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?
Culture-independent analysis of polymicrobial infectionThe assessment of bacterial infections has recently been facilitated by the use of cultureindependent tools. These strategies have led to fresh insights into the often complex and dynamic relationships between host and microbes. One field for which recent findings highlight the importance of defining these complex relationships is that of chronic polymicrobial infections: these respond very differently to antibiotic treatment than predicted by conventional models of infections [1][2][3][4][5][6]. Further, evidence is emerging that the manner by which antibiotics provide clinical benefit in chronic polymicrobial infections might differ from the traditionally accepted mechanisms [7]. Whilst presenting opportunities for the development of novel therapeutic strategies [8,9], these new findings also necessitate a fundamental re-evaluation of the way in which the impact of antimicrobial treatment is assessed in vivo.