Developing Synthetic Full-Length SARS-CoV-2 cDNAs and Reporter Viruses for High-Throughput Antiviral Drug Screening
Bibliographic record
Abstract
The continuing spread of SARS-CoV-2 and the associated morbidity and mortality, especially in vulnerable populations, highlight the need for the development of antiviral therapeutics. Reverse genetics systems and reporter viruses are valuable for antiviral screening by simplifying methods to detect and quantify virus infections. This study aimed to generate wild-type and Nluc reporter full-length SARS-CoV-2 molecular clones and viruses as tools for high-throughput antiviral assays. The large SARS-CoV-2 genome (~30 kb) makes cDNA cloning and virus rescue technically challenging, so we opted to use cDNA chemical synthesis services to generate full-length wild-type and reporter Delta and Omicron clones. Clone-derived Delta and Omicron wild-type and reporter viruses were successfully rescued and showed replication kinetics comparable to patient-derived isolates. Nluc reporter viruses displayed stable luciferase expression that correlated with viral titres, supporting their reliability as replication substitutes. Antiviral assays measuring replication inhibition by Remdesivir, Molnupiravir, and Nirmatrelvir, based on Nluc expression, yielded IC50 values and selectivity indices consistent with published ranges. Finally, Delta Nluc viruses replicated in primary human bronchial epithelial cells, demonstrating the application of clone-derived viruses in physiologically relevant models. The SARS-CoV-2 cDNA clones and Nluc reporter viruses derived from DNA synthesis services provide a rapid, scalable reverse genetics platform for generating new viruses and developing assays to rapidly assess antiviral compounds against current and emerging SARS-CoV-2 variants or coronaviruses that may emerge in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".