Mycobiome-microbiome cross-talk analysis during acute pulmonary exacerbation: focus on climax-attack model in cystic fibrosis

Author:

Florian Lussac-Sorton 1, Sébastien Imbert 1, Perrine Sorret 2, Louise-Eva Vandenborght 3, Florence Francis-Oliviero 4, Raphael
Enaud 5, Marta Alvalos-Fernandez 2, Thierry Schaeverbeke 6, Patrick Berger 3, Michael Fayon 3, Rodolphe Thiebaut 2, Laurence
Delhaes* 1;7

Author address:

1 Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000, Univ. Bordeaux, Bordeaux, France; 2 Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France; 3 Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000, Bordeaux, France; 4
CHU Bordeaux, Department of Public Health, Bordeaux, France; 5 Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000, CRCM Pédiatrique, CHU de Bordeaux, Bordeaux, France; 6
CHU de Bordeaux, Univ. Bordeaux, FHU ACRONIM, Bordeaux, France; 7 Service de Parasitologie Mycologie, FHU ACRONIM, Bordeaux, France

Full conference title:

European Congress of Clinical Microbiology and Infectious Diseases 2020

Date: 24 June 2020

Abstract:

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: laurence.delhaes@gmail.com

Abstract Number: 2065

Link to conference website:

Link Conference abstract: 

ECCMID 2020

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