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