Metabolic engineering of Escherichia coli for squalene overproduction
Bibliographic record
Abstract
ABSTRACT Squalene, a lipophilic triterpene with multifaceted bioactivities, faces bioproduction bottlenecks in microbial hosts due to inefficient biosynthetic pathways and limited storage capacity. Here, we address these challenges through systems metabolic engineering integrating redox-balanced 3-hydroxy-3-methyl glutaryl coenzyme A reductase (HMGR) variants and membrane lipid remodeling. By developing a hybrid HMGRs system combining NADPH-dependent and NADH-preferred enzymes, squalene production reached 852.06 ± 28.95 mg/L with balanced cofactor utilization. Subsequent engineering of membrane morphology and lipid metabolism generated lipid-enriched elongated cells, through the overexpression of dgs , murG and plsC , boosting squalene production to 970.86 ± 55.67 mg/L. Implementation of delayed induction strategies coupled with 10% dodecane overlay as an in situ recovery system achieved a final squalene titer of 1267.01 mg/L in a 3 L bioreactor. Mechanistic studies revealed fatty acid (FA) and phosphatidylethanolamine (PE) as key reservoirs for squalene in E . coli , with dgs overexpression specifically promoting cellular elongation. This article provides comprehensive insights into engineering strategies and mechanistic perspectives, establishing a universal framework for hydrophobic metabolite biomanufacturing in prokaryotic hosts.
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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 | 0.001 | 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".