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Record W2091097167 · doi:10.1111/pbi.12165

Transient down‐regulation of the <scp>RNA</scp> silencing machinery increases efficiency of <i><scp>A</scp>grobacterium</i>‐mediated transformation of <scp>A</scp>rabidopsis

2014· article· en· W2091097167 on OpenAlexaff
Andriy Bilichak, Youli Yao, Igor Kovalchuk

Bibliographic record

VenuePlant Biotechnology Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBiologyGene silencingTransformation (genetics)ArabidopsisCell biologyMutantAgrobacteriumArgonauteRNA interferenceAgrobacterium tumefaciensGeneRNAMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Agrobacterium tumefaciens is a plant pathogen that is widely used in plant transformation. As the process of transgenesis includes the delivery of single-stranded T-DNA molecule, we hypothesized that transformation rate may negatively correlate with the efficiency of the RNA-silencing machinery. Using mutants compromised in either the transcriptional or post-transcriptional gene-silencing pathways, two inhibitors of stable transformation were revealed-AGO2 and NRPD1a. Furthermore, an immunoprecipitation experiment has shown that NRPD1, a subunit of Pol IV, directly interacts with Agrobacterium T-DNA in planta. Using the Tobacco rattle virus (TRV)--based virus-induced gene silencing (VIGS) technique, we demonstrated that the transient down-regulation of the expression of either AGO2 or NRPD1a genes in reproductive organs of Arabidopsis, leads to an increase in transformation rate. We observed a 6.0- and 3.5-fold increase in transformation rate upon transient downregulation of either AGO2 or NRPD1a genes, respectively. This is the first report demonstrating the increase in the plant transformation rate via VIGS-mediated transient down-regulation of the components of epigenetic machinery in reproductive tissue.

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)0.002
metaresearch head score (Gemma)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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