Cross-talk of Nod1 and Nod2 signaling pathways with IFNγ (172.33)
Bibliographic record
Abstract
Abstract The importance of Nod1- and Nod2-mediated PGN-recognition for the elicitation of antimicrobial immunity has been highlighted by observations demonstrating that animals deficient in Nod1 and Nod2 are more susceptible to several bacterial infections. Moreover, we recently revealed that Nod1-/- and Nod2-/- animals exert altered microbe-specific T cells producing IFNγ. However, sole Nod1 and Nod2 stimulation fails to elicit the Ag-specific type 1 immune response suggesting that PGN recognition by Nod1 and Nod2 acts in concert with (an)other signal(s) for the priming of protective immunity. Since IFNγ provides an innate mechanism of resistance to many intracellular pathogens, we hypothesize that the synergistic activity of Nod1 and Nod2 with IFNγ exerts critical antimicrobial activity through the enhanced priming of CXCR3+ Ag-specific CD4+ and CD8+T cells. Following our hypothesis we acquired preliminary data demonstrating that stimulation of murine and human macrophages and DC by a combination of IFNγ and Nod1- or Nod2 agonists, induces the synergistic release of the chemokines RANTES, MIG and IP-10, known to chemoattract Th1 and NK cells as well as the upregulation of co-stimulatory molecules such as MHC class II, CD40 CD80 and CD86. These finding demonstrate that the synergistic stimulation of IFNγ with Nod1- or Nod2-agonists leads to the potent induction of CXCR3 and CCL5 ligating chemokines and the subsequent polarization of protective antimicrobial type 1 immunity in vivo.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".