Multi-strain phage induced clearance of bacterial infections

Area of Science:

• Microbiology
• Virology
• Computational Biology

Background:

• Antibiotic resistance is a growing threat, driving interest in phage therapy as an alternative treatment.
• Bacteria can evolve resistance to bacteriophages (phages), challenging the long-term efficacy of phage therapeutics.
• Phage cocktails, mixtures of multiple phages, are proposed to mitigate the emergence of phage-resistant bacterial mutants.

Purpose of the Study:

• To investigate the efficacy of phage cocktails against bacterial infections using theoretical and computational models.
• To analyze how phage cocktails perform compared to single-phage treatments, considering factors like bacterial resistance and host immunity.
• To explore the impact of phage traits and receptor complementarity on therapeutic outcomes in both immunocompetent and immunocompromised hosts.

Main Methods:

• Development and application of computational models simulating in vivo phage therapy.
• Utilizing a two-phage cocktail model inspired by Pseudomonas aeruginosa and phages LUZ19v and PAK_P1, which target different surface receptors.
• Performing Luria-Delbrück fluctuation tests to confirm the low probability of simultaneous resistance evolution.

Main Results:

• Phage cocktails targeting independent bacterial receptors enhance treatment outcomes, particularly in immunocompromised hosts.
• Cocktails significantly reduce the likelihood of bacteria evolving simultaneous resistance to multiple phages.
• Therapeutic efficacy is robust across variations in phage-bacteria interactions and host immune responses.

Conclusions:

• Phage cocktails offer a promising strategy to overcome bacterial resistance in phage therapy.
• The design of effective phage cocktails should consider viral life history traits and receptor complementarity.
• Computational modeling is a valuable tool for optimizing phage cocktail deployment in clinical settings.