Pathogenic interactions drive lung function decline.
Lower respiratory tract infections associated with cystic fibrosis have been the subject of a great deal of research. As such, they have become one of the paradigms for polymicrobial infections. The literature focuses, however understandably, on the presence of pathogens in isolation, or univariate measures like number of species, to predict decline of lung function and ignores large swathes of data. Here, we suggest that looking at the interactions between species identified by 16S rRNA gene sequencing, rather than at species singularly, will elucidate hitherto unknown properties of these complicated infections. To confirm our suspicions, pooled samples from studies conducted by our laboratory, sequenced using the same pipeline, assessed microbiome wide associations to lung function. Further, pathogenic interactions between species were found to be limited to the most abundant species, which composed of “known” pathogens (Pseudomonas, Staphylococcus, Achromobacter, and Stenotrophomonas) and commensals. This observation is crucial for the better understanding of polymicrobial infections and treatment of these conditions, whilst providing a simple framework for expanding this research into other disease states. The adoption of ecological principles into infection science can provide better understanding and options to those suffering from chronic conditions. The method presented here allows researchers to know the community they are dealing with, linked to a clinically relevant function output (lung function), to create clear hypotheses from observational data that can be ratified through statistical validation and future experimental studies.