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 preclinicalMecp2-heterozygousfemale mouse model (Het). The emergence of computational neuroethology tools allows 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 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 suggests differential effects on neural circuitry, opens new avenues to investigate the underlying molecular and cellular mechanisms of heterogeneity, and design better studies for stratifying therapeutics.Significance StatementA long-standing problem in the field of neuropsychiatric disorders is the reliable identification of heterogeneous endophenotypes in animal models for the disorders. This problem has clear implications in identifying etiology and therapeutic targets. The emergence of accessible computational neuroethology tools is a powerful solution in systematic characterization of animal behaviors in resolving this heterogeneity. Using DeepLabCut and multidimensional analysis of an ethologically relevant social cognition task, we identify two distinct populations exhibiting delay in efficient behavior and regression in a female mouse model for Rett syndrome. The novel identification of two populations in genotypically-identical mice has profound implications on both etiology and personalized therapeutic approaches.