Predictive genome-wide CRISPR-mediated gene downregulation for enhanced bioproduction

CRISPR interference (CRISPRi) has emerged as a valuable tool for redirecting metabolic flux to enhance bioproduction. However, conventional approaches for identifying target genes for CRISPRi-mediated downregulation have largely relied on heuristic methods and trial and error, which are labor-intensive and time-consuming. Additionally, the ability to achieve multigene knockdowns is limited by the constraints of molecular cloning techniques required for building multiplexed CRISPRi systems. In this study, we describe two novel methodologies FluxRETAP (Flux-REaction TArget Prioritization, a Genome-Scale Modeling Technique) and VAMMPIRE (a Versatile Assembly Method for MultiPlexing CRISPRi-mediated downREgulation). FluxRETAP accurately identified gene targets whose knockdown led to substantial increase of isoprenol titers, outperforming traditional heuristic selection. The use of VAMMPIRE enabled accurate and position-independent assembly of CRISPRi constructs containing up to five sgRNA arrays. The integration of FluxRETAP and VAMMPIRE has the potential to advance metabolic engineering by rapidly identifying CRISPRi-mediated knockdowns and knockdown combinations that enhance bioproduction titers, with potential applicability to other microbial systems.