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bioRxiv · 10.1101/2024.01.31.578163

Chromosome segment scanning for gain- or loss-of-function screening (CHASING) and its application in metabolic engineering

Abstract

Constructing a library of thousands of single-gene knockout or interference strains is a powerful tool to understand the relation between genotype and phenotype, but it is labor and cost intensive. Powered by the computer-aided gene annotation and functional grouping of non-essential genes, we showed that targeting a single gene directly to a specific observed phenotype could be quickly achieved for a specific microorganism via constructing a library of strains containing single chromosome-segment-deletion per strain. As a proof-of-concept, a genome-scale library consisting of 70 chromosome-segment-deletion strains for B. subtilis was constructed by CRISPR-based methods and strains with six loss-of- and gain-of-function phenotypes were screened out. To facilitate the rapid genotyping, we developed a web tool to visualize the potential targets of each chromosome segment associated with a particular function, successfully identifying the genes for valuable representative phenotypes. To apply the library to metabolic engineering, the hosts with improved production capacity of acetoin and lycopene were screened in the presence of pathway genes. This work demonstrated the significance of our strategy of chromosome segment scanning for gain- or loss-of-function screening (CHASING) on functional genomics investigation, robust chassis engineering, and chemical overproduction. O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/578163v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@19b4938org.highwire.dtl.DTLVardef@1e41d2eorg.highwire.dtl.DTLVardef@137d4b3org.highwire.dtl.DTLVardef@6d4bfc_HPS_FORMAT_FIGEXP M_FIG TOC C_FIG

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BibTeXRIS

Xia, Y., Sun, L., Liang, Z., Han, Z., Li, J., Guo, Y., Dong, P., Huo, Y.-X., Guo, S.. 2024-02-02. Chromosome segment scanning for gain- or loss-of-function screening (CHASING) and its application in metabolic engineering. https://doi.org/10.1101/2024.01.31.578163

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