Comparative genomic analysis of multiple strains of two unusual plant pathogens: Pseudomonas corrugata and Pseudomonas mediterranea
Bibliographic record
Abstract
The non-fluorescent pseudomonads, Pseudomonas corrugata (Pcor) and P. mediterranea (Pmed), are closely related species that cause pith necrosis, a disease of tomato that causes severe crop losses. However, they also show strong antagonistic effects against economically important pathogens, demonstrating their potential for utilization as biological control agents. In addition, their metabolic versatility makes them attractive for the production of commercial biomolecules and bioremediation. An extensive comparative genomics study is required to dissect the mechanisms that Pcor and Pmed employ to cause disease, prevent disease caused by other pathogens, and to mine their genomes for genes that encode proteins involved in commercially important chemical pathways. Here, we present the draft genomes of nine Pcor and Pmed strains from different geographical locations. This analysis covered significant genetic heterogeneity and allowed in-depth genomic comparison. All examined strains were able to trigger symptoms in tomato plants but not all induced a hypersensitive-like response in Nicotiana benthamiana. Genome-mining revealed the absence of type III secretion system and known type III effector-encoding genes from all examined Pcor and Pmed strains. The lack of a type III secretion system appears to be unique among the plant pathogenic pseudomonads. Several gene clusters coding for type VI secretion system were detected in all genomes. Genome-mining also revealed the presence of gene clusters for biosynthesis of siderophores, polyketides, non-ribosomal peptides, and hydrogen cyanide. A highly conserved quorum sensing system was detected in all strains, although species specific differences were observed. Our study provides the basis for in-depth investigations regarding the molecular mechanisms underlying virulence strategies in the battle between plants and microbes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".