Multidimensional analysis of a social behavior identifies regression and phenotypic heterogeneity in a female mouse model for Rett syndrome

Abstract

Regression is a key feature of neurodevelopmental disorders such as Autism Spectrum Disorder, Fragile X Syndrome and Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene Methyl CpG-Binding Protein 2 (MECP2). It is characterized by an early period of typical development with subsequent regression of previously acquired motor and speech skills in girls. The syndromic phenotypes are individualistic and dynamic over time. Thus far, it has been difficult to capture these dynamics and syndromic heterogeneity in the preclinical Mecp2-heterozygous female mouse model (Het). The emergence of computational neuroethology tools allow for robust analysis of complex and dynamic behaviors to model endophenotypes in pre-clinical models. Towards this first step, we utilized DeepLabCut, a marker-less pose estimation software to quantify trajectory kinematics, and multidimensional analysis to characterize behavioral heterogeneity in Het over trials in the previously benchmarked, ethologically relevant social cognition task of pup retrieval. We report the identification of two distinct phenotypes of adult Het: Het that display a delay in efficiency in early days and then improve over days like wild-type mice, and Het that regress and perform worse in later days. Furthermore, regression is dependent on age, behavioral context, and is identifiable in early days of retrieval. Together, the novel identification of two populations of Het suggest differential effects on neural circuitry and opens new directions of exploration to investigate the underlying molecular and cellular mechanisms, and better design experimental therapeutics.