Rewiring Escherichia coli central carbon metabolism for the sustainable bioconversion of waste glycerol into biodegradable polyhydroxybutyrate
Bibliographic record
Abstract
The biomaterial polyhydroxybutyrate (PHB) is a promising, renewable, and green alternative polymer. In this study, a method to incorporate gluconate 6-phosphate dehydratase (edd) deletion in Escherichia coli expressing PHB biosynthesis genes from Cupriavidus necator strain A-04 was proposed. The growth of the edd-deficient strain, which is defective in the glycolytic Entner–Doudoroff pathway, decreased significantly in Luria-Bertani (LB) medium supplemented with glucose. Surprisingly, compared with the wild-type strain, the recombinant edd-deficient strain expressing PHB biosynthesis genes exhibited expeditious PHB accumulation with a high PHB content. The edd-deficient strain reached the highest PHB concentration of 7.6 g/L and a 93 wt% PHB content within 30 h of flask-scale cultivation when commercial glucose was used as the sole carbon source. In addition, the resulting strain was able to utilize crude glycerol waste from the biodiesel industry for PHB accretion with a 74.8 wt% content in 24 h. The PHB yields obtained from glucose and crude glycerol waste were 0.37 and 0.20 g PHB/g substrate, respectively. These findings not only broaden the understanding of the effect of glucose metabolism on PHB production but also provide promising candidates for the production of polyhydroxyalkanoates in the future.
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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 | 0.000 | 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 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".