Metabolome and transcriptome analysis reveal the effect of methyl jasmonate on phytosterol biosynthesis in <i>Brassica napus</i>
Bibliographic record
Abstract
Abstract Phytosterols are a group of nonpharmacological alternatives to prevent or control dyslipidemias and cardiovascular disease. Increasing the phytosterol content in rapeseed oil is important to enhance daily phytosterol intake. However, the mechanisms of biosynthesis and regulation of phytosterol in rapeseed remain unclear. In this study, two representative rapeseed cultivars with extremely high (H286) and low (H174) phytosterol content were selected and treated with various concentrations (0.5−5.0 mM) of methyl jasmonate (MeJA). The results showed that treatment with 1 mM MeJA increased the phytosterol content of H174 and H286 by 17% and 27%, respectively. Based on the multiomics data, a gene‐phytosterol regulatory network was constructed. We deduced that MeJA down‐regulated the expression level of BnaA07.SCL15 , BnaC05.MYB61 , and BnaC03.AGL2 , thereby promoting the phytosterol biosynthesis , and which were validated through the transient expression in tobacco. Notably, overexpression of Arabidopsis BnaA07.SCL15 exhibited a significant decrease in their phytosterol content. Additionally, an integrative analysis of the high‐resolution metabolome and transcriptome revealed that the accumulation patterns of 997 metabolites were highly correlated with their corresponding gene expression patterns. MeJA also significantly affected flavonoid biosynthesis, α‐linolenic acid metabolism, and amino acid metabolism. Furthermore, BnaA09.TT8 and BnaC09.TT8 were found to regulate of flavonoids. Overall, this study provides valuable insights into the phytosterol biosynthesis in rapeseed and offers a simple and effective approach for improving rapeseed quality.
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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 | 0.001 |
| 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".