Metabolic engineering of Escherichia coli for the utilization of ethanol
Bibliographic record
Abstract
Abstract Background The fuel ethanol industry has made tremendous progress in the last decades. Ethanol can be obtained by fermentation using a variety of biomass materials as the feedstocks. However, few studies have been conducted on ethanol utilization by microorganisms. The price of petroleum-derived ethanol, easily made by the hydrolysis of ethylene, is even lower than that of bioethanol. If ethanol can be metabolized by microorganisms to produce value-added chemicals, it will open a new door for the utilization of inexpensive ethanol resources. Results We constructed an engineered Escherichia coli strain which could utilize ethanol as the sole carbon source. The alcohol dehydrogenase and aldehyde dehydrogenase from Aspergillus nidulans was introduced into E. coli and the recombinant strain acquired the ability to grow on ethanol. Cell growth continued when ethanol was supplied after glucose starvation and 2.24 g L−1 of ethanol was further consumed during the shake-flasks fermentation process. Then ethanol was further used for the production of mevalonic acid by heterologously expressing its biosynthetic pathway. Deuterium-labeled ethanol-D6 as the feedstock confirmed that mevalonic acid was synthesized from ethanol. Conclusions This study demonstrated the possibility of using ethanol as the carbon source by engineered E. coli strains. It can serve as the basis for the construction of more robust strains in the future though the catabolic capacity of ethanol should be further improved.
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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 | 0.001 |
| 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".