Applying Artificial Intelligence to Identify Common Targets for Treatment of Asthma, Eczema, and Food Allergy.

IF 2.5 4区 医学 Q3 ALLERGY International Archives of Allergy and Immunology Pub Date : 2024-01-01 Epub Date: 2023-11-21 DOI:10.1159/000534827
Hei Man Liu, Andre Rayner, Andrew R Mendelsohn, Anastasia Shneyderman, Michelle Chen, Frank W Pun
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Abstract

Introduction: Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients.

Methods: We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease.

Results: Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets' dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies.

Conclusion: Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets.

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应用人工智能识别哮喘、湿疹和食物过敏治疗的共同靶点。
导读:过敏性疾病是一种常见的疾病,其特征是对非病原体的外来抗原产生异常的免疫反应。通常食物过敏的患者还会患有哮喘和湿疹。鉴于这些疾病的相似性和缺乏有效的治疗方法,针对多种过敏的共同目标开发新的治疗方法将为患者提供一种高效且经济的治疗方法。方法:利用人工智能驱动的靶点发现平台熊猫组学(PandaOmics),识别治疗哮喘、湿疹和食物过敏的常见靶点。分别从15、11和6个与哮喘(558例,315例对照)、湿疹(441例,371例对照)和食物过敏(208例,106例对照)相关的转录组学数据集中产生32例病例对照比较,并分配到三个荟萃分析中以确定目标。针对每种变态反应性疾病,熊猫组学对Top-100个高置信度靶点和Top-100个新靶点进行优先排序。结果:所有三种过敏性疾病的六个常见高置信度靶点(即IL4R, IL5, JAK1, JAK2, JAK3和NR3C1)已批准用于治疗哮喘和湿疹的药物。根据这些靶点的异常表达谱及其在变态反应性疾病中的作用机制,提出了三个潜在的治疗靶点。IL5之所以被选为高可信度的靶标,是因为它与过敏有密切的关系。PTAFR被确定为药物再利用,而RNF19B被选择为治疗创新的新靶点。对哮喘、湿疹和食物过敏中常见的失调通路的分析揭示了具有良好特征的疾病特征和可能构成过敏病理生理学基础的新生物学过程。结论:总的来说,我们的研究剖析了过敏性疾病的共同病理生理,揭示了人工智能在探索新的治疗靶点方面的力量。
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来源期刊
CiteScore
5.60
自引率
3.60%
发文量
105
审稿时长
2 months
期刊介绍: ''International Archives of Allergy and Immunology'' provides a forum for basic and clinical research in modern molecular and cellular allergology and immunology. Appearing monthly, the journal publishes original work in the fields of allergy, immunopathology, immunogenetics, immunopharmacology, immunoendocrinology, tumor immunology, mucosal immunity, transplantation and immunology of infectious and connective tissue diseases.
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