Differences in silencing of mismatched targets by sliced versus diced siRNAs
Bibliographic record
Abstract
It has been reported that the two major types of RNA interference triggers, the classical Dicer-generated small RNAs (siRNAs), which function with all members of the Argonaute (Ago) protein family in mammals, and the Ago2-sliced small RNAs (sli-siRNAs), which function solely through Ago2, have similar potency in target cleavage and repression. Here, we show that sli-siRNAs are generally more potent than siRNAs in silencing mismatched targets. This phenomenon is usually more apparent in targets that have mismatched nucleotides in the 3' supplementary region than in targets with mismatches in the seed region. We demonstrate that Ago2 slicer activity is a major factor contributing to the greater silencing efficiency of sli-siRNA against mismatched targets and that participation of non-slicing Agos in silencing mismatched siRNA targets may dilute the slicing ability of Ago2. The difference in length of the mature guide RNA used in sli-RISCs and si-RISCs may also contribute to the observed difference in knockdown efficiency. Our data suggest that a sli-siRNA guide strand is likely to have substantially stronger off-target effects than a guide strand with the same sequence in a classical siRNA and that Dicer and non-slicing Agos may play pivotal roles in controlling siRNA target specificity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".