The Nucleotide-binding Leucine-rich Repeat (NLR) Family Member NLRX1 Mediates Protection against Experimental Autoimmune Encephalomyelitis and Represses Macrophage/Microglia-induced Inflammation
Bibliographic record
Abstract
The nucleotide binding domain and leucine-rich repeat-containing (NLR) family of proteins is known to activate innate immunity, and the inflammasome-associated NLRs are prime examples. In contrast, the concept that NLRs can inhibit innate immunity is still debated, and the impact of such inhibitory NLRs in diseases shaped by adaptive immune responses is entirely unexplored. This study demonstrates that, in contrast to other NLRs that activate immunity, NLRX1 plays a protective role in experimental autoimmune encephalomyelitis (EAE), a mouse model for multiple sclerosis. When compared with wild-type controls, Nlrx1(-/-) mice have significantly worsened clinical scores and heightened CNS tissue damage during EAE. NLRX1 does not alter the production of encephalitogenic T cells in the peripheral lymphatic tissue, but Nlrx1(-/-) mice are more susceptible to adoptively transferred myelin-reactive T cells. Analysis of the macrophage and microglial populations indicates that NLRX1 reduces activation during both active and passive EAE models. This work represents the first case of an NLR that attenuates microglia inflammatory activities and protects against a neurodegenerative disease model caused by autoreactive T cells.
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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".