Cargando…

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug‐...

Descripción completa

Detalles Bibliográficos
Autores principales: Khaledi, Ariane, Weimann, Aaron, Schniederjans, Monika, Asgari, Ehsaneddin, Kuo, Tzu‐Hao, Oliver, Antonio, Cabot, Gabriel, Kola, Axel, Gastmeier, Petra, Hogardt, Michael, Jonas, Daniel, Mofrad, Mohammad RK, Bremges, Andreas, McHardy, Alice C, Häussler, Susanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059009/
https://www.ncbi.nlm.nih.gov/pubmed/32048461
http://dx.doi.org/10.15252/emmm.201910264
_version_ 1783503960705859584
author Khaledi, Ariane
Weimann, Aaron
Schniederjans, Monika
Asgari, Ehsaneddin
Kuo, Tzu‐Hao
Oliver, Antonio
Cabot, Gabriel
Kola, Axel
Gastmeier, Petra
Hogardt, Michael
Jonas, Daniel
Mofrad, Mohammad RK
Bremges, Andreas
McHardy, Alice C
Häussler, Susanne
author_facet Khaledi, Ariane
Weimann, Aaron
Schniederjans, Monika
Asgari, Ehsaneddin
Kuo, Tzu‐Hao
Oliver, Antonio
Cabot, Gabriel
Kola, Axel
Gastmeier, Petra
Hogardt, Michael
Jonas, Daniel
Mofrad, Mohammad RK
Bremges, Andreas
McHardy, Alice C
Häussler, Susanne
author_sort Khaledi, Ariane
collection PubMed
description Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug‐resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.
format Online
Article
Text
id pubmed-7059009
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-70590092020-03-11 Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics Khaledi, Ariane Weimann, Aaron Schniederjans, Monika Asgari, Ehsaneddin Kuo, Tzu‐Hao Oliver, Antonio Cabot, Gabriel Kola, Axel Gastmeier, Petra Hogardt, Michael Jonas, Daniel Mofrad, Mohammad RK Bremges, Andreas McHardy, Alice C Häussler, Susanne EMBO Mol Med Articles Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug‐resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections. John Wiley and Sons Inc. 2020-02-12 2020-03-06 /pmc/articles/PMC7059009/ /pubmed/32048461 http://dx.doi.org/10.15252/emmm.201910264 Text en © 2020 The Authors. Published under the terms of the CC BY 4.0 license This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Khaledi, Ariane
Weimann, Aaron
Schniederjans, Monika
Asgari, Ehsaneddin
Kuo, Tzu‐Hao
Oliver, Antonio
Cabot, Gabriel
Kola, Axel
Gastmeier, Petra
Hogardt, Michael
Jonas, Daniel
Mofrad, Mohammad RK
Bremges, Andreas
McHardy, Alice C
Häussler, Susanne
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
title Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
title_full Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
title_fullStr Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
title_full_unstemmed Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
title_short Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
title_sort predicting antimicrobial resistance in pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059009/
https://www.ncbi.nlm.nih.gov/pubmed/32048461
http://dx.doi.org/10.15252/emmm.201910264
work_keys_str_mv AT khalediariane predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT weimannaaron predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT schniederjansmonika predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT asgariehsaneddin predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT kuotzuhao predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT oliverantonio predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT cabotgabriel predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT kolaaxel predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT gastmeierpetra predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT hogardtmichael predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT jonasdaniel predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT mofradmohammadrk predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT bremgesandreas predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT mchardyalicec predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics
AT hausslersusanne predictingantimicrobialresistanceinpseudomonasaeruginosawithmachinelearningenabledmoleculardiagnostics