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Record W2981656086 · doi:10.1101/815845

The <i>Brassica napus</i> Wall-Associated Kinase-Like (WAKL) gene <i>Rlm9</i> provides race-specific blackleg resistance

2019· preprint· en· W2981656086 on OpenAlexafffund
Nicholas J. Larkan, Lisong Ma, Parham Haddadi, Miles Buchwaldt, Isobel A. P. Parkin, Mohammad Djavaheri, M. Hossein Borhan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaSaskatchewan Canola Development Commission
KeywordsBlacklegLeptosphaeria maculansPathosystemR geneBiologyGeneGeneticsBrassicaPlant disease resistanceBotany

Abstract

fetched live from OpenAlex

Abstract In plants, race-specific defense against microbial pathogens is facilitated by resistance ( R ) genes which correspond to specific pathogen avirulence ( Avr ) genes. This study reports the cloning of a blackleg R gene from Brassica napus (canola); Rlm9 , which encodes a wall-associated kinase-like (WAKL) protein, a newly-discovered class of race-specific plant RLK resistance genes. Rlm9 provides race-specific resistance against isolates of Leptosphaeria maculans carrying the corresponding avirulence gene AvrLm5-9 , representing only the second WAKL-type R gene described to date. The Rlm9 protein is predicted to be cell membrane-bound yet appears to have no direct interaction with AvrLm5-9. Rlm9 forms part of a distinct evolutionary family of RLK proteins in B. napus, and while little is yet known about WAKL function, the Brassica-Leptosphaeria pathosystem may prove to be a model system by which the mechanism of fungal avirulence protein recognition by WAKL-type R genes can be determined.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.014
GPT teacher head0.193
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2019
Admission routes2
Has abstractyes

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