Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia.
Microbiology Unit, Alfred Health, Melbourne, Australia.
Elife. 2023 Oct 10;12:RP87406. doi: 10.7554/eLife.87406.
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, , and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for 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.
代谢能力在细菌种内有很大差异,导致生态位分离,以及毒力和抗菌药物敏感性的差异。基于基因组规模的代谢模型是研究个体代谢潜力的有用工具,随着基因组测序的快速扩展,有大量数据可用于比较分析。然而,目前很少有工具可以大规模构建菌株特异性代谢模型。在这里,我们描述了 Bactabolize,这是一种基于参考的工具,可快速生成菌株特异性代谢模型和生长表型预测。我们描述了一种针对优先抗微生物病原体的泛参考模型,以及一种使用草图基因组组装作为 Bactabolize 输入的质量控制框架。Bactabolize 衍生的 KPPR1 参考菌株模型在 507 种底物和 2317 种敲除突变体生长预测方面的表现与目前可用的自动方法 CarveMe 和 gapseq 相当或更好。通过我们系统定义的质量控制标准的新型草图基因组导致模型具有高度的完整性(与从匹配的完整基因组中得出的模型相比,捕获的基因和反应≥99%)和高精度(平均值为 0.97,n=10)。我们预计本文描述的工具和框架将促进大规模代谢建模分析,从而加深我们对细菌种内多样性的理解,并为优先病原体提供新的控制策略。