Imperial College London
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ProfessorMohamedShamji

Faculty of Medicine, National Heart & Lung Institute

Professor of Immunology and Allergy
 
 
 
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Contact

 

+44 (0)20 7594 3476m.shamji99 Website

 
 
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Location

 

Room 111Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Agache:2023:10.1016/j.jaci.2022.06.028,
author = {Agache, I and Shamji, MH and Kermani, NZ and Vecchi, G and Favaro, A and Layhadi, JA and Heider, A and Akbas, DS and Filipaviciute, P and Wu, LYD and Cojanu, C and Laculiceanu, A and Akdis, CA and Adcock, IM},
doi = {10.1016/j.jaci.2022.06.028},
journal = {Journal of Allergy and Clinical Immunology},
pages = {128--137},
title = {Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic airways},
url = {http://dx.doi.org/10.1016/j.jaci.2022.06.028},
volume = {151},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: Unsupervised clustering of biomarkers derived from non-invasive samples such as nasal fluid is less evaluated as a tool for describing asthma endotypes. OBJECTIVE: To evauate whether protein expression in nasal fluid would identify distinct clusters of asthmatics with specific lower airway molecular phenotypes. METHODS: Unsupervised clustering of 168 nasal inflammatory and immune proteins and Shapley values was used to stratify 43 severe asthmatic patients (ENDANA) using a two 'modelling blocks' machine learning (ML) approach. This algorithm was also applied to nasal brushings transcriptomics from U-BIOPRED. Feature reduction and functional gene analysis were used to compare proteomic and transcriptomic clusters. Gene set variation analysis (GSVA) provided enrichment scores (ESs) of the ENDANA protein signature within U-BIOPRED sputum and blood. RESULTS: The nasal protein ML model identified two 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 4/18 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, JAK-STAT, TLR and infection activation. GSVA 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, non-invasive test for non-T2 asthma that is currently unavailable.
AU  - Agache,I
AU  - Shamji,MH
AU  - Kermani,NZ
AU  - Vecchi,G
AU  - Favaro,A
AU  - Layhadi,JA
AU  - Heider,A
AU  - Akbas,DS
AU  - Filipaviciute,P
AU  - Wu,LYD
AU  - Cojanu,C
AU  - Laculiceanu,A
AU  - Akdis,CA
AU  - Adcock,IM
DO  - 10.1016/j.jaci.2022.06.028
EP  - 137
PY  - 2023///
SN  - 0091-6749
SP  - 128
TI  - Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic airways
T2  - Journal of Allergy and Clinical Immunology
UR  - http://dx.doi.org/10.1016/j.jaci.2022.06.028
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36154846
UR  - https://www.sciencedirect.com/science/article/pii/S0091674922012234?via%3Dihub
UR  - http://hdl.handle.net/10044/1/100056
VL  - 151
ER  -
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