Genome-wide characterization of SARS-CoV-2 cytopathogenic proteins in the search of antiviral targets
Bibliographic record
Abstract
Abstract Therapeutic inhibition of critical viral functions is important for curtailing coronavirus disease-2019 (COVID-19). We sought to identify antiviral targets through genome-wide characterization of SARS-CoV-2 proteins that are crucial for viral pathogenesis and that cause harmful cytopathic effects. All twenty-nine viral proteins were tested in a fission yeast cell-based system using inducible gene expression. Twelve proteins including eight non-structural proteins (NSP1, NSP3, NSP4, NSP5, NSP6, NSP13, NSP14 and NSP15) and four accessory proteins (ORF3a, ORF6, ORF7a and ORF7b) were identified that altered cellular proliferation and integrity, and induced cell death. Cell death correlated with the activation of cellular oxidative stress. Of the twelve proteins, ORF3a was chosen for further study in mammalian cells. In human pulmonary and kidney epithelial cells, ORF3a induced cellular oxidative stress associated with apoptosis and necrosis, and caused activation of pro-inflammatory response with production of the cytokines TNF-α, IL-6, and IFN-β1, possibly through the activation of NF-κB. To further characterize the mechanism, we tested a natural ORF3a Beta variant, Q57H, and a mutant with deletion of the highly conserved residue, ΔG188. Compared to wild type ORF3a, the ΔG188 variant yielded more robust activation of cellular oxidative stress, cell death, and innate immune response. Since cellular oxidative stress and inflammation contribute to cell death and tissue damage linked to the severity of COVID-19, our findings suggest that ORF3a is a promising, novel therapeutic target against COVID-19. Significance The ongoing SARS-CoV-2 pandemic has claimed over 5 million lives with more than 250 million people infected world-wide. While vaccines are effective, the emergence of new viral variants could jeopardize vaccine protection. Antiviral drugs provide an alternative to battle against COVID-19. Our goal was to identify viral therapeutic targets that can be used in antiviral drug discovery. Utilizing a genome-wide functional analysis in a fission yeast cell-based system, we identified twelve viral candidates, including ORF3a, which cause cellular oxidative stress, inflammation and apoptosis and necrosis that contribute to COVID-19. Our findings indicate that antiviral agents targeting ORF3a could greatly impact COVID-19.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".