Recent Advances in the Evolutionary Engineering of Industrial Biocatalysts James D. Winkler, Katy C. Kao PII: DOI: Reference:
S0888-7543(14)00183-9 doi: 10.1016/j.ygeno.2014.09.006 YGENO 8664
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Please cite this article as: James D. Winkler, Katy C. Kao, Recent Advances in the Evolutionary Engineering of Industrial Biocatalysts, Genomics (2014), doi: 10.1016/j.ygeno.2014.09.006
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Recent Advances in the Evolutionary Engineering of
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Industrial Biocatalysts
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James D. Winkler1 , Katy C. Kao1
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Department of Chemical Engineering, Texas A&M University, College Station, TX, United States
of America
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ACCEPTED MANUSCRIPT Running Head: Recent Advances in Evolutionary Engineering
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Keywords: evolutionary engineering, mutagenesis, automation, selection, adaptation, complex
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phenotypes
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Corresponding author: Katy C. Kao
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Department of Chemical Engineering, Texas A&M University,
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College Station, TX, United States of America
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Phone 979 845 5571
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Email: [email protected]
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ACCEPTED MANUSCRIPT Abstract
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Evolutionary engineering has been used to improve key industrial strain traits, such
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as carbon source utilization, tolerance to adverse environmental conditions, and resis-
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tance to chemical inhibitors, for many decades due to its technical simplicity and effec-
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tiveness. The lack of need for prior genetic knowledge underlying the phenotypes of
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interest makes this a powerful approach for strain development for even species with
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minimal genotypic information. While the basic experimental procedure for labora-
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tory adaptive evolution has remained broadly similar for many years, a range of recent
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advances show promise for improving the experimental workflows for evolutionary
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engineering by accelerating the pace of evolution, simplifying the analysis of evolved
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mutants, and providing new ways of linking desirable phenotypes to selectable charac-
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teristics. This review aims to highlight some of these recent advances and discuss how
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they may be used to improve industrially relevant microbial phenotypes.
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ACCEPTED MANUSCRIPT Introduction
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Biocatalyst robustness is an important parameter of microbial strains used for industrial fermenta-
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tion. Cells in these processes are subjected to variety of different stresses, including thermal, acid,
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and osmotic stresses, in addition to toxic effects from growth substrates and both desired and side
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products. Improving the tolerance of strains to these physiochemical insults is therefore of great
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interest for industrial applications. Rational methods for improving these strain characteristics have
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been largely stymied by the fact that the underlying genetic determinants for most tolerance phe-
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notypes are largely unknown. One way of circumventing this obstacle is to use inverse engineering
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approaches, which involve generating strains with the desired phenotype through random methods
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and then analyzing the genetics, transcriptomics, proteomics, and/or metabolomics of the mutants
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to identify the causative mutations underlying the phenotype. These approaches do not require
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a priori genetic knowledge of the organism, and can therefore be used with any organism if the
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phenotypes-of-interest are growth-coupled.
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Evolutionary engineering, also known as adaptive laboratory evolution or whole-cell directed
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evolution, is the pre-eminent method for both improving industrial strains and analyzing these
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complex tolerance phenotypes due to its simplicity and effectiveness. The technique involves the
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continued propagation of a microbial population under a desired selective pressure. Fitter mutants
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naturally arise from random mutations during DNA replication or other sources, and will increase
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in frequency in proportion to their fitness (relative to the mean fitness of the population). Evolu-
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tionary engineering can also be combined with other methods such as random mutagenesis in order
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to generate more genetic diversity for selection. Adaptive evolution has been successfully utilized
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to generate strains with traits such as enhanced substrate utilization, tolerance to fermentation con-
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ditions, and resistance to toxic compounds [1, 2, 8, 10, 11, 16, 37, 42, 52, 56, 57]. Combined with
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the increasing availability of affordable high-throughput genomic technologies, molecular charac-
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terization of beneficial mutants isolated from the evolved populations is beginning to advance our
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fundamental knowledge on the genetic determinants and molecular mechanisms associated with
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industrially relevant complex phenotypes. This growing body of knowledge will be of great use
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ACCEPTED MANUSCRIPT for future rational engineering of tolerance and other growth-linked traits. In this review, we focus
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on recent advances in evolutionary engineering that help to simplify strain improvement and anal-
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ysis, as well as systems biology approaches for analyzing the changes associated with phenotype
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improvement.
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Recent Innovations in Evolutionary Engineering
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Several novel new thrusts towards changing the standard paradigm of evolutionary engineering
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have recently been demonstrated in the literature. These advances can be loosely grouped into sev-
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eral distinct categories: methods for increasing, identifying, and exploiting diversity within evolv-
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ing populations, new schema for evolving metabolite overproducing strains, parallelization and
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automation of cultivation systems, and novel tools for understanding the genetic bases of evolved
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phenotypes. Figure 1 shows how improvements in these areas would affect the execution of adap-
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tive evolution experiments. Based on improvements in these areas, we speculate about future re-
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search directions in evolutionary engineering and how these new tools may change our use of this
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established strain improvement tool.
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Increasing Genetic Diversity
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Since intrinsic mutation rates are typically low for most organisms (on the order of 10−9 -10−10
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bp−1 generation−1 ) under most growth conditions [30, 33], even a relatively small and transient in-
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crease in mutation rate can significantly improve the probability of generating a chance beneficial
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mutation. Mutagenesis, using chemicals or radiation, is often used to increase the genetic diversity
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of a population prior to or during an evolution experiment [32, 46]. The use of mutator strains
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with defective DNA repair systems can also be used to increase genetic diversity [17]. Although
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higher mutation rates increase the frequency of beneficial mutations in the population, it also leads
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to an increase in deleterious mutations (often in the same genetic backgrounds); thus, methods to
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control mutation rate during adaptive evolution have the benefit of transiently increasing genetic
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ACCEPTED MANUSCRIPT diversity while reducing the risk of accumulating deleterious mutations. The GREACE (Genome
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Replication Engineering Assisted Continuous Evolution) method [34] was recently developed for
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this purpose and relies on the introduction of a library of defective error-prone polymerases mutD
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(dnaQ) into a target strain. After evolution in the condition of interest, the plasmid-borne defective
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mutD can be cured by short-term propagation in the absence of antibiotics for plasmid maintenance,
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returning the mutation rate of the evolved strain to normal levels for subsequent analyses. Another
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example of mutation rate manipulation that couples mutation rate to desired product formation was
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recently introduced by Chou and Keasling, where the strain mutation rate is inversely dependent
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on the intracellular concentrations of particular metabolites [9]. Both approaches may be useful to
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transiently raise mutation rates until the desired phenotypic objectives have been reached. Subse-
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quent analysis of the evolved strains is simplified by returning the mutation rate to the wild-type
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baseline, ensuring the evolved phenotypes are sufficiently stable for downstream analysis.
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Interrogating Diverse Populations
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Although the true objective of industrial evolutionary engineering experiments is to generate supe-
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rior production strains, knowledge of the underlying genotypes is crucial for downstream rational
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strain engineering and for improving our fundamental knowledge of biological systems. However,
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growth under selection generally results in genetically heterogeneous populations that contain a
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variety of adaptive, neutral, and deleterious adaptive mutations [4], making it difficult to identify
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all genotypes present with typical, low-throughput, and often arbitrary isolation procedures. Com-
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petition between these diverse lineages makes it difficult to detect all potentially adaptive mutations
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from within the population, reducing our ability to understand the genetic changes that occurred
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over the history of evolution experiments. Our laboratory recently introduced a tool for visual-
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izing evolutionary dynamics in real time (VERT) [25, 43] that allows for the direct detection of
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clonal interference in a population. Competing clones, expressing different fluorescent proteins,
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can therefore be isolated based on the observed population dynamics. Computational tools have
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also been developed to standardize the isolation procedure as well [58]. VERT has been suc-
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ACCEPTED MANUSCRIPT cessfully employed to study several tolerance phenotypes, including solvent, drug, and biomass
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inhibitor tolerance [1, 21, 43]. Other labeling systems, such as DNA barcodes, can be used to
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a similar effect with significantly higher resolution; however, the isolation of single mutants-of-
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interest and mutation linkage determination will be more challenging.
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Visualization of clonal interference, though effective as an evolutionary tool, cannot provide
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data on all genotypes present in a population since even labeled subpopulations may be heteroge-
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neous. With the advent of affordable whole genome sequencing, however, evolutionary engineers
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now have the power to uncover all mutations present above a certain frequency within an evolving
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population by direct sequencing of the entire population. Two recent studies used this approach
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to study population dynamics and types of adaptive mutations that may occur during evolution
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[29, 31] in ways that would be impossible with more traditional interrogation methods. The prin-
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cipal limitation of this approach stems from properties in common Next Generation Sequencing
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(NGS) platforms, namely their short read lengths (100-500 bp) and the need for deep sequencing
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to identify rare genotypes in the population. Genomic rearrangements and copy number variations
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are difficult to identify in heterogeneous population samples, and linkage information between
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widely separated mutations cannot be extracted; for example, cohorts of mutations were observed
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to expand and contract together in evolving populations of yeast, but not all are beneficial when
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combined [31]. Even with these restrictions in mind, knowledge of all detectable mutations over the
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duration of an experiment will provide unprecedented amount of information on the evolutionary
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dynamics in the population, and with data from parallel experiments to help narrow down neutral or
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deleterious hitchhikers from beneficial mutations will likely reveal novel mechanisms that confer
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the phenotypes-of-interest.
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Exploiting Genetic Diversity for Phenotypic Improvement
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Beyond creating, visualizing, and quantifying diversity within an evolving population, additional
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focus has recently been given to help harness this diversity to speed adaptive evolution. One ob-
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stacle reducing the effectiveness of evolution experiments is clonal interference, which arises from
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ACCEPTED MANUSCRIPT the competition of genetically distinct mutants within a population. One approach to reducing this
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effect is to permit genetic exchange between the lineages (i.e., sex) [35]. This process allows adap-
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tive mutations to spread throughout an evolving population, and can potentially combine different
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mutations from competing mutants into a single genetic background. In asexual reproduction,
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multiple, independent mutations acquired sequentially would be required to combine multiple ben-
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eficial mutations, significantly increasing the amount of time required to achieve the same level
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of improvement expected in sexual populations. In addition, any deleterious mutation present
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in adaptive mutants cannot be readily removed in asexual evolution (known as Muller’s ratchet,
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[18]), which can be alleviated by sexual recombination. Classic means of recombination include
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genome shuffling by protoplast fusion [40, 47, 63] and by exploiting sexual cycles in some labora-
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tory species (S. cerevisae) [19]. These methods, though generally successful in their stated goals
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of increasing diversity and permitting interclonal gene flow, are low-throughput and are not very
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efficient, making them difficult to apply to highly parallel evolutionary engineering projects.
