Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq
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 COVID19. 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 PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 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 COVID19, 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.
Data availability
Full PhIP-Seq data for all cohorts presented is available for download at Dryad at https://doi.org/10.5061/dryad.qfttdz0k4. All available deidentified clinical data for this study is available in Supplemental Table 1.
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Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-SeqDryad Digital Repository, doi:10.5061/dryad.qfttdz0k4.
Article and author information
Author details
Funding
National Institute of Allergy and Infectious Diseases (5P01AI118688)
- Mark S Anderson
UCSF-CTSI TL1 (TR001871)
- Zoe Quandt
Division of Intramural Research, National Institute of Allergy and Infectious Diseases (1 ZIA AI001222)
- Luigi D Notarangelo
National Institute of Child Health and Development (1R61HD105590)
- Adriana Tremoulet
- Jane C Burns
Multiple sources**
- Jean-Laurent Casanova
FRM (EA20170638020)
- Paul Bastard
MD-PhD program of the Imagine Institute
- Paul Bastard
National Institute of Allergy and Infectious Diseases (1ZIAAI001175)
- Michail S Lionakis
National Institute of Diabetes and Digestive and Kidney Diseases (1F30DK123915)
- Sara E Vazquez
Chan Zuckerberg Biohub
- Joseph L DeRisi
Parker Institute for Cancer Immunotherapy
- Mark S Anderson
Juvenile Diabetes Research Foundation United States of America
- Mark S Anderson
Helmsley Charitable Trust
- Mark S Anderson
National Institute of General Medical Sciences (5T32GM007618)
- Mark S Anderson
American Diabetes Association (1-19-PDF-131)
- Zoe Quandt
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.**multiple sources includes: The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute, the Rockefeller University, the St. Giles Foundation, the National Institutes of Health (NIH) (R01AI088364 and R01AI163029), the National Center for Advancing Translational Sciences (NCATS), NIH Clinical and Translational Science Award (CTSA) program (UL1TR001866), the Fisher Center for Alzheimer's Research Foundation, the Meyer Foundation, the JPB Foundation, the French National Research Agency (ANR) under the Investments for the Future" program (ANR-10-IAHU-01)
Ethics
Human subjects: Detailed information on consent, where applicable, is available in the methods section of the manuscript.
Reviewing Editor
- Antony Rosen, Johns Hopkins University School of Medicine, United States
Publication history
- Received: March 23, 2022
- Preprint posted: March 25, 2022 (view preprint)
- Accepted: October 10, 2022
- Accepted Manuscript published: October 27, 2022 (version 1)
- Version of Record published: November 30, 2022 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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