Engineering Next-Generation CRISPR Platforms for Mammalian Transcriptional Control
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Kristof, Andrew
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Abstract
The synthetic control of gene expression is essential for understanding cellular function. Recently, the advent of targeted DNA-binding proteins that can be coupled with protein subdomains capable of altering local transcription (effectors) has enabled precise gene expression control strategies in any eukaryotic host, including human and other mammalian cells. Despite the vast promise of CRISPR-dCas9 technology as a broadly applicable genetic tool for highly specific gene expression control, current systems exhibit highly variable performance across both gene targets and cell lines. Furthermore, knowledge of effector behavior, and how multiple effectors work in tandem to regulate gene expression, remains elusive. To address these limitations, we systematically classified the activity of candidate effector domains, explored the effects of attaching them in various combinations, and utilized this knowledge to engineer highly potent CRISPR-based platforms.
First, we constructed a fluorescence-based assay to simultaneously quantify the expression level and activity of candidate transcription factor subdomains. After discovering a panel of high-activity repressor domains, we designed several combinatorial libraries fusing these new effectors to gold-standard systems for CRISPR-mediated gene suppression (CRISPRi) and discovered five novel domain combinations improving performance. Through robust validation, we present a next-generation CRISPR repression system, ZIM3(KRAB)-MeCP2(t), that significantly outperformed prior systems across cell lines, gene targets, and recruitment approaches. Second, we performed in-depth functional characterization of one canonical repressor protein, MeCP2, and identified two unique subdomains that individually promote effector activity, even beyond levels observed with ZIM3(KRAB)-MeCP2(t). Using confocal microscopy, we determined that nuclear localization specificity is a key determinant underlying CRISPR system performance and demonstrated further that appending nuclear localization signal (NLS) domains to a variety of transcriptional effectors vastly improves their performance. Third, we performed functional analysis of a larger panel (~80 total) of transcription factor subdomains. By quantifying activity across multiple cell lines, we construct a novel classification system and identify numerous domains that can behave both as activators and repressors depending on biological context. Using this framework, we designed combinatorial libraries to explore how specific activator and repressor subdomains work together to regulate gene expression and identified several potent activator-activator effector combinations. Finally, by incorporating several novel domains and nuclear localization optimization, we introduce a new best-in-class CRISPR repression system, ZIM3(KRAB)-NID-MXD1-BPSV40NLS, which demonstrates the strongest reported gene knockdown capabilities to-date compared to existing CRISPR tools.
This work represents a fundamental step forward in understanding the activity, context-specific behavior, and cooperativity of transcriptional protein subdomains, and how this knowledge can inform the design improved tools for CRISPR-mediated gene regulation. Furthermore, we envision that our novel, top-performing repression system, ZIM3(KRAB)-NID-MXD1-BPSV40NLS, will be a particularly valuable tool, enabling unmatched gene knockdown capability across genes of interest, cell types, and applications.
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2024-12-08
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Dissertation (PhD)