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In an effort to circumvent this limitation of conventional recombination tools, a conjugation-
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based evolution system was recently introduced by Winkler and Kao [59], built on previous work by
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Cooper that demonstrated that conjugation proficient E. coli evolved more quickly than conjugation
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incompetent controls [12]. The key innovation of this new method is the design of a strain capable
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of efficient, bidirectional mating between strains, allowing for interclonal chromosomal DNA ex-
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change, significantly increasing the rate of recombination within a population during growth. The
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F conjugation system used by Winkler and Kao is capable of continuous mating in liquid culture,
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allowing evolution and recombination to take place simultaneously, even under selection. Rates of
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phenotypic improvement during evolution can be significantly increased as a result, depending on
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the fitness landscape describing the phenotype of interest [59]. In contrast, genome shuffling by
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protoplast fusion must be performed in the absence of any selection due to the fragility of the re-
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combinants generated by the procedure, imposing a large bottleneck due to the loss of cells during
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the shuffling and re-inoculation process. Recombinants generated by conjugation are immediately
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subjected to selection, simplifying the generation of strains with improved growth phenotypes. The
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ACCEPTED MANUSCRIPT combination of continuous mating under selection with the improved efficiency of conjugation is a
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significant improvement over previous methods, and the system has recently been demonstrated to
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be an effective evolutionary tool by improving the E. coli osmotolerance phenotype [60]. A similar
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mass mating system, based on the natural yeast mating cycle, has also been developed by Hughes et
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al. [22]. Since the theoretical advantages of recombination are significant [13, 18], we expect that
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further refinements of these systems and development of additional tools to enable recombination
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will play a substantial role in the evolutionary engineering field for the foreseeable future.
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Connecting Fitness to Production
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One limitation in the use of adaptive evolution is the required coupling between growth and desired
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phenotypes, such as consumption of a non-ideal carbon source or tolerance of various inhibitors.
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A fundamental, though largely unstated, assumption underlying the previous sections is that there
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exists a simple linkage between the selective pressure of interest and growth rate or viability. The
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desired mutants are therefore those that grow faster or survive more frequently in the selective
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environments. However, industrial strains are used to synthesize products-of-interest that are gen-
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erally not growth-linked, so adaptive laboratory evolution cannot be applied to evolve strains with
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enhanced productivity. To circumvent this limitation, strain engineering efforts that force the cou-
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pling between product formation and growth by manipulation of redox balancing in the host strain
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have been used [15, 24, 50]. In some cases, the selective pressure used in evolutionary engineer-
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ing can also be designed to provide a growth advantage to better producers. A recent effort by
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our group successfully demonstrated the use of this approach in using oxidative stress to improve
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productivity of carotenoids in yeast [44]. The use of anti-metabolites or metabolite analogs have
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also been applied in evolutionary engineering as selective pressures to either improve productiv-
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ity [6, 49] or improve substrate utilization [38]. The overriding problem with these approaches is
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that each selection experiment must be uniquely designed for the pathway and desired metabolite,
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assuming a viable selective pressure exists at all.
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Though engineering growth-product linkages will remain a formidable problem in the near fu10
ACCEPTED MANUSCRIPT ture, significant progress has been made in designing more generic systems that can link product
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formation to strain fitness. An innovative approach linking a metabolite-sensing riboswitch with a
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tetracycline resistance cassette (tetA) was recently employed to evolve E. coli for improved lysine
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and tryptophan productivity [62]. In this case, expression of tetA, the tetracycline resistance gene,
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was inversely proportional to the concentration of the amino acid of interest. Yang and coworkers
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took advantage of the fact that high levels of tetA expression confer a Ni2+ -sensitive phenotype,
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so simple propagation in the presence of Ni2+ was sufficient to select for mutants with large im-
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provements in amino acid accumulation. The main challenge in generalizing this method is the
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engineering of suitable riboswitches for each metabolite, though this area has recently seen great
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strides [5, 7]. An alternative approach called FREP (feedback-regulated evolution of phenotype) by
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Chou and Keasling has also been successfully applied to increase production of isoprenoids and ty-
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rosine in E. coli through adaptive evolution [9]. Their method relied on making the expression of a
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dominant mutator allele (mutD5) inversely proportional to the concentration of a target metabolite,
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so that low producers would rapidly accumulate mutations that may improve metabolite produc-
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tion. High producers, in contrast, are more genetically stable due to decreased expression of the
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mutator allele. Both methods represent the first steps towards a more general method for linking
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strain productivity to fitness, and allow metabolic engineers to combine the stochasticity of adap-
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tive evolution with large library sizes permitted by selection (∼ 1010 individuals) for more rapid
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strain improvement. As additional sensors are designed, evolved, or discovered in nature, it is likely
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that the evolutionary engineering approach to producer development will become a complementary
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alternative to the more rational strain engineering approaches used at the moment.
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Parellelization and Automation of Evolution
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One of the factors limiting the utility of adaptive laboratory evolution is the capital expense and
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manual effort required. In our experience, one person can feasibly handle an experiment consisting
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of 24-30 replicate batch cultures, while performing the necessary maintenance and quantification
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procedures (e.g., archival storage, contamination tests, adjustment of selective pressure, pheno-
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ACCEPTED MANUSCRIPT type improvement analysis, etc). This scale is fairly limiting, as it is difficult to draw statistical
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conclusions about strain improvement or potential adaptive mechanisms from a small number of
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replicate cultures. The amount of manual intervention required also introduces an element of bias
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into the experiment, especially when trying to adjust selective pressures in a consistent manner as
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population fitness improves.
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Several new cultivation systems with increased parallelism and automation have recently been
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developed as a means of overcoming these limitations. For example, control systems and reac-
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tors themselves can now be inexpensively constructed and customized according to user needs
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[36, 54]. One particularly innovative tool is the "morbidostat" developed by Toprak et al. [55],
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which combines a continuous bioreactor array with a feedback control system for adjusting in situ
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selective pressure automatically. The population growth rate is, therefore, maintained at a constant
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level throughout the experiment without manual adjustment. The advantages of the system were
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demonstrated by evolving E. coli for resistance to several antibiotics to show that starkly different
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selective pressure regimes were needed to maintain the desired level of inhibition. They also in-
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ferred structural properties of the underlying fitness landscapes [39] based on the ramping schemes
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used by the feedback controller in the system, providing additional insight as to how adaptation can
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proceed in response to antibiotic challenges. The use of this approach will clearly improve con-
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sistency of selective pressures within experiments while enabling researchers to obtain quantitative
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data concerning the rate of adaptation for target inhibitors. As the evolutionary process is stochas-
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tic, these high-throughput, small-scale, and low-cost bioreactor designs allow even small labs the
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ability to run multiple parallel evolutionary experiments in order to identify possible evolutionary
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trajectories.
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Liquid handling robots and microfluidics have also been increasingly used to dramatically im-
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prove experimental throughput. A fully automated evolution system that propagates microbial cul-
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tures in microplates was recently introduced by Horinouchi and coworkers [20] and successfully
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used to evolve strains with improved tolerance of a range of industrially relevant stressors. Other
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automated systems, also based on batch growth in microtiter plates, has also been used elsewhere
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ACCEPTED MANUSCRIPT [28, 31]. While these approaches rely on robotics to handle liquid transfers and inoculations, mi-
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croscale chemostat systems based on the maintenance of populations inside liquid droplets have
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also been introduced [23], potentially allowing thousands of populations to be maintained in paral-
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lel. The decreasing cost of liquid handling robots and microfluidic devices will help enable future
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development of high throughput approaches. It is important to note that the small population sizes
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imposed by typical plate-based high throughput evolutionary systems may not be desirable in all
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situations, as the genetic diversity and evolutionary trajectories are more limited compared to larger
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population sizes. However, the advantages of extensive replication and automated handling are nu-
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merous: it facilitates detection of divergent or antagonistic adaptation pathways, and it can provide
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data for theoretical analyses of evolutionary dynamics, clonal interference, or other aspects of pop-
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ulation genetics. Combined with advances in NGS and genetic analysis, these new high throughput
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cultivation tools promise to simplify and streamline the process of evolutionary engineering, while
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providing extensive information on parallel adaptation routes. Knowledge gained from the resulting
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adaptive mutants can be used to improve existing industrially strains.
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Understanding Evolved Genotypes
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Despite advances in other areas, characterization of evolved strains remains a laborious and low-
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throughput process. The most common approach for uncovering genotype-phenotype linkages
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involves reconstructing each potentially adaptive mutation into the unevolved parental strain, fol-
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lowed by the appropriate phenotypic characterization (growth rate, tolerance, etc) to confirm its
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putative effect. Traditional reconstruction techniques, such as selection-counterselection cassette
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replacement, transduction, site-directed mutagenesis, and others [14, 48, 53], while powerful, re-
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quire a significant amount of time to produce a single reconstructed mutant. When the number of
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mutations per strain is large, or when it is necessary to examine interactions (positive or negative
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epistasis) between adaptive mutations [26, 37], then reconstruction quickly becomes the limiting
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step for understanding evolved mutants. The introduction of collections of defined deletion strains
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for E. coli and S. cerevisae [3, 61], in addition to the ASKA overexpression plasmid collection
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ACCEPTED MANUSCRIPT for E. coli [27], has significantly improved the ability of researchers to more rapidly analyze cer-
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tain types of evolved mutations (e.g. inactivating mutations or mutations resulting in increased
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gene expression). However, for those mutations occurring in non-model organisms or those that do
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not result in gene disruption or increased expression, each potentially adaptive mutation must be
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manually reconstructed using the appropriate tools.
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One promising approach towards reducing the time required for strain reconstruction and genotype-
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phenotype analysis in bacteria is a new method named REGRES (recursive genomewide recombi-
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nation and sequencing) introduced by Quandt and colleagues [41]. REGRES is based on the F con-
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jugation system, in which strains containing the mutations of interest are converted into Hfr (high
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frequency recombinant-forming) strains using integrative F plasmids, and subsequently mated to
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F- recipients. The donor is then selected against using antibiotics, yielding a library of transcon-
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jugants that contain heterozygous genomes of donor and recipient DNA. These transconjugants
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can be screened or selected again for the sought after phenotype, followed by NGS or Sanger se-
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quencing to identify the causative mutations underlying the observed phenotypes. This approach
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was used to identify the genetic bases for the aerobic citrate utilization trait that arose during the
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long-term evolution experiment in the Lenski laboratory [4] without the need for manual strain re-
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construction, which poses a significant challenge given the large number of accumulated mutations
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in the isolates after more than 50,000 generations of evolution. Further improvements in recom-
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bineering tools, including the rapid disruption of multiple genes simultaneously [45, 51], will also
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help to decrease the burden of strain analysis in the future.
