4.8 Article

Autoantibody discovery across monogenic, acquired, and COVID-19-associated autoimmunity with scalable PhIP-seq

期刊

ELIFE
卷 11, 期 -, 页码 -

出版社

eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.78550

关键词

PhIP-seq; autoantibody; autoantigen; COVID-19; APS1; IPEX; Human

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资金

  1. National Institute of Allergy and Infectious Diseases [5P01AI118688, 1ZIAAI001175]
  2. National Institute of Diabetes and Digestive and Kidney Diseases [1F30DK123915]
  3. Chan Zuckerberg Biohub
  4. Parker Institute for Cancer Immunotherapy
  5. Juvenile Diabetes Research Foundation United States of America
  6. Helmsley Charitable Trust
  7. National Institute of General Medical Sciences [5T32GM007618]
  8. American Diabetes Association [1-19-PDF-131]
  9. UCSF-CTSI [TL1TR001871]
  10. Division of Intramural Research, National Institute of Allergy and Infectious Diseases [1 ZIA AI001222]
  11. Eunice Kennedy Shriver National Institute of Child Health and Human Development [1R61HD105590]
  12. Laboratory of Human Genetics of Infectious Diseases
  13. FRM [EA20170638020]
  14. Imagine Institute

向作者/读者索取更多资源

Phage immunoprecipitation sequencing (PhIP-seq) is a unbiased method for discovering autoantibodies and studying immune dysregulation in various diseases. This study developed and validated a high throughput extension of PhIP-seq, which can be applied to autoimmune and inflammatory diseases, providing prediction of disease status and detection of autoantigens.
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.

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