Looking beyond the DNA sequence: the relevance of DNA methylation processes for the stress–diathesis model of depression
Bibliographic record
Abstract
The functioning of the hypothalamic-pituitary-adrenal (HPA) axis and serotonergic (5-HT) system are known to be intertwined with mood. Alterations in these systems are often associated with depression. However, neither are sufficient to cause depression in and of themselves. It is now becoming increasingly clear that the environment plays a crucial role, particularly, the perinatal environment. In this review, we posit that early environmental stress triggers a series of epigenetic mechanisms that adapt the genome and programme the HPA axis and 5-HT system for survival in a harsh environment. We focus on DNA methylation as it is the most stable epigenetic mark. Given that DNA methylation patterns are in large part set within the perinatal period, long-term gene expression programming by DNA methylation is especially vulnerable to environmental insults during this period. We discuss specific examples of genes in the 5-HT system (serotonin transporter) and HPA axis (glucocorticoid receptor and arginine vasopressin enhancer) whose DNA methylation state is associated with early life experience and may potentially lead to depression vulnerability. We conclude with a discussion on the relevance of studying epigenetic mechanisms in peripheral tissue as a proxy for those occurring in the human brain and suggest avenues for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".