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Record W2095283819 · doi:10.1111/tpj.12573

<scp>HSP</scp>90s are required for <scp>NLR</scp> immune receptor accumulation in Arabidopsis

2014· article· en· W2095283819 on OpenAlexafffund
Shuai Huang, Jacqueline Monaghan, X.P. Zhong, Ling Lin, Tongjun Sun, Oliver Xiaoou Dong, Xin Li

Bibliographic record

VenueThe Plant Journal · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHsp90Ubiquitin ligaseImmune receptorArabidopsisImmune systemReceptorBiologyHeat shock proteinCell biologyMutantUbiquitinF-box proteinGenetic screenGeneGenetics

Abstract

fetched live from OpenAlex

Heat shock proteins (HSPs) serve as molecular chaperones for diverse client proteins in many biological processes. In plant immunity, cytosolic HSP90s participate in the assembly, stability control and/or activation of immune receptor complexes. In this paper we report that in addition to the well-established positive roles that HSP90 isoforms play in plant immunity, they are also involved in the negative regulation of immune receptor accumulation. Point mutations in two HSP90 genes, HSP90.2 and HSP90.3, were identified from a forward genetic screen designed to isolate mutants with enhanced disease resistance. We found that specific mutations in HSP90.2 and HSP90.3 lead to heightened accumulation of immune receptors, including SNC1, RPS2 and RPS4. HSP90s may assist SGT1 in the formation of SCF E3 ubiquitin ligase complexes that target immune receptors for degradation. Such regulation is critical for maintaining appropriate levels of immune receptor proteins to avoid autoimmunity.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.354
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.042
GPT teacher head0.302
Teacher spread0.260 · 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

Citations83
Published2014
Admission routes2
Has abstractyes

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