Cell‐specific expression of salicylate hydroxylase in an attempt to separate localized HR and systemic signalling establishing SAR in tobacco
Bibliographic record
Abstract
Abstract There is conflicting evidence concerning the nature of the long-distance signal responsible for establishing the systemic acquired resistance (SAR) state following a local response to an incompatible plant/pathogen interaction. We outline standard inoculation procedures and terminology for experiments used to characterize SAR in Nicotiana tabacum and show that leaf development (age) has dramatic affects on TMV lesion size which needs to be taken into account in experimental design. TMV infection was more efficient at inducing SAR than primary infection with avirulent bacteria. We have examined the effect on SAR induction of altering the accumulation of salicylic acid (SA), through the expression of a salicylate hydroxylase gene (SH-L), in different phases of lesion development using the hydrogen peroxide-responsive AoPR1 promoter and the salicylate-responsive PR1a promoter. Suppression of SA accumulation during the early phases of lesion development in AoPR1-SH-L transgenic tobacco resulted in an attenuated form of SAR compared to wild-type plants, whereas SAR was not exhibited in PR1a-SH-L plants. However, interpretation of data from these experiments was complicated by virus escape from inoculated leaves. Using a GUS reporter it was discovered that the CaMV35S promoter was not expressed constitutively in all cell types of petioles and stems, particularly phloem tissue, whereas the PR1a promoter demonstrated induced expression in the phloem following TMV infection. We suggest two hypotheses for why PR1a-SH-L transgenics do not display SAR: either the systemic expression of PR1a-SH-L is sufficient to suppress SAR, or SA synthesis or translocation in the phloem is essential for SAR.
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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) | 0.001 | 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".