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‐...
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| Published in | EMBO molecular medicine Vol. 12; no. 3; pp. e10264 - n/a |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
06.03.2020
EMBO Press EMBOpress John Wiley and Sons Inc Springer Nature |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1757-4676 1757-4684 1757-4684 |
| DOI | 10.15252/emmm.201910264 |
Cover
| Abstract | 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.
Synopsis
The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles.
Genome and transcriptome data of 414 clinical isolates was combined for biomarker identification using information on gene expression, gene presence or absence, and single nucleotide variations.
For some antibiotics, transcriptome information greatly improves resistance prediction.
Depending on the antibiotic, 37–93 biomarkers are sufficient to obtain high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values.
Biomarkers include known resistance conferring genes (e.g. gyrA, oprD, ampC, efflux pumps) as well as unexpected and potential novel candidates.
Graphical Abstract
The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. |
|---|---|
| AbstractList | Abstract 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. 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. Synopsis The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. Genome and transcriptome data of 414 clinical isolates was combined for biomarker identification using information on gene expression, gene presence or absence, and single nucleotide variations. For some antibiotics, transcriptome information greatly improves resistance prediction. Depending on the antibiotic, 37–93 biomarkers are sufficient to obtain high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. Biomarkers include known resistance conferring genes (e.g. gyrA, oprD, ampC, efflux pumps) as well as unexpected and potential novel candidates. Graphical Abstract The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. 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.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. 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. 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. 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. The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. 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. Synopsis The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. Genome and transcriptome data of 414 clinical isolates was combined for biomarker identification using information on gene expression, gene presence or absence, and single nucleotide variations. For some antibiotics, transcriptome information greatly improves resistance prediction. Depending on the antibiotic, 37–93 biomarkers are sufficient to obtain high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. Biomarkers include known resistance conferring genes (e.g. gyrA, oprD, ampC, efflux pumps) as well as unexpected and potential novel candidates. The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. 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. Finally, 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. |
| Author | Jonas, Daniel Khaledi, Ariane Mofrad, Mohammad RK Kola, Axel Gastmeier, Petra Schniederjans, Monika Kuo, Tzu‐Hao Häussler, Susanne Hogardt, Michael Cabot, Gabriel McHardy, Alice C Weimann, Aaron Asgari, Ehsaneddin Bremges, Andreas Oliver, Antonio |
| AuthorAffiliation | 10 Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Lab Berkeley CA USA 8 Institute of Medical Microbiology and Infection Control University Hospital Frankfurt Frankfurt/Main Germany 4 German Center for Infection Research (DZIF) Braunschweig Germany 9 Faculty of Medicine Institute for Infection Prevention and Hospital Epidemiology Medical Center‐University of Freiburg Freiburg Germany 3 Computational Biology of Infection Research Helmholtz Centre for Infection Research Braunschweig Germany 6 Servicio de Microbiología y Unidad de Investigación Hospital Universitario Son Espases Instituto de Investigación Sanitaria Illes Balears (IdISPa) Palma de Mallorca Spain 1 Department of Molecular Bacteriology Helmholtz Centre for Infection Research Braunschweig Germany 5 Molecular Cell Biomechanics Laboratory Departments of Bioengineering and Mechanical Engineering University of California Berkeley CA USA 7 Institute of Hygiene and Environmental Medicine Charité – Univers |
| AuthorAffiliation_xml | – name: 8 Institute of Medical Microbiology and Infection Control University Hospital Frankfurt Frankfurt/Main Germany – name: 7 Institute of Hygiene and Environmental Medicine Charité – Universitätsmedizin Berlin Berlin Germany – name: 1 Department of Molecular Bacteriology Helmholtz Centre for Infection Research Braunschweig Germany – name: 3 Computational Biology of Infection Research Helmholtz Centre for Infection Research Braunschweig Germany – name: 9 Faculty of Medicine Institute for Infection Prevention and Hospital Epidemiology Medical Center‐University of Freiburg Freiburg Germany – name: 5 Molecular Cell Biomechanics Laboratory Departments of Bioengineering and Mechanical Engineering University of California Berkeley CA USA – name: 2 Molecular Bacteriology Group TWINCORE‐Centre for Experimental and Clinical Infection Research Hannover Germany – name: 4 German Center for Infection Research (DZIF) Braunschweig Germany – name: 10 Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Lab Berkeley CA USA – name: 6 Servicio de Microbiología y Unidad de Investigación Hospital Universitario Son Espases Instituto de Investigación Sanitaria Illes Balears (IdISPa) Palma de Mallorca Spain |
