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...
As one of the few cellular traits that can be quantified across the tree of life, DNA-replication fidelity provides an excellent platform for understanding fundamental evolutionary processes. Furthermore, because mutation is the ultimate source of all genetic variation, clarifying why mutation rates vary is crucial for understanding all areas of biology. A potentially revealing hypothesis for mutation-rate evolution is that natural selection primarily operates to improve replication fidelity, with the ultimate limits to what can be achieved set by the power of random genetic drift. This drift-barrier hypothesis is consistent with comparative measures of mutation rates, provides a simple explanation for the existence of error-prone polymerases and yields a formal counter-argument to the view that selection fine-tunes gene-specific mutation rates.
When properly determined, spontaneous mutation rates are a more accurate and biologically meaningful reflection of the underlying mutagenic mechanism than are mutation frequencies. Because bacteria grow exponentially and mutations arise stochastically, methods to estimate mutation rates depend on theoretical models that describe the distribution of mutant numbers among parallel cultures, as in the original Luria-Delbrück fluctuation analysis. An accurate determination of mutation rate depends on understanding the strengths and limitations of these methods, and how to design fluctuation assays to optimize a given method. In this paper we describe a number of methods to estimate mutation rates, give brief accounts of their derivations, and discuss how they behave under various experimental conditions.The purpose of this article is to provide experimental and mathematical methods for determining spontaneous mutation rates in bacterial cultures. Mutations are heritable changes in an organism's DNA (or RNA for RNA-based organisms). Spontaneous mutations are mutations that occur in the absence of an exogenous DNA damaging agent. These include DNA polymerase errors, mutations induced by endogenous agents, as well as deletions, duplications, and insertions. It is generally assumed that most spontaneous mutations that occur during growth are linked to DNA replication. Although we have not included mutations induced by DNA damaging agents in this article, the methods discussed also can be applied to mutations induced by DNA base analogues or by low, nonlethal doses of other mutagens. We assume that our readers are interested in the mechanism of mutation, in mutation rates among natural isolates of bacteria, and/or in the influence of genetic background or environment on the mutational process. The alternative to determining a mutation rate is to determine a mutant frequency, that is simply to average the fraction of mutant bacteria in a few replicate cultures. There are two reasons for making the effort to determine a mutation rate instead of a frequency.First, when properly determined, the mutation rate is more accurate and reproducible than the mutant frequency. This is, indeed, the fundamental fact that was exploited in the famous Luria and Delbrück experiment (1). During exponential growth every cell has a low but nonzero probability of sustaining a mutation during its lifetime, and this probability is what we call the mutation rate. After a cell sustains a mutation it produces a clone of mutants, and the size of this clone will depend on when during the growth of the population the mutation occurred. Thus, even though the growth of the population may be deterministic, the final number of mutants in the population reflects an underlying stochastic process. Because of the exponential growth of mutants, low probability events occurring early during the growth of a population have huge consequences. This makes mutant frequency, no matter how many cultures are Corresponding author: Patricia L. Foster, Ph.D., S107,...
The Escherichia coli MutS and MutL proteins have been conserved throughout evolution, although their combined functions in mismatch repair (MMR) are poorly understood. We have used biochemical and genetic studies to ascertain a physiologically relevant mechanism for MMR. The MutS protein functions as a regional lesion sensor. ADP-bound MutS specifically recognizes a mismatch. Repetitive rounds of mismatch-provoked ADP-->ATP exchange results in the loading of multiple MutS hydrolysis-independent sliding clamps onto the adjoining duplex DNA. MutL can only associate with ATP-bound MutS sliding clamps. Interaction of the MutS-MutL sliding clamp complex with MutH triggers ATP binding by MutL that enhances the endonuclease activity of MutH. Additionally, MutL promotes ATP binding-independent turnover of idle MutS sliding clamps. These results support a model of MMR that relies on two dynamic and redundant ATP-regulated molecular switches.
Spontaneous mutations arise as a result of cellular processes that act upon or damage DNA. Accurate determination of spontaneous mutation rates can contribute to our understanding of these processes and the enzymatic pathways that deal with them. The methods that are used to calculate mutation rates are based on the model for the expansion of mutant clones originally described by Luria and Delbrück and extended by Lea and Coulson. The accurate determination of mutation rates depends on understanding the strengths and limitations of these methods and how to optimize a fluctuation assay for a given method. This chapter describes the proper design of a fluctuation assay, several of the methods used to calculate mutation rates, and ways to evaluate the results statistically.
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