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Record W4400736105 · doi:10.1038/s44318-024-00174-6

A viral effector blocks the turnover of a plant NLR receptor to trigger a robust immune response

2024· article· en· W4400736105 on OpenAlexaff
Chunli Wang, Min Zhu, Hao Hong, Jia Li, Chongkun Zuo, Yu Zhang, Yajie Shi, Suyu Liu, Haohua Yu, Yuling Yan, Jing Chen, Lingna Shangguan, Rongzhen Chen, Karen Thulasi Devendrakumar, Xiaorong Tao

Bibliographic record

VenueThe EMBO Journal · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsCanada's Michael Smith Genome Sciences Centre
FundersNational Key Research and Development Program of ChinaHuazhong Agricultural UniversityNanjing Agricultural UniversityGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsEffectorBiologyImmune systemCell biologyUbiquitin ligaseImmune receptorPathogenReceptorLeucine-rich repeatUbiquitinImmunologyBiochemistryKinaseGene

Abstract

fetched live from OpenAlex

Plant intracellular nucleotide-binding and leucine-rich repeat immune receptors (NLRs) play a key role in activating a strong pathogen defense response. Plant NLR proteins are tightly regulated and accumulate at very low levels in the absence of pathogen effectors. However, little is known about how this low level of NLR proteins is able to induce robust immune responses upon recognition of pathogen effectors. Here, we report that, in the absence of effector, the inactive form of the tomato NLR Sw-5b is targeted for ubiquitination by the E3 ligase SBP1. Interaction of SBP1 with Sw-5b via only its N-terminal domain leads to slow turnover. In contrast, in its auto-active state, Sw-5b is rapidly turned over as SBP1 is upregulated and interacts with both its N-terminal and NB-LRR domains. During infection with the tomato spotted wilt virus, the viral effector NSm interacts with Sw-5b and disrupts the interaction of Sw-5b with SBP1, thereby stabilizing the active Sw-5b and allowing it to induce a robust immune response.

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)0.003
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 score0.843
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0010.000

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 head0.031
GPT teacher head0.262
Teacher spread0.231 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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