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

Author summaryClostridioides difficile is the causative agent in approximately 73% of healthcare-acquired gastrointestinal infections, resulting in a significant healthcare burden. Its toxins are crucial to virulence and play a key role in establishing a nutritional niche for C. difficile. Highly virulent strains with high toxin production can lead to worse outcomes for patients with C. difficile infection (CDI), such as progression to pseudomembranous colitis, toxic megacolon, and in some cases, death. Improving our understanding of how these toxins are regulated through their environment and intracellular metabolism could allow us to attenuate C. difficile virulence in infected patients. Therefore, we have compiled gene expression data of C. difficile grown in 16 different conditions to investigate how toxin production changes in response to the environment. We have integrated these data with a genome-scale metabolic model of C. difficile, allowing us to simulate the intracellular metabolism in high and low toxin producing states. Our network analysis of metabolism and toxin production predicts metabolic patterns in high and low toxin-producing states and provides insights into metabolic regulation of toxins. Additionally, our analysis highlights new proteins that could serve as anti-toxin targets. 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.