Community composition shapes microbial-specific phenotypes in a cystic fibrosis polymicrobial model system.
Fabrice Jean-PierreThomas H HamptonDaniel SchultzDeborah A HoganMarie-Christine GroleauEric DézielGeorge A O'ToolePublished in: eLife (2023)
Interspecies interactions can drive the emergence of unexpected microbial phenotypes that are not observed when studying monocultures. The cystic fibrosis (CF) lung consists of a complex environment where microbes, living as polymicrobial biofilm-like communities, are associated with negative clinical outcomes for persons with CF (pwCF). However, the current lack of in vitro models integrating the microbial diversity observed in the CF airway hampers our understanding of why polymicrobial communities are recalcitrant to therapy in this disease. Here, integrating computational approaches informed by clinical data, we built a mixed community of clinical relevance to the CF lung composed of Pseudomonas aeruginosa , Staphylococcus aureus , Streptococcus sanguinis , and Prevotella melaninogenica . We developed and validated this model biofilm community with multiple isolates of these four genera. When challenged with tobramycin, a front-line antimicrobial used to treat pwCF, the microorganisms in the polymicrobial community show altered sensitivity to this antibiotic compared to monospecies biofilms. We observed that wild-type P. aeruginosa is sensitized to tobramycin in a mixed community versus monoculture, and this observation holds across a range of community relative abundances. We also report that LasR loss-of-function, a variant frequently detected in the CF airway, drives tolerance of P. aeruginosa to tobramycin specifically in the mixed community. Our data suggest that the molecular basis of this community-specific recalcitrance to tobramycin for the P. aeruginosa lasR mutant is increased production of phenazines. Our work supports the importance of studying a clinically relevant model of polymicrobial biofilms to understand community-specific traits relevant to infections.
Keyphrases
- cystic fibrosis
- pseudomonas aeruginosa
- mental health
- staphylococcus aureus
- healthcare
- biofilm formation
- microbial community
- lung function
- stem cells
- dna methylation
- electronic health record
- gene expression
- genome wide
- machine learning
- cell therapy
- escherichia coli
- acinetobacter baumannii
- bone marrow
- data analysis
- replacement therapy