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Conclusions
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These improvements in evolutionary engineering techniques, though enormously beneficial from
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both scientific and engineering perspectives, have focused primarily on refinements of the same ex-
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perimental procedure. In effect, a scientist evolving E. coli for an improved phenotype in the 1950s
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or the 2000s would have very similar workflows during the actual evolution experiment, despite
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ACCEPTED MANUSCRIPT the seismic changes in other areas of the biotechnology field. It is no longer sufficient to merely
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improve the phenotype of one organism in a single condition using evolution; given that most in-
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dustrial environments involve a range of stressors (temperature, osmolarity, and so forth) using
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different microbial species and strains, we must be able to quickly evolve for new traits, dissect
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their tolerance mechanisms, and then transfer these findings to other organisms to increase their
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industrial utility. While it is likely that adaptive laboratory evolution will continue to be performed
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in much the same way for the foreseeable future, we speculate that evolutionary engineering will
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eventually be able to accomplish this seminal goal as a result of continuing improvement in the
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tools and methodologies available to evolutionary engineers.
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Acknowledgements
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We gratefully acknowledge support from the Norman Hackerman Advanced Research Program
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(grant number 000512-0004-2011) for this work.
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Literature Cited
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NU
SC
RI
PT
274
287
[1] María P Almario, Luis H Reyes, and Katy C Kao. Evolutionary engineering of Saccharomyces
288
cerevisiae for enhanced tolerance to hydrolysates of lignocellulosic biomass. Biotechnology
289
and Bioengineering, 110(10):2616–2623, 2013.
290
[2] Shota Atsumi, Tung-Yun Wu, Iara MP Machado, Wei-Chih Huang, Pao-Yang Chen, Matteo
291
Pellegrini, and James C Liao. Evolution, genomic analysis, and reconstruction of isobutanol
292
tolerance in Escherichia coli. Molecular Systems Biology, 6(1), 2010.
293
[3] Tomoya Baba, Takeshi Ara, Miki Hasegawa, Yuki Takai, Yoshiko Okumura, Miki Baba, Kir-
294
ill A Datsenko, Masaru Tomita, Barry L Wanner, and Hirotada Mori. Construction of Es-
295
cherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Molecular
296
Systems Biology, 2(1), 2006. 15
ACCEPTED MANUSCRIPT
299
300
Nature Reviews Genetics, 14(12):827–839, 2013. [5] Chase L Beisel and Christina D Smolke. Design principles for riboswitch function. PLoS
PT
298
[4] Jeffrey E Barrick and Richard E Lenski. Genome dynamics during experimental evolution.
Computational Biology, 5(4):e1000363, 2009.
RI
297
[6] Jeanne Bonomo, Michael D Lynch, Tanya Warnecke, James V Price, and Ryan T Gill.
302
Genome-scale analysis of anti-metabolite directed strain engineering. Metabolic Engineer-
303
ing, 10(2):109–120, 2008.
NU
SC
301
[7] Andrew L Chang, Joshua J Wolf, and Christina D Smolke. Synthetic rna switches as a tool
305
for temporal and spatial control over gene expression. Current Opinion in Biotechnology,
306
23(5):679–688, 2012.
MA
304
[8] Kian-Kai Cheng, Baek-Seok Lee, Takeshi Masuda, Takuro Ito, Kazutaka Ikeda, Akiyoshi
308
Hirayama, Lingli Deng, Jiyang Dong, Kazuyuki Shimizu, Tomoyoshi Soga, et al. Global
309
metabolic network reorganization by adaptive mutations allows fast growth of Escherichia
310
coli on glycerol. Nature Communications, 5, 2014.
312
TE
AC CE P
311
D
307
[9] Howard H Chou and Jay D Keasling. Programming adaptive control to evolve increased metabolite production. Nature Communications, 4, 2013.
313
[10] Tom M Conrad, Michael Frazier, Andrew R Joyce, Byung-Kwan Cho, Eric M Knight,
314
Nathan E Lewis, Robert Landick, and Bernhard Ø Palsson. Rna polymerase mutants found
315
through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media.
316
Proceedings of the National Academy of Sciences, 107(47):20500–20505, 2010.
317
[11] Tom M Conrad, Andrew R Joyce, M Kenyon Applebee, Christian L Barrett, Bin Xie,
318
Yuan Gao, and Bernhard Ø Palsson. Whole-genome resequencing of Escherichia coli K-12
319
MG1655 undergoing short-term laboratory evolution in lactate minimal media reveals flexible
320
selection of adaptive mutations. Genome Biology, 10(10):R118, 2009.
16
ACCEPTED MANUSCRIPT
323
324
mutations in populations of Escherichia coli. PLoS Biology, 5(9):e225, 2007. [13] James F Crow and Motoo Kimura. Evolution in sexual and asexual populations. American
PT
322
[12] Tim F Cooper. Recombination speeds adaptation by reducing competition between beneficial
Naturalist, pages 439–450, 1965.
RI
321
[14] Hilary M. Ellis, Daiguan Yu, Tina DiTizio, and Donald L. Court. High efficiency mutage-
326
nesis, repair, and engineering of chromosomal DNA using single-stranded oligonucleotides.
327
Proceedings of the National Academy of Sciences, 98(12):6742–6746, 2001.
NU
SC
325
[15] Stephen S Fong, Anthony P Burgard, Christopher D Herring, Eric M Knight, Frederick R
329
Blattner, Costas D Maranas, and Bernhard O Palsson. In silico design and adaptive evolu-
330
tion of Escherichia coli for production of lactic acid. Biotechnology and Bioengineering,
331
91(5):643–648, 2005.
D
MA
328
[16] Hani Goodarzi, Bryson D Bennett, Sasan Amini, Marshall L Reaves, Alison K Hottes,
333
Joshua D Rabinowitz, and Saeed Tavazoie. Regulatory and metabolic rewiring during lab-
334
oratory evolution of ethanol tolerance in E. coli. Molecular Systems Biology, 6(1), 2010.
336
337
338
339
340
AC CE P
335
TE
332
[17] Alan Greener, Marie Callahan, and Bruce Jerpseth. An efficient random mutagenesis technique using an E. colimutator strain. Molecular Biotechnology, 7(2):189–195, 1997. [18] John Haigh. The accumulation of deleterious genes in a population: Muller’s ratchet. Theoretical population biology, 14(2):251–267, 1978. [19] Ira Herskowitz. Life cycle of the budding yeast saccharomyces cerevisiae. Microbiological Reviews, 52(4):536, 1988.
341
[20] Takaaki Horinouchi, Teruaki Minamoto, Shingo Suzuki, Hiroshi Shimizu, and Chikara Furu-
342
sawa. Development of an Automated Culture System for Laboratory Evolution. Journal of
343
Laboratory Automation, page 2211068214521417, 2014.
17
ACCEPTED MANUSCRIPT 344
345
[21] Mian Huang, Mark McClellan, Judith Berman, and Katy C Kao. Evolutionary dynamics of Candida albicans during in vitro evolution. Eukaryotic Cell, 10(11):1413–1421, 2011. [22] Stephen R Hughes, Ronald E Hector, Joseph O Rich, Nasib Qureshi, Kenneth M Bischoff,
347
Bruce S Dien, Badal C Saha, Siqing Liu, Elby J Cox, John S Jackson, David E Sterner,
348
Tauseef R Butt, Joshua LaBaer, and Michael A Cotta. Automated yeast mating protocol
349
using open reading frames from Saccharomyces cerevisiae genome to improve yeast strains
350
for cellulosic ethanol production. Journal of Laboratory Automation, 14(4):190–199, 2009.
NU
SC
RI
PT
346
[23] Slawomir Jakiela, Tomasz S Kaminski, Olgierd Cybulski, Douglas B Weibel, and Piotr
352
Garstecki. Bacterial Growth and Adaptation in Microdroplet Chemostats. Angewandte
353
Chemie, 125(34):9076–9079, 2013.
MA
351
[24] Kaemwich Jantama, MJ Haupt, Spyros A Svoronos, Xueli Zhang, JC Moore, KT Shan-
355
mugam, and LO Ingram. Combining metabolic engineering and metabolic evolution to
356
develop nonrecombinant strains of Escherichia coli C that produce succinate and malate.
357
Biotechnology and Bioengineering, 99(5):1140–1153, 2008.
AC CE P
TE
D
354
358
[25] Katy C Kao and Gavin Sherlock. Molecular characterization of clonal interference during
359
adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nature Genetics,
360
40(12):1499–1504, 2008.
361
[26] Aisha I Khan, Duy M Dinh, Dominique Schneider, Richard E Lenski, and Tim F Cooper.
362
Negative epistasis between beneficial mutations in an evolving bacterial population. Science,
363
332(6034):1193–1196, 2011.
364
[27] Masanari Kitagawa, Takeshi Ara, Mohammad Arifuzzaman, Tomoko Ioka-Nakamichi, Eiji
365
Inamoto, Hiromi Toyonaga, and Hirotada Mori. Complete set of ORF clones of Escherichia
366
coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biolog-
367
ical research. DNA Research, 12(5):291–299, 2006.
18
ACCEPTED MANUSCRIPT [28] Sergey Kryazhimskiy, Daniel P. Rice, Elizabeth R. Jerison, and Michael M. Desai. Global
369
epistasis makes adaptation predictable despite sequence-level stochasticity. 344(6191):1519–
370
1522, 2014.
PT
368
[29] Daniel J Kvitek and Gavin Sherlock. Whole Genome, Whole Population Sequencing Reveals
372
That Loss of Signaling Networks Is the Major Adaptive Strategy in a Constant Environment.
373
PLoS Genetics, 9(11):e1003972, 2013.
375
SC
[30] Gregory I Lang and Andrew W Murray. Estimating the per-base-pair mutation rate in the
NU
374
RI
371
yeast Saccharomyces cerevisiae. Genetics, 178(1):67–82, 2008. [31] Gregory I Lang, Daniel P Rice, Mark J Hickman, Erica Sodergren, George M Weinstock,
377
David Botstein, and Michael M Desai. Pervasive genetic hitchhiking and clonal interference
378
in forty evolving yeast populations. Nature, 500(7464):571–574, 2013.
