Impact of Fetal Glucocorticoid Exposure on Gene Expression Profiles in the Adult Adrenal Gland
Bibliographic record
Abstract
An adverse in‐utero environment increases the risk of development of adult diseases such as hypertension, diabetes and the metabolic syndrome. Although elevated circulating fetal levels of glucocorticoids are critically involved in programming events of adult diseases, the genetic basis is not completely understood. The aim of this study was to examine the impact of elevated fetal glucocorticoid levels on changes in gene expression in the adult adrenal gland via a genome‐wide approach. The adrenal gland was selected for analysis since it secretes hormones that can directly influence cardiovascular, endocrine and metabolic functions. Total mRNA from adrenal glands was isolated from 5 wk old male CD1 mice exposed prenatally (GD14‐21) via maternal injections of the synthetic glucocorticoid dexamethasone (DEX; 100 ug/kg/day i.p.) or saline (control). To probe changes in neuroendocrine transcriptome induced in fetal programming, total mRNA was hybridized to Affymetrix GeneChip Mouse Genome 430v2.0 Arrays (~39K genes). Data was analyzed using a model‐based expression algorithm (dCHIP) and filtered (>1.5 fold change in gene expression; statistical significance, p<0.05). Results indicate that only 0.7% of genes (290 genes) were differentially expressed in adrenal glands of mice prenatally exposed to DEX. Widespread alterations in gene expression in diverse systems were observed, including catecholamine biosynthesis (DDC, PNMT), stress proteins (HSP27, HSP68, HSP70) and oxidative stress. These global changes induced by elevated fetal glucocorticoids suggest altered adrenal function may contribute to the pathogenesis of fetal programming of adult 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".