Strain engineering for high-level 5-aminolevulinic acid production in Escherichia coli
Bibliographic record
Abstract
As issues surrounding depleting fossil fuels, climate change, and various other environmental impacts are becoming more prevalent, there is a growing interest in technological shifts toward a bio-based economy. Various advanced biotechnological tools have been developed to customize cell factories for the production of a wide range of complex fine chemicals from renewable feedstock. Herein, we report development of a microbial bioprocess for high-level and potentially economical production of 5-aminolevulinic acid (5-ALA), a valuable non-proteinogenic amino acid with multiple applications in medical, agricultural, and food industries, using Escherichia coli as a cell factory. We first implemented the Shemin (i.e., C4) pathway for heterologous 5-ALA biosynthesis in E. coli. To reduce, but not to abolish, the carbon flux toward essential tetrapyrrole/porphyrin biosynthesis, we applied Clustered Regularly Interspersed Short Palindromic Repeats interference (CRISPRi) to repress hemB expression, leading to extracellular 5-ALA accumulation. We then applied metabolic engineering strategies to direct more dissimilated carbon flux toward the key precursor of succinyl-CoA for enhanced 5-ALA biosynthesis. Using these engineered E. coli strains for bioreactor cultivation, we successfully demonstrated high-level 5-ALA biosynthesis solely from glycerol (~30 g l-1) under both microaerobic and aerobic conditions, achieving up to 5.95 g l-1 (36.9% yield) and 6.93 g l-1 (50.9% yield) 5-ALA, respectively. This study represents one of the most effective bio-based production of 5-ALA from a structurally unrelated carbon to date, highlighting the importance of integrated strain engineering and bioprocessing strategies to enhance bio-based production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".