RON‐regulated innate immunity is protective in an animal model of multiple sclerosis
Bibliographic record
Abstract
The tyrosine kinase receptor RON and its ligand, macrophage stimulating protein (MSP), exert inhibitory effects on systemic innate immunity, but their CNS expression and impact on human neuroinflammatory diseases are unknown were RON and MSP present in human brain perivascular macrophages and microglia, but RON mRNA and protein abundance in the CNS were diminished in both MS patients and the MS animal model, experimental autoimmune encephalomyelitis (EAE). Treatment of differentiated human monocytoid cells with MSP resulted in significant reduction of interleukin (IL)-1beta, tumor necrosis factor (TNF)-alpha and MMP-9 mRNA levels, whereas minimal effects were observed in human astrocytes. After induction of EAE, RON knockout and heterozygote animals exhibited significantly increased CNS proinflammatory gene (TNF-alpha, MMP-12) expression compared with wild-type littermate controls, although IL-4 levels were suppressed in both RON-deficient groups. Neurological disease in RON-deficient animals showed a more rapid onset with overall worsened severity, together with exacerbated demyelination, axonal injury, and neuroinflammation after EAE induction. The proto-oncogene, c-Cbl, which modulates ubiquitylation of RON, was increased in glia in both MS brains and EAE spinal cords. Thus, the MSP-RON pathway represents a novel regulatory mechanism within the CNS by which innate immunity and its pathogenic effects could be targeted for future therapeutic interventions.
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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) | 0.001 | 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".