Back

The D614G mutation of SARS-CoV-2 spike protein enhances viral infectivity and decreases neutralization sensitivity to individual convalescent sera

Hu, J.; He, C. L.; Gao, Q.; Zhang, G. J.; Cao, X. X.; Long, Q. X.; Deng, H. J.; Huang, L. Y.; Chen, J.; Wang, K.; Tang, N.; Huang, A. L.

2020-06-20 microbiology
10.1101/2020.06.20.161323 bioRxiv
Show abstract

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike (S) protein that mediates SARS-CoV-2 entry into host cells is a major target for vaccines and therapeutics. Thus, insights into its sequence variations are key to understanding the infection and antigenicity of SARS-CoV-2. A dominant mutational variant at position 614 of the S protein (aspartate to glycine, D614G mutation) was observed in the SARS-CoV-2 genome sequence obtained from the Nextstrain database. Using a pseudovirus-based assay, we identified that S-D614 and S-G614 protein pseudotyped viruses share a common receptor, human angiotensin-converting enzyme 2 (ACE2), which could be blocked by recombinant ACE2 with the fused Fc region of human IgG1. However, S-D614 and S-G614 protein demonstrated functional differences. First, S-G614 protein could be cleaved by serine protease elastase-2 more efficiently. Second, S-G614 pseudovirus infected 293T-ACE2 cells significantly more efficiently than did the S-D614 pseudovirus, especially in the presence of elastase-2. Third, an elastase inhibitor approved for clinical use blocked elastase-enhanced S-G614 pseudovirus infection. Moreover, 93% (65/70) convalescent sera from patients with COVID-19 could neutralize both S-D614 and S-G614 pseudoviruses with comparable efficiencies, but about 7% (5/70) convalescent sera showed reduced neutralizing activity against the S-G614 pseudovirus. These findings have important implications for SARS-CoV-2 transmission and immune interventions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.