Mammalian sialyltransferases allow efficient <i>Escherichia coli</i>-based production of mucin-type O-glycoproteins but can also transfer Kdo
Bibliographic record
Abstract
The prospect of producing human-like glycoproteins in bacteria is becoming attractive as an alternative to already-established but costly mammalian cell expression systems. We previously described an Escherichia coli expression platform that uses a dual-plasmid approach to produce simple mucin type O-glycoproteins: one plasmid encoding the target protein and another O-glycosylation machinery. Here, we expand the capabilities of our platform to carry out sialylation and demonstrate the high-yielding production of human interferon α2b and human growth hormone bearing mono- and disialylated T-antigen glycans. This is achieved through engineering an E. coli strain to produce CMP-Neu5Ac and introducing various α-2,3- and α-2,6 mammalian or bacterial sialyltransferases into our O-glycosylation operons. We further demonstrate that mammalian sialyltransferases, including porcine ST3Gal1, human ST6GalNAc2 and human ST6GalNAc4, are very effective in vivo and outperform some of the bacterial sialyltransferases tested, including Campylobacter jejuni Cst-I and Cst-II. In the process, we came upon a way of modifying T-Antigen with Kdo, using a previously uncharacterised Kdo-transferase activity of porcine ST3Gal1. Ultimately, the heterologous expression of mammalian sialyltransferases in E. coli shows promise for the further development of bacterial systems in therapeutic glycoprotein 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".