Enhanced Resistance to<i>Fusarium graminearum</i>in Transgenic Arabidopsis Plants Expressing a Modified Plant Thionin
Bibliographic record
Abstract
The fungal pathogen Fusarium graminearum causes Fusarium head blight (FHB) on wheat, barley, and other grains. FHB results in yield reductions and contaminates grain with trichothecene mycotoxins, which threaten food safety and food security. Innovative mechanisms for controlling FHB are urgently needed. We have previously shown that transgenic tobacco and citrus plants expressing a modified thionin (Mthionin) exhibited enhanced resistance toward several bacterial pathogens. The aim of this study was to investigate whether overexpression of Mthionin could be similarly efficacious against F. graminearum, and whether transgenic expression of Mthionin impacts the plant microbiome. Transgenic Arabidopsis plants expressing Mthionin were generated and confirmed. When challenged with F. graminearum, Mthionin-expressing plants showed less disease and fungal biomass in both leaves and inflorescences compared with control plants. When infiltrated into leaves, macroconidia of F. graminearum germinated at lower rates and produced less hyphal growth in Arabidopsis leaves expressing Mthionin. Moreover, marker genes related to defense signaling pathways were expressed at significantly higher levels after F. graminearum infection in Mthionin transgenic Arabidopsis plants. However, Mthionin expression did not appreciably alter the overall microbiome associated with transgenic plants grown under controlled conditions; across leaves and roots of Mthionin-expressing and control transgenic plants, only a few bacterial and fungal taxa differed, and differences between Mthionin transformants were of similar magnitude compared with control plants. In sum, our data indicate that Mthionin is a promising candidate to produce transgenic crops for reducing FHB severity and ultimately mycotoxin contamination.
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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".