Effects of Chemical Modifications on siRNA Strand Selection in Mammalian Cells
Bibliographic record
Abstract
Small interfering RNAs (siRNAs) enable efficient gene silencing through RNA interference (RNAi) mechanisms. The RNAi machinery relies on an RNA-guided nuclease, Argonaute-2 (Ago2), which preferentially selects a single strand from an siRNA duplex. Complementarity between the selected strand and an RNA target strand leads to silencing through cleavage. The U.S. Food and Drug Administration's recent approval of two siRNA drugs has reignited optimism for RNAi therapeutics. Despite this recent success in the field, off-target effects are still a major concern; however, chemical modifications have shown promise in mitigating some off-target gene silencing. To evaluate the impact of novel chemical modifications on strand selection, we developed a quantitative polymerase chain reaction-based assay that is compatible with several pre-existing siRNA libraries and was used to characterize chemically modified siRNAs. siRNAs bearing azobenzene and propargyl modifications at the central region of the passenger strand significantly improved strand selection. On the other hand, folic acid-modified siRNAs improved strand selection best when placed at the 3' terminus. This study highlights the development and utility of a convenient method to evaluate the impact that novel chemical modifications have on strand-specific gene silencing 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) | 0.000 | 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) | 0.001 | 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".