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

doi:10.3934/medsci.2018.3.238

AIMS Medical Science

2018, Volume 5, Issue 3: 238-251. doi: 10.3934/medsci.2018.3.238

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

1.
University of Sherbrooke, 3001 12e Avenue Nord, Sherbrooke, QC, Canada
2.
McGill University, 740 Penfield, Av Room 2203, Montreal, QC, H3A 0G1, Canada

Received: 26 March 2018 Accepted: 26 April 2018 Published: 30 May 2018

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.
- behavioral genotype differences,
- automated behavioral assessment,
- R program,
- supervised learning,
- home cage behavior,
- long-term continuous recording,
- Nlrp12 knock-out,
- ASC knock-out,
- inflammasome deficient mice
Citation: Katsiaryna V Gris, Kenzo Yamamoto, Marjan Gharagozloo, Shaimaa Mahmoud, Camille Simard, Pavel Gris, Denis Gris. Exhaustive behavioral profile assay to detect genotype differences between wild-type, inflammasome-deficient, and Nlrp12 knock-out mice[J]. AIMS Medical Science, 2018, 5(3): 238-251. doi: 10.3934/medsci.2018.3.238

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Abstract

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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