Glucoraphanin and sulforaphane biosynthesis by melatonin mediating nitric oxide in hairy roots of broccoli (Brassica oleracea L. var. italica Planch): insights from transcriptome data
Bibliographic record
Abstract
Abstract Glucoraphanin (GRA) is present in the seeds and nutrient organs of broccoli and is the precursor of the anti-cancer compound sulforaphane (SF). The hairy roots obtained by infecting broccoli ( Brassica oleracea L. var. Italic Planch) leaves with Agrobacterium rhizogenes (ATCC15834) are phytohormonally autonomous, genetically stable, and can produce large amounts of the anti-cancer substance SF. Melatonin (MT) is a natural hormone widely found in plants. Studies have shown that melatonin can regulate the synthesis of secondary metabolites of downstream targets by mediating the synthesis of signal molecules. However, whether MT regulates the synthesis of NO and H 2 O 2 and mediates the synthesis mechanism of secondary metabolites, GRA and SF, is not yet clear. In this study, the hairy roots of broccoli were treated with 500 μmol/L MT, and the genome of broccoli ( Brassica oleracea L. var. botrytis L) was used as the reference genome for transcriptome analysis. By this approach, we found that MT regulates the synthesis of NO and H 2 O 2 and mediates the synthesis of secondary metabolites GRA and SF. GO annotations indicated that DEGs involved in the MT treatment of broccoli hairy roots were mainly related to catalytic activity, cells, and metabolic processes; the KEGG pathway analysis indicated that MT treatment likely affects the hormone signal transduction process in broccoli hairy roots; broccoli hairy roots were treated with 500 μmol/L MT for 0, 6, 12, 20, and 32 h, respectively; compared with 0 h, the yield of GRA and SF increased under the other treatments. The highest yields of GRA and SF occurred at 12 h. The NO content was the highest at 12 h, and the H 2 O 2 content was positively correlated with MT concentration. The content of NO and H 2 O 2 were regulated, and the content of GRA and SF was increased under MT treatment. NO synthase inhibitor (L-NAME and TUN) could effectively inhibit the content of NO in broccoli hairy roots and reduce GRA and SF yield; MT could regulate NO levels by regulating NO synthesis-related enzymes and could alleviate the reduction of NO content in tissue cells caused by NO synthase inhibitor and promote NO synthesis. These results have important theoretical implications for understanding the regulation of GRA and SF synthesis events by NO and H 2 O 2 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".