SARS-CoV-2 and the CGG-CGG Furin Site Genetic Fingerprint: Five Years Later
Bibliographic record
Abstract
The key evolutionary step leading to the pandemic virus was the acquisition of the furin cleavage motif at the S protein S1/S2 junction. This insertion led to a gain of function for SARS-CoV-2, in which the virus's S protein became a substrate protein for human furin. The corresponding 12 nucleotide fragment inserted into the S gene in a SARS-CoV-2 precursor included the CGG-CGG genetic fingerprint coding the furin arginine pair. The arginine CGG codon was (still is) rare in the virus, even more two CGGs in a row. Afterwards the probable human origin of that motif has been proposed (BMC Genomic Data 24:71, 2023). Synonymous base substitutions or arginine codon usage bias at the CGG-CGG fingerprint was one of the evidences supporting the hypothesis. Based on 2025 SARS-CoV-2 isolates the aim of this work is follow the evolution of the furin site arginine pair code. From GISAID database 17,506 SARS-CoV-2 complete genomes were downloaded, with collection dates from January 1, 2025 to February 18, 2925. Using Perl programs the S gene sequences were retrieved. 62 out of 15,390 (0.4028%) S-protein sequences showed arginine codon usage bias at the S gene CGG-CGG fingerprint. The SARS-CoV-2 lineage distribution of the 2025 sample is shown. The XEC (44.5%) and KP.3.1.1 (13.8%) lineages were the majority. Lineage KP.3.1.1 was also the majority in CGG-CGG codon usage bias analyses, grouped into two main population groups of origin Japan and Canada. In the 2025 working sample 125 out of 1,620 (7,71%) Japan and 47 out of 4,793 (0,98%) Canada Ontario KP.3.1.1. isolates showed CGG-CGG optimization. The results shown are in agreement with previous studies, although in large samples the percentage (probability) of SARS-CoV-2 S gene furin site arginine codon optimization appears weak, it increases significantly when focusing on specific lineages or population groups.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".