Epigenetic modulation of glucocorticoid receptors in posttraumatic stress disorder
Bibliographic record
Abstract
Some individuals suffering from posttraumatic stress disorder (PTSD) exhibit lower basal salivary cortisol and higher glucocorticoid receptor (GR) sensitivity. Recent studies suggest that epigenetic mechanisms regulate the activity of cortisol and GR. As a means to combine and cross-validate those findings, we compared cortisol, GR expression and promoter methylation levels in peripheral T lymphocytes of healthy controls versus individuals endorsing a diagnosis of lifetime PTSD. Thirty subjects with lifetime (current or remitted) PTSD and 16 subjects never exposed to trauma were recruited. Salivary cortisol was collected at six time points over the course of a single weekday and analyzed utilizing a time-resolved fluorescence immunoassay. GR expression (GRtotal, 1B, 1C, 1F and 1H) was measured by quantitative RT-PCR. DNA methylation levels in human glucocorticoid receptor (hGR) 1B and 1C variant's promoter were quantified by epityper in T lymphocytes isolated by magnetic-assisted cell sorting. Individuals with lifetime PTSD have lower morning cortisol release, higher mRNA expression of hGRtotal, 1B, and 1C and lower overall methylation levels in hGR 1B and 1C promoters. Cortisol levels were inversely correlated with hGR 1B mRNA expression. Moreover, overall and CpG site-specific methylation levels were inversely correlated with hGRtotal and 1B mRNA expression. There was no difference between current and remitted PTSD across cortisol, GR expression mRNA and DNA methylation data. Traumatic events induce DNA methylation alterations in distinct promoters of hGR with transcriptional modifications that associate with hypoactive hypothalamus-pituitary-adrenal axis in individuals with PTSD. Our results also point toward an important role of hGR 1B variant in PTSD.
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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".