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Abstract Background Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi‐omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development. Objectives To determine whether multi‐omics integration of sputum leads to improved granularity of the molecular classification of severe asthma. Methods We analyzed six ‐omics data blocks–microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing–from induced sputum samples of 57 severe asthma patients, 15 mild‐moderate asthma patients, and 13 healthy volunteers in the U‐BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi‐omics datasets of the 72 asthmatics. Results Five stable omics‐associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL‐22 cytokine activation, with the prediction of therapeutic response to anti‐IL22 antibody therapy. Conclusion Multi‐omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high‐dimensional interactions will contribute to a more comprehensive understanding of complex endotypes. Key Points Unsupervised clustering on sputum multi‐omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL‐22.

Background Idiopathic pulmonary fibrosis (IPF) is an irreversible disorder with a poor prognosis. The incomplete understanding of IPF pathogenesis and the lack of accurate animal models is limiting the development of effective treatments. Thus, the selection of clinically relevant animal models endowed with similarities with the human disease in terms of lung anatomy, cell biology, pathways involved and genetics is essential. The bleomycin (BLM) intratracheal murine model is the most commonly used preclinical assay to evaluate new potential therapies for IPF. Here, we present the findings derived from an integrated histomorphometric and transcriptomic analysis to investigate the development of lung fibrosis in a time-course study in a BLM rat model and to evaluate its translational value in relation to IPF.Methods Rats were intratracheally injected with a double dose of BLM (days 0–4) and sacrificed at days 7, 14, 21, 28 and 56. Histomorphometric analysis of lung fibrosis was performed on left lung sections. Transcriptome profiling by RNAseq was performed on the right lung lobes and results were compared with nine independent human gene-expression IPF studies.Results The histomorphometric and transcriptomic analyses provided a detailed overview in terms of temporal gene-expression regulation during the establishment and repair of the fibrotic lesions. Moreover, the transcriptomic analysis identified three clusters of differentially coregulated genes whose expression was modulated in a time-dependent manner in response to BLM. One of these clusters, centred on extracellular matrix (ECM)-related process, was significantly correlated with histological parameters and gene sets derived from human IPF studies.Conclusions The model of lung fibrosis presented in this study lends itself as a valuable tool for preclinical efficacy evaluation of new potential drug candidates. The main finding was the identification of a group of persistently dysregulated genes, mostly related to ECM homoeostasis, which are shared with human IPF.