Production of itaconic acid using metabolically engineered Escherichia coli
Bibliographic record
Abstract
An Escherichia coli system was engineered for the heterologous production of itaconic acid via the expression of cis-aconitate decarboxylase gene (cad), and then maximal itaconic acid levels produced by engineered E. coli were evaluated. Expression of cad in E. coli grown in Luria-Bertani (LB) medium without glucose in a test tube resulted in 0.07 g/L itaconic acid production after 78 h at 20°C. To increase itaconic acid production, E. coli recombinants were constructed by inactivating the isocitrate dehydrogenase gene (icd) and/or the isocitrate lyase gene (aceA). Expression of cad and inactivation of icd resulted in 0.35 g/L itaconic acid production after 78 h, whereas aceA inactivation had no effect on itaconic acid production. The intracellular itaconate concentration in the Δicd strain was higher than that in the cad-expressing strain without icd inactivation, which suggests that the extracellular secretion of itaconate in E. coli is the rate-determining step during itaconic acid production. pH-stat cultivation using the cad-expressing Δicd strain in LB medium with 3% glucose in a jar fermenter resulted in 1.71 g/L itaconic acid production after 97 h at 28°C. To further increase itaconic acid production, the aconitase B gene (acnB) was overexpressed in the cad-expressing Δicd strain. Simultaneous overexpression of acnB with the expression of cad in the Δicd strain led to 4.34 g/L itaconic acid production after 105 h. Our findings indicate that icd inactivation and acnB overexpression considerably enhance itaconic acid production in cad-expressing E. coli.
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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 | 0.000 | 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".