Back

Differential Effects of 5-HT7 Receptor Signaling in the Noradrenergic System in a Rat Model of Treatment-Resistant Depression

Bruzos-Cidon, C.; Llamosas, N.; Bengoetxea, H.; Lafuente, J. V.; Ugedo, L.; Torrecilla, M.

2026-01-05 neuroscience
10.64898/2026.01.05.697635 bioRxiv
Show abstract

IntroductionTreatment-resistant depression (TRD) is a major clinical challenge, and its neurobiological basis remains unclear. The locus coeruleus (LC), a key noradrenergic hub, shows altered activity in TRD models, and serotonergic 5-HT7 receptors have emerged as potential modulators of this circuit. MethodsWe assessed the impact of 5-HT7 receptor activation on LC function in Wistar Kyoto rats, an established TRD model, using in vivo extracellular recordings, in vitro patch-clamp assays, and receptor expression analysis. ResultsSystemic administration of AS19, a selective 5-HT7 receptor agonist, increased LC neuronal firing in both strains, with a significantly greater effect in Wistar Kyoto rats. This response was prevented by a 5-HT7 antagonist confirming receptor specificity. Glutamatergic blockade altered AS19 effects in a strain-dependent manner, whereas in vitro assays revealed no differential postsynaptic modulation and similar presynaptic inhibition across groups. Western blot analysis showed elevated 5-HT7 receptor expression in the LC, ventral hippocampus, and amygdala of Wistar Kyoto rats, alongside reduced expression in the prefrontal cortex. ConclusionThese findings indicate that 5-HT7 receptor activation exerts complex, circuit-dependent modulation of LC activity, influenced by glutamatergic and serotonergic inputs. Enhanced receptor expression in limbic regions and altered LC responsiveness may contribute to TRD pathophysiology and highlight 5-HT7 signaling as a promising target for novel therapeutic strategies.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.