Characterization of stably transfected fusion protein GFP‐estrogen receptor‐α in MCF‐7 human breast cancer cells
Bibliographic record
Abstract
Tagging hormone receptors with the green fluorescent protein (GFP) has increased our knowledge of ligand dependent sub-cellular trafficking of hormone receptors. However, the effect of the tagged hormone receptor expression on the corresponding wild type hormone receptor and endogenous gene expression has not been investigated. In this study, we constructed a MCF-7 cell line stably expressing GFP-tagged human estrogen receptor-alpha (ER) under control of the tetracycline-on system to determine the effect of GFP-ER expression on cell proliferation and expression of endogenous ER and hormone-responsive genes. Further, the inducible system was applied to determine the ligand dependent turnover rates of GFP-ER protein and mRNA. Our results demonstrate that GFP-ER expression did not affect cell cycling. Independent of ligand, GFP-ER markedly reduced the level of endogenous ER mRNA and protein, suggesting that ER negatively autoregulates its expression. Cisplatin cross-linking studies showed that GFP-ER is associated with nuclear DNA in situ, suggesting that GFP-ER is partially replacing ER at estrogen response elements. Furthermore, GFP-ER expression did not affect the estradiol induced temporal expression of hormone responsive genes c-myc and pS2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.002 |
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".