Methods for Determining Spontaneous Mutation Rates

Foster, Patricia L.

doi:10.1016/s0076-6879(05)09012-9

Cited by 274 publications

(346 citation statements)

References 28 publications

(39 reference statements)

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“…Analyzing fluctuation data and postplating growth on 13 canavanine: There are many methods for calculating mutation rates from fluctuation data (Foster 2006), of which the Ma-Sandri-Sarkar maximum-likelihood method is preferred because it is the most accurate, it is valid for any range of the expected number of mutation events per culture (m), and 95% confidence intervals can be calculated by an empirically determined set of equations (Stewart 1994;Rosche and Foster 2000). For estimates of mutation rates and 95% confidence intervals generated from this method to be accurate the data must approximate the Luria-Delbrück distribution.…”

Section: Resultsmentioning

confidence: 99%

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Estimating the Per-Base-Pair Mutation Rate in the YeastSaccharomyces cerevisiae

2008

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Although mutation rates are a key determinant of the rate of evolution they are difficult to measure precisely and global mutations rates (mutations per genome per generation) are often extrapolated from the per-base-pair mutation rate assuming that mutation rate is uniform across the genome. Using budding yeast, we describe an improved method for the accurate calculation of mutation rates based on the fluctuation assay. Our analysis suggests that the per-base-pair mutation rates at two genes differ significantly (3.80 3 10 À10 at URA3 and 6.44 3 10 À10 at CAN1) and we propose a definition for the effective target size of genes (the probability that a mutation inactivates the gene) that acknowledges that the mutation rate is nonuniform across the genome.

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Section: Resultsmentioning

confidence: 99%

“…Mutation rate is calculated from the equation m ¼ m/N, where N is the average number of cells per culture (approximately equal to the number of cell divisions per culture since the initial inoculum is much smaller than N ). Ninety-five percent confidence intervals on m and m were assigned using Equations 29 and 30 from Foster (2006).…”

Section: Methodsmentioning

confidence: 99%

Estimating the Per-Base-Pair Mutation Rate in the YeastSaccharomyces cerevisiae

2008

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“…Mutation rates to Nal R or Rif R were estimated using fluctuation tests as described (76). Further details are given in SI Materials and Methods.…”

Section: Methodsmentioning

confidence: 99%

Rate and molecular spectrum of spontaneous mutations in the bacteriumEscherichia colias determined by whole-genome sequencing

Lee¹,

Popodi

Tang³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Knowledge of the rate and nature of spontaneous mutation is fundamental to understanding evolutionary and molecular processes. In this report, we analyze spontaneous mutations accumulated over thousands of generations by wild-type Escherichia coli and a derivative defective in mismatch repair (MMR), the primary pathway for correcting replication errors. The major conclusions are (i) the mutation rate of a wild-type E. coli strain is ∼1 × 10 −3 per genome per generation; (ii) mutations in the wild-type strain have the expected mutational bias for G:C > A:T mutations, but the bias changes to A:T > G:C mutations in the absence of MMR; (iii) during replication, A:T > G:C transitions preferentially occur with A templating the lagging strand and T templating the leading strand, whereas G:C > A:T transitions preferentially occur with C templating the lagging strand and G templating the leading strand; (iv) there is a strong bias for transition mutations to occur at 5′ApC3′/3′TpG5′ sites (where bases 5′A and 3′T are mutated) and, to a lesser extent, at 5′GpC3′/3′CpG5′ sites (where bases 5′G and 3′C are mutated); (v) although the rate of small (≤4 nt) insertions and deletions is high at repeat sequences, these events occur at only 1/10th the genomic rate of base-pair substitutions. MMR activity is genetically regulated, and bacteria isolated from nature often lack MMR capacity, suggesting that modulation of MMR can be adaptive. Thus, comparing results from the wild-type and MMR-defective strains may lead to a deeper understanding of factors that determine mutation rates and spectra, how these factors may differ among organisms, and how they may be shaped by environmental conditions. evolution | mutation accumulation | neutral mutation | mutational hotspots | indels M utations are the source of variation upon which natural selection acts; thus, a complete understanding of evolutionary processes must include an accurate assessment of mutation rates and of the molecular spectrum of mutational events. In addition, we need to know whether, and how, intrinsic and extrinsic factors influence mutational processes. This understanding must be founded on baseline parameters established by analyzing mutations that accumulate in a neutral fashion, unbiased by selective pressures. Much of our knowledge of spontaneous mutation is based on mutations that occur in nonessential reporter genes during short-term laboratory culture of microorganisms (1). An alternative approach is to compare presumably neutral mutations that have accumulated over evolutionary time periods in diverged species (2). Both methods have substantial uncertainties. The experimental approach may use reporter loci that are not representative of the whole genome and necessarily incorporates assumptions about the expression and neutrality of the mutant phenotypes. The historical approach relies on estimated divergence times and the absence of selective pressure on synonymous sequence changes. High-throughput whole-genome sequencing allows some of these limitations to be...

