Cortactin associates with N-cadherin adhesions and mediates intercellular adhesion strengthening in fibroblasts
Bibliographic record
Abstract
The regulation of N-cadherin-mediated intercellular adhesion strength in fibroblasts is poorly characterized; this is due, in part, to a lack of available quantitative models. We used a recombinant N-cadherin chimeric protein and a Rat 2 fibroblast, donor-acceptor cell model, to study the importance of cortical actin filaments and cortactin in the strengthening of N-cadherin adhesions. In wash-off assays, cytochalasin D (1 microM) reduced intercellular adhesion by threefold, confirming the importance of cortical actin filaments in strengthening of N-cadherin-mediated adhesions. Cortactin, an actin filament binding protein, spatially colocalized to, and directly associated with, nascent N-cadherin adhesion complexes. Transfection of Rat-2 cells with cortactin-specific, RNAi oligonucleotides reduced cortactin protein by 85% and intercellular adhesion by twofold compared with controls (P<0.005) using the donor-acceptor model. Cells with reduced cortactin exhibited threefold less N-cadherin-mediated intercellular adhesion strength compared with controls in wash-off assays using N-cadherin-coated beads. Immunoprecipitation and immunoblotting showed that N-cadherin-associated cortactin was phosphorylated on tyrosine residue 421 after intercellular adhesion. While tyrosine phosphorylation of cortactin was not required for recruitment to N-cadherin adhesions it was necessary for cadherin-mediated intercellular adhesion strength. Thus cortactin, and phosphorylation of its tyrosine residues, are important for N-cadherin-mediated intercellular adhesion strength.
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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".