Visualizing evolution in real time to determine the molecular mechanisms of n-butanol tolerance in Escherichia coli

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Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.A daptive laboratory evolution (ALE) is a growing field facilitated by whole-genome sequencing. The process of ALE involves the continuous culturing of an organism over multiple generations. During an ALE experiment, mutations arise, and those beneficial to the selection pressure are fixed over time in the population. Most ALE experiments analyze a perturbation from a reference state to another (e.g., environmental [1,2] or genetic [3]). After adaptation, understanding what genetic changes enabled an increase in fitness is often desirable (4). Generally there are two methods of evolving microorganisms: batch cultures and chemostats. Each method has its own advantages and disadvantages, in terms of maintenance, growth environment, and selection pressures (5). Applications of ALE are numerous and include those for biotechnological goals, such as improving tolerance to a given compound of interest (6-8), or more progressive uses such as improving electrical current consumption in an organism (9). In addition, there has been a significant focus on using ALE to understand antibiotic resistance to given compounds (i.e., drugs) in order to combat clinical resistance (10). A number of in-depth reviews on ALE have appeared ...

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confidence: 99%

Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium

LaCroix

Sandberg

O’Brien

et al. 2015

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“…Several nonnative microbial systems have been engineered for its production, including Escherichia coli (1), Lactobacillus brevis (2), Pseudomonas putida (3), Bacillus subtilis (3), and Saccharomyces cerevisiae (4). However, this solvent is highly toxic to microorganisms, imposing a limit on the productivity of bio-based production and leading to the development of simultaneous fermentation and separation techniques to mitigate the toxic effects of the biofuel (5) and efforts to identify the genetic determinants and molecular mechanisms associated with n-butanol tolerance for reverse engineering of more robust strains (6)(7)(8)(9)(10)(11)(12). Prior strain engineering efforts include overexpression of GroESL in Clostridium acetobutylicum (resulting in a 50% improvement in total growth in 0.75% [vol/vol] n-butanol) (12) and recently in E. coli (resulting in a 2.8-fold increase in total growth after 48 h in 0.75% [vol/vol] n-butanol) (11) and overexpression of gene CAC1869 in C. acetobutylicum (resulting in an 81% increase in total growth after 12 h) (13).…”

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confidence: 99%

“…We previously reported the use of an adaptive laboratory evolution-based method called visualizing evolution in real time (VERT) to study n-butanol tolerance in E. coli (7). Using a twocolor VERT system (with green fluorescent protein [GFP]-labeled and yellow fluorescent protein [YFP]-labeled cells, allowing the tracking of independent lineages), we isolated several n-butanoltolerant adaptive mutants throughout the evolution and used whole-genome transcriptome profiling and resequencing analyses to identify the underlying n-butanol tolerance mechanisms.…”

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confidence: 99%

Genetic Determinants forn-Butanol Tolerance in Evolved Escherichia coli Mutants: Cross Adaptation and Antagonistic Pleiotropy betweenn-Butanol and Other Stressors

Reyes

Abdelaal

Kao

2013

Self Cite

bCross-tolerance and antagonistic pleiotropy have been observed between different complex phenotypes in microbial systems. These relationships between adaptive landscapes are important for the design of industrially relevant strains, which are generally subjected to multiple stressors. In our previous work, we evolved Escherichia coli for enhanced tolerance to the biofuel n-butanol and discovered a molecular mechanism of n-butanol tolerance that also conferred tolerance to the cationic antimicrobial peptide polymyxin B in one specific lineage (green fluorescent protein [GFP] labeled) in the evolved population. In this work, we aim to identify additional mechanisms of n-butanol tolerance in an independent lineage (yellow fluorescent protein [YFP] labeled) from the same evolved population and to further explore potential cross-tolerance and antagonistic pleiotropy between n-butanol tolerance and other industrially relevant stressors. Analysis of the transcriptome data of the YFP-labeled mutants allowed us to discover additional membrane-related and osmotic stress-related genes that confer n-butanol tolerance in E. coli. Interestingly, the n-butanol resistance mechanisms conferred by the membrane-related genes appear to be specific to n-butanol and are in many cases antagonistic with isobutanol and ethanol. Furthermore, the YFP-labeled mutants showed cross-tolerance between n-butanol and osmotic stress, while the GFP-labeled mutants showed antagonistic pleiotropy between n-butanol and osmotic stress tolerance.

“…This simplification skews the BMR calculations to include only beneficial mutations that were fixed in the population. There is a potential that other beneficial mutations are possible but were not observed due to either clonal interference or genetic drift (34). As observed in the fitness trajectories for passage sizes of 0.01% and 0.001%, not all experiments were able to make jumps to occupy all of the states.…”

Section: Resultsmentioning

confidence: 99%

A Model for Designing Adaptive Laboratory Evolution Experiments

LaCroix

Palsson

Feist

2017

The occurrence of mutations is a cornerstone of the evolutionary theory of adaptation, capitalizing on the rare chance that a mutation confers a fitness benefit. Natural selection is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining when to consider an experiment complete and for balancing outcomes with available resources (i.e., laboratory supplies, personnel, and time). To design and to better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to the optimization of ALE experiments. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim, to explain differences in experimental outcomes. Furthermore, a beneficial mutation rate of 10 Ϫ6.9 to 10 Ϫ8.4 mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in serial passage batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results described here will aid in the design of ALE experiments to fit the exact needs of a project while taking into account the resources required and will lower the barriers to entry for this experimental technique.IMPORTANCE ALE is a widely used scientific technique to increase scientific understanding, as well as to create industrially relevant organisms. The manner in which ALE experiments are conducted is highly manual and uniform, with little optimization for efficiency. Such inefficiencies result in suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in an optimized fashion and can design experiments to generate greater fitness in an accelerated time frame, thereby pushing the limits of what adaptive laboratory evolution can achieve. KEYWORDS Escherichia coli, adaptive evolution, evolutionary biologyA daptive laboratory evolution (ALE) has been performed in vitro for decades, and the field is expanding. ALE involves subjecting a population of organisms to a given environment, in the laboratory, and allowing natural selection to increase the overall fitness of the population. In laboratory settings, this is typically performed with organisms possessing short generation times. The basic principles governing ALE experiments are easily understood across a breadth of disciplines, which has led to its adoption in many laboratories (1, 2). The recent growth in the use of ALE can be attributed to the ease of access and decreasing costs of genome sequencing (3-5). Decreasing sequencing costs have led to increased investigation of genomic, transcriptomic, and additional data types over the course of evolution (5). Wh...