Author ORCID Identifier
Date of Graduation
5-2024
Document Type
Dissertation (PhD)
Program Affiliation
Quantitative Sciences
Degree Name
Doctor of Philosophy (PhD)
Advisor/Committee Chair
Traver Hart, Ph.D.
Committee Member
Hsiming (Sidney) Wang, Ph.D.
Committee Member
Guillermina Lozano, Ph.D.
Committee Member
Eduardo Vilar-Sanchez, M.D., Ph.D.
Committee Member
Jeffrey Chang, Ph.D.
Committee Member
John Paul Shen, M.D.
Abstract
Discovering synthetic lethal interactions between genes holds the key to uncovering cancer vulnerabilities, enabling the development of more effective drugs for patients. However, identifying these vulnerabilities in the complex genome of human, which comprises thousands of genes, poses a significant challenge. One alternative approach to investigate these interactions involves exploring enriched sources of synthetic lethal interactions, such as paralog pairs. In recent years, a couple of studies have conducted dual-gene knockout experiments on paralog pairs using different approaches to identify synthetic lethal interactions. In this study, we conducted a meta-analysis of CRISPR genetic interaction screens. We identified a candidate set of synthetic lethals that are independent of background, and showed that the Cas12a platform exhibited enhanced sensitivity and consistency. Building on this knowledge, we developed a platform capable of expressing four independent enCas12a guide RNAs from a single promoter. Using this platform, we designed a whole-genome library that targets not only all protein coding genes but also targets ~5,000 paralog pairs, triples and quads. This library is 30% smaller than current whole-genome libraries and requires fivefold fewer reagents than other dual-gene knockout studies to assess genetic interactions between gene pairs. We screened this library in different cell lines and showed its high sensitivity and efficiency.
Keywords
CRISPR, enCas12a, Paralog Pairs, Synthetic lethal interactions, Gene Editing, Whole-genome CRISPR library, Dual-gene knockout
Included in
Bioinformatics Commons, Cancer Biology Commons, Computational Biology Commons, Genetics Commons, Genomics Commons