RIP2 Is Required for NOD Signaling But Not for Th1 Cell Differentiation and Cellular Allograft Rejection
Bibliographic record
Abstract
Two previous reports that receptor-interacting protein (RIP)-2 knockout (RIP2-/-) mice had defective nuclear factor-kappa B (NF-kappaB) signaling and T helper (Th)1 immune responses had led us to believe that this putative serine-threonine kinase might be a possible target for transplant immunosuppression. Thus, we tested whether RIP2-/- mice were able to reject vascularized allografts. Surprisingly, we found that T cells from RIP2-/- mice proliferated and produced interferon (IFN)-gamma after allostimulation in vitro. Moreover, naïve RIP2-/- CD4+ T cells differentiated normally into Th1 or Th2 cells under appropriate cytokine microenvironments. Consistent with these findings, no difference in allograft survival was observed between wild-type and RIP2-/- recipient mice, and rejection had similar pathology and cytokine profiles in both types of recipients. RIP2 deficiency was associated with defective NOD signaling, but this did not affect T-cell receptor (TCR)-dependent activation of the canonical NF-kappaB signaling or expression of NF-kappaB genes in rejecting allografts. Our data demonstrate that RIP2-deficient mice have intact canonical NF-kappaB signaling and can mount Th1-mediated alloresponses and reject vascularized allografts as efficiently as wild-type mice, thus arguing against RIP2 as a primary target for immunosuppression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".