AI 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.
Article Abstract
Loss of function of the RNA-binding protein TDP-43 (TDP-LOF) is a hallmark of amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders. Here we describe TDP-REG, which exploits the specificity of cryptic splicing induced by TDP-LOF to drive protein expression when and where the disease process occurs. The SpliceNouveau algorithm combines deep learning with rational design to generate customizable cryptic splicing events within protein-coding sequences. We demonstrate that expression of TDP-REG reporters is tightly coupled to TDP-LOF in vitro and in vivo. TDP-REG enables genomic prime editing to ablate the UNC13A cryptic donor splice site specifically upon TDP-LOF. Finally, we design TDP-REG vectors encoding a TDP-43/Raver1 fusion protein that rescues key pathological cryptic splicing events, paving the way for the development of precision therapies for TDP43-related disorders.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616720 | PMC |
| http://dx.doi.org/10.1126/science.adk2539 | DOI Listing |
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