Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Minimal Cut Sets (MCSs) are crucial for understanding metabolic network vulnerabilities.
• Traditional methods for finding MCSs in genome-scale metabolic models (GSMMs) are computationally intensive, especially for large networks.
• Identifying MCSs can reveal gene, mRNA, or enzyme targets for disrupting cellular functions.

Purpose of the Study:

• To develop and evaluate a novel computational tool, aspefm, for efficient MCS computation in GSMMs.
• To apply the aspefm tool to a medically relevant bacterial consortium model.
• To identify potential therapeutic targets for microbial consortia by analyzing MCSs and interspecies metabolite exchange.

Main Methods:

• Utilized the link between MCSs and Elementary Flux Modes (EFMs) within a logic programming framework.
• Developed the aspefm tool for computing MCSs of any size from GSMMs.
• Applied aspefm to a consortium model of Staphylococcus aureus and Pseudomonas aeruginosa, incorporating constraints for metabolite exchange.

Main Results:

• The aspefm tool demonstrated superior performance in computing large-sized MCSs compared to mixed-integer linear programming methods.
• Identified interspecies metabolite exchanges, such as inosine, as critical for rescuing individual species' growth.
• Discovered that specific enzyme targets can be identified for therapeutic applications that are robust against interspecies metabolite rescue.

Conclusions:

• The aspefm tool offers an efficient approach for MCS identification in GSMMs.
• Interspecies metabolite exchange significantly impacts the robustness of microbial consortium functions.
• The study provides a list of promising enzyme targets for developing novel antimicrobial strategies against bacterial consortia.