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Bacillus subtilis is an industrially important microorganism that is often used as a microbial cell factory for the production of recombinant proteins due to its food safety, rapid growth, and powerful secretory capacity. However, the lack of data on functional genes related to recombinant protein production has hindered the further development of B. subtilis cell factories. Here, a strategy combining genome‐wide CRISPRi screening and targeted CRISPRa activation to enhance recombinant protein expression is proposed. First, a CRISPRi library covering a total of 4225 coding genes (99.7%) in the B. subtilis genome and built the corresponding high‐throughput screening methods is constructed. Twelve key genes for recombinant protein expression are identified, including targets without relevant functional annotations. Meanwhile, the transcription of recombinant protein genes by CRISPRa is up‐regulated. These screened or selected genes can be easily applied to metabolic engineering by constructing sgRNA arrays. The relationship between differential pathways and recombinant protein expression in engineered strains by transcriptome analysis is also revealed. High‐density fermentation and generalisability validation results prove the reliability of the strategy. This method can be extended to other industrial hosts to support functional gene annotation and the design of novel cell factories. Based on genome‐wide CRISPRi screening, 12 repressor genes favorable for recombinant protein production are identified. sgRNA arrays are constructed to combine these novel targets with a CRISPRa‐driven activation strategy to achieve comprehensive multi‐gene regulation. The high‐yielding strains that are obtained have improved the yield of a variety of recombinant proteins.

Lactate is an important monomer for the synthesis of poly-lactate (PLA), which is a substitute for the petrochemical plastics. To achieve the goal of high lactate titer, rate, and yield for commercial production, efficient lactate production pathway is needed as well as genetic targets that affect high lactate production and tolerance. In this study, an LldR-based d-lactate biosensor with a broad dynamic range was first applied into Zymomonas mobilis to select mutant strains with strong GFP fluorescence, which could be the mutant strains with increased d-lactate production. Then, LldR-based d-lactate biosensor was combined with a genome-wide CRISPR interference (CRISPRi) library targeting the entire genome to generate thousands of mutants with gRNA targeting different genetic targets across the whole genome. Specifically, two mutant libraries were selected containing 105 and 104 mutants with different interference sites from two rounds of fluorescence-activated cell sorting (FACS), respectively. Two genetic targets of ZMO1323 and ZMO1530 were characterized and confirmed to be associated with the increased d-lactate production, further knockout of ZMO1323 and ZMO1530 resulted in a 15% and 21% increase of d-lactate production, respectively. This work thus not only established a high-throughput approach that combines genome-scale CRISPRi and biosensor-assisted screening to identify genetic targets associated with d-lactate production in Z. mobilis, but also provided a feasible high-throughput screening approach for rapid identification of genetic targets associated with strain performance for other industrial microorganisms. [Display omitted] •A heterologous LldR-based d-lactate biosensor constructed for Zymomonas mobilis.•A d-lactate biosensor-assisted CRISPRi screening approach developed.•Genetic targets associated with high d-lactate production identified.•Reducing the expression of ZMO1323 and ZMO1530 increased d-lactate production.

Background The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and scalability. However, the cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters. Results We present CRISPulator, a Monte Carlo method-based computational tool that simulates the impact of screen parameters on the robustness of screen results, thereby enabling users to build intuition and insights that will inform their experimental strategy. CRISPulator enables the simulation of screens relying on either CRISPR interference (CRISPRi) or CRISPR nuclease (CRISPRn). Pooled screens based on cell growth/survival, as well as fluorescence-activated cell sorting according to fluorescent reporter phenotypes are supported. CRISPulator is freely available online ( http://crispulator.ucsf.edu ). Conclusions CRISPulator facilitates the design of pooled genetic screens by enabling the exploration of a large space of experimental parameters in silico, rather than through costly experimental trial and error. We illustrate its power by deriving non-obvious rules for optimal screen design.