Endotypes of severe neutrophilic and eosinophilic asthma from multi‐omics integration of U‐BIOPRED sputum samples

Author:

Kermani Nazanin Zounemat12ORCID,Li Chuan‐Xing3,Versi Ali1,Badi Yusef1,Sun Kai2,Abdel‐Aziz Mahmoud I4,Bonatti Martina3,Maitland‐van der Zee Anke‐Hilse4,Djukanovic Ratko5,Wheelock Åsa36,Dahlen Sven‐Erik3,Howarth Peter5,Guo Yike2,Chung Kian Fan12ORCID,Adcock Ian M.12ORCID,

Affiliation:

1. National Heart and Lung Institute Imperial College London London UK

2. Data Science Institute Imperial College London London UK

3. Respiratory Medicine Unit Department of Medicine & Centre for Molecular Medicine Karolinska Institutet Stockholm Sweden

4. Department of Pulmonology Amsterdam UMC University of Amsterdam Amsterdam The Netherlands

5. NIHR Southampton Respiratory Biomedical Research Unit and Clinical and Experimental Sciences Southampton UK

6. Institute of Environmental Medicine Centre for Allergy Research Karolinska Institute Stockholm Sweden

Abstract

AbstractBackgroundClustering 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.ObjectivesTo determine whether multi‐omics integration of sputum leads to improved granularity of the molecular classification of severe asthma.MethodsWe 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.ResultsFive 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.ConclusionMulti‐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.

Funder

Innovative Medicines Initiative

Publisher

Wiley

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