Rapid reorganization of serotonin projections and antidepressant response to 5-HT1A-biased agonist NLX-101 in fluoxetine-resistant cF1ko mice
Bibliographic record
Abstract
Selective serotonin (5-HT) reuptake inhibitors (SSRIs) like fluoxetine remain a first-line treatment for major depression, but are effective in less than half of patients and can take 4-8 weeks to show results. In this study, we examined cF1ko mice with genetically induced upregulation of 5-HT1A autoreceptors that reduces 5-HT neuronal activity. These mice display anxiety- and depression-related behaviors that did not respond to chronic fluoxetine treatment. We examined treatment with NLX-101, a biased agonist that preferentially targets 5-HT1A heteroreceptors. By testing different doses of NLX-101, we found that a dose of 0.2 mg/kg was effective in reducing depression-related behavior in cF1ko mice without causing hypothermia, a 5-HT1A autoreceptor-mediated response. After 1 h, this dose activated dorsal raphe 5-HT neurons and cells in the medial prefrontal cortex (mPFC), increasing nuclear c-fos labelling in cF1ko mice. In cF1ko mice but not wild-type littermates, 0.2 mg/kg NLX-101 administered 1 h prior to each behavioral test for two weeks reduced depressive behavior in the forced swim test, but increased anxiety-related behaviors in the open field, elevated plus maze, and novelty suppressed feeding tests. During this treatment, NLX-101 induced widespread increases in the density of 5-HT axons, varicosities, and especially synaptic and triadic structures, particularly in depression-related brain regions including mPFC, hippocampal CA1 and CA2/3, amygdala and nucleus accumbens of cF1ko mice. Overall, NLX-101 was rapid and effective in reducing depressive behavior in SSRI-resistant mice, but also induced anxiety-related behaviors. The increase in serotonin innervation induced by intermittent NLX-101 may contribute to its behavioral actions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".