Back

SARS-COV-2 γ variant acquires spike P681H or P681R for improved viral fitness

Yang, X.-J.

2021-10-19 evolutionary biology
10.1101/2021.10.16.464641 bioRxiv
Show abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) evolves and generates different variants through a continuously branching model. Four variants of concern have been the major pandemic drivers around the world. One important question is how they may evolve and generate subvariants, some of which may be even more virulent and drive the pandemic further. While investigating how {gamma} (or P.1) variant has been evolving, I noticed the spike substitution P681H in a group of genomes encoding a new subvariant, which has been designated P.1.7. This subvariant has become the dominant P.1 sublineage in Brazil, Italy, Spain and Peru, supporting that P681H confers evolutionary advantage to P.1.7. In Brazil and Peru, P.1.7 was still responsible for ~30% and ~40% cases, respectively, in August 2021. However, it has been competed out by {delta}1 (a {delta} subvariant) in both countries, Italy and Spain, suggesting that P.1.7 is not as virulent as {delta}1. In addition, 160 P.1 genomes possess a related substitution, P681R, and 120 of them encode a new subvariant, designated P.1.8. This P.1 subvariant carries two additional spike substitutions, T470N and C1235F, located at the receptor-binding pocket and cytoplasmic tail of spike protein, respectively. More P.1.8 genomes have been identified than P.1 genomes that encode P681R but not T470N and C1235F, suggesting that these two substitutions improve virulence of P.1.8 subvariant. Some P.1 genomes carry other substitutions (such as N679K, V687L and C1250F) that affect the furin cleavage site or cytoplasmic tail of spike protein. Thus, to improve viral fitness and expand its evolutionary cage, {gamma} variant acquires mutations to finetune the furin cleavage site and cytoplasmic tail of spike protein.

Matching journals

The top 7 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.