Safe and Sensitive Antiviral Screening Platform Based on Recombinant Human Coronavirus OC43 Expressing the Luciferase Reporter Gene
Bibliographic record
Abstract
Human coronaviruses (HCoVs) cause 15 to 30% of mild upper respiratory tract infections. However, no specific antiviral drugs are available to prevent or treat HCoV infections to date. Here, we developed four infectious recombinant HCoVs-OC43 (rHCoVs-OC43) which express the Renilla luciferase (Rluc) reporter gene. Among these four rHCoVs-OC43, rOC43-ns2DelRluc (generated by replacing ns2 with the Rluc gene) showed robust luciferase activity with only a slight impact on its growth characteristics. Additionally, this recombinant virus remained stable for at least 10 passages in BHK-21 cells. rOC43-ns2DelRluc was comparable to its parental wild-type virus (HCoV-OC43-WT) with respect to the quantity of the antiviral activity of chloroquine and ribavirin. We showed that chloroquine strongly inhibited HCoV-OC43 replication in vitro, with a 50% inhibitory concentration (IC50) of 0.33 μM. However, ribavirin showed inhibition of HCoV-OC43 replication only at high concentrations which may not be applicable to humans in clinical treatment, with an IC50 of 10 μM. Furthermore, using a luciferase-based small interfering RNA (siRNA) screening assay, we identified double-stranded-RNA-activated protein kinase (PKR) and DEAD box RNA helicases (DDX3X) that exhibited antiviral activities, which were further verified by the use of HCoV-OC43-WT. Therefore, rOC43-ns2DelRluc represents a promising safe and sensitive platform for high-throughput antiviral screening and quantitative analysis of viral replication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".