Strategies for improving the efficacy of combinatorial antibiotic therapy in chronic infections

项目总结/文摘

Project Summary/Abstract The spread of antibiotic resistance is a growing concern as the emergence of resistance mechanisms among human pathogens is occurring more rapidly than the development of new antimicrobial agents. This issue contributes to the inability to fully clear persistent infections such as chronic wound and lung infections, which represent a major source of human morbidity and mortality. In turn, the inability to eradicate these persistent infections creates more opportunities for the evolution of novel microbial mechanisms to circumvent therapeutic treatment, exacerbating the problem of antibiotic resistance. There are multiple aspects of the chronic infection environment that contribute to therapeutic failure and the emergence of antibiotic resistance. First, several stressors encountered at the host-pathogen interface are mutagenic, which helps drive evolutionary adaptation in these sites. Second, the polymicrobial nature of many chronic infections can contribute to the spread of resistance mechanisms via horizontal gene transfer. The presence of polymicrobial communities can also further compound the issue of therapeutic clearance of infection since interspecies microbial interactions are known to alter bacterial physiological and lead to antimicrobial tolerance. In this proposal, we seek to target both the microbial evolutionary trajectory at the host-pathogen interface and the polymicrobial nature of chronic infections to design improved therapeutic strategies for eradication of pathogens contributing to otherwise persistent infections. In Aim 1, we propose to target antibiotic resistant isolates through the identification of vulnerability tradeoffs that can occur as the cell shifts its fundamental physiology to cope with antibiotic exposure. In addition to published examples of this phenomenon, we demonstrate our ability to uncover novel examples of tradeoffs that can be exploited to eradicate otherwise recalcitrant microorganisms. We seek to uncover more examples of vulnerability tradeoffs and determine the effectiveness of targeting these tradeoffs in a murine model of chronic wound infection. In Aim 2, we establish polymicrobial community wound pathogen models and use a methodology that we propose can be adapted for use in the clinical laboratory to demonstrate shifts in antibiotic efficacy driven by polymicrobial interactions. We demonstrate that both polymicrobial synergism (a reduction in antibiotic efficacy in complex bacterial communities) and polymicrobial antagonism (an increase in antibiotic efficacy in the context of a polymicrobial consortium) can be readily observed. Preliminary data suggest that combinatorial treatment strategies can be developed to exploit polymicrobial antagonism to overcome synergistic interactions. We propose to validate this strategy in a murine model of chronic wound infection. Together, these Aims will be used to identify antibiotic treatment strategies that will extend the efficacy of the currently available repertoire of antibiotics.