Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli

Quan, Selwyn; Ray, J. Christian J.; Kwota, Zakari; Duong, Trang Thu; Balázsi, Gábor; Cooper, Tim F.; Monds, Russell D.

doi:10.1371/journal.pgen.1002444

Cited by 56 publications

(79 citation statements)

References 58 publications

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“…Plasmids were constructed using enzymatic assembly methods (28). Expression plasmids were constructed using a lowcopy plasmid with a pSC101 origin (pRM102) (29), and coding sequences for the relevant genes were amplified from E. coli MG1655 with the appropriate homology regions for assembly (Table S3). Gene deletions and fluorescent protein fusions were introduced into the chromosome using allelic exchange methods with suicide plasmids (30).…”

Section: Methodsmentioning

confidence: 99%

A dynamically assembled cell wall synthesis machinery buffers cell growth

Lee

Tropini

Hsin

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Assembly of protein complexes is a key mechanism for achieving spatial and temporal coordination in processes involving many enzymes. Growth of rod-shaped bacteria is a well-studied example requiring such coordination; expansion of the cell wall is thought to involve coordination of the activity of synthetic enzymes with the cytoskeleton via a stable complex. Here, we use singlemolecule tracking to demonstrate that the bacterial actin homolog MreB and the essential cell wall enzyme PBP2 move on timescales orders of magnitude apart, with drastically different characteristic motions. Our observations suggest that PBP2 interacts with the rest of the synthesis machinery through a dynamic cycle of transient association. Consistent with this model, growth is robust to large fluctuations in PBP2 abundance. In contrast to stable complex formation, dynamic association of PBP2 is less dependent on the function of other components of the synthesis machinery, and buffers spatially distributed growth against fluctuations in pathway component concentrations and the presence of defective components. Dynamic association could generally represent an efficient strategy for spatiotemporal coordination of protein activities, especially when excess concentrations of system components are inhibitory to the overall process or deleterious to the cell.

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Section: Methodsmentioning

confidence: 99%

A dynamically assembled cell wall synthesis machinery buffers cell growth

Lee

Tropini

Hsin

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…These results demonstrate that the rise of the BYS2E01-745 lineage is driven by intracohort epistasis between the hsl7 and kel1 mutations that combine to produce a high fitness genotype. The pairwise epistatic interaction between hsl7 and kel1 impacts fitness in a synergistic manner, representing one of few such examples to emerge from a long-term evolution experiment (22)(23)(24)(25)(26).…”

Section: Bys1a08mentioning

confidence: 99%

Hitchhiking and epistasis give rise to cohort dynamics in adapting populations

Buskirk

Peace

Lang

2017

Proc. Natl. Acad. Sci. U.S.A.

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Beneficial mutations are the driving force of adaptive evolution. In asexual populations, the identification of beneficial alleles is confounded by the presence of genetically linked hitchhiker mutations. Parallel evolution experiments enable the recognition of common targets of selection; yet these targets are inherently enriched for genes of large target size and mutations of large effect. A comprehensive study of individual mutations is necessary to create a realistic picture of the evolutionarily significant spectrum of beneficial mutations. Here we use a bulk-segregant approach to identify the beneficial mutations across 11 lineages of experimentally evolved yeast populations. We report that nearly 80% of detected mutations have no discernible effects on fitness and less than 1% are deleterious. We determine the distribution of driver and hitchhiker mutations in 31 mutational cohorts, groups of mutations that arise synchronously from low frequency and track tightly with one another. Surprisingly, we find that one-third of cohorts lack identifiable driver mutations. In addition, we identify intracohort synergistic epistasis between alleles of hsl7 and kel1, which arose together in a low-frequency lineage.experimental evolution | cohorts | fitness | epistasis A daptation is a fundamental biological process. The identification and characterization of the genetic mechanisms underlying adaptive evolution remains a central challenge in biology. To identify beneficial mutations, recent studies have characterized thousands of first-step mutations and systematic deletion and amplification mutations in the yeast genome (1, 2). These unbiased screens provide a wealth of information regarding the spectrum of beneficial mutations, their fitness effects, and the biological processes under selection. However, this information alone cannot predict which mutations will ultimately succeed in an evolutionary context as genetic interactions and population dynamics also impart substantial influence on the adaptive outcomes.Early theoretical models assume that beneficial mutations are rare, such that once a beneficial mutation escapes drift, it will fix (3-5). For most microbial populations, however, multiple beneficial mutations will arise and spread simultaneously, leading to complex dynamics of clonal interference and genetic hitchhiking (6-9), and in many cases, multiple mutations track tightly with one another through time as mutational cohorts (10-13). The fate of each mutation is therefore dependent not only on its own fitness effect, but on the fitness effects of and interactions between all mutations in the population. Many beneficial mutations will be lost due to drift and clonal interference, whereas many neutral (and even deleterious) mutations will fix by hitchhiking. The influence of clonal interference and genetic hitchhiking on the success of mutations makes it difficult to identify beneficial mutations from sequenced clones or population samples. The extent of genetic hitchhiking and its evolutionary significanc...

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“…Strains generated using ALE often have multiple mutations (20,21), and determining causality for a phenotype can require a significant effort (22)(23)(24). Despite the growing availability of genome engineering tools (22,25,26), determining causality is still a time-consuming process.…”

mentioning

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

Appl Environ Microbiol

233

319

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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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Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli

Cited by 56 publications

References 58 publications

A dynamically assembled cell wall synthesis machinery buffers cell growth

A dynamically assembled cell wall synthesis machinery buffers cell growth

Hitchhiking and epistasis give rise to cohort dynamics in adapting populations

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

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