Rice RNA‐dependent RNA polymerase 6 acts in small RNA biogenesis and spikelet development
Bibliographic record
Abstract
Higher plants have evolved multiple RNA-dependent RNA polymerases (RDRs), which work with Dicer-like (DCL) proteins to produce different classes of small RNAs with specialized molecular functions. Here we report that OsRDR6, the rice (Oryza sativa L.) homolog of Arabidopsis RDR6, acts in the biogenesis of various types and sizes of small RNAs. We isolated a rice osrdr6-1 mutant, which was temperature sensitive and showed spikelet defects. This mutant displays reduced accumulation of tasiR-ARFs, the conserved trans-acting siRNAs (tasiRNAs) derived from the TAS3 locus, and ectopic expression of tasiR-ARF target genes, the Auxin Response Factors (including ARF2 and ARF3/ETTIN). The loss of tasiR-mediated repression of ARFs in osrdr6-1 can explain its morphological defects, as expression of two non-targeted ARF3 gene constructs (ARF3muts) in a wild-type background mimics the osrdr6 and osdcl4-1 mutant phenotypes. Small RNA high-throughput sequencing also reveals that besides tasiRNAs, 21-nucleotide (nt) phased small RNAs are also largely dependent on OsRDR6. Unexpectedly, we found that osrdr6-1 has a strong impact on the accumulation of 24-nt phased small RNAs, but not on unphased ones. Our work uncovers the key roles of OsRDR6 in small RNA biogenesis and directly illustrates the crucial functions of tasiR-ARFs in rice development.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".