A mutation spectra database for bacterial and mammalian genes

Mutational signatures indicative of environmental stress in bacteria

Evolutionary innovations are dependent on mutations. Mutation rates are increased by adverse conditions in the laboratory, but there is no evidence that stressful environments that do not directly impact on DNA leave a mutational imprint on extant genomes. Mutational spectra in the laboratory are normally determined with unstressed cells but are unavailable with stressed bacteria. To by-pass problems with viability, selection effects, and growth rate differences due to stressful environments, in this study we used a set of genetically engineered strains to identify the mutational spectrum associated with nutritional stress. The strain set members each had a fixed level of the master regulator protein, RpoS, which controls the general stress response of Escherichia coli. By assessing mutations in cycA gene from 485 cycloserine resistant mutants collected from as many independent cultures with three distinct perceived stress (RpoS) levels, we were able establish a dose-dependent relationship between stress and mutational spectra. The altered mutational patterns included base pair substitutions, single base pair indels, longer indels, and transpositions of different insertion sequences. The mutational spectrum of low-RpoS cells closely matches the genome-wide spectrum previously generated in laboratory environments, while the spectra of high RpoS, high perceived stress cells more closely matches spectra found in comparisons of extant genomes. Our results offer an explanation of the uneven mutational profiles such as the transition-transversion biases observed in extant genomes and provide a framework for assessing the contribution of stress-induced mutagenesis to evolutionary transitions and the mutational emergence of antibiotic resistance and disease states.

Bayesian analysis of mutational spectra

Studies that examine both the frequency of gene mutation and the pattern or spectrum of mutational changes can be used to identify chemical mutagens and to explore the molecular mechanisms of mutagenesis. In this article, we propose a Bayesian hierarchical modeling approach for the analysis of mutational spectra. We assume that the total number of independent mutations and the numbers of mutations falling into different response categories, defined by location within a gene and/or type of alteration, follow binomial and multinomial sampling distributions, respectively. We use prior distributions to summarize past information about the overall mutation frequency and the probabilities corresponding to the different mutational categories. These priors can be chosen on the basis of data from previous studies using an approach that accounts for heterogeneity among studies. Inferences about the overall mutation frequency, the proportions of mutations in each response category, and the category-specific mutation frequencies can be based on posterior distributions, which incorporate past and current data on the mutant frequency and on DNA sequence alterations. Methods are described for comparing groups and for assessing dose-related trends. We illustrate our approach using data from the literature.

In search of a mutational hotspot

In vitro selection was used to define sequence contexts that significantly enhanced the mutagenic potential of 7, 8-dihydro-8-oxoguanine (8-oxoG). Contexts that simultaneously reduced the efficiency of 8-oxoG cleavage by formamidopyrimidine DNA N-glycosylase and increased the efficiency of misincorporating A opposite the lesion by DNA polymerase were isolated from a pool of 4(8) random octanucleotide sequences. Kinetic analysis showed that the combined effects of poor repair and high miscoding resulted in 10(2)- to 10(3)-fold increase in the mutagenic potential of 8-oxoG. Furthermore, the isolated sequence contexts correlated strongly with G --> T transversion hotspots in spontaneous mutational spectra reported for the Escherichia coli lacI and human p53 and factor IX genes. We present an example directly linking the interplay between DNA repair and replication to a "high risk sequence" for base substitution.