Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies

Abstract

Clostridioides difficile pathogenesis is mediated through its two toxin proteins, TcdA and TcdB, which induce intestinal epithelial cell death and inflammation. It is possible to alter C. difficile toxin production by changing various metabolite concentrations within the extracellular environment. However, it is unknown which intracellular metabolic pathways are involved and how they regulate toxin production. To investigate the response of intracellular metabolic pathways to diverse nutritional environments and toxin production states, we use previously published genome-scale metabolic models of C. difficile strains CD630 and CDR20291 (iCdG709 and iCdR703). We integrated publicly available transcriptomic data with the models using the RIPTiDe algorithm to create 16 unique contextualized C. difficile models representing a range of nutritional environments and toxin states. We used Random Forest with flux sampling and shadow pricing analyses to identify metabolic patterns correlated with toxin states and environment. Specifically, we found that arginine and ornithine uptake is particularly active in low toxin states. Additionally, uptake of arginine and ornithine is highly dependent on intracellular fatty acid and large polymer metabolite pools. We also applied the metabolic transformation algorithm (MTA) to identify model perturbations that shift metabolism from a high toxin state to a low toxin state. This analysis expands our understanding of toxin production in C. difficile and identifies metabolic dependencies that could be leveraged to mitigate disease severity.

Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes

“…S3a ). These imply that toxin production in communities is likely impacted by regulatory networks and other cellular processes 71–73 that are shaped by gut microbiota inter-species interactions.…”

“…In addition, strain-level variability played a larger role in toxin production in communities than inferred gLV growth-mediated inter-species interactions. Since toxin production is tightly linked with metabolism 73,86 , genotypic variations among C. difficile strains would impact their toxin production profiles. The lack of an informative relationship between growth-mediated inter-species interactions and toxin production suggests that inhibiting C. difficile growth may not always protect against CDI unless C. difficile is excluded from the community.…”

“…S3a). These imply that toxin production in communities is likely impacted by regulatory networks and other cellular processes [71][72][73] that are shaped by gut microbiota inter-species interactions. Some stresses including nutrient limitations have been reported to induce C. difficile toxin production 72,74 .…”

Clostridioides difficile-mucus interactions encompass shifts in gene expression, metabolism, and biofilm formation

“…To assess the metabolic potential of C. difficile during growth in mucus, we used RIPTiDe to contextualize an established genome-scale metabolic network reconstruction (GENRE) for C. difficile R20291 with our transcriptomic data [49][50][51] . To recapitulate conditions from the RNA-Seq experiment, we allowed the model to use mucin-derived monosaccharides for conditions with mucus and excluded them from the no-mucus condition.…”

“…We contextualized a published C. difficile R20291 genome-scale metabolic network reconstruction (GENRE) for conditions with and without mucus as described [49][50][51] , using transcript per million values from the RNA-Seq experiment and the maxfit_contextualize() function in RIPTiDe with default settings. The model was constrained to fit minimal media conditions used (CDMM with or without mucus) 49 . Bray-Curtis dissimilarity, nonmetric multidimensional scaling, and PERMANOVA statistical testing were performed using the vegan R package (v.2.6-4) 50 .…”