Manipulating Intracellular Oxidative Conditions to Enhance Porphyrin Production in Escherichia coli
Bibliographic record
Abstract
Being essential intermediates for the biosynthesis of heme, chlorophyll, and several other biologically critical compounds, porphyrins have wide practical applications. However, up till now, their bio-based production remains challenging. In this study, we identified potential metabolic factors limiting the biosynthesis of type-III stereoisomeric porphyrins in Escherichia coli. To alleviate this limitation, we developed bioprocessing strategies by redirecting more dissimilated carbon flux toward the HemD-enzymatic pathway to enhance the production of type-III uroporphyrin (UP-III), which is a key precursor for heme biosynthesis. Our approaches included the use of antioxidant reagents and strain engineering. Supplementation with ascorbic acid (up to 1 g/L) increased the UP-III/UP-I ratio from 0.62 to 2.57. On the other hand, overexpression of ROS-scavenging genes such as sod- and kat-genes significantly enhanced UP production in E. coli. Notably, overexpression of sodA alone led to a 72.9% increase in total porphyrin production (1.56 g/L) while improving the UP-III/UP-I ratio to 1.94. Our findings highlight the potential of both antioxidant supplementation and strain engineering to mitigate ROS-induced oxidative stress and redirect more dissimilated carbon flux toward the biosynthesis of type-III porphyrins in E. coli. This work offers an effective platform to enhance the bio-based production of porphyrins.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".