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Irene A Chen - One of the best experts on this subject based on the ideXlab platform.
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mapping a systematic ribozyme Fitness Landscape reveals a frustrated evolutionary network for self aminoacylating rna
Journal of the American Chemical Society, 2019Co-Authors: Abe Pressman, Ziwei Liu, Evan Janzen, Celia Blanco, Ulrich F Muller, Gerald F Joyce, Robert Pascal, Irene A ChenAbstract:Molecular evolution can be conceptualized as a walk over a "Fitness Landscape", or the function of Fitness (e.g., catalytic activity) over the space of all possible sequences. Understanding evolution requires knowing the structure of the Fitness Landscape and identifying the viable evolutionary pathways through the Landscape. However, the Fitness Landscape for any catalytic biomolecule is largely unknown. The evolution of catalytic RNA is of special interest because RNA is believed to have been foundational to early life. In particular, an essential activity leading to the genetic code would be the reaction of ribozymes with activated amino acids, such as 5(4 H)-oxazolones, to form aminoacyl-RNA. Here we combine in vitro selection with a massively parallel kinetic assay to map a Fitness Landscape for self-aminoacylating RNA, with nearly complete coverage of sequence space in a central 21-nucleotide region. The method (SCAPE: sequencing to measure catalytic activity paired with in vitro evolution) shows that the Landscape contains three major ribozyme families (Landscape peaks). An analysis of evolutionary pathways shows that, while local optimization within a ribozyme family would be possible, optimization of activity over the entire Landscape would be frustrated by large valleys of low activity. The sequence motifs associated with each peak represent different solutions to the problem of catalysis, so the inability to traverse the Landscape globally corresponds to an inability to restructure the ribozyme without losing activity. The frustrated nature of the evolutionary network suggests that chance emergence of a ribozyme motif would be more important than optimization by natural selection.
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comprehensive experimental Fitness Landscape and evolutionary network for small rna
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Jose I Jimenez, Ramon Xulvibrunet, Gregory W Campbell, Rebecca Turkmacleod, Irene A ChenAbstract:The origin of life is believed to have progressed through an RNA world, in which RNA acted as both genetic material and functional molecules. The structure of the evolutionary Fitness Landscape of RNA would determine natural selection for the first functional sequences. Fitness Landscapes are the subject of much speculation, but their structure is essentially unknown. Here we describe a comprehensive map of a Fitness Landscape, exploring nearly all of sequence space, for short RNAs surviving selection in vitro. With the exception of a small evolutionary network, we find that Fitness peaks are largely isolated from one another, highlighting the importance of historical contingency and indicating that natural selection would be constrained to local exploration in the RNA world.
Tobias Warnecke - One of the best experts on this subject based on the ideXlab platform.
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Fitness Landscape of a dynamic rna structure
PLOS Genetics, 2021Co-Authors: Valerie W C Soo, Jacob B Swadling, Andre J Faure, Tobias WarneckeAbstract:RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to Fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5’ splice site. Following cleavage at the 5’ splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3’ splice site. Using an in vivo reporter system that couples splicing activity to Fitness in E. coli, we demonstrate that Fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the Fitness Landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular Fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single Fitness Landscape.
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Fitness Landscape of a dynamic rna structure
bioRxiv, 2020Co-Authors: Valerie W C Soo, Jacob B Swadling, Andre J Faure, Tobias WarneckeAbstract:RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to Fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron that come together to form the P1 extension (P1ex) helix at the 59 splice site and, following cleavage at the 59 splice site, dissociate to allow formation of an alternative helix (P10) at the 3′ splice site. Using an in vivo reporter system that couples splicing activity to Fitness in E. coli, we demonstrate that Fitness is driven jointly by constraints on P1ex and P10 formation and that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Importantly, using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we show that the Fitness Landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular Fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study transient but important conformational states, with the capacity to provide critical insights into the evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help us to reverse-engineer dynamic interactions and critical non-native states from a single Fitness Landscape.
Claudia Bank - One of the best experts on this subject based on the ideXlab platform.
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evolution in the light of Fitness Landscape theory
Trends in Ecology and Evolution, 2019Co-Authors: Ines Fragata, Alexandre Blanckaert, Marco Antonio Dias Louro, David A Liberles, Claudia BankAbstract:By formalizing the relationship between genotype or phenotype and Fitness, Fitness Landscapes harbor information on molecular and evolutionary constraints. The shape of the Fitness Landscape determines the potential for adaptation and speciation, as well as our ability to predict evolution. Consequently, Fitness Landscape theory has been invoked across the natural sciences and across multiple levels of biological organization. We review here the existing literature on Fitness Landscape theory by describing the main types of Fitness Landscape models, and highlight how these are increasingly integrated into an applicable statistical framework for the study of evolution. Specifically, we demonstrate how the interpretation of experimental studies with respect to Fitness Landscape models enables a direct link between evolution, molecular biology, and systems biology.
