Lung microbiota analyses in asthma mouse model

Background: Asthma is a frequent chronic inflammatory bronchial disease affecting more than 300 million patients worldwide, 70% of whom secondary to allergy. The diversity of asthmatic endotypes contributes to their complexity. Many factors, such as the environment, allergen sensitization pathways associate with microbiota, influence asthma natural course and explain its phenotypic heterogeneity. Here, we compared a mouse model of house dust mite (HDM)-induced allergic asthma sensitized via various routes for the clinical features of asthma, immune responses, the lung barrier and dysbiosis. Method: Mice were sensitized HDM by oral, nasal or percutaneous routes. Lung function, barrier integrity, immune response and microbiota composition were analyzed. Results: Severe impairment of respiratory function was observed in the mice sensitized by the nasal and cutaneous paths. It was associated with epithelial dysfunction characterized by an increased permeability secondary to junction protein disruption. Conversely, such sensitization paths induced a mixed eosinophilic and neutrophilic inflammatory response with high IL-17 airways secretion. In contrast, oral sensitized mice showed a mild impairment of respiratory function. Epithelial dysfunction was lighter with increased mucus production but conserved epithelial junctions. Lung Th2 and eosinophilic inflammation were observed. Considering lung microbiota, sensitization provoked a significant loss diversity. At the genus level, Cutibacterium, Acinetobacter, Streptococcus and Lactobacillus were found to be modulated according to the sensitization pathway. An increase in anti-inflammatory microbiota metabolites was observed in the oral group. Conclusion: Our study highlights the strong involvement of the sensitization route in allergic asthma physiopathology and the critical phenotypic diversity in a mouse model.Reads were processed with microSysMics (https://bio.tools/microSysMics), a workflow built around the QIIME2 toolbox, chaining softwares in order to automatize metabarcoding analysis.Funding provided by: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100022077Award Number:Analysis of microbiota composition. Bacterial DNA was extracted from BAL pellets according to the manufacturer's instructions (Macherey Nagel, genomic DNA from tissue, Nucleospin Tissue). The microbiota composition was analyzed by 16S sequencing with permission from Biofortis (Saint-Herblain, France). Raw sequencing data were obtained from a single Illumina MiSeq run as 250 bp paired-end reads targeting the V3-V4 region (Primers : Bakt_341F 5′-CCTACGGGNGGCWGCAG-3′,Bakt_805R 5′ GACTACHVGGGTATCTAATCC-3) of the 16S rDNA gene. Reads were processed with microSysMics (https://bio.tools/microSysMics), a workflow built around the QIIME2 toolbox, chaining softwares in order to automatize metabarcoding analysis. PCR primers and remaining Illumina adapters were removed with Cutadapt. ASVs inference and count estimation were performed with dada2 using a trimming length of 220 and default parameters. We used a Naive Bayes Classifier pre-trained on the SILVA 99% reference database (Release 138) to assign ASVs to taxa