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Record W4399671788 · doi:10.1038/s41467-024-49425-4

The effector-triggered immunity landscape of tomato against Pseudomonas syringae

2024· article· en· W4399671788 on OpenAlexafffund
Fabien Lonjon, Yan Lai, Nasrin Askari, Niharikaa Aiyar, Cedoljub Bundalovic-Torma, Bradley Laflamme, Pauline W. Wang, Darrell Desveaux, David S. Guttman

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Toronto
FundersMitacsScience Foundation Ireland
KeywordsPseudomonas syringaeEffectorBiologyVirulenceSolanumPlant ImmunityArabidopsisArabidopsis thalianaPathogenCropBiotechnologyBotanyMicrobiologyAgronomyGeneticsGeneMutantCell biology

Abstract

fetched live from OpenAlex

Tomato (Solanum lycopersicum) is one of the world's most important food crops, and as such, its production needs to be protected from infectious diseases that can significantly reduce yield and quality. Here, we survey the effector-triggered immunity (ETI) landscape of tomato against the bacterial pathogen Pseudomonas syringae. We perform comprehensive ETI screens in five cultivated tomato varieties and two wild relatives, as well as an immunodiversity screen on a collection of 149 tomato varieties that includes both wild and cultivated varieties. The screens reveal a tomato ETI landscape that is more limited than what was previously found in the model plant Arabidopsis thaliana. We also demonstrate that ETI eliciting effectors can protect tomato against P. syringae infection when the effector is delivered by a non-virulent strain either prior to or simultaneously with a virulent strain. Overall, our findings provide a snapshot of the ETI landscape of tomatoes and demonstrate that ETI can be used as a biocontrol treatment to protect crop plants.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.417

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 science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.258
Teacher spread0.246 · 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

Citations23
Published2024
Admission routes2
Has abstractyes

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