Metabolic Usage and Glycan Destinations of GlcNAz in <i>E. coli</i>
Bibliographic record
Abstract
Bacteria use a diverse range of carbohydrates to generate a profusion of glycans, with amino sugars, such as N -acetylglucosamine (GlcNAc), being prevalent in the cell wall and in many exopolysaccharides. The primary substrate for GlcNAc-containing glycans, UDP-GlcNAc, is the product of the bacterial hexosamine pathway and a key target for bacterial metabolic glycan engineering. Using the strategy of expressing NahK, to circumvent the hexosamine pathway, it is possible to directly feed the analogue of GlcNAc, N -azidoacetylglucosamine (GlcNAz), for metabolic labeling in Escherichia coli . The cytosolic production of UDP-GlcNAz was confirmed by using fluorescence-assisted polyacrylamide gel electrophoresis. The key question of where GlcNAz is incorporated was interrogated by analyzing potential sites including peptidoglycan (PGN), the biofilm-related exopolysaccharide poly-β-1,6- N -acetylglucosamine (PNAG), lipopolysaccharide (LPS), and the enterobacterial common antigen (ECA). The highest levels of incorporation were observed in PGN with lower levels in PNAG and no observable incorporation in LPS or ECA. The promiscuity of the PNAG synthase (PgaCD) toward UDP-GlcNAz in vitro and the lack of undecaprenyl-pyrophosphoryl-GlcNAz intermediates generated in vivo confirmed the incorporation preferences. The results of this work will guide the future development of carbohydrate-based probes and metabolic engineering strategies.
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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 | 0.001 | 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".