A comparison between the role of enniatins and deoxynivalenol in Fusarium virulence on different tissues of common wheat
Bibliographic record
Abstract
BACKGROUND: Fusarium graminearum and Fusarium avenaceum are two of the most important causal agents of Fusarium head blight (FHB) of wheat. They can produce mycotoxins that accumulate in infected wheat heads, including deoxynivalenol (DON) and enniatins (ENNs), produced by F. graminearum and F. avenaceum, respectively. While the role of DON as a virulence factor in F. graminearum toward wheat is well known, ENNs in F. avenaceum has been poorly explored. Results obtained to-date indicate that ENNs may confer an advantage to F. avenaceum only on particular hosts. RESULTS: In this study, with the use of ENN-producing and ENN non-producing F. avenaceum strains, the role of ENNs on F. avenaceum virulence was investigated on the root, stem base and head of common wheat, and compared with the role of DON, using DON-producing and DON non-producing F. graminearum strains. The DON-producing F. graminearum strain showed a significantly higher ability to cause symptoms and colonise each of the tested tissues than the non-producing strain. On the other hand, the ability to produce ENNs increased initial symptoms of the disease and fungal biomass accumulation, measured by qPCR, only in wheat heads, and not in roots or stem bases. LC-MS/MS analysis was used to confirm the presence of ENNs and DON in the different strains, and results, both in vitro and in wheat heads, were consistent with the genetics of each strain. CONCLUSION: While the key role of DON on F. graminearum virulence towards three different wheat tissues was noticeable, ENNs seemed to have a role only in influencing F. avenaceum virulence on common wheat heads probably due to an initial delay in the appearance of symptoms.
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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".