Modelling Hypertrophic and Dilated Cardiomyopathy in zebrafish (Danio rerio)

Abstract

Hypertrophic and dilated cardiomyopathies are the two most prevalent cardiomyopathy
subgroups. Hypertrophic cardiomyopathy is characterised by left ventricular hypertrophy and
is mainly caused by mutations on genes encoding proteins of the sarcomere. The hallmark of
dilated cardiomyopathy is dysfunction and dilation of the left ventricle and can be caused by
genetic and non-genetic causes. The mutations that cause cardiomyopathies, and the
phenotypes that result from them, are highly heterogeneous making diagnosis and treatment
for individual patients complex. New animal models of these precise mutations would allow
detailed studies into their effects and allow a platform for screening of therapeutics. This project
focuses on mutations in TNNT2 and ACTC1, which are known to lead to either one of the
cardiomyopathies. Zebrafish (Danio rerio) were used to model the diseases by introducing
specific base changes (knock-ins) in the tnnt2a and actc1a/actc2 genes homologous to TNNT2
and ACTC1 respectively. Generation of the knock-in models was attempted using the
CRISPR/Cas9 technique and following the homology-directed repair pathway. Microinjections
were performed in zebrafish embryos at the one cell stage, and DNA from injected larvae was
extracted and sequenced. Results suggest that the reagent design and creation of knock out
models were successful. For the tnnt2a mutation, sequencing analysis showed no knock-in
generation, even with chemical modulators to increase precise genome editing (PGE)
efficiency present. Similarly, for the actc1a and actc2 mutations, sequencing analysis suggests
that there is no knock-in. However, visual inspection of the sequencing traces revealed the
potential for PGE in two samples. Additionally, some zebrafish carrying indel mutations in
these actin genes were viable and are being grown up to establish stable lines. Further research
is required to address the limitations of this study and enhance the efficiency of PGE, with the
goal of accurately generating disease models and effectively assessing their phenotypes

Date of Award	10 Dec 2024
Original language	English
Awarding Institution	University of Bristol
Supervisor	Beck J Richardson (Supervisor) & Danielle M Paul (Supervisor)