Necrotrophic Pathogens Use the Salicylic Acid Signaling Pathway to Promote Disease Development in Tomato
Bibliographic record
Abstract
Plants use different immune pathways to combat pathogens. The activation of the jasmonic acid (JA)-signaling pathway is required for resistance against necrotrophic pathogens; however, to combat biotrophic pathogens, the plants activate mainly the salicylic acid (SA)-signaling pathway. SA can antagonize JA signaling and vice versa. NPR1 (noninducible pathogenesis-related 1) is considered a master regulator of SA signaling. NPR1 interacts with TGA transcription factors, ultimately leading to the activation of SA-dependent responses. SA has been shown to promote disease development caused by the necrotrophic pathogen Botrytis cinerea through NPR1, by suppressing the expression of two JA-dependent defense genes, proteinase inhibitors I and II. We show here that the transcription factor TGA1.a contributes to disease development caused by B. cinerea in tomato by suppressing the expression of proteinase inhibitors I and II. Finally, we present evidence that the SA-signaling pathway contributes to disease development caused by another necrotrophic pathogen, Alternaria solani, in tomato. Disease development promoted by SA through NPR1 requires the TGA1.a transcription factor. These data highlight how necrotrophs manipulate the SAsignaling pathway to promote their disease in tomato.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".