Characterisation of the transcriptome and proteome of SARS-CoV-2 reveals a cell passage induced in-frame deletion of the furin-like cleavage site from the spike glycoprotein
Bibliographic record
Abstract
BACKGROUND: SARS-CoV-2 is a recently emerged respiratory pathogen that has significantly impacted global human health. We wanted to rapidly characterise the transcriptomic, proteomic and phosphoproteomic landscape of this novel coronavirus to provide a fundamental description of the virus's genomic and proteomic potential. METHODS: We used direct RNA sequencing to determine the transcriptome of SARS-CoV-2 grown in Vero E6 cells which is widely used to propagate the novel coronavirus. The viral transcriptome was analysed using a recently developed ORF-centric pipeline. Allied to this, we used tandem mass spectrometry to investigate the proteome and phosphoproteome of the same virally infected cells. RESULTS: Our integrated analysis revealed that the viral transcripts (i.e. subgenomic mRNAs) generally fitted the expected transcription model for coronaviruses. Importantly, a 24 nt in-frame deletion was detected in over half of the subgenomic mRNAs encoding the spike (S) glycoprotein and was predicted to remove a proposed furin cleavage site from the S glycoprotein. Tandem mass spectrometry identified over 500 viral peptides and 44 phosphopeptides in virus-infected cells, covering almost all proteins predicted to be encoded by the SARS-CoV-2 genome, including peptides unique to the deleted variant of the S glycoprotein. CONCLUSIONS: Detection of an apparently viable deletion in the furin cleavage site of the S glycoprotein, a leading vaccine target, shows that this and other regions of SARS-CoV-2 proteins may readily mutate. The furin site directs cleavage of the S glycoprotein into functional subunits during virus entry or exit and likely contributes strongly to the pathogenesis and zoonosis of this virus. Our data emphasises that the viral genome sequence should be carefully monitored during the growth of viral stocks for research, animal challenge models and, potentially, in clinical samples. Such variations may result in different levels of virulence, morbidity and mortality.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".