Comparative Genomics of Host-Specific Virulence in <i>Pseudomonas syringae</i>
Bibliographic record
Abstract
While much study has gone into characterizing virulence factors that play a general role in disease, less work has been directed at identifying pathogen factors that act in a host-specific manner. Understanding these factors will help reveal the variety of mechanisms used by pathogens to suppress or avoid host defenses. We identified candidate Pseudomonas syringae host-specific virulence genes by searching for genes whose distribution among natural P. syringae isolates was statistically associated with hosts of isolation. We analyzed 91 strains isolated from 39 plant hosts by DNA microarray-based comparative genomic hybridization against an array containing 353 virulence-associated (VA) genes, including 53 type III secretion system effectors (T3SEs). We identified individual genes and gene profiles that were significantly associated with strains isolated from cauliflower, Chinese cabbage, soybean, rice, and tomato. We also identified specific horizontal gene acquisition events associated with host shifts by mapping the array data onto the core genome phylogeny of the species. This study provides the largest suite of candidate host-specificity factors from any pathogen, suggests that there are multiple ways in which P. syringae isolates can adapt to the same host, and provides insight into the evolutionary mechanisms underlying host adaptation.
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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".