Identification of N,N′,N″-triacetylfusarinine C as a key metabolite for root rot disease virulence in American ginseng
Bibliographic record
Abstract
It is estimated that 20–30% of ginseng crops in Canada are lost to root rot each harvest. This disease is commonly caused by fungal infection with Ilyonectria, previously known as Cylindrocarpon. Previous reports have linked the virulence of fungal disease to the production of siderophores, a class of small-molecule iron chelators. However, these siderophores have not been identified in Ilyonectria. High-resolution LC–MS/MS was used to screen Ilyonectria and Cylindrocarpon strain extracts for secondary metabolite production. These strains were also tested for their ability to cause root rot in American ginseng and categorized as virulent or avirulent. The differences in detected metabolites between the virulent and avirulent strains were compared with a focus on siderophores. For the first time, a siderophore N,N′,N″-triacetylfusarinine C (TAFC) has been identified in Ilyonectria, and it appears to be linked to disease virulence. Siderophore production was suppressed as the concentration of iron increased, which is in agreement with previous reports. The identification of the siderophore produced by Ilyonectria gives us further insight into the root rot disease that heavily affects ginseng crop yields. This research identifies a molecular pathway previously unknown for ginseng root rot and could lead to new disease treatment options.
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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) | 0.000 | 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".