bioRxivImmunologyPreprint
B cell receptor repertoire analysis unveils dynamic antibody response and severity markers in COVID-19 patients
Abstract
Humoral and cell mediated immunity are critical against viral infections. The knowledge of composition, diversity, gene usage of the B cell repertoires helps in determining the immune response to SARS-CoV-2 infection. Examining B cell response provides insights on therapeutic antibodies, disease severity markers and aids in predicting vaccine response. We have analyzed public domain immunoglobulin sequencing data from PBMCs of SARS-CoV-2 infected individuals to gain a better understanding of B cell repertoire in patients. Public clonotypes showed increased usage of IGHV3, IGHV4, IGKV1, IGKV3, IGLV3 and IGLV2 family genes during the acute phase infection. Identical CDR3 sequences were identified for heavy (H), kappa (K) and lambda (L) chains across individuals, indicating the convergence of B cell selection during SARS-CoV-2 infection. While the immune repertoire dynamically changed over the course of convalescence, there were persistent clones across early and late timepoints. The diversity of antibody repertoire, measured by Shannon-Weiner diversity index for H and K chains, reduced during the acute phase of infection. In addition, the repertoire diversity was low in severe patients compared to patients with mild or moderate symptoms. Increased usage of IGHV4-59 gene was observed in COVID-19 patients with severe symptoms requiring ventilator support at 2 weeks and 3 weeks post symptom onset. IGHV4-59 is reported to have rheumatoid factor (RF) activity with high affinity for IgG and the elevated level of IGHV4-59 provides a potential mechanism for the increased autoimmune responses in severe patients. Correlation of the clinical features with the B cell receptor repertoire dynamics elucidated public antibody clonotypes and disease severity markers for COVID-19.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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