Systematic analysis of severity in a widely used cognitive depression model for mice
2020
Mallien, Anne S | Hager, Christine | Palme, Rupert | Talbot, Steven R | Vogt, Miriam A | Pfeiffer, Natascha | Brandwein, Christiane | Struve, Birgitta | Inta, Dragos | Chourbaji, Sabine | Hellweg, Rainer | Vollmayr, Barbara | Bleich, Andre | Gass, Peter
Animal models in psychiatric research are indispensable for insights into mechanisms of behaviour and mental disorders. Distress is an important aetiological factor in psychiatric diseases, especially depression, and is often used to mimic the human condition. Modern bioethics requires balancing scientific progress with animal welfare concerns. Therefore, scientifically based severity assessment of procedures is a prerequisite for choosing the least compromising paradigm according to the 3Rs principle. Evidence-based severity assessment in psychiatric animal models is scarce, particularly in depression research. Here, we assessed severity in a cognitive depression model by analysing indicators of stress and well-being, including physiological (body weight and corticosterone metabolite concentrations) and behavioural (nesting and burrowing behaviour) parameters. Additionally, a novel approach for objective individualised severity grading was employed using clustering of voluntary wheel running (VWR) behaviour. Exposure to the paradigm evoked a transient elevation of corticosterone, but neither affected body weight, nesting or burrowing behaviour. However, the performance in VWR was impaired after recurrent stress exposure, and the individual severity level increased, indicating that this method is more sensitive in detecting compromised welfare. Interestingly, the direct comparison to a somatic, chemically induced colitis model indicates less distress in the depression model. Further objective severity assessment studies are needed to classify the severity of psychiatric animal models in order to balance validity and welfare, reduce the stress load and thus promote refinement.
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