Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.
NPJ Syst Biol Appl. 2020 Oct 20;6(1):31. doi: 10.1038/s41540-020-00151-9.
Hospital acquired Clostridioides (Clostridium) difficile infection is exacerbated by the continued evolution of C. difficile strains, a phenomenon studied by multiple laboratories using stock cultures specific to each laboratory. Intralaboratory evolution of strains contributes to interlaboratory variation in experimental results adding to the challenges of scientific rigor and reproducibility. To explore how microevolution of C. difficile within laboratories influences the metabolic capacity of an organism, three different laboratory stock isolates of the C. difficile 630 reference strain were whole-genome sequenced and profiled in over 180 nutrient environments using phenotypic microarrays. The results identified differences in growth dynamics for 32 carbon sources including trehalose, fructose, and mannose. An updated genome-scale model for C. difficile 630 was constructed and used to contextualize the 28 unique mutations observed between the stock cultures. The integration of phenotypic screens with model predictions identified pathways enabling catabolism of ethanolamine, salicin, arbutin, and N-acetyl-galactosamine that differentiated individual C. difficile 630 laboratory isolates. The reconstruction was used as a framework to analyze the core-genome of 415 publicly available C. difficile genomes and identify areas of metabolism prone to evolution within the species. Genes encoding enzymes and transporters involved in starch metabolism and iron acquisition were more variable while C. difficile distinct metabolic functions like Stickland fermentation were more consistent. A substitution in the trehalose PTS system was identified with potential implications in strain virulence. Thus, pairing genome-scale models with large-scale physiological and genomic data enables a mechanistic framework for studying the evolution of pathogens within microenvironments and will lead to predictive modeling to combat pathogen emergence.
医院获得性艰难梭菌（梭状芽孢杆菌）感染因艰难梭菌菌株的持续进化而加剧，这一现象已被多个实验室使用针对每个实验室的库存培养物进行研究。菌株的实验室内进化导致实验结果的实验室间差异，增加了科学严谨性和可重复性的挑战。为了探索实验室内部艰难梭菌的微观进化如何影响生物体的代谢能力，对三种不同的艰难梭菌 630 参考菌株的实验室库存分离株进行了全基因组测序，并使用表型微阵列在 180 多种营养环境中进行了分析。结果确定了在 32 种碳源（包括海藻糖、果糖和甘露糖）的生长动态方面的差异。构建了一个更新的艰难梭菌 630 基因组规模模型，并用于将在库存培养物之间观察到的 28 个独特突变进行背景化处理。表型筛选与模型预测的结合，确定了使乙醇胺、水杨苷、熊果苷和 N-乙酰半乳糖胺分解代谢的途径，这些途径可区分单个艰难梭菌 630 实验室分离株。该重建被用作分析 415 个公开可用的艰难梭菌基因组的核心基因组的框架，并确定了该物种中易发生进化的代谢途径。参与淀粉代谢和铁获取的酶和转运蛋白编码基因更具变异性，而艰难梭菌独特的代谢功能，如 Stickland 发酵则更为一致。鉴定出海藻糖 PTS 系统中的一个替代物，可能对菌株的毒力有潜在影响。因此，将基因组规模模型与大规模生理和基因组数据相结合，为在微环境中研究病原体进化提供了一个机制框架，并将导致针对病原体出现的预测建模。
