Increased NOD2-mediated recognition of <i>N</i>-glycolyl muramyl dipeptide
Bibliographic record
Abstract
Peptidoglycan-derived muramyl dipeptide (MDP) activates innate immunity via the host sensor NOD2. Although MDP is N-acetylated in most bacteria, mycobacteria and related Actinomycetes convert their MDP to an N-glycolylated form through the action of N-acetyl muramic acid hydroxylase (NamH). We used a combination of bacterial genetics and synthetic chemistry to investigate whether N-glycolylation of MDP alters NOD2-mediated immunity. Upon infecting macrophages with 12 bacteria, tumor necrosis factor (TNF) alpha secretion was NOD2 dependent only with mycobacteria and other Actinomycetes (Nocardia and Rhodococcus). Disruption of namH in Mycobacterium smegmatis obrogated NOD2-mediated TNF secretion, which could be restored upon gene complementation. In mouse macrophages, N-glycolyl MDP was more potent than N-acetyl MDP at activating RIP2, nuclear factor kappaB, c-Jun N-terminal kinase, and proinflammatory cytokine secretion. In mice challenged intraperitoneally with live or killed mycobacteria, NOD2-dependent immune responses depended on the presence of bacterial namH. Finally, N-glycolyl MDP was more efficacious than N-acetyl MDP at inducing ovalbumin-specific T cell immunity in a model of adjuvancy. Our findings indicate that N-glycolyl MDP has a greater NOD2-stimulating activity than N-acetyl MDP, consistent with the historical observation attributing exceptional immunogenic activity to the mycobacterial cell wall.
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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".