Muramyl Dipeptide Induces NOD2-Dependent Ly6Chigh Monocyte Recruitment to the Lungs and Protects Against Influenza Virus Infection
Bibliographic record
Abstract
Bacterial peptidoglycan-derived muramyl dipeptide (MDP) and derivatives have long-recognized antiviral properties but their mechanism of action remains unclear. In recent years, the pattern-recognition receptor NOD2 has been shown to mediate innate responses to MDP. Here, we show that MDP treatment of mice infected with Influenza A virus (IAV) significantly reduces mortality, viral load and pulmonary inflammation in a NOD2-dependent manner. Importantly, the induction of type I interferon (IFN) and CCL2 chemokine was markedly increased in the lungs following MDP treatment and correlated with a NOD2-dependent enhancement in circulating monocytes. Mechanistically, the protective effect of MDP could be explained by the NOD2-dependent transient increase in recruitment of Ly6C(high) "inflammatory" monocytes and, to a lesser extent, neutrophils to the lungs. Indeed, impairment in both Ly6C(high) monocyte recruitment and survival observed in infected Nod2-/- mice treated with MDP was recapitulated in mice deficient for the chemokine receptor CCR2 required for CCL2-mediated Ly6C(high) monocyte migration from the bone marrow into the lungs. MDP-induced pulmonary monocyte recruitment occurred normally in IAV-infected and MDP-treated Ips-1-/- mice. However, IPS-1 was required for improved survival upon MDP treatment. Finally, mycobacterial N-glycolyl MDP was more potent than N-acetyl MDP expressed by most bacteria at reducing viral burden while both forms of MDP restored pulmonary function following IAV challenge. Overall, our work sheds light on the antiviral mechanism of a clinically relevant bacterial-derived compound and identifies the NOD2 pathway as a potential therapeutic target against IAV.
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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 | 0.001 |
| 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".