Phosphorylation Is Involved in the Activation of Metal-regulatory Transcription Factor 1 in Response to Metal Ions
Bibliographic record
Abstract
We have studied the role of phosphorylation in the activation of metal-regulatory transcription factor-1 (MTF-1) and metallothionein (MT) gene expression. We showed that MTF-1 is phosphorylated in vivo and that zinc stimulates MTF-1 phosphorylation 2-4-fold. Several kinase inhibitors were used to examine the possible involvement of kinase cascades in the activation of MTF-1. Metal-induced MT gene expression was abrogated by protein kinase C (PKC), c-Jun N-terminal kinase (JNK), phosphoinositide 3-kinase, and tyrosine-specific protein kinases inhibitors, as assayed by Northern analysis and by cotransfection experiments using a metal regulatory element-luciferase reporter plasmid. The extracellular signal-activated protein kinase and the p38 kinase cascades did not appear to be essential for the activation of MT gene transcription by metals. By using dominant-negative mutants of PKC, JNK, mitogen-activated kinase kinase 4 (MKK4), and MKK7, we provide further evidence supporting a role for PKC and JNK in the activation of MTF-1 in response to metals. Notably, increased MTF-1 DNA binding in response to zinc and MTF-1 nuclear localization was not inhibited in cells preincubated with the different kinase inhibitors despite strong inhibition of MTF-1-mediated gene expression. This suggests that phosphorylation is essential for MTF-1 transactivation function. We hypothesize that metal-induced phosphorylation of MTF-1 is one of the primary events leading to increased MTF-1 activity. Thus, metal ions such as cadmium could activate MTF-1 and induce MT gene expression by stimulating one or several kinases in the MTF-1 signal transduction pathway.
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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) | 0.001 | 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 | 0.001 |
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".