Flow-dependent Smad2 phosphorylation and TGIF nuclear localization in human aortic endothelial cells
Bibliographic record
Abstract
Endothelial cells respond to fluid flow stimulation through transient and sustained signal pathway activation. Smad2 is a signaling molecule and transcription factor in the Smad signaling pathway, traditionally associated with TGF-β. Although phosphorylation of Smad2 in the receptor-dependent COOH-terminal region is the most appreciated way Smad2 is activated to affect gene expression, phosphorylation may also occur in the MH1-MH2 linker region (L-psmad2). Here, we show that in human aortic endothelial cells (HAEC), Smad2 was both preferentially phosphorylated in the linker region and localized to the nucleus in a flow-dependent manner. The Smad corepressor transforming growth interacting factor (TGIF) was also found to have flow-dependent nuclear localization. Tissue studies confirmed this L-psmad2 generation trend in rat aorta, indicating likely importance in arterial tissue. HAEC-based inhibitor studies demonstrated that L-psmad2 levels were not related to MAPK phosphorylation, but instead followed the pattern of pAkt(473), both with and without the phosphatidylinositol 3-kinase inhibitor PI-103. Akt and Smad species were also shown to directly interact under flow relative to static controls. To further evaluate impacts of PI-103 treatment, expression profiles for two TGF-β and shear stress-dependent genes were determined and showed that mRNAs were lower from untreated 10 dyn/cm(2) than 2 dyn/cm(2) average shear stress cultures. However, upon exposure to PI-103, this trend was reversed, with a stronger response observed at 10 dyn/cm(2). Taken together, the results of this work suggest that fluid flow exposure may influence endothelial gene expression by a novel mechanism involving Akt, L-psmad2, and TGIF.
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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) | 0.002 | 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".