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

Rivett, Damian W ORCID: https://orcid.org/0000-0002-1852-6137, Hatfield, Lauren R ORCID: https://orcid.org/0000-0002-7513-3014, Gavillet, Helen, Hardman, Michelle and van der Gast, Christopher ORCID: https://orcid.org/0000-0003-1101-4048 (2025) Bacterial interactions underpin worsening lung function in cystic fibrosis-associated infections. mBio, 16 (1). e0145624. ISSN 2150-7511

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Abstract

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 Pseudomonas, Staphylococcus, Stenotrophomonas, and Achromobacter) and commensals. This observation is crucial for better understanding of polymicrobial infections and treatment of these conditions while 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 statistical ecology approach presented here enables clear hypotheses from observational data that can be ratified through subsequent manipulative experimental studies. Moreover, it can also be used to support the design and construction of clinically relevant in vitro models of polymicrobial infections.