Global changes in gene expression associated with plant pathogen tolerance
Bibliographic record
Abstract
A plant can respond to the threat of a pathogen through resistance defenses or through tolerance. Resistance has been widely studied in many host pathogen systems but little is known about genetic changes which underlie a tolerant interaction; in many instances it is unclear if the pathogen is even detected. In this study we have used a recently developed model system for a tolerant tomato interaction with the fungal wilt pathogen, Verticillium dahliae . Amount of disease was estimated by a disease index and amount of pathogen by quantitative PCR. The objective was to use microarray technology to examine global changes in gene expression in a susceptible and tolerant interaction compared with uninoculated controls. The results indicate that genetic changes can be dramatically different and some genes that are strongly elevated in the susceptible interaction are actually down regulated in tolerance. Similar levels of fungal DNA and up regulation of many pathogenesis related genes in both interactions indicate the presence of fungus is clearly recognized by the plant but other changes correlate with the absence of symptoms in the tolerant interaction. For example, a gene encoding a known 14‐3‐3 regulatory protein and a number of genes normally affected by this protein are suppressed, including genes which may contribute to foliar necrosis and cell death in the susceptible interaction. This raises the possibility that the wilt symptoms, chlorosis and necrosis which are observed in the susceptible interaction are actually programmed to further limit the growth of the pathogen and protect the general tomato population. Supported by the Natural Sciences and Engineering Research Council of Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".