The tree peony nuclear factor Y transcription factor <scp>PrNF‐YC2</scp> promotes seed oil accumulation
Bibliographic record
Abstract
Seed oil not only provides energy for seed postgermination development but also provides essential nutrients and raw materials for human products. However, the transcriptional regulatory mechanism controlling seed oil accumulation remains largely unknown. Tree peony (Paeonia rockii) is an emerging woody oilseed crop in China that is known for its high-quality seed oil. Here, we revealed that a tree peony nuclear factor Y transcription factor, PrNF-YC2, is expressed predominantly in developing seeds and functions as an essential positive regulator of seed oil accumulation. PrNF-YC2 promoted oil accumulation in both transient ectopic overexpression Nicotiana benthamiana leaves and stable transgenic Arabidopsis thaliana seeds, globally upregulating the expression of genes involved in oil accumulation. In contrast, PrNF-YC2-silenced tree peony leaves using a virus-induced gene silencing system showed reduced oil content and expression of oil synthesis-related genes, including four master positive regulators contributing to oil accumulation, namely, LEAFY COTYLEDON1 (LEC1), ABSCISIC ACID INSENSITIVE3 (ABI3), FUSCA3 (FUS3), and WRINKLED1 (WRI1). We demonstrated that PrNF-YC2 directly activates PrLEC1 and PrABI3 alone and indirectly activates PrFUS3 and PrWRI1 by interacting with PrLEC1. Moreover, interaction with PrLEC1 also enhances the activation capacity of PrNF-YC2. The activation of these four master positive regulators by PrNF-YC2 triggered the upregulation of numerous oil synthesis-related genes, thus promoting oil accumulation. These findings provide new insights into the regulatory mechanism of seed oil accumulation and manipulation of PrNF-YC2 may be beneficial for enhancing oil yield in tree peony and other oilseed crops.
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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) | 0.002 | 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".