D
MA
376
[32] Dae-Hee Lee, Adam M Feist, Christian L Barrett, and Bernhard Ø Palsson. Cumulative num-
380
ber of cell divisions as a meaningful timescale for adaptive laboratory evolution of Escherichia
381
coli. PLoS One, 6(10):e26172, 2011.
383
AC CE P
382
TE
379
[33] Peter A Lind and Dan I Andersson. Whole-genome mutational biases in bacteria. Proceedings of the National Academy of Sciences, 105(46):17878–17883, 2008.
384
[34] Guodong Luan, Zhen Cai, Yin Li, Yanhe Ma, et al. Genome replication engineering as-
385
sisted continuous evolution (GREACE) to improve microbial tolerance for biofuels produc-
386
tion. Biotechnology for Biofuels, 6(1):137, 2013.
387
388
389
390
[35] Gabriel Marais and Brian Charlesworth. Genome evolution: recombination speeds up adaptive evolution. Current Biology, 13(2):R68–R70, 2003. [36] Aaron W Miller, Corrie Befort, Emily O Kerr, and Maitreya J Dunham. Design and use of multiplexed chemostat arrays. Journal of Visualized Experiments, (72), 2013.
19
ACCEPTED MANUSCRIPT [37] J. Minty, A. Lesnefsky, F. Lin, Y. Chen, T. Zaroff, A. Veloso, B. Xie, C. McConnell, R. Ward,
392
D. Schwartz, J.M. Rouillard, Y. Gao, E. Gulari, and X.N. Lin. Evolution combined with
393
genomic study elucidates genetic bases of isobutanol tolerance in Escherichia coli. Microbial
394
Cell Factories, 10(1):18, 2011.
PT
391
[38] Ali Mohagheghi, Jeff Linger, Holly Smith, Shihui Yang, Nancy Dowe, and Philip T Pienkos.
396
Improving xylose utilization by recombinant Zymomonas mobilis strain 8b through adaptation
397
using 2-deoxyglucose. Biotechnology for Biofuels, 7(1):19, 2014.
SC
NU
399
[39] H Allen Orr.
Fitness and its role in evolutionary genetics.
10(8):531–539, 2009.
Nature Reviews Genetics,
MA
398
RI
395
[40] Ranjan Patnaik, Susan Louie, Vesna Gavrilovic, Kim Perry, Willem PC Stemmer, Chris M
401
Ryan, and Stephen del Cardayré. Genome shuffling of lactobacillus for improved acid toler-
402
ance. Nature Biotechnology, 20(7):707–712, 2002.
TE
D
400
[41] Erik M Quandt, Daniel E Deatherage, Andrew D Ellington, George Georgiou, and Jeffrey E
404
Barrick. Recursive genomewide recombination and sequencing reveals a key refinement step
405
in the evolution of a metabolic innovation in Escherichia coli. Proceedings of the National
406
Academy of Sciences, page 201314561, 2013.
407
408
AC CE P
403
[42] Amparo Querol, M Teresa Fernández-Espinar, Eladio Barrio, et al. Adaptive evolution of wine yeast. International Journal of Food Microbiology, 86(1):3–10, 2003.
409
[43] Luis H Reyes, Maria P Almario, James Winkler, Margarita M Orozco, and Katy C Kao. Visu-
410
alizing evolution in real time to determine the molecular mechanisms of n-butanol tolerance
411
in Escherichia coli. Metabolic Engineering, 14(5):579–590, 2012.
412
413
[44] Luis H Reyes, Jose M Gomez, and Katy C Kao. Improving carotenoids production in yeast via adaptive laboratory evolution. Metabolic Engineering, 21:26–33, 2014.
414
[45] Owen W Ryan, Jeffrey M Skerker, Matthew J Maurer, Xin Li, Jordan C Tsai, Snigdha Poddar,
415
Michael E Lee, Will DeLoache, John E Dueber, Adam P Arkin, and Jamie H D Cate. Selection 20
ACCEPTED MANUSCRIPT 416
of chromosomal DNA libraries using a multiplex CRISPR system. eLife, pages e03703–
417
e03703, 2014.
420
421
PT
Metabolic Engineering, pages 129–169. Springer, 2001.
RI
419
[46] Uwe Sauer. Evolutionary engineering of industrially important microbial phenotypes. In
[47] R. Schnettler, U. Zimmermann, and C. C. Emeis. Large-scale production of yeast hybrids by electrofusion. FEMS Microbiology Letters, 24(1):81–85, 1984.
SC
418
[48] Shyam K Sharan, Lynn C Thomason, Sergey G Kuznetsov, Donald L Court, et al. Recombi-
423
neering: a homologous recombination-based method of genetic engineering. Nature Proto-
424
cols, 4(2):206–223, 2009.
MA
development in Escherichia coli. Metabolic Engineering, 13(6):674–681, 2011.
D
426
[49] Kevin M Smith and James C Liao. An evolutionary strategy for isobutanol production strain
TE
425
NU
422
[50] Marco Sonderegger and Uwe Sauer. Evolutionary engineering of Saccharomyces cerevisiae
428
for anaerobic growth on xylose. Applied and Environmental Microbiology, 69(4):1990–1998,
429
2003.
430
431
AC CE P
427
[51] Chan Woo Song and Sang Yup Lee. Rapid one-step inactivation of single or multiple genes in escherichia coli. Biotechnology Journal, 8(7):776–784, 2013.
432
[52] Boris U Stambuk, Barbara Dunn, Sergio L Alves, Eduarda H Duval, and Gavin Sherlock.
433
Industrial fuel ethanol yeasts contain adaptive copy number changes in genes involved in
434
vitamin B1 and B6 biosynthesis. Genome Research, 19(12):2271–2278, 2009.
435
436
[53] N Sternberg and R Hoess. The molecular genetics of bacteriophage P1. Annual Review of Genetics, 17(1):123–154, 1983.
437
[54] Chris N. Takahashi, Aaron W. Miller, Felix Ekness, Maitreya J. Dunham, and Eric Klavins.
438
A Low Cost, Customizable Turbidostat for Use in Synthetic Circuit Characterization. ACS
439
Synthetic Biology, 2014. 21
ACCEPTED MANUSCRIPT [55] Erdal Toprak, Adrian Veres, Jean-Baptiste Michel, Remy Chait, Daniel L Hartl, and Roy
441
Kishony. Evolutionary paths to antibiotic resistance under dynamically sustained drug selec-
442
tion. Nature Genetics, 44(1):101–105, 2012.
PT
440
[56] Valeria Wallace-Salinas and Marie F Gorwa-Grauslund. Adaptive evolution of an indus-
444
trial strain of Saccharomyces cerevisiae for combined tolerance to inhibitors and temperature.
445
Biotechnology for Biofuels, 6(1):151, 2013.
SC
RI
443
[57] Yongze Wang, Ryan Manow, Christopher Finan, Jinhua Wang, Erin Garza, and Shengde
447
Zhou. Adaptive evolution of nontransgenic Escherichia coli kc01 for improved ethanol tol-
448
erance and homoethanol fermentation from xylose. Journal of Industrial Microbiology and
449
Biotechnology, 38(9):1371–1377, 2011.
452
453
MA
D
VERT system. Journal of Biological engineering, 6(1):1–8, 2012.
TE
451
[58] James Winkler and Katy C Kao. Computational identification of adaptive mutants using the
[59] James Winkler and Katy C Kao. Harnessing recombination to speed adaptive evolution in
AC CE P
450
NU
446
Escherichia coli. Metabolic Engineering, 14(5):487–495, 2012.
454
[60] James D Winkler, Carlos Garcia, Michelle Olson, Emily Callaway, and Katy C Kao. Evolved
455
Osmotolerant Escherichia coli Mutants Frequently Exhibit Defective N-Acetylglucosamine
456
Catabolism and Point Mutations in Cell Shape-Regulating Protein MreB. Applied and Envi-
457
ronmental Microbiology, 80(12):3729–3740, 2014.
458
[61] Elizabeth A. Winzeler, Daniel D. Shoemaker, Anna Astromoff, Hong Liang, Keith Ander-
459
son, Bruno Andre, Rhonda Bangham, Rocio Benito, Jef D. Boeke, Howard Bussey, An-
460
gela M. Chu, Carla Connelly, Karen Davis, Fred Dietrich, Sally Whelen Dow, Mohamed
461
El Bakkoury, Franc?oise Foury, Stephen H. Friend, Erik Gentalen, Guri Giaever, Johannes H.
462
Hegemann, Ted Jones, Michael Laub, Hong Liao, Nicole Liebundguth, David J. Lockhart,
463
Anca Lucau-Danila, Marc Lussier, Nasiha M’Rabet, Patrice Menard, Michael Mittmann, Chai
22
ACCEPTED MANUSCRIPT Pai, Corinne Rebischung, Jose L. Revuelta, Linda Riles, Christopher J. Roberts, Petra Ross-
465
MacDonald, Bart Scherens, Michael Snyder, Sharon Sookhai-Mahadeo, Reginald K. Storms,
466
Steeve Veronneau, Marleen Voet, Guido Volckaert, Teresa R. Ward, Robert Wysocki, Grace S.
467
Yen, Kexin Yu, Katja Zimmermann, Peter Philippsen, Mark Johnston, and Ronald W. Davis.
468
Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Anal-
469
ysis. Science, 285(5429):901–906, 1999.
SC
RI
PT
464
[62] Jina Yang, Sang Woo Seo, Sungho Jang, So-I Shin, Chae Hyun Lim, Tae-Young Roh, and
471
Gyoo Yeol Jung. Synthetic RNA devices to expedite the evolution of metabolite-producing
472
microbes. Nature Communications, 4:1413, 2013.
MA
NU
470
[63] Ying-Xin Zhang, Kim Perry, Victor A Vinci, Keith Powell, Willem PC Stemmer, and
474
Stephen B del Cardayré. Genome shuffling leads to rapid phenotypic improvement in bacteria.
475
Nature, 415(6872):644–646, 2002.
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Figure 1: Principal variables that can be adjusted when performing adaptive evolution experiments. A) use of mutagens or mutagenic strains to increase adaptation rate to stressors. B) Detection of clonal interference (mutant competition) to observe independent mutant lineages within a population. C) modification of the applied selective pressure through various means, D) mutant characterization using high-throughput technologies, and E) increased parallelism to detect favored adaptive mechanisms. Advances that alter these experimental properties and more are explored in the text.
