385347 Tracking Combinatorially Engineered Libraries at the Genome Scale

Thursday, November 20, 2014: 3:33 PM
214 (Hilton Atlanta)
Ramsey Ibrahim Zeitoun, Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO and Ryan T. Gill, Chemical and Biological Engineering, University of Colorado, Boulder, CO

Phenotypic changes driven by evolution have bred the functionality and diversity found in life. Applying an evolution-like approach towards generating organisms with desired function requires methods to effectively assess diversity through phenotype to genotype relationships. Although organisms can be reprogrammed to create billions of targeted combinatorial mutants in a day, utilization of this diversity is impeded by the epistatic relationship between mutations, which dictate a complex genotype-phenotype landscape. Currently, assessing epistasis by evaluating combinations of mutations in combinatorial mutant libraries with single-cell resolution is limited by low-throughput genotyping approaches. To enable the high-throughput evaluation of combinatorial mutation space, we introduce a systematic method to track combinatorial engineered libraries and to assess the diverse genotypes of a population at the single-genotype level. Distal genetic sites were linked to create a single construct containing up to 10 sites of interest at a density of about 110 bp/site. This approach is shown to be generalizable from thermodynamic and kinetic constraints while being compatible with current high-throughput DNA sequencing technologies. Populations are quantitatively evaluated with single-genotype resolution by linking in an emulsion PCR format while taking further considerations to reduce undesired recombination events.  Finally, we demonstrate deconstruction and evaluation of populations containing four degenerate combinatorially engineered ribosome binding sites in multiplexed automated genome engineering (MAGE) generated hydrolysate tolerance libraries and further application towards evaluating combinations of oncogenic mutations in human ovarian (cervical) cancer cell lines.  This method can be used to effectively close the loop of the genome engineering cycle by allowing the depth of genomic information available to be effectively collected and further applied to a diverse subset of complex phenomena.

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