5′-Heterogeneity of Glucocorticoid Receptor Messenger RNA Is Tissue Specific: Differential Regulation of Variant Transcripts by Early-Life Events
Bibliographic record
Abstract
Glucocorticoid receptor (GR) gene expression is regulated in a complex tissue-specific manner, notably by early-life environmental events that program tissue GR levels. We have identified and characterized several new rat GR mRNAs. All encode a common protein, but differ in their 5'-leader sequences as a consequence of alternate splicing of, potentially, 11 different exon 1 sequences. Most are located in a 3-kb CpG island, upstream of exon 2, that exhibits substantial promoter activity in transfected cells. Ribonuclease (RNase) protection analysis demonstrated significant levels of six alternate exons 1 in vivo in rat, with differences between liver, hippocampus, and thymus reflecting tissue-specific differences in promoter activity. Two of the alternate exons 1 (exons 1(6) and 1(10)) were expressed in all tissues examined, together present in 77-87% of total GR mRNA. The remaining GR transcripts contained tissue-specific alternate first exons. Importantly, tissue-specific first exon usage was altered by perinatal environmental manipulations. Postnatal handling, which permanently increases GR in the hippocampus, causing attenuation of stress responses, selectively elevated GR mRNA containing the hippocampus-specific exon 1(7). Prenatal glucocorticoid exposure, which increases hepatic GR expression and produces adult hyperglycemia, decreased the proportion of hepatic GR mRNA containing the predominant exon 1(10), suggesting an increase in a minor exon 1 variant. Such tissue specificity of promoter usage allows differential GR regulation and programming.
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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".