Respective contributions of Arabidopsis <scp>DCL</scp>2 and <scp>DCL</scp>4 to <scp>RNA</scp> silencing
Bibliographic record
Abstract
Dicer proteins are central to the different mechanisms involving RNA interference. Plants have evolved multiple DICER-LIKE (DCL) copies, thus enabling functional diversification. In Arabidopsis, DCL2 and DCL4 process double-stranded RNA into 22 and 21 nucleotide small interfering (si)RNAs, respectively, and have overlapping functions with regards to virus and transgene silencing. Nonetheless, some studies have reported that dcl2 or dcl4 single mutations are sometimes sufficient to hinder silencing. To better dissect the role of DCL2 and DCL4, we analyzed silencing kinetics and efficiencies using different transgenic systems in single and double mutant backgrounds. The results indicate that DCL2 stimulates transitivity and secondary siRNA production through DCL4 while being sufficient for silencing on its own. Notably, silencing of 35S-driven transgenes functions more efficiently in dcl4 mutants, indicating that DCL4 mostly obscures DCL2 in wild-type plants. Nonetheless, in a dcl4 mutant compromised in phloem-originating silencing, ectopically expressed DCL2 allows restoration of silencing, suggesting that DCL2 is not, or poorly, expressed in phloem. Remarkably, this ectopic DCL2 contribution to phloem-originating silencing is dependent on the activity of RNA-DEPENDENT RNA POLYMERASE6. These results indicate that, despite differences in the silencing activity of their small RNA products, DCL2 and DCL4 mostly act redundantly yet hierarchically when present simultaneously.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".