Application of hydrocarbon and perfluorocarbon oxygen vectors to enhance heterologous production of hyaluronic acid in engineered <i>Bacillus subtilis</i>
Bibliographic record
Abstract
In microbial cultivations for hyaluronic acid (HA) production, oxygen can be a limiting substrate due to its poor solubility in aqueous medium and the substantial increase in culture viscosity at relatively low HA titers. Shear stress due to the high agitation and aeration rates required to overcome oxygen limitation may reduce the quality (i.e., molecular weight) of HA, and production costs associated with power consumption and supplemental oxygen may be excessive. Here, we report the application of oxygen vectors to the heterologous production of HA in engineered Bacillus subtilis, leading to significantly improved culture performance. We first derived an improved HA-producing strain of B. subtilis through engineering of the promoter driving coexpression of seHas and tuaD, leading to high-level HA production. Out of seven potential oxygen vectors evaluated in a preliminary screening, significant improvements to the HA titer and/or cell density were observed in cultures containing n-heptane, n-hexadecane, perfluoromethyldecalin, and perfluoro-1,3-dimethylcyclohexane. Adjustments to the vector concentration, timing of vector addition, and the agitation rate resulted in further enhancements, with the HA titer reaching up to 4.5 g/L after only 10 hr cultivation. Moreover, our results indicate that certain vectors may alter the functional expression of Class I hyaluronan synthase (HAS) in B. subtilis, and that higher shear rates may drive more carbon flux through the HA biosynthetic pathway without negatively affecting the MW. Our study demonstrates the efficacy of oxygen vectors to enhance heterologous HA production in B. subtilis, and provides valuable insight for future bioprocess development in microbial HA production.
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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".