Engineering of cell membrane to enhance heterologous production of hyaluronic acid in <i>Bacillus subtilis</i>
Bibliographic record
Abstract
Hyaluronic acid (HA) is a high-value biopolymer used in the biomedical, pharmaceutical, cosmetic, and food industries. Current methods of HA production, including extraction from animal sources and streptococcal cultivations, are associated with high costs and health risks. Accordingly, the development of bioprocesses for HA production centered on robust "Generally Recognized as Safe (GRAS)" organisms such as Bacillus subtilis is highly attractive. Here, we report the development of novel strains of B. subtilis in which the membrane cardiolipin (CL) content and distribution has been engineered to enhance the functional expression of heterologously expressed hyaluronan synthase (HAS) of Streptococcus equisimilis (SeHAS), in turn, improving the culture performance for HA production. Elevation of membrane CL levels via overexpressing components involved in the CL biosynthesis pathway, and redistribution of CL along the lateral membrane via repression of the cell division initiator protein FtsZ resulted in increases to the HA titer of up to 204% and peak molecular weight of up to 2.2 MDa. Moreover, removal of phosphatidylethanolamine and neutral glycolipids from the membrane of HA-producing B. subtilis via inactivation of pssA and ugtP, respectively, has suggested the lipid dependence for functional expression of SeHAS. Our study demonstrates successful application of membrane engineering strategies to develop an effective platform for biomanufacturing of HA with B. subtilis strains expressing Class I streptococcal HAS.
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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".