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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".