Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Abstract

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

Using theE. coliAlleleome in Strain Design

“…The original ICT ALE experiments were adapted for tolerance to toxic concentrations of an industrially relevant chemical from a set of 11 different chemicals (8), where 10 of the experiments resulted in relatively clear outcomes. Genomic features hosting potentially beneficial mutations were identified by observing the amount of replicate ALEs a genomic feature was mutated in; if a genomic feature is mutated in a large proportion of replicate ALEs, it is said to demonstrate convergence (4) (Figure 1A).…”

“…In addition to convergence, mutated features were also statistically associated with conditions to understand which mutated features were potentially beneficial for conditions of interest (4) (Figure 1A). Some individual genomic features were mutated at low frequencies across ICT ALE experiments, though their operons were frequently mutated, indicating that the systems in which they encode were important targets for mutations.…”

“…Recent research has demonstrated the value of maximizing variants to genomic features of interest to understand functional changes that beneficial variants converge on (3,4).…”

“…Recent research has demonstrated the value of maximizing variants to genomic features of interest to understand functional changes that beneficial variants converge on (3,4). The field of pangenomics research has been aggregating genomes of numerous species isolated from a variety of environmental and clinical conditions, generally referred to as wild-type strains (WT), and developing methods to compare their gene sets and alleles (5,6).…”

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

“…Recently, machine learning workflows tailored for the analysis of relevant data have proven to be useful in providing inference on the hidden layers of regulation, such as condition-specifically modulated gene sets and their TFs [101] and evolutionary constraints with different degrees of contribution to adaptive phenotypes [107] . Meta-analysis of large-scale mutational data [9] can also be extrapolated to predict novel genome engineering targets unforeseen in contemporary ALE designs [108] . The AlphaFold deep learning network designed for protein structural prediction [109] enables facile analysis of the implication of structural variants of mutant enzymes on strain phenotype [34] .…”