A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates

ABSTRACT Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine...

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Autores principales:	Nathan B. Pincus, Egon A. Ozer, Jonathan P. Allen, Marcus Nguyen, James J. Davis, Deborah R. Winter, Chih-Hsien Chuang, Cheng-Hsun Chiu, Laura Zamorano, Antonio Oliver, Alan R. Hauser
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	Pseudomonas aeruginosa genome analysis machine learning modeling prediction virulence Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/1908f46e3086477ca33102afad2a73da
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spelling	oai:doaj.org-article:1908f46e3086477ca33102afad2a73da2021-11-15T15:56:45ZA Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates10.1128/mBio.01527-202150-7511https://doaj.org/article/1908f46e3086477ca33102afad2a73da2020-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.01527-20https://doaj.org/toc/2150-7511ABSTRACT Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.Nathan B. PincusEgon A. OzerJonathan P. AllenMarcus NguyenJames J. DavisDeborah R. WinterChih-Hsien ChuangCheng-Hsun ChiuLaura ZamoranoAntonio OliverAlan R. HauserAmerican Society for MicrobiologyarticlePseudomonas aeruginosagenome analysismachine learningmodelingpredictionvirulenceMicrobiologyQR1-502ENmBio, Vol 11, Iss 4 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pseudomonas aeruginosa genome analysis machine learning modeling prediction virulence Microbiology QR1-502
spellingShingle	Pseudomonas aeruginosa genome analysis machine learning modeling prediction virulence Microbiology QR1-502 Nathan B. Pincus Egon A. Ozer Jonathan P. Allen Marcus Nguyen James J. Davis Deborah R. Winter Chih-Hsien Chuang Cheng-Hsun Chiu Laura Zamorano Antonio Oliver Alan R. Hauser A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates
description	ABSTRACT Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.
format	article
author	Nathan B. Pincus Egon A. Ozer Jonathan P. Allen Marcus Nguyen James J. Davis Deborah R. Winter Chih-Hsien Chuang Cheng-Hsun Chiu Laura Zamorano Antonio Oliver Alan R. Hauser
author_facet	Nathan B. Pincus Egon A. Ozer Jonathan P. Allen Marcus Nguyen James J. Davis Deborah R. Winter Chih-Hsien Chuang Cheng-Hsun Chiu Laura Zamorano Antonio Oliver Alan R. Hauser
author_sort	Nathan B. Pincus
title	A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates
title_short	A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates
title_full	A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates
title_fullStr	A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates
title_full_unstemmed	A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates
title_sort	genome-based model to predict the virulence of <named-content content-type="genus-species">pseudomonas aeruginosa</named-content> isolates
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/1908f46e3086477ca33102afad2a73da
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A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates

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