RT Journal Article
SR Electronic
T1 Data-Driven Prediction of CRISPR-Based Transcription Regulation for Programmable Control of Metabolic Flux
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 119396
DO 10.1101/119396
A1 Jiayuan Sheng
A1 Weihua Guo
A1 Christine Ash
A1 Brendan Freitas
A1 Mitchell Paoletti
A1 Xueyang Feng
YR 2017
UL http://biorxiv.org/content/early/2017/03/22/119396.abstract
AB 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.