Data-Driven Prediction of CRISPR-Based Transcription Regulation for Programmable Control of Metabolic Flux

TL;DR

This paper presents a data-driven model for designing CRISPR-based transcription regulators that enable programmable, multiplex control of gene expression to optimize metabolic fluxes in yeast for chemical production.

Contribution

It introduces a novel predictive model for designing CRISPR-based regulators that can simultaneously activate and repress genes for metabolic engineering.

Findings

Achieved programmable control of metabolic fluxes in yeast

Developed a predictive model for guide RNA targeting

Demonstrated improved control of gene expression

Abstract

Multiplex and multi-directional control of metabolic pathways is crucial for metabolic engineering to improve product yield of fuels, chemicals, and pharmaceuticals. To achieve this goal, artificial transcriptional regulators such as CRISPR-based transcription regulators have been developed to specifically activate or repress genes of interest. Here, we found that by deploying guide RNAs to target on DNA sites at different locations of genetic cassettes, we could use just one synthetic CRISPR-based transcriptional regulator to simultaneously activate and repress gene expressions. By using the pairwise datasets of guide RNAs and gene expressions, we developed a data-driven predictive model to rationally design this system for fine-tuning expression of target genes. We demonstrated that this system could achieve programmable control of metabolic fluxes when using yeast to produce versatile chemicals. We anticipate that this master CRISPR-based transcription regulator will be a valuable addition to the synthetic biology toolkit for metabolic engineering, speeding up the design-build-test cycle in industrial biomanufacturing as well as generating new biological insights on the fates of eukaryotic cells.