Construction of cell factory through combinatorial metabolic engineering for efficient production of itaconic acid
Bibliographic record
Abstract
Abstract Background Itaconic acid, an unsaturated C5 dicarbonic acid, has significant market demand and prospects. It has numerous biological functions, such as anti-cancer, anti-inflammatory, and anti-oxidative in medicine, and is an essential renewable platform chemical in industry. However, the development of industrial itaconic acid production by Aspergillus terreus, the current standard production strain, is hampered by the unavoidable drawbacks of that species. Developing a highly efficient cell factory is essential for the sustainable and green production of itaconic acid. Results This study employed combinatorial engineering strategies to construct Escherichia coli cells to produce itaconic acid efficiently. Two essential genes (cis-aconitate decarboxylase (CAD) encoding gene cadA and aconitase (ACO) encoding gene acn) employed various genetic constructs and plasmid combinations to create 12 recombination E. coli strains to be screened. Among them, E. coli BL-CAC exhibited the highest titer with citrate as substrate, and the induction and reaction conditions were further systematically optimized. Subsequently, employing enzyme evolution to optimize rate-limiting enzyme CAD and synthesizing protein scaffolds to co-localize ACO and CAD were used to improve itaconic acid biosynthesis efficiency. Under the optimized reaction conditions combined with the feeding control strategy, itaconic acid titer reached 398.07 mM (51.79 g/L) of engineered E. coli BL-CAR470E-DS/A-CS cells as a catalyst with the highest specific production of 9.42 g/g(DCW) among heterologous hosts at 48 h. Conclusions The excellent catalytic performance per unit biomass shows the potential for high-efficiency production of itaconic acid and effective reduction of catalytic cell consumption. This study indicates that it is necessary to continuously explore engineering strategies to develop high-performance cell factories to break through the existing bottleneck and achieve the economical commercial production of itaconic acid.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".