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Exhaustive behavioral profile assay to detect genotype differences between wild-type, inflammasome-deficient, and Nlrp12 knock-out mice

Katsiaryna V Gris Kenzo Yamamoto Marjan Gharagozloo Shaimaa Mahmoud Camille Simard Pavel Gris Denis Gris

*Corresponding author: Denis Gris denis.gris@usherbrooke.ca


Technological advances in computer vision led to the development of various algorithms that are designed to analyze human and animal behavior. We have used an algorithm based on hidden Markov model to monitor the behavior of two different knock-out mice and a healthy control. The goal of this study was to detect behavioral changes in mice with single gene deletion in different inflammatory molecular pathways. Mice with elevated inflammatory activity were compared to wild-type and to mice that lack an inflammasome, a multiprotein complex that processes main inflammatory cytokines, IL-1 beta and IL-18. Importantly, there were no previous reports of any behavioral abnormalities in these mice. We used 10 days continuous recording analysis of 34 behavioral activities. Resulting data were analyzed using R programming. Within the dataset, we found a large number of statistically significant correlations and therefore used factor analysis and hierarchical clustering to reduce the dimensionality of the data that resulted in the 6 factors and 6 clusters. We found that 3 factors and 4 clusters were significantly different between three groups of mice. In conclusion, by using computerized video assessment method paired with R, we have found profound differences where differences could not have been detected by a naked eye. This method allows for fast, accurate, sensitive, and unbiased evaluating of multiparametric behavioral arrays in biomedical research.

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Article URL   http://www.aimspress.com/medicalScience/article/2032.html
Article ID   medsci-05-03-238
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