A combination of RBD and NTD neutralizing antibodies limits the generation of SARS-CoV-2 spike neutralization-escape mutants

Most known SARS-CoV-2 neutralizing antibodies (nAbs), including those approved by the FDA for emergency use, inhibit viral infection by targeting the receptor-binding domain (RBD) of the spike (S) protein. Variants of concern (VOC) carrying mutations in the RBD or other regions of S reduce the effectiveness of many nAbs and vaccines by evading neutralization. Therefore, therapies that are less susceptible to resistance are urgently needed. Here, we characterized the memory B-cell repertoire of COVID-19 convalescent donors and analyzed their RBD and non-RBD nAbs. We found that many of the non-RBD-targeting nAbs were specific to the N-terminal domain (NTD). Using neutralization assays with authentic SARS-CoV-2 and a recombinant vesicular stomatitis virus carrying SARS-CoV-2 S protein (rVSV-SARS2), we defined a panel of potent RBD and NTD nAbs. Next, we used a combination of neutralization-escape rVSV-SARS2 mutants and a yeast display library of RBD mutants to map their epitopes. The most potent RBD nAb competed with hACE2 binding and targeted an epitope that includes residue F490. The most potent NTD nAb epitope included Y145, K150 and W152. As seen with some of the natural VOC, the neutralization potencies of COVID-19 convalescent sera were reduced by 4-16-fold against rVSV-SARS2 bearing Y145D, K150E or W152R spike mutations. Moreover, we found that combining RBD and NTD nAbs modestly enhanced their neutralization potential. Notably, the same combination of RBD and NTD nAbs limited the development of neutralization-escape mutants in vitro, suggesting such a strategy may have higher efficacy and utility for mitigating the emergence of VOC. ImportanceThe US FDA has issued emergency use authorizations (EUAs) for multiple investigational monoclonal antibody (mAb) therapies for the treatment of mild to moderate COVID-19. These mAb therapeutics are solely targeting the receptor binding domain of the SARS-CoV-2 spike protein. However, the N-terminal domain of the spike protein also carries crucial neutralizing epitopes. Here, we show that key mutations in the N-terminal domain can reduce the neutralizing capacity of convalescent COVID-19 sera. We report that a combination of two neutralizing antibodies targeting the receptor binding and N-terminal domains may have higher efficacy and is beneficial to combat the emergence of virus variants.

Cytotoxic lymphocytes are dysregulated in multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and multiple organ involvement in individuals under 21 years following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To identify genes, pathways and cell types driving MIS-C, we sequenced the blood transcriptomes of MIS-C cases, pediatric cases of coronavirus disease 2019, and healthy controls. We define a MIS-C transcriptional signature partially shared with the transcriptional response to SARS-CoV-2 infection and with the signature of Kawasaki disease, a clinically similar condition. By projecting the MIS-C signature onto a co-expression network, we identified disease gene modules and found genes downregulated in MIS-C clustered in a module enriched for the transcriptional signatures of exhausted CD8+ T-cells and CD56dimCD57+ NK cells. Bayesian network analyses revealed nine key regulators of this module, including TBX21, a central coordinator of exhausted CD8+ T-cell differentiation. Together, these findings suggest dysregulated cytotoxic lymphocyte response to SARS-Cov-2 infection in MIS-C.

Where to look next? Eye movements reduce local uncertainty.

How do we decide where to look next? During natural, active vision, we move our eyes to gather task-relevant information from the visual scene. Information theory provides an elegant framework for investigating how visual stimulus information combines with prior knowledge and task goals to plan an eye movement. We measured eye movements as observers performed a shape-learning and -matching task, for which the task-relevant information was tightly controlled. Using computational models, we probe the underlying strategies used by observers when planning their next eye movement. One strategy is to move the eyes to locations that maximize the total information gained about the shape, which is equivalent to reducing global uncertainty. Observers' behavior may appear highly similar to this strategy, but a rigorous analysis of sequential fixation placement reveals that observers may instead be using a local rule: fixate only the most informative locations, that is, reduce local uncertainty.

An information maximization model of eye movements.

We propose a sequential information maximization model as a general strategy for programming eye movements. The model reconstructs high-resolution visual information from a sequence of fixations, taking into account the fall-off in resolution from the fovea to the periphery. From this framework we get a simple rule for predicting fixation sequences: after each fixation, fixate next at the location that minimizes uncertainty (maximizes information) about the stimulus. By comparing our model performance to human eye movement data and to predictions from a saliency and random model, we demonstrate that our model is best at predicting fixation locations. Modeling additional biological constraints will improve the prediction of fixation sequences. Our results suggest that information maximization is a useful principle for programming eye movements.

When is scene identification just texture recognition?

Subjects were asked to identify scenes after very brief exposures (<70 ms). Their performance was always above chance and improved with exposure duration, confirming that subjects can get the gist of a scene with one fixation. We propose that a simple texture analysis of the image can provide a useful cue towards rapid scene identification. Our model learns texture features across scene categories and then uses this knowledge to identify new scenes. The texture analysis leads to similar identifications and confusions as subjects with limited processing time. We conclude that early scene identification can be explained with a simple texture recognition model.