Abrogated Freud-1/Cc2d1a Repression of 5-HT1A Autoreceptors Induces Fluoxetine-Resistant Anxiety/Depression-Like Behavior
Bibliographic record
Abstract
Freud-1/Cc2d1a represses the gene transcription of serotonin-1A (5-HT1A) autoreceptors, which negatively regulate 5-HT tone. To test the role of Freud-1 in vivo , we generated mice with adulthood conditional knock-out of Freud-1 in 5-HT neurons ( cF1ko ). In cF1ko mice, 5-HT1A autoreceptor protein, binding and hypothermia response were increased, with reduced 5-HT content and neuronal activity in the dorsal raphe. The cF1ko mice displayed increased anxiety- and depression-like behavior that was resistant to chronic antidepressant (fluoxetine) treatment. Using conditional Freud-1/5-HT1A double knock-out ( cF1/1A dko ) to disrupt both Freud-1 and 5-HT1A genes in 5-HT neurons, no increase in anxiety- or depression-like behavior was seen upon knock-out of Freud-1 on the 5-HT1A autoreceptor-negative background; rather, a reduction in depression-like behavior emerged. These studies implicate transcriptional dysregulation of 5-HT1A autoreceptors by the repressor Freud-1 in anxiety and depression and provide a clinically relevant genetic model of antidepressant resistance. Targeting specific transcription factors, such as Freud-1, to restore transcriptional balance may augment response to antidepressant treatment. SIGNIFICANCE STATEMENT Altered regulation of the 5-HT1A autoreceptor has been implicated in human anxiety, major depression, suicide, and resistance to antidepressants. This study uniquely identifies a single transcription factor, Freud-1, as crucial for 5-HT1A autoreceptor expression in vivo . Disruption of Freud-1 in serotonin neurons in mice links upregulation of 5-HT1A autoreceptors to anxiety/depression-like behavior and provides a new model of antidepressant resistance. Treatment strategies to reestablish transcriptional regulation of 5-HT1A autoreceptors could provide a more robust and sustained antidepressant response.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".