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Record W1993712847 · doi:10.1104/pp.112.199646

RDR1 and SGS3, Components of RNA-Mediated Gene Silencing, Are Required for the Regulation of Cuticular Wax Biosynthesis in Developing Inflorescence Stems of Arabidopsis  

2012· article· en· W1993712847 on OpenAlexafffund
Patricia Lam, Lifang Zhao, Heather E. McFarlane, Mytyl Aiga, Vivian Lam, Tanya S. Hooker, Ljerka Kunst

Bibliographic record

VenuePLANT PHYSIOLOGY · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsArabidopsisBiologyRNAWaxRepressorArabidopsis thalianaCell biologyRibonucleaseGeneBiochemistryMutantGene expression

Abstract

fetched live from OpenAlex

The cuticle is a protective layer that coats the primary aerial surfaces of land plants and mediates plant interactions with the environment. It is synthesized by epidermal cells and is composed of a cutin polyester matrix that is embedded and covered with cuticular waxes. Recently, we have discovered a novel regulatory mechanism of cuticular wax biosynthesis that involves the ECERIFERUM7 (CER7) ribonuclease, a core subunit of the exosome. We hypothesized that at the onset of wax production, the CER7 ribonuclease degrades an mRNA specifying a repressor of CER3, a wax biosynthetic gene whose protein product is required for wax formation via the decarbonylation pathway. In the absence of this repressor, CER3 is expressed, leading to wax production. To identify the putative repressor of CER3 and to unravel the mechanism of CER7-mediated regulation of wax production, we performed a screen for suppressors of the cer7 mutant. Our screen resulted in the isolation of components of the RNA-silencing machinery, RNA-DEPENDENT RNA POLYMERASE1 and SUPPRESSOR OF GENE SILENCING3, implicating RNA silencing in the control of cuticular wax deposition during inflorescence stem development in Arabidopsis (Arabidopsis thaliana).

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)0.000
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 score0.657
Threshold uncertainty score0.118

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.048
GPT teacher head0.214
Teacher spread0.167 · 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

Citations114
Published2012
Admission routes2
Has abstractyes

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