Kasmanas Jonas Coelho, Magnúsdóttir Stefanía, Zhang Junya, Smalla Kornelia, Schloter Michael, Stadler Peter F, de Leon Ferreira de Carvalho André Carlos Ponce, Rocha Ulisses
Department of Applied and Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany.
Institute of Mathematics and Computer Sciences, University of São Paulo, 13566-590 São Carlos, Brazil.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf072.
Comparative genomics, genetic spread analysis, and context-aware ranking are crucial in understanding microbial dynamics' impact on public health. gSpreadComp streamlines the path from in silico analysis to hypothesis generation. By integrating comparative genomics, genome annotation, normalization, plasmid-mediated gene transfer, and microbial resistance-virulence risk-ranking into a unified workflow, gSpreadComp facilitates hypothesis generation from complex microbial datasets.
The gSpreadComp workflow works through 6 modular steps: taxonomy assignment, genome quality estimation, antimicrobial resistance (AMR) gene annotation, plasmid/chromosome classification, virulence factor annotation, and downstream analysis. Our workflow calculates gene spread using normalized weighted average prevalence and ranks potential resistance-virulence risk by integrating microbial resistance, virulence, and plasmid transmissibility data and producing an HTML report. As a use case, we analyzed 3,566 metagenome-assembled genomes recovered from human gut microbiomes across diets. Our findings indicated consistent AMR across diets, with diet-specific resistance patterns, such as increased bacitracin in vegans and tetracycline in omnivores. Notably, ketogenic diets showed a slightly higher resistance-virulence rank, while vegan and vegetarian diets encompassed more plasmid-mediated gene transfer.
The gSpreadComp workflow aims to facilitate hypothesis generation for targeted experimental validations by the identification of concerning resistant hotspots in complex microbial datasets. Our study raises attention to a more thorough study of the critical role of diet in microbial community dynamics and the spread of AMR. This research underscores the importance of integrating genomic data into public health strategies to combat AMR. The gSpreadComp workflow is available at https://github.com/mdsufz/gSpreadComp/.
比较基因组学、基因传播分析和上下文感知排序对于理解微生物动态对公共卫生的影响至关重要。gSpreadComp简化了从计算机分析到假设生成的流程。通过将比较基因组学、基因组注释、归一化、质粒介导的基因转移以及微生物抗性 - 毒力风险排序整合到一个统一的工作流程中，gSpreadComp有助于从复杂的微生物数据集中生成假设。
gSpreadComp工作流程通过6个模块化步骤进行：分类学分配、基因组质量评估、抗菌抗性（AMR）基因注释、质粒/染色体分类、毒力因子注释和下游分析。我们的工作流程使用归一化加权平均流行率计算基因传播，并通过整合微生物抗性、毒力和质粒可传播性数据并生成HTML报告来对潜在的抗性 - 毒力风险进行排名。作为一个应用案例，我们分析了从不同饮食的人类肠道微生物群中回收的3566个宏基因组组装基因组。我们的研究结果表明不同饮食中的AMR具有一致性，同时存在特定饮食的抗性模式，例如纯素食者中杆菌肽增加，杂食者中四环素增加。值得注意的是，生酮饮食显示出略高的抗性 - 毒力排名，而纯素食和素食饮食中包含更多质粒介导的基因转移。
gSpreadComp工作流程旨在通过识别复杂微生物数据集中令人担忧的抗性热点，促进针对性实验验证的假设生成。我们的研究提请人们更加深入地研究饮食在微生物群落动态和AMR传播中的关键作用。这项研究强调了将基因组数据整合到公共卫生策略中以对抗AMR的重要性。gSpreadComp工作流程可在https://github.com/mdsufz/gSpreadComp/获取。
