Mannose-Binding Lectin Recognizes Peptidoglycan via the <i>N</i> -Acetyl Glucosamine Moiety, and Inhibits Ligand-Induced Proinflammatory Effect and Promotes Chemokine Production by Macrophages
Bibliographic record
Abstract
Peptidoglycan (PGN) is the major cell wall component (90%, w/w) of Gram-positive bacteria and consists of N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc) disaccharide repeating arrays that are cross-linked by short peptides. We hypothesized that PGN is a ligand for pathogen-associated pattern-recognition proteins. Mannose-binding lectin (MBL) and serum amyloid component P are two carbohydrate-binding innate immune proteins present in the blood. In this study we show that human MBL, but not serum amyloid component P, binds significantly to PGN via its C-type lectin domains, and that the interaction can be more effectively competed by GlcNAc than by MurNAc. Surface plasmon resonance analyses show that native MBL binds immobilized PGN with high avidity. Competition experiments also show that both native MBL and MBL(n/CRD), a 48-kDa recombinant trimeric fragment of MBL containing neck and carbohydrate recognition domains, have higher affinity for GlcNAc than for MurNAc. Protein arrays and ELISA show that PGN increases the secretion of TNF-alpha, IL-8, IL-10, MCP-2, and RANTES from PMA-stimulated human monocytic U937 cells. Interestingly, the presence of MBL together with PGN increases the production of IL-8 and RANTES, but reduces that of TNF-alpha. Our results indicate that Gram-positive bacterial is a biologically relevant ligand for MBL, and that the collectin preferentially binds to the GlcNAc moiety of the PGN via its C-type lectin domains. MBL inhibits PGN-induced production of proinflammatory cytokines while enhancing the production of chemokines by macrophages, which suggests that MBL may down-regulate macrophage-mediated inflammation while enhancing phagocyte recruitment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".