The phosphoinositide 3-kinase pathway and glycogen synthase kinase-3 positively regulate the activity of metal-responsive transcription factor-1 in response to zinc ions
Bibliographic record
Abstract
Metal-responsive transcription factor-1 (MTF-1) is a metal-regulatory transcription factor essential for induction of the genes encoding metallothioneins (MTs) in response to transition metal ions. Activation of MTF-1 is dependent on the interaction of zinc with the zinc fingers of the protein. In addition, phosphorylation is essential for MTF-1 transactivation. We previously showed that inhibition of phosphoinositide 3-kinase (PI3K) abrogated Mt expression and metal-induced MTF-1 activation in human hepatocellular carcinoma (HCC) HepG2 and mouse L cells, thus showing that the PI3K signaling pathway positively regulates MTF-1 activity and Mt gene expression. However, it has also been reported that inhibition of PI3K has no significant effects on Mt expression in immortalized epithelial cells and increases Mt expression in HCC cells. To further characterize the role of the PI3K pathway on the activity of MTF-1, transfection experiments were performed in HEK293 and HepG2 cells in presence of glycogen synthase kinase-3 (GSK-3), mTOR-C1, and mTOR-C2 inhibitors, as well as of siRNAs targeting Phosphatase and TENsin homolog (PTEN). We showed that inhibition of the mTOR-C2 complex inhibits the activity of MTF-1 in HepG2 and HEK293 cells, while inhibition of the mTOR-C1 complex or of PTEN stimulates MTF-1 activity in HEK293 cells. These results confirm that the PI3K pathway positively regulates MTF-1 activity. Finally, we showed that GSK-3 is required for MTF-1 activation in response to zinc ions.
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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) | 0.001 | 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".