Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms
Department of Biotechnology, the Catholic University of Korea, Gyeonggi 14662, Republic of KoreaCorrespondence to:
J. Microbiol. Biotechnol. 2020; 30(6): 793-803
Published June 28, 2020
Copyright © The Korean Society for Microbiology and Biotechnology.
As microorganisms are repeatedly subcultured in the laboratories, the lag phase is shortened and the growth rate is increased compared to the initial culture. This is the result of “struggling for existence”, as Darwin stated. During the process of competing in an environment with limited resource, adaptive mutations are passed down, which bring about changes in the whole population. In this way, unintended evolution occurs over a short period. Adaptive laboratory evolution (ALE) is a narrow-experimental evolution that mimics this natural phenomenon in laboratory and derives the desired phenotype. It is possible to change the environment by applying artificial selection pressure and obtain the ameliorated organism generated by the accumulation of beneficial mutations via natural selection [1, 2]. ALE was first used by the evolutionary scientist Dallinger in the seven-year high-temperature adaptation experiment  and has since been applied to studies on various organisms, from microalgae, mammalian cells, and viruses, to standard model organisms such as
Efficient rational engineering of cellular metabolism is possible only if comprehensive knowledge of metabolic pathways is acquired; however, this aspect is elusive even in the most well-characterized model organism,
Industrial microorganisms have long been exploited as key producers in almost every field, including food, pharmaceuticals, and other value-added chemical production. Efforts to eliminate obstacles and maximize productivity in bio-based processes are still ongoing, and ALE is one of the most effective approaches for this endeavor. In particular, microorganisms have the advantages like easy control of culture conditions, short generation time, easy manufacturing, and storage of living fossil records for each period ; therefore, research on microorganisms utilizing ALE has been actively conducted. Recently, a multi-disciplinary approach termed “systems metabolic engineering”, which encompasses systems biology, synthetic biology, and evolutionary engineering with the existing metabolic engineering approaches, has paved the way for the industrial use of microbial ALE.
This review presents recent ALE studies on industrial microorganisms, major considerations for efficient experimental design, and highlights the utility of various tools and strategies for strain optimization.
Empirical Studies of ALE for Industrial Application
The development of industrial strains aims to maximize productivity (product formation in unit time) and yield (product formation per substrate consumption) . For industrial process efficiency, ALE of an industrial host can contribute to cut down the cycle time and improve product titer, strain tolerance, and substrate utilization ability. Application of ALE for strain improvement is typically categorized as 1) optimization of growth on a specific nutrient and induction of prototrophy, 2) growing under harsh conditions (non-optimal pH and temperature) or tolerating adverse environment like inhibitors generated during the process, 3) production of new substances or increment of product titer.
Empirical ALE studies aimed for improved productivity of value-added products and substrate flexibility of strains were listed to identify the preferred production strain, the evolutionary duration, and conditions for each product. These include examples of
Practical Considerations of ALE
There are several factors that the researcher should consider for successful ALE results. First, the mode of cultivation should be determined by considering the characteristics of the host strain, available man-power, and other costs incurred during the process. Batch and continuous culture are the two main methods of culturing microorganisms. As described in the review by Sandberg
Second, it is essential to apply the appropriate selection pressure to screen the desired phenotype from the entire population. For the purpose of increasing the growth rate of organisms, maximizing the use of alternative carbon sources, harsh conditions (non-optimal pH and temperature), or tolerance for substances generated during production, the organisms favorable for survival in specific conditions reproduce rapidly and the desired phenotype and growth rate are coupled, facilitating screening. For example, overproducer of antimicrobial substances can be easily identified by superior cell viability in hostile environment [39, 40]. However, the phenotype is sometimes not linked to an improvement in growth rate. Overproduction of growth-irrelevant metabolite or an evolution-induced altered mutation acts as a metabolic burden thus reducing the growth rate or biomass . Therefore, metabolic pathways are adjusted to couple desired phenotypes to growth for growth-based selection. This method of applying artificial evolutionary pressure by metabolic engineering was proposed with the name “metabolic engineering-guided evolution (MGE)” . Considerable numbers of the studies presented in Table 1 started with a mutant rather than a wild type strain, or a genetically modified mid-point strain, to prevent efflux of resources that interfere with production or to exert evolutionary pressure. However, in the process of MGE, prior knowledge for rational design is required, making its application limited to targeting well-known materials; it also may require complicated manipulation. In addition to MGE, as an alternative to screening growth-uncoupled phenotype, the transcription factor-based or riboswitch-based biosensors has recently emerged as detectors of intracellular substances not related to growth [27, 29, 62]. In the ALE study of
The third is the time span, that is, when to stop the ALE and obtain the final evolution product. Determining the end point is entirely up to the researcher's decision. Generally, due to the tendency for most causal mutations to fix early, beneficial mutation rates tend to decrease with longer evolutionary periods , although this does not mean that organisms stop adaptive evolution. A generation is generally the scale used to indicate the period of evolution, and days and transferred numbers are also displayed depending on the experimental method. Because of ambiguous criteria, it is difficult to compare experiments for evolutionary periods, even when the experiments evolve the same strain. For this reason, cumulative cell divisions (CCDs) have been proposed as a new time scale. CCD utilizes the total cumulative number of the whole population, which is substantially proportional to the mutation rate, as a denominator so that it can describe an accurate quantitative criterion for ALE’s execution time .
