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ABSTRACT: Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

PROVIDER: PRJEB52901 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR9745020.fastq.gz	Fastqsanger.gz
	ERR9745030.fastq.gz	Fastqsanger.gz
	ERR9745040.fastq.gz	Fastqsanger.gz
	ERR9745050.fastq.gz	Fastqsanger.gz
	ERR9745060.fastq.gz	Fastqsanger.gz

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Project description:Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

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Inhibiting DDX3X triggers tumor-intrinsic type I interferon response and enhances anti-tumor immunity

Project description:Accumulating evidence has shown that cellular double-stranded RNAs (dsRNAs) induce antiviral innate immune responses in human normal and malignant cancer cells. However, it is not fully understood how endogenous ‘self’ dsRNA homeostasis is regulated in the cell. Here, we show that an RNA-binding protein, DEAD-box RNA helicase 3X (DDX3X), prevents the aberrant accumulation of cellular dsRNAs. Loss of DDX3X induces dsRNA sensor-mediated type I interferon signaling and innate immune response in breast cancer cells due to abnormal cytoplasmic accumulation of dsRNAs. Dual depletion of DDX3X and a dsRNA-editing protein, ADAR1 synergistically activates the cytosolic dsRNA pathway in breast cancer cell. Moreover, inhibiting DDX3X enhances the antitumor activity by increasing tumor intrinsic-type I interferon response, antigen presentation, and tumor-infiltration of cytotoxic T cells as well as dendritic cells in breast tumors, which may lead to the development of breast cancer therapy by targeting DDX3X in combination with immune checkpoint blockade. To assess the impact of DDX3X on the gene expression in the breast cancer, we stably depleted DDX3X in breast cancer MCF7 cells using a short hairpin RNA (shRNA)-mediated knockdown, and performed a genome-wide transcriptome analysis using a next-generation RNA deep sequencing.

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Saturating mutagenesis with assembled CRISPR-Cas9 ribonucleoprotein complexes.

Project description:The CRISPR-Cas9 system enables efficient sequence-specific mutagenesis for creating germline mutants of model organisms. Key constraints in vivo remain the expression and delivery of active Cas9-guideRNA ribonucleoprotein complexes (RNPs) with minimal toxicity, variable mutagenesis efficiencies depending on targeting sequence, and high mutation mosaicism. Here, we established in vitro-assembled, fluorescent Cas9-sgRNA RNPs in stabilizing salt solution to achieve maximal mutagenesis efficiency in zebrafish embryos. Sequence analysis of targeted loci in individual embryos reveals highly efficient bi-allelic mutagenesis that reaches saturation at several tested gene loci. Such virtually complete mutagenesis reveals preliminary loss-of-function phenotypes for candidate genes in somatic mutant embryos for subsequent generation of stable germline mutants. We further show efficient targeting of functional non-coding elements in gene-regulatory regions using saturating mutagenesis towards uncovering functional control elements in transgenic reporters and endogenous genes. Our results suggest that in vitro assembled, fluorescent Cas9-sgRNA RNPs provide a rapid reverse-genetics tool for direct and scalable loss-of-function studies beyond zebrafish applications.

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Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Project description:Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code.

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