Using theE. coliAlleleome in Strain Design

Abstract

Leveraging observed variants in strain design is a promising technique for creating strains with specific properties. Adaptive laboratory evolution (ALE) experiments generate variants that enhance fitness under specific conditions and can contribute to application-specific strain designs. Further, the wild-type (WT) coding alleleome of an organism, the complete set of its genes' WT alleles, can provide an additional amount and diversity of variants not yet accessible from the aggregation of ALE experiment results. This study used both an ALE mutation database (3093 genomes) and a large set of WT genomes (12,661 genomes) to explore the solution space of genes involved in tolerance to 10 conditions of industrial importance. To accomplish this, ALE variants for 22 genes previously identified as potentially important for industrial chemical tolerance were collected and supplemented with all available variants from the WT coding alleleome. A total of 4879 variants were reintroduced and used in 10 selection experiments. Although ALE variants were generally found to be more advantageous, specific WT variants were more highly enriched than ALE variants in certain conditions. Additionally, ALE and WT variants rarely overlapped on AA positions, but their clustering did coincide with where highly enriched variants were ultimately located. For genes primarily hosting potential gain-of-function variations, substitutions predicted to have a conservative impact frequently outperformed more radical substitutions. Case studies demonstrated that maximizing the amount of variants enabled easier identification of variant trends, which in turn can be used to better understand areas and characteristics of genes that can be feasibly varied, resulting in what can be thought of as a genome design variable. The combination of ALE and WT variants is a promising approach for use in future projects to better constrain and ultimately achieve practical coverage in the exploration of feasible sequence solution space.

Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing

“…It is hypothesized that an individual ALE experiment selects mutations that are readily accessible and sufficiently effective, though not necessarily the most potent . Experimental evidence further emphasizes this potential by demonstrating that different substitutions to a single gene can induce a phenotype across a range of intensities . ALEdb (), a publicly accessible database of aggregated ALE mutations, has the potential to provide data that could lead to a more comprehensive understanding of gain-of-function mutations and result in a more effective strain design solution. , …”