Regulation and different functions of the animal microRNA‐induced silencing complex
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it