scholarly journals Effects of intra-gene fitness interactions on the benefit of sexual recombination

2006 ◽  
Vol 34 (4) ◽  
pp. 560-561 ◽  
Author(s):  
R.A. Watson ◽  
D.M. Weinreich ◽  
J. Wakeley
Keyword(s):  
Nucleotide Sequence ◽  
Point Mutation ◽  
Sequence Space ◽  
Fitness Effect ◽  
Epistatic Interactions ◽  
Genetic Sequence ◽  
High Fitness ◽  
Sexual Recombination ◽  
Evolutionary Trajectories ◽  
Asexual Populations

Whereas spontaneous point mutation operates on nucleotides individually, sexual recombination manipulates the set of nucleotides within an allele as an essentially particulate unit. In principle, these two different scales of variation enable selection to follow fitness gradients in two different spaces: in nucleotide sequence space and allele sequence space respectively. Epistasis for fitness at these two scales, between nucleotides and between genes, may be qualitatively different and may significantly influence the advantage of mutation-based and recombination-based evolutionary trajectories respectively. We examine scenarios where the genetic sequence within a gene strongly influences the fitness effect of a mutation in that gene, whereas epistatic interactions between sites in different genes are weak or absent. We find that, in cases where beneficial alleles of a gene differ from one another at several nucleotide sites, sexual populations can exhibit enormous benefit compared with asexual populations: not only discovering fit genotypes faster than asexual populations, but also discovering high-fitness genotypes that are effectively not evolvable in asexual populations.

scholarly journals Antibiotic Resistance Evolution Is Contingent on the Quorum-Sensing Response in Pseudomonas aeruginosa

2019 ◽  
Vol 36 (10) ◽  
pp. 2238-2251 ◽  
Author(s):  
Sara Hernando-Amado ◽  
Fernando Sanz-García ◽  
José Luis Martínez
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Quorum Sensing ◽  
Wild Type ◽  
Resistance Mutations ◽  
Epistatic Interactions ◽  
Adaptive Mutations ◽  
Resistance Evolution ◽  
Evolutionary Trajectories

Abstract Different works have explored independently the evolution toward antibiotic resistance and the role of eco-adaptive mutations in the adaptation to a new habitat (as the infected host) of bacterial pathogens. However, knowledge about the connection between both processes is still limited. We address this issue by comparing the evolutionary trajectories toward antibiotic resistance of a Pseudomonas aeruginosa lasR defective mutant and its parental wild-type strain, when growing in presence of two ribosome-targeting antibiotics. Quorum-sensing lasR defective mutants are selected in P. aeruginosa populations causing chronic infections. Further, we observed they are also selected in vitro as a first adaptation for growing in culture medium. By using experimental evolution and whole-genome sequencing, we found that the evolutionary trajectories of P. aeruginosa in presence of these antibiotics are different in lasR defective and in wild-type backgrounds, both at the phenotypic and the genotypic levels. Recreation of a set of mutants in both genomic backgrounds (either wild type or lasR defective) allowed us to determine the existence of negative epistatic interactions between lasR and antibiotic resistance determinants. These epistatic interactions could lead to mutual contingency in the evolution of antibiotic resistance when P. aeruginosa colonizes a new habitat in presence of antibiotics. If lasR mutants are selected first, this would constraint antibiotic resistance evolution. Conversely, when resistance mutations (at least those studied in the present work) are selected, lasR mutants may not be selected in presence of antibiotics. These results underlie the importance of contingency and epistatic interactions in modulating antibiotic resistance evolution.

scholarly journals Predictive profiling of SARS-CoV-2 variants by deep mutational learning

2021 ◽  
Author(s):  
Joseph M Taft ◽  
Cedric R Weber ◽  
Beichen Gao ◽  
Roy A Ehling ◽  
Jiami Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Severe Acute Respiratory Syndrome ◽  
Sequence Space ◽  
Neutralizing Antibodies ◽  
Spike Protein ◽  
Receptor Binding Domain ◽  
Primary Target ◽  
Engineering Technology ◽  
Evolutionary Trajectories

