Evolution of a single gene highlights the complexity underlying molecular descriptions of fitness

Peña, Matthew I.; Itallie, Elizabeth Van; Bennett, Matthew R.; Shamoo, Yousif

doi:10.1063/1.3453623

Cited by 6 publications

(10 citation statements)

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“…It remains to be seen, however, whether it is possible to elucidate and discern ancient adaptive steps from adjustments taken by a modern cell to a maladapted gene. When challenged with an ancestral component, will the engineered bacteria accumulate direct mutations on the ancestral component and “re-trace” the evolutionary history of this component by changing its sequence to be closer to the modern variant (Lind et al 2010; Pena et al 2010)? Alternatively, are compensatory mutations non-directional due to the very large solution space, and therefore the organism may be expected to respond to the ancient perturbation through modifications and modulation outside of the ancestral gene-coding region (Larios-Sanz and Travisano 2009)?…”

Section: Introductionmentioning

confidence: 99%

Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein

Kacar

Sanyal

et al. 2017

J Mol Evol

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The ability to design synthetic genes and engineer biological systems at the genome scale opens new means by which to characterize phenotypic states and the responses of biological systems to perturbations. One emerging method involves inserting artificial genes into bacterial genomes and examining how the genome and its new genes adapt to each other. Here we report the development and implementation of a modified approach to this method, in which phylogenetically inferred genes are inserted into a microbial genome, and laboratory evolution is then used to examine the adaptive potential of the resulting hybrid genome. Specifically, we engineered an approximately 700-million-year-old inferred ancestral variant of tufB, an essential gene encoding elongation factor Tu, and inserted it in a modern Escherichia coli genome in place of the native tufB gene. While the ancient homolog was not lethal to the cell, it did cause a twofold decrease in organismal fitness, mainly due to reduced protein dosage. We subsequently evolved replicate hybrid bacterial populations for 2000 generations in the laboratory and examined the adaptive response via fitness assays, whole genome sequencing, proteomics, and biochemical assays. Hybrid lineages exhibit a general adaptive strategy in which the fitness cost of the ancient gene was ameliorated in part by upregulation of protein production. Our results suggest that an ancient–modern recombinant method may pave the way for the synthesis of organisms that exhibit ancient phenotypes, and that laboratory evolution of these organisms may prove useful in elucidating insights into historical adaptive processes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00239-017-9781-0) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein

Kacar

Sanyal

et al. 2017

J Mol Evol

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“…Several orthologous protein substitution experiments demonstrated successful transfer of an alien gene to a foreign genome (Bershtein et al 2015;LariosSanz and Travisano 2009;Lind et al 2010), resulting in a fitness decrease similar to our approach, which was referred to as the "weak link approach" (Counago et al 2006). After evolving these organisms under laboratory conditions, it was demonstrated that direct accumulation of convergent mutations on the extant alien gene exhibits adaptive behavior (Counago et al 2006;Miller et al 2010;Pena et al 2010). In this study, however, we detected that adaptive mutations compensating for the fitness detriment of the suboptimal ancestral gene occurred outside of the foreign gene that was introduced, including within the promoter region of the ancient gene.…”

Section: Discussionmentioning

confidence: 99%

“…(Color figure online) researchers to address the role of chance and necessity at the molecular level? The key to resolving these prior questions is, at least in part, to assess the degree to which our system tracks or differs from experimental systems that replace genomic components with homologs obtained from other extant organisms (Acevedo-Rocha et al 2013;Agashe et al 2013;Andersson and Hughes 2009;Pena et al 2010;Urbanczyk et al 2012).…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

“…It remains to be seen, however, whether it is possible to elucidate and discern ancient adaptive steps from adjustments taken by a modern cell to a maladapted gene. When challenged with an ancestral component, will the engineered bacteria accumulate direct mutations on the ancestral component and "re-trace" the evolutionary history of this component by changing its sequence to be closer to the modern variant (Lind et al 2010;Pena et al 2010)? Alternatively, are compensatory mutations non-directional due to the very large solution space, and therefore the organism may be expected to respond to the ancient perturbation through modifications and modulation outside of the ancestral gene-coding region (Larios-Sanz and Travisano 2009) ? To what degree will the adaptive pathways of the modified organism recapitulate the organism's evolutionary history and thus allow Amino acid sequences were obtained from the NCBI database and aligned using Clustal Omega (Sievers et al 2011).…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

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Experimental evolution of Escherichia coli harboring an ancient translation protein