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S0888-7543(14)00183-9 doi: 10.1016/j.ygeno.2014.09.006 YGENO 8664
To appear in:
Genomics
Received date: Accepted date:
11 July 2014 16 September 2014
Please cite this article as: James D. Winkler, Katy C. Kao, Recent Advances in the Evolutionary Engineering of Industrial Biocatalysts, Genomics (2014), doi: 10.1016/j.ygeno.2014.09.006
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ACCEPTED MANUSCRIPT
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Recent Advances in the Evolutionary Engineering of
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Industrial Biocatalysts
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James D. Winkler1 , Katy C. Kao1
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Department of Chemical Engineering, Texas A&M University, College Station, TX, United States
of America
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ACCEPTED MANUSCRIPT Running Head: Recent Advances in Evolutionary Engineering
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Keywords: evolutionary engineering, mutagenesis, automation, selection, adaptation, complex
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phenotypes
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Corresponding author: Katy C. Kao
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Department of Chemical Engineering, Texas A&M University,
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College Station, TX, United States of America
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Phone 979 845 5571
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Email: [email protected]
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ACCEPTED MANUSCRIPT Abstract
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Evolutionary engineering has been used to improve key industrial strain traits, such
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as carbon source utilization, tolerance to adverse environmental conditions, and resis-
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tance to chemical inhibitors, for many decades due to its technical simplicity and effec-
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tiveness. The lack of need for prior genetic knowledge underlying the phenotypes of
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interest makes this a powerful approach for strain development for even species with
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minimal genotypic information. While the basic experimental procedure for labora-
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tory adaptive evolution has remained broadly similar for many years, a range of recent
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advances show promise for improving the experimental workflows for evolutionary
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engineering by accelerating the pace of evolution, simplifying the analysis of evolved
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mutants, and providing new ways of linking desirable phenotypes to selectable charac-
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teristics. This review aims to highlight some of these recent advances and discuss how
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they may be used to improve industrially relevant microbial phenotypes.
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ACCEPTED MANUSCRIPT Introduction
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Biocatalyst robustness is an important parameter of microbial strains used for industrial fermenta-
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tion. Cells in these processes are subjected to variety of different stresses, including thermal, acid,
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and osmotic stresses, in addition to toxic effects from growth substrates and both desired and side
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products. Improving the tolerance of strains to these physiochemical insults is therefore of great
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interest for industrial applications. Rational methods for improving these strain characteristics have
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been largely stymied by the fact that the underlying genetic determinants for most tolerance phe-
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notypes are largely unknown. One way of circumventing this obstacle is to use inverse engineering
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approaches, which involve generating strains with the desired phenotype through random methods
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and then analyzing the genetics, transcriptomics, proteomics, and/or metabolomics of the mutants
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to identify the causative mutations underlying the phenotype. These approaches do not require
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a priori genetic knowledge of the organism, and can therefore be used with any organism if the
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phenotypes-of-interest are growth-coupled.
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Evolutionary engineering, also known as adaptive laboratory evolution or whole-cell directed
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evolution, is the pre-eminent method for both improving industrial strains and analyzing these
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complex tolerance phenotypes due to its simplicity and effectiveness. The technique involves the
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continued propagation of a microbial population under a desired selective pressure. Fitter mutants
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naturally arise from random mutations during DNA replication or other sources, and will increase
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in frequency in proportion to their fitness (relative to the mean fitness of the population). Evolu-
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tionary engineering can also be combined with other methods such as random mutagenesis in order
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to generate more genetic diversity for selection. Adaptive evolution has been successfully utilized
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to generate strains with traits such as enhanced substrate utilization, tolerance to fermentation con-
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ditions, and resistance to toxic compounds [1, 2, 8, 10, 11, 16, 37, 42, 52, 56, 57]. Combined with
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the increasing availability of affordable high-throughput genomic technologies, molecular charac-
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terization of beneficial mutants isolated from the evolved populations is beginning to advance our
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fundamental knowledge on the genetic determinants and molecular mechanisms associated with
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industrially relevant complex phenotypes. This growing body of knowledge will be of great use
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ACCEPTED MANUSCRIPT for future rational engineering of tolerance and other growth-linked traits. In this review, we focus
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on recent advances in evolutionary engineering that help to simplify strain improvement and anal-
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ysis, as well as systems biology approaches for analyzing the changes associated with phenotype
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improvement.
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Recent Innovations in Evolutionary Engineering
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Several novel new thrusts towards changing the standard paradigm of evolutionary engineering
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have recently been demonstrated in the literature. These advances can be loosely grouped into sev-
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eral distinct categories: methods for increasing, identifying, and exploiting diversity within evolv-
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ing populations, new schema for evolving metabolite overproducing strains, parallelization and
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automation of cultivation systems, and novel tools for understanding the genetic bases of evolved
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phenotypes. Figure 1 shows how improvements in these areas would affect the execution of adap-
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tive evolution experiments. Based on improvements in these areas, we speculate about future re-
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search directions in evolutionary engineering and how these new tools may change our use of this
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established strain improvement tool.
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Increasing Genetic Diversity
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Since intrinsic mutation rates are typically low for most organisms (on the order of 10−9 -10−10
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bp−1 generation−1 ) under most growth conditions [30, 33], even a relatively small and transient in-
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crease in mutation rate can significantly improve the probability of generating a chance beneficial
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mutation. Mutagenesis, using chemicals or radiation, is often used to increase the genetic diversity
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of a population prior to or during an evolution experiment [32, 46]. The use of mutator strains
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with defective DNA repair systems can also be used to increase genetic diversity [17]. Although
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higher mutation rates increase the frequency of beneficial mutations in the population, it also leads
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to an increase in deleterious mutations (often in the same genetic backgrounds); thus, methods to
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control mutation rate during adaptive evolution have the benefit of transiently increasing genetic
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ACCEPTED MANUSCRIPT diversity while reducing the risk of accumulating deleterious mutations. The GREACE (Genome
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Replication Engineering Assisted Continuous Evolution) method [34] was recently developed for
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this purpose and relies on the introduction of a library of defective error-prone polymerases mutD
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(dnaQ) into a target strain. After evolution in the condition of interest, the plasmid-borne defective
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mutD can be cured by short-term propagation in the absence of antibiotics for plasmid maintenance,
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returning the mutation rate of the evolved strain to normal levels for subsequent analyses. Another
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example of mutation rate manipulation that couples mutation rate to desired product formation was
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recently introduced by Chou and Keasling, where the strain mutation rate is inversely dependent
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on the intracellular concentrations of particular metabolites [9]. Both approaches may be useful to
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transiently raise mutation rates until the desired phenotypic objectives have been reached. Subse-
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quent analysis of the evolved strains is simplified by returning the mutation rate to the wild-type
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baseline, ensuring the evolved phenotypes are sufficiently stable for downstream analysis.
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Interrogating Diverse Populations
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Although the true objective of industrial evolutionary engineering experiments is to generate supe-
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rior production strains, knowledge of the underlying genotypes is crucial for downstream rational
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strain engineering and for improving our fundamental knowledge of biological systems. However,
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growth under selection generally results in genetically heterogeneous populations that contain a
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variety of adaptive, neutral, and deleterious adaptive mutations [4], making it difficult to identify
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all genotypes present with typical, low-throughput, and often arbitrary isolation procedures. Com-
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petition between these diverse lineages makes it difficult to detect all potentially adaptive mutations
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from within the population, reducing our ability to understand the genetic changes that occurred
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over the history of evolution experiments. Our laboratory recently introduced a tool for visual-
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izing evolutionary dynamics in real time (VERT) [25, 43] that allows for the direct detection of
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clonal interference in a population. Competing clones, expressing different fluorescent proteins,
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can therefore be isolated based on the observed population dynamics. Computational tools have
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also been developed to standardize the isolation procedure as well [58]. VERT has been suc-
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ACCEPTED MANUSCRIPT cessfully employed to study several tolerance phenotypes, including solvent, drug, and biomass
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inhibitor tolerance [1, 21, 43]. Other labeling systems, such as DNA barcodes, can be used to
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a similar effect with significantly higher resolution; however, the isolation of single mutants-of-
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interest and mutation linkage determination will be more challenging.
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Visualization of clonal interference, though effective as an evolutionary tool, cannot provide
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data on all genotypes present in a population since even labeled subpopulations may be heteroge-
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neous. With the advent of affordable whole genome sequencing, however, evolutionary engineers
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now have the power to uncover all mutations present above a certain frequency within an evolving
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population by direct sequencing of the entire population. Two recent studies used this approach
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to study population dynamics and types of adaptive mutations that may occur during evolution
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[29, 31] in ways that would be impossible with more traditional interrogation methods. The prin-
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cipal limitation of this approach stems from properties in common Next Generation Sequencing
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(NGS) platforms, namely their short read lengths (100-500 bp) and the need for deep sequencing
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to identify rare genotypes in the population. Genomic rearrangements and copy number variations
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are difficult to identify in heterogeneous population samples, and linkage information between
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widely separated mutations cannot be extracted; for example, cohorts of mutations were observed
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to expand and contract together in evolving populations of yeast, but not all are beneficial when
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combined [31]. Even with these restrictions in mind, knowledge of all detectable mutations over the
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duration of an experiment will provide unprecedented amount of information on the evolutionary
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dynamics in the population, and with data from parallel experiments to help narrow down neutral or
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deleterious hitchhikers from beneficial mutations will likely reveal novel mechanisms that confer
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the phenotypes-of-interest.
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Exploiting Genetic Diversity for Phenotypic Improvement
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Beyond creating, visualizing, and quantifying diversity within an evolving population, additional
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focus has recently been given to help harness this diversity to speed adaptive evolution. One ob-
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stacle reducing the effectiveness of evolution experiments is clonal interference, which arises from
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ACCEPTED MANUSCRIPT the competition of genetically distinct mutants within a population. One approach to reducing this
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effect is to permit genetic exchange between the lineages (i.e., sex) [35]. This process allows adap-
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tive mutations to spread throughout an evolving population, and can potentially combine different
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mutations from competing mutants into a single genetic background. In asexual reproduction,
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multiple, independent mutations acquired sequentially would be required to combine multiple ben-
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eficial mutations, significantly increasing the amount of time required to achieve the same level
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of improvement expected in sexual populations. In addition, any deleterious mutation present
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in adaptive mutants cannot be readily removed in asexual evolution (known as Muller’s ratchet,
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[18]), which can be alleviated by sexual recombination. Classic means of recombination include
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genome shuffling by protoplast fusion [40, 47, 63] and by exploiting sexual cycles in some labora-
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tory species (S. cerevisae) [19]. These methods, though generally successful in their stated goals
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of increasing diversity and permitting interclonal gene flow, are low-throughput and are not very
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efficient, making them difficult to apply to highly parallel evolutionary engineering projects.
