Endophytic commensal bacteria capitalize on the AvrPto-FER pathway to enhance proliferation during early stages of pathogen invasion
Bibliographic record
Abstract
Leaves typically harbor a community of pre-existing beneficial and commensal bacteria that contribute to plant health. When pathogens invade, plants employ a series of strategies to response to the invasion, including the modulation of the microbial community structure. However, it remains unclear how commensal bacteria respond to pathogen at the early stage, and whether this response is specifically regulated. Here, we show that infection of Arabidopsis thaliana leaves by the pathogen Pseudomonas syringae pv. tomato DC3000 leads to a significant increase in the population of commensal bacteria, characterized by enrichment of Gammaproteobacteria and Alphaproteobacteria, alongside a reduction in Firmicutes and Betaproteobacteria. This cascade of events specifically occurs because AvrPto, an effector secreted by Pst DC3000, targets and inhibits the host receptor kinase FER, resulting in suppression of FER-mediated pattern-triggered immunity via the previously identified RIPK-RBOHD module. This specific suppression via FER pathway creates a condition that facilitates rapid proliferation of pre-existing commensal bacteria during early pathogen invasion. Our work provides a paradigm for the study of the interaction and ecological generality between commensal bacteria and pathogens with spatiotemporal patterns.
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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 | 0.000 |
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".