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Record W2884212651 · doi:10.3390/ijms19072142

Colletotrichum higginsianum as a Model for Understanding Host–Pathogen Interactions: A Review

2018· review· en· W2884212651 on OpenAlexaff
Yaqin Yan, Qinfeng Yuan, Jintian Tang, Junbin Huang, Tom Hsiang, Yangdou Wei, Lu Zheng

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversity of SaskatchewanUniversity of Guelph
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaNational Natural Science Foundation of China
KeywordsColletotrichumHost (biology)BiologyPathogenFungal pathogenAppressoriumMicrobiologyEcologyPathogenicity

Abstract

fetched live from OpenAlex

Colletotrichum higginsianum is a hemibiotrophic ascomycetous fungus that causes economically important anthracnose diseases on numerous monocot and dicot crops worldwide. As a model pathosystem, the Colletotrichum–Arabidopsis interaction has the significant advantage that both organisms can be manipulated genetically. The goal of this review is to provide an overview of the system and to point out recent significant studies that update our understanding of the pathogenesis of C. higginsianum and resistance mechanisms of Arabidopsis against this hemibiotrophic fungus. The genome sequence of C. higginsianum has provided insights into how genome structure and pathogen genetic variability has been shaped by transposable elements, and allows systematic approaches to longstanding areas of investigation, including infection structure differentiation and fungal–plant interactions. The Arabidopsis-Colletotrichum pathosystem provides an integrated system, with extensive information on the host plant and availability of genomes for both partners, to illustrate many of the important concepts governing fungal–plant interactions, and to serve as an excellent starting point for broad perspectives into issues in plant pathology.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.716
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.385
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations78
Published2018
Admission routes1
Has abstractyes

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