Background: Lung infections play a critical role in cystic fibrosis (CF) pathogenesis, especially in CF pulmonary exacerbation (CFPE) and decreases in respiratory function (i.e., FEV1 decline). CF respiratory tract is now considered as a polymicrobial ecosystem that NGS allowed to analyze deeply in terms of mycobiome and microbiome. To the best of our knowledge, no data have been generated in regards of the mycobiome and the microbiome together during CFPE.
Materials/methods: Thirty-three sputa isolated from patients with and without CFPE underwent targeted metagenomics, based on analysis of bacterial and fungal rRNA regions (16S and ITS2 regions). Inter-kingdom network and adapted Phy-Lasso method were used to highlight correlations in compositional data. Given the limited number of samples, we chosen to apply a penalized regression method with bootstrap (bootstrap-enhanced Phy-Lasso) for examining associations between microbial genera and clinically relevant features (here CFPE and/or FEV1 decline).
Results: As previously described, the decline in respiratory function (FEV1) was associated with a decrease in bacterial diversity. The inter-kingdom network revealed three main clusters organized around Aspergillus, Candida, and Scedosporium genera. We confirmed by in vitro experimentations the cross-domain positive interactions between Aspergillus and Streptococcus predicted by the correlation network. We identified Aspergillus and Malassezia to be associated with CFPE. Scedosporium plus Pseudomonas were associated with a decline in FEV1. Collectively, our findings (inter-kingdom network analysis, in vitro co-culture results, and feature selection based on bootstrap-enhanced Phy-Lasso) pave the way for deciphering the role of fungi in CF lung disease at the ecological level by proposing a new version of the recently-described Climax-Attack Model (CAM).
Conclusions: Altogether, these results highlighted the complexity of the microbial community interacting within the CF respiratory tract, and suggested the suitability of developing ecological models such as CAM. For the first time, we included documented mycobiome data into CAM that opens new lines of thoughts about the physiopathology of CF lung disease and future perspectives to improve its therapeutic management.
Presenter email address: [email protected]
Full conference title:
- ECCMID 30th (2020)