Serotonin transactivation of PDGFβ receptors results in a heterologous desensitization to subsequent transactivation stimuli
Bibliographic record
Abstract
The platelet‐derived growth factor receptor type β (PDGFRβ) is an important receptor tyrosine kinase for neuronal development and survival. In addition to being activated by PDGF ligands, PDGFRβ can be transactivated by G protein‐coupled receptors (GPCRs), an intracellular, ligand‐independent mechanism for activating growth factor receptors. Treatment of primary cortical neurons and SH‐SY5Y cells with 5‐HT results in a transactivation of PDGFRβ and an activation of ERK1/2. Pretreatment of these cells with fluoxetine, a serotonin‐selective reuptake inhibitor (SSRI) appears to block 5‐HT‐induced transactivation of PDGFRβ. However, we demonstrate that fluoxetine itself is able to transactivate PDGFRβ, possibly by binding 5‐HT2 receptors, and this initial transactivation prevents or desensitizes PDGFRβ to subsequent transactivation stimuli. To our knowledge this is the first report of a heterologous desensitization of the transactivation phenomenon. Given the recent development of the “neurotrophic factor hypothesis” for depression, the ability of fluoxetine to transactivate PDGFRβ provides an intriguing link between this widely‐used anti‐depressant drug and promoting growth factor receptor activity in neurons. This work was support by the National Science and Engineering Research Council of Canada.
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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 | 0.001 |
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".