The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

scholarly journals Learning the pattern of epistasis linking genotype and phenotype in a protein

2017 ◽  
Author(s):  
Frank J. Poelwijk ◽  
Michael Socolich ◽  
Rama Ranganathan
Keyword(s):  
Experimental Study ◽  
Sequence Space ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
High Order ◽  
Epistatic Interactions ◽  
Stepwise Variation ◽  
Experimental Strategies ◽  
Variation And Selection ◽  
Entacmaea Quadricolor

Understanding the pattern of epistasis – the non-independence of mutations – is critical for relating genotype and phenotype in biological systems. However, the complexity of potential epistatic interactions has limited approaches to this problem at any level. To develop practical strategies, we carried out a comprehensive experimental study of epistasis between all mutations that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein. The data demonstrate significant high-order epistatic interactions between mutations, but also reveals extraordinary sparsity, enabling novel experimental strategies and sequence-based statistical methods for learning the relevant epistasis. The sequence space linking the parental fluorescent proteins is functionally connected through paths of single mutations; thus, high-order epistasis in proteins is consistent with evolution through stepwise variation and selection. This work initiates a path towards characterizing epistasis in proteins in general.

scholarly journals DISSEQT—DIStribution-based modeling of SEQuence space Time dynamics†

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
R Henningsson ◽  
G Moratorio ◽  
A V Bordería ◽  
M Vignuzzi ◽  
M Fontes
Keyword(s):  
Sequence Space ◽  
Rna Virus ◽  
Population Based ◽  
Space Time ◽  
Fitness Landscapes ◽  
Microbial Populations ◽  
Dynamic Nature ◽  
Time Dynamics ◽  
Low Dimension ◽  
Evolutionary Trajectories

Abstract Rapidly evolving microbes are a challenge to model because of the volatile, complex, and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. The pipeline is openly available on GitHub (https://github.com/rasmushenningsson/DISSEQT.jl, accessed 23 June 2019) and Synapse (https://www.synapse.org/#!Synapse: syn11425758, accessed 23 June 2019), covering the entire workflow from read alignment to visualization of results. Our pipeline is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype–phenotype maps. We illustrate its utility and capacity with examples from evolving RNA virus populations, which present one of the highest degrees of genetic heterogeneity within a given population found in nature. Using our pipeline, we empirically reconstruct the evolutionary trajectories of evolving populations in sequence space and genotype–phenotype fitness landscapes. We show that while sequence space is vastly multidimensional, the relevant genetic space of evolving microbial populations is of intrinsically low dimension. In addition, evolutionary trajectories of these populations can be faithfully monitored to identify the key minority genotypes contributing most to evolution. Finally, we show that empirical fitness landscapes, when reconstructed to include minority variants, can predict phenotype from genotype with high accuracy.

scholarly journals Environmental change exposes beneficial epistatic interactions in a catalytic RNA

2012 ◽  
Vol 279 (1742) ◽  
pp. 3418-3425 ◽  
Author(s):  
Eric J. Hayden ◽  
Andreas Wagner
Keyword(s):  
Environmental Change ◽  
Fitness Landscape ◽  
Point Mutations ◽  
Relative Fitness ◽  
Catalytic Rna ◽  
Fitness Effect ◽  
Epistatic Interactions ◽  
Group I ◽  
The Cost ◽  
Genetic Context

Natural selection drives populations of individuals towards local peaks in a fitness landscape. These peaks are created by the interactions between individual mutations. Fitness landscapes may change as an environment changes. In a previous contribution, we discovered a variant of the Azoarcus group I ribozyme that represents a local peak in the RNA fitness landscape. The genotype at this peak is distinguished from the wild-type by four point mutations. We here report ribozyme fitness data derived from constructing all possible combinations of these point mutations. We find that these mutations interact epistatically. Importantly, we show that these epistatic interactions change qualitatively in the three different environments that we studied. We find examples where the relative fitness of a ribozyme can change from neutral or negative in one environment, to positive in another. We also show that the fitness effect of a specific GC–AU base pair switch is dependent on both the environment and the genetic context. Moreover, the mutations that we study improve activity at the cost of decreased structural stability. Environmental change is ubiquitous in nature. Our results suggest that such change can facilitate adaptive evolution by exposing new peaks of a fitness landscape. They highlight a prominent role for genotype–environment interactions in doing so.

