scholarly journals The causes of epistasis

2011 ◽  
Vol 278 (1725) ◽  
pp. 3617-3624 ◽  
Author(s):  
J. Arjan G. M. de Visser ◽  
Tim F. Cooper ◽  
Santiago F. Elena
Keyword(s):  
Protein Function ◽  
Metabolic Networks ◽  
Fitness Landscape ◽  
Research Effort ◽  
Empirical Work ◽  
Fitness Effects ◽  
Landscape Models ◽  
Genetic Robustness ◽  
Multiple Dimensions ◽  
Proximate Causes

Since Bateson's discovery that genes can suppress the phenotypic effects of other genes, gene interactions—called epistasis—have been the topic of a vast research effort. Systems and developmental biologists study epistasis to understand the genotype–phenotype map, whereas evolutionary biologists recognize the fundamental importance of epistasis for evolution. Depending on its form, epistasis may lead to divergence and speciation, provide evolutionary benefits to sex and affect the robustness and evolvability of organisms. That epistasis can itself be shaped by evolution has only recently been realized. Here, we review the empirical pattern of epistasis, and some of the factors that may affect the form and extent of epistasis. Based on their divergent consequences, we distinguish between interactions with or without mean effect, and those affecting the magnitude of fitness effects or their sign. Empirical work has begun to quantify epistasis in multiple dimensions in the context of metabolic and fitness landscape models. We discuss possible proximate causes (such as protein function and metabolic networks) and ultimate factors (including mutation, recombination, and the importance of natural selection and genetic drift). We conclude that, in general, pleiotropy is an important prerequisite for epistasis, and that epistasis may evolve as an adaptive or intrinsic consequence of changes in genetic robustness and evolvability.

Resilient Individuals Improve Evolutionary Search

2006 ◽  
Vol 12 (1) ◽  
pp. 17-34 ◽  
Author(s):  
Terence Soule
Keyword(s):  
Evolutionary Computation ◽  
Artificial Life ◽  
Fitness Landscape ◽  
Evolutionary Search ◽  
Negative Effects ◽  
Evolutionary Dynamic ◽  
Genetic Robustness ◽  
Over Time ◽  
Evolving Populations

Results from the artificial life community show that under some conditions evolving populations converge on broader, but less fit peaks in the fitness landscape and avoid more fit, but narrower peaks. Results from the evolutionary computation community show that over time genotypes evolve to become more resilient, where resiliency (or genetic robustness) is defined as the ability of an individual to resist the potentially negative effects of genetic operations. This article demonstrates a previously unobserved evolutionary dynamic: in populations initially favoring a low, broad fitness peak, increases in resiliency result in the population shifting to a higher, narrower fitness peak. In these cases increasing resiliency is a necessary precondition for finding narrower peaks.

scholarly journals Analyses of HIV Proteases Variants at the Threshold of Viability Reveals Relationships Between Processing Efficiency and Fitness

2021 ◽  
Author(s):  
Gily Schneider-Nachum ◽  
Julia Flynn ◽  
David Mavor ◽  
Celia A Schiffer ◽  
Daniel N A Bolon
Keyword(s):  
Viral Replication ◽  
Protein Function ◽  
Processing Efficiency ◽  
Peptide Cleavage ◽  
Fitness Effects ◽  
Biochemical Function ◽  
Primary Determinant ◽  
Biochemical Mechanisms ◽  
Rate Limiting ◽  
Hiv 1

Abstract Investigating the relationships between protein function and fitness provides keys for understanding biochemical mechanisms that underly evolution. Mutations with partial fitness defects can delineate the threshold of biochemical function required for viability. We utilized a previous deep mutational scan of HIV-1 protease (PR) to identify variants with 15-45% defects in replication and analyzed the biochemical function of eight variants (L10M, L10S, V32C, V32I, A71V, A71S, Q92I, Q92N). We purified each variant and assessed the efficiency of peptide cleavage for three cut sites (MA-CA, TF-PR, PR-RT) as well as a gel-based analyses of processing of purified Gag. The cutting activity of at least one site was perturbed relative to WT protease for all variants, consistent with cutting activity being a primary determinant of fitness effects. We examined the correlation of fitness defects with cutting activity of different sites. MA-CA showed the weakest correlation (R2=0.02) with fitness, suggesting relatively weak coupling with viral replication. In contrast, cutting of the TF-PR site showed the strongest correlation with fitness (R2=0.53). Cutting at the TF-PR site creates a new PR protein with a free N-terminus that is critical for activity. Our findings indicate that increasing the pool of active PR is rate limiting for viral replication making this an ideal step to target with inhibitors.

