ERβ agonist alters RNA splicing factor expression and has a longer window of antidepressant effectiveness than estradiol after long-term ovariectomy
Bibliographic record
Abstract
Background: Estrogen therapy (ET), an effective treatment for perimenopausal depression, often fails to ameliorate symptoms when initiated late after the onset of menopause. Our previous work has suggested that alternative splicing of RNA might mediate these differential effects of ET. Methods: Female Sprague–Dawley rats were treated with estradiol (E2) or vehicle 6 days (early ET) or 180 days (late ET) after ovariectomy (OVX). We investigated the differential expression of RNA splicing factors and tryptophan hydroxylase 2 (TPH2) protein using a customized RT2 Profiler PCR Array, reverse-transcription polymerase chain reaction, immunoprecipitation and behaviour changes in clinically relevant early and late ET. Results: Early ET, but not late ET, prolonged swimming time in the forced swim test and reduced anxiety-like behaviours in the elevated plus maze. It reversed OVX-increased (SFRS7 and SFRS16) or OVX-decreased (ZRSR2 and CTNNB1) mRNA levels of splicing factors and ERβ splicing changes in the brains of OVX rats. Early ET, but not late ET, also increased the expression of TPH2 and decreased monoamine oxidase A levels in the dorsal raphe in the brains of OVX rats. In late ET, only diarylpropionitrile (an ERβ-specific agonist) achieved similar results — not E2 (an ERα and ERβ agonist) or propylpyrazoletriol (an ERα-specific agonist). Limitations: Our experimental paradigm mimicked early and late ET in the clinical setting, but the contribution of age and OVX might be difficult to distinguish. Conclusion: These findings suggest that ERβ alternative splicing and altered responses in the regulatory system for serotonin may mediate the antidepressant efficacy of ET associated with the timing of therapy initiation. It is likely that ERβ-specific ligands would be effective estrogen-based antidepressants late after the onset of menopause.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".