Maturation of SARS-CoV-2 Spike-specific memory B cells drives resilience to viral escape

Memory B cells (MBCs) generate rapid antibody responses upon secondary encounter with a pathogen. Here, we investigated the kinetics, avidity and cross-reactivity of serum antibodies and MBCs in 155 SARS-CoV-2 infected and vaccinated individuals over a 16-month timeframe. SARS-CoV-2-specific MBCs and serum antibodies reached steady-state titers with comparable kinetics in infected and vaccinated individuals. Whereas MBCs of infected individuals targeted both pre- and postfusion Spike (S), most vaccine-elicited MBCs were specific for prefusion S, consistent with the use of prefusion-stabilized S in mRNA vaccines. Furthermore, a large fraction of MBCs recognizing postfusion S cross-reacted with human betacoronaviruses. The avidity of MBC-derived and serum antibodies increased over time resulting in enhanced resilience to viral escape by SARS-CoV-2 variants, including Omicron BA.1 and BA.2 sub-lineages, albeit only partially for BA.4 and BA.5 sublineages. Overall, the maturation of high-affinity and broadly-reactive MBCs provides the basis for effective recall responses to future SARS-CoV-2 variants.

Predicting the mutational drivers of future SARS-CoV-2 variants of concern

SARS-CoV-2 evolution threatens vaccine- and natural infection-derived immunity, and the efficacy of therapeutic antibodies. Herein we sought to predict Spike amino acid changes that could contribute to future variants of concern. We tested the importance of features comprising epidemiology, evolution, immunology, and neural network-based protein sequence modeling. This resulted in identification of the primary biological drivers of SARS-CoV-2 intra-pandemic evolution. We found evidence that resistance to population-level host immunity has increasingly shaped SARS-CoV-2 evolution over time. We identified with high accuracy mutations that will spread, at up to four months in advance, across different phases of the pandemic. Behavior of the model was consistent with a plausible causal structure wherein epidemiological variables integrate the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future, and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. This modeling approach may be applied to any pathogen with genomic surveillance data, and so may address other rapidly evolving pathogens such as influenza, and unknown future pandemic viruses.

A human antibody that broadly neutralizes betacoronaviruses protects against SARS-CoV-2 by blocking the fusion machinery

The repeated spillovers of {beta}-coronaviruses in humans along with the rapid emergence of SARS-CoV-2 escape variants highlight the need to develop broad coronavirus therapeutics and vaccines. Five monoclonal antibodies (mAbs) were isolated from COVID-19 convalescent individuals and found to cross-react with multiple {beta}-coronavirus spike (S) glycoproteins by targeting the stem helix. One of these mAbs, S2P6, cross-reacts with more than twenty human and animal {beta}-coronavirus S glycoproteins and broadly neutralizes SARS-CoV-2 and pseudotyped viruses from the sarbecovirus, merbecovirus and embecovirus subgenera. Structural and functional studies delineate the molecular basis of S2P6 cross-reactivity and broad neutralization and indicate that this mAb blocks viral entry by inhibiting membrane fusion. S2P6 protects hamsters challenged with SARS-CoV-2 (including the B.1.351 variant of concern) through direct viral neutralization and Fc-mediated effector functions. Serological and B cell repertoire analyses indicate that antibodies targeting the stem helix are found in some convalescent donors and vaccinees but are predominantly of narrow specificity. Germline reversion of the identified cross-reactive mAbs revealed that their unmutated ancestors are specific for the endemic OC43 or HKU1 viruses and acquired enhanced affinity and breadth through somatic mutations. These data demonstrate that conserved epitopes in the coronavirus fusion machinery can be targeted by protective antibodies and provide a framework for structure-guided design of pan-{beta}-coronavirus vaccines eliciting broad protection.

Structural basis for broad sarbecovirus neutralization by a human monoclonal antibody

The recent emergence of SARS-CoV-2 variants of concern (VOC) and the recurrent spillovers of coronaviruses in the human population highlight the need for broadly neutralizing antibodies that are not affected by the ongoing antigenic drift and that can prevent or treat future zoonotic infections. Here, we describe a human monoclonal antibody (mAb), designated S2x259, recognizing a highly conserved cryptic receptor-binding domain (RBD) epitope and cross-reacting with spikes from all sarbecovirus clades. S2x259 broadly neutralizes spike-mediated entry of SARS-CoV-2 including the B.1.1.7, B.1.351, P.1 and B.1.427/B.1.429 VOC, as well as a wide spectrum of human and zoonotic sarbecoviruses through inhibition of ACE2 binding to the RBD. Furthermore, deep-mutational scanning and in vitro escape selection experiments demonstrate that S2x259 possesses a remarkably high barrier to the emergence of resistance mutants. We show that prophylactic administration of S2x259 protects Syrian hamsters against challenges with the prototypic SARS-CoV-2 and the B.1.351 variant, suggesting this mAb is a promising candidate for the prevention and treatment of emergent VOC and zoonotic infections. Our data unveil a key antigenic site targeted by broadly-neutralizing antibodies and will guide the design of pan-sarbecovirus vaccines.

Transfer transcriptomic signatures for infectious diseases

The modulation of the transcriptome is among the earliest responses to infection, and vaccination. However, defining transcriptome signatures of disease is challenging because logistic, technical and cost factors limit the size and representativeness of samples in clinical studies. These limitations lead to poor performance of signatures when applied to new datasets or varying study settings. Using a novel approach, we leverage existing transcriptomic signatures as classifiers in unseen datasets from prospective studies, with the goal of predicting individual outcomes. Machine learning allowed the identification of sets of genes, which we name transfer transcriptomic signatures, that are predictive across diverse datasets and/or species (rhesus to humans) and that are also suggestive of activated pathways and cell type composition. We demonstrate the usefulness of transfer signatures in two use cases: progression of latent to active tuberculosis, and severity of COVID-19 and influenza A H1N1 infection. The broad significance of our work lies in the concept that a small set of archetypal human immunophenotypes, captured by transfer signatures, can explain a larger set of responses to diverse diseases.