Stepwise metabolic engineering of Escherichia coli to produce triacylglycerol rich in medium-chain fatty acids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it