Transcriptome-based identification of novel endotypes in adult atopic dermatitis

Alain Lefevre-Utile; Melissa Saichi; Peter Olah; Marc Delord; Bernhard Homey; Vassili Soumelis

doi:10.1111/all.15150

Transcriptome-based identification of novel endotypes in adult atopic dermatitis

Alain Lefevre-Utile, Melissa Saichi, Peter Olah, Marc Delord, Bernhard Homey, Vassili Soumelis

Population Health Sciences

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

Background: Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking. Methods: To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies. Results: Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p =.02) and S. aureus (SA) colonization (p =.02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort. Conclusion: Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.

Original language	English
Pages (from-to)	1486-1498
Number of pages	13
Journal	Allergy
Volume	77
Issue number	5
Early online date	12 Nov 2021
DOIs	https://doi.org/10.1111/all.15150
Publication status	Published - 28 Apr 2022

Keywords

atopic dermatitis
clustering
endotype
transcriptome

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10.1111/all.15150

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@article{587fdd50598748e9977473803faa1536,

title = "Transcriptome-based identification of novel endotypes in adult atopic dermatitis",

abstract = "Background: Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking. Methods: To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies. Results: Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p =.02) and S. aureus (SA) colonization (p =.02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort. Conclusion: Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.",

keywords = "atopic dermatitis, clustering, endotype, transcriptome",

author = "Alain Lefevre-Utile and Melissa Saichi and Peter Olah and Marc Delord and Bernhard Homey and Vassili Soumelis",

note = "Funding Information: This work was supported by the , grant FDM201806006187, to Alain Lef{\`e}vre‐Utile. The MAARS Consortium has received funding from the FP7/2007–2013 (Grant 261366). Thanks to Nuala Mooney, Andrei Zinovyev, and Elise Amblard for their critical reviews. Thanks to Cristina Ghirelli for the scientific editing. Fondation pour la Recherche M{\'e}dicale Publisher Copyright: {\textcopyright} 2021 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.",

year = "2022",

month = apr,

day = "28",

doi = "10.1111/all.15150",

language = "English",

volume = "77",

pages = "1486--1498",

journal = "Allergy",

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number = "5",

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T1 - Transcriptome-based identification of novel endotypes in adult atopic dermatitis

AU - Lefevre-Utile, Alain

AU - Saichi, Melissa

AU - Olah, Peter

AU - Delord, Marc

AU - Homey, Bernhard

AU - Soumelis, Vassili

N1 - Funding Information: This work was supported by the , grant FDM201806006187, to Alain Lefèvre‐Utile. The MAARS Consortium has received funding from the FP7/2007–2013 (Grant 261366). Thanks to Nuala Mooney, Andrei Zinovyev, and Elise Amblard for their critical reviews. Thanks to Cristina Ghirelli for the scientific editing. Fondation pour la Recherche Médicale Publisher Copyright: © 2021 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

PY - 2022/4/28

Y1 - 2022/4/28

N2 - Background: Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking. Methods: To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies. Results: Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p =.02) and S. aureus (SA) colonization (p =.02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort. Conclusion: Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.

AB - Background: Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking. Methods: To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies. Results: Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p =.02) and S. aureus (SA) colonization (p =.02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort. Conclusion: Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.

KW - atopic dermatitis

KW - clustering

KW - endotype

KW - transcriptome

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U2 - 10.1111/all.15150

DO - 10.1111/all.15150

M3 - Article

SN - 0105-4538

VL - 77

SP - 1486

EP - 1498

JO - Allergy

JF - Allergy

IS - 5

ER -

Transcriptome-based identification of novel endotypes in adult atopic dermatitis

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Keywords

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