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Qing Wang

Publications and source records attributed to Qing Wang.

2 recordsLinked to original sources

CRISPR-Cas9 targeted deletion of the C9orf72 repeat expansion mutation corrects cellular phenotypes in patient-derived iPS cells

The large hexanucleotide (GGGGCC) repeat expansion in the non-coding promoter region of C9orf72 is the leading cause of Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). Mechanisms underlying neurodegeneration are not clear, and both a C9orf72 loss of function and a gain of toxicity, in the form of RNA foci or dipeptide repeat deposition, are implicated. CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9-mediated genome editing is an attractive strategy for disease modeling and therapeutic intervention. Here we show that this system can be utilized to completely remove the large repeat expansion mutation within C9orf72 in patient-derived induced pluripotent stem (iPS) cells. Removal of the mutation prevented RNA foci formation and promoter hypermethylation, two phenotypes of the C9orf72 mutation. Interestingly, these changes did not significantly alter C9orf72 expression at the mRNA or protein level. This work provides a proof-of-principle for the use of CRISPR-Cas9-mediated excision of the pathogenic C9orf72 repeat expansion as a therapeutic strategy in FTD/ALS.\n\nOne Sentence SummaryCRISPR-Cas9-mediated excision of the large C9orf72 repeat expansion mutation rescues RNA foci formation and promoter hypermethylation without altering C9orf72 transcript and protein expression.

Neuroscience

RNA-seq: primary cells, cell lines and heat stress

Transcriptome analysis by RNA-seq has emerged as a high-throughput, cost-effective means to evaluate the expression pattern of genes in organisms. Unlike other methods, such as microarrays or quantitative PCR, RNA-seq is a target free method that permits analysis of essentially any RNA that can be amplified from a cell or tissue. At its most basic, RNA-seq can determine individual gene expression levels by counting the number of times a particular transcript was found in the sequence data. Transcript levels can be compared across multiple samples to identify differentially expressed genes and infer differences in biological states between the samples. We have used this approach to examine gene expression patterns in chicken and human cells, with particular interest in determining response to heat stress.

Genomics