Differential metabolic response of barley genotypes, varying in resistance, to trichothecene‐producing and ‐nonproducing ( <i>tri5</i> <sup> <i>−</i> </sup> ) isolates of <i>Fusarium graminearum</i>
Bibliographic record
Abstract
Resistance of barley to Fusarium graminearum was studied using a pair each of resistant and susceptible black and yellow barley lines. The spikelets were inoculated with a trichothecene‐producing isolate, a trichothecene‐nonproducing isolate ( tri5 − ), or a mock solution. Spikelets were collected 72 h after inoculation and metabolites were analysed using a LC‐hybrid MS system. Metabolite abundances were used to identify the constitutive (RRC) and induced resistance‐related metabolites (RRI). The pathogen virulence factor, DON, and its plant detoxification product, DON‐3‐ O ‐glucoside (D3G), were also identified and designated as resistance‐indicator (RI) metabolites. The RRC, RRI and RI metabolites were putatively identified. Jasmonic acid was significantly induced in barley following inoculation with a trichothecene‐producing isolate, but not with a tri5 − isolate. The former isolate reduced the induction of both the number and amount of RR metabolites. The metabolites cinnamic acid, sinapoyl alcohol, coniferin, catechin and naringin were identified only in response to the inoculation with a tri5 − mutant. The abundances of p ‐coumaric acid, coniferaldehyde and sinapaldehyde increased more in response to the tri5 − mutant than to the trichothecene‐producing isolate. The total amount of DON synthesized and its conversion to D3G varied greatly between the resistant and susceptible black barley, but not in yellow barley. Interestingly, an increase in the amount of total DON produced was associated with a decrease in the conversion of DON to D3G. The roles of RRC, RRI and RI metabolites in plant defence and their further use as potential biomarkers in screening are discussed.
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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) | 0.001 | 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".