ABSTRACT
Memory B cells (MBCs) represent a second layer of immune protection against SARS-CoV-2. Whether MBCs elicited by mRNA vaccines can recognize the Omicron variant is of major concern. We used bio-layer interferometry to assess the affinity against the receptor-binding-domain (RBD) of Omicron spike of 313 naturally expressed monoclonal IgG that were previously tested for affinity and neutralization against VOC prior to Omicron. We report here that Omicron evades recognition from a larger fraction of these antibodies than any of the previous VOCs. Additionally, whereas 30% of these antibodies retained high affinity against Omicron-RBD, our analysis suggest that Omicron specifically evades antibodies displaying potent neutralizing activity against the D614G and Beta variant viruses. Further studies are warranted to understand the consequences of a lower memory B cell potency on the overall protection associated with current vaccines.
ABSTRACT
How a previous SARS-CoV-2 infection may amplify and model the memory B cell (MBC) response elicited by mRNA vaccines was addressed by a comparative longitudinal study of two cohorts, naive individuals and disease-recovered patients, up to 2 months after vaccination. The quality of the memory response was assessed by analysis of the VDJ repertoire, affinity and neutralization against variants of concerns (VOC), using unbiased cultures of 2452 MBCs. Upon boost, the MBC pool of recovered patients selectively expanded, further matured and harbored potent neutralizers against VOC. Maturation of the MBC response in naive individuals was much less pronounced. Nevertheless, and as opposed to their weaker neutralizing serum response, half of their RBD-specific MBCs displayed high affinity towards multiple VOC and one-third retained neutralizing potency against B.1.351. Thus, repeated vaccine challenges could reduce these differences by recall of affinity-matured MBCs and allow naive vaccinees to cope efficiently with VOC.
ABSTRACT
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time. Waning antibody levels lead to reduced sensitivity of serological diagnostic tests over time. This undermines the utility of serological surveillance as the SARS-CoV-2 pandemic progresses into its second year. Here we develop a multiplex serological test for measuring antibodies of three isotypes (IgG, IgM, IgA) to five SARS-CoV-2 antigens (Spike (S), receptor binding domain (RBD), Nucleocapsid (N), Spike subunit 2, Membrane-Envelope fusion) and the Spike proteins of four seasonal coronaviruses. We measure antibody responses in several cohorts of French and Irish hospitalized patients and healthcare workers followed for up to eleven months after symptom onset. The data are analysed with a mathematical model of antibody kinetics to quantify the duration of antibody responses accounting for inter-individual variation. One year after symptoms, we estimate that 36% (95% range: 11%, 94%) of anti-S IgG remains, 31% (9%, 89%) anti-RBD IgG remains, and 7% (1%, 31%) anti-N IgG remains. Antibodies of the IgM isotype waned more rapidly, with 9% (2%, 32%) anti-RBD IgM remaining after one year. Antibodies of the IgA isotype also waned rapidly, with 10% (3%, 38%) anti-RBD IgA remaining after one year. Quantitative measurements of antibody responses were used to train machine learning algorithms for classification of previous infection and estimation of time since infection. The resulting diagnostic test classified previous infections with 99% specificity and 98% (95% confidence interval: 94%, 99%) sensitivity, with no evidence for declining sensitivity over the time scale considered. The diagnostic test also provided accurate classification of time since infection into intervals of 0 - 3 months, 3 - 6 months, and 6 - 12 months. Finally, we present a computational method for serological reconstruction of past SARS-CoV-2 transmission using the data from this test when applied to samples from a single cross-sectional sero-prevalence survey.
ABSTRACT
BackgroundInfection with SARS-CoV-2 induces an antibody response targeting multiple antigens that changes over time. This complexity presents challenges and opportunities for serological diagnostics. MethodsA multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples from patients in French hospitals with RT-qPCR confirmed SARS-CoV-2 infection (n = 259), and negative control serum samples collected before the start of the SARS-CoV-2 epidemic (n = 335). A random forests algorithm was trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection. A mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the potential sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low transmission settings. ResultsIgG antibody responses to trimeric Spike protein identified individuals with previous RT-qPCR confirmed SARS-CoV-2 infection with 91.6% sensitivity (95% confidence interval (CI); 87.5%, 94.5%) and 99.1% specificity (95% CI; 97.4%, 99.7%). Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% sensitivity (95% CI; 96.5%, 99.6%) and 99.3% specificity (95% CI; 97.6%, 99.8%). Informed by prior data from other coronaviruses, we estimate that one year following infection a monoplex assay with optimal anti-Stri IgG cutoff has 88.7% sensitivity (95% CI: 63.4%, 97.4%), and that a multiplex assay can increase sensitivity to 96.4% (95% CI: 80.9%, 100.0%). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 1%, below the false positivity rate of many other assays. ConclusionSerological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. This provides potential solutions to two pressing challenges for SARS-CoV-2 serological surveillance: classifying individuals who were infected greater than six months ago, and measuring seroprevalence in serological surveys in very low transmission settings.
