Potent neutralization of SARS-CoV-2 variants of concern by an antibody with an uncommon genetic signature and structural mode of spike recognition.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineages that are more transmissible and resistant to currently approved antibody therapies poses a considerable challenge to the clinical treatment of coronavirus disease (COVID-19). Therefore, the need for ongoing discovery efforts to identify broadly reactive monoclonal antibodies to SARS-CoV-2 is of utmost importance. Here, we report a panel of SARS-CoV-2 antibodies isolated using the linking B cell receptor to antigen specificity through sequencing (LIBRA-seq) technology from an individual who recovered from COVID-19. Of these antibodies, 54042-4 shows potent neutralization against authentic SARS-CoV-2 viruses, including variants of concern (VOCs). A cryoelectron microscopy (cryo-EM) structure of 54042-4 in complex with the SARS-CoV-2 spike reveals an epitope composed of residues that are highly conserved in currently circulating SARS-CoV-2 lineages. Further, 54042-4 possesses uncommon genetic and structural characteristics that distinguish it from other potently neutralizing SARS-CoV-2 antibodies. Together, these findings provide motivation for the development of 54042-4 as a lead candidate to counteract current and future SARS-CoV-2 VOCs.

Self-assembling synthetic nanoadjuvant scaffolds cross-link B cell receptors and represent new platform technology for therapeutic antibody production.

Host antibody responses are pivotal for providing protection against infectious agents. We have pioneered a new class of self-assembling micelles based on pentablock copolymers that enhance antibody responses while providing a low inflammatory environment compared to traditional adjuvants. This type of "just-right" immune response is critical in the rational design of vaccines for older adults. Here, we report on the mechanism of enhancement of antibody responses by pentablock copolymer micelles, which act as scaffolds for antigen presentation to B cells and cross-link B cell receptors, unlike other micelle-forming synthetic block copolymers. We exploited this unique mechanism and developed these scaffolds as a platform technology to produce antibodies in vitro. We show that this novel approach can be used to generate laboratory-scale quantities of therapeutic antibodies against multiple antigens, including those associated with SARS-CoV-2 and Yersinia pestis, further expanding the value of these nanomaterials to rapidly develop countermeasures against infectious diseases.

Fab-dimerized glycan-reactive antibodies are a structural category of natural antibodies.

Natural antibodies (Abs) can target host glycans on the surface of pathogens. We studied the evolution of glycan-reactive B cells of rhesus macaques and humans using glycosylated HIV-1 envelope (Env) as a model antigen. 2G12 is a broadly neutralizing Ab (bnAb) that targets a conserved glycan patch on Env of geographically diverse HIV-1 strains using a unique heavy-chain (VH) domain-swapped architecture that results in fragment antigen-binding (Fab) dimerization. Here, we describe HIV-1 Env Fab-dimerized glycan (FDG)-reactive bnAbs without VH-swapped domains from simian-human immunodeficiency virus (SHIV)-infected macaques. FDG Abs also recognized cell-surface glycans on diverse pathogens, including yeast and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike. FDG precursors were expanded by glycan-bearing immunogens in macaques and were abundant in HIV-1-naive humans. Moreover, FDG precursors were predominately mutated IgM+IgD+CD27+, thus suggesting that they originated from a pool of antigen-experienced IgM+ or marginal zone B cells.

Use of machine learning to identify a T cell response to SARS-CoV-2.

The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.