Dipeptidyl peptidase DPF-3 is a gatekeeper of microRNA Argonaute compensation in animals
Bibliographic record
Abstract
MicroRNAs (miRNAs) are essential regulators involved in multiple biological processes. To achieve their gene repression function, they are loaded in miRNA-specific Argonautes to form the miRNA-induced silencing complex (miRISC). Mammals and C. elegans possess more than one paralog of miRNA-specific Argonautes, but the dynamic between them remains unclear. Here, we report the conserved dipeptidyl peptidase DPF-3 as an interactor of the miRNA-specific Argonaute ALG-1 in C. elegans. Knockout of dpf-3 increases ALG-2 levels and miRISC formation in alg-1 loss-of-function animals, thereby compensating for ALG-1 loss and rescuing miRNA-related defects observed. DPF-3 can cleave an ALG-2 N-terminal peptide in vitro but does not appear to rely on this catalytic activity to regulate ALG-2 in vivo. This study uncovers the importance of DPF-3 in the miRNA pathway and provides insights into how multiple miRNA Argonautes contribute to achieving proper miRNA-mediated gene regulation in animals. Argonaute proteins are key effectors in the microRNA pathway. Here, the authors show that the conserved peptidase DPF-3 regulates Argonautes in C. elegans, and that loss of dpf-3 restores function and fitness in animals lacking the microRNA Argonaute ALG-1.
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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 | 0.001 |
| 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".