Abstract 352: The role of p62 (SQTSM1) in the transforming growth factor β signaling pathway
Bibliographic record
Abstract
Abstract Transforming growth factor beta (TGFβ) is a cytokine that regulates many cellular processes, including cellular adhesion, proliferation and apoptosis. Its canonical downstream effectors include Smad2/3 proteins, which are phosphorylated and then translocate to the nucleus to alter gene transcriptional programs and promote processes such as epithelial-to-mesenchymal transition (EMT). Previous studies in our lab have shown that atypical Protein Kinase C (aPKC) isoforms associate with TGFβ receptors and modulate receptor trafficking and signal transduction. An aPKC-associated protein, p62 (SQSTM1) has been implicated in TGFβ-dependent EMT, however the mechanisms remain unclear.Here, we investigate the localization of p62 and its potential roles in modulating TGFβ signaling via knockdown and overexpression studies. Using antibody feeding and immunofluorescence microscopy, we support previous findings showing that p62 localizes to late endosomes. In addition, using a co-immunoprecipitation approach, we observed that p62 may associate with TGFβ type II receptor. To explore the functional role of p62 in TGFβ signaling, we conducted protein silencing using siRNA. We observed TGFβ-independent decreases in E-cadherin expression. However these changes were independent of the phosphorylation status or nuclear translocation of Smad2.P62 also plays an important role in autophagy by targeting proteins for degradation. Recently, prolonged TGFβ stimulation has been shown to induce cellular autophagy. Therefore, it is in our interest to characterize the relationship between TGFβ-dependent EMT and autophagy, and to determine whether p62 has a regulatory role between these two processes. Citation Format: Evelyn Ng, Adrian Gunaratne, John Di Guglielmo. The role of p62 (SQTSM1) in the transforming growth factor β signaling pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 352. doi:10.1158/1538-7445.AM2017-352
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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) | 0.004 | 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".