Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

Frontiers in Microbiology
Pieter-Jan Van CampAleksey Porollo

Abstract

Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provi...Continue Reading

References

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Feb 23, 2020·International Journal of Molecular Sciences·Pieter-Jan Van CampAleksey Porollo

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Citations

Apr 27, 2021·Frontiers in Microbiology·Flavia MarinelliPaolo Visca
Dec 10, 2021·Klinicheskaia laboratornaia diagnostika·A V ChaplinD O Korostin
Dec 25, 2021·Journal of the Pediatric Infectious Diseases Society·David B Haslam
Dec 14, 2021·Frontiers in Cellular and Infection Microbiology·Bilal AslamZulqarnain Baloch

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Datasets Mentioned

BETA
PRJNA587095

Methods Mentioned

BETA
BioSample
dissect

Software Mentioned

BLASTx
DIAMOND
WGS
Shiny
USEARCH
LASSO
R
ESKAPE
XGBoost
SRA

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