Atypical Protein Kinase C Phosphorylates Par6 and Facilitates Transforming Growth Factor β-Induced Epithelial-to-Mesenchymal Transition
Bibliographic record
Abstract
Epithelial-to-mesenchymal transition (EMT) is controlled by cellular signaling pathways that trigger the loss of cell-cell adhesion and lead to the restructuring of the cell cytoskeleton. Transforming growth factor β (TGF-β) has been shown to regulate cell plasticity through the phosphorylation of Par6 on a conserved serine residue (S345) by the type II TGF-β receptor. We show here that atypical protein kinase C (aPKC) is an essential component to this signaling pathway in non-small-cell lung cancer (NSCLC) cells. We show that the aPKC, PKCι, interacts with TGF-β receptors through Par6 and that these proteins localize to the leading edge of migrating cells. Furthermore, Par6 phosphorylation on serine 345 by TGF-β receptors is enhanced in the presence of aPKC. aPKC kinase activity, as well as an association with Par6, were found to be important for Par6 phosphorylation. In effect, small interfering RNA-targeting aPKC reduces TGF-β-induced RhoA and E-cadherin loss, cell morphology changes, stress fiber production, and the migration of NSCLC cells. Interestingly, reintroduction of a phosphomimetic Par6 (Par6-S345E) into aPKC-silenced cells rescues both RhoA and E-cadherin loss with TGF-β stimulation. In conclusion, our results suggest that aPKCs cooperate with TGF-β receptors to regulate phospho-Par6-dependent EMT and cell migration.
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) | 0.003 | 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".