Genetic responses in a plant‐endophyte interplay
Bibliographic record
Abstract
A plant can respond to the threat of a pathogen through resistance defenses or tolerance. Resistance has been widely studied in many host pathosystems but little is known about genetic events in tolerance. In this study a recently developed model for a tolerant tomato‐fungal (Verticillium dahliae) wilt pathosystem was used to examine changes in gene expression during both a tolerant interaction and in competition with a susceptible infection. Relative changes in mRNA levels were measured using a customized TVR (tomato‐Verticillium response) DNA chip and actual changes in cellular proteins were assessed using two dimensional gel electophoresis and MS‐based protein identification. Heat maps were used to identify responding groups of proteins and individual groups were examined further for common functions and other relationships including correspondence between mRNA and protein changes. In addition to elevated levels of defense gene responses to pathogen colonization, suppression or blocked responses were evident in mixed infections. These included some transcription factors and PR proteins such as class IV chitinases and beta glucanases known to target fungal spores and mycelium. Taken together the results reveal intriguing but complex molecular changes with potential agricultural benefits. Supported by NSERC (R.N.N. and J.R.), NIH, NHLBI (A.K.) and a Canadian Commonwealth Scholarship (H.O.S.).
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".