Graduate Student Medical College of Wisconsin Milwaukee, Wisconsin, United States
Disclosure(s):
Nathan Witman, PhD: No financial relationships to disclose
Introduction/Rationale: Autoreactive antibodies are pathological in many infectious diseases and can lead to chronic autoimmune disease. Severe COVID-19 has been shown to elicit strong autoantibodies with broad reactivity towards both viral and self-antigens, and recent work has shown mRNA vaccinees develop similar autoantibodies but to a lesser extent. Determining the drivers of these antibodies is critical for understanding how to circumvent infection-driven autoimmunity.
Methods: To do this, we generated recombinant antibodies from plasmablasts and expanded B-cells of severe COVID-19 patients using single-cell RNA sequencing and tested their reactivities.
Results: We found a subset of cloned antibodies exhibited polyreactivity towards both viral and self-antigens. Notably, despite overlapping antigen specificities, analysis using a protein language model revealed no shared paratope convergence across these clones. Instead, structural analysis revealed that autoreactivity was highly correlated with cationic residues in the heavy chain (H)CDR3, and docking predictions showed charge-based dependency rather than epitope similarity as the most likely mode of interaction. Germline reversion, which reduced positive charge in HCDR3s, led to diminished viral binding and reduced affinity for nuclear antigens, implicating charge as a driver of reactivity.
Conclusion: These findings, together with independent evidence that the SARS-CoV-2 spike protein has evolved to lose negative surface charges over time, suggest a model in which electrostatic surface charge of the virus may favor the expansion of highly charged antiviral antibodies that are inadvertently autoreactive. Our study identifies charge-based interactions as a unifying mechanism connecting antiviral immunity to autoantibody emergence in both infection, and potentially vaccination, providing a conceptual framework for understanding and mitigating infection-related autoimmunity.
