Iterative SCRaMbLE for Engineering Synthetic Genome Modules and Chromosomes
Xinyu LuiD, Klaudia CiurkotiD, Glen-Oliver F. GowersiD, William M. ShawiD, Tom EllisiD
Abstract
Synthetic biology offers the possibility of synthetic genomes with customised gene content and modular organisation. In eukaryotes, building whole custom genomes is still many years away, but work in Saccharomyces cerevisiae yeast is closing-in on the first synthetic eukaryotic genome with genome-wide design changes. A key design change throughout the synthetic yeast genome is the introduction of LoxPsym site sequences. These enable inducible genomic rearrangements in vivo via expression of Cre recombinase via SCRaMbLE (Synthetic Chromosome Recombination and Modification by LoxPsym-mediated Evolution). When paired with selection, SCRaMbLE can quickly generate strains with phenotype improvements by diversifying gene arrangement and content in LoxPsym-containing regions. Here, we demonstrate how iterative cycles of SCRaMbLE can be used to reorganise synthetic genome modules and synthetic chromosomes for improved functional performance under selection. To achieve this, we developed SCOUT (SCRaMbLE Continuous Output and Universal Tracker), a reporter system that allows SCRaMbLEd cells to be sorted into a high diversity pool. When coupled with long-read sequencing, SCOUT enables high-throughput mapping of genotype abundance and correlation of gene content and arrangement with growth-related phenotypes. Iterative SCRaMbLE was applied here to yeast strains with a full synthetic chromosome, and to strains with synthetic genome modules encoding the gene set for histidine biosynthesis. Five synthetic designs for HIS modules were constructed and tested, and we investigated how SCRaMbLE reorganised the poorest performing design to give improved growth under selection. The results of iterative SCRaMbLE serve as a quick route to identify genome module designs with optimised function in a selected condition and offer a powerful tool to generate datasets that can inform the design of modular genomes in the future.
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