Fluoxetine affects GluK2 editing, glutamate-evoked Ca<sup>2+</sup> influx and extracellular signal-regulated kinase phosphorylation in mouse astrocytes
Bibliographic record
Abstract
BACKGROUND: We sought to study the effects of chronic exposure to fluoxetine - a selective serotonin reuptake inhibitor (SSRI) and specific 5-HT(2B) receptor agonist in astrocytes - on the expression of kainate receptors (GluK1-5) in cultured astrocytes and in intact brains in mice and on GluK2 editing by adenosine deaminase acting on RNA (ADAR), as well as the ensuing effects of fluoxetine on glutamate-mediated Ca(2+) influx and extracellular signal-regulated kinase (ERK)(1/2) phosphorylation in astrocytes. METHODS: We performed reverse transcription-polymerase chain reaction (PCR) to assess mRNA expression. We analyzed RNA editing with amplification refractory mutation system PCR and complementary DNA sequencing. Protein expression and ERK phosphorylation were assessed using Western blots. We studied gene silencing with specific small interfering RNAs (siRNA), and we studied intracellular Ca(2+) using fluorometry. RESULTS: All GluK subunits were present in the brain in vivo, and GluK2-5 subunits were present in cultured astrocytes. Fluoxetine upregulated GluK2 and ADAR2. Enhanced GluK2 editing by fluoxetine abolished glutamate-mediated increases in intra cellular Ca(2+) and ERK(1/2) phosphorylation. Enhanced editing of GluK2 was prevented by siRNA against the 5-HT(2B) receptor or ADAR2. LIMITATIONS: Limitations of our study include the use of an in vitro system, but our cultured cells in many respects behave like in vivo astrocytes. CONCLUSION: Fluoxetine alters astrocytic glutamatergic function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".