Epigenetic Control of Adaptive or Homeostatic Splicing of Alternative Exons and <i>Prolactin</i> Gene Expression During Interval-Training Activities of Pituitary Cells
Bibliographic record
Abstract
Abstract Interval-training activities induce adaptive cellular changes without altering their fundamental identity, but the precise underlying molecular mechanisms are not fully understood. In this study, we demonstrate that interval-training depolarization (ITD) of pituitary cells triggers distinct adaptive or homeostatic splicing responses of alternative exons. This occurs while preserving the steady-state expression of the Prolactin and other hormone genes. The nature of these splicing responses depends on the exon’s DNA methylation status, the methyl-C-binding protein MeCP2 and its associated CA-rich motif-binding hnRNP L. Interestingly, the steady expression of the Prolactin gene is also reliant on MeCP2, whose disruption during ITD leads to exacerbated overexpression and multi-exon aberrant splicing of the hormone gene transcripts, similar to the observed hyperprolactinemia or activity-dependent aberrant splicing in Rett Syndrome. Therefore, depending on how many times cells are stimulated, exons may exhibit different splicing responses to cell activities. During the ITD, epigenetic control is crucial for both adaptive or homeostatic splicing and the steady expression of the Prolactin hormone gene. Disruption in this regulation may have significant implications for the development of progressive diseases.
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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) | 0.001 | 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".