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Record W2093529391 · doi:10.1094/phyto-04-12-0086-r

Gene Expression Profiling of a Compatible Interaction Between Douglas-Fir and the Root Rot Fungal Pathogen <i>Phellinus sulphurascens</i>

2013· article· en· W2093529391 on OpenAlexaff
Rona N. Sturrock, A.K.M. Ekramoddoullah

Bibliographic record

VenuePhytopathology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersU.S. Forest Service
KeywordsPathosystemBiologyRoot rotInoculationGeneGene expressionFungusBotanycDNA libraryPolymerase chain reactionGene expression profilingHorticultureGenetics

Abstract

fetched live from OpenAlex

Douglas-fir (DF) (Pseudotsuga menziesii) is one of the largest and most economically important coniferous species in western North America. Its productivity is greatly affected by the root rot fungus Phellinus sulphurascens Pilát. Evidence of resistance by DF to fungal root pathogens such as P. sulphurascens has been reported but mechanisms of resistance in this compatible pathosystem are not yet known. To better understand the DF-P. sulphurascens interaction, especially at the molecular level, we selected 12 diverse plant genes already identified as defense-related from a cDNA library constructed using root tissues from P. sulphurascens-infected DF seedlings. Using quantitative reverse-transcriptase polymerase chain reaction on infected DF root samples collected at five different time points after inoculation, we found that P. sulphurascens infection significantly elevated expression of the 12 selected genes. In most cases the highest expression level was recorded within 2 to 3 days after inoculation. The constructed cDNA library, which is enriched with defense-related host genes and a number of fungal genes, will continue to serve as a useful resource for future larger-scale gene discovery and functional research on the P. sulphurascens and DF pathosystem.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.515

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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