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

Area of Science:

• Microbiology
• Systems Biology
• Computational Biology

Background:

• Clostridioides difficile is a major cause of healthcare-associated infections, leading to pseudomembranous colitis.
• Understanding C. difficile's complex regulatory and metabolic networks is crucial for developing effective treatments.

Purpose of the Study:

• To develop predictive models for comprehensive systems analysis of C. difficile.
• To identify key genes, regulatory mechanisms, and metabolic requirements essential for C. difficile growth in the intestinal environment.

Main Methods:

• Leveraged 151 published transcriptomes to generate an EGRIN (Environmental Gene Regulatory Influence Network) model.
• Advanced a metabolic model by adding and curating metabolic reactions, including nutrient uptake.
• Developed a PRIME (Predictive Regulatory Interactions in Metabolic Environments) model to link gene regulation with metabolism.

Main Results:

• Organized 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules.
• Identified 14 essential amino acids, diverse carbohydrates, and 10 metabolic genes for C. difficile intestinal growth.
• Uncovered how transcription factors like CcpA and CodY modulate essential metabolic processes for C. difficile growth.

Conclusions:

• The developed models provide a systems-level understanding of C. difficile virulence.
• Identified critical metabolic and regulatory targets for potential therapeutic strategies.
• An interactive web portal offers access to these resources for collaborative research.