Role of coactivators SRC-1 and CARM1 in estrogen receptor-alpha and beta-dependent cell proliferation in the dentate gyrus of adult female rats
Bibliographic record
Abstract
Nuclear receptors such as the estrogen receptors (ER) require the presence of coactivator proteins, such as the steroid receptor coactivator (SRC-1) and coactivator-associated arginine methyltransferase (CARM1) to enhance the transcription of target genes. Importantly, in vitro work suggests that ER and ER differ in the ability to recruit coactivators such as SRC-1. For example, SRC-1 has a strong affinity for ER and a weaker affinity for ER. Interestingly, both ER and ER are individually involved in estradiol-enhanced cell proliferation in the dentate gyrus of adult female rats. In addition, previous work suggests a role for CARM1 in cell proliferation and for SRC-1 in cell differentiation, therefore the present study aimed to determine whether proliferating cells in the dentate gyrus of the hippocampus co-express the coactivators SRC-1 and CARM1. We also aimed to determine whether ER and ER agonists would result in altered expression of SRC-1 and CARM1 in new proliferating cells in the dentate gyrus. To investigate this, adult female rats were ovariectomized and treated with either the ER agonist Propyl-pyrazole triol (PPT), the ER agonist diarylpropionitrile (DPN), estradiol benzoate (EB), or vehicle (CTRL). Rats were then injected with BrdU (200 mg/kg) and sacrificed 24 hours later. Preliminary data suggests that DPN, PPT and EB increase cell proliferation in the dentate gyrus compared to the vehicle-injected group. Interestingly, the number of proliferating cell expressing SRC-1 is similar in all groups, suggesting that neither of the ER agonists nor EB treatment affects the co-expression of BrdU+ cells with SRC-1. However, additional measurements are currently being done to investigate whether CARM-1 is differentially expressed in proliferating cells in the hippocampus following selective ER agonist treatment.
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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 | 0.001 | 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 | 0.000 |
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 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".