Increased Serotonin-1A (5-HT1A) Autoreceptor Expression and Reduced Raphe Serotonin Levels in Deformed Epidermal Autoregulatory Factor-1 (Deaf-1) Gene Knock-out Mice
Bibliographic record
Abstract
Altered regulation of the serotonin-1A (5-HT1A) receptor gene is implicated in major depression and mood disorders. The functional human 5-HT1A C(-1019)G promoter polymorphism (rs6295), which prevents the binding of Deaf-1/NUDR leading to dysregulation of the receptor, has been associated with major depression. In cell models Deaf-1 displays dual activity, repressing 5-HT1A autoreceptor expression in serotonergic raphe cells while enhancing postsynaptic 5-HT1A heteroreceptor expression in nonserotonergic neurons. A functional Deaf-1 binding site on the mouse 5-HT1A promoter was recognized by Deaf-1 in vitro and in vivo and mediated dual activity of Deaf-1 on 5-HT1A gene transcription. To address regulation by Deaf-1 in vivo, Deaf-1 knock-out mice bred to a C57BL/6 background were compared with wild-type siblings for changes in 5-HT1A RNA and protein by quantitative RT-PCR, in situ hybridization, and immunofluorescence. In the dorsal raphe, Deaf-1 knock-out mice displayed increased 5-HT1A mRNA, protein, and 5-HT1A-positive cell counts but reduced 5-HT levels, whereas other serotonergic markers, such as tryptophan hydroxylase (TPH)- or 5-HT-positive cells and TPH2 RNA levels, were unchanged. By contrast, 5-HT1A mRNA and 5-HT1A-positive cells were reduced in the frontal cortex of Deaf-1-null mice, with no significant change in hippocampal 5-HT1A RNA, protein, or cell counts. The region-specific alterations of brain 5-HT1A gene expression and reduced raphe 5-HT content in Deaf-1(-/-) mice indicate the importance of Deaf-1 in regulation of 5-HT1A gene expression and provide insight into the role of the 5-HT1A G(-1019) allele in reducing serotonergic neurotransmission by derepression of 5-HT1A autoreceptors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".