scholarly journals Genetic background affects epistatic interactions between two beneficial mutations

2013 ◽  
Vol 9 (1) ◽  
pp. 20120328 ◽  
Author(s):  
Yinhua Wang ◽  
Carolina Díaz Arenas ◽  
Daniel M. Stoebel ◽  
Tim F. Cooper
Keyword(s):  
Escherichia Coli ◽  
Genetic Background ◽  
General Pattern ◽  
Experimental Studies ◽  
Natural Isolate ◽  
Phenotypic Effect ◽  
Fitness Effect ◽  
Epistatic Interactions ◽  
Beneficial Mutations ◽  
E Coli

The phenotypic effect of mutations can depend on their genetic background, a phenomenon known as epistasis. Many experimental studies have found that epistasis is pervasive, and some indicate that it may follow a general pattern dependent on the fitness effect of the interacting mutations. These studies have, however, typically examined the effect of interactions between a small number of focal mutations in a single genetic background. Here, we extend this approach by considering how the interaction between two beneficial mutations that were isolated from a population of laboratory evolved Escherichia coli changes when they are added to divergent natural isolate strains of E. coli . We find that interactions between the focal mutations and the different genetic backgrounds are common. Moreover, the pair-wise interaction between the focal mutations also depended on their genetic background, being more negative in backgrounds with higher absolute fitness. Together, our results indicate the presence of interactions between focal mutations, but also caution that these interactions depend quantitatively on the wider genetic background.

scholarly journals Phylogeny and Sequence Space: A Combined Approach to Analyze the Evolutionary Trajectories of Homologous Proteins. The Case Study of Aminodeoxychorismate Synthase

2019 ◽  
Vol 68 (1) ◽  
pp. 139-156
Author(s):  
Sylvain Lespinats ◽  
Olivier De Clerck ◽  
Benoît Colange ◽  
Vera Gorelova ◽  
Delphine Grando ◽  
...  
Keyword(s):  
Sequence Space ◽  
Combined Approach ◽  
Homologous Proteins ◽  
Evolutionary Trajectories

scholarly journals Epistasis between antibiotic resistance mutations and genetic background shape the fitness effect of resistance across species of Pseudomonas

2016 ◽  
Vol 283 (1830) ◽  
pp. 20160151 ◽  
Author(s):  
T. Vogwill ◽  
M. Kojadinovic ◽  
R. C. MacLean
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Mutations ◽  
Bacterial Strains ◽  
Fitness Effect ◽  
Epistatic Interactions ◽  
Fitness Effects ◽  
Cost Of Resistance ◽  
Pathogen Populations ◽  
The Cost ◽  
Antibiotic Resistance Mutations

Antibiotic resistance often evolves by mutations at conserved sites in essential genes, resulting in parallel molecular evolution between divergent bacterial strains and species. Whether these resistance mutations are having parallel effects on fitness across bacterial taxa, however, is unclear. This is an important point to address, because the fitness effects of resistance mutations play a key role in the spread and maintenance of resistance in pathogen populations. We address this idea by measuring the fitness effect of a collection of rifampicin resistance mutations in the β subunit of RNA polymerase ( rpoB ) across eight strains that span the diversity of the genus Pseudomonas . We find that almost 50% of rpoB mutations have background-dependent fitness costs, demonstrating that epistatic interactions between rpoB and the rest of the genome are common. Moreover, epistasis is typically strong, and it is the dominant genetic determinant of the cost of resistance mutations. To investigate the functional basis of epistasis, and because rpoB plays a central role in transcription, we measured the effects of common rpoB mutations on transcriptional efficiency across three strains of Pseudomonas . Transcriptional efficiency correlates strongly to fitness across strains, and epistasis arises because individual rpoB mutations have differential effects on transcriptional efficiency in different genetic backgrounds.

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