Genome editing mediated by engineered nucleases, including Transcription Activator-Like Effector Nucleases (TALENs) and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) / CRISPR-associated (Cas) systems, holds great potential in a broad range of applications, including biomedical studies and disease treatment. In addition to creating cell lines and disease models, this technology allows generation of well-defined, genetically modified cells and organisms with novel characteristics that can be used to cure diseases, study gene functions, and facilitate drug development. However, achieving both high efficiency and high specificity remains a major challenge in nuclease-based genome editing. The objectives of this thesis were to optimize the design of TALENs to achieve high on-target cleavage activity, and analyze the off-target effect of CRISPR/Cas to help achieve high specificity. Based on experimental evaluation of >200 TALENs, we compared three different TALEN architectures, proposed new TALEN design rules, and developed a Scoring Algorithm for Predicting TALEN Activity (SAPTA) to identify optimal target sites with high activity. We also performed a systematic study to demonstrate the off-target cleavage by CRISPR/Cas9 when DNA sequences contain insertions or deletions compared to the RNA guide strand. Our results strongly indicate the need to perform comprehensive off-target analysis, and suggest specific guidelines for reducing potential off-target cleavage of CRISPR/Cas9 systems. The studies performed in this thesis work provide important insight and powerful tools for the optimization of engineered nucleases in genome editing, thus making a significant contribution to biomedical engineering and medical applications.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54249 |
| Date | 07 January 2016 |
| Creators | Lin, Yanni |
| Contributors | Bao, Gang |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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