Modulation of the wheat transcriptome by TaZFP13D \nunder well‑watered and drought conditions
Bibliographic record
Abstract
Maintaining global food security in the context of climate changes will be an important challenge in the next century. Improving abiotic stress tolerance of major crops such as wheat can contribute to this goal. This can be achieved by the identification of the genes involved and their use to develop tools for breeding programs aiming to generate better adapted cultivars. Recently, we identified the wheat TaZFP13D gene encoding Zinc Finger Protein 13D as a new gene improving water-stress tolerance. The current work analyzes the TaZFP13D-dependent transcriptome modifications that occur in well-watered and dehydration conditions to better understand its function during normal growth and during drought. Plants that overexpress TaZFP13D have a higher biomass under well-watered conditions, indicating a positive effect of the protein on growth. Survival rate and stress recovery after a severe drought stress are improved compared to wild-type plants. The latter is likely due the higher activity of key antioxidant enzymes and concomitant reduction of drought-induced oxidative damage. Conversely, down-regulation of TaZFP13D decreases drought tolerance and protection against drought-induced oxidative damage. RNA-Seq transcriptome analysis identified many genes regulated by TaZFP13D that are known to improve drought tolerance. The analysis also revealed several genes involved in the photosynthetic electron transfer chain known to improve photosynthetic efficiency and chloroplast protection against drought-induced ROS damage. This study highlights the important role of TaZFP13D in wheat drought tolerance, contributes to unravel the complex regulation governed by TaZFPs, and suggests that it could be a promising marker to select wheat cultivars with higher drought tolerance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".