NKG2D and Its Ligand MULT1 Contribute to Disease Progression in a Mouse Model of Multiple Sclerosis
Bibliographic record
Abstract
NKG2D is an activating receptor expressed on the surface of immune cells including subsets of T lymphocytes. NKG2D binds multiple ligands (NKG2DL) whose expression are differentially triggered in a cell type and stress specific manner. The NKG2D-NKG2DL interaction has been involved in autoimmune disorders but its role in animal models of multiple sclerosis (MS) remains incompletely resolved. Here we show that NKG2D and its ligand MULT1 contribute to the pathobiology of experimental autoimmune encephalomyelitis (EAE). MULT1 protein levels are increased in the central nervous system (CNS) at EAE disease peak; soluble MULT1 is elevated in the cerebrospinal fluid of both active and passive EAE. We establish that such soluble MULT1 enhances effector functions (e.g. IFN production) of activated CD8 T lymphocytes from wild type but not from NKG2D-deficient (Klrk1-/-) mice in vitro. The adoptive transferred of activated T lymphocytes from wild type donors induced a significantly reduced EAE disease in Klrk1-/- compared to wild type (Klrk1+/+) recipients. Characterization of T lymphocytes infiltrating the CNS of recipient mice shows that donor (CD45.1) rather than endogenous (CD45.2) CD4 T cells are the main producers of key cytokines (IFN, GM-CSF). In contrast, infiltrating CD8 T lymphocytes include mainly endogenous (CD45.2) cells exhibiting effector properties (NKG2D, granzyme B and IFN). Our data support the notion that endogenous CD8 T cells contribute to passive EAE pathobiology in a NKG2D-dependent manner. Collectively, our results point to the deleterious role of NKG2D and its MULT1 in the pathobiology of a MS mouse model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".