Multidimensional analysis of behavior predicts genotype with high accuracy in a mouse model of Angelman syndrome

Abstract

Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss of expression of the maternal copy of the UBE3A gene. Individuals with AS have a multifaceted behavioral phenotype consisting of deficits in motor function, epilepsy, cognitive impairment, sleep abnormalities, as well as other comorbidities. Effectively modeling this behavioral profile and measuring behavioral improvement will be crucial for the success of ongoing and future clinical trials. Foundational studies have defined an array of behavioral phenotypes in the AS mouse model. However, no single behavioral test is able to fully capture the complex nature of AS—in mice, or in children. We performed multidimensional analysis (principal component analysis + k-means clustering) to quantify the performance of AS model mice (n = 148) and wild-type littermates (n = 138) across eight behavioral domains. This approach correctly predicted the genotype of mice based on their behavioral profile with ~95% accuracy, and remained effective with reasonable sample sizes (n = ~12–15). Multidimensional analysis was effective using different combinations of behavioral inputs and was able to detect behavioral improvement as a function of treatment in AS model mice. Overall, multidimensional behavioral analysis provides a tool for evaluating the effectiveness of preclinical treatments for AS. Multidimensional analysis of behavior may also be applied to rodent models of related neurodevelopmental disorders, and may be particularly valuable for disorders where individual behavioral tests are less reliable than in AS.

Spared nerve injury decreases motivation in long-access homecage-based operant tasks in mice

“…Prior work has demonstrated that integrating performance across various behavioral assays can lead to more sensitive and reliable measures of the overall socalled "emotionality" of preclinical rodent models. 9,13,38,90 Whether this same approach would apply for injury-induced negative affect is unclear. To determine whether a normalized "emotionality" behavioral z-score could reflect chronic neuropathic injury, we next tested this concept by normalizing each individual output from a single behavioral assay (eg, % time in center and distance travelled in the open field; Table 1) into a single assay score.…”

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

“…For multidimensional analysis of behavior profiles, we performed data selection, standardization, principal component analysis (PCA), k-means clustering, and validation (Fig. 2b) (Popovitz et al, 2021;Tanas et al, 2022). We utilized PUMBAA (phenotyping using a multidimensional behavioral analysis algorithm, https://github.com/sidorovlab/PUMBA A) for all multidimensional analysis (Tanas et al, 2022).…”

“…2b) (Popovitz et al, 2021;Tanas et al, 2022). We utilized PUMBAA (phenotyping using a multidimensional behavioral analysis algorithm, https://github.com/sidorovlab/PUMBA A) for all multidimensional analysis (Tanas et al, 2022).…”

“…The emergence of computational neuroethology tools allows for fast, reliable, and systematic detection of recorded movement over time to extract multiple metrics in different contexts. This extraction is critical for identifying unique sub-populations and assessing the therapeutic value of manipulations for pre-clinical animal studies for psychiatric disorders (Luxem et al, 2023;Popovitz et al, 2021;Shemesh & Chen, 2023;Tanas et al, 2022;Yamamoto et al, 2018). Thus, to capture the complexity of individual variations in a mosaic Mecp2-heterozygous population over multiple days of pup retrieval behavior, we used DeepLabCut, a marker-less pose estimation software, to derive animal trajectories, and performed multidimensional analysis of trajectory kinematics in adolescent and adult female WT and Het (Mathis et al, 2018;Nath et al, 2019).…”

Neuropathic injury drives a generalized negative affective state in mice

“…The exception to that was in the more challenging operant tasks (i.e., FR3 and PR; Figures 6-9). Prior work has demonstrated that integrating performance across various behavioral assays can lead to more sensitive and reliable measures of the overall so-called 'emotionality' of preclinical rodent models [9,12,35,78]. Whether this same approach would apply for injury-induced negative affect is unclear.…”