哮喘和过敏的系统生物学和大数据——最新发现和新出现的挑战
2019/11/12
摘要
哮喘是由免疫和呼吸功能障碍引起的常见疾病，通常与过敏有关。系统的观点可能有助于揭示哮喘和过敏的复杂性。我们的目的是概述过敏和哮喘研究中使用的系统生物学方法。具体来说，我们描述最近的“组学”水平的发现，并研究如何将这些发现系统地整合以产生进一步的见解。当前的研究表明，过敏是由遗传和表观遗传因素驱动的，与微生物群和饮食等环境因素一致，导致早期免疫系统发育紊乱和关键免疫炎症途径内的平衡失调。遗传易感性和暴露的差异导致哮喘和其他过敏性疾病表现的异质性。机器学习方法正在被用来探索这种异质性，并探究与哮喘和变态反应的亚表型相关的病理生理模式或“内型”。正在基于基因组，转录组和蛋白质组学数据建立数学模型，预测或区分疾病表型，并描述哮喘背后的生物分子网络。系统生物学在变态反应和哮喘研究中的应用正在迅速发展，迄今为止取得了丰硕成果。然而，这项研究的规模和多学科性质意味着它伴随着新的挑战。最终，希望系统医学将组学数据整合到临床实践中，可以为更精确，个性化和有效地治疗哮喘铺平道路。
Systems biology and big data in asthma and allergy - recent discoveries and emerging challenges.
Tang HHF, Sly PD, Holt PG, Holt KE, Inouye M.
Abstract
Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight. Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic, and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.
哮喘是由免疫和呼吸功能障碍引起的常见疾病，通常与过敏有关。系统的观点可能有助于揭示哮喘和过敏的复杂性。我们的目的是概述过敏和哮喘研究中使用的系统生物学方法。具体来说，我们描述最近的“组学”水平的发现，并研究如何将这些发现系统地整合以产生进一步的见解。当前的研究表明，过敏是由遗传和表观遗传因素驱动的，与微生物群和饮食等环境因素一致，导致早期免疫系统发育紊乱和关键免疫炎症途径内的平衡失调。遗传易感性和暴露的差异导致哮喘和其他过敏性疾病表现的异质性。机器学习方法正在被用来探索这种异质性，并探究与哮喘和变态反应的亚表型相关的病理生理模式或“内型”。正在基于基因组，转录组和蛋白质组学数据建立数学模型，预测或区分疾病表型，并描述哮喘背后的生物分子网络。系统生物学在变态反应和哮喘研究中的应用正在迅速发展，迄今为止取得了丰硕成果。然而，这项研究的规模和多学科性质意味着它伴随着新的挑战。最终，希望系统医学将组学数据整合到临床实践中，可以为更精确，个性化和有效地治疗哮喘铺平道路。
(中日友好医院呼吸与危重症医学科 王瑞茵 摘译 林江涛 审校)
(Eur Respir J. 2019 Oct 16. pii: 1900844. doi: 10.1183/13993003.00844-2019. [Epub ahead of print])
(Eur Respir J. 2019 Oct 16. pii: 1900844. doi: 10.1183/13993003.00844-2019. [Epub ahead of print])
Systems biology and big data in asthma and allergy - recent discoveries and emerging challenges.
Tang HHF, Sly PD, Holt PG, Holt KE, Inouye M.
Abstract
Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight. Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic, and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.
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抗IgE单克隆抗体奥马珠单抗通过与Fcγ受体结合引起不良反应
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哮喘严重程度评分系统（ASSESS）的开发和初步验证
