ziming , Liu Ziqi , Jiang Rongfeng , Wang Xiaoli , Liu Jialin , Gao Junhui , Wang Xinjun 

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

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.712886

DOI=10.3389/fmicb.2021.712886

ISSN=1664-302X

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 (K. pneumoniae) is one of the most significant members of the genus Klebsiella in the Enterobacteriaceae family, and also a common nonsocial 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 (SNPs) 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 model with XGBoost algorithm and deep neural network (DNN) algorithm to predict MICs. 
Three kinds of machine learning and deep learning models were used to predict MICs: XGBoost classification model, XGBoost regression model and deep neural network (DNN) regression model. Our models have all achieved good accuracy (above 80%). Through external verification, some of the selected features were found to be related to drug resistance.