Diverse Genetic Mechanisms Enable <i>Pseudomonas syringae</i> to Rapidly Overcome Effector‐Triggered Immunity
Bibliographic record
Abstract
Bacterial plant pathogens pose a serious threat to worldwide crop yields and cause widespread food insecurity. One common approach to limit pathogen proliferation on critical crops is to genetically engineer cultivars that harbour resistance genes capable of recognising and responding to critical bacterial virulence factors like type III secreted effectors. Unfortunately, these resistance barriers are often overcome by pathogen evolution within just a few seasons. In this study, we explore the evolutionary mechanisms that enable pathogens to overcome plant resistance by leveraging two Pseudomonas syringae pv. maculicola strains that differ in their ability to cause disease on the model host Arabidopsis thaliana. We first characterise the molecular basis of the adaptation that enabled the P. syringae pv. maculicola PmaES4326 to overcome rps5-mediated resistance through a direct modification to its hopAR1 effector. We then show that through in planta evolution, the initially nonpathogenic strain P. syringae pv. maculicola PmaYM7930 can rapidly adapt to overcome rps5-mediated resistance via low-frequency mutations that do not involve direct modifications to hopAR1. This result was especially surprising because hopAR1 is known to be associated with mobile genetic elements that enable increased evolutionary plasticity. The rapid ability of P. syringae to overcome effector-triggered immunity without direct modifications to hopAR1 reveals that the genetic mechanisms enabling pathogens to overcome host resistance are more diverse than is currently recognised. This result has important implications for the development of more stably resistant crops that can resist various forms of pathogen evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".