Functional dissection of human protease μ‐calpain in cell migration using RNAi
Bibliographic record
Abstract
Calpains are a family of calcium-dependent cysteine proteases involved in a variety of cellular functions. Two isoforms, m-calpain and mu-calpain, have been implicated in cell migration. However, since conventional inhibitors used for the studies of the functions of these enzymes lack specificity, the individual physiological function and biochemical mechanism of these two isoforms, especially mu-calpain, are not clear. In contrast, RNA interference has the potential to allow a sequence-specific destruction of target RNA for functional assay of gene of interest. In the present study, we found that small interfering RNAs-mediated knockdown of mu-calpain expression in MCF-7 cells that do not express m-Calpain led to a reduction of cell migration. This isoform-specific function of mu-calpain was further confirmed by the rescue experiment as overexpression of mu-calpain but not m-calpain could restore the cell migration rate. Knockdown of mu-calpain also altered cell morphology with increased filopodial projections and a highly elongated tail that seemed to prevent cell spreading and migration with reduced rear detachment ability. Furthermore, knockdown of mu-calpain decreased the proteolytic products of filamin and talin, which were specifically rescued by overexpression of mu-calpain but not m-calpain, suggesting that their proteolysis could be one of the key mechanisms by which mu-calpain regulates cell migration.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".