Integrative transcriptome and metabolome evaluation of melanin biosynthesis in <i>Phyllostachys nigra</i> during low-temperature growth
Bibliographic record
Abstract
Plant melanin is an organic molecule commonly used in medicine, food, and chemical industries. However, the molecular underpinnings of plant melanin's biosynthesis and its regulation are still unclear. Phyllostachys nigra is well known for its ornamental value because of its black culms. The black pigments enriched in the epidermis and cortex of P. nigra were identified to be melanin by analyses of its physical and chemical properties. Moreover, the biosynthesis of melanin was examined using comprehensive transcriptomic and metabolomic analyses in P. nigra when grown at low temperatures. Nontargeted metabolite profiling revealed that some indoles, including serotonin, 3-indoleacetic acid, and 1H-indole-3-acetamide, were significantly enriched in P. nigra when grown at low temperatures. Parallel transcriptomic analysis showed that a set of structural genes involved in serotonin biosynthesis was significantly upregulated by low temperatures. By integrating the transcriptome data and weighted gene co-expression network analysis, the essential transcription factors that putatively regulate the biosynthesis of serotonins were revealed. Among those, PnWRKY19-3 was functionally tested and shown to increase the serotonin content in transgenic rice by upregulating OsT5H under low temperature conditions. These findings suggest that PnWRKY19-3 may play a positive role in promoting melanin formation in the culms of P. nigra. According to the two functional genomic platforms, it appears that low temperature stimulates melanin formation in P. nigra by inducing the biosynthesis of indoles. Our research provides new insights into melanin biosynthesis in bamboo, which may be vital to other plant species.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".