Diverse Evolutionary Mechanisms Shape the Type III Effector Virulence Factor Repertoire in the Plant Pathogen Pseudomonas syringae
Bibliographic record
Abstract
Many gram-negative pathogenic bacteria directly translocate effector proteins into eukaryotic host cells via type III delivery systems. Type III effector proteins are determinants of virulence on susceptible plant hosts; they are also the proteins that trigger specific disease resistance in resistant plant hosts. Evolution of type III effectors is dominated by competing forces: the likely requirement for conservation of virulence function, the avoidance of host defenses, and possible adaptation to new hosts. To understand the evolutionary history of type III effectors in Pseudomonas syringae, we searched for homologs to 44 known or candidate P. syringae type III effectors and two effector chaperones. We examined 24 gene families for distribution among bacterial species, amino acid sequence diversity, and features indicative of horizontal transfer. We assessed the role of diversifying and purifying selection in the evolution of these gene families. While some P. syringae type III effectors were acquired recently, others have evolved predominantly by descent. The majority of codons in most of these genes were subjected to purifying selection, suggesting selective pressure to maintain presumed virulence function. However, members of 7 families had domains subject to diversifying selection.
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 | 0.001 | 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 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".