Segú-Vergés, C, Gómez, J, Terradas, P et al. (4 more authors) (2023) Unveiling chronic spontaneous urticaria pathophysiology through systems biology. Journal of Allergy and Clinical Immunology, 151 (4). pp. 1005-1014. ISSN 0091-6749
Abstract
Background
Chronic spontaneous urticaria (CSU) is a rare, heterogeneous, severely debilitating, and often poorly controlled skin disease resulting in an itchy eruption which can be persistent. Antihistamines and omalizumab, an anti-IgE monoclonal antibody, are the only licenced therapies. Although CSU pathogenesis is not yet fully understood, mast cell activation through the IgE:FcεRI (high affinity IgE receptor) axis appears central to the disease process.
Objective
We sought to model CSU pathophysiology and identify in silico the mechanism of action of different CSU therapeutic strategies currently in use or under development.
Methods
Therapeutic Performance Mapping System (TPMS) technology, based on systems biology and machine learning, was used to create a CSU interactome, validated with gene expression data from CSU patients, and a CSU model, which was used to evaluate CSU pathophysiology and the mechanism of action of different therapeutic strategies.
Results
Our models reflect the known role of mast cells activation as central process of CSU pathophysiology, as well as recognized roles for different therapeutic strategies over this and other innate and adaptive immune processes. They also allow determining similarities and differences between them: anti-IgE and Bruton tyrosine kinase (BTK) inhibitors present a more direct role on mast cell biology through abrogation of FcεRI signalling activity, while anti-interleukins and anti-Siglec-8 have a role on adaptive immunity modulation.
Conclusion
In silico CSU models reproduced known CSU and therapeutic strategies features. Our results could help advance understanding of therapeutic mechanisms of action, and further advance treatment research by patient profile.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | machine learning; chronic spontaneous urticaria; system biology; artificial intelligence; mast cells |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Institute of Rheumatology & Musculoskeletal Medicine (LIRMM) (Leeds) > Inflammatory Arthritis (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jan 2023 14:25 |
Last Modified: | 22 Nov 2023 14:21 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jaci.2022.12.809 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194865 |