<i>Pantoea ananatis</i> -triggered systemic resistance requires root sensing through the LORE receptor kinase in Arabidopsis
Bibliographic record
Abstract
Abstract Systemic immunity induced by beneficial bacteria helps plants to cope with subsequent pathogen attacks. However, how these bacteria are sensed by plants and how this sensing results in a systemic response is not well understood. Recently, the Brassicaceae LIPOOLIGOSACCHARIDE-SPECIFIC REDUCED ELICITATION (LORE) receptor kinase was identified as the direct sensor of bacterial medium-chain-length 3-hydroxy fatty acids (mc-3-OH-FAs) and 3-(3-hydroxyalkanoyloxy)alkanoic acids (HAAs), which activate systemic Brassicaceae immunity. Here, we show that the bacterium Pantoea ananatis BRT175, which produces HAAs, can colonize Arabidopsis thaliana roots independently of the production of these compounds. P. ananatis triggers induced systemic resistance (ISR) to the necrotrophic pathogen Botrytis cinerea , but not to the hemibiotrophic pathogen Pseudomonas syringae pv. tomato . Importantly, this ISR can be mimicked by HAAs or 3-hydroxy-decanoic acid (3-OH-C 10 ). Both P. ananatis -triggered and 3-OH-C 10 -triggered ISR against B. cinerea are mediated by LORE sensing, involve salicylic acid, jasmonic acid and ethylene signaling pathways, and activate the expression of similar defense genes in infected leaves. Thus, 3-OH-C 10 and HAAs are perceived by a pattern recognition receptor (PRR) in the root and activate a local immune response in roots as well as systemic immunity in leaves. Our study demonstrates that these lipidic microbe-associated molecular patterns (MAMPs), produced by beneficial microorganisms, are necessary and sufficient to trigger rhizobacteria-driven ISR. It further highlights the role of these MAMPs in differentially activating systemic resistance against biotrophic and necrotrophic 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) | 0.001 | 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 | 0.001 |
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".