Characterization of the mouse metal-regulatory-element-binding proteins, metal element protein-1 and metal regulatory transcription factor-1
Bibliographic record
Abstract
Metal activation of metallothionein gene transcription depends mainly on the presence of regulatory DNA sequences termed metal-regulatory elements (MREs) and involves MRE-binding transcription factor-1 (MTF-1) interacting with the MREs in a Zn(2+)-dependent manner. We previously identified and characterized a nuclear protein, termed metal element protein-1 (MEP-1), specifically binding with high affinity to MRE elements. The precise relationship between MTF-1 and MEP-1 was unclear, and to determine whether MEP-1 and MTF-1 were distinct protein species, we performed DNA binding analyses to characterize the binding properties of both proteins. Electrophoretic mobility-shift assays showed that MTF-1, produced in COS cells, produces a slower-migrating band compared with that obtained with purified MEP-1. Using an anti-MTF-1 antibody, we showed that both the MTF-1-MRE and the MEP-1-MRE complexes are supershifted by an anti-MTF-1 antibody, thus demonstrating that MEP-1 is antigenically related to MTF-1. RNase protection analyses carried out with RNA prepared from different tissues and cell lines failed to reveal the presence of MTF-1 splicing variants. This indicates that MEP-1 may be a proteolytic fragment of MTF-1. MTF-1 DNA-binding activity was rapidly activated in vivo by Zn(2+) ions but not by Cd(2+), UV irradiation or PMA, and occurred on ice as well as at 21 degrees C. In control and Zn(2+)-treated cell extracts, DNA-binding activity was not enhanced in vitro following the addition of exogenous Zn(2+) or a preincubation at 37 degrees C. However, recombinant MTF-1 produced in vitro required Zn(2+) activation for DNA binding. Interestingly, treatment of nuclear extracts with calf intestine phosphatase completely abrogated MTF-1 DNA-binding activity, thus suggesting that phosphorylation is involved in the regulation of MTF-1 activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".