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.

我们提出了对艰难梭菌（伪膜性结肠炎的病因）进行全面系统分析的预测模型。通过利用 151 个已发表的转录组，我们生成了一个 EGRIN 模型，该模型将 90% 的艰难梭菌基因组织成由 297 个共同调控模块组成的转录调控网络，涉及孢子形成、碳水化合物转运和代谢中的基因。通过添加和管理代谢反应（包括营养吸收）来推进代谢模型，我们发现了 14 种氨基酸、多种碳水化合物和 10 个代谢基因对于艰难梭菌在肠道环境中的生长至关重要。最后，我们开发了一个 PRIME 模型来揭示 EGRIN 推断的转录因子（例如 CcpA 和 CodY）的组合基因调控如何调节基本代谢过程，以使艰难梭菌相对于共生定殖生长。艰难梭菌交互式门户网站提供对这些模型资源的访问，以支持艰难梭菌特定环境毒力机制的协作系统级研究。