Identification of a synthetic muramyl peptide derivative with enhanced Nod2 stimulatory capacity
Bibliographic record
Abstract
Muramyl peptides (MPs) represent the building blocks of bacterial peptidoglycan, a critical component of bacterial cell walls. MPs are well characterized for their immunomodulatory properties, and numerous studies have delineated the role of MPs or synthetic MP analogs in host defense, adjuvanticity and inflammation. More recently, Nod1 and Nod2 have been identified as the host sensors for specific MPs, and, in particular, Nod2 was shown to detect muramyl dipeptide (MDP), a MP found in both Gram-positive and Gram-negative bacterial cell walls. Because mutations in Nod2 are associated with the etiology of Crohn's disease, there is a need to identify synthetic MP analogs that could potentiate Nod2-dependent immunity. Here, we analyzed the Nod2-activating property of 36 MP analogs that had been tested previously for their adjuvanticity and anti-infectious activity. Using a luciferase-based screen, we demonstrate that addition of a methyl group to the second amino acid of MDP generates a MDP derivative with enhanced Nod2-activating capacity. We further validated these results in murine macrophages, human dendritic cells and in vivo. These results offer a basis for the rational development of synthetic MPs that could be used in the treatment of inflammatory disorders that have been associated with Nod2 dysfunction, such as Crohn's disease.
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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".