Bacterial interactions underpin worsening lung function in cystic fibrosis-associated infections.

Chronic lung infections are the primary cause of morbidity and early mortality in cystic fibrosis (CF) and, as such, have been the subject of a great deal of research. Subsequently, they have become one of the key paradigms for polymicrobial infections. The literature, however, has traditionally focused 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, could elucidate hitherto unknown properties of these complicated infections. To confirm this, pooled samples from studies conducted by our laboratory, sequenced using the same pipeline, were used to assess microbiome-wide associations to lung function. We found pathogenic interactions between species were limited to the most abundant species, which were composed of canonical CF pathogens (including IMPORTANCE: Research studies have repeatedly demonstrated that chronic lung infection in cystic fibrosis is polymicrobial and consequently does not adhere to the single microbe-based Koch's postulates. Despite the plethora of evidence, the role of the constituent taxa present is largely unknown. Here we demonstrate how an ecological modeling perspective on lung infection microbiota can tease out potential interactions that alter progression of disease. Using techniques akin to genome-wide association studies, we show and validate 22 taxa, present in the chronic respiratory disease associated with cystic fibrosis, which have significant interactions that are negatively associated with patient lung function, the majority of which are "non-pathogenic" organisms. This work highlights the need to understand the interactive landscapes of the microbiomes to fully appreciate the complexity and treat chronic lung infections. Furthermore, this presents testable hypotheses for manipulative experiments in model systems to elucidate key mechanisms to driving disease progression.