LOSS OF RIP2 EXACERBATES MURINE EXPERIMENTAL AUTOIMMUNE ENCEPHALOMYELITIS (129.16)
Bibliographic record
Abstract
Abstract Receptor interacting protein 2 (RIP2), a putative serine-threonine kinase, is a modulator of the activation of both innate and adaptive immune responses. RIP2-deficient mice have impaired NF-κB signaling and defects in Th1-mediated immune responses. Since activated NF-κB signaling in hematopoietic tissue and in the central nervous system (CNS) has been implicated in the pathogenesis of experimental autoimmune encephalomyelitis (EAE), we hypothesized that deficient expression of RIP2, a regulator of the NF-κB pathway, would affect the course of EAE. To test this hypothesis, we induced EAE with MOG35-55 peptide, in C57Bl/6 wild type (WT) and RIP2−/− mice. We found that RIP2−/− mice exhibited exacerbated EAE as compared to WT mice, with both a higher clinical score and increased demyelination. Disease induction in both genotypes was T cell-dependent as demonstrated by adoptive T cell transfer. However, EAE in RIP2−/− mice did not have an accelerated course and did not show increased T cell priming ex vivo. Rather, the exacerbated EAE in RIP2−/− mice was associated with a higher percentage of IL-17-producing T cells, both in the periphery and in the CNS, while the percentage of IFN-γ-producing cells was the same in WT and RIP2−/− mice. Our data demonstrate that RIP2 is a key in vivo regulator of Th17-associated autoimmunity in the CNS. Such a role is likely due to its activity on the NF-κB pathway.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".