Nuclear Factor-κB Regulates Estrogen Receptor-α Transcription in the Human Heart
Bibliographic record
Abstract
Estrogen receptor (ER)-mediated effects have been associated with the modulation of myocardial hypertrophy in animal models and in humans, but the regulation of ER expression in the human heart has not yet been analyzed. In various cell lines and tissues, multiple human estrogen receptor alpha (hERalpha) mRNA isoforms are transcribed from distinct promoters and differ in their 5'-untranslated regions. Using PCR-based strategies, we show that in the human heart the ERalpha mRNA is transcribed from multiple promoters, namely, A, B, C, and F, of which the F-promoter is most frequently used variant. Transient transfection reporter assays in a human cardiac myocyte cell line (AC16) with F-promoter deletion constructs demonstrated a negative regulatory region within this promoter. Site-directed mutagenesis and electrophoretic mobility shift assays indicated that NF-kappaB binds to this region. An inhibition of NF-kappaB activity by parthenolide significantly increased the transcriptional activity of the F-promoter. Increasing NF-kappaB expression by tumor necrosis factor-alpha reduced the expression of ERalpha, indicating that the NF-kappaB pathway inhibits expression of ERalpha in human cardiomyocytes. Finally, 17beta-estradiol induced the transcriptional activity of hERalpha promoters A, B, C, and F. In conclusion, inflammatory stimuli suppress hERalpha expression via activation and subsequent binding of NF-kappaB to the ERalpha F-promoter, and 17beta-estradiol/hERalpha may antagonize the inhibitory effect of NF-kappaB. This suggests interplay between estrogen/estrogen receptors and the pro-hypertrophic and inflammatory responses to NF-kappaB.
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) | 0.000 | 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".