Adaptive evolution of genomically recoded Escherichia coli

Abstract

Efforts are underway to construct several recoded genomes anticipated to exhibit multivirus resistance, enhanced nonstandard amino acid (nsAA) incorporation, and capability for synthetic biocontainment. Although our laboratory pioneered the first genomically recoded organism ( strain C321.∆A), its fitness is far lower than that of its nonrecoded ancestor, particularly in defined media. This fitness deficit severely limits its utility for nsAA-linked applications requiring defined media, such as live cell imaging, metabolic engineering, and industrial-scale protein production. Here, we report adaptive evolution of C321.∆A for more than 1,000 generations in independent replicate populations grown in glucose minimal media. Evolved recoded populations significantly exceeded the growth rates of both the ancestral C321.∆A and nonrecoded strains. We used next-generation sequencing to identify genes mutated in multiple independent populations, and we reconstructed individual alleles in ancestral strains via multiplex automatable genome engineering (MAGE) to quantify their effects on fitness. Several selective mutations occurred only in recoded evolved populations, some of which are associated with altering the translation apparatus in response to recoding, whereas others are not apparently associated with recoding, but instead correct for off-target mutations that occurred during initial genome engineering. This report demonstrates that laboratory evolution can be applied after engineering of recoded genomes to streamline fitness recovery compared with application of additional targeted engineering strategies that may introduce further unintended mutations. In doing so, we provide the most comprehensive insight to date into the physiology of the commonly used C321.∆A strain.

Predicting Parallelism and Quantifying Divergence in Experimental Evolution

“…Between covariance is unlikely to affect the degree to detect divergent evolution. MPD (3) of each treatment and and F 1 statistics across all treatments are not significantly different from the null expectation when covariance between individuals within the same treatment is removed for data from [52] in a), b) and data from [53] in c), d) . The black dots and lines in a) and c) represent the mean and 95% standardized CIs from null simulations while the colored dots represent the observed standardized values of MPD (3) .…”

“…Observed F statistics were not significantly different from the null expectation in absence of within-group covariance for the datasets examined [52, 53]. This result suggests that while covariance between populations is difficult to detect in evolution experiments with moderate replication (e.g., n=4-6), this low signal provides the added advantage of not having to be concerned with how different environments or backgrounds affect covariance between genes (i.e., the Behrens–Fisher problem [16, 54]).…”

“…To examine the degree that covariance between replicates affects the ability to distinguish between populations evolving under different conditions, we examined two datasets from studies with moderate within-treatment replication. The first dataset examined the spectrum of mutations in genomically recoded E. coli MG1655, where 14 replicate populations of the following strains were serially transferred: (1) the non-recoded ancestor (ECNR2), (2) a strain where UAG stop codons were replaced with UAA and the class I peptide release factor 1 was deleted (C321.ΔA), (3) a C321.ΔA derivative with engineered reversions to three off-target mutations (C321.ΔA-v2), and (4) a C321.ΔA derivative recoded to restore RF1 (C321) [53]. The second study was more focused on the consequence of microbial life cycles in different environments.…”

From Designing the Molecules of Life to Designing Life: Future Applications Derived from Advances in DNA Technologies

“…We recently evolved GROs for the first time and for more than 1000 generations.I ni ndependent sequenced clones,w e observed selective mutations that compensated for intentional genome design alterations as well as unintentional offtarget errors that occurred during engineering. [37] Similar evolution of GROs and synthetic organisms in the future would provide unique insights into genome pliability.…”

Optimizing the power to identify the genetic basis of complex traits with Evolve and Resequence studies

“…Such a powerful design usually requires large population sizes (> 1000), several replicates (> 5) and multiple generations of selection (> 90). Hence E&R studies are mostly suitable for small organisms having short generation times such as fruit flies, (Turner et al, 2011;Turner and Miller, 2012;Orozco-Terwengel et al, 2012;, nematodes (Teotonio et al, 2012), yeast (Kosheleva and Desai, 2017), bacteria (Wannier et al, 2018) and mice (Keightley and Bulfield, 1993). But even for model organisms, E&R studies come at a considerable cost in terms of time and sequencing effort.…”