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Record W4414453699 · doi:10.1093/plcell/koaf232

A plant RNA virus hijacks a dual-specificity phosphatase to attenuate MAPK-mediated immunity for robust infection

2025· article· en· W4414453699 on OpenAlexafffund
Yameng Luan, Xue Jiang, Yuting Wang, Mengzhu Chai, Fangfang Li, Aiming Wang, Xiaoyun Wu, Xiaofei Cheng

Bibliographic record

VenueThe Plant Cell · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersNatural Science Foundation of Heilongjiang ProvinceAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsTurnip mosaic virusPhosphataseArabidopsis thalianaArabidopsisPlant ImmunityImmunityPlant virusProtein phosphatase 2Innate immune system

Abstract

fetched live from OpenAlex

Mitogen-activated protein kinase (MAPK) cascades play vital roles in plant responses to biotic and abiotic stresses; however, their regulation during viral infection and the mechanisms by which viruses counteract these defenses remain poorly understood. Here, we report that the Arabidopsis thaliana atypical dual-specificity phosphatase (DSP) DSP4 negatively regulates plant immunity against turnip mosaic virus (TuMV), a member of the Potyviridae family. Subcellular localization, fractionation, and mutagenesis revealed that DSP4 is anchored to the cellular membrane via its C-terminus. Notably, only the membrane-bound form of DSP4 interacts with and dephosphorylates the MAPKs, MPK6 and MPK3, which redundantly restrict TuMV infection. Furthermore, TuMV P3 protein binds to DSP4, maintaining it on the membrane to dephosphorylate MPKs, whereas DSP4 is typically released from the membrane during immune priming. These findings unveil a molecular mechanism wherein TuMV P3 exploits this membrane-associated phosphatase to dampen MAPK-mediated immunity and promote virus infection.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.787

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 studies0.0010.000
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 head0.052
GPT teacher head0.251
Teacher spread0.199 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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