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Record W2125111015 · doi:10.3389/fpls.2015.00235

Integrating data on the Arabidopsis NPR1/NPR3/NPR4 salicylic acid receptors; a differentiating argument

2015· article· en· W2125111015 on OpenAlexafffund
Xiahezi Kuai, Brandon J. MacLeod, Charles Després

Bibliographic record

VenueFrontiers in Plant Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNPR1Systemic acquired resistanceWRKY protein domainArabidopsisBiologyUbiquitin ligaseReceptorTranscription factorCell biologySalicylic acidBiochemistryGeneUbiquitinMutantInternal medicineMedicine

Abstract

fetched live from OpenAlex

Salicylic acid (SA) is a mandatory plant metabolite in the deployment of systemic acquired resistance (SAR), a broad-spectrum systemic immune response induced by local inoculation with avirulent pathogens. The NPR1 transcription co-activator is the central node positively regulating SAR. SA was the last of the major hormones to be without a known receptor. Recently, NPR1 was shown to be the direct link between SA and gene activation. This discovery seems to be controversial. NPR1 being an SA-receptor is reminiscent of the mammalian steroid receptors, which are transcription factors whose binding to DNA is dependent on the interaction with a ligand. Unlike steroid receptors, NPR1 does not bind directly to DNA, but is recruited to promoters by the TGA family of transcription factors to form an enhanceosome. In Arabidopsis, NPR1 is part of a multigene family in which two other members, NPR3 and NPR4, have also been shown to interact with SA. NPR3/NPR4 are negative regulators of immunity and act as substrate adaptors for the recruitment of NPR1 to an E3-ubiquitin ligase, leading to its subsequent degradation by the proteasome. In this perspective, we will stress-test in a friendly way the current NPR1/NPR3/NPR4 model.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.054
GPT teacher head0.250
Teacher spread0.195 · 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 designNot applicable
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

Citations68
Published2015
Admission routes2
Has abstractyes

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