MUC1 Initiates Src-CrkL-Rac1/Cdc42–Mediated Actin Cytoskeletal Protrusive Motility after Ligating Intercellular Adhesion Molecule-1
Bibliographic record
Abstract
MUC1, a transmembrane glycoprotein of the mucin family, when aberrantly expressed on breast cancer cells is correlated with increased lymph node metastases. We have previously shown that MUC1 binds intercellular adhesion molecule-1 (ICAM-1) on surrounding accessory cells and facilitates transendothelial migration of MUC1-bearing cells. Nevertheless, the underlying molecular mechanism is still obscure. In the present study, we used a novel assay of actin cytoskeletal reorganization to show that by ligating ICAM-1, MUC1 triggers Rac1- and Cdc42-dependent actin cytoskeletal protrusive activity preferentially at the heterotypic cell-cell contact sites. Further, we show that these MUC1/ICAM-1 interaction-initiated lamellipodial and filopodial protrusions require Src family kinase and CT10 regulator of kinase like (CrkL) accompanied by the rapid formation of a Src-CrkL signaling complex at the MUC1 cytoplasmic domain. Through inhibition of Src kinase activity, we further revealed that Src is required for recruiting CrkL to the MUC1 cytoplasmic domain as well as mediating the observed actin cytoskeleton dynamics. These findings suggest a novel MUC1-Src-CrkL-Rac1/Cdc42 signaling cascade following ICAM-1 ligation, through which MUC1 regulates cytoskeletal reorganization and directed cell motility during 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) | 0.000 | 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) | 0.001 | 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".