Hypoxia acts through multiple signaling pathways to induce metallothionein transactivation by the metal-responsive transcription factor-1 (MTF-1)
Bibliographic record
Abstract
Metal-responsive transcription factor-1 (MTF-1) is essential for the induction of genes encoding metallothionein by metals and hypoxia. Here, we studied the mechanism controlling the activation of MTF-1 by hypoxia. Hypoxia activation of Mt gene transcription is dependent on the presence of metal regulatory elements (MREs) in the promoter of Mt genes. We showed that MREa and MREd are the main elements controlling mouse Mt-1 gene induction by hypoxia. Transfection experiments in Mtf-1-null cells showed that MTF-1 is essential for induction by hypoxia. Chromatin immunoprecipitation analysis showed that MTF-1 DNA-binding activity was strongly enhanced in the presence of zinc but not by hypoxia. Notably, hypoxia inducible factor- (HIF) 1α was recruited to the Mt-1 promoter in response to hypoxia but not to zinc. MTF-1 activation was inhibited by PKC, JNK, and PI3K inhibitors and by the electron transport chain inhibitors rotenone and myxothiazol, but not by the antioxidant N-acetylcysteine. We showed that prolyl-hydroxylase inhibitors can activate MTF-1, but this activation requires the presence of HIF-1α. Finally, HIF-dependent transcription is enhanced in the presence of MTF-1 and induction of an MRE promoter is stimulated by HIF-1α, thus indicating cooperation between these 2 factors. However, coimmunoprecipitation experiments did not suggest direct interaction between MTF-1 and HIF-1α.
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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) | 0.001 | 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".