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Record W1997446463 · doi:10.1371/journal.pone.0092503

The LmSNF1 Gene Is Required for Pathogenicity in the Canola Blackleg Pathogen Leptosphaeria maculans

2014· article· en· W1997446463 on OpenAlexafffund
Jie Feng, Hui Zhang, Stephen E. Strelkov, Sheau‐Fang Hwang

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of AlbertaAgriculture Food and Rural Development
FundersAgriculture and Agri-Food CanadaSaskatchewan Canola Development Commission
KeywordsLeptosphaeria maculansBlacklegCanolaBiologyVirulenceGeneGene knockoutMicrobiologyGeneticsBotanyBrassica

Abstract

fetched live from OpenAlex

Leptosphaeria maculans is a fungal pathogen causing blackleg in canola. Its virulence has been attributed, among other factors, to the activity of hydrolytic cell wall degrading enzymes (CWDEs). Studies on the pathogenicity function of CWDEs in plant pathogenic fungi have been difficult due to gene redundancy. In microorganisms many CWDE genes are repressed by glucose and derepressed by the function of the sucrose non-fermenting protein kinase 1 gene (SNF1). To address the molecular function of SNF1 in L. maculans, the ortholog of SNF1 (LmSNF1) was cloned and functionally characterized using a gene knockout strategy. Growth of the LmSNF1 knockout strains was severely disrupted, as was sporulation, spore germination and the ability to attach on the plant surface. When inoculated on canola cotyledons, the LmSNF1 knockout strains could not cause any symptoms, indicating the loss of pathogenicity. The expression of 11 selected CWDE genes and a pathogenicity gene (LopB) was significantly down-regulated in the LmSNF1 knockout strains. In conclusion, knockout of LmSNF1 prevents L. maculans from properly derepressing the production of CWDEs, compromises the utilization of certain carbon sources, and impairs fungal pathogenicity on canola.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.392

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 head0.046
GPT teacher head0.221
Teacher spread0.175 · 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

Citations39
Published2014
Admission routes2
Has abstractyes

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