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Associations between fungal and bacterial microbiota of airways and asthma endotypes

      Background

      The relationship between asthma, atopy, and underlying type 2 (T2) airway inflammation is complex. Although the bacterial airway microbiota is known to differ in asthmatic patients, the fungal and bacterial markers that discriminate T2-high (eosinophilic) and T2-low (neutrophilic/mixed-inflammation) asthma and atopy are still incompletely identified.

      Objectives

      The aim of this study was to demonstrate the fungal microbiota structure of airways in asthmatic patients associated with T2 inflammation, atopy, and key clinical parameters.

      Methods

      We collected endobronchial brush (EB) and bronchoalveolar lavage (BAL) samples from 39 asthmatic patients and 19 healthy subjects followed by 16S gene and internal transcribed spacer–based microbiota sequencing. The microbial sequences were classified into exact sequence variants. The T2 phenotype was defined by using a blood eosinophil count with a threshold of 300 cells/μL.

      Results

      Fungal diversity was significantly lower in EB samples from patients with T2-high compared with T2-low inflammation; key fungal genera enriched in patients with T2-high inflammation included Trichoderma species, whereas Penicillium species was enriched in patients with atopy. In BAL fluid samples the dominant genera were Cladosporium, Fusarium, Aspergillus, and Alternaria. Using generalized linear models, we identified significant associations between specific fungal exact sequence variants and FEV1, fraction of exhaled nitric oxide values, BAL fluid cell counts, and corticosteroid use. Investigation of interkingdom (bacterial-fungal) co-occurrence patterns revealed different topologies between asthmatic patients and healthy control subjects. Random forest models with fungal classifiers predicted asthma status with 75% accuracy for BAL fluid samples and 80% accuracy for EB samples.

      Conclusions

      We demonstrate clear differences in bacterial and fungal microbiota in asthma-associated phenotypes. Our study provides additional support for considering microbial signatures in delineating asthma phenotypes.

      Graphical abstract

      Key words

      Abbreviations used:

      AA (Asthmatic patient with atopy), ABC (ATP-binding cassette), ANA (Asthmatic patient without atopy), ANCOM (Analysis of composition of microbiomes), BMI (Body mass index), CA (Control subject with atopy), CNA (Control subject without atopy), BAL (Bronchoalveolar lavage), BH-FDR (Benjamini-Hochberg false discovery rate), EB (Endobronchial brush), ESV (Exact sequence variant), Feno (Fraction of exhaled nitric oxide), GLM (Generalized linear regression model), ICS (Inhaled corticosteroid), ITS (Internal transcribed spacer), NMDS (Nonmetric multidimensional scaling), OCS (Oral corticosteroid), OOB (Out-Of-Bag), PERMANOVA (Permutational multivariate analysis of variance), PICRUSt (Phylogenetic Reconstruction of Unobserved States), T2 (Type 2), WGCNA (Weighted correlation network analysis)
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