Targeting calpastatin pharmacologically restores synaptic proteolysis and preserves motor neurons survival and function in C9orf72 ALS
Bibliographic record
Abstract
A hexanucleotide repeat expansion (GGGGCC) in the C9orf72 gene is the most prevalent genetic cause of ALS, with early neuromuscular junction (NMJ) dysfunction being a key pathological feature. Current therapies provide only limited symptomatic relief, underscoring the need for targeted, mechanism-based interventions. Using a C9orf72 ALS zebrafish model (C9-miR) and patient-derived induced pluripotent stem cell (iPSC) motor neurons, we identified significant downregulation of calpastatin, the endogenous inhibitor of calpains, a calcium-dependent protease family implicated in neurodegeneration. We demonstrate that restoring calpastatin activity with a cell-permeable calpastatin-derived peptide or the small molecule, calpeptin, ameliorates locomotor deficits and NMJ dysfunction in the C9-miR zebrafish model. These interventions enhance synaptic vesicle turnover and quantal release at the NMJ while improving motor neuron excitability and synaptic integrity in iPSC-derived motor neurons. N-terminomic/TAILS mass spectrometry revealed direct calpain-mediated cleavage of synaptic proteins in motor neurons derived from C9orf72 patients. Proteolysis of novel ALS-relevant synaptic and axonal proteins is prevented by calpeptin and calpastatin peptide treatments. Our findings establish the calpastatin as a pivotal regulator of synaptic function in C9orf72-associated ALS and identify it as a promising therapeutic target, offering a novel strategy to restore synaptic transmission and potentially halt disease progression.
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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 | 0.001 | 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".