Mutual antagonism of estrogen receptors alpha and beta and their preferred interactions with steroid receptor coactivators in human osteoblastic cell lines
Bibliographic record
Abstract
Estrogen is a major sex steroid that affects the growth, maintenance, and homeostasis of the skeleton. Two isoforms of the estrogen receptor (ERalpha and ERbeta) mediate the transcriptional effects of estrogen. Although both isoforms of ER are present and functional in some human osteoblast (OB) cell lines, there is minimal information on the differential regulation of transcription by ERalpha and ERbeta homo- or heterodimers. This report demonstrates that ERalpha and ERbeta coexpression decreases the transcriptional capacity (relative to each ER isoform alone) on an estrogen response element-dependent reporter gene in OBs but not in other non-osteoblastic cell lines. These data suggest that ERalpha and ERbeta coexpression can differentially influence the degree of transcriptional activation in certain cell types. Interestingly, the overexpression of the steroid hormone receptor coactivator-1 (SRC1) resulted in preferential transcriptional enhancement by ERbeta as well as coexpressed ERalpha and ERbeta, whereas SRC2 overexpression appeared to preferentially enhance ERalpha transactivation. SRC3 overexpression failed to enhance estrogen-dependent transcription of any ER combination in OBs. Similar overexpression experiments in COS7 cells exhibited preferential enhancement of ERalpha function with all SRCs, including SRC3. Our data also demonstrated that SRC3 mRNA is reduced in osteoblastic cells, suggesting that SRC3 may have only a minor role in these cells. These data suggest that the transactivation capacity of various ER isoforms is both SRC species and cell type dependent.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".