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Record W4410493658 · doi:10.3390/jof11050391

Autophagy-Related Proteins (ATGs) Are Differentially Required for Development and Virulence of Sclerotinia sclerotiorum

2025· article· en· W4410493658 on OpenAlexafffund
Thilini Weerasinghe, Josh Li, Jiayang Gao, Lei Tian, Yan Xu, Yihan Gong, Weijie Huang, Yuelin Zhang, Liwen Jiang, Xin Li

Bibliographic record

VenueJournal of Fungi · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesFujian Agriculture and Forestry UniversityNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSclerotinia sclerotiorumVirulenceBiologyAutophagyMutantAppressoriumATG8Cell biologyPathogenGeneticsFungal pathogenGeneMicrobiologyBotanyApoptosis

Abstract

fetched live from OpenAlex

Sclerotinia sclerotiorum is a devastating fungal pathogen that can colonize numerous crops. Despite its economic importance, the regulation of its development and pathogenicity remains poorly understood. From a forward genetic screen in S. sclerotiorum, six UV mutants were identified with loss-of-function mutations in SsATG1, SsATG2, SsATG4, SsATG5, SsATG9, and SsATG26. Functional validation through gene knockouts revealed that each ATG is essential for sclerotia formation, although the morphology of appressoria was not significantly altered in the mutants. Different levels of virulence attenuation were observed among these mutants. Autophagy, monitored using GFP-ATG8, showed dynamic activities during sclerotia development. These findings suggest that macroautophagy and pexophagy contribute to sclerotia maturation and virulence processes. Future work will reveal how autophagy controls target organelle or protein turnover to regulate these processes.

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 score0.549
Threshold uncertainty score0.156

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 studies0.0000.000
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 head0.020
GPT teacher head0.218
Teacher spread0.197 · 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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