Comparative Analysis of NADPH-Cytochrome P450 Reductases From Legumes for Heterologous Production of Triterpenoids in Transgenic Saccharomyces cerevisiae
Bibliographic record
Abstract
Triterpenoids are plant specialized metabolites with various pharmacological activities. They are widely distributed in higher plants, such as legumes. Because of their low accumulation in plants, there is a need for improving triterpenoid production. Cytochrome P450 monooxygenases (CYPs) play critical roles in the structural diversification of triterpenoids. To perform site-specific oxidations, CYPs require the electrons that are transferred by NADPH-cytochrome P450 reductase (CPR). Plants possess two main CPR classes, class I and class II. CPR classes I and II have been reported to be responsible for primary and specialized (secondary) metabolism, respectively. In this study, we first analyzed the CPR expression level of three legumes species,Medicago truncatula,Lotus japonicus, andGlycyrrhiza uralensis, showing that the expression level of CPR class I was lower and more stable, while that of CPR class II was higher in almost all the samples. We then co-expressed different combinations of CYP716As and CYP72As with different CPR classes from these three legumes in transgenic yeast. We found that CYP716As worked better with CPR-I from the same species, while CYP72As worked better with any CPR-IIs. Using engineered yeast strains, CYP88D6 paired with class II GuCPR produced the highest level of 11-oxo-β-amyrin, the important precursor of high-value metabolites glycyrrhizin. This study provides insight into co-expressing genes from legumes for heterologous production of triterpenoids in yeast.
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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) | 0.001 | 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".