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Record W2149359829 · doi:10.1093/jxb/eru390

PERK–KIPK–KCBP signalling negatively regulates root growth in Arabidopsis thaliana

2014· article· en· W2149359829 on OpenAlexafffund
Tania Humphrey, Katrina E. Haasen, May Grace Aldea-Brydges, He Sun, Yara Zayed, Emily Indriolo, Daphne R. Goring

Bibliographic record

VenueJournal of Experimental Botany · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaArizona Biomedical Research Commission
KeywordsArabidopsisKinaseArabidopsis thalianaBiologyCell biologyGeneMutantGenetics

Abstract

fetched live from OpenAlex

The Arabidopsis proline-rich, extensin-like receptor-like kinases (PERKs) are a small group of receptor-like kinases that are thought to act as sensors at the cell wall through their predicted proline-rich extracellular domains. In this study, we focused on the characterization of a subclade of three Arabidopsis predicted PERK genes, PERK8, -9, and -10, for which no functions were known. Yeast two-hybrid interaction studies were conducted with the PERK8,- 9, and -10 cytosolic kinase domains, and two members of the Arabidopsis AGC VIII kinase family were identified as interacting proteins: AGC1-9 and the closely related kinesin-like calmodulin-binding protein (KCBP)-interacting protein kinase (KIPK). As KIPK has been identified previously as an interactor of KCBP, these interactions were also examined further and confirmed in this study. Finally, T-DNA mutants for each gene were screened for altered phenotypes under different conditions, and from these screens, a role for the PERK, KIPK, and KCBP genes in negatively regulating root growth was uncovered.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.254
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

Citations57
Published2014
Admission routes2
Has abstractyes

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