O-acetylation controls the glycosylation of bacterial serine-rich repeat glycoproteins
Bibliographic record
Abstract
The serine-rich repeat (SRR) glycoproteins of gram-positive bacteria are a family of adhesins that bind to a wide range of host ligands, and expression of SRR glycoproteins is linked with enhanced bacterial virulence. The biogenesis of these surface glycoproteins involves their intracellular glycosylation and export via the accessory Sec system. Although all accessory Sec components are required for SRR glycoprotein export, Asp2 of Streptococcus gordonii also functions as an O-acetyltransferase that modifies GlcNAc residues on the SRR adhesin gordonii surface protein B (GspB). Because these GlcNAc residues can also be modified by the glycosyltransferases Nss and Gly, it has been unclear whether the post-translational modification of GspB is coordinated. We now report that acetylation modulates the glycosylation of exported GspB. Loss of O-acetylation due to aps2 mutagenesis led to the export of GspB glycoforms with increased glucosylation of the GlcNAc moieties. Linkage analysis of the GspB glycan revealed that both O-acetylation and glucosylation occurred at the same C6 position on GlcNAc residues and that O-acetylation prevented Glc deposition. Whereas streptococci expressing nonacetylated GspB with increased glucosylation were significantly reduced in their ability to bind human platelets in vitro, deletion of the glycosyltransferases nss and gly in the asp2 mutant restored platelet binding to WT levels. These findings demonstrate that GlcNAc O-acetylation controls GspB glycosylation, such that binding via this adhesin is optimized. Moreover, because O-acetylation has comparable effects on the glycosylation of other SRR adhesins, acetylation may represent a conserved regulatory mechanism for the post-translational modification of the SRR glycoprotein family.
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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".