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Autoantigenomics: Holistic characterization of autoantigen repertoires for a better understanding of autoimmune diseases.
Autoimmunity Reviews ( IF 9.2 ) Pub Date : 2019-12-12 , DOI: 10.1016/j.autrev.2019.102450
Christian P Moritz 1 , Stéphane Paul 2 , Oda Stoevesandt 3 , Yannick Tholance 4 , Jean-Philippe Camdessanché 1 , Jean-Christophe Antoine 1
Affiliation  

Autoimmune diseases are mostly characterized by autoantibodies in the patients' serum or cerebrospinal fluid, representing diagnostic or prognostic biomarkers. For decades, research has focused on single autoantigens or panels of single autoantigens. In this article, we advocate to broaden the focus by addressing the entire autoantigen repertoire in a systemic "omics-like" way. This approach aims to capture the enormous biodiversity in the sets of targeted antigens and pave the way toward a more holistic understanding of the concerted character of antibody-related humoral immune responses. Ongoing technological progress permits high-throughput screenings of thousands of autoantigens in parallel, e.g., via protein microarrays, phage display, or immunoprecipitation with mass spectrometry. We argue that the time is right for combining omics and autoantibody screening approaches into "autoantigenomics" as a novel omics subcategory. In this article, we introduce the concept of autoantigenomics, describe its roots and application options, and demarcate the method from related holistic approaches such as systems serology or immune-related transcriptomics and proteomics. We suggest the following extendable method set to be applied to autoantigen repertoires: (1) principal component analysis, (2) hierarchical cluster analysis, (3) partial least-square discriminant analysis or orthogonal projections to latent structures discriminant analysis, (4) analysis of the repertoire sizes in disease groups and clinical subgroups, (5) overrepresentation analyses using databases like those of Gene Ontology, Reactome Pathway, or DisGeNET, (6) analysis of pathways that are significantly targeted by specific repertoires, and (7) machine learning approaches. In an unsupervised way, these methods can identify clusters of autoantigens sharing certain functional or spatial properties, or clusters of patients comprising clinical subgroups potentially useful for patient stratification. In a supervised way, these methods can lead to prediction models that may eventually assist diagnosis and prognosis. The untargeted autoantigenomics approach allows for the systematic survey of antibody-related humoral immune responses. This may enhance our understanding of autoimmune diseases in a more comprehensive way compared to current single or panel autoantibodies approaches.

中文翻译:

自身基因组学:全面描述自身抗原库,以更好地了解自身免疫性疾病。

自身免疫性疾病的主要特征是患者血清或脑脊液中的自身抗体,代表诊断或预后生物标志物。几十年来,研究一直集中在单一自身抗原或单一自身抗原的面板上。在本文中,我们主张通过以系统的“类组学”方式解决整个自身抗原库来扩大关注范围。这种方法旨在捕获目标抗原集中的巨大生物多样性,并为对抗体相关的体液免疫反应的协调特征更全面的了解铺平道路。正在进行的技术进步允许并行地(例如,通过蛋白质微阵列,噬菌体展示或质谱免疫沉淀)对数千种自身抗原进行高通量筛选。我们认为,现在是将组学组和自身抗体筛查方法结合到一个新的组学子类别“自体基因组学”中的时候了。在本文中,我们介绍了自身抗基因组学的概念,描述了其自身的根源和应用选项,并从相关的整体方法(例如系统血清学或与免疫相关的转录组学和蛋白质组学)中划分了该方法。我们建议将以下可扩展方法集应用于自身抗原库:(1)主成分分析,(2)层次聚类分析,(3)偏最小二乘判别分析或对潜在结构判别分析的正交投影,(4)分析(5)使用诸如Gene Ontology,Reactome Pathway或DisGeNET之类的数据库进行的过分表达分析,(6)分析特定曲目显着针对的路径,以及(7)机器学习方法。以无监督的方式,这些方法可以识别具有某些功能或空间特性的自身抗原簇,或包含可能对患者分层有用的临床亚组的患者簇。以一种有监督的方式,这些方法可以导致最终可以帮助诊断和预后的预测模型。非靶向的自身抗基因组学方法可用于系统地调查抗体相关的体液免疫反应。与当前的单一或专门小组自身抗体方法相比,这可能以更全面的方式增强我们对自身免疫性疾病的理解。以无监督的方式,这些方法可以识别具有某些功能或空间特性的自身抗原簇,或包含可能对患者分层有用的临床亚组的患者簇。以一种有监督的方式,这些方法可以导致最终可以帮助诊断和预后的预测模型。非靶向的自身抗基因组学方法可用于系统地调查抗体相关的体液免疫反应。与当前的单一或专门小组自身抗体方法相比,这可能以更全面的方式增强我们对自身免疫性疾病的理解。以无监督的方式,这些方法可以识别具有某些功能或空间特性的自身抗原簇,或包含可能对患者分层有用的临床亚组的患者簇。以一种有监督的方式,这些方法可以导致最终可以帮助诊断和预后的预测模型。非靶向的自身抗基因组学方法可用于系统地调查抗体相关的体液免疫反应。与当前的单一或专门小组自身抗体方法相比,这可能以更全面的方式增强我们对自身免疫性疾病的理解。这些方法可以产生预测模型,最终可以帮助诊断和预后。非靶向的自身抗基因组学方法可用于系统地调查抗体相关的体液免疫反应。与当前的单一或专门小组自身抗体方法相比,这可能以更全面的方式增强我们对自身免疫性疾病的理解。这些方法可以产生预测模型,最终可以帮助诊断和预后。非靶向的自身抗基因组学方法可用于系统地调查抗体相关的体液免疫反应。与当前的单一或专门小组自身抗体方法相比,这可以更全面的方式增强我们对自身免疫性疾病的理解。
更新日期:2019-12-13
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