Regulation and different functions of the animal microRNA‐induced silencing complex
Bibliographic record
Abstract
Among the different types of small RNAs, microRNAs (miRNAs) are key players in controlling gene expression at the mRNA level. To be active, they must associate with an Argonaute protein to form the miRNA induced silencing complex (miRISC) and binds to specific mRNA through complementarity sequences. The miRISC binding to an mRNA can lead to multiple outcomes, the most frequent being inhibition of the translation and/or deadenylation followed by decapping and mRNA decay. In the last years, several studies described different mechanisms modulating miRISC functions in animals. For instance, the regulation of the Argonaute protein through post-translational modifications can change the miRISC gene regulatory activity as well as modulate its binding to proteins, mRNA targets and miRISC stability. Furthermore, the presence of RNA binding proteins and multiple miRISCs at the targeted mRNA 3' untranslated region (3'UTR) can also affect its function through cooperation or competition mechanisms, underlying the importance of the 3'UTR environment in miRNA-mediated repression. Another way to regulate the miRISC function is by modulation of its interactors, forming different types of miRNA silencing complexes that affect gene regulation differently. It is also reported that the subcellular localization of several components of the miRNA pathway can modulate miRISC function, suggesting an important role for vesicular trafficking in the regulation of this essential silencing complex. This article is categorized under: RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes Regulatory RNAs/RNAi/Riboswitches > RNAi: Mechanisms of Action Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".