Nod2-Dependent Th2 Polarization of Antigen-Specific Immunity
Bibliographic record
Abstract
While a number of microbial-associated molecular patterns have been known for decades to act as adjuvants, the mechanisms and the signaling pathways underlying their action have remained elusive. Here, we examined the unfolding of the adaptive immune response induced by Nod2 in vivo upon activation by its specific ligand, muramyl dipeptide, a component of peptidoglycan. Our findings demonstrate that this bacterial sensor triggers a potent Ag-specific immune response with a Th2-type polarization profile, characterized by the induction of IL-4 and IL-5 by T cells and IgG1 Ab responses. Nod2 was also found to be critical for the induction of both Th1- and Th2-type responses following costimulation with TLR agonists. Importantly, the synergistic responses to Nod2 and TLR agonists seen in vivo were recapitulated by dendritic cells in vitro, suggesting that these cells likely play a central role in the integration of Nod2- and TLR-dependent signals for driving the adaptive immune response. Taken together, our results identify Nod2 as a critical mediator of microbial-induced potentiation and polarization of Ag-dependent immunity. Moreover, these findings affect our understanding of Crohn's diseases pathogenesis, where lack of Nod2-dependent Th2 signaling in a subset of these patients might explain heightened Th1-mediated inflammation at the level of the intestinal mucosa.
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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".