Reconstructing curcumin biosynthesis in yeast reveals the implication of caffeoyl-shikimate esterase in phenylpropanoid metabolic flux
Bibliographic record
Abstract
Curcumin is a polyphenolic natural product from the roots of turmeric (Curcuma longa). It has been a popular coloring and flavoring agent in food industries with known health benefits. The conventional phenylpropanoid pathway is known to proceed from phenylalanine via p-coumaroyl-CoA intermediate. Although hydroxycinnamoyl-CoA: shikimate hydroxycinnamoyl transferase (HCT) plays a key catalysis in the biosynthesis of phenylpropanoid products at the downstream of p-coumaric acid, a recent discovery of caffeoyl-shikimate esterase (CSE) showed that an alternative pathway exists. Here, the biosynthetic efficiency of the conventional and the alternative pathway in producing feruloyl-CoA was examined using curcumin production in yeast. A novel modular multiplex genome-edit (MMG)-CRISPR platform was developed to facilitate rapid integrations of up to eight genes into the yeast genome in two steps. Using this MMG-CRISPR platform and metabolic engineering strategies, the alternative CSE phenylpropanoid pathway consistently showed higher titers (2-19 folds) of curcumin production than the conventional pathway in engineered yeast strains. In shake flask cultures using a synthetic minimal medium without phenylalanine, the curcumin production titer reached up to 1.5 mg/L, which is three orders of magnitude (∼4800-fold) improvement over non-engineered base strain. This is the first demonstration of de novo curcumin biosynthesis in yeast. Our work shows the critical role of CSE in improving the metabolic flux in yeast towards the phenylpropanoid biosynthetic pathway. In addition, we showcased the convenience and reliability of modular multiplex CRISPR/Cas9 genome editing in constructing complex synthetic pathways 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".