Protein Kinases Alter the Allosteric Modulation of the Serotonin Transporter<i>In Vivo</i>and<i>In Vitro</i>
Bibliographic record
Abstract
AIM: Studies using S- and R-enantiomers of the SSRI citalopram have shown that R-citalopram exerts an antagonistic effect on the efficacy of the antidepressant S-citalopram (escitalopram) through an interaction at an allosteric modulator site on the serotonin transporter (SERT). Here, we show that protein kinase signaling systems are involved in the allosteric modulation of the SERT in vivo and in vitro. METHODS: We assessed the effects of nonspecific protein kinase inhibitor staurosporine in the action of escitalopram and/or R-citalopram using electrophysiological and behavioral assays in rats and cell surface SERT expression measures in serotoninergic cells. RESULTS: Acute administration of R-citalopram counteracted the escitalopram-induced suppression of the serotonin (5-HT) neuronal firing activity and increase of the head twitches number following L-5-hydroxytryptophan injection. Importantly, these counteracting effects of R-citalopram were abolished by prior systemic administration of staurosporine. Interestingly, the preventing effect of staurosporine on 5-HT neuronal firing activity was abolished by direct activation of protein kinase C with phorbol 12-myristate 13-acetate. Finally, in vitro, quantification of the amount of cell surface-expressed SERT molecules revealed that R-citalopram prevented escitalopram-induced SERT internalization that was completely altered by staurosporine. CONCLUSION: Taken together, these results highlight for the first time an involvement of protein kinases in the allosteric modulation of SERT 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".