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Record W2583501014 · doi:10.1139/cjb-2016-0304

Salinity-regulated expression of genes involved in GABA metabolism and signaling

2017· article· en· W2583501014 on OpenAlexafffundvenue
Adel Zarei, Greta Z. Chiu, Guanghui Yu, Christopher P. Trobacher, Barry J. Shelp

Bibliographic record

VenueBotany · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyWRKY protein domainMYBGene expressionArabidopsisMutantCalmodulinArabidopsis thalianaGene expression profilingBiochemistryWild typeOrnithine decarboxylase antizymeCell biologyGeneEnzymeOrnithine decarboxylase

Abstract

fetched live from OpenAlex

4-Aminobutyrate (GABA) is a nonproteinogenic amino acid that functions in stress tolerance and signaling. Here, we report that salinity stress and elevated GABA levels coincided with the induction of glutamate decarboxylase 4 (GAD4) expression in Arabidopsis thaliana (L.) Heynh. ecotype Col-0. In-silico and microarray analysis revealed the over-representation of binding sites for WRKY and MYB transcription factors in the GAD4 promoter, as well as their co-expression with GAD4. Transcript profiling of liquid culture-grown, wild-type plantlets subjected to salinity stress for up to two days confirmed that GAD4 expression is associated with the inducible co-expression of WRKY28, WRKY30, WRKY40, MYB2, MYB15, and MYB108, as well as calmodulin-like 37 and aluminum-activated malate transporter 2, suggesting the involvement of gene regulation, protein activation, and anion transport in GABA accumulation. Transcript profiling of shoots from soil-grown, wild-type plants and corresponding single and double GAD mutants subjected to two days of salinity stress suggests that the GABA accumulation could involve post-translational activation of pre-existing GAD1 and GAD2 by elevated cytosolic calmodulin, as well as induction of GAD4 expression.

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 score0.815
Threshold uncertainty score0.144

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 head0.049
GPT teacher head0.283
Teacher spread0.234 · 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

Citations31
Published2017
Admission routes3
Has abstractyes

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