Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic

被引:9
|
作者
Agache, Ioana [1 ,2 ]
Shamji, Mohamed H. [3 ,4 ]
Kermani, Nazanin Zounemat [3 ,5 ]
Vecchi, Giulia [6 ]
Favaro, Alberto [6 ]
Layhadi, Janice A. [3 ,4 ]
Heider, Anja [7 ]
Akbas, Didem Sanver [3 ,4 ]
Filipaviciute, Paulina [3 ,4 ]
Wu, Lily Y. D. [3 ,4 ]
Cojanu, Catalina [1 ,2 ]
Laculiceanu, Alexandru [1 ,2 ]
Akdis, Cezmi A. [7 ,8 ]
Adcock, Ian M. [3 ,4 ]
机构
[1] Transylvania Univ, Fac Med, 2A Pictor Ion Andreescu, Brasov 500051, Romania
[2] Theramed Healthcare, Brasov, Romania
[3] Imperial Coll London, Natl Heart & Lung Inst, Sir Alexander Fleming Bldg, London SW7 2AZ, England
[4] NIHR Biomed Res Ctr, London, England
[5] Imperial Coll London, Data Sci Inst, London, England
[6] Fac Sci Ltd, London, England
[7] Christine Kuhne Ctr Allergy Res & Educ, Davos, Switzerland
[8] Univ Zurich, Swiss Inst Allergy & Asthma Res SIAF, Davos, Switzerland
基金
英国医学研究理事会; 英国工程与自然科学研究理事会; 英国惠康基金;
关键词
Severe asthma; T2; asthma; biomarkers; nasal prote-omics; machine learning; trascriptome-associated cluster; endotypes;
D O I
10.1016/j.jaci.2022.06.028
中图分类号
R392 [医学免疫学];
学科分类号
100102 ;
摘要
Background: Unsupervised clustering of biomarkers derived from noninvasive samples such as nasal fluid is less evaluated as a tool for describing asthma endotypes. Objective: We sought to evaluate whether protein expression in nasal fluid would identify distinct clusters of patients with asthma with specific lower airway molecular phenotypes. Methods: Unsupervised clustering of 168 nasal inflammatory and immune proteins and Shapley values was used to stratify 43 patients with severe asthma (endotype of noneosinophilic asthma) using a 2 "modeling blocks"machine learning approach. This algorithm was also applied to nasal brushings transcriptomics from U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes). Feature reduction and functional gene analysis were used to compare proteomic and transcriptomic clusters. Gene set variation analysis provided enrichment scores of the endotype of noneosinophilic asthma protein signature within U-BIOPRED sputum and blood. Results: The nasal protein machine learning model identified 2 severe asthma endotypes, which were replicated in U-BIOPRED nasal transcriptomics. Cluster 1 patients had significant airway obstruction, small airways disease, air trapping, decreased diffusing capacity, and increased oxidative stress, although only 4 of 18 were current smokers. Shapley identified 20 cluster defining proteins. Forty-one proteins were significantly higher in cluster 1. Pathways associated with proteomic and transcriptomic clusters were linked to TH1, TH2, neutrophil, Janus kinase-signal transducer and activator of transcription, TLR, and infection activation. Gene set variation analysis of the nasal protein and gene signatures were enriched in subjects with sputum neutrophilic/mixed granulocytic asthma and in subjects with a molecular phenotype found in sputum neutrophil-high subjects. Conclusions: Protein or gene analysis may indicate molecular phenotypes within the asthmatic lower airway and provide a simple, noninvasive test for non-type 2 immune response asthma that is currently unavailable. (J Allergy Clin Immunol 2023;151:128-37.)
引用
收藏
页码:128 / 137
页数:10
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