关键词: APS1 COVID-19 IPEX PhIP-seq autoantibody autoantigen human immunology inflammation

Mesh : Humans Autoantibodies Autoantigens / metabolism Autoimmune Diseases Autoimmunity Bacteriophages / metabolism COVID-19 Homeodomain Proteins Immunoprecipitation Proteome

来  源:   DOI:10.7554/eLife.78550   PDF(Pubmed)

Abstract:
Phage immunoprecipitation sequencing (PhIP-seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-seq for autoantigen discovery, including our previous work (Vazquez et al., 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), and finally, mild and severe forms of COVID-19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as prodynorphin (PDYN) in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in two patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID-19, including the endosomal protein EEA1. Together, scaled PhIP-seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.
摘要:
噬菌体免疫沉淀测序(PhIP-seq)允许无偏倚,在各种疾病环境中发现全蛋白质组自身抗体,随着疾病特异性自身抗原的鉴定,人们对以前知之甚少的免疫失调形式提供了新的见解。尽管PhIP-seq成功用于自身抗原发现,包括我们以前的工作(Vazquez等人。,2020),当前的协议本质上很难扩展以适应大量病例,重要的是,健康的控制。这里,我们开发并验证了PhIP-seq在各种自身免疫性和炎症性疾病病因中的高通量扩展,包括APS1、IPEX、RAG1/2缺乏,川崎病(KD),儿童多系统炎症综合征(MIS-C),最后,轻度和重度形式的COVID-19。我们证明了这些缩放的数据集能够实现机器学习方法,从而实现对疾病状态的强大预测,以及检测已知和新型自身抗原的能力,如APS1患者的强啡肽(PDYN),IPEX患者的肠道表达蛋白BEST4和BTNL8。值得注意的是,在两名RAG1/2缺乏症患者中也发现了BEST4抗体,其中一人患有很早发性IBD。MIS-C和KD的缩放PhIP-seq检查显示罕见,重叠抗原,包括CGNL1,以及严重COVID-19中几种强烈富集的推定肺炎相关抗原,包括内体蛋白EEA1。一起,PhIP-seq为广泛评估不同起源和病因的自身免疫性疾病之间的罕见和常见自身抗原重叠提供了有价值的工具.
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