Stepwise metabolic engineering of Escherichia coli to produce triacylglycerol rich in medium-chain fatty acids
Bibliographic record
Abstract
Triacylglycerols (TAGs) rich in medium-chain fatty acids (MCFAs, C10–14 fatty acids) are valuable feedstocks for biofuels and chemicals. Natural sources of TAGs rich in MCFAs are restricted to a limited number of plant species, which are unsuitable for mass agronomic production. Instead, the modification of seed or non-seed tissue oils to increase MCFA content has been investigated. In addition, microbial oils are considered as promising sustainable feedstocks for providing TAGs, although little has been done to tailor the fatty acids in microbial TAGs. Here, we first assessed various wax synthase/acyl-coenzyme A:diacylglycerol acyltransferases, phosphatidic acid phosphatases, acyl-CoA synthetases as well as putative fatty acid metabolism regulators for producing high levels of TAGs in Escherichia coli . Activation of endogenous free fatty acids with tailored chain length via overexpression of the castor thioesterase RcFatB and the subsequent incorporation of such fatty acids into glycerol backbones shifted the TAG profile in the desired way. Metabolic and nutrient optimization of the engineered bacterial cells resulted in greatly elevated TAG levels (399.4 mg/L) with 43.8% MCFAs, representing the highest TAG levels in E. coli under shake flask conditions. Engineered cells were observed to contain membrane-bound yet robust lipid droplets. We introduced a complete Kennedy pathway into non-oleaginous E . coli towards developing a bacterial platform for the sustainable production of TAGs rich in MCFAs. Strategies reported here illustrate the possibility of prokaryotic cell factories for the efficient production of TAGs rich in MCFAs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".