A cross-section of the fitness landscape of dihydrofolate reductase

Aita, Takuyo; Iwakura, Masahiro; Husimi, Yuzuru

doi:10.1093/protein/14.9.633

Cited by 67 publications

(89 citation statements)

References 11 publications

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“…Nonetheless, both landscapes share common features, such as the existence of fitness holes due to unconditionally lethal genotypes or the presence of pervasive epistatic interactions. The topographies of both empirical landscapes match pretty well with the expectations from a random uncorrelated landscape, lying somewhere between the extreme case of the house-of-cards model (31,41), in which the fitness of each genotype is absolutely independent of the fitness of the other genotypes, and the less radical case of the rough Mount Fuji model (31,42), which combines properties of both the house-of-cards model and a purely multiplicative landscape.…”

Section: Discussionsupporting

confidence: 53%

“…The ruggedness of the landscape can be evaluated using several different measures (Table 2). For instance, the ratio of mean slope to roughness, , (31) showed similar values for both hosts, and in both cases the values were Ͼ1, indicating that the landscapes are rugged in relationship to the average slope of the peaks. A recently proposed measure of epistasis is the correlation between fitness effects of a given genotype and all their one-step neighbors, i.e., (32).…”

Section: Resultsmentioning

confidence: 79%

“…measures how much the slope of a given peak spikes out from the average surface in which it exists. A value of Ͼ Ͼ1 means that a peak emerges from an otherwise flat surface, similar to a Mount Fuji landscape; in contrast, a value of Յ1 indicates that the peak's slope does not differ substantially from the background surface, that is, it is surrounded by many small peaks with similar slopes (31). measures the similarity between the fitness levels of genotypes that occupy nearby positions in the landscape.…”

Section: Methodsmentioning

confidence: 99%

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Effect of Host Species on Topography of the Fitness Landscape for a Plant RNA Virus

Cervera

Lalić

Elena

2016

J Virol

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Adaptive fitness landscapes are a fundamental concept in evolutionary biology that relate the genotypes of individuals to their fitness. In the end, the evolutionary fate of evolving populations depends on the topography of the landscape, that is, the numbers of accessible mutational pathways and possible fitness peaks (i.e., adaptive solutions). For a long time, fitness landscapes were only theoretical constructions due to a lack of precise information on the mapping between genotypes and phenotypes. In recent years, however, efforts have been devoted to characterizing the properties of empirical fitness landscapes for individual proteins or for microbes adapting to artificial environments. In a previous study, we characterized the properties of the empirical fitness landscape defined by the first five mutations fixed during adaptation of tobacco etch potyvirus (TEV) to a new experimental host, Arabidopsis thaliana. Here we evaluate the topography of this landscape in the ancestral host Nicotiana tabacum. By comparing the topographies of the landscapes for the two hosts, we found that some features remained similar, such as the existence of fitness holes and the prevalence of epistasis, including cases of sign and reciprocal sign epistasis that created rugged, uncorrelated, and highly random topographies. However, we also observed significant differences in the fine-grained details between the two landscapes due to changes in the fitness and epistatic interactions of some genotypes. Our results support the idea that not only fitness tradeoffs between hosts but also topographical incongruences among fitness landscapes in alternative hosts may contribute to virus specialization. IMPORTANCEDespite its importance for understanding virus evolutionary dynamics, very little is known about the topography of virus adaptive fitness landscapes, and even less is known about the effects that different host species and environmental conditions may have on this topography. To bridge this gap, we evaluated the topography of a small fitness landscape formed by all genotypes that result from every possible combination of the first five mutations fixed during adaptation of TEV to the novel host A. thaliana. To assess the effect that host species may have on this topography, we evaluated the fitness of every genotype in both the ancestral and novel hosts. We found that both landscapes share some macroscopic properties, such as the existence of holes and being highly rugged and uncorrelated, yet they differ in microscopic details due to changes in the magnitude and sign of fitness and epistatic effects. T he effects of mutations can be influenced by their interactions with other mutations, with the environment, or both. Epistatic interactions among genetic loci determine the ruggedness of fitness landscapes. In the absence of epistasis, landscapes are single peaked and smooth (1). For such simple landscapes, predicting the result of evolution is an easy task. In contrast, epistatic interactions create curvature in the lands...

