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

被引:4
作者
Vezina, Ben [1 ]
Watts, Stephen C. [1 ]
Hawkey, Jane [1 ]
Cooper, Helena B. [1 ]
Judd, Louise M. [1 ]
Jenney, Adam W. J. [2 ]
Monk, Jonathan M. [3 ]
Holt, Kathryn E. [1 ,4 ]
Wyres, Kelly L. [1 ]
机构
[1] Monash Univ, Cent Clin Sch, Dept Infect Dis, Melbourne, Australia
[2] Microbiol Unit, Alfred Hlth, Melbourne, Australia
[3] Univ Calif San Diego, Dept Bioengn, San Diego, CA USA
[4] London Sch Hyg & Trop Med, Dept Infect Biol, London, England
来源
ELIFE | 2023年 / 12卷
基金
澳大利亚研究理事会; 英国医学研究理事会;
关键词
Klebsiella pneumoniae; software; metabolic modelling; strain-specific; Klebsiella pneumoniae species complex; bacterial; Other; KLEBSIELLA-PNEUMONIAE; ESCHERICHIA-COLI; DIVERSITY; VIRULENCE;
D O I
10.7554/eLife.87406
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:22
相关论文
empty
未找到相关数据