Distinct Rho GTPase Activities Regulate Epithelial Cell Localization of the Adhesion Molecule CEACAM1: Involvement of the CEACAM1 Transmembrane Domain
Bibliographic record
Abstract
CEACAM1 is an intercellular adhesion glycoprotein. As CEACAM1 plays an important role in epithelial cell signaling and functions, we have examined its localization in epithelial cells. We have observed that distribution at cell contacts is not always seen in these cells, suggesting that CEACAM1 localization might be regulated. In Swiss 3T3 cells, the targeting of CEACAM1 at cell-cell boundaries is regulated by the Rho GTPases. In the present study, we have used the MDCK epithelial cells to characterize the effects of the Rho GTPases and their effectors on CEACAM1 intercellular targeting. Activated Cdc42 and Rac1 or their downstream effector PAK1 targeted CEACAM1 to sites of cell-cell contacts. On the other hand, neither activated RhoA nor activated Rho kinase directed CEACAM1 to cell boundaries, resulting in a condensed distribution of CEACAM1 at the cell surface. Interestingly, inhibition of this pathway resulted in CEACAM1 intercellular localization suggesting that a tightly regulated balance of Rho GTPase activities is necessary to target CEACAM1 at cell-cell boundaries. In addition, using CEACAM1 mutants and chimeric fusion constructs containing domains of the colony-stimulating factor receptor, we have shown that the transmembrane domain of CEACAM1 is responsible for the Cdc42-induced targeting at cell-cell contacts.
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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".