<i>Ralstonia solanacearum</i> pandemic lineage strain <scp>UW551</scp> overcomes inhibitory xylem chemistry to break tomato bacterial wilt resistance
Bibliographic record
Abstract
Plant-pathogenic Ralstonia strains cause bacterial wilt disease by colonizing xylem vessels of many crops, including tomato. Host resistance is the best control for bacterial wilt, but resistance mechanisms of the widely used Hawaii 7996 tomato breeding line (H7996) are unknown. Using growth in ex vivo xylem sap as a proxy for host xylem, we found that Ralstonia strain GMI1000 grows in sap from both healthy plants and Ralstonia-infected susceptible plants. However, sap from Ralstonia-infected H7996 plants inhibited Ralstonia growth, suggesting that in response to Ralstonia infection, resistant plants increase inhibitors in their xylem sap. Consistent with this, reciprocal grafting and defence gene expression experiments indicated that H7996 wilt resistance acts in both above- and belowground plant parts. Concerningly, H7996 resistance is broken by Ralstonia strain UW551 of the pandemic lineage that threatens highland tropical agriculture. Unlike other Ralstonia, UW551 grew well in sap from Ralstonia-infected H7996 plants. Moreover, other Ralstonia strains could grow in sap from H7996 plants previously infected by UW551. Thus, UW551 overcomes H7996 resistance in part by detoxifying inhibitors in xylem sap. Testing a panel of xylem sap compounds identified by metabolomics revealed that no single chemical differentially inhibits Ralstonia strains that cannot infect H7996. However, sap from Ralstonia-infected H7996 contained more phenolic compounds, which are known to be involved in plant antimicrobial defence. Culturing UW551 in this sap reduced total phenolic levels, indicating that the resistance-breaking Ralstonia strain degrades these chemical defences. Together, these results suggest that H7996 tomato wilt resistance depends in part on inducible phenolic compounds in xylem sap.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".