Kaçar

Tran

et al. 2016

Preprint

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The ability to design synthetic genes and engineer biological systems at the genome scale opens new means by which to characterize phenotypic states and the responses of biological systems to perturbations. One emerging method involves inserting artificial genes into bacterial genomes, and examining how the genome and its new genes adapt to each other. Here we report the development and implementation of a modified approach to this method, in which phylogenetically inferred genes are inserted into a microbial genome, and laboratory evolution is then used to examine the adaptive potential of the resulting hybrid genome. Specifically, we engineered an approximately 700-million-year old inferred ancestral variant of tufB, an essential gene encoding Elongation Factor Tu, and inserted it in a modern Escherichia coli genome in place of the native tufB gene. While the ancient homolog was not lethal to the cell, it did cause a two-fold decrease in organismal fitness, mainly due to reduced protein dosage. We subsequently evolved replicate hybrid bacterial populations for 2,000 generations in the laboratory, and examined the adaptive response via fitness assays, whole-genome sequencing, proteomics and biochemical assays. We found that the hybrid lineages exhibit a general adaptive strategy in which the fitness cost of the ancient gene was ameliorated in part by up-regulation of protein production. We expect that this ancient-modern recombinant method may pave the way for the synthesis of organisms that exhibit ancient phenotypes, and that laboratory evolution of these organisms may prove useful in elucidating insights into historical adaptive processes.

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“…52,53 Two examples of this type of experiment are reported in this issue. The work by Pena et al 54 shows that the nonlinearity in the function relating enzyme activity to fitness is sufficient to explain at a population level the dynamics of sweeps and clonal interference seen during adaptation to a changing environment. From a different perspective, Ogbunugafor et al 55 describe the concept of environmental robustness.…”

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confidence: 99%

Introduction to Focus Issue: Genetic Interactions

Segrè

Marx

2010

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The perturbation of a gene in an organism's genome often causes changes in the organism's observable properties or phenotypes. It is not obvious a priori whether the simultaneous perturbation of two genes produces a phenotypic change that is easily predictable from the changes caused by individual perturbations. In fact, this is often not the case: the nonlinearity and interdependence between genetic variants in determining phenotypes, also known as epistasis, is a prevalent phenomenon in biological systems. This focus issue presents recent developments in the study of epistasis and genetic interactions, emphasizing the broad implications of this phenomenon in evolutionary biology, functional genomics, and human diseases. © 2010 American Institute of Physics. ͓doi:10.1063/1.3456057͔A long-standing question in biology is how the instructions written in the heritable blueprint of a living system (its genotype) determine its observable properties (its phenotype).1,2 The genotype-phenotype mapping is important for many reasons, from gaining fundamental understanding of how evolution works, 1,3 to uncovering the molecular mechanisms of genetic disease, 4,5 and identifying the "Achilles' heels" of microbial pathogens.6,7 One way of probing this mapping is to study how different versions of the genotype (either naturally occurring or artificially generated) produce different phenotypes. 8,9This focus issue brings together different viewpoints and approaches to understanding epistasis, 3,10-12 i.e., the nonlinearities often present in the genotype-phenotype mapping.To introduce the concept of epistasis, let us start with a highly oversimplified view where the myriad instructions written in a genome can be thought of as discrete, binary units ͑"genes"͒, whose values determine an array of quantitative phenotypic traits. Assume for simplicity that an unperturbed organism has all genes set to zero. Single perturbation experiments may then be performed, switching individual genes to the value one. In a "first order" approximation of biology, one may be satisfied with knowing how the perturbation of each gene affects the phenotypes. For example, imagine that gene A exerts some control over metabolic rate, such that switching A from zero to one decreases the metabolic rate by 10%. It may be the case that another gene B influences metabolic rate to the same extent. The first order approximation might still work if the perturbations of multiple genes combine according to a simple, general law. For example, the metabolic rate may be affected additively, so that simultaneous switching of A or B decreases metabolic rate by 20%.Yet, since the early days of genetics, it is known that living systems can deviate quite dramatically from such a simple linear behavior. An extreme case is one in which the effect of a double perturbation is drastically enhanced relative to the effects of individual perturbations ͑synergistic effect͒. In the example above, this may mean that the simultaneous switching of A and B gives a metabolic rate of zer...

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Evolution of a single gene highlights the complexity underlying molecular descriptions of fitness

Cited by 6 publications

References 46 publications

Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein

Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein

Experimental evolution of Escherichia coli harboring an ancient translation protein

Introduction to Focus Issue: Genetic Interactions

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