Two interacting basic helix-loop-helix transcription factors control flowering time in rice
Bibliographic record
Abstract
Flowering time is one of the most important agronomic traits affecting the adaptation and yield of rice (Oryza sativa). Heading date 1 (Hd1) is a key factor in the photoperiodic control of flowering time. In this study, two basic helix-loop-helix (bHLH) transcription factors, Hd1 Binding Protein 1 (HBP1) and Partner of HBP1 (POH1) were identified as transcriptional regulators of Hd1. We generated knockout mutants of HBP1 and ectopically expressed transgenic lines of the two bHLH transcription factors and used these lines to investigate the roles of these two factors in regulating flowering time. HBP1 physically associated with POH1 forming homo- or heterodimers to perform their functions. Both HBP1 and POH1 bound directly to the cis-acting elements located in the promoter of Hd1 to activate its expression. CRISPR/Cas9-generated knockout mutations of HBP1, but not POH1 mutations, promoted earlier flowering time; conversely, HBP1 and POH1 overexpression delayed flowering time in rice under long-day and short-day conditions by activating the expression of Hd1 and suppressing the expression of Early heading date 1 (Ehd1), Heading date 3a (Hd3a), and Rice Flowering locus T 1 (RFT1), thus controlling flowering time in rice. Our findings revealed a mechanism for flowering time control through transcriptional regulation of Hd1 and laid theoretical and practical foundations for improving the growth period, adaptation, and yield of rice.
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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".