Coping with Environmental Eukaryotes; Identification of Pseudomonas syringae Genes during the Interaction with Alternative Hosts or Predators
Bibliographic record
Abstract
Understanding the molecular mechanisms underpinning the ecological success of plant pathogens is critical to develop strategies for controlling diseases and protecting crops. Recent observations have shown that plant pathogenic bacteria, particularly Pseudomonas, exist in a range of natural environments away from their natural plant host e.g., water courses, soil, non-host plants. This exposes them to a variety of eukaryotic predators such as nematodes, insects and amoebae present in the environment. Nematodes and amoeba in particular are bacterial predators while insect herbivores may act as indirect predators, ingesting bacteria on plant tissue. We therefore postulated that bacteria are probably under selective pressure to avoid or survive predation and have therefore developed appropriate coping mechanisms. We tested the hypothesis that plant pathogenic Pseudomonas syringae are able to cope with predation pressure and found that three pathovars show weak, but significant resistance or toxicity. To identify the gene systems that contribute to resistance or toxicity we applied a heterologous screening technique, called Rapid Virulence Annotation (RVA), for anti-predation and toxicity mechanisms. Three cosmid libraries for P. syringae pv. aesculi, pv. tomato and pv. phaseolicola, of approximately 2000 cosmids each, were screened in the susceptible/non-toxic bacterium Escherichia coli against nematode, amoebae and an insect. A number of potential conserved and unique genes were identified which included genes encoding haemolysins, biofilm formation, motility and adhesion. These data provide the first multi-pathovar comparative insight to how plant pathogens cope with different predation pressures and infection of an insect gut and provide a foundation for further study into the function of selected genes and their role in ecological success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".