Genomic screens identify a new phytobacterial microbe-associated molecular pattern and the cognate Arabidopsis receptor-like kinase that mediates its immune elicitation
Bibliographic record
Abstract
BACKGROUND: The recognition of microbe-associated molecular patterns during infection is central to the mounting of an effective immune response. In spite of their importance, it remains difficult to identify these molecules and the host receptors required for their perception, ultimately limiting our understanding of the role of these molecules in the evolution of host-pathogen relationships. RESULTS: We employ a comparative genomics screen to identify six new immune eliciting peptides from the phytopathogenic bacterium Pseudomonas syringae. We then perform a reverse genetic screen to identify Arabidopsis thaliana leucine-rich repeat receptor-like kinases required for the recognition of these elicitors. We test the six elicitors on 187 receptor-like kinase knock-down insertion lines using a high-throughput peroxidase-based immune assay and identify multiple lines that show decreased immune responses to specific peptides. From this primary screen data, we focused on the interaction between the xup25 peptide from a bacterial xanthine/uracil permease and the Arabidopsis receptor-like kinase xanthine/uracil permease sensing 1; a family XII protein closely related to two well-characterized receptor-like kinases. We show that xup25 treatment increases pathogenesis-related gene induction, callose deposition, seedling growth inhibition, and resistance to virulent bacteria, all in a xanthine/uracil permease sensing 1-dependent manner. Finally, we show that this kinase-like receptor can bind the xup25 peptide directly. These results identify xup25 as a P. syringae microbe-associated molecular pattern and xanthine/uracil permease sensing 1 as a receptor-like kinase that detects the xup25 epitope to activate immune responses. CONCLUSIONS: The present study demonstrates an efficient method to identify immune elicitors and the plant receptors responsible for their perception. Further exploration of these molecules will increase our understanding of plant-pathogen interactions and the basis for host specificity.
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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) | 0.002 | 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".