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“…The explanation for this pattern is that plating efficiency is a random sampling effect that is contributing to the fluctuation effect. On the other hand, the fitness value of the mutants is key; a fitness of 0.8 provokes a strong reduction in the detected variance effect so that the discrimination between the spontaneous and adaptive hypotheses becomes less apparent [7]. To improve the power of the test under this fitness scenario, the solution is to increase the population size, i.e.…”

Section: Example Exercisesmentioning

confidence: 99%

“…5 using the basic estimator proposed initially by Luria and Delbrü ck ( [1]; see also refs. [7,8]). Given that a Poisson distribution can be assumed for the number of mutations, the mutation rate can be expressed as l ¼ m/(N t -N 0 ) where m is the number of mutations that occurred in a culture during a finite time interval.…”

Section: Design Of the Practicalmentioning

confidence: 99%

Teaching the fluctuation test in silico by using mutate: A program to distinguish between the adaptive and spontaneous mutation hypotheses

Carvajal-Rodríguez¹

2012

Biochem Molecular Bio Educ

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Mutate is a program developed for teaching purposes to impart a virtual laboratory class for undergraduate students of Genetics in Biology. The program emulates the so‐called fluctuation test whose aim is to distinguish between spontaneous and adaptive mutation hypotheses in bacteria. The plan is to train students in certain key multidisciplinary aspects of current genetics such as sequence databases, DNA mutations, and hypothesis testing, while introducing the fluctuation test. This seminal experiment was originally performed studying Escherichia coli resistance to the infection by bacteriophage T1. The fluctuation test initiated the modern bacterial genetics that 25 years later ushered in the era of the recombinant DNA. Nowadays we know that some deletions in fhuA, the gene responsible for E. coli membrane receptor of T1, could cause the E. coli resistance to this phage. For the sake of simplicity, we will introduce the assumption that a single mutation generates the resistance to T1. During the practical, the students use the program to download some fhuA gene sequences, manually introduce some stop codon mutations, and design a fluctuation test to obtain data for distinguishing between preadaptative (spontaneous) and induced (adaptive) mutation hypotheses. The program can be launched from a browser or, if preferred, its executable file can be downloaded from http://webs.uvigo.es/acraaj/MutateWeb/Mutate.html. It requires the Java 5.0 (or higher) Runtime Environment (freely available at http://www.java.com).

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Methods for Determining Spontaneous Mutation Rates

Cited by 274 publications

References 28 publications

Estimating the Per-Base-Pair Mutation Rate in the YeastSaccharomyces cerevisiae

Estimating the Per-Base-Pair Mutation Rate in the YeastSaccharomyces cerevisiae

Rate and molecular spectrum of spontaneous mutations in the bacteriumEscherichia colias determined by whole-genome sequencing

Teaching the fluctuation test in silico by using mutate: A program to distinguish between the adaptive and spontaneous mutation hypotheses

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