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Section: Discussionsupporting

confidence: 53%

Section: Resultsmentioning

confidence: 79%

Section: Methodsmentioning

confidence: 99%

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Effect of Host Species on Topography of the Fitness Landscape for a Plant RNA Virus

Cervera

Lalić

Elena

2016

J Virol

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“…Moreover, because multiple alleles at the same site are contained within the landscape, we may study whether changes in the shape of the landscape are site-or amino acid-specific. We computed various landscape statistics (roughness-to-slope ratio, fraction of epistasis, and the recently proposed gamma statistics; SI Appendix, Supporting Information 1: Extended Materials and Methods) (10,11,14) and compared them with expectations from theoretical landscape models [NK (36)(37)(38), RMF (39,40), HoC (41), egg-box landscapes (14); for brief definitions of these terms, see SI Appendix, Supporting Information 2: Overview of Different Fitness Landscape Models Introduced in the Main Text]. Whenever necessary, we provide an analytical extension of the used statistic to the case of multiallelic landscapes (Materials and Methods).…”

Section: Resultsmentioning

confidence: 99%

“…4), generally based on the combination of previously observed beneficial mutations or on the dissection of an observed adaptive walk (i.e., a combination of mutations that have been observed to be beneficial in concert). Secondly, theoretical research has proposed different landscape architectures [such as the House-of-Cards (HoC), the Kauffman NK (NK), and the Rough Mount Fuji (RMF) model], studied their respective properties, and developed a number of statistics that characterize the landscape and quantify the expected degree of epistasis (i.e., interaction effects between mutations) (10)(11)(12)(13)(14).…”

mentioning

confidence: 99%

On the (un)predictability of a large intragenic fitness landscape

Bank

Matuszewski

Hietpas

et al. 2016

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

116

147

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The study of fitness landscapes, which aims at mapping genotypes to fitness, is receiving ever-increasing attention. Novel experimental approaches combined with next-generation sequencing (NGS) methods enable accurate and extensive studies of the fitness effects of mutations, allowing us to test theoretical predictions and improve our understanding of the shape of the true underlying fitness landscape and its implications for the predictability and repeatability of evolution. Here, we present a uniquely large multiallelic fitness landscape comprising 640 engineered mutants that represent all possible combinations of 13 amino acid-changing mutations at 6 sites in the heat-shock protein Hsp90 in Saccharomyces cerevisiae under elevated salinity. Despite a prevalent pattern of negative epistasis in the landscape, we find that the global fitness peak is reached via four positively epistatic mutations. Combining traditional and extending recently proposed theoretical and statistical approaches, we quantify features of the global multiallelic fitness landscape. Using subsets of the data, we demonstrate that extrapolation beyond a known part of the landscape is difficult owing to both local ruggedness and amino acid-specific epistatic hotspots and that inference is additionally confounded by the nonrandom choice of mutations for experimental fitness landscapes.evolution | adaptation | epistasis | fitness landscape | mutagenesis S ince first proposed by Sewall Wright in 1932 (1), the idea of a fitness landscape relating genotype (or phenotype) to the reproductive success of an individual has inspired evolutionary biologists and mathematicians alike (2-4). With the advancement of molecular and systems biology toward large and accurate datasets, the fitness landscape concept has received increasing attention across other subfields of biology (5-9). The shape of the fitness landscape carries information on the repeatability and predictability of evolution, the potential for adaptation, the importance of genetic drift, the likelihood of convergent and parallel evolution, and the degree of optimization that is (theoretically) achievable (4). Unfortunately, the dimensionality of a complete fitness landscape of an organism-that is, a mapping of all possible combinations of mutations to their respective fitness effects-is much too high to be assessed experimentally. With the development of experimental approaches that allow for the assessment of full fitness landscapes of tens to hundreds of mutations, there is growing interest in statistics that capture the features of the landscape and that relate an experimental landscape to theoretical landscapes of similar architecture, which have been studied extensively (10). It is, however, unclear whether this categorization allows for an extrapolation to unknown parts of the landscape, which would be the first step toward quantifying predictability-an advancement that would yield impacts far beyond the field of evolutionary biology, in particular for the clinical study of drug-resistan...

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