Interaction between a J-domain co-chaperone and a specific Argonaute protein contributes to microRNA function in animals
Bibliographic record
Abstract
ABSTRACT MicroRNAs (miRNAs) are essential regulators of several biological processes. They are loaded onto Argonaute (AGO) proteins to achieve their repressive function, forming the microRNA-Induced Silencing Complex (miRISC). While several AGO proteins are expressed in plants and animals, it is still unclear why specific AGOs are strictly binding miRNAs. Here, we identified the co-chaperone DNJ-12 as a new interactor of ALG-1, one of the two major miRNA-specific AGOs in Caenorhabditis elegans . DNJ-12 does not interact with ALG-2, the other major miRNA-specific AGO, and PRG-1 and RDE-1, two AGOs involved in other small RNA pathways, making it a specific actor in ALG-1-dependent miRNA-mediated gene silencing. The loss of DNJ-12 causes developmental defects associated with defective miRNA function. Using the Auxin Inducible Degron (AID) system, a powerful tool to acutely degrade proteins in specific tissues, we show that DNJ-12 depletion hampers ALG-1 interaction with HSP70, a chaperone required for miRISC loading in vitro . Moreover, DNJ-12 depletion leads to the decrease of several miRNAs and prevents their loading onto ALG-1. This study uncovers the importance of a co-chaperone for the miRNA function in vivo and provides insights to explain how different small RNAs associate with specific AGO in animals.
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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 | 0.000 | 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".