TabHLH489 suppresses nitrate signaling by inhibiting the function of TaNLP7‐3A in wheat
Bibliographic record
Abstract
Nitrate not only serves as the primary nitrogen source for terrestrial plants but also serves as a critical signal in regulating plant growth and development. Understanding how plant responses to nitrate availability is essential for improving nitrogen use efficiency in crops. Herein, we demonstrated that the basic helix-loop-helix (bHLH) transcription factor TabHLH489 plays a crucial negative regulatory role in wheat nitrate signaling. Overexpressing TabHLH489 significantly reduced nitrate-promoted wheat growth and grain yield. Transcriptomic analysis showed that approximately 75% of nitrate-responsive genes were no longerregulated by nitrate in the TabHLH489 overexpression lines. TabHLH489 directly interacts with TaNLP7-3A, the wheat homolog protein of NIN-like protein 7 (NLP7), a central transcription factor in nitrate signaling. This interaction impairs TaNLP7-3A's ability to bind DNA, thereby inhibiting its transcriptional activity. Moreover, TabHLH489 induces the accumulation of reactive oxygen species (ROS) to reduce the nuclear localization of TaNLP7-3A, thereby diminishing its effectiveness in regulating the plant nitrogen response. These findings highlight the intricate regulatory mechanism by which TabHLH489 modulates TaNLP7-3A activity through direct interaction and ROS-mediated inhibition of nuclear localization. Our research highlights the critical roles of TabHLH489 and TaNLP7-3A in modulating nitrate signaling, providing new gene targets for developing wheat varieties with enhanced nitrogen use efficiency.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".