Exotic glycerol dehydrogenase expressing Escherichia coli increases yield of 2,3-butanediol
Bibliographic record
Abstract
The thriving of biodiesel industry has led to produce 10% (v/v) crude glycerol, thus creating an overflow problem. Biofuel production is restricted by Escherichia coli due to its toxicity to bacterial cells. Therefore, a platform chemical and fuel additive 2,3-butanediol (2,3-BD) with low toxicity to microbes could be a promising alternative for biofuel production by recombinant E. coli using glycerol as the sole substrate. A novel expression system of E. coli was developed to express the dhaD gene encoding glycerol dehydrogenase (GDH) to produce value-added metabolic products through aerobic biotransformation of glycerol. The dhaD gene obtained from Klebsiella pneumoniae SRP2 was expressed in E. coli BL21(DE3)pLysS using an E. coli–K. pneumoniae shuttle vector pJET1.2/blunt consisting of chloramphenicol-resistance gene under the control of the T7lac promotor. RT-PCR analysis and dhaD overexpression confirmed that the 2,3-BD synthesis pathway gene was expressed on RNA and protein levels. Therefore, the recombinant E. coli exhibited a 38.9-fold higher enzyme activity (312.57 units/mg protein), yielding 8.97 g/L 2,3-BD, a 2.4-fold increase with respect to the non-recombinant strain. The engineered strain E. coli BL21(DE3)pLysS/pJET1.2/blunt-dhaD, carrying the 2,3-BD pathway gene dhaD from our newly isolated Klebsiella pneumoniae SRP2 strain, displayed the best ability to synthesize 2,3-BD from low-cost biomass glycerol. The value of expression of an important glycerol metabolism gene dhaD is the highest ever achieved with an engineered E. coli strain. From these results, the first reported dhaD expression system has paved the way for improvement of 2,3-BD production and is efficient for another heterologous gene expression in E. coli.
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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 | 0.001 |
| 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".