Full-spike deep mutational scanning helps predict the evolutionary success of SARS-CoV-2 clades

Bernadeta Dadonaite, Jack Brown, Teagan E McMahon, Ariana G Farrell, Daniel Asarnow, Cameron Stewart, Jenni Logue, Ben Murrell, Helen Y Chu, David Veesler, Jesse D Bloom
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引用次数: 1

Abstract

SARS-CoV-2 variants acquire mutations in spike that promote immune evasion and impact other properties that contribute to viral fitness such as ACE2 receptor binding and cell entry. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning to measure how >9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry, or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully impacted ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456, and 473--however, the antigenic impacts of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.
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全突峰深度突变扫描有助于预测SARS-CoV-2进化枝的进化成功
SARS-CoV-2变异体在突刺中获得突变,促进免疫逃避,并影响其他有助于病毒适应性的特性,如ACE2受体结合和细胞进入。了解突变如何影响这些突刺表型,可以深入了解病毒当前和潜在的未来进化。在这里,我们使用假病毒深度突变扫描来测量整个XBB.1.5和BA.2刺突上的9,000个突变如何影响ACE2结合、细胞进入或从人类血清中逃逸。我们发现,在SARS-CoV-2进化过程中,受体结合域(RBD)外的突变对ACE2结合产生了有意义的影响。我们还测量了XBB.1.5尖峰突变如何影响最近感染SARS-CoV-2的个体的血清中和作用。最强的血清逃逸突变位于RBD的357、420、440、456和473位点,然而,这些突变的抗原影响因个体而异。我们还在RBD外发现了强逃逸突变;然而,它们中的许多减少ACE2结合,表明它们通过调节RBD构象起作用。值得注意的是,人类SARS-CoV-2分支的生长速度在很大程度上可以通过测量突变对刺突表型的影响来解释,这表明我们的数据可以更好地预测病毒的进化。
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