Antiviral Antibody Fc Dysfunction Is Linked to Increased IgG1 Fucosylation in Long COVID
Year: 2026
Type: preprint
Abstract: Background: Persistent SARS-CoV-2 antigen is a leading hypothesis for post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID. Impaired Fc-mediated antiviral antibody function could contribute to inefficient clearance of infected cells and viral antigen despite preserved antibody levels. Therefore, we tested whether PASC is associated with altered Fcγ receptor-mediated antibody function and differences in IgG1 Fc fucosylation, a major regulator of Fc effector function. Methods: Serum samples from RECOVER-Adult participants with prior SARS-CoV-2 infection (99 PASC, 139 non-PASC) were analyzed for anti-SARS-CoV-2 spike receptor-binding domain (RBD) IgG and subclasses, FcγRIIIa- and FcγRIIa-mediated reporter activity as surrogate measures of antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP), anti-RBD IgG1 Fc glycosylation, and circulating fucose-modifying enzymes. Results: Despite higher anti-RBD IgG and IgG1 abundance, PASC sera showed lower ADCC- and ADCP-associated FcγR reporter activity, including after normalization for antibody abundance. Anti-RBD IgG1 showed greater fucosylation in PASC samples (30 PASC, 34 non-PASC). Serum FUCA2 was lower in PASC samples (24 PASC, 34 non-PASC), whereas FUCA1 and soluble CD16a levels did not differ between the two groups. Conclusion: PASC is associated with impaired antiviral IgG Fc function, increased IgG1 Fc fucosylation, and reduced circulating FUCA2, identifying a glycoimmune axis that may contribute to impaired Fc-dependent antigen clearance and represents a potentially targetable pathway in Long COVID.
Source: medRxiv
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Cohort and biomarkers for COVID-19 severity, natural history, and reinfection, COVID-19 Network of Networks Expanding Clinical and Translational approaches to Predict Severe Illness in Children (CONNECT to Predict SIck Children), AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids, New Jersey Alliance for Clinical Translational Science: NJ ACTS, Stanford Integrated Cardiovascular/Pulmonary Residency Research Training Program, OTA-21-015A Post-Acute Sequelae of SARS-CoV-2 Infection Initiative: NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core
OpenAlex ID: W7214751819