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The Fitness Landscape of the codon space across environments
Heredity, 2018Co-Authors: Ines Fragata, Sebastian Matuszewski, Mark A. Schmitz, Thomas Bataillon, Jeffrey D. Jensen, Claudia BankAbstract:Fitness Landscapes map the relationship between genotypes and Fitness. However, most Fitness Landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the Fitness effects of synonymous mutations and their potential impact on adaptation on a Fitness Landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of Fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon Fitness Landscape across amino acid positions and environments, and quantify how the consideration of synonymous Fitness effects changes the evolutionary dynamics on these Fitness Landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in Fitness Landscape studies.
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on the un predictability of a large intragenic Fitness Landscape
Proceedings of the National Academy of Sciences of the United States of America, 2016Co-Authors: Claudia Bank, Sebastian Matuszewski, Ryan T Hietpas, Jeffrey D. JensenAbstract: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.
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on the un predictability of a large intragenic Fitness Landscape
bioRxiv, 2016Co-Authors: Claudia Bank, Sebastian Matuszewski, Ryan T Hietpas, Jeffrey D. JensenAbstract:The study of Fitness Landscapes, which aims at mapping genotypes to Fitness, is receiving ever-increasing attention. Novel experimental approaches combined with 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 multi-allelic Fitness Landscape comprised of 640 engineered mutants that represent all possible combinations of 13 amino-acid changing mutations at six 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 multi-allelic Fitness Landscape. Using subsets of this 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 non-random choice of mutations for experimental Fitness Landscapes.
Hailong Zhang - One of the best experts on this subject based on the ideXlab platform.
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evolution on the biophysical Fitness Landscape of an rna virus
Molecular Biology and Evolution, 2018Co-Authors: Assaf Rotem, Adrian W R Serohijos, Connie B Chang, Joshua Wolfe, Audrey Fischer, Thomas Mehoke, Hailong ZhangAbstract:Viral evolutionary pathways are determined by the Fitness Landscape, which maps viral genotype to Fitness. However, a quantitative description of the Landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical Fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.
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tuning the course of evolution on the biophysical Fitness Landscape of an rna virus
bioRxiv, 2016Co-Authors: Assaf Rotem, Adrian W R Serohijos, Connie B Chang, Joshua Wolfe, Audrey Fischer, Thomas Mehoke, Hailong Zhang, Ye Tao, Lloyd W Ung, Jeongmo ChoiAbstract:Predicting viral evolution remains a major challenge with profound implications for public health. Viral evolutionary pathways are determined by the Fitness Landscape, which maps viral genotype to Fitness. However, a quantitative description of the Landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical Fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics device, the Evolution Chip, which propagates millions of independent viral sub-populations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs shape viral evolution.
Sebastian Bonhoeffer - One of the best experts on this subject based on the ideXlab platform.
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recombination accelerates adaptation on a large scale empirical Fitness Landscape in hiv 1
PLOS Genetics, 2014Co-Authors: Danesh Moradigaravand, Roger D Kouyos, Trevor Hinkley, Mojgan Haddad, Christos J Petropoulos, Jan Engelstadter, Sebastian BonhoefferAbstract:Recombination has the potential to facilitate adaptation. In spite of the substantial body of theory on the impact of recombination on the evolutionary dynamics of adapting populations, empirical evidence to test these theories is still scarce. We examined the effect of recombination on adaptation on a large-scale empirical Fitness Landscape in HIV-1 based on in vitro Fitness measurements. Our results indicate that recombination substantially increases the rate of adaptation under a wide range of parameter values for population size, mutation rate and recombination rate. The accelerating effect of recombination is stronger for intermediate mutation rates but increases in a monotonic way with the recombination rates and population sizes that we examined. We also found that both Fitness effects of individual mutations and epistatic Fitness interactions cause recombination to accelerate adaptation. The estimated epistasis in the adapting populations is significantly negative. Our results highlight the importance of recombination in the evolution of HIV-I.
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exploring the complexity of the hiv 1 Fitness Landscape
PLOS Genetics, 2012Co-Authors: Roger D Kouyos, Gabriel E Leventhal, Trevor Hinkley, Mojgan Haddad, Jeannette M Whitcomb, Christos J Petropoulos, Sebastian BonhoefferAbstract:Although Fitness Landscapes are central to evolutionary theory, so far no biologically realistic examples for large-scale Fitness Landscapes have been described. Most currently available biological examples are restricted to very few loci or alleles and therefore do not capture the high dimensionality characteristic of real Fitness Landscapes. Here we analyze large-scale Fitness Landscapes that are based on predictive models for in vitro replicative Fitness of HIV-1. We find that these Landscapes are characterized by large correlation lengths, considerable neutrality, and high ruggedness and that these properties depend only weakly on whether Fitness is measured in the absence or presence of different antiretrovirals. Accordingly, adaptive processes on these Landscapes depend sensitively on the initial conditions. While the relative extent to which mutations affect Fitness on their own (main effects) or in combination with other mutations (epistasis) is a strong determinant of these properties, the Fitness Landscape of HIV-1 is considerably less rugged, less neutral, and more correlated than expected from the distribution of main effects and epistatic interactions alone. Overall this study confirms theoretical conjectures about the complexity of biological Fitness Landscapes and the importance of the high dimensionality of the genetic space in which adaptation takes place.