A role for cathepsin Z in neuroinflammation provides mechanistic support for an epigenetic risk factor in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Hypomethylation of the cathepsin Z locus has been proposed as an epigenetic risk factor for multiple sclerosis (MS). Cathepsin Z is a unique lysosomal cysteine cathepsin expressed primarily by antigen presenting cells. While cathepsin Z expression has been associated with neuroinflammatory disorders, a role for cathepsin Z in mediating neuroinflammation has not been previously established. METHODS: Experimental autoimmune encephalomyelitis (EAE) was induced in both wildtype mice and mice deficient in cathepsin Z. The effects of cathepsin Z-deficiency on the processing and presentation of the autoantigen myelin oligodendrocyte glycoprotein, and on the production of IL-1β and IL-18 were determined in vitro from cells derived from wildtype and cathepsin Z-deficient mice. The effects of cathepsin Z-deficiency on CD4+ T cell activation, migration, and infiltration to the CNS were determined in vivo. Statistical analyses of parametric data were performed by one-way ANOVA followed by Tukey post-hoc tests, or by an unpaired Student's t test. EAE clinical scoring was analyzed using the Mann-Whitney U test. RESULTS: We showed that mice deficient in cathepsin Z have reduced neuroinflammation and dramatically lowered circulating levels of IL-1β during EAE. Deficiency in cathepsin Z did not impact either the processing or the presentation of MOG, or MOG- specific CD4+ T cell activation and trafficking. Consistently, we found that cathepsin Z-deficiency reduced the efficiency of antigen presenting cells to secrete IL-1β, which in turn reduced the ability of mice to generate Th17 responses-critical steps in the pathogenesis of EAE and MS. CONCLUSION: Together, these data support a novel role for cathepsin Z in the propagation of IL-1β-driven neuroinflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".