Successful ALE results depend on the heterogeneity of the population resulting from mutations [19, 64]. Although artificial mutagenesis may induce mutation of the parent strain to increase diversity, adjusting the passage size (the amount of transferred cells) that can act as a bottleneck during serial transfer can also affect diversity . It is also important to keep the cell state constant for each cycle. In addition, considering that the growth rate increases while the fitness increase decreases as the culture is prolonged, it seems trivial, but critical, to optimize the passage method, such as gradually increasing the passage frequency and reducing the passage size.
Exploiting Genetic Engineering Tools to Assist Evolution
ALE can be accompanied by genetic engineering to reconstruct metabolic pathways or to identify causality between mutations and phenotypic changes, as analyzed by WGS of evolutionary strains . Classical metabolic engineering tools such as lambda red recombinase expression vector of
The mutation rate in ALE is accelerated by population heterogeneity . Therefore, to secure genetically diverse libraries, random mutagenesis of whole cells using mutagens, such as N‐methyl-N'-nitro-N-nitrosoguanidine, ethyl methane sulfonate, can be performed using the same principle as the classical in vitro directed evolution [33, 70, 71]. However, the mutation rate increases in case of traditional in vivo mutagenesis, while the efficiency may decrease because of hitch-hiking (neutral, deleterious) and non-target mutations. These limitations can be overcome by targeted in vivo mutagenesis. Typical targeted in vivo mutagenesis includes an orthogonal replication pair (vector and DNA polymerase), ColE1/Pol , OrthoRep , and Muta T7 , which are composed of a phage-derived promoter, RNA polymerase, and DNA-damaging enzyme (cytidine deaminase). Because the characteristics of each method (applicable species and mutation introduction method) are different, it is important to use them properly according to the specific purpose. Furthermore, mutagenesis of the target sequence can be achieved using modified CRISPR-Cas technology such as EvolvR , CasPER , CREATE .
As to altering complex phenotypes, genome evolution approaches including genome shuffling , global transcription machinery engineering (gTME) , and MAGE (multiplex automated genome engineering)  are suitable. Sandberg
As these approaches are capable of mutation control, they can compensate for the disadvantages of the existing random mutagenesis, such as an unstable genotype and nonhomogeneous phenotype of the final strain; thus, they could be promising strategies for industrial strain development.
ALE for Developing Non-Recombinant Strains
Productivity and product quality may be degraded due to environments that are not suitable for growth, such as depletion of nutrients, low oxygen, and high osmolarity, which can occur on an industrial scale [84, 85]. Therefore, as stated above, ALE is often accompanied by genetic manipulation. However, unlike the production of fine chemicals without genetic materials, genetically modified microorganisms (GMMs) are rarely commercialized in traditional food and probiotics industries . Apart from the definition of food-grade genetically modified organisms (GMOs) and the enactment of related laws, the production of GMO-derived food has been a constant debate due to the combination of negative consumer perceptions and distrust of conglomerates . The use of GMMs, mostly composed of lactic acid bacteria and yeast, is disinclined because of the possibility of viable GMOs in the product to colonize during the digestive period in the human body, even if they pass through the stomach . Although two genetically modified
Therefore, fermentation strains have been ameliorated through classical methods, such as random mutagenesis, and natural conjugation and transformation . These methods are not subject to the strict regulations related to GMOs, but have the disadvantage that it is difficult to express brand-new characteristics not inherent in the strain. Furthermore, in the case of brewing yeast, sexual hybridization is intricate due to the loss of the spore-forming ability and sexual reproduction caused by industrial domestication [84, 91, 92], and random mutagenesis is also limited because of the complex genome structure of the production strain. This may also lead to the loss of the desired phenotype . Experimental evolution is drawing attention as an efficient and non-genetic engineering technique that can solve these problems. In adaptive evolution, strain aneuploidy and polyploidy are not a problem [93, 94]. Additionally, there is no need for the complicated work of screening strains of the desired phenotype and prior knowledge of genes involved in the attribute is not required . For this reason, in the case of strains used in fermentation or probiotics, it is advantageous to improve varieties through ALE rather than genetic manipulation for commercialization.
