Publications by authors named "Maria Pisliakova"
Article Synopsis
- The study focuses on TDP-REG, a tool designed to take advantage of the specific splicing changes caused by TDP-43 loss of function (TDP-LOF), which is linked to ALS and similar neurodegenerative diseases.
- It utilizes a deep learning algorithm, SpliceNouveau, to create customizable splicing events that enhance protein expression correlated with the disease state, both in lab settings (in vitro) and in living organisms (in vivo).
- TDP-REG allows for targeted editing of genetic sequences, potentially correcting harmful splicing effects and leading to new precision treatment approaches for disorders related to TDP-43.
A system enabling the expression of therapeutic proteins specifically in diseased cells would be transformative, providing greatly increased safety and the possibility of pre-emptive treatment. Here we describe "TDP-REG", a precision medicine approach primarily for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), which exploits the cryptic splicing events that occur in cells with TDP-43 loss-of-function (TDP-LOF) in order to drive expression specifically in diseased cells. In addition to modifying existing cryptic exons for this purpose, we develop a deep-learning-powered algorithm for generating customisable cryptic splicing events, which can be embedded within virtually any coding sequence.
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