The scaffold protein Ajuba suppresses CdGAP activity in epithelia to maintain stable cell-cell contacts
Bibliographic record
Abstract
Levels of active Rac1 at epithelial junctions are partially modulated via interaction with Ajuba, an actin binding and scaffolding protein. Here we demonstrate that Ajuba interacts with the Cdc42 GTPase activating protein CdGAP, a GAP for Rac1 and Cdc42, at cell-cell contacts. CdGAP recruitment to junctions does not require Ajuba; rather Ajuba seems to control CdGAP residence at sites of cell-cell adhesion. CdGAP expression potently perturbs junctions and Ajuba binding inhibits CdGAP activity. Ajuba interacts with Rac1 and CdGAP via distinct domains and can potentially bring them in close proximity at junctions to facilitate activity regulation. Functionally, CdGAP-Ajuba interaction maintains junctional integrity in homeostasis and diseases: (i) gain-of-function CdGAP mutants found in Adams-Oliver Syndrome patients strongly destabilize cell-cell contacts and (ii) CdGAP mRNA levels are inversely correlated with E-cadherin protein expression in different cancers. We present conceptual insights on how Ajuba can integrate CdGAP binding and inactivation with the spatio-temporal regulation of Rac1 activity at junctions. Ajuba provides a novel mechanism due to its ability to bind to CdGAP and Rac1 via distinct domains and influence the activation status of both proteins. This functional interplay may contribute towards conserving the epithelial tissue architecture at steady-state and in different pathologies.
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 | 0.001 |
| 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".