Kelch-type F-box protein TaFBK34 improves wheat seedling tolerance to heat stress
Bibliographic record
Abstract
BACKGROUND: Based on the climate change and more extreme temperature events in the past 30 years, heat stress (HS) has become one of the most detrimental abiotic stresses that affect crop growth and development, limit their geographical distribution, and reduce yield. As a typical chimonophilous crop, wheat is very sensitive to high temperature. Deciphering the molecular mechanism of the wheat response to high temperature will help in the development of cultivars that perform better under HS. RESULTS: In this study, we identified a wheat Kelch-type F-box gene, TaFBK34. Overexpression of TaFBK34 wheat plants (TaFBK34-OE) showed stronger heat tolerance compared to the wild type, while plants with attenuated TaFBK34 (TaFBK34-RNAi) exhibited the phenotype of heat sensitivity. Increased expression of antioxidant-related genes and a heat-shock protein gene was observed in TaFBK34-OE plants compared with the wild type, coinciding with higher activities of the antioxidant enzymes, accumulation of proline and soluble sugar, reduced malondialdehyde and reactive oxygen species content. The opposite trends were observed in TaFBK34-RNAi lines. TaFBK34 interacts with the ADP-ribosylation factor, TaARL2. Compared to the wild type, more TaARL2 protein accumulated in TaFBK34-RNAi lines after HS treatment; moreover, TaARL2 continued to increase after MG132 (a proteasome inhibitor) injection for 12 h + 37 °C for 12 h, indicating that TaARL2 is involved in the response to HS and is degraded by the 26S proteasome. CONCLUSIONS: These findings show that TaFBK34 improves wheat tolerance to HS, at least in part through an interaction with the TaARL2 protein, and provides potential applications of these genes for the improvement of wheat.
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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) | 0.002 | 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".