Ultrasound of alternating frequencies and variable emotional impact evokes depressive syndrome in mice and rats

• A 3-week ultrasound of 20–45 kHz frequencies induces a depressive state in rodents.
• Altered brain SERT, BDNF, and 5-HT2A/1A receptors parallel this condition.
• Fluoxetine is able to counter this ultrasound induced depressive state.
• This paradigm can serve as a non-invasive emotional stress model in rats and mice.

Emotional stress is primarily triggered by the cognitive processing of negative input; it is regarded as a serious pathogenetic factor of depression that is challenging to model in animals. While available stress paradigms achieve considerable face and construct validity in modelling depressive disorders, broader use of naturalistic stressors instead of the more prevalent models with artificial challenges inducing physical discomfort or pain may substantially contribute to the development of novel antidepressants. Here, we investigated whether a 3-week exposure of Wistar rats and Balb/c mice to unpredictably alternating frequencies of ultrasound between the ranges of 20–25 and 25–45 kHz, which are known to correspond with an emotionally negative and with a neutral emotional state, respectively, for small rodents in nature, can induce behavioural and molecular depressive-like changes. Both rats and mice displayed decreased sucrose preference, elevated “despair” behaviour in a swim test, reduced locomotion and social exploration. Rats showed an increased expression of SERT and 5-HT2A receptor, a decreased expression of 5-HT1A receptor in the prefrontal cortex and hippocampus, diminished BDNF on gene and protein levels in the hippocampus. Fluoxetine, administered to rats at the dose of 10 mg/kg, largely precluded behavioural depressive-like changes. Thus, the here applied paradigm of emotional stress is generating an experimental depressive state in rodents, which is not related to any physical stressors or pain. In essence, this ultrasound stress model, besides enhancing animal welfare, is likely to provide improved validity in the modelling of clinical depression and may help advance translational research and drug discovery for this disorder.