The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Abstract

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

Figure 1.. Adaptive Laboratory Evolution

A) Microbes are cultured in a desired growth environment for an extended period of time, allowing natural selection to enrich for mutant strains (altered coloration) with improved fitness. This example depicts ALE via serial propagation of batch cultures. B) Evolved strains are characterized for phenotypic improvements relative to the ancestral strain, using whatever “fitness” metric is appropriate given the evolutionary environment. C) Evolved strains have their DNA sequenced to reveal the adaptive mutations enabling phenotypic improvement. This example case depicts the fixation of two successive mutations targeting the same genetic region.

Figure 2:. A Categorization of ALE Studies

A diagram of different categories of use for laboratory evolution experiments, detailing the percent makeup of studies examined (159 total). ‘Growth rate optimization’ illustrates fitness development over the course of an ALE, with noticeable fitness jumps. ‘Increase tolerance’ illustrates an experimental schematic of an initially mixed population acquiring beneficial mutations (red cells) that promote cell survival in a constantly increasing external stress environment (e.g., pH, antibiotics, temperature, etc). ‘Substrate utilization’ and ‘Increase Product Yield/titer’ illustrate evolutionary pathways enabled via ALE that enhance the organism’s ability to make use of alternative nutrient sources (colored circles), and to increase production of metabolites of interest (colored squares), respectively. Lastly, ‘General Discovery’ encompasses studies that examined ALEs at a genetic or systems level in greater detail.

Figure 3:. ALE for use in the Design, Build, Test, Learn cycle.

A) The typical Design, Build, Test, Learn cycle used in metabolic engineering to generate a strain with a desired property. B) Augmentation of the cycle where ALE is included in the Build step to rescue a strain that has decreased fitness due to a perturbation, or to optimize a strain after removal or addition of genetic content. C) Augmentation of the cycle where a collection of mutations (e.g., ALEdb (Phaneuf et al., 2018)) associated to a particular phenotype is leveraged for the Design step D) Augmentation of the cycle where ALE can be used to completely replace the Design and Build steps and a desirable strain is acquired directly from ALE when a phenotype can be tied to selection without engineering.