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Stratification of asthma phenotypes by airway proteomic signatures
Ctr Prote Res, Biol Sci, Southampton, Hants, England;NIHR Southampton Biomed Res Ctr, Clin & Expt Sci, Fac Med, Southampton, Hants, England;Univ Southampton, Inst Life Sci, Ctr Prote Res, Southampton, Hants, England.
Ctr Prote Res, Biol Sci, Southampton, Hants, England;NIHR Southampton Biomed Res Ctr, Clin & Expt Sci, Fac Med, Southampton, Hants, England;Univ Southampton, Ctr Prote Res, Inst Life Sci, Southampton, Hants, England.
NIHR Southampton Biomed Res Ctr, Clin & Expt Sci, Fac Med, Southampton, Hants, England;Univ Southampton, Southampton, Hants, England.
Ctr Prote Res, Biol Sci, Southampton, Hants, England;Univ Southampton, Math Sci, Southampton, Hants, England.
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2019 (English)In: Journal of Allergy and Clinical Immunology, ISSN 0091-6749, E-ISSN 1097-6825, Vol. 144, no 1, p. 70-82Article in journal (Refereed) Published
Abstract [en]

Background: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. Objective: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. Methods: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. Results: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. Conclusion: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 144, no 1, p. 70-82
Keywords [en]
Asthma, proteomics, biomarkers, eosinophils, neutrophils
National Category
Respiratory Medicine and Allergy
Identifiers
URN: urn:nbn:se:uu:diva-390913DOI: 10.1016/j.jaci.2019.03.013ISI: 000473432800011PubMedID: 30928653OAI: oai:DiVA.org:uu-390913DiVA, id: diva2:1343282
Funder
EU, FP7, Seventh Framework Programme, FP7/2007-2013Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2019-08-16Bibliographically approved

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