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

Though Clostridioides difficile is among the most studied anaerobes, we know little about the systems level interplay of metabolism and regulation that underlies its ability to negotiate complex immune and commensal interactions while colonizing the human gut. We have compiled publicly available resources, generated through decades of work by the research community, into two models and a portal to support comprehensive systems analysis of C. difficile. First, by compiling a compendium of 148 transcriptomes from 11 studies we have generated an Environment and Gene Regulatory Influence Network (EGRIN) model that organizes 90% of all genes in the C. difficile genome into 297 high quality modules based on evidence for their conditional co-regulation by at least 120 transcription factors. EGRIN predictions, validated with independently-generated datasets, have recapitulated previously characterized C. difficile regulons of key transcriptional regulators, refined and extended membership of genes within regulons, and implicated new genes for sporulation, carbohydrate transport and metabolism. Findings further predict pathogen behaviors in in vivo colonization, and interactions with beneficial and detrimental commensals. Second, by advancing a constraints-based metabolic model, we have discovered that 15 amino acids, diverse carbohydrates, and 24 genes across glyoxylate, Wood-Ljungdahl, nucleotide, amino acid, and carbohydrate metabolism are essential to support growth of C. difficile within an intestinal environment. Models and supporting resources are accessible through an interactive web portal (http://networks.systemsbiology.net/cdiff-portal/) to support collaborative systems analyses of C. difficile.