Regulation of Alternative Splicing by Steroid Hormones
Bibliographic record
Abstract
Steroid hormone signaling pathways are critical for organismal development and act through binding to nuclear receptors (NRs) driving transcriptional regulation. In this review, we summarize evidence for another-underrated-mechanism of action for steroid hormones: their ability to modulate the alternative splicing of pre-messenger RNA. Thirty years ago, pioneering studies used in vitro transfection of plasmids expressing alternative exons under the control of hormone-responsive promoters in cell lines. These studies demonstrated that steroid hormones binding to their NRs affected both gene transcription and alternative splicing outcomes. The advent of exon arrays and next-generation sequencing has allowed researchers to observe the effect of steroid hormones at the whole-transcriptome level. These studies demonstrate that steroid hormones regulate alternative splicing in a time-, gene-, and tissue-specific manner. We provide examples of the mechanisms by which steroid hormones regulate alternative splicing including 1) recruitment of dual-function proteins that behave as coregulators and splicing factors, 2) transcriptional regulation of splicing factor levels, 3) the alternative splicing of splicing factors or transcription factors that feed-forward regulate steroid hormone signaling, and 4) regulation of elongation rate. Experiments performed in vivo and in cancer cell lines highlight that steroid hormone-mediated alternative splicing occurs both in physiological and pathophysiologic states. Studying the effect of steroid hormones on alternative splicing is a fruitful avenue for research that should be exploited to discover new targets for therapeutic intervention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".