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Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models

Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid e...

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Autores principales: Vezina, Ben, Watts, Stephen C, Hawkey, Jane, Cooper, Helena B, Judd, Louise M, Jenney, Adam WJ, Monk, Jonathan M, Holt, Kathryn E, Wyres, Kelly L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564454/
https://www.ncbi.nlm.nih.gov/pubmed/37815531
http://dx.doi.org/10.7554/eLife.87406
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author Vezina, Ben
Watts, Stephen C
Hawkey, Jane
Cooper, Helena B
Judd, Louise M
Jenney, Adam WJ
Monk, Jonathan M
Holt, Kathryn E
Wyres, Kelly L
author_facet Vezina, Ben
Watts, Stephen C
Hawkey, Jane
Cooper, Helena B
Judd, Louise M
Jenney, Adam WJ
Monk, Jonathan M
Holt, Kathryn E
Wyres, Kelly L
author_sort Vezina, Ben
collection PubMed
description Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here, we describe Bactabolize, a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae, and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically defined quality control criteria resulted in models with a high degree of completeness (≥99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.
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spelling pubmed-105644542023-10-11 Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models Vezina, Ben Watts, Stephen C Hawkey, Jane Cooper, Helena B Judd, Louise M Jenney, Adam WJ Monk, Jonathan M Holt, Kathryn E Wyres, Kelly L eLife Computational and Systems Biology Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here, we describe Bactabolize, a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae, and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically defined quality control criteria resulted in models with a high degree of completeness (≥99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens. eLife Sciences Publications, Ltd 2023-10-10 /pmc/articles/PMC10564454/ /pubmed/37815531 http://dx.doi.org/10.7554/eLife.87406 Text en © 2023, Vezina, Watts et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Vezina, Ben
Watts, Stephen C
Hawkey, Jane
Cooper, Helena B
Judd, Louise M
Jenney, Adam WJ
Monk, Jonathan M
Holt, Kathryn E
Wyres, Kelly L
Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
title Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
title_full Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
title_fullStr Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
title_full_unstemmed Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
title_short Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
title_sort bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564454/
https://www.ncbi.nlm.nih.gov/pubmed/37815531
http://dx.doi.org/10.7554/eLife.87406
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