Neural circuits and behavior in mouse models of Ube3a disorders
As we move towards promising clinical trials for AS, a major challenge is deciding how to measure improvement after treatment. This study will apply mathematical approaches to data from new and existing behavioral tests using the Angelman syndrome mouse model to determine a single behavior score for each AS mouse that captures performance on all of the different behavior tests. The researchers will determine whether this score correlates with the established EEG biomarker in the AS mouse model and whether the score and biomarker improves when UBE3A is restored in the mouse model.
Ultimately, this study will allow researchers to carefully compare different therapeutic approaches in the AS mouse model to determine which one works best. Also, it will provide an important proof-of-concept where this same approach may be applied to AS patient data before and after therapeutic treatment to generate better outcome measures for AS trials.
Why This Study is Important
This work helps condense multiple mouse phenotypes into a single score. This is important for evaluating whether a therapeutic is working well compared to other interventions or therapeutics by making it easier to score mouse improvement without bias. This can help companies and research labs make better decisions about different AS therapeutics.
An approach called “multidimensional analysis,” was developed. This approach combines the results of many different behavioral tests and summarizes behavior as a single severity score.
Using this approach, researchers were able to combine and simplify data from all of the behavioral tests into a single “Angelman severity score” number. Looking at a single severity score allowed researchers to predict a mouse’s genotype with >95% accuracy. It was also demonstrated that the Angelman severity score improved meaningfully in a group of mice where paternal Ube3a was unsilenced at birth.
Using this severity score, will help labs and companies test the effectiveness of many different types of treatments preclinically to evaluate the best candidates to move forward towards clinical trials.