CRISPR-based Control of Biosynthetic Pathways and Genetic Biosensors, Informed by a Quantitative Investigation of Metabolic Burden

Abstract

The heterologous, engineered genetics often used for biosynthetic chemical production share resources with host gene expression. While the host’s endogenous expression has evolved along with complex regulatory networks to balance supply and demand, or cost and benefit, heterologous expression requires engineered control systems to avoid overly burdening the host. Optimizing the genetic controllers of pathway enzymes, for example, achieves greater likelihood of acceptable balance between cost and production, avoiding reductions in growth rate, population homogeneity, and production titers or circuit performance. Here, we first investigate the metabolic burden of genetic control systems implemented with CRISPR transcriptional activation and T7 RNA polymerase in E. coli, finding that the overhead cost of CRISPR-based control is relatively high, but that expansion of the circuit to several control nodes that share the overhead cost is an inexpensive addition. We next analyze a set of feedback control circuits suggesting that closed circuits can limit excessive heterologous gene expression at a metabolic cost comparable to simpler, and less stable, open circuitry. Finally, we build metabolic pathways producing valuable chemicals—the aromatic amino acid derivative p-aminocinnamic acid and the human milk oligosaccharide lacto-N-tetraose—making use of CRISPR-based control’s expandability into broad, open circuits controlling not only the pathway enzymes, but an potentially large array of endogenously-targeted metabolic regulators. As part of the CRISPR circuit for LNT production, we develop a novel in vivo biosensor, based on a ligand-responsive scRNA switch, to report on production levels, and use it alongside traditional analysis to query a library of pathway expression variants. These varied proofs-of-concept illustrate the impressive and versatile capabilities of CRISPR-based genetic control when applied to biosynthetic pathways, and are informed by an enhanced understanding of heterologous contributors to metabolic burden in biosynthetic hosts.