Genomic variability and plasticity of <i>Pseudomonas</i> causing coffee leaf spots in Minas Gerais state, Brazil
Bibliographic record
Abstract
Abstract Foliar spots caused by Pseudomonas coronafaciens pv. garcae (Pcg), Pseudomonas amygdali pv. tabaci (Pat) and Pseudomonas cichorii (Pch) are major bacterial diseases that can reduce coffee production. However, little is known about the genetic diversity and molecular mechanisms underlying the pathogenicity to coffee plants of these bacteria. In this study, genome sequences of Pcg, Pat and Pch strains isolated from coffee plants in Minas Gerais state, Brazil, were used to assess their variability and plasticity, and compare their type III secretion system (T3SS) and apoplastic effector repertoires as well as tabtoxin biosynthetic/detoxification genes. Genomic diversity was found for all three phytopathogens, among which Pch possesses the highest number of exclusive proteins. The Pcg genome is the most stable whereas that of Pch is the most plastic, which is related to their host ranges. When compared with those of Pseudomonas syringae pv. tomato DC3000, hrp / hrc gene sequences are more conserved in Pcg and Pat than in Pch, which also possesses the smallest T3SS and the largest apoplastic effector repertoires. The only T3SS effector family common to all three pathogens is AvrE, suggesting that, as for other plant– Pseudomonas interactions, it may play a crucial role for pathogenicity towards coffee plants. Apoplastic proteins associated with maintaining the redox balance and degrading proteins/peptides not previously described as important in plant–bacteria interactions were found. Gain/loss of the tabtoxin biosynthetic cluster with retention of the antitoxin gene was observed, indicating that tabtoxin production is not a limiting factor for the occurrence of mixed infections.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".