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In an effort to circumvent this limitation of conventional recombination tools, a conjugation-
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based evolution system was recently introduced by Winkler and Kao [59], built on previous work by
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Cooper that demonstrated that conjugation proficient E. coli evolved more quickly than conjugation
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incompetent controls [12]. The key innovation of this new method is the design of a strain capable
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of efficient, bidirectional mating between strains, allowing for interclonal chromosomal DNA ex-
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change, significantly increasing the rate of recombination within a population during growth. The
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F conjugation system used by Winkler and Kao is capable of continuous mating in liquid culture,
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allowing evolution and recombination to take place simultaneously, even under selection. Rates of
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phenotypic improvement during evolution can be significantly increased as a result, depending on
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the fitness landscape describing the phenotype of interest [59]. In contrast, genome shuffling by
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protoplast fusion must be performed in the absence of any selection due to the fragility of the re-
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combinants generated by the procedure, imposing a large bottleneck due to the loss of cells during
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the shuffling and re-inoculation process. Recombinants generated by conjugation are immediately
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subjected to selection, simplifying the generation of strains with improved growth phenotypes. The
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ACCEPTED MANUSCRIPT combination of continuous mating under selection with the improved efficiency of conjugation is a
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significant improvement over previous methods, and the system has recently been demonstrated to
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be an effective evolutionary tool by improving the E. coli osmotolerance phenotype [60]. A similar
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mass mating system, based on the natural yeast mating cycle, has also been developed by Hughes et
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al. [22]. Since the theoretical advantages of recombination are significant [13, 18], we expect that
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further refinements of these systems and development of additional tools to enable recombination
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will play a substantial role in the evolutionary engineering field for the foreseeable future.
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Connecting Fitness to Production
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One limitation in the use of adaptive evolution is the required coupling between growth and desired
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phenotypes, such as consumption of a non-ideal carbon source or tolerance of various inhibitors.
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A fundamental, though largely unstated, assumption underlying the previous sections is that there
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exists a simple linkage between the selective pressure of interest and growth rate or viability. The
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desired mutants are therefore those that grow faster or survive more frequently in the selective
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environments. However, industrial strains are used to synthesize products-of-interest that are gen-
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erally not growth-linked, so adaptive laboratory evolution cannot be applied to evolve strains with
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enhanced productivity. To circumvent this limitation, strain engineering efforts that force the cou-
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pling between product formation and growth by manipulation of redox balancing in the host strain
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have been used [15, 24, 50]. In some cases, the selective pressure used in evolutionary engineer-
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ing can also be designed to provide a growth advantage to better producers. A recent effort by
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our group successfully demonstrated the use of this approach in using oxidative stress to improve
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productivity of carotenoids in yeast [44]. The use of anti-metabolites or metabolite analogs have
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also been applied in evolutionary engineering as selective pressures to either improve productiv-
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ity [6, 49] or improve substrate utilization [38]. The overriding problem with these approaches is
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that each selection experiment must be uniquely designed for the pathway and desired metabolite,
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assuming a viable selective pressure exists at all.
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Though engineering growth-product linkages will remain a formidable problem in the near fu10
ACCEPTED MANUSCRIPT ture, significant progress has been made in designing more generic systems that can link product
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formation to strain fitness. An innovative approach linking a metabolite-sensing riboswitch with a
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tetracycline resistance cassette (tetA) was recently employed to evolve E. coli for improved lysine
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and tryptophan productivity [62]. In this case, expression of tetA, the tetracycline resistance gene,
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was inversely proportional to the concentration of the amino acid of interest. Yang and coworkers
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took advantage of the fact that high levels of tetA expression confer a Ni2+ -sensitive phenotype,
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so simple propagation in the presence of Ni2+ was sufficient to select for mutants with large im-
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provements in amino acid accumulation. The main challenge in generalizing this method is the
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engineering of suitable riboswitches for each metabolite, though this area has recently seen great
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strides [5, 7]. An alternative approach called FREP (feedback-regulated evolution of phenotype) by
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Chou and Keasling has also been successfully applied to increase production of isoprenoids and ty-
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rosine in E. coli through adaptive evolution [9]. Their method relied on making the expression of a
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dominant mutator allele (mutD5) inversely proportional to the concentration of a target metabolite,
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so that low producers would rapidly accumulate mutations that may improve metabolite produc-
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tion. High producers, in contrast, are more genetically stable due to decreased expression of the
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mutator allele. Both methods represent the first steps towards a more general method for linking
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strain productivity to fitness, and allow metabolic engineers to combine the stochasticity of adap-
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tive evolution with large library sizes permitted by selection (∼ 1010 individuals) for more rapid
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strain improvement. As additional sensors are designed, evolved, or discovered in nature, it is likely
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that the evolutionary engineering approach to producer development will become a complementary
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alternative to the more rational strain engineering approaches used at the moment.
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Parellelization and Automation of Evolution
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One of the factors limiting the utility of adaptive laboratory evolution is the capital expense and
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manual effort required. In our experience, one person can feasibly handle an experiment consisting
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of 24-30 replicate batch cultures, while performing the necessary maintenance and quantification
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procedures (e.g., archival storage, contamination tests, adjustment of selective pressure, pheno-
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ACCEPTED MANUSCRIPT type improvement analysis, etc). This scale is fairly limiting, as it is difficult to draw statistical
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conclusions about strain improvement or potential adaptive mechanisms from a small number of
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replicate cultures. The amount of manual intervention required also introduces an element of bias
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into the experiment, especially when trying to adjust selective pressures in a consistent manner as
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population fitness improves.
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Several new cultivation systems with increased parallelism and automation have recently been
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developed as a means of overcoming these limitations. For example, control systems and reac-
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tors themselves can now be inexpensively constructed and customized according to user needs
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[36, 54]. One particularly innovative tool is the "morbidostat" developed by Toprak et al. [55],
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which combines a continuous bioreactor array with a feedback control system for adjusting in situ
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selective pressure automatically. The population growth rate is, therefore, maintained at a constant
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level throughout the experiment without manual adjustment. The advantages of the system were
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demonstrated by evolving E. coli for resistance to several antibiotics to show that starkly different
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selective pressure regimes were needed to maintain the desired level of inhibition. They also in-
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ferred structural properties of the underlying fitness landscapes [39] based on the ramping schemes
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used by the feedback controller in the system, providing additional insight as to how adaptation can
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proceed in response to antibiotic challenges. The use of this approach will clearly improve con-
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sistency of selective pressures within experiments while enabling researchers to obtain quantitative
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data concerning the rate of adaptation for target inhibitors. As the evolutionary process is stochas-
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tic, these high-throughput, small-scale, and low-cost bioreactor designs allow even small labs the
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ability to run multiple parallel evolutionary experiments in order to identify possible evolutionary
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trajectories.
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Liquid handling robots and microfluidics have also been increasingly used to dramatically im-
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prove experimental throughput. A fully automated evolution system that propagates microbial cul-
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tures in microplates was recently introduced by Horinouchi and coworkers [20] and successfully
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used to evolve strains with improved tolerance of a range of industrially relevant stressors. Other
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automated systems, also based on batch growth in microtiter plates, has also been used elsewhere
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ACCEPTED MANUSCRIPT [28, 31]. While these approaches rely on robotics to handle liquid transfers and inoculations, mi-
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croscale chemostat systems based on the maintenance of populations inside liquid droplets have
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also been introduced [23], potentially allowing thousands of populations to be maintained in paral-
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lel. The decreasing cost of liquid handling robots and microfluidic devices will help enable future
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development of high throughput approaches. It is important to note that the small population sizes
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imposed by typical plate-based high throughput evolutionary systems may not be desirable in all
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situations, as the genetic diversity and evolutionary trajectories are more limited compared to larger
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population sizes. However, the advantages of extensive replication and automated handling are nu-
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merous: it facilitates detection of divergent or antagonistic adaptation pathways, and it can provide
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data for theoretical analyses of evolutionary dynamics, clonal interference, or other aspects of pop-
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ulation genetics. Combined with advances in NGS and genetic analysis, these new high throughput
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cultivation tools promise to simplify and streamline the process of evolutionary engineering, while
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providing extensive information on parallel adaptation routes. Knowledge gained from the resulting
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adaptive mutants can be used to improve existing industrially strains.
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Understanding Evolved Genotypes
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Despite advances in other areas, characterization of evolved strains remains a laborious and low-
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throughput process. The most common approach for uncovering genotype-phenotype linkages
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involves reconstructing each potentially adaptive mutation into the unevolved parental strain, fol-
241
lowed by the appropriate phenotypic characterization (growth rate, tolerance, etc) to confirm its
242
putative effect. Traditional reconstruction techniques, such as selection-counterselection cassette
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replacement, transduction, site-directed mutagenesis, and others [14, 48, 53], while powerful, re-
244
quire a significant amount of time to produce a single reconstructed mutant. When the number of
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mutations per strain is large, or when it is necessary to examine interactions (positive or negative
246
epistasis) between adaptive mutations [26, 37], then reconstruction quickly becomes the limiting
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step for understanding evolved mutants. The introduction of collections of defined deletion strains
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for E. coli and S. cerevisae [3, 61], in addition to the ASKA overexpression plasmid collection
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ACCEPTED MANUSCRIPT for E. coli [27], has significantly improved the ability of researchers to more rapidly analyze cer-
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tain types of evolved mutations (e.g. inactivating mutations or mutations resulting in increased
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gene expression). However, for those mutations occurring in non-model organisms or those that do
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not result in gene disruption or increased expression, each potentially adaptive mutation must be
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manually reconstructed using the appropriate tools.
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One promising approach towards reducing the time required for strain reconstruction and genotype-
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phenotype analysis in bacteria is a new method named REGRES (recursive genomewide recombi-
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nation and sequencing) introduced by Quandt and colleagues [41]. REGRES is based on the F con-
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jugation system, in which strains containing the mutations of interest are converted into Hfr (high
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frequency recombinant-forming) strains using integrative F plasmids, and subsequently mated to
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F- recipients. The donor is then selected against using antibiotics, yielding a library of transcon-
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jugants that contain heterozygous genomes of donor and recipient DNA. These transconjugants
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can be screened or selected again for the sought after phenotype, followed by NGS or Sanger se-
262
quencing to identify the causative mutations underlying the observed phenotypes. This approach
263
was used to identify the genetic bases for the aerobic citrate utilization trait that arose during the
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long-term evolution experiment in the Lenski laboratory [4] without the need for manual strain re-
265
construction, which poses a significant challenge given the large number of accumulated mutations
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in the isolates after more than 50,000 generations of evolution. Further improvements in recom-
267
bineering tools, including the rapid disruption of multiple genes simultaneously [45, 51], will also
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help to decrease the burden of strain analysis in the future.
