Fluoxetine and its metabolite norfluoxetine induce microglial apoptosis
Bibliographic record
Abstract
Inflammatory insult to the central nervous system (CNS) can lead to development of depression, and subsequently depression is the most frequent psychiatric comorbidity following ischemic stroke, often limiting recovery and rehabilitation in patients. The initiators of inflammatory pathways in the CNS are microglia activated in response to acute ischemic stress, and anti-depressants have been shown to have anti-inflammatory effects in the CNS, promoting neuronal survival following ischemic insult. We have previously shown that the selective serotonin reuptake inhibitors (SSRIs) fluoxetine and citalopram promote neuronal survival after oxygen-glucose deprivation, an in vitro model of ischemia, by attenuating the release of glutamate and D-serine from activated microglia. Interestingly, we found that fluoxetine-treated microglial cultures contained fewer numbers of cells compared to other groups and hypothesized that fluoxetine and citalopram attenuated the release of glutamate and D-serine by promoting the apoptosis of microglia. The present study aimed to test and compare antidepressants from three distinct classes (tricyclics, monoamine oxidase inhibitors, and SSRIs) on microglial apoptosis. Primary microglia were treated with 1 μg/mL lipopolysaccharide and/or 10 μM antidepressants, and various apoptotic markers were assayed. Fluoxetine and its metabolite norfluoxetine decreased protein levels in cell lysates, decreased cell viability of microglia, and increased the expression of the apoptotic marker cleaved-caspase 3 in microglia. Live/dead nuclear staining also showed that fluoxetine- or norfluoxetine-treated cultures contained greater numbers of dying microglial cells compared to vehicle-treated cultures. Cultures treated with citalopram, phenelzine, or imipramine showed no evidence of inducing microglial apoptosis. Our results demonstrate that fluoxetine and norfluoxetine induce the apoptotic death of microglia, which may serve as a mechanism to attenuate the release of glutamate and D-serine from activated microglia. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.
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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 | 0.000 |
| 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".