Proteomic insights into the mechanisms of deoxynivalenol resistance in Triticum aestivum
Bibliographic record
Abstract
Fusarium head blight (FHB) is a globally relevant cereal crop disease resulting from infection with fungal pathogens, including Fusarium spp., with Fusarium graminearum being the primary causative agent. A distinctive and devastating factor of this disease is the production of deoxynivalenol (DON), a mycotoxin, which inhibits eukaryotic protein synthesis to weaken and kill cells in infected host tissues, threatening food safety for humans and livestock. In this thesis, I investigated the regulation of host response to infection with the known virulence factor of FHB, deoxynivalenol. My findings elucidated our understanding of distinct DON detoxification responses corresponding to these parameters: 24 vs. 120 h post-inoculation, low [0.1 mg/mL] and high [1.0 mg/mL] DON, FHB-resistant vs. -susceptible cultivars through the production of proteins known to detoxify DON and with hypothesized DON-detoxifying capabilities (e.g., glutathione transferases and glycosyltransferases). Next, I developed an in vitro assay for the quantification of DON-degrading capabilities for these prioritized candidate proteins. Continuing to develop our understanding of the biochemical methods used to mitigate the effects of DON in planta is a useful approach to identifying biomarkers for selective breeding of mycotoxin-resistant cultivars in the future.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".