Asthma is characterised by heterogeneous clinical phenotypes. Our objective was to determine molecular phenotypes of asthma by analysing sputum cell transcriptomics from 104 moderate-to-severe asthmatic subjects and 16 nonasthmatic subjects.After filtering on the differentially expressed genes between eosinophil- and noneosinophil-associated sputum inflammation, we used unbiased hierarchical clustering on 508 differentially expressed genes and gene set variation analysis of specific gene sets.We defined three transcriptome-associated clusters (TACs): TAC1 (characterised by immune receptors , and ), TAC2 (characterised by interferon-, tumour necrosis factor-α- and inflammasome-associated genes) and TAC3 (characterised by genes of metabolic pathways, ubiquitination and mitochondrial function). TAC1 showed the highest enrichment of gene signatures for interleukin-13/T-helper cell type 2 (Th2) and innate lymphoid cell type 2. TAC1 had the highest sputum eosinophilia and exhaled nitric oxide fraction, and was restricted to severe asthma with oral corticosteroid dependency, frequent exacerbations and severe airflow obstruction. TAC2 showed the highest sputum neutrophilia, serum C-reactive protein levels and prevalence of eczema. TAC3 had normal to moderately high sputum eosinophils and better preserved forced expiratory volume in 1 s. Gene-protein coexpression networks from TAC1 and TAC2 extended this molecular classification.We defined one Th2-high eosinophilic phenotype TAC1, and two non-Th2 phenotypes TAC2 and TAC3, characterised by inflammasome-associated and metabolic/mitochondrial pathways, respectively.

Background: Sputum analysis in asthma is used to define airway inflammatory processes and may guide therapy. Objective: To determine differential gene and protein expression in sputum samples from patients with severe asthma (SA) compared to mildmoderate non-smoking asthmatics (MMA). Methods: Induced sputum was obtained from non-smoking SA (SAn), smokers/ex-smokers with SA (SAsm), MMA and healthy non-smoking controls. Differential cell counts, microarray analysis of cell pellets and SOMAscan analysis of sputum analytes was performed. CRID3 was used to inhibit the inflammasome in a mouse model of severe asthma. Results: Eosinophilic and mixed neutrophilic/eosinophilic inflammation were more prevalent in SA compared to MMA. 42 genes probes were upregulated (>2-fold) in SAn compared to MMA including IL-1R family and NRLP3 inflammasome members (FDR<0.05). The inflammasome proteins NLRP1, NLRP3 and NLRC4 were associated with neutrophilic asthma and with sputum IL--13-induced Th2 signature and IL1RL1 mRNA expression. These differences were sputum-specific since no activation of NLRP3 or enrichment of IL-1R family genes in bronchial brushings or biopsies in SA was observed. Expression of NLRP3 and of the IL-1R family genes was validated in the Airway Disease Endotyping for Personalized Therapeutics (ADEPT) cohort. Inflammasome inhibition using CRID3 prevented airway hyperresponsiveness and airway inflammation (both neutrophilia and eosinophilia) in a mouse model of severe allergic asthma.Conclusion: IL1RL1 gene expression is associated with eosinophilic SA whilst NLRP3 inflammasome expression is highest in neutrophilic SA. Th2-driven eosinophilic inflammation and neutrophil-associated inflammasome activation may represent interacting pathways in SA.Imperial College of Science, Technology and Medicine We enclose a revised version of the above manscript entitled 'Sputum transcriptomics reveal upregulation of IL-1 receptor family members in severe asthma' by Rossios and collagues.We have responded to the Reviewer's comments in a point by point manner below and have incorporated the changes requested.We hope that the manuscript is now acceptable for publication. Responses to CommentsImperial College of Science, Technology and Medicine EDITOR'S SPECIFIC COMMENTS: Thank you for your thoughtful revision of this manuscript. However, I agree with Reviewer 2 in that adjusting for cell composition will allow you to determine whether your results are driven largely by differences in cellular composition or by true differences in gene expression. This will affect the interpretation of your results and provide important biological insight. Response: we have added this detail as detailed in response to Reviewers 1 and 2 below. COMMENTS FROM REVIEWER #1:The authors have addressed most of my original comments and have rewritten some sections of the manuscript to increase overall clarity. Response: We thank the Reviewer for their helpful comments which have improved the paper considerably.The one issue they did not address is...

RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity

Combining a EGFR TKI with BEV extended PFS and protected against brain metastasis. Those effects were probably due to the reduction of circulating S100A9-positive MDSCs by BEV, which leads to restoration of effective antitumor immunity. Our data also support the rationale for a BEV-immune checkpoint inhibitor combination.