Bibliographic record
Abstract
N-Acetyl-D-neuraminic acid (Neu5Ac) has attracted considerable interest due to its promising potential applications in medicine. Significant efforts have been made in whole-cell biocatalyst for Neu5Ac production, but the processes often result in suboptimal performance due to poor expression of enzymes, imbalances of pathway components, disturbance of competing pathways, and barriers of mass transport. In this study, we engineered Escherichia coli strains capable of producing Neu5Ac by assembling a two-step heterologous pathway consisting of N-acetyl-D-glucosamine 2-epimerase (AGE) and Neu5Ac aldolase (NanA). Multiple approaches were used to improve the efficiency of the engineered pathway and process for enhanced Neu5Ac production. Firstly, we identified that NanA was the rate-controlling enzyme in this pathway. With increased expression of NanA, a ninefold increase in Neu5Ac production (65 mM) was observed. Secondly, knocking out nanTEK genes blocked Neu5Ac uptake and the competing pathway, which kept the reactions to the synthetic direction as the final product went outside of the cells and enhanced the Neu5Ac production by threefold, resulting in 173.8 mM of Neu5Ac. Thirdly, we improved the performance of the system by promoting substrate transport and optimizing concentrations of substrates. An overall whole-cell biocatalytic process was developed and a maximum titer of 240 mM Neu5Ac (74.2 g/L) was achieved, with productivity of 6.2 g Neu5Ac/L/h and conversion yield of 40 % from GlcNAc. The engineered strain could be reused for at least five cycles with a productivity of >6 g/L/h. It is a cost-effective process for Neu5Ac production with potential applications in large-scale industrial production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".