A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape

Firnberg, Elad; Labonte, Jason W.; Gray, Jeffrey J.; Ostermeier, Marc

doi:10.1093/molbev/msw021

Cited by 124 publications

(254 citation statements)

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“…Similarly, ACG can serve as an initiation codon (37), but its initiation efficiency is only 1-3% of that of ATG (34). In addition, both GTG and ACG initiation codons are frequently observed in comprehensive TEM-1 mutagenesis libraries selected at low levels of ampicillin (38). Reducing the concentration of TEM-1 is a simple strategy to mitigate mistranslation costs, but it is only viable where amounts of ampicillin are so low that TEM-1 expression can be reduced without adverse effects.…”

Section: Discussionmentioning

confidence: 99%

Mistranslation drives the evolution of robustness in TEM-1 β-lactamase

Bratulić

Gerber

Wagner

2015

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

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How biological systems such as proteins achieve robustness to ubiquitous perturbations is a fundamental biological question. Such perturbations include errors that introduce phenotypic mutations into nascent proteins during the translation of mRNA. These errors are remarkably frequent. They are also costly, because they reduce protein stability and help create toxic misfolded proteins. Adaptive evolution might reduce these costs of protein mistranslation by two principal mechanisms. The first increases the accuracy of translation via synonymous "high fidelity" codons at especially sensitive sites. The second increases the robustness of proteins to phenotypic errors via amino acids that increase protein stability. To study how these mechanisms are exploited by populations evolving in the laboratory, we evolved the antibiotic resistance gene TEM-1 in Escherichia coli hosts with either normal or high rates of mistranslation. We analyzed TEM-1 populations that evolved under relaxed and stringent selection for antibiotic resistance by single molecule real-time sequencing. Under relaxed selection, mistranslating populations reduce mistranslation costs by reducing TEM-1 expression. Under stringent selection, they efficiently purge destabilizing amino acid changes. More importantly, they accumulate stabilizing amino acid changes rather than synonymous changes that increase translational accuracy. In the large populations we study, and on short evolutionary timescales, the path of least resistance in TEM-1 evolution consists of reducing the consequences of translation errors rather than the errors themselves. molecular evolution | mutational robustness | phenotypic mutations | protein stability | antibiotic resistance

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Section: Discussionmentioning

confidence: 99%

Mistranslation drives the evolution of robustness in TEM-1 β-lactamase

Bratulić

Gerber

Wagner

2015

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

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“…However, even when the issue of a changing environment is ignored (readers are referred to [5] for a detailed model of a fitness landscape affected by environmental change), as we do for the purpose of this review, substantial challenges remain when charting the fitness landscapes of genomes of any appreciable size. First, the number of all possible genotypes (or sequence space) is beyond our theoretical computational capacity even for a single protein sequence, although attempts to explore larger volumes of sequence space are being reported for proteins [6][7][8] and small RNA molecules [9][10][11]. Second, we typically obtain sequence data from extant organisms and, therefore, we chart the sequence landscape from a biased set of sequences that mostly correspond to genotypes conferring high fitness.…”

Section: Introductionmentioning

confidence: 99%

Topological features of rugged fitness landscapes in sequence space

2015

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“…Note however, that DMS studies in which the functional capacity of a (mutant) protein (i.e., the protein fitness) cannot be directly related to organismal fitness (for a recent review on the topic see Boucher et al 2016) do not adhere to the statistical framework presented here. Examples include recent DMS studies, which were based on fluorescence (as in Sarkisyan et al 2016), antibiotic resistance (e.g., Jacquier et al 2013;Firnberg et al 2014), and binding selection using protein display technologies (Fowler et al 2010;Whitehead et al 2012;Olson et al 2014).…”

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confidence: 99%

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

et al. 2016

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The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deepsequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.KEYWORDS experimental design; experimental evolution; distribution of fitness effects; mutation; population genetics M UTATIONS provide the fuel for evolutionary change, and their fitness effects critically influence the course and dynamics of evolution. The distribution of fitness effects (DFE) lies at the heart of many evolutionary concepts, such as the genetic basis of complex traits (Eyre-Walker 2010) and diseases (Keightley and Eyre-Walker 2010), the rate of adaptation to a new environment (Gerrish and Lenski 1998;Orr 1998Orr , 2005b, the maintenance of genetic variation (Charlesworth et al. 1995), and the relative importance of selection and drift in molecular evolution (Ohta 1977(Ohta , 1992Kimura 1979). Unsurprisingly, considerable effort has been devoted, both empirically (e.g., Sawyer et al. 2003;Sousa et al. 2012;Gordo and Campos 2013;Bernet and Elena 2015) and theoretically (e.g., Gillespie 1983;Orr 2005a;Martin and Lenormand 2006b;Connallon and Clark 2015;Rice et al. 2015), to assess the fraction of all possible mutations that are beneficial, neutral, or deleterious. Until recently, the two main approaches for assessing the DFE have been based either on the analysis of polymorphism and divergence data (Jensen et al. 2008;Keightley and Eyre-Walker 2010;Schneider et al. 2011) or on laboratory evolution studies ...

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A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape

Cited by 124 publications

References 0 publications

Mistranslation drives the evolution of robustness in TEM-1 β-lactamase

Mistranslation drives the evolution of robustness in TEM-1 β-lactamase

Topological features of rugged fitness landscapes in sequence space

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

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