ABSTRACT
The first ever US Food and Drug Administration-approved messenger RNA vaccines are highly protective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1-3. However, the contribution of each dose to the generation of antibodies against SARS-CoV-2 spike (S) protein and the degree of protection against novel variants warrant further study. Here, we investigated the B cell response to the BNT162b2 vaccine by integrating B cell repertoire analysis with single-cell transcriptomics pre- and post-vaccination. The first vaccine dose elicits a recall response of IgA+ plasmablasts targeting the S subunit S2. Three weeks after the first dose, we observed an influx of minimally mutated IgG+ memory B cells that targeted the receptor binding domain on the S subunit S1 and likely developed from the naive B cell pool. This response was strongly boosted by the second dose and delivers potently neutralizing antibodies against SARS-CoV-2 and several of its variants.
Subject(s)
Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , B-Lymphocytes/immunology , BNT162 Vaccine/immunology , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Antibodies, Neutralizing/blood , Antibodies, Viral/blood , COVID-19/immunology , COVID-19/prevention & control , Humans , Immunoglobulin A/blood , Immunoglobulin A/immunology , Immunoglobulin G/blood , Immunoglobulin G/immunology , Memory T Cells/immunology , Protein Domains/immunology , Vaccine EfficacyABSTRACT
Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms.