Rewiring central metabolism in Komagataella phaffii for efficient mannose synthesis
Bibliographic record
Abstract
BACKGROUND: Mannose has wide-ranging applications but microbial fermentation remains underdeveloped compared to biotransformation for its production. The yeast Komagataella phaffii stands out as a premier synthetic biology platform, renowned for its safety profile and exceptional suitability for high-density fermentation. This established chassis organism is ideally positioned for large-scale mannose production through targeted rewiring of its mannose biosynthetic pathway via metabolic engineering. RESULTS: K. phaffii was metabolically engineered for efficient mannose production using a dual carbon source system: glycerol for biomass generation and glucose for mannose synthesis. To redirect carbon flux toward fructose-6-phosphate (F6P) accumulation at the glycolytic node, glycolytic flux was attenuated by knocking out the phosphofructokinase II (pfk2) gene and downregulating phosphofructokinase I (pfk1). Simultaneously, pentose phosphate pathway flux was reduced by downregulating glucose-6-phosphate dehydrogenase (zwf1). To enhance mannose biosynthesis, conversion of F6P into mannose was promoted by suppressing phosphomannose isomerase (PAS_chr3_1115) and overexpressing the Escherichia coli-derived phosphatase gene yniC. Additionally, three genes involved in arabinitol and ribitol production (PAS_chr2-2_0019, PAS_chr4_0754, and PAS_chr4_0988) were deleted to suppress byproduct accumulation. The engineered strain achieved ~ 121.1 g/L mannose in high-cell-density, fed-batch fermentation, representing the highest reported titer via microbial fermentation to date. CONCLUSIONS: This study achieved efficient mannose production in K. phaffii by remodeling central metabolism. It not only offers a new route for mannose biosynthesis but also establishes a model framework for engineering K. phaffii to produce other high-value bioactive compounds.
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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 | 0.000 | 0.000 |
| 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".