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Record W2599218736 · doi:10.7554/elife.23684

Regulation of plant immune receptor accumulation through translational repression by a glycine-tyrosine-phenylalanine (GYF) domain protein

2017· article· en· W2599218736 on OpenAlexafffund
Zhongshou Wu, Shuai Huang, Xiaobo Zhang, Di Wu, Shitou Xia, Xin Li

Bibliographic record

VenueeLife · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsBiologyPsychological repressionCell biologyTyrosineTranslation (biology)EIF4EGeneticsBiochemistryGeneGene expressionMessenger RNA

Abstract

fetched live from OpenAlex

Plant immunity is tightly regulated to ensure proper defense against surrounding microbial pathogens without triggering autoimmunity, which negatively impacts plant growth and development. Immune receptor levels are intricately controlled by RNA processing and post-translational modification events, such as ubiquitination. It remains unknown whether, and if yes, how, plant immune receptor homeostasis is regulated at the translational level. From a mutant, snc1-enhancing (muse) forward genetic screen, we identified MUSE11/EXA1, which negatively regulates nucleotide-binding leucine-rich repeat (NLR) receptor mediated defence. EXA1 contains an evolutionarily conserved glycine-tyrosine-phenylalanine (GYF) domain that binds proline-rich sequences. Genetic and biochemical analysis revealed that loss of EXA1 leads to heightened NLR accumulation and enhanced resistance against virulent pathogens. EXA1 also associates with eIF4E initiation factors and the ribosome complex, likely contributing to the proper translation of target proteins. In summary, our study reveals a previously unknown mechanism of regulating NLR homeostasis through translational repression by a GYF protein.

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 score0.477
Threshold uncertainty score0.435

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 studies0.0010.000
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 head0.059
GPT teacher head0.306
Teacher spread0.247 · 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

Citations42
Published2017
Admission routes2
Has abstractyes

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