Tools for Advancing Genome Engineering on the Protein, Pathway, and Genome Scale | AIChE

Tools for Advancing Genome Engineering on the Protein, Pathway, and Genome Scale

Authors 

Freed, E., University of Colorado
Pines, G., University of Colorado, Boulder

Coordinating the action of biological parts (promoters, regulators, genes, etc.) to construct new genomes and biological systems with desirable phenotypes is one of the main challenges facing the field of metabolic engineering. Recent advancements in DNA synthesis, and improved understanding of homologous recombination and the CRISPR-Cas system enable new approaches that integrate rational design and directed evolution to both create novel biological parts and inform how to assemble these parts into systems. We present several genome engineering tools with applications on the protein, pathway, and full genome scale.

We have developed a method for protein engineering based on the CRISPR-Cas RNA-guided nuclease system. We couple desired point mutations within the open reading frame with a common mutation in the guide RNA recognition site, thus enabling a CRISPR-induced cell death of wild type cells and the survival of mutants. This approach enriches significantly the mutated cells within the population without the need for an antibiotic selection marker.

We have applied this approach to protein engineering orthogonal regulators, namely the tet repressor (TetR). Coupled with a unique selection for orthogonal DNA and mRNA binding, we present TetR mutants that allow increased control of cellular circuits on the transcriptional and post-transcriptional levels. This method can potentially be scaled to the pathway level as well.

At the genome scale, we are working on improved methods to link genomic alterations to traits. This new method, TRMR2.0, allows the fine-tuning of the expression level of each gene in the E. coli genome in parallel. Molecular barcoding allows rapid tracking of genes and expression levels conferring desirable phenotypes. Finally, we developed a mathematical model of chromosomal segregation with the goal of optimizing genome engineering efforts to produce more stable and reliable genotype alterations.