Higher anthocyanin accumulation associated with higher transcription levels of anthocyanin biosynthesis genes in spinach
Bibliographic record
Abstract
Spinach (Spinacia oleracea L.) is widely cultivated as an economically important green leafy vegetable crop for fresh and processing consumption. The red-purple spinach shows abundant anthocyanin accumulation in the leaf and leaf petiole. However, the molecular mechanisms of anthocyanin synthesis in this species are still undetermined. In the present study, we investigated pigment formation and identified anthocyanin biosynthetic genes in spinach. We also analyzed the expression of these genes in purple and green cultivars by quantitative PCR. The accumulation of anthocyanin showed that it was the dominant pigment resulting in the red coloration in spinach. In total, 22 biosynthesis genes and 25 regulatory genes were identified in spinach, based on the spinach genomic and transcriptomic database. Furthermore, the expression patterns of genes encoding enzymes indicated that SoPAL, SoUFGT3, and SoUFGT4 were possible candidate genes for anthocyanin biosynthesis in red-purple spinach. The expression patterns of transcription factors indicated that two SoMYB genes, three SobHLH genes, and one SoWD40 gene were drastically up-regulated and co-expression in red-purple spinach, suggesting an essential role of regulatory genes in the anthocyanin biosynthesis of spinach. These results will enhance our understanding of the molecular mechanisms of anthocyanin biosynthesis in purple spinach.
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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".