Metabolomic Profiling of Fungal Pathogens Responsible for Root Rot in American Ginseng
Bibliographic record
Abstract
Ginseng root is an economically valuable crop in Canada at high risk of yield loss caused by the pathogenic fungus Ilyonectria mors-panacis, formerly known as Cylindrocarpon destructans. While this pathogen has been well-characterized from morphological and genetic perspectives, little is known about the secondary metabolites it produces and their role in pathogenicity. We used an untargeted tandem liquid chromatography-mass spectrometry (LC-MS)-based approach paired with global natural products social molecular networking (GNPS) to compare the metabolite profiles of virulent and avirulent Ilyonectria strains. The ethyl acetate extracts of 22 I. mors-panacis strains and closely related species were analyzed by LC-MS/MS. Principal component analysis of LC-MS features resulted in two distinct groups, which corresponded to virulent and avirulent Ilyonectria strains. Virulent strains produced more types of compounds than the avirulent strains. The previously reported I. mors-panacis antifungal compound radicicol was present. Additionally, a number of related resorcyclic acid lactones (RALs) were putatively identified, namely pochonins and several additional derivatives of radicicol. Pochonins have not been previously reported in Ilyonectria spp. and have documented antimicrobial activity. This research contributes to our understanding of I. mors-panacis natural products and its pathogenic relationship with ginseng.
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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 | 0.001 | 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".