However, there are limits to improvements without genetic modification. Several yeast studies have produced plant secondary metabolites through heterologous expression [95, 96], but relying only on evolution without external DNA sources cannot drive non-intrinsic pathways, thus limiting the development and production of new food flavors. This may lead to serious defects that do not lower unit costs. Another trepidation when using non-recombinant is the difficulty in identifying the source of the production strain. A unique strain is the valuable original technology of the company. However, the non-recombinant strain is naturally derived from the ancestral wild type, and it can be difficult to distinguish clearly because there is no characteristic factor to distinguish the strain due to the absence of an artificial marker, which might lead a conflict of interests between industries. However, despite these shortcomings, at this point, where the GMO controversy has not easily faded for decades, the improvement of production strains using experimental evolution without the influx of external DNA is anticipated to be used as a reasonable and practical strategy to ensure stable profits.
A Genome-Scale Metabolic Model for Insight into Integrated Cellular Systems
The optimization of cell factories is an iterative process (design-build-test-learn [DBTL] cycle) consisting of strain construction, evaluation, and data analysis to obtain the desired product [97, 98]. In the process of constructing an overproducer, the collaboration of ALE with the genome-scale metabolic model (GEM) , one of several mathematical models describing metabolic processes, facilitates obtaining a desirable phenotype . GEM is an
Furthermore, GEM and ALE can also be used together to derive the desired phenotype. In contrast with
Automation of ALE
In the process of manual cultivation for a long time, several practical problems arise. First, it is very laborious to continuously and frequently monitor cells during long-term experiments. Since most ALEs are performed for increasing growth rates, the monitoring intervals should be shorter as cells evolve to accurately measure growth rates; however, there are limitations to such manual approach. In addition, the batch culture used in many ALE studies is more strenuous and error-prone because additional manual processes, such as periodic passage, are required. These problems can be dealt with by the introduction of full or partial automation. For example, using a machine, the passage of batch culture can be performed at a frequency 3-7 times higher than the manual process, so it is not restricted by the experimenter's schedule, allowing passage at a constant growth stage and reducing fluctuation of passage size. This can prevent beneficial mutation loss [104, 105]. The problem of maintaining exponential growth and the appropriate selective pressure level can also be solved, unwanted effects caused by manual adjustment can be minimized, and genetic diversity can be secured, resulting in a shorter time to obtain an endpoint. In addition, it is possible to consolidate statistical significance by increasing the number of independent replicates beyond the capacity of a person to perform , readily introducing changes in the culture environment (substrate gradients, temperature, etc.) and monitoring the status in real time. Accordingly, ALE studies that automate repetitive batch cultivation processes have been continuously reported [59,81,106-108]. By measuring the optical density (OD), cell growth status can be checked and then automatically passed to the next vessel at the right time to take advantage of the aforementioned, as well as to measure the pH and dissolved oxygen in real-time to monitor the immediate response of the cells to the environment. There are also types of machinery that enhance the automatic aspect of continuous culture. An morbidostat can detect a population increase and adjust the levels of chemicals acting as the selection pressure, as feedback. A fluorostat, which combines fluorescence detection with a turbidostat, can be applied to determine gene expression levels [109-111]. Recently, eVOLVER, a framework consisting of open-source software and flexible hardware that can customize experimental conditions according to a specific purpose, was developed. It was introduced as a tool that can be reconfigured in the laboratory, and a detailed user guide to operate this platform is provided [112, 113]. High-throughput cultivation techniques can be used to derive the outcome of experimental evolution more quickly and accurately, and the acquired knowledge and outcomes can be used industrially, such as improved understanding of bioprocessing and the resulting improved strains. However, considering that most of the automated systems introduced above are laboratory scales of μL and mL, verification through scale-up is required.
In conclusion, connecting a theoretical study directly to the success of industrialization remains a challenging task. The fragmentary knowledge of host metabolic pathways is insufficient to predict the downstream effects caused by the complex network in the cell. The scale of the culture and the industrial process at the laboratory level are different, which is also a major factor that hampers the practical use of the developed strain. Thus, it is inevitable that considerable trials and errors in the process of constructing a microbial cell factory are conducted. The point is that this series of processes should not just end in failure, but it is the interpretation of the results that should complement the underlying knowledge and reduce the development process itself, resulting in cost savings and industrial competitiveness. In this regard, ALE combined with systems biology and synthetic biology tools is a prominent strategy that can be used in the design and build steps of the Design-Build-Test-Learn (DBTL) cycle for microbial strain development (Fig. 1). The
Recent progress in tools and strategies for ALE.Adaptive laboratory evolution (ALE) is one of the most preferred approaches to optimize host in the design-build-test-learn (DBTL) cycle. It elicits desired outcome efficiently along with genome-scale metabolic model, genetic engineering tools and computational technology as well as aids the amelioration of non-recombinant strain in the food industry. CRISPR-Cas system, Clustered Regularly Interspaced Palindromic Repeats- CRISPR-associated proteins system; MAGE, multiplex automated genomic engineering.
This work was financially supported by the grants from the National Research Foundation of Korea (2016R1E1A1A01943552). KP appreciates the support from the research fund 2018 from the CUK.
Conflict of Interest
The authors have no financial conflicts of interest to declare.
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