Loss of calpains-1 and -2 prevents repair of plasma membrane scrape injuries, but not small pores, and induces a severe muscular dystrophy
Bibliographic record
Abstract
The ubiquitous calpains, calpain-1 and -2, play important roles in Ca2+-dependent membrane repair. Mechanically active tissues like skeletal muscle are particularly reliant on mechanisms to repair and remodel membrane injury, such as those caused by eccentric damage. We demonstrate that calpain-1 and -2 are master effectors of Ca2+-dependent repair of mechanical plasma membrane scrape injuries, although they are dispensable for repair/removal of small wounds caused by pore-forming agents. Using CRISPR gene-edited human embryonic kidney 293 (HEK293) cell lines, we established that loss of both calpains-1 and -2 ( CAPNS1−/−) virtually ablates Ca2+-dependent repair of mechanical scrape injuries but does not affect injury or recovery from perforation by streptolysin-O or saponin. In contrast, cells with targeted knockout of either calpain-1 ( CAPN1−/−) or -2 ( CAPN2−/−) show near-normal repair of mechanical injuries, inferring that both calpain-1 and calpain-2 are equally capable of conducting the cascade of proteolytic cleavage events to reseal a membrane injury, including that of the known membrane repair agent dysferlin. A severe muscular dystrophy in a murine model with skeletal muscle knockout of Capns1 highlights vital roles for calpain-1 and/or -2 for health and viability of skeletal muscles not compensated for by calpain-3 ( CAPN3). We propose that the dystrophic phenotype relates to loss of maintenance of plasma membrane/cytoskeletal networks by calpains-1 and -2 in response to directed and dysfunctional Ca2+-signaling, pathways hyperstimulated in the context of membrane injury. With CAPN1 variants associated with spastic paraplegia, a severe dystrophy observed with muscle-specific loss of calpain-1 and -2 activity identifies CAPN2 and CAPNS1 as plausible candidate neuromuscular disease genes.
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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) | 0.002 | 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".