On the availability of microRNA-induced silencing complexes, saturation of microRNA-binding sites and stoichiometry
Bibliographic record
Abstract
Several authors have suggested or inferred that modest changes in microRNA expression can potentiate or impinge on their capacity to mediate gene repression, and that doing so could play a significant role in diseases. Such interpretations are based on several assumptions, namely: (i) changes in microRNA expression correlate with changes in the availability of mature, functional miRISC, (ii) changes in microRNA expression can significantly alter the stoichiometry of miRISC populations with their cognate targets, (iii) and this, in turn, can result in changes in miRISC silencing output. Here, we experimentally challenge those assumptions by quantifying and altering the availability of miRISC across several families of microRNAs. Doing so revealed a surprising fragmentation in the miRISC functional pool, striking differences in the availability of miRNA families and saturability of miRNA-mediated silencing. Furthermore, we provide direct experimental evidence that only a limited subset of miRNAs, defined by a conjuncture of expression threshold, miRISC availability and low target site abundance, is susceptible to competitive effects through microRNA-binding sites.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".