Light prevents pathogen-induced aqueous microenvironments via potentiation of salicylic acid signaling
Bibliographic record
Abstract
Abstract Upon establishment of an infection, many plant pathogens induce an aqueous microenvironment in the extracellular space of their host, resulting in water-soaked lesions. In the case of Pseudomonas syringae ( Pst ), this is accomplished through the activity of water-soaking effectors that stimulate abscisic acid (ABA) production and signaling, which results in stomatal closure. This reduces transpiration and induces a microenvironment favorable for bacterial growth. Stomata are also highly sensitive to environmental conditions, including light and circadian rhythm. Here, we show that a period of darkness is required for water-soaking, and that a constant light regime abrogates the water-soaking activity of Pst effectors. Additionally, we show that constant light induces resistance against Pst and that this effect requires salicylic acid (SA). An increase in SA production upon infection under constant light did not affect effector-induced ABA signaling, but rather abrogated ABA’s ability to induce stomatal closure. Indeed, under normal diurnal light regimes, application of a SA analog is sufficient to prevent the ability of the pathogen to induce stomatal closure and a water-rich niche in the apoplast. Our results provide a novel approach to interfering with a common virulence strategy, as well as providing a physiological mechanism by which SA functions in defense against certain pathogens.
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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) | 0.002 | 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".