Granzyme B-inhibitor serpina3n induces neuroprotection in vitro and in vivo
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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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".