Zhang Wei, Zhang Yu, Li Lifei, Chen Rongchang, Shi Fei
Department of Infectious Diseases, the First Affiliated Hospital (Shenzhen People's Hospital), School of Medicine, Southern University of Science and Technology, Shenzhen, China.
Department of Infectious Diseases, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Front Allergy. 2024 Nov 5;5:1496392. doi: 10.3389/falgy.2024.1496392. eCollection 2024.
Asthma has become one of the most serious chronic respiratory diseases threatening people's lives worldwide. The pathogenesis of asthma is complex and driven by numerous cells and their interactions, which contribute to its genetic and phenotypic heterogeneity. The clinical characteristic is insufficient for the precision of patient classification and therapies; thus, a combination of the functional or pathophysiological mechanism and clinical phenotype proposes a new concept called "asthma endophenotype" representing various patient subtypes defined by distinct pathophysiological mechanisms. High-throughput omics approaches including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome enable us to investigate the pathogenetic heterogeneity of diverse endophenotypes and the underlying mechanisms from different angles. In this review, we provide a comprehensive overview of the roles of diverse cell types in the pathophysiology and heterogeneity of asthma and present a current perspective on their contribution into the bidirectional interaction between airway inflammation and airway remodeling. We next discussed how integrated analysis of multi-omics data via machine learning can systematically characterize the molecular and biological profiles of genetic heterogeneity of asthma phenotype. The current application of multi-omics approaches on patient stratification and therapies will be described. Integrating multi-omics and clinical data will provide more insights into the key pathogenic mechanism in asthma heterogeneity and reshape the strategies for asthma management and treatment.
哮喘已成为全球范围内威胁人类生命的最严重慢性呼吸系统疾病之一。哮喘的发病机制复杂，由众多细胞及其相互作用驱动，这导致了其遗传和表型的异质性。临床特征不足以实现患者分类和治疗的精准性；因此，功能或病理生理机制与临床表型的结合提出了一个名为“哮喘内表型”的新概念，它代表了由不同病理生理机制定义的各种患者亚型。包括基因组学、表观基因组学、转录组学、蛋白质组学、代谢组学和微生物组学在内的高通量组学方法使我们能够从不同角度研究各种内表型的致病异质性及其潜在机制。在这篇综述中，我们全面概述了不同细胞类型在哮喘病理生理学和异质性中的作用，并就它们在气道炎症与气道重塑之间双向相互作用中的贡献提出了当前观点。接下来，我们讨论了如何通过机器学习对多组学数据进行综合分析，以系统地表征哮喘表型遗传异质性的分子和生物学特征。将描述多组学方法在患者分层和治疗方面的当前应用。整合多组学和临床数据将为哮喘异质性的关键致病机制提供更多见解，并重塑哮喘管理和治疗策略。
