计算生物学
表观遗传学
清脆的
生物
基因组编辑
基因
基因传递
稳健性(进化)
生物信息学
遗传学
遗传增强
作者
Giovanni A. Carosso,Robin W. Yeo,T. Blair Gainous,M. Zaki Jawaid,Yang Xiao,James Y.S. Kim,Kavita Jadhav,Nina Juan-Sing,Siddaraju V. Boregowda,Vincent Cutillas,Lei S. Qi,Alexandra Collin de l’Hortet,Timothy P. Daley,Daniel Hart
标识
DOI:10.1101/2023.06.02.543492
摘要
Abstract Programmable epigenetic modulators provide a powerful toolkit for controlling gene expression in novel therapeutic applications, but recent discovery efforts have primarily selected for potency of effect rather than contextual robustness or durability thereof. Current CRISPR-based tools are further limited by large cargo sizes that impede clinical delivery and, in gene activation contexts, by brief activity windows that preclude transient, single-dose strategies such as lipid nanoparticle (LNP) delivery. To address these limitations, we perform high-throughput screening to discover novel classes of transcriptional modulators derived from thousands of human, viral, and archaeal proteomes. We identify high-potency activators capable of mitotically stable gene activation in a multitude of cellular contexts and leverage machine learning models to rationally engineer variants with improved activities. In liver and T-cells, novel hypercompact activators (64 to 98 amino acids) derived from vIRF2 core domain (vCD) achieve superior potency and durable activation lasting weeks beyond the current large activators (∼five-fold larger). In a humanized mouse model, we target a human hypercholesterolemia susceptibility gene and achieve activation persisting five weeks after a single dose by LNP delivery. Our discovery pipeline provides a predictive rubric for the development of contextually robust, potent, and persistent activators of compact size, broadly advancing the therapeutic potential of epigenetic gene activation.
科研通智能强力驱动
Strongly Powered by AbleSci AI