Abstract 175: Atypical PKC knockdown alters TGFβ signaling potential
Bibliographic record
Abstract
Abstract Transforming growth factor beta (TGFβ) regulates many important cellular processes including growth control and cell migration. In epithelial tumor cells, TGFβ signaling is fundamentally altered and induces epithelial to mesenchymal transition (EMT) leading to increased migratory potential. We have recently reported that the inhibition of atypical PKC isoforms (aPKC) extends TGFβ induced Smad signaling by altering receptor trafficking. Here we report that siRNA targeting of aPKC in A549 lung adenocarcinoma cells extends temporal P-Smad2 signaling but does not alter the transcript levels of Snail and Slug, as assessed by qPCR analysis. Interestingly, TGFβ was not observed to have as robust an increase in MMP9 and PAI-1 levels in aPKC-silenced cells compared to cells transfected with control siRNA. To further analyze the functional consequences, we assessed EMT by immunofluorescence microscopy analysis as well as immunoblotting for epithelial and mesenchymal cell markers. We found that TGFβ-dependent EMT was significantly reduced in aPKC silenced cells. We also uncovered that in addition to increased P-Smad2 signaling, P38 MAPK phosphorylation was increased and extended in aPKC silenced cells. Finally, we discovered that aPKC phosphorylates Par6 on a conserved serine residue that has been shown to regulate EMT and metastasis in breast cancer. Taken together, we believe that aPKC regulates several aspects of TGFβ signaling, including P-Smad2, P-P38 and P-Par6 levels. Consequently, convergence of these signals in aPKC silenced cells leads to reduced EMT and migratory potential. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 175. doi:1538-7445.AM2012-175
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".