Integrated transcriptome and metabolome analysis revealed that HaMYB1 modulates anthocyanin accumulation to deepen sunflower flower color
Bibliographic record
Abstract
KEY MESSAGE: HanMYB1 was found to play positive roles in the modulation of anthocyanins metabolism based on the integrative analysis of different color cultivars and the related molecular genetic analyses. As a high value ornamental and edible crop with various colors, sunflowers (Helianthus annuus L.) provide an ideal system to understand the formation of flower color. Anthocyanins are major pigments in higher plants, which is associated with development of flower colors and ability of oxidation resistance. Here, we performed an integrative analysis of the transcriptome and flavonoid metabolome in five sunflower cultivars with different flower colors. According to differentially expressed genes and differentially accumulated flavonoids, these cultivars could be grouped into yellow and red. The results showed that more anthocyanins were accumulated in the red group flowers, especially the chrysanthemin. Some anthocyanins biosynthesis-related genes like UFGT (UDP-glycose flavonoid glycosyltransferase) also expressed more in the red group flowers. A MYB transcriptional factor, HanMYB1, was found to play vital positive roles in the modulation of anthocyanins metabolism by the integrative analysis. Overexpressed HanMYB1 in tobacco could deepen the flower color, increase the accumulation of anthocyanins and directly active the express of UFGT genes. Our findings indicated that the MYB transcriptional factors provide new insight into the dynamic regulation of the anthocyanin biosynthesis in facilitating sunflower color formation and anthocyanin accumulation.
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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".