Open AccessIdentification of antibiotic resistance and virulence‐encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning
Author(s) -
Lu Jiayue,
Chen Jifan,
Liu Congcong,
Zeng Yu,
Sun Qiaoling,
Li Jiaping,
Shen Zhangqi,
Chen Sheng,
Zhang Rong
Publication year - 2022
Publication title -
microbial biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.287
H-Index - 74
ISSN - 1751-7915
DOI - 10.1111/1751-7915.13960
Subject(s) - klebsiella pneumoniae , virulence , antibiotic resistance , microbiology and biotechnology , antibiotics , biology , pathogen , drug resistance , gene , escherichia coli , genetics
Summary Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K . pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical K . pneumoniae strains. Rapid differentiation of clinical K . pneumoniae with high resistance/hypervirulence from classical K. pneumoniae would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence‐encoding factors and resistance phenotypes from K . pneumoniae strains. A total of 71 K . pneumoniae strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on K . pneumoniae strains were determined. Seven thousand four hundred fifty‐five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning‐based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence‐encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify K . pneumoniae with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back‐testing the Raman‐CNN platform on 71 K . pneumoniae strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.