Genetic, epigenetic and posttranscriptional mechanisms for treatment of major depression: the 5-HT1A receptor gene as a paradigm
Bibliographic record
Abstract
Major depression and anxiety are highly prevalent and involve chronic dysregulation of serotonin, but they remain poorly understood. Here, we review novel transcriptional (genetic, epigenetic) and posttranscriptional (microRNA, alternative splicing) mechanisms implicated in mental illness, focusing on a key serotonin-related regulator, the serotonin 1A (5-HT1A) receptor. Functional single-nucleotide polymorphisms and stress-induced DNA methylation of the 5-HT1A promoter converge to differentially alter pre- and postsynaptic 5-HT1A receptor expression associated with major depression and reduced therapeutic response to serotonergic antidepressants. Major depression is also associated with altered levels of splice factors and microRNA, posttranscriptional mechanisms that regulate RNA stability. The human 5-HT1A 3′-untranslated region is alternatively spliced, removing microRNA sites and increasing 5-HT1A expression, which is reduced in major depression and may be genotype-dependent. Thus, the 5-HT1A receptor gene illustrates the convergence of genetic, epigenetic and posttranscriptional mechanisms in gene expression, neurodevelopment and neuroplasticity, and major depression. Understanding gene regulatory mechanisms could enhance the detection, categorization and personalized treatment of major depression.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".