Energetic basis of excited-state enzyme design and function

2021 ◽  
Author(s):  
Chi-Yun Lin ◽  
Matthew Romei ◽  
Irimpan Mathews ◽  
Steven Boxer
Keyword(s):  
Excited State ◽  
Protein Function ◽  
Fluorescent Protein ◽  
De Novo ◽  
Fitness Landscape ◽  
Quantitative Model ◽  
Efficient Solutions ◽  
Catalyst Design ◽  
Quantum Yields ◽  
Fluorescence Quantum Yields

The last decades have witnessed an explosion of de novo protein designs with a remarkable range of scaffolds. It remains challenging, however, to design catalytic functions that are competitive with naturally occurring counterparts as well as biomimetic or non-biological catalysts. Although directed evolution often offers efficient solutions, the fitness landscape remains opaque. Green fluorescent protein (GFP), which has revolutionized biological imaging and assays, is one of the most re-designed proteins. While not an enzyme in the conventional sense, GFPs feature competing excited-state decay pathways with the same steric and electrostatic origins as conventional ground-state catalysts, and they exert exquisite control over multiple reaction outcomes through the same principles. Thus, GFP is an “excited-state enzyme”. Herein we show that rationally designed mutants and hybrids that contain environmental mutations and substituted chromophores provide the basis for a quantitative model and prediction that describes the influence of sterics and electrostatics on excited-state catalysis of GFPs. As both perturbations can selectively bias photoisomerization pathways, GFPs with fluorescence quantum yields (FQYs) and photoswitching characteristics tailored for specific applications could be predicted and then demonstrated. The underlying energetic landscape, readily accessible via spectroscopy for GFPs, offers an important missing link in the design of protein function that is generalizable to catalyst design.

scholarly journals Evaluating the within-host fitness effects of mutations fixed during virus adaptation to different ecotypes of a new host

2015 ◽  
Vol 370 (1675) ◽  
pp. 20140292 ◽  
Author(s):  
Julia Hillung ◽  
José M. Cuevas ◽  
Santiago F. Elena
Keyword(s):  
Genetic Variation ◽  
Fitness Landscape ◽  
Host Population ◽  
Gene Model ◽  
New Host ◽  
Host Fitness ◽  
Fitness Effects ◽  
Rate Of Spread ◽  
Virus Diversity ◽  
Gene For Gene

The existence of genetic variation for resistance in host populations is assumed to be essential to the spread of an emerging virus. Models predict that the rate of spread slows down with the increasing frequency and higher diversity of resistance alleles in the host population. We have been using the experimental pathosystem Arabidopsis thaliana —tobacco etch potyvirus (TEV) to explore the interplay between genetic variation in host's susceptibility and virus diversity. We have recently shown that TEV populations evolving in A. thaliana ecotypes that differ in susceptibility to infection gained within-host fitness, virulence and infectivity in a manner compatible with a gene-for-gene model of host–parasite interactions: hard-to-infect ecotypes were infected by generalist viruses, whereas easy-to-infect ecotypes were infected by every virus. We characterized the genomes of the evolved viruses and found cases of host-driven convergent mutations. To gain further insights in the mechanistic basis of this gene-for-gene model, we have generated all viral mutations individually as well as in specific combinations and tested their within-host fitness effects across ecotypes. Most of these mutations were deleterious or neutral in their local ecotype and only a very reduced number had a host-specific beneficial effect. We conclude that most of the mutations fixed during the evolution experiment were so by drift or by selective sweeps along with the selected driver mutation. In addition, we evaluated the ruggedness of the underlying adaptive fitness landscape and found that mutational effects were mostly multiplicative, with few cases of significant epistasis.

scholarly journals HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

2013 ◽  
Vol 19 ◽  
pp. 349-360 ◽  
Author(s):  
Raphael Z. Sangeda ◽  
Kristof Theys ◽  
Gertjan Beheydt ◽  
Soo-Yon Rhee ◽  
Koen Deforche ◽  
...  
Keyword(s):  
Treatment Failure ◽  
Fitness Landscape ◽  
Sequence Data ◽  
Landscape Models ◽  
Treatment Pressure ◽  
Hiv 1

The Dynamic Changes in Roles of Learning through the Baldwin Effect

2007 ◽  
Vol 13 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Reiji Suzuki ◽  
Takaya Arita
Keyword(s):  
Phenotypic Plasticity ◽  
Fitness Landscape ◽  
Evolutionary Model ◽  
Hill Climbing ◽  
Complex Environments ◽  
Baldwin Effect ◽  
Epistatic Interactions ◽  
Genetic Robustness ◽  
The Baldwin Effect ◽  
Insight Into

The interaction between evolution and learning called the Baldwin effect is a two-step evolutionary scenario caused by the balances between benefit and cost of learning in general. However, little is known about the dynamic evolution of these balances in complex environments. Our purpose is to give a new insight into the benefit and cost of learning by focusing on the quantitative evolution of phenotypic plasticity under the assumption of epistatic interactions. For this purpose, we have constructed an evolutionary model of quantitative traits by using an extended version of Kauffman's NK fitness landscape. Phenotypic plasticity is introduced into our model; whether each phenotype is plastic or not is genetically defined, and plastic phenotypes can be adjusted by learning. The simulation results clearly show that drastic changes in roles of learning cause three-step evolution through the Baldwin effect and also cause the evolution of genetic robustness against mutations. We also conceptualize four different roles of learning by using a hill-climbing image of a population on a fitness landscape.

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