Rapid combinatorial rewiring of metabolic networks for enhanced poly(3- hydroxybutyrate) production in Corynebacterium glutamicum
Bibliographic record
Abstract
Abstract Background: The disposal of plastic waste is a major environmental challenge. With recent advances in microbial genetic and metabolic engineering technologies, microbial polyhydroxyalkanoates (PHAs) are being used as next-generation biomaterials to replace petroleum-based synthetic plastics in a sustainable future. However, the relatively high production cost of bioprocesses hinders the production and application of microbial PHAs on an industrial scale. Results: Here, we describe a rapid strategy to rewire metabolic networks in an industrial microorganism, Corynebacterium glutamicum, for the enhanced production of poly(3-hydroxybutyrate) (PHB). A three-gene PHB biosynthetic pathway in Rasltonia eutropha was refactored for high-level gene expression. A fluorescence-based quantification assay for cellular PHB content using BODIPY was devised for the rapid fluorescence-activated cell sorting (FACS)-based screening of a large combinatorial metabolic network library constructed in C. glutamicum. Rewiring metabolic networks across the central carbon metabolism enabled highly efficient production of PHB up to 29% of dry cell weight, which is the highest PHB content ever reported in C. glutamicumusing a sole carbon source. Conclusions: We successfully constructed a heterologous PHB biosynthetic pathway and rapidly optimized metabolic networks across central metabolism in C. glutamicum for enhanced production of PHB using glucose or fructose as the sole carbon source in minimal medium. We expect that this FACS-based metabolic rewiring framework will accelerate strain engineering processes for the production of diverse biochemicals and biopolymers.
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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 | 0.000 |
| 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 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".