Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data

ABSTRACT Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polyg...

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Autores principales:	Nenad Macesic, Oliver J. Bear Don’t Walk, Itsik Pe’er, Nicholas P. Tatonetti, Anton Y. Peleg, Anne-Catrin Uhlemann
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	genotype phenotype prediction antimicrobial resistance machine learning Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/b206dea6959348dd85e0c18e57dc1f9e
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spelling	oai:doaj.org-article:b206dea6959348dd85e0c18e57dc1f9e2021-12-02T19:46:20ZPredicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data10.1128/mSystems.00656-192379-5077https://doaj.org/article/b206dea6959348dd85e0c18e57dc1f9e2020-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00656-19https://doaj.org/toc/2379-5077ABSTRACT Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance. IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.Nenad MacesicOliver J. Bear Don’t WalkItsik Pe’erNicholas P. TatonettiAnton Y. PelegAnne-Catrin UhlemannAmerican Society for Microbiologyarticlegenotypephenotypepredictionantimicrobial resistancemachine learningMicrobiologyQR1-502ENmSystems, Vol 5, Iss 3 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	genotype phenotype prediction antimicrobial resistance machine learning Microbiology QR1-502
spellingShingle	genotype phenotype prediction antimicrobial resistance machine learning Microbiology QR1-502 Nenad Macesic Oliver J. Bear Don’t Walk Itsik Pe’er Nicholas P. Tatonetti Anton Y. Peleg Anne-Catrin Uhlemann Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data
description	ABSTRACT Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance. IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.
format	article
author	Nenad Macesic Oliver J. Bear Don’t Walk Itsik Pe’er Nicholas P. Tatonetti Anton Y. Peleg Anne-Catrin Uhlemann
author_facet	Nenad Macesic Oliver J. Bear Don’t Walk Itsik Pe’er Nicholas P. Tatonetti Anton Y. Peleg Anne-Catrin Uhlemann
author_sort	Nenad Macesic
title	Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data
title_short	Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data
title_full	Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data
title_fullStr	Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data
title_full_unstemmed	Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data
title_sort	predicting phenotypic polymyxin resistance in <named-content content-type="genus-species">klebsiella pneumoniae</named-content> through machine learning analysis of genomic data
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/b206dea6959348dd85e0c18e57dc1f9e
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Predicting Phenotypic Polymyxin Resistance in <named-content content-type="genus-species">Klebsiella pneumoniae</named-content> through Machine Learning Analysis of Genomic Data

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