Manipulating the sleeping beauty mutase operon for the production of 1-propanol in engineered Escherichia coli
Bibliographic record
Abstract
BACKGROUND: While most resources in biofuels were directed towards implementing bioethanol programs, 1-propanol has recently received attention as a promising alternative biofuel. Nevertheless, no microorganism has been identified as a natural 1-propanol producer. In this study, we manipulated a novel metabolic pathway for the synthesis of 1-propanol in the genetically tractable bacterium Escherichia coli. RESULTS: E. coli strains capable of producing heterologous 1-propanol were engineered by extending the dissimilation of succinate via propionyl-CoA. This was accomplished by expressing a selection of key genes, i.e. (1) three native genes in the sleeping beauty mutase (Sbm) operon, i.e. sbm-ygfD-ygfG from E. coli, (2) the genes encoding bifunctional aldehyde/alcohol dehydrogenases (ADHs) from several microbial sources, and (3) the sucCD gene encoding succinyl-CoA synthetase from E. coli. Using the developed whole-cell biocatalyst under anaerobic conditions, production titers up to 150 mg/L of 1-propanol were obtained. In addition, several genetic and chemical effects on the production of 1-propanol were investigated, indicating that certain host-gene deletions could abolish 1-propanol production as well as that the expression of a putative protein kinase (encoded by ygfD/argK) was crucial for 1-propanol biosynthesis. CONCLUSIONS: The study has provided a novel route for 1-propanol production in E. coli, which is subjected to further improvement by identifying limiting conversion steps, shifting major carbon flux to the productive pathway, and optimizing gene expression and culture conditions.
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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) | 0.000 | 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".