Photoperiodic flowering regulators are required for nitrogen-dependent flowering delay in rice under long-day condition
Bibliographic record
Abstract
Plants recognize environmental information as external signals, which they use to coordinate developmental processes for their survival and reproduction. In the agricultural production of rice, the primary staple crop worldwide, soil nitrogen (N) is an important environmental factor. Heading date, a key trait for rice quality and yield, is modulated by N fertilisation; however, the molecular mechanisms underlying N-dependent flowering regulation remain unclear. Here, we conduct a genome-wide association study using differences in heading dates of Japanese rice cultivars grown under different N conditions. We identify Hd6, which is known to be involved in photoperiodic flowering regulation, as a key signalling component in the phenotypic variation observed under various N conditions. Further analyses using near-isogenic lines reveal that not only Hd6 but also Hd2 and Hd1 are required for the delayed flowering caused by N fertilisation. We also discover that Hd6 regulates floral inducer genes by stabilizing Hd2 through phosphorylation in response to N conditions and that the Hd6-Hd2 module can counteract the transcriptional regulation of Hd1. This study elucidates the molecular pathway directly linking N responses to flowering regulation in rice, providing insights for novel breeding and cultivation strategies. The molecular mechanisms underlying nitrogen (N)-dependent flowering regulation remain unclear. Here, the authors reveal Hd6, a known factor involved in photoperiodic flowering regulation, as a key signaling component in rice heading date variation under different N conditions.
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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".