Granzyme B-inhibitor serpina3n induces neuroprotection in vitro and in vivo
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is an autoimmune inflammatory and neurodegenerative disease of the central nervous system (CNS). It is widely accepted that inflammatory cells play major roles in the pathogenesis of MS, possibly through the use of serine protease granzyme B (GrB) secreted from the granules of cytotoxic T cells. We have previously identified GrB as a mediator of axonal injury and neuronal death. In this study, our goal was to evaluate the effect of GrB inhibition in the human system in vitro, and in vivo in EAE using the newly isolated GrB-inhibitor serpina3n. METHODS: We used a well-established in vitro model of neuroinflammation characterized by a co-culture system between human fetal neurons and lymphocytes. In vivo, we induced EAE in 10- to 12-week-old female C57/BL6 mice and treated them intravenously with serpina3n. RESULTS: In the in vitro co-culture system, pre-treatment of lymphocytes with serpina3n prevented neuronal killing and cleavage of the cytoskeletal protein alpha-tubulin, a known substrate for GrB. Moreover, in EAE, 50 μg serpina3n substantially reduced the severity of the disease. This dose was administered intravenously twice at days 7 and 20 post EAE induction. serpina3n treatment reduced axonal and neuronal injury compared to the vehicle-treated control group and maintained the integrity of myelin. Interestingly, serpina3n treatment did not seem to reduce the infiltration of immune cells (CD4(+) and CD8(+) T cells) into the CNS. CONCLUSION: Our data suggest further studies on serpina3n as a potentially novel therapeutic strategy for the treatment of inflammatory-mediated neurodegenerative diseases such as MS.
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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 | 0.000 | 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 | 0.000 |
| 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 it