Luria-Delbrück Estimation of Turnip Mosaic Virus Mutation Rate
            <i>In Vivo</i>

Iglesia, Francisca de la; Martínez, Fernando; Hillung, Julia; Cuevas, José M.; Gerrish, Philip J.; Daròs, José‐Antonio; Elena, Santiago F.

doi:10.1128/jvi.06909-11

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…The maximum likelihood estimator is the solution of , and its asymptotic variance is computed from (see [29] ). This is essentially the method used by de la Iglesia et al [18] in a similar case.…”

Section: Methodsmentioning

confidence: 99%

“…Even under the most careful monitoring, final numbers of cells vary [16] . Yet, final number data are rarely reported in fluctuation analysis experiments, although exceptions exist such as [17] , [18] . Theoretical models considering variations in the population size have previously been proposed by Angerer [19] and Komarova et al [20] .…”

Section: Introductionmentioning

confidence: 99%

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Unbiased Estimation of Mutation Rates under Fluctuating Final Counts

Ycart

Véziris

2014

PLoS ONE

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Estimation methods for mutation rates (or probabilities) in Luria-Delbrück fluctuation analysis usually assume that the final number of cells remains constant from one culture to another. We show that this leads to systematically underestimate the mutation rate. Two levels of information on final numbers are considered: either the coefficient of variation has been independently estimated, or the final number of cells in each culture is known. In both cases, unbiased estimation methods are proposed. Their statistical properties are assessed both theoretically and through Monte-Carlo simulation. As an application, the data from two well known fluctuation analysis studies on Mycobacterium tuberculosis are reexamined.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unbiased Estimation of Mutation Rates under Fluctuating Final Counts

Ycart

Véziris

2014

PLoS ONE

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“…We measured the mutation rate of VSV by the Luria-Delbrück fluctuation test, a standard estimation method [44] that has been used previously in several viruses including poliovirus [45] , vesicular stomatitis virus [46] , influenza A virus [47] , measles virus [48] , turnip mosaic virus [49] , and bacteriophages φ6 [20] and Qβ [50] . To score mutants, we used a monoclonal antibody against the envelope glycoprotein G and determined the probability of appearance of monoclonal antibody resistance (MAR) mutants in independent cultures (null-class method).…”

Section: Resultsmentioning

confidence: 99%

Variation in RNA Virus Mutation Rates across Host Cells

Combe¹,

Sanjuán

2014

PLoS Pathog

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It is well established that RNA viruses exhibit higher rates of spontaneous mutation than DNA viruses and microorganisms. However, their mutation rates vary amply, from 10−6 to 10−4 substitutions per nucleotide per round of copying (s/n/r) and the causes of this variability remain poorly understood. In addition to differences in intrinsic fidelity or error correction capability, viral mutation rates may be dependent on host factors. Here, we assessed the effect of the cellular environment on the rate of spontaneous mutation of the vesicular stomatitis virus (VSV), which has a broad host range and cell tropism. Luria-Delbrück fluctuation tests and sequencing showed that VSV mutated similarly in baby hamster kidney, murine embryonic fibroblasts, colon cancer, and neuroblastoma cells (approx. 10−5 s/n/r). Cell immortalization through p53 inactivation and oxygen levels (1–21%) did not have a significant impact on viral replication fidelity. This shows that previously published mutation rates can be considered reliable despite being based on a narrow and artificial set of laboratory conditions. Interestingly, we also found that VSV mutated approximately four times more slowly in various insect cells compared with mammalian cells. This may contribute to explaining the relatively slow evolution of VSV and other arthropod-borne viruses in nature.

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“…For instance, in trans-complementation studies, selection may not be fully removed if the expression level or timing of the plasmid gene copy does not match the wild-type expression profile or if this region of the viral genome contains functional RNA structures or other cis-acting elements. Here, we measured the mutation rate of bacteriophage Qb as substitutions per nucleotide per round of copying (s/n/r), using the Luria-Delbrück fluctuation test, a standard estimation method that has been used previously for at least six other RNA viruses (Sedivy et al 1987;Suárez et al 1992;Schrag et al 1999;Chao et al 2002;Furió et al 2005;de La Iglesia et al 2012). To do so, we engineered amber mutant viruses that could grow only on an Escherichia coli amber suppressor strain and then scored revertants to a functional codon by assaying for viral growth in nonsuppressor cells.…”

mentioning

confidence: 99%

Correlation Between Mutation Rate and Genome Size in Riboviruses: Mutation Rate of Bacteriophage Qβ

et al. 2013

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Genome sizes and mutation rates covary across all domains of life. In unicellular organisms and DNA viruses, they show an inverse relationship known as Drake's rule. However, it is still unclear whether a similar relationship exists between genome sizes and mutation rates in RNA genomes. Coronaviruses, the RNA viruses with the largest genomes (30 kb), encode a proofreading 39 exonuclease that allows them to increase replication fidelity. However, it is unknown whether, conversely, the RNA viruses with the smallest genomes tend to show particularly high mutation rates. To test this, we measured the mutation rate of bacteriophage Qb, a 4.2-kb levivirus. Amber reversion-based Luria-Delbrück fluctuation tests combined with mutant sequencing gave an estimate of 1.4 3 10 24 substitutions per nucleotide per round of copying, the highest mutation rate reported for any virus using this method. This estimate was confirmed using a direct plaque sequencing approach and after reanalysis of previously published estimates for this phage. Comparison with other riboviruses (all RNA viruses except retroviruses) provided statistical support for a negative correlation between mutation rates and genome sizes. We suggest that the mutation rates of RNA viruses might be optimized for maximal adaptability and that the value of this optimum may in turn depend inversely on genome size.

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Luria-Delbrück Estimation of Turnip Mosaic Virus Mutation Rate In Vivo

Cited by 17 publications

References 26 publications

Unbiased Estimation of Mutation Rates under Fluctuating Final Counts

Unbiased Estimation of Mutation Rates under Fluctuating Final Counts

Variation in RNA Virus Mutation Rates across Host Cells

Correlation Between Mutation Rate and Genome Size in Riboviruses: Mutation Rate of Bacteriophage Qβ

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