Nuclear Localization of the Transcriptional Regulator MIER1α Requires Interaction with HDAC1/2 in Breast Cancer Cells
Bibliographic record
Abstract
MIER1α is a transcriptional regulator that functions in gene repression through its ability to interact with various chromatin modifiers and transcription factors. We have also shown that MIER1α interacts with ERα and inhibits estrogen-stimulated growth. While MIER1α is localized in the nucleus of MCF7 cells, previous studies have shown that it does not contain a nuclear localization signal. In this report, we investigate the mechanism involved in transporting MIER1α into the nucleus. We explored the possibility that MIER1α is transported into the nucleus through a 'piggyback' mechanism. One obvious choice is via interaction with ERα, however we demonstrate that nuclear targeting of MIER1α does not require ERα. Knockdown of ERα reduced protein expression to 22% of control, but did not alter the percentage of cells with nuclear MIER1α (98% nuclear with scrambled shRNA vs. 95% with ERα shRNA). Further evidence was obtained using two stable transfectants derived from the ER-negative MDA231 cell line: MC2 (ERα+) and VC5 (ERα-). Confocal analysis showed no difference in MIER1α localization (86% nuclear in MC2 vs. 89% in VC5). These data demonstrate that ERα is not involved in nuclear localization of MIER1α. To identify the critical MIER1α sequence, we performed a deletion analysis and determined that the ELM2 domain was necessary and sufficient for nuclear localization. This domain binds HDAC1 & 2, therefore we investigated their role. Confocal analysis of an MIER1α containing an ELM2 point mutation previously shown to abolish HDAC binding revealed that this mutation results in almost complete loss of nuclear targeting: 10% nuclear vs. 97% with WT-MIER1α. Moreover, double knockdown of HDAC1 and 2 caused a reduction in percent nuclear from 86% to 44%. The results of this study demonstrate that nuclear targeting of MIER1α requires an intact ELM2 domain and is dependent on interaction with HDAC1/2.
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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 | 0.001 |
| 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".