Unbiased discovery of autoantibodies associated with severe COVID-19 via genome-scale self-assembled DNA-barcoded protein libraries

Authors

• Joel J. Credle, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Jonathan Gunn, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Puwanat Sangkhapreecha, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Daniel R. Monaco, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Xuwen Alice Zheng, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Hung-Ji Tsai, Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, UK.
• Azaan Wilbon, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• William R. Morgenlander, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Andre Rastegar, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Yi Dong, Center for Cell Dynamics and Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Sahana Jayaraman, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Lorenzo Tosi, Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA.
• Biju Parekkadan, Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA.
• Alan N. Baer, Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Mario Roederer, ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
• Evan M. Bloch, Division of Transfusion Medicine, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Aaron A. Tobian, Division of Transfusion Medicine, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Israel Zyskind, Department of Pediatrics, NYU Langone Medical Center, New York City, NY, USA.
• Jonathan I. Silverberg, Department of Dermatology, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
• Avi Z. Rosenberg, Division of Kidney-Urologic Pathology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Andrea L. Cox, Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Tom Lloyd, Department of Neurology and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• Andrew L. Mammen, Department of Neurology and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
• H Benjamin Larman, Institute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. hlarman1@jhmi.edu.

Document Type

Journal Article

Publication Date

8-1-2022

Journal

Nature biomedical engineering

Volume

6

Issue

8

DOI

10.1038/s41551-022-00925-y

Abstract

Pathogenic autoreactive antibodies that may be associated with life-threatening coronavirus disease 2019 (COVID-19) remain to be identified. Here, we show that self-assembled genome-scale libraries of full-length proteins covalently coupled to unique DNA barcodes for analysis by sequencing can be used for the unbiased identification of autoreactive antibodies in plasma samples. By screening 11,076 DNA-barcoded proteins expressed from a sequence-verified human ORFeome library, the method, which we named MIPSA (for Molecular Indexing of Proteins by Self-Assembly), allowed us to detect circulating neutralizing type-I and type-III interferon (IFN) autoantibodies in five plasma samples from 55 patients with life-threatening COVID-19. In addition to identifying neutralizing type-I IFN-α and IFN-ω autoantibodies and other previously known autoreactive antibodies in patient plasma, MIPSA enabled the detection of as yet unidentified neutralizing type-III anti-IFN-λ3 autoantibodies that were not seen in healthy plasma samples or in convalescent plasma from ten non-hospitalized individuals with COVID-19. The low cost and simple workflow of MIPSA will facilitate unbiased high-throughput analyses of protein-antibody, protein-protein and protein-small-molecule interactions.

APA Citation

Credle, Joel J.; Gunn, Jonathan; Sangkhapreecha, Puwanat; Monaco, Daniel R.; Zheng, Xuwen Alice; Tsai, Hung-Ji; Wilbon, Azaan; Morgenlander, William R.; Rastegar, Andre; Dong, Yi; Jayaraman, Sahana; Tosi, Lorenzo; Parekkadan, Biju; Baer, Alan N.; Roederer, Mario; Bloch, Evan M.; Tobian, Aaron A.; Zyskind, Israel; Silverberg, Jonathan I.; Rosenberg, Avi Z.; Cox, Andrea L.; Lloyd, Tom; Mammen, Andrew L.; and Benjamin Larman, H, "Unbiased discovery of autoantibodies associated with severe COVID-19 via genome-scale self-assembled DNA-barcoded protein libraries" (2022). GW Authored Works. Paper 1561.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1561

Department

Dermatology