Inferring population genetics parameters of evolving viruses using time-series data

Zinger, Tal; Gelbart, Maoz; Miller, Danielle; Pennings, Pleuni S.; Stern, Adi

doi:10.1093/ve/vez011

Cited by 11 publications

(9 citation statements)

References 59 publications

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“…We formally estimated the posterior distributions of all parameters in W using an ABC-SMC ( 67 ) [as implemented in astroABC ( 68 )]. As a summary statistic, we used the mean ℓ 1 distance between the simulated trajectories of cheater frequencies and the empirical trajectories of the cheater frequencies of either line A or line B

, similar to ( 69 ), where T is the number of passages that were sequenced. Prior distributions for all parameters were assumed to be uniform over [0,4) unless mentioned otherwise.…”

Section: Methodsmentioning

confidence: 99%

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Competition between social cheater viruses is driven by mechanistically different cheating strategies

et al. 2020

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Cheater viruses, also known as defective interfering viruses, cannot replicate on their own yet replicate faster than the wild type upon coinfection. While there is growing interest in using cheaters as antiviral therapeutics, the mechanisms underlying cheating have been rarely explored. During experimental evolution of MS2 phage, we observed the parallel emergence of two independent cheater mutants. The first, a point deletion mutant, lacked polymerase activity but was advantageous in viral packaging. The second synonymous mutant cheater displayed a completely different cheating mechanism, involving an altered RNA structure. Continued evolution revealed the demise of the deletion cheater and rise of the synonymous cheater. A mathematical model inferred that while a single cheater is expected to reach an equilibrium with the wild type, cheater demise arises from antagonistic interactions between coinfecting cheaters. These findings highlight layers of parasitism: viruses parasitizing cells, cheaters parasitizing intact viruses, and cheaters may parasitize other cheaters.

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, similar to ( 69 ), where T is the number of passages that were sequenced. Prior distributions for all parameters were assumed to be uniform over [0,4) unless mentioned otherwise.…”

Section: Methodsmentioning

confidence: 99%

“…, similar to (69), where T is the number of passages that were sequenced. Prior distributions for all parameters were assumed to be uniform over [0,4) unless mentioned otherwise.…”

Section: Mathematical Modelmentioning

confidence: 99%

Competition between social cheater viruses is driven by mechanistically different cheating strategies

et al. 2020

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“…By combining laboratory virus evolution with highthroughput mutagenesis or sequencing, virologists have characterized the distribution of mutational fitness effects with unprecedented detail, such that the effect of nearly every possible individual mutation can now be measured (19). However, the question remains to what extent these experiments, which are typically carried out in simple cell culture systems, capture important aspects of viral evolution in nature.…”

Section: Developing More Relevant Experimental Evolution Systemsmentioning

confidence: 99%

Five Challenges in the Field of Viral Diversity and Evolution

et al. 2021

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Viral diversity and evolution play a central role in processes such as disease emergence, vaccine failure, drug resistance, and virulence. However, significant challenges remain to better understand and manage these processes. Here, we discuss five of these challenges. These include improving our ability to predict viral evolution, developing more relevant experimental evolutionary systems, integrating viral dynamics and evolution at different scales, systematic appraisal of the virosphere, and deepening our understanding of virus-virus interactions. Intensifying future research on these areas should improve our ability to combat viral diseases, as well as to exploit viral diversity and evolution for biotechnological purposes.

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“…Serially sampled sequences allow the estimation of important evolutionary genetic parameters that is not possible with data obtained at a single time point. For example, various methods were proposed to estimate the strength of natural selection and effective population size from allele frequency trajectories (Bollback et al 2008, Steinrücken et al 2014, Schraiber et al 2016, Ferrer-Admetlla et al 2016, Zinger et al 2019. These studies assumed that nucleotide sequences are obtained from discrete time points, therefore giving discrete series of allele frequencies in a population.…”

Section: Introductionmentioning

confidence: 99%

Inference of population genetic parameters from the continuously serial-sampled sequences of human seasonal influenza A/H3N2

Croze¹,

Kim

2020

Preprint

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Basic summary statistics that quantify the population genetic structure of influenza virus are important for understanding and inferring the evolutionary and epidemiological processes. However, global virus sequences were sampled continuously over several decades, scattered semi-randomly over time. This temporal structure of samples and the small effective size of viral population make it difficult to use conventional methods to calculate summary statistics. Here we define statistics that overcome this problem by correcting for sampling time difference in quantifying a pairwise sequence difference. A simple method of linear regression jointly estimates the mutation rate and the level of sequence polymorphism, thus providing the estimate of the effective population size. It also leads to the definition of Wright’s FST for arbitrary time-series data. In addition, as an alternative to Tajima’s D statistic or site frequency spectrum, mismatch distribution corrected for sampling time differences can be obtained and compared between actual and simulated data. Application of these methods to seasonal influenza A/H3N2 viruses sampled between 1980 and 2017 and sequences simulated under the model of recurrent positive selection with meta-population dynamics allowed us to estimate the synonymous mutation rate and find parameter values of selection and demographic structure that fit the observation. We found that the mutation rates of HA and PB1 segments before 2007 were particularly high, and that adding recurrent positive selection in our model was essential for the genealogical structure of the HA segment. Methods developed here can be generally applied to population genetic inferences using serially sampled genetic data.

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Inferring population genetics parameters of evolving viruses using time-series data

Cited by 11 publications

References 59 publications

Competition between social cheater viruses is driven by mechanistically different cheating strategies

Competition between social cheater viruses is driven by mechanistically different cheating strategies

Five Challenges in the Field of Viral Diversity and Evolution

Inference of population genetic parameters from the continuously serial-sampled sequences of human seasonal influenza A/H3N2

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