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Conclusions
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These improvements in evolutionary engineering techniques, though enormously beneficial from
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both scientific and engineering perspectives, have focused primarily on refinements of the same ex-
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perimental procedure. In effect, a scientist evolving E. coli for an improved phenotype in the 1950s
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or the 2000s would have very similar workflows during the actual evolution experiment, despite
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ACCEPTED MANUSCRIPT the seismic changes in other areas of the biotechnology field. It is no longer sufficient to merely
275
improve the phenotype of one organism in a single condition using evolution; given that most in-
276
dustrial environments involve a range of stressors (temperature, osmolarity, and so forth) using
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different microbial species and strains, we must be able to quickly evolve for new traits, dissect
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their tolerance mechanisms, and then transfer these findings to other organisms to increase their
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industrial utility. While it is likely that adaptive laboratory evolution will continue to be performed
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in much the same way for the foreseeable future, we speculate that evolutionary engineering will
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eventually be able to accomplish this seminal goal as a result of continuing improvement in the
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tools and methodologies available to evolutionary engineers.
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Acknowledgements
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We gratefully acknowledge support from the Norman Hackerman Advanced Research Program
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(grant number 000512-0004-2011) for this work.
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Literature Cited
AC CE P
TE
D
MA
NU
SC
RI
PT
274
287
[1] María P Almario, Luis H Reyes, and Katy C Kao. Evolutionary engineering of Saccharomyces
288
cerevisiae for enhanced tolerance to hydrolysates of lignocellulosic biomass. Biotechnology
289
and Bioengineering, 110(10):2616–2623, 2013.
290
[2] Shota Atsumi, Tung-Yun Wu, Iara MP Machado, Wei-Chih Huang, Pao-Yang Chen, Matteo
291
Pellegrini, and James C Liao. Evolution, genomic analysis, and reconstruction of isobutanol
292
tolerance in Escherichia coli. Molecular Systems Biology, 6(1), 2010.
293
[3] Tomoya Baba, Takeshi Ara, Miki Hasegawa, Yuki Takai, Yoshiko Okumura, Miki Baba, Kir-
294
ill A Datsenko, Masaru Tomita, Barry L Wanner, and Hirotada Mori. Construction of Es-
295
cherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Molecular
296
Systems Biology, 2(1), 2006. 15
ACCEPTED MANUSCRIPT
299
300
Nature Reviews Genetics, 14(12):827–839, 2013. [5] Chase L Beisel and Christina D Smolke. Design principles for riboswitch function. PLoS
PT
298
[4] Jeffrey E Barrick and Richard E Lenski. Genome dynamics during experimental evolution.
Computational Biology, 5(4):e1000363, 2009.
RI
297
[6] Jeanne Bonomo, Michael D Lynch, Tanya Warnecke, James V Price, and Ryan T Gill.
302
Genome-scale analysis of anti-metabolite directed strain engineering. Metabolic Engineer-
303
ing, 10(2):109–120, 2008.
NU
SC
301
[7] Andrew L Chang, Joshua J Wolf, and Christina D Smolke. Synthetic rna switches as a tool
305
for temporal and spatial control over gene expression. Current Opinion in Biotechnology,
306
23(5):679–688, 2012.
MA
304
[8] Kian-Kai Cheng, Baek-Seok Lee, Takeshi Masuda, Takuro Ito, Kazutaka Ikeda, Akiyoshi
308
Hirayama, Lingli Deng, Jiyang Dong, Kazuyuki Shimizu, Tomoyoshi Soga, et al. Global
309
metabolic network reorganization by adaptive mutations allows fast growth of Escherichia
310
coli on glycerol. Nature Communications, 5, 2014.
312
TE
AC CE P
311
D
307
[9] Howard H Chou and Jay D Keasling. Programming adaptive control to evolve increased metabolite production. Nature Communications, 4, 2013.
313
[10] Tom M Conrad, Michael Frazier, Andrew R Joyce, Byung-Kwan Cho, Eric M Knight,
314
Nathan E Lewis, Robert Landick, and Bernhard Ø Palsson. Rna polymerase mutants found
315
through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media.
316
Proceedings of the National Academy of Sciences, 107(47):20500–20505, 2010.
317
[11] Tom M Conrad, Andrew R Joyce, M Kenyon Applebee, Christian L Barrett, Bin Xie,
318
Yuan Gao, and Bernhard Ø Palsson. Whole-genome resequencing of Escherichia coli K-12
319
MG1655 undergoing short-term laboratory evolution in lactate minimal media reveals flexible
320
selection of adaptive mutations. Genome Biology, 10(10):R118, 2009.
16
ACCEPTED MANUSCRIPT
323
324
mutations in populations of Escherichia coli. PLoS Biology, 5(9):e225, 2007. [13] James F Crow and Motoo Kimura. Evolution in sexual and asexual populations. American
PT
322
[12] Tim F Cooper. Recombination speeds adaptation by reducing competition between beneficial
Naturalist, pages 439–450, 1965.
RI
321
[14] Hilary M. Ellis, Daiguan Yu, Tina DiTizio, and Donald L. Court. High efficiency mutage-
326
nesis, repair, and engineering of chromosomal DNA using single-stranded oligonucleotides.
327
Proceedings of the National Academy of Sciences, 98(12):6742–6746, 2001.
NU
SC
325
[15] Stephen S Fong, Anthony P Burgard, Christopher D Herring, Eric M Knight, Frederick R
329
Blattner, Costas D Maranas, and Bernhard O Palsson. In silico design and adaptive evolu-
330
tion of Escherichia coli for production of lactic acid. Biotechnology and Bioengineering,
331
91(5):643–648, 2005.
D
MA
328
[16] Hani Goodarzi, Bryson D Bennett, Sasan Amini, Marshall L Reaves, Alison K Hottes,
333
Joshua D Rabinowitz, and Saeed Tavazoie. Regulatory and metabolic rewiring during lab-
334
oratory evolution of ethanol tolerance in E. coli. Molecular Systems Biology, 6(1), 2010.
336
337
338
339
340
AC CE P
335
TE
332
[17] Alan Greener, Marie Callahan, and Bruce Jerpseth. An efficient random mutagenesis technique using an E. colimutator strain. Molecular Biotechnology, 7(2):189–195, 1997. [18] John Haigh. The accumulation of deleterious genes in a population: Muller’s ratchet. Theoretical population biology, 14(2):251–267, 1978. [19] Ira Herskowitz. Life cycle of the budding yeast saccharomyces cerevisiae. Microbiological Reviews, 52(4):536, 1988.
341
[20] Takaaki Horinouchi, Teruaki Minamoto, Shingo Suzuki, Hiroshi Shimizu, and Chikara Furu-
342
sawa. Development of an Automated Culture System for Laboratory Evolution. Journal of
343
Laboratory Automation, page 2211068214521417, 2014.
17
ACCEPTED MANUSCRIPT 344
345
[21] Mian Huang, Mark McClellan, Judith Berman, and Katy C Kao. Evolutionary dynamics of Candida albicans during in vitro evolution. Eukaryotic Cell, 10(11):1413–1421, 2011. [22] Stephen R Hughes, Ronald E Hector, Joseph O Rich, Nasib Qureshi, Kenneth M Bischoff,
347
Bruce S Dien, Badal C Saha, Siqing Liu, Elby J Cox, John S Jackson, David E Sterner,
348
Tauseef R Butt, Joshua LaBaer, and Michael A Cotta. Automated yeast mating protocol
349
using open reading frames from Saccharomyces cerevisiae genome to improve yeast strains
350
for cellulosic ethanol production. Journal of Laboratory Automation, 14(4):190–199, 2009.
NU
SC
RI
PT
346
[23] Slawomir Jakiela, Tomasz S Kaminski, Olgierd Cybulski, Douglas B Weibel, and Piotr
352
Garstecki. Bacterial Growth and Adaptation in Microdroplet Chemostats. Angewandte
353
Chemie, 125(34):9076–9079, 2013.
MA
351
[24] Kaemwich Jantama, MJ Haupt, Spyros A Svoronos, Xueli Zhang, JC Moore, KT Shan-
355
mugam, and LO Ingram. Combining metabolic engineering and metabolic evolution to
356
develop nonrecombinant strains of Escherichia coli C that produce succinate and malate.
357
Biotechnology and Bioengineering, 99(5):1140–1153, 2008.
AC CE P
TE
D
354
358
[25] Katy C Kao and Gavin Sherlock. Molecular characterization of clonal interference during
359
adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nature Genetics,
360
40(12):1499–1504, 2008.
361
[26] Aisha I Khan, Duy M Dinh, Dominique Schneider, Richard E Lenski, and Tim F Cooper.
362
Negative epistasis between beneficial mutations in an evolving bacterial population. Science,
363
332(6034):1193–1196, 2011.
364
[27] Masanari Kitagawa, Takeshi Ara, Mohammad Arifuzzaman, Tomoko Ioka-Nakamichi, Eiji
365
Inamoto, Hiromi Toyonaga, and Hirotada Mori. Complete set of ORF clones of Escherichia
366
coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biolog-
367
ical research. DNA Research, 12(5):291–299, 2006.
18
ACCEPTED MANUSCRIPT [28] Sergey Kryazhimskiy, Daniel P. Rice, Elizabeth R. Jerison, and Michael M. Desai. Global
369
epistasis makes adaptation predictable despite sequence-level stochasticity. 344(6191):1519–
370
1522, 2014.
PT
368
[29] Daniel J Kvitek and Gavin Sherlock. Whole Genome, Whole Population Sequencing Reveals
372
That Loss of Signaling Networks Is the Major Adaptive Strategy in a Constant Environment.
373
PLoS Genetics, 9(11):e1003972, 2013.
375
SC
[30] Gregory I Lang and Andrew W Murray. Estimating the per-base-pair mutation rate in the
NU
374
RI
371
yeast Saccharomyces cerevisiae. Genetics, 178(1):67–82, 2008. [31] Gregory I Lang, Daniel P Rice, Mark J Hickman, Erica Sodergren, George M Weinstock,
377
David Botstein, and Michael M Desai. Pervasive genetic hitchhiking and clonal interference
378
in forty evolving yeast populations. Nature, 500(7464):571–574, 2013.
