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Rice RNA‐dependent RNA polymerase 6 acts in small RNA biogenesis and spikelet development

2012· article· en· W1839389162 on OpenAlexfundno aff
Xianwei Song, Dekai Wang, Lijia Ma, Zhiyu Chen, Pingchuan Li, Xia Cui, Chunyan Liu, Shouyun Cao, Chengcai Chu, Yuezhi Tao, Xiaofeng Cao

Bibliographic record

VenueThe Plant Journal · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsBiologySmall RNARNAGeneticsTrans-acting siRNASmall interfering RNACell biologyArgonauteMutantGeneRNA silencingRNA interference

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.235
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations124
Published2012
Admission routes1
Has abstractyes

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