The C‐type lectin receptor MGL senses<i>N</i>‐acetylgalactosamine on the unique<i>Staphylococcus aureus</i>ST395 wall teichoic acid
Bibliographic record
Abstract
Staphylococcus aureus is a common skin commensal but is also associated with various skin and soft tissue pathologies. Upon invasion, S. aureus is detected by resident innate immune cells through pattern-recognition receptors (PRRs), although a comprehensive understanding of the specific molecular interactions is lacking. Recently, we demonstrated that the PRR langerin (CD207) on epidermal Langerhans cells senses the conserved β-1,4-linked N-acetylglucosamine (GlcNAc) modification on S. aureus wall teichoic acid (WTA), thereby increasing skin inflammation. Interestingly, the S. aureus ST395 lineage as well as certain species of coagulase-negative staphylococci (CoNS) produce a structurally different WTA molecule, consisting of poly-glycerolphosphate with α-O-N-acetylgalactosamine (GalNAc) residues, which are attached by the glycosyltransferase TagN. Here, we demonstrate that S. aureus ST395 strains interact with the human Macrophage galactose-type lectin (MGL; CD301) receptor, which is expressed by dendritic cells and macrophages in the dermis. MGL bound S. aureus ST395 in a tagN- and GalNAc-dependent manner but did not interact with different tagN-positive CoNS species. However, heterologous expression of Staphylococcus lugdunensis tagN in S. aureus conferred phage infection and MGL binding, confirming the role of this CoNS enzyme as GalNAc-transferase. Functionally, the detection of GalNAc on S. aureus ST395 WTA by human monocyte-derived dendritic cells significantly enhanced cytokine production. Together, our findings highlight differential recognition of S. aureus glycoprofiles by specific human innate receptors, which may affect downstream adaptive immune responses and pathogen clearance.
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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) | 0.001 | 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".