D
MA
376
[32] Dae-Hee Lee, Adam M Feist, Christian L Barrett, and Bernhard Ø Palsson. Cumulative num-
380
ber of cell divisions as a meaningful timescale for adaptive laboratory evolution of Escherichia
381
coli. PLoS One, 6(10):e26172, 2011.
383
AC CE P
382
TE
379
[33] Peter A Lind and Dan I Andersson. Whole-genome mutational biases in bacteria. Proceedings of the National Academy of Sciences, 105(46):17878–17883, 2008.
384
[34] Guodong Luan, Zhen Cai, Yin Li, Yanhe Ma, et al. Genome replication engineering as-
385
sisted continuous evolution (GREACE) to improve microbial tolerance for biofuels produc-
386
tion. Biotechnology for Biofuels, 6(1):137, 2013.
387
388
389
390
[35] Gabriel Marais and Brian Charlesworth. Genome evolution: recombination speeds up adaptive evolution. Current Biology, 13(2):R68–R70, 2003. [36] Aaron W Miller, Corrie Befort, Emily O Kerr, and Maitreya J Dunham. Design and use of multiplexed chemostat arrays. Journal of Visualized Experiments, (72), 2013.
19
ACCEPTED MANUSCRIPT [37] J. Minty, A. Lesnefsky, F. Lin, Y. Chen, T. Zaroff, A. Veloso, B. Xie, C. McConnell, R. Ward,
392
D. Schwartz, J.M. Rouillard, Y. Gao, E. Gulari, and X.N. Lin. Evolution combined with
393
genomic study elucidates genetic bases of isobutanol tolerance in Escherichia coli. Microbial
394
Cell Factories, 10(1):18, 2011.
PT
391
[38] Ali Mohagheghi, Jeff Linger, Holly Smith, Shihui Yang, Nancy Dowe, and Philip T Pienkos.
396
Improving xylose utilization by recombinant Zymomonas mobilis strain 8b through adaptation
397
using 2-deoxyglucose. Biotechnology for Biofuels, 7(1):19, 2014.
SC
NU
399
[39] H Allen Orr.
Fitness and its role in evolutionary genetics.
10(8):531–539, 2009.
Nature Reviews Genetics,
MA
398
RI
395
[40] Ranjan Patnaik, Susan Louie, Vesna Gavrilovic, Kim Perry, Willem PC Stemmer, Chris M
401
Ryan, and Stephen del Cardayré. Genome shuffling of lactobacillus for improved acid toler-
402
ance. Nature Biotechnology, 20(7):707–712, 2002.
TE
D
400
[41] Erik M Quandt, Daniel E Deatherage, Andrew D Ellington, George Georgiou, and Jeffrey E
404
Barrick. Recursive genomewide recombination and sequencing reveals a key refinement step
405
in the evolution of a metabolic innovation in Escherichia coli. Proceedings of the National
406
Academy of Sciences, page 201314561, 2013.
407
408
AC CE P
403
[42] Amparo Querol, M Teresa Fernández-Espinar, Eladio Barrio, et al. Adaptive evolution of wine yeast. International Journal of Food Microbiology, 86(1):3–10, 2003.
409
[43] Luis H Reyes, Maria P Almario, James Winkler, Margarita M Orozco, and Katy C Kao. Visu-
410
alizing evolution in real time to determine the molecular mechanisms of n-butanol tolerance
411
in Escherichia coli. Metabolic Engineering, 14(5):579–590, 2012.
412
413
[44] Luis H Reyes, Jose M Gomez, and Katy C Kao. Improving carotenoids production in yeast via adaptive laboratory evolution. Metabolic Engineering, 21:26–33, 2014.
414
[45] Owen W Ryan, Jeffrey M Skerker, Matthew J Maurer, Xin Li, Jordan C Tsai, Snigdha Poddar,
415
Michael E Lee, Will DeLoache, John E Dueber, Adam P Arkin, and Jamie H D Cate. Selection 20
ACCEPTED MANUSCRIPT 416
of chromosomal DNA libraries using a multiplex CRISPR system. eLife, pages e03703–
417
e03703, 2014.
420
421
PT
Metabolic Engineering, pages 129–169. Springer, 2001.
RI
419
[46] Uwe Sauer. Evolutionary engineering of industrially important microbial phenotypes. In
[47] R. Schnettler, U. Zimmermann, and C. C. Emeis. Large-scale production of yeast hybrids by electrofusion. FEMS Microbiology Letters, 24(1):81–85, 1984.
SC
418
[48] Shyam K Sharan, Lynn C Thomason, Sergey G Kuznetsov, Donald L Court, et al. Recombi-
423
neering: a homologous recombination-based method of genetic engineering. Nature Proto-
424
cols, 4(2):206–223, 2009.
MA
development in Escherichia coli. Metabolic Engineering, 13(6):674–681, 2011.
D
426
[49] Kevin M Smith and James C Liao. An evolutionary strategy for isobutanol production strain
TE
425
NU
422
[50] Marco Sonderegger and Uwe Sauer. Evolutionary engineering of Saccharomyces cerevisiae
428
for anaerobic growth on xylose. Applied and Environmental Microbiology, 69(4):1990–1998,
429
2003.
430
431
AC CE P
427
[51] Chan Woo Song and Sang Yup Lee. Rapid one-step inactivation of single or multiple genes in escherichia coli. Biotechnology Journal, 8(7):776–784, 2013.
432
[52] Boris U Stambuk, Barbara Dunn, Sergio L Alves, Eduarda H Duval, and Gavin Sherlock.
433
Industrial fuel ethanol yeasts contain adaptive copy number changes in genes involved in
434
vitamin B1 and B6 biosynthesis. Genome Research, 19(12):2271–2278, 2009.
435
436
[53] N Sternberg and R Hoess. The molecular genetics of bacteriophage P1. Annual Review of Genetics, 17(1):123–154, 1983.
437
[54] Chris N. Takahashi, Aaron W. Miller, Felix Ekness, Maitreya J. Dunham, and Eric Klavins.
438
A Low Cost, Customizable Turbidostat for Use in Synthetic Circuit Characterization. ACS
439
Synthetic Biology, 2014. 21
ACCEPTED MANUSCRIPT [55] Erdal Toprak, Adrian Veres, Jean-Baptiste Michel, Remy Chait, Daniel L Hartl, and Roy
441
Kishony. Evolutionary paths to antibiotic resistance under dynamically sustained drug selec-
442
tion. Nature Genetics, 44(1):101–105, 2012.
PT
440
[56] Valeria Wallace-Salinas and Marie F Gorwa-Grauslund. Adaptive evolution of an indus-
444
trial strain of Saccharomyces cerevisiae for combined tolerance to inhibitors and temperature.
445
Biotechnology for Biofuels, 6(1):151, 2013.
SC
RI
443
[57] Yongze Wang, Ryan Manow, Christopher Finan, Jinhua Wang, Erin Garza, and Shengde
447
Zhou. Adaptive evolution of nontransgenic Escherichia coli kc01 for improved ethanol tol-
448
erance and homoethanol fermentation from xylose. Journal of Industrial Microbiology and
449
Biotechnology, 38(9):1371–1377, 2011.
452
453
MA
D
VERT system. Journal of Biological engineering, 6(1):1–8, 2012.
TE
451
[58] James Winkler and Katy C Kao. Computational identification of adaptive mutants using the
[59] James Winkler and Katy C Kao. Harnessing recombination to speed adaptive evolution in
AC CE P
450
NU
446
Escherichia coli. Metabolic Engineering, 14(5):487–495, 2012.
454
[60] James D Winkler, Carlos Garcia, Michelle Olson, Emily Callaway, and Katy C Kao. Evolved
455
Osmotolerant Escherichia coli Mutants Frequently Exhibit Defective N-Acetylglucosamine
456
Catabolism and Point Mutations in Cell Shape-Regulating Protein MreB. Applied and Envi-
457
ronmental Microbiology, 80(12):3729–3740, 2014.
458
[61] Elizabeth A. Winzeler, Daniel D. Shoemaker, Anna Astromoff, Hong Liang, Keith Ander-
459
son, Bruno Andre, Rhonda Bangham, Rocio Benito, Jef D. Boeke, Howard Bussey, An-
460
gela M. Chu, Carla Connelly, Karen Davis, Fred Dietrich, Sally Whelen Dow, Mohamed
461
El Bakkoury, Franc?oise Foury, Stephen H. Friend, Erik Gentalen, Guri Giaever, Johannes H.
462
Hegemann, Ted Jones, Michael Laub, Hong Liao, Nicole Liebundguth, David J. Lockhart,
463
Anca Lucau-Danila, Marc Lussier, Nasiha M’Rabet, Patrice Menard, Michael Mittmann, Chai
22
ACCEPTED MANUSCRIPT Pai, Corinne Rebischung, Jose L. Revuelta, Linda Riles, Christopher J. Roberts, Petra Ross-
465
MacDonald, Bart Scherens, Michael Snyder, Sharon Sookhai-Mahadeo, Reginald K. Storms,
466
Steeve Veronneau, Marleen Voet, Guido Volckaert, Teresa R. Ward, Robert Wysocki, Grace S.
467
Yen, Kexin Yu, Katja Zimmermann, Peter Philippsen, Mark Johnston, and Ronald W. Davis.
468
Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Anal-
469
ysis. Science, 285(5429):901–906, 1999.
SC
RI
PT
464
[62] Jina Yang, Sang Woo Seo, Sungho Jang, So-I Shin, Chae Hyun Lim, Tae-Young Roh, and
471
Gyoo Yeol Jung. Synthetic RNA devices to expedite the evolution of metabolite-producing
472
microbes. Nature Communications, 4:1413, 2013.
MA
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[63] Ying-Xin Zhang, Kim Perry, Victor A Vinci, Keith Powell, Willem PC Stemmer, and
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Stephen B del Cardayré. Genome shuffling leads to rapid phenotypic improvement in bacteria.
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Nature, 415(6872):644–646, 2002.
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Figure 1: Principal variables that can be adjusted when performing adaptive evolution experiments. A) use of mutagens or mutagenic strains to increase adaptation rate to stressors. B) Detection of clonal interference (mutant competition) to observe independent mutant lineages within a population. C) modification of the applied selective pressure through various means, D) mutant characterization using high-throughput technologies, and E) increased parallelism to detect favored adaptive mechanisms. Advances that alter these experimental properties and more are explored in the text.
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