Comparative Analysis of β-Carotene Hydroxylase Genes for Astaxanthin Biosynthesis
Bibliographic record
Abstract
Astaxanthin (3,3'-dihydroxy-4,4'-diketo-β-carotene) (1) is a carotenoid of significant commercial value due to its superior antioxidant potential, application as a component of animal feeds, and ongoing research that links its application to the treatment and prevention of human pathologies. The high commercial cost of 1 is also based upon its complex synthesis. Chemical synthesis has been demonstrated, but produces a mixture of stereoisomers with limited applications. Production from biological sources is limited to natural producers with complex culture requirements. The biosynthetic pathway for 1 is well studied; however, questions remain that prevent optimized production in heterologous systems. Presented is a direct comparison of 12 β-carotene (2) hydroxylases derived from archaea, bacteria, cyanobacteria, and plants. Expression in Escherichia coli enables a comparison of catalytic activity with respect to zeaxanthin (3) and 1 biosynthesis. The most suitable β-carotene hydroxylases were subsequently expressed from an efficient dual expression vector, enabling 1 biosynthesis at levels up to 84% of total carotenoids. This supports efficient 1 biosynthesis by balanced expression of β-carotene ketolase and β-carotene hydroxylase genes. Moreover, our work suggests that the most efficient route for astaxanthin biosynthesis proceeds by hydroxylation of β-carotene to zeaxanthin, followed by ketolation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".