Reducing expression of a nitrate‐responsive <scp>bZIP</scp> transcription factor increases grain yield and N use in wheat
Bibliographic record
Abstract
Nitrogen (N) plays critical role in plant growth; manipulating N assimilation could be a target to increase grain yield and N use. Here, we show that ABRE-binding factor (ABF)-like leucine zipper transcription factor TabZIP60 mediates N use and growth in wheat. The expression of TabZIP60 is repressed when the N-deprived wheat plants is exposed to nitrate. Knock down of TabZIP60 through RNA interference (RNAi) increases NADH-dependent glutamate synthase (NADH-GOGAT) activity, lateral root branching, N uptake and spike number, and improves grain yield more than 25% under field conditions, while overexpression of TabZIP60-6D had the opposite effects. Further investigation shows TabZIP60 binds to ABRE-containing fragment in the promoter of TaNADH-GOGAT-3B and negatively regulates its expression. Genetic analysis reveals that TaNADH-GOGAT-3B overexpression overcomes the spike number and yield reduction caused by overexpressing TabZIP60-6D. As such, TabZIP60-mediated wheat growth and N use is associated with its negative regulation on TaNADH-GOGAT expression. These findings indicate that TabZIP60 and TaNADH-GOGAT interaction plays important roles in mediating N use and wheat growth, and provides valuable information for engineering N use efficiency and yield in 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) | 0.001 | 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".