Forward and robust selection of the most potent and noncellular toxic siRNAs from RNAi libraries
Bibliographic record
Abstract
Use of highly potent small interfering RNAs (siRNAs) can substantially reduce dose-dependent cytotoxic and off-target effects. We developed a genetic forward approach by fusing the cytosine deaminase gene with targets for the robust identification of highly potent siRNAs from RNA interference (RNAi) libraries that were directly delivered into cells via bacterial invasion. We demonstrated that two simple drug selection cycles performed conveniently in a single container predominately enriched two siRNAs targets the MVP gene (siMVP) and one siRNA targets the egfp gene (siEGFP) in surviving cells and these proved to be the most effective siRNAs reported. Furthermore, the potent siRNAs isolated from the surviving cells possessed noncellular toxic characteristics. Interestingly, the length of highly potent siMVPs identified could be as short as 16-mer, and increasing the length of their native sequences dramatically reduced RNAi potency. These results suggest that the current approach can robustly discover the most potent and nontoxic siRNAs in the surviving cells, and thus has great potential in facilitating RNAi applications by minimizing the dose-dependent and sequence nonspecific side effects of siRNAs.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".