Differential accumulation of phenolic compounds and expression of related genes in black- and yellow-seeded Brassica napus
Bibliographic record
Abstract
Developing yellow-seeded Brassica napus (rapeseed) with improved qualities is a major breeding goal. The intermediate and final metabolites of the phenylpropanoid and flavonoid pathways affect not only oil quality but also seed coat colour of B. napus. Here, the accumulation of phenolic compounds was analysed in the seed coats of black-seeded (ZY821) and yellow-seeded (GH06) B. napus. Using toluidine blue O staining and liquid chromatography-mass spectrometry, histochemical and biochemical differences were identified in the accumulation of phenolic compounds between ZY821 and GH06. Two and 13 unique flavonol derivatives were detected in ZY821 and GH06, respectively. Quantitative real-time PCR analysis revealed significant differences between ZY821 and GH06 in the expression of common phenylpropanoid biosynthetic genes (BnPAL and BnC4H), common flavonoid biosynthetic genes (BnTT4 and BnTT6), anthocyanin- and proanthocyandin-specific genes (BnTT3 and BnTT18), proanthocyandin-specific genes (BnTT12, BnTT10, and BnUGT2) and three transcription factor genes (BnTTG1, BnTTG2, and BnTT8) that function in the flavonoid biosynthetic pathway. These data provide insight into pigment accumulation in B. napus, and serve as a useful resource for researchers analysing the formation of seed coat colour and the underlying regulatory mechanisms in B. napus.
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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".