Revealing the dynamics of polymicrobial infections: implications for antibiotic therapy

Abstract

As a new generation of culture-independent analytical strategies emerge, the amount of data on polymicrobial infections will increase dramatically. For these data to inform clinical thinking, and in turn to maximise benefits for patients, an appropriate framework for their interpretation is required. Here, we use cystic fibrosis (CF) lower airway infections as a model system to examine how conceptual and technological advances can address two clinical questions that are central to improved management of CF respiratory disease. Firstly, can markers of the microbial community be identified that predict a change in infection dynamics and clinical outcomes? Secondly, can these new strategies directly characterize the impact of antimicrobial therapies, allowing treatment efficacy to be both assessed and optimized?

Figure 1

Diagnostic strategies for acute, single-agent infections as compared to chronic acute infections. Abbreviation: Q-PCR, quantitative-PCR.

Figure 2

Current pathway for determination of CFPE treatment efficacy as compared to direct characterization based on molecular genetic analysis.

Figure 3

P. aeruginosa loads in sputum sample before and after IV antibiotics, with and without PMA treatment. Sputum aliquots collected before and after 14-day courses of IV antibiotics were either subjected to direct DNA extraction or were first treated with PMA to exclude non-viable cells. Determination of P. aeruginosa load was then performed by P. aeruginosa-specific RT-PCR. A significant decrease in load was only identified where PMA treatment was employed (P<0.05, paired t-test). Error bars represent SEM. Adapted from Ref. [51]. Abbreviation: cfu, colony-forming units.