Cadherin-cadherin engagement promotes cell survival via Rac/Cdc42 and Stat3
Bibliographic record
Abstract
2522 Stat3 (signal transducer and activator of transcription-3) is activated by a number of receptor and non-receptor tyrosine kinases, while a constitutively active form of Stat3 alone is sufficient to induce neoplastic transformation. We recently demonstrated a dramatic increase in the activity of Stat3 in breast carcinoma as well as normal epithelial cells and fibroblasts, as a consequence of cell to cell adhesion (Oncogene 23:2600). Given the generally accepted, positive role of Stat3 in proliferation, the Stat3 activity increase observed in post-confluent cells, that is when cells do not divide, was an unexpected observation. In the present report we demonstrate that Stat3 can also be activated through homophilic interactions by E-cadherin and cadherin-11, two members of the classical type I and II cadherin family of surface receptors, responsible for the formation of cell to cell junctions. Furthermore, by plating cells onto surfaces coated with fragments encompassing the two outermost domains of these cadherins, we demonstrate that cadherin engagement can activate Stat3, even in the absence of direct cell to cell contact. Unexpectedly, our results revealed for the first time a dramatic surge in total Rac1 and Cdc42 protein levels by cadherin engagement, and an increase in Rac1 and Cdc42 activity, which is responsible for the Stat3 stimulation observed upon cadherin ligation. Inhibition of cadherin interactions using a peptide, a soluble cadherin fragment or genetic ablation induced apoptosis, pointing to an significant role of this pathway in cell survival signalling, a finding which could also have important therapeutic implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".