Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile. [Display omitted] •The EGRIN model uncovers regulatory responses of C. difficile in multiple contexts•Genes and pathways needed to support C. difficile growth in vivo are identified•PRIME predicts in vivo synergistic epistasis between transcription factor networks•The C. difficile portal makes all tools and resources available to the public C. difficile is one of the leading causes of hospital-acquired infections. Arrieta-Ortiz et al. report three predictive models (with extensive validations) for dissecting interplay of regulation and metabolism that underlies host-pathogen interactions of C. difficile. The C. difficile interactive web portal provides access to these models, compiled datasets, and algorithms.