Reduced neutralisation of SARS-CoV-2 omicron B.1.1.529 variant by post-immunisation serum

Dejnirattisai, Wanwisa; Shaw, R. H.; Supasa, Piyada; Liu, Chang; Stuart, Arabella; Pollard, Andrew J.; Liu, Xinxue; Lambe, Teresa; Crook, Derrick W.; Stuart, D.; Mongkolsapaya, Juthathip; Nguyen‐Van‐Tam, Jonathan S; Snape, Matthew D; Screaton, Gavin

doi:10.1016/s0140-6736(21)02844-0

Cited by 329 publications

(229 citation statements)

References 5 publications

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“…Antibody responses are generally sufficient to neutralize the Alpha variant to similar levels as the original Wuhan strain in mRNA vaccine recipients and in convalescent individuals. However, the Beta, Gamma, Delta and Omicron variants showed on average an 9 fold, 4-fold, 4-fold and 20 to 40-fold reduced sensitivity respectively to neutralization by sera from convalescent patients as well as from vaccine recipients 19,24-25 .…”

Section: Introductionmentioning

confidence: 93%

Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines

Gils

Lavell

Appelman

et al. 2021

Preprint

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Emerging and future SARS-CoV-2 variants may jeopardize the effectiveness of vaccination campaigns. We performed a head-to-head comparison of the ability of sera from individuals vaccinated with either one of four vaccines (BNT162b2, mRNA-1273, AZD1222 or Ad26.COV2.S) to recognize and neutralize the four SARS-CoV-2 variants of concern (VOCs; Alpha, Beta, Gamma and Delta). Four weeks after completing the vaccination series, SARS-CoV-2 wild-type neutralizing antibody titers were highest in recipients of BNT162b2 and mRNA-1273 (median titers of 1891 and 3061, respectively), and substantially lower in those vaccinated with the adenovirus vector-based vaccines AZD1222 and Ad26.COV2.S (median titers of 241 and 119, respectively). VOCs neutralization was reduced in all vaccine groups, with the largest (5.8-fold) reduction in neutralization being observed against the Beta variant. Overall, the mRNA vaccines appear superior to adenovirus vector-based vaccines in inducing neutralizing antibodies against VOCs four weeks after the final vaccination.

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Section: Introductionmentioning

confidence: 93%

Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines

Gils

Lavell

Appelman

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These results are broadly consistent with both a) predictions that the Omicron spike protein structure can evade antibodies and b) experiments measuring significantly lower neutralization by vaccinated and convalescent sera as compared to previous variants—including Beta, which, as Table 1 indicates, is significantly immune evasive itself. [166]–[171] This demonstrates the ability of the neural network model to generalize, as it predicts a reduction in neutralizing antibody titers of Omicron much greater than any other previously characterized variant considered individually. By contrast, when tested XGBoost an alternative methodology, it consistently overfit to the training data and predicted no more than a 2 or 3-fold reduction for any combination of maximum tree depth, regularization, and other hyperparameters.…”

Section: Resultsmentioning

confidence: 83%

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

Sokhansanj

Zhao

Rosen

2021

Preprint

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As the COVID-19 pandemic continues, the SARS-CoV-2 virus continues to rapidly mutate and change in ways that impact virulence, transmissibility, and immune evasion. Genome sequencing is a critical tool, as other biological techniques can be more costly, time-consuming, and difficult. However, the rapid and complex evolution of SARS-CoV-2 challenges conventional sequence analysis methods like phylogenetic analysis. The virus picks up and loses mutations independently in multiple subclades, often in novel or unexpected combinations, and, as for the newly emerged Omicron variant, sometimes with long explained branches. We propose interpretable deep sequence models trained by machine learning to complement conventional methods. We apply Transformer-based neural network models developed for natural language processing to analyze protein sequences. We add network layers to generate sample embeddings and sequence-wide attention to interpret models and visualize multiscale patterns. We demonstrate and validate our framework by modeling SARS-CoV-2 and coronavirus taxonomy. We then develop an interpretable predictive model of disease severity that integrates SARS-CoV-2 spike protein sequence and patient demographic variables, using publicly available data from the GISAID database. We also apply our model to Omicron. Based on knowledge prior to the availability of empirical data for Omicron, we predict: 1) reduced neutralization antibody activity (15-50 fold) greater than any previously characterized variant, varying between Omicron sublineages, and 2) reduced risk of severe disease (by 35-40%) relative to Delta. Both predictions are in accord with recent epidemiological and experimental data.

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“…No reuse allowed without permission. vaccinated and/or infected donors [40][41][42][43] . They also confer resistance or complete escape from the majority of clinical antibody candidates 43 .…”

Section: Neutralization Of the Omicron Variantmentioning

confidence: 99%

Engineering SARS-CoV-2 neutralizing antibodies for increased potency and reduced viral escape

Zhao

Keating

Ozorowski

et al. 2022

Preprint

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The rapid spread of SARS-CoV-2 variants poses a constant threat of escape from monoclonal antibody and vaccine countermeasures. Mutations in the ACE2 receptor binding site on the surface S protein have been shown to disrupt antibody binding and prevent viral neutralization. Here, we use a directed evolution-based approach to engineer three neutralizing antibodies for enhanced binding to S protein. The engineered antibodies showed increased in vitro functional activity in terms of neutralization potency and/or breadth of neutralization against viral variants. Deep mutational scanning revealed that higher binding affinity reduced the total number of viral escape mutations. Studies in the Syrian hamster model showed two examples where the affinity matured antibody provided superior protection compared to the parental antibody. These data suggest that monoclonal antibodies for anti-viral indications could benefit from in vitro affinity maturation to reduce viral escape pathways and appropriate affinity maturation in vaccine immunization could help resist viral variation.

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Reduced neutralisation of SARS-CoV-2 omicron B.1.1.529 variant by post-immunisation serum

Cited by 329 publications

References 5 publications

Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines

Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

Engineering SARS-CoV-2 neutralizing antibodies for increased potency and reduced viral escape

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