Viral evolution: beyond drift and shift

Greenbaum, Benjamin; Ghedin, Elodie

doi:10.1016/j.mib.2015.06.015

Cited by 23 publications

(14 citation statements)

References 52 publications

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“…Additionally, fruitful applications may be found in the analysis of evolutionary lineages in viral populations, such as influenza [41] and HIV [42], where lineage trees have been obtained using temporal sequencing data. Quantifying the strength of selection on viral traits, such as antigenic determinants, and inferring their fitness landscape is an important challenge in the field [43–45] which the method presented here could address. The application of this new lineage analysis tool to broader biological contexts may unravel the roles of phenotypic heterogeneity in diverse cellular and evolutionary phenomena.…”

Section: Discussionmentioning

confidence: 99%

Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data

2017

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Recent advances in single-cell time-lapse microscopy have revealed non-genetic heterogeneity and temporal fluctuations of cellular phenotypes. While different phenotypic traits such as abundance of growth-related proteins in single cells may have differential effects on the reproductive success of cells, rigorous experimental quantification of this process has remained elusive due to the complexity of single cell physiology within the context of a proliferating population. We introduce and apply a practical empirical method to quantify the fitness landscapes of arbitrary phenotypic traits, using genealogical data in the form of population lineage trees which can include phenotypic data of various kinds. Our inference methodology for fitness landscapes determines how reproductivity is correlated to cellular phenotypes, and provides a natural generalization of bulk growth rate measures for single-cell histories. Using this technique, we quantify the strength of selection acting on different cellular phenotypic traits within populations, which allows us to determine whether a change in population growth is caused by individual cells’ response, selection within a population, or by a mixture of these two processes. By applying these methods to single-cell time-lapse data of growing bacterial populations that express a resistance-conferring protein under antibiotic stress, we show how the distributions, fitness landscapes, and selection strength of single-cell phenotypes are affected by the drug. Our work provides a unified and practical framework for quantitative measurements of fitness landscapes and selection strength for any statistical quantities definable on lineages, and thus elucidates the adaptive significance of phenotypic states in time series data. The method is applicable in diverse fields, from single cell biology to stem cell differentiation and viral evolution.

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Section: Discussionmentioning

confidence: 99%

Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data

2017

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“…Mutation and evolution in herpesviruses result not only from base substitutions but also from recombination between strains and, to a lesser extent, between species. Recombination in herpesviruses can provide a driving force for evolutionary shifts, akin to that associated with reassortment in segmented RNA viruses ( 26 ). HTS studies of laboratory-generated recombinants of HSV-1 have revealed a bias toward breakpoints being detected in repetitive tracts, intergenic regions, and areas of higher G+C content ( 27 ).…”

Section: Recombination As a Driving Force In Dna Virus Evolutionmentioning

confidence: 99%

Impacts of Genome-Wide Analyses on Our Understanding of Human Herpesvirus Diversity and Evolution

Renner

Szpara

2018

J Virol

109

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Until fairly recently, genome-wide evolutionary dynamics and within-host diversity were more commonly examined in the context of small viruses than in the context of large double-stranded DNA viruses such as herpesviruses. The high mutation rates and more compact genomes of RNA viruses have inspired the investigation of population dynamics for these species, and recent data now suggest that herpesviruses might also be considered candidates for population modeling. High-throughput sequencing (HTS) and bioinformatics have expanded our understanding of herpesviruses through genome-wide comparisons of sequence diversity, recombination, allele frequency, and selective pressures. Here we discuss recent data on the mechanisms that generate herpesvirus genomic diversity and underlie the evolution of these virus families. We focus on human herpesviruses, with key insights drawn from veterinary herpesviruses and other large DNA virus families. We consider the impacts of cell culture on herpesvirus genomes and how to accurately describe the viral populations under study. The need for a strong foundation of high-quality genomes is also discussed, since it underlies all secondary genomic analyses such as RNA sequencing (RNA-Seq), chromatin immunoprecipitation, and ribosome profiling. Areas where we foresee future progress, such as the linking of viral genetic differences to phenotypic or clinical outcomes, are highlighted as well.

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“…We have formulated a theory that predicts optimal responses under fluctuating conditions, and which could be broadly applied to study diverse systems from immune responses [41, 42] and viral dynamics [43] to physiological adaptations [35], drug resistance [44–46], and cooperation [47] in phenotypically diverse cellular populations.…”

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confidence: 99%

Evolutionary Phase Transitions in Random Environments

Skanata

Kussell

2016

Phys. Rev. Lett.

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We present analytical results for long-term growth rates of structured populations in randomly fluctuating environments, which we apply to predict how cellular response networks evolve. We show that networks which respond rapidly to a stimulus will evolve phenotypic memory exclusively under random (i.e. non-periodic) environments. We identify the evolutionary phase diagram for simple response networks, which we show can exhibit both continuous and discontinuous transitions. Our approach enables exact analysis of diverse evolutionary systems, from viral epidemics to emergence of drug resistance.

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Viral evolution: beyond drift and shift

Cited by 23 publications

References 52 publications

Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data

Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data

Impacts of Genome-Wide Analyses on Our Understanding of Human Herpesvirus Diversity and Evolution

Evolutionary Phase Transitions in Random Environments

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