Precision Modelling of Rare Genetic Disease Variants

: Establishing Xenopus as a Next Generation Tool

Abstract

One in 17 people will have or develop a rare disease with a genetic component. For these people, the pathway to improved diagnostic, preventative and therapeutic strategies can be found in the abundance of individuals’ DNA sequence data. As well as identifying the gene causing a disease about one time in three, next-generation sequencing will uncover a wealth of variants of unknown significance (VUS). For many VUS, determining disease causality in silico is not attainable, a problem that is now increasingly addressed through model organism studies. This project reports the research outputs from a local collaboration between clinicians, clinical geneticists and Xenopus biologists in the South of England, which aimed to understand how broadly Xenopus models could be applied to better understanding and diagnostic rates of rare genetic diseases in Wessex.
The first part of this report outlines the processes developed to select VUS and generate patient- directed CRISPR/Cas9 knock-out novel-disease-gene animals and lines, revealing how CRISPR/Cas9 modelling of variants in Xenopus is contributing to the clinic. Furthermore, a standardised phenotyping pipeline is described and applied to characterise the phenotypes of these lines. This has included discovering and measuring a search behaviour strategy used by Xenopus tadpoles that is equivalent to human behaviour and was found to be altered in a Xenopus model of a human neurodevelopmental disorder. The second part of this project defines an approach to knock-in patient-specific variants and begins to explore the possibility of using precision gene-editing techniques in Xenopus to analyse the function of VUS. Overall, this data adds to the growing body of evidence demonstrating the success of CRISPR/Cas9 screens in Xenopus and highlights the need for optimised methods for precision modelling in the frog.

Date of Award	2 Dec 2021
Original language	English
Awarding Institution	University of Portsmouth
Supervisor	Matt Guille (Supervisor), Sarah Ennis (Supervisor) & Colin Sharpe (Supervisor)