Directing Trophic Divergence in Plant-Pathogen Interactions: Antagonistic Phytohormones With NO Doubt?
Bibliographic record
Abstract
A fundamental process culminating in the mechanisms of plant-pathogen interactions is the regulation of trophic divergence into biotrophic, hemibiotrophic, and necrotrophic interactions. Plant hormones, of almost all types, play significant roles in this regulatory apparatus. In plant-pathogen interactions, two classical mechanisms underlying hormone-dependent trophic divergence are long recognized. While salicylic acid dominates in the execution of host defense response against biotrophic and early-stage hemibiotrophic pathogens, jasmonic acid, and ethylene are key players facilitating host defense response against necrotrophic and later-stage hemibiotrophic pathogens. Evidence increasingly suggests that trophic divergence appears to be modulated by more complex signaling networks. Acting antagonistically or agonistically, other hormones such as auxins, cytokinins, abscisic acid, gibberellins, brassinosteroids, and strigolactones, as well as nitric oxide, are emerging candidates in the regulation of trophic divergence. In this review, the latest advances in the dynamic regulation of trophic divergence are summarized, emphasizing common and contrasting hormonal and nitric oxide signaling strategies deployed in plant-pathogen interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".