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Record W2339819641 · doi:10.1104/pp.15.01954

Two redundant receptor-like cytoplasmic kinases function downstream of pattern recognition receptors to regulate activation of SA biosynthesis in Arabidopsis

2016· article· en· W2339819641 on OpenAlexafffund
Qing Kong, Tongjun Sun, Na Qu, Junling Ma, Meng Li, Yu-ti Cheng, Qian Zhang, Di Wu, Zhibin Zhang, Yuelin Zhang

Bibliographic record

VenuePLANT PHYSIOLOGY · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyBiosynthesisMutantReceptorSignal transductionArabidopsisCell biologyKinaseWRKY protein domainTranscription factorBiochemistryPattern recognition receptorFunction (biology)GeneInnate immune system

Abstract

fetched live from OpenAlex

Salicylic acid (SA) serves as a critical signaling molecule in plant defense. Two transcription factors, SARD1 and CBP60g, control SA biosynthesis through regulating pathogen-induced expression of Isochorismate Synthase1, which encodes a key enzyme for SA biosynthesis. Here, we report that Pattern-Triggered Immunity Compromised Receptor-like Cytoplasmic Kinase1 (PCRK1) and PCRK2 function as key regulators of SA biosynthesis. In the pcrk1 pcrk2 double mutant, pathogen-induced expression of SARD1, CBP60g, and ICS1 is greatly reduced. The pcrk1 pcrk2 double mutant, but neither of the single mutants, exhibits reduced accumulation of SA and enhanced disease susceptibility to bacterial pathogens. Both PCRK1 and PCRK2 interact with the pattern recognition receptor FLS2, and treatment with pathogen-associated molecular patterns leads to rapid phosphorylation of PCRK2. Our data suggest that PCRK1 and PCRK2 function downstream of pattern recognition receptor in a signal relay leading to the activation of SA biosynthesis.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.752

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)0.0010.000

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 head0.020
GPT teacher head0.210
Teacher spread0.191 · 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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePLANT PHYSIOLOGYSame topicPlant-Microbe Interactions and ImmunityFrench-language works237,207