Three-component systems represent a common pathway for extracytoplasmic addition of pentofuranose sugars into bacterial glycans
Bibliographic record
Abstract
Cell surface glycans are major drivers of antigenic diversity in bacteria. The biochemistry and molecular biology underpinning their synthesis are important in understanding host–pathogen interactions and for vaccine development with emerging chemoenzymatic and glycoengineering approaches. Structural diversity in glycostructures arises from the action of glycosyltransferases (GTs) that use an immense catalog of activated sugar donors to build the repeating unit and modifying enzymes that add further heterogeneity. Classical Leloir GTs incorporate α- or β-linked sugars by inverting or retaining mechanisms, depending on the nucleotide sugar donor. In contrast, the mechanism of known ribofuranosyltransferases is confined to β-linkages, so the existence of α-linked ribofuranose in some glycans dictates an alternative strategy. Here, we use Citrobacter youngae O1 and O2 lipopolysaccharide O antigens as prototypes to describe a widespread, versatile pathway for incorporating side-chain α-linked pentofuranoses by extracytoplasmic postpolymerization glycosylation. The pathway requires a polyprenyl phosphoribose synthase to generate a lipid-linked donor, a MATE-family flippase to transport the donor to the periplasm, and a GT-C type GT (founding the GT136 family) that performs the final glycosylation reaction. The characterized system shares similarities, but also fundamental differences, with both cell wall arabinan biosynthesis in mycobacteria, and periplasmic glucosylation of O antigens first discovered in Salmonella and Shigella . The participation of auxiliary epimerases allows the diversification of incorporated pentofuranoses. The results offer insight into a broad concept in microbial glycobiology and provide prototype systems and bioinformatic guides that facilitate discovery of further examples from diverse species, some in currently unknown glycans.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".