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Record W2530478975 · doi:10.1038/ncomms13179

NIN-like protein 8 is a master regulator of nitrate-promoted seed germination in Arabidopsis

2016· article· en· W2530478975 on OpenAlexafffund
Dawei Yan, Vanathy Easwaran, Vivian Chau, Masanori Okamoto, Matthew Ierullo, Mitsuhiro Kimura, Akira Endo, Ryoichi Yano, Asher Pasha, Yunchen Gong, Yong‐Mei Bi, Nicolas Provart, David S. Guttman, Anne Krapp, Steven J. Rothstein, Eiji Nambara

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversity of GuelphCanada Research ChairsUniversity of Toronto
FundersPrecursory Research for Embryonic Science and TechnologyNatural Sciences and Engineering Research Council of CanadaInstitut National de la Recherche AgronomiqueScuola Superiore Sant'AnnaShimane UniversityAcademia SinicaAgence Nationale de la Recherche
KeywordsArabidopsisGerminationAbscisic acidNitrate reductaseNitrateMutantCatabolismBiologyRegulatorCell biologyBotanyBiochemistryEnzymeGeneEcology

Abstract

fetched live from OpenAlex

Seeds respond to multiple different environmental stimuli that regulate germination. Nitrate stimulates germination in many plants but how it does so remains unclear. Here we show that the Arabidopsis NIN-like protein 8 (NLP8) is essential for nitrate-promoted seed germination. Seed germination in nlp8 loss-of-function mutants does not respond to nitrate. NLP8 functions even in a nitrate reductase-deficient mutant background, and the requirement for NLP8 is conserved among Arabidopsis accessions. NLP8 reduces abscisic acid levels in a nitrate-dependent manner and directly binds to the promoter of CYP707A2, encoding an abscisic acid catabolic enzyme. Genetic analysis shows that NLP8-mediated promotion of seed germination by nitrate requires CYP707A2. Finally, we show that NLP8 localizes to nuclei and unlike NLP7, does not appear to be activated by nitrate-dependent nuclear retention of NLP7, suggesting that seeds have a unique mechanism for nitrate signalling.

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 score0.874
Threshold uncertainty score0.180

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 science0.0010.000
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 head0.028
GPT teacher head0.244
Teacher spread0.217 · 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

Citations239
Published2016
Admission routes2
Has abstractyes

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