SARS-COV-2 γ variant acquires spike P681H or P681R for improved viral fitness
Bibliographic record
Abstract
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 γ (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 δ1 (a δ subvariant) in both countries, Italy and Spain, suggesting that P.1.7 is not as virulent as δ1. 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, γ variant acquires mutations to finetune the furin cleavage site and cytoplasmic tail of spike protein.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".