Systems biology analysis of theClostridioides difficilecore-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence

Hospital acquiredClostridioides(Clostridium)difficileinfection is exacerbated by the continued evolution ofC. difficilestrains, 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 ofC. difficilewithin laboratories influences the metabolic capacity of an organism, three different laboratory stock isolates of theC. difficile630 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 forC. difficile630 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 individualC. difficile630 laboratory isolates. The reconstruction was used as a framework to analyze the core-genome of 415 publicly availableC. difficilegenomes 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 whileC. difficiledistinct 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.