The serotonin reuptake blocker citalopram destabilizes fictive locomotor activity in salamander axial circuits through 5-HT1A receptors
Bibliographic record
Abstract
Serotoninergic (5-HT) neurons are powerful modulators of spinal locomotor circuits. Most studies about 5-HT modulation focused on the effect of exogenous 5-HT and these studies provided key information about the cellular mechanisms involved. Less is known about the effects of increased release of endogenous 5-HT with selective serotonin reuptake inhibitors. Such molecules were shown to destabilize the locomotor output of spinal limb networks through 5-HT1A receptors. However, in tetrapods little is known about the effects of increased 5-HT release on the locomotor output of axial networks, which are coordinated with limb circuits during locomotion from basal vertebrates to mammals. Here, we examined the effect of citalopram on fictive locomotion generated in axial segments of isolated spinal cords in salamanders, a tetrapod where raphe 5-HT reticulospinal neurons and intraspinal 5-HT neurons are present as in other vertebrates. Using electrophysiological recordings of ventral roots, we show that fictive locomotion generated by bath-applied glutamatergic agonists is destabilized by citalopram. Citalopram-induced destabilization was prevented by a 5-HT1A receptor antagonist, whereas a 5-HT1A receptor agonist destabilized fictive locomotion. Using immunofluorescence experiments, we found 5-HT-positive fibers and varicosities in proximity with motoneurons and glutamatergic interneurons that are likely involved in rhythmogenesis. Our results show that increasing 5-HT release has a deleterious effect on axial locomotor activity through 5-HT1A receptors. This is consistent with studies in limb networks of turtle and mouse, suggesting that this part of the complex 5-HT modulation of spinal locomotor circuits is common to limb and axial networks in limbed vertebrates.
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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) | 0.001 | 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".