The overarching goal of this New Aim 4 is to test the hypothesis that SARS-CoV-2 (CoV-2) causes autoimmune disease (AI) in a subset of infected patients. Our preliminary studies on 336 COVID-19 samples from 282 COVID19 patients (four COVID-19 cohorts in three geographically distinct regions) have identified autoantibodies and clinical evidence of AI. To test the hypothesis that autoantibodies develop following CoV-2 infection, we will use autoantigen arrays to identify proteins targeted by autoantibodies, some of which may cause pathogenic inflammatory responses that could mediate lung, skin, and other tissue injury, dysregulated coagulation, endothelial dysfunction, and vasculopathy. We will then test the hypothesis that autoantibodies develop through different mechanisms including molecular mimicry and generation of receptor-blocking anti-cytokine antibodies (ACA) in response to ?cytokine storm?. We hypothesize that infection with CoV-2 induces 2 different outcomes: (i) the desired outcome - protective responses that neutralize CoV-2; or (ii) pathogenic responses that lead to symptomatic autoimmunity or autoinflammatory disease.
Aim 4. 1 will test the hypothesis that CoV-2 causes development of autoantibodies and classifiable autoimmune diseases by leveraging our custom ?COVID-19 Autoantigen Array? comprising common autoantigens from diseases that affect the lung, endothelium and skin.
Aim 4. 2 will characterize serum antibodies specific for proteins from CoV-2 and other coronaviruses, and correlate with autoantibodies in Aim 1, by using our ?COVID-19 Viral Array? capable of simultaneously quantitating antibodies against many different wild-type and mutant viral proteins and peptides. Viral responses will be correlated with clinical outcomes including development of autoantibodies, and progression to clinical autoimmunity.
Aim 4. 3 will test the hypothesis that CoV-2 causes autoimmunity through mechanisms including cross-reactivity (molecular mimicry) and cytokine storm which generates receptor-blocking ACA. We will use a variety of lab-based techniques to explore these mechanisms, including purification of antigen-specific IgG from serum, co-immunoprecipitation, and cross-binding assays. Together, the proposed experiments will begin to quantify the impact of CoV-2 on AI, identify which antigens and specific AI are associated with CoV-2, and contribute to our mechanistic understanding of COVID-19 pathogenesis, setting the stage for large-scale epidemiology studies to determine the extent of autoimmunity that results from CoV-2 infection, as well as longterm impacts on the health care system and economy.

Public Health Relevance

We will validate using PCR and microfluidics an 11 gene transcript profile, recently discovered using a ?Big Data? approach, that predicts influenza vaccine responsiveness. We will explore the underlying biology of the pathways we have discovered and will translate the assay to a sensitive and rapid new platform that uses Giant MagnetoResistive Sensors.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Special Emphasis Panel (ZAI1)
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Gordon, Jennifer L
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Stanford University
Internal Medicine/Medicine
Schools of Medicine
United States
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Gee, Marvin H; Han, Arnold; Lofgren, Shane M et al. (2018) Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell 172:549-563.e16
Vallania, Francesco; Tam, Andrew; Lofgren, Shane et al. (2018) Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases. Nat Commun 9:4735
Bongen, Erika; Vallania, Francesco; Utz, Paul J et al. (2018) KLRD1-expressing natural killer cells predict influenza susceptibility. Genome Med 10:45
Cheung, Peggie; Vallania, Francesco; Warsinske, Hayley C et al. (2018) Single-Cell Chromatin Modification Profiling Reveals Increased Epigenetic Variations with Aging. Cell 173:1385-1397.e14
Sweeney, Timothy E; Thomas, Neal J; Howrylak, Judie A et al. (2018) Multicohort Analysis of Whole-Blood Gene Expression Data Does Not Form a Robust Diagnostic for Acute Respiratory Distress Syndrome. Crit Care Med 46:244-251
Degn, Søren E; van der Poel, Cees E; Firl, Daniel J et al. (2017) Clonal Evolution of Autoreactive Germinal Centers. Cell 170:913-926.e19
Perkins, Tiffany; Rosenberg, Jacob M; Le Coz, Carole et al. (2017) Smith-Magenis Syndrome Patients Often Display Antibody Deficiency but Not Other Immune Pathologies. J Allergy Clin Immunol Pract 5:1344-1350.e3
Lofgren, Shane; Hinchcliff, Monique; Carns, Mary et al. (2016) Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity. JCI Insight 1:e89073