Systematic analysis of goal-related movement sequences during maternal behavior in a female mouse model for Rett syndrome

Abstract

Parenting is an ethologically relevant social behavior consisting of stereotypic components involving the care and nourishment of young. First-time rodent dams seek and gather wandering/scattered pups back to the nest (pup retrieval), an essential aspect of maternal care. Over the decades, qualitative observations of the behaving animal have been presented in quantitative discrete units. However, systematic analysis of the dynamic sequences of goal-related movements that comprise the entire behavioral sequence, which would be ultimately essential for understanding the underlying neurobiology, is usually not analyzed. Here, we present systematic analysis of pup retrieval behavior across three days in alloparental female mice (Surrogates or Sur) of two genotypes; Mecp2Heterozygotes (Het), a female mouse model for a neuropsychiatric disorder called Rett syndrome and their wild type (WT) siblings. Additionally, we analyzed CBA/CaJ and C57BL/6J WT surrogates for within-strain comparisons. Frame-by-frame analysis over different phases was performed manually using DataVyu software. We previously showed that Het are inefficient, by measuring latency and errors, at pup retrieval. Here, we show that the sequence of searching, pup-approach and good retrieval crystallizes over time for WT; this sequence does not crystallize in Het. We found that goal-related movements of Het in different phases were similar to WT, suggesting context-driven atypical dynamic patterns in Het. We also identified pup approach and pup grooming as atypical tactile interactions between pups and Het, which contribute to inefficient pup retrieval. Day-by-day analysis showed dynamic changes in goal-related movements in individual animals across genotypes and strains in response to the growing pups. Overall, our approach 1) embraces natural variation in individual mice on different days of pup retrieval behavior, 2) establishes a "gold-standard" manually curated dataset to next build behavioral repertoires using machine learning approaches, and 3) identifies distinct atypical tactile sensory processing in a female mouse model for Rett syndrome.