Design and evaluation of small interfering RNAs for the treatment of Severe Acute Respiratory Syndrome-Coronavirus-2
Bibliographic record
Abstract
SARS-CoV-2 is the virus responsible for the COVID-19 pandemic. As the 2019 coronavirus disease continues to spread, it will be useful to have as many effective treatment options as possible. This research has the potential to create a siRNA treatment that is safe, effective, and practical in design and administration; 192 siRNAs were designed to target conserved regions of the SARS-CoV-2 genome. The first aim of this study is to confirm, via sequence analysis, that these target sites have remained highly conserved over the course of the pandemic. Multiple sequence alignments were generated for the first half of 30,312 full SARS-CoV-2 genomes, which were averaged and compared with the Wuhan-Hu-1 reference genome. Most target sites maintained a very high level of conservation, suggesting that potential repressor siRNAs could be effective in a majority of infected individuals. To evaluate the efficacy of the 192 test siRNAs, we cloned sections of the SARS-CoV-2 RNA genome into GFP fusion genes. Some of these constructs were transfected in different conditions to set up a screening assay based on GFP expression. Preliminary data on the setup of this GFP reporter assay show that the M, N, E, ORF8, and ORF10 constructs produced a good GFP signal, whereas the S, F1, F2 and F3 constructs did not produce a sufficiently strong GFP signal to detect above background. In a preliminary experiment, we evaluated siRNAs targeting the M, N, and E open reading frames and found some to be efficacious. Future directions for this project include generating alignments of the second half of the SARS-CoV-2 genome for a complete sequence conservation estimate, and cell metabolism assays for supplementing visual observations of siRNA toxicity, optimization of GFP readout, and screening of all designed siRNAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 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 teacher head, 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".