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bioRxiv · 10.1101/2023.06.25.546437

Endocannabinoid system modulation alters fine motor behavior in mice: insights from 3D motion capture

Abstract

The neuromodulatory endocannabinoid system is a promising target for therapeutic interventions. One of the well-known behavioral effects of cannabinoid CB1 receptor activation with exogenous ligands such as THC is the inhibition of locomotor activity. However, the behavioral effects of endogenous cannabinoids are not understood. Enhancing endocannabinoid signaling offers an advantageous therapeutic strategy with limited cannabimimetic side effects, but their effects on motor function remain unclear. To reveal even the finest changes in motor function during voluntary locomotor tasks in mice, we adapted a high-speed, high-resolution marker-based motion capture, which so far has not been available in freely moving mice. Here we show that inhibition of distinct endocannabinoid metabolic pathways produces opposite effects on locomotor behavior that differ from those induced by exogenous cannabinoid receptor ligands. Selective upregulation of endocannabinoids 2-arachidonoylglycerol (2-AG) or N-arachidonoylethanolamine (AEA, anandamide) with inhibitors of their degradation (MJN110 and PF3845, respectively), produced bidirectional effects: MJN110 enhanced and PF3845 suppressed locomotor activity. Consistent differences in whole-body movement and precise step kinematics were found under distinct treatments, while analysis of locomotory episodes revealed invariant temporal microstructure, pointing towards motivational rather than motor-related mechanisms of action. The results show that the effects of manipulations of endocannabinoid system on locomotion are more diverse than previously assumed and result in distinct kinematic phenotypes.

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BibTeXRIS

Ignatowska-Jankowska, B. M., Gurkan Ozer, A., Kuck, A., Niphakis, M. J., Ogasawara, D., Cravatt, B. F., Uusisaari, M. Y.. 2023-06-26. Endocannabinoid system modulation alters fine motor behavior in mice: insights from 3D motion capture. https://doi.org/10.1101/2023.06.25.546437

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