Combination therapy delays antimicrobial resistance after adaptive laboratory evolution of Staphylococcus aureus.

Abstract

Antibiotic resistance, driven by misuse and overuse of antibiotics, is one of the greatest threats against human health. The antimicrobial pressure during prolonged antibiotic treatment of chronic bacterial infections selects for resistance. While antibiotic combinations may reduce resistance emergence, antibiotic-tolerant persister cells can serve as a reservoir for resistance development. Therefore, targeting these cells with anti-persister drugs might provide a novel strategy for resistance prevention. In this study, we conducted 42 days of adaptive laboratory evolution using Staphylococcus aureus exposed to rifampicin, ciprofloxacin, daptomycin, and vancomycin, alone or in combination with the anti-persister drug mitomycin C. We monitored antibiotic susceptibility daily and assessed phenotypic changes in growth and biofilm formation in evolved strains. Whole-genome sequencing revealed mutations linked to antibiotic resistance and phenotypic shifts. Rifampicin resistance developed within a few days, while ciprofloxacin and daptomycin emerged in approximately 3 weeks. Treatments with vancomycin or mitomycin C resulted in minimal changes in susceptibility. While combination therapy delayed resistance, it did not fully prevent it. Notably, the combination of rifampicin with mitomycin C maintained rifampicin susceptibility throughout the long-term evolution experiment. Sub-inhibitory antibiotic treatments selected for both previously characterized and novel mutations, including unprecedented alterations in the nucleotide excision repair system and azoreductase following mitomycin C exposure. The delayed resistance development observed with combination therapy, particularly mitomycin C's ability to suppress rifampicin resistance, suggests potential therapeutic applications. Future studies should evaluate the clinical efficacy of anti-persister drugs in preventing resistance across different bacterial pathogens and infection models.