Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic airways
Copyright © 2022. Published by Elsevier Inc..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:151 |
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| Enthalten in: |
The Journal of allergy and clinical immunology - 151(2023), 1 vom: 15. Jan., Seite 128-137 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Agache, Ioana [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarkers |
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| Anmerkungen: |
Date Completed 10.01.2023 Date Revised 03.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jaci.2022.06.028 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM346711045 |
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| 245 | 1 | 0 | |a Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic airways |
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| 500 | |a Date Revised 03.02.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022. Published by Elsevier Inc. | ||
| 520 | |a BACKGROUND: Unsupervised clustering of biomarkers derived from noninvasive samples such as nasal fluid is less evaluated as a tool for describing asthma endotypes | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Severe asthma | |
| 650 | 4 | |a T2 asthma | |
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| 650 | 4 | |a nasal proteomics | |
| 650 | 4 | |a trascriptome-associated cluster | |
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| 700 | 1 | |a Shamji, Mohamed H |e verfasserin |4 aut | |
| 700 | 1 | |a Kermani, Nazanin Zounemat |e verfasserin |4 aut | |
| 700 | 1 | |a Vecchi, Giulia |e verfasserin |4 aut | |
| 700 | 1 | |a Favaro, Alberto |e verfasserin |4 aut | |
| 700 | 1 | |a Layhadi, Janice A |e verfasserin |4 aut | |
| 700 | 1 | |a Heider, Anja |e verfasserin |4 aut | |
| 700 | 1 | |a Akbas, Didem Sanver |e verfasserin |4 aut | |
| 700 | 1 | |a Filipaviciute, Paulina |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Lily Y D |e verfasserin |4 aut | |
| 700 | 1 | |a Cojanu, Catalina |e verfasserin |4 aut | |
| 700 | 1 | |a Laculiceanu, Alexandru |e verfasserin |4 aut | |
| 700 | 1 | |a Akdis, Cezmi A |e verfasserin |4 aut | |
| 700 | 1 | |a Adcock, Ian M |e verfasserin |4 aut | |
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