| Author_xml | – sequence: 1 givenname: Ariane surname: Khaledi fullname: Khaledi, Ariane organization: Department of Molecular Bacteriology, Helmholtz Centre for Infection Research, Molecular Bacteriology Group, TWINCORE‐Centre for Experimental and Clinical Infection Research – sequence: 2 givenname: Aaron orcidid: 0000-0003-4597-2471 surname: Weimann fullname: Weimann, Aaron organization: Molecular Bacteriology Group, TWINCORE‐Centre for Experimental and Clinical Infection Research, Computational Biology of Infection Research, Helmholtz Centre for Infection Research, German Center for Infection Research (DZIF) – sequence: 3 givenname: Monika surname: Schniederjans fullname: Schniederjans, Monika organization: Department of Molecular Bacteriology, Helmholtz Centre for Infection Research, Molecular Bacteriology Group, TWINCORE‐Centre for Experimental and Clinical Infection Research – sequence: 4 givenname: Ehsaneddin surname: Asgari fullname: Asgari, Ehsaneddin organization: Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California – sequence: 5 givenname: Tzu‐Hao surname: Kuo fullname: Kuo, Tzu‐Hao organization: Computational Biology of Infection Research, Helmholtz Centre for Infection Research – sequence: 6 givenname: Antonio surname: Oliver fullname: Oliver, Antonio organization: Servicio de Microbiología y Unidad de Investigación Hospital Universitario Son Espases, Instituto de Investigación Sanitaria Illes Balears (IdISPa) – sequence: 7 givenname: Gabriel surname: Cabot fullname: Cabot, Gabriel organization: Servicio de Microbiología y Unidad de Investigación Hospital Universitario Son Espases, Instituto de Investigación Sanitaria Illes Balears (IdISPa) – sequence: 8 givenname: Axel surname: Kola fullname: Kola, Axel organization: Institute of Hygiene and Environmental Medicine, Charité – Universitätsmedizin Berlin – sequence: 9 givenname: Petra surname: Gastmeier fullname: Gastmeier, Petra organization: Institute of Hygiene and Environmental Medicine, Charité – Universitätsmedizin Berlin – sequence: 10 givenname: Michael surname: Hogardt fullname: Hogardt, Michael organization: Institute of Medical Microbiology and Infection Control, University Hospital Frankfurt – sequence: 11 givenname: Daniel surname: Jonas fullname: Jonas, Daniel organization: Faculty of Medicine, Institute for Infection Prevention and Hospital Epidemiology, Medical Center‐University of Freiburg – sequence: 12 givenname: Mohammad RK surname: Mofrad fullname: Mofrad, Mohammad RK organization: Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Lab – sequence: 13 givenname: Andreas orcidid: 0000-0001-6739-7899 surname: Bremges fullname: Bremges, Andreas organization: Computational Biology of Infection Research, Helmholtz Centre for Infection Research, German Center for Infection Research (DZIF) – sequence: 14 givenname: Alice C orcidid: 0000-0003-2370-3430 surname: McHardy fullname: McHardy, Alice C email: Alice.McHardy@helmholtz-hzi.de organization: Computational Biology of Infection Research, Helmholtz Centre for Infection Research, German Center for Infection Research (DZIF) – sequence: 15 givenname: Susanne orcidid: 0000-0001-6141-9102 surname: Häussler fullname: Häussler, Susanne email: Susanne.Haeussler@helmholtz-hzi.de organization: Department of Molecular Bacteriology, Helmholtz Centre for Infection Research, Molecular Bacteriology Group, TWINCORE‐Centre for Experimental and Clinical Infection Research |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32048461$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1627933$$D View this record in Osti.gov |
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| Snippet | Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial... Abstract Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species,... |
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| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | e10264 |
| SubjectTerms | Anti-Bacterial Agents - pharmacology Antibiotic resistance Antibiotics Antimicrobial agents Antimicrobial resistance Bacteria BASIC BIOLOGICAL SCIENCES biomarkers Ciprofloxacin clinical isolates Cloning Drug resistance Drug Resistance, Bacterial EMBO02 EMBO09 EMBO23 Gene expression Genome, Bacterial Genomes Genotype & phenotype Infections Learning algorithms Machine Learning Microbial Sensitivity Tests molecular diagnostics Mutation Nucleotide sequence Pathogens Pathology, Molecular Phylogenetics Prediction models Pseudomonas aeruginosa Pseudomonas aeruginosa - drug effects Research & Experimental Medicine Transcriptome Whole genome sequencing |
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| Title | Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics |
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