Abstract
Trinucleotide mutational signatures extracted from cancer genomes provide clues useful in understanding the roles of mutagens and mutagenic mechanisms in cancer development. The lack of a simple method for genome-wide analysis of alterations induced by mutagens hampers the identification of trinucleotide signatures of mutagen exposure and evaluation of their relationships with human cancers. Here, we describe a novel approach to facilitate analysis of chemically induced mutations in bacterial cells by detection of increased frequencies of base substitutions after mutagen exposure, using paired-end overlapping next-generation sequencing. DNA samples from Salmonella typhimurium strain TA100, exposed to three alkylating agents, ethylnitrosourea (ENU), methylnitrosourea (MNU), and ethyl methansulphonate (EMS), were analysed. The G:C > A:T mutation frequency was increased in all samples, whereas A:T base pair substitution frequencies were increased specifically in samples exposed to ENU, consistent with previous reports. Mutation patterns in the context of 96 possible trinucleotide formats in these samples exhibited a sharp peak corresponding to an NpCpY consensus sequence, which is similar to the mutational signature of alkylating agents in human cancer. These results indicate that our approach can be useful in facilitating the understanding of mechanisms underlying chemical mutagenicity and for identification of unknown causal mutagens in human cancer.
Highlights
Cancer genomics studies have uncovered somatic mutations in cancer genomes and revealed their roles in cancer development[1]
We performed hierarchical clustering of these patterns, together with the 21 human cancer mutational signatures validated by Alexandrov et al The results indicate that all three patterns formed a cluster with signature 11, indicating that our method could identify the trinucleotide sequence changes characteristic of chemical exposure, as determined from signatures extracted from human cancer
The background error frequencies of control samples were about 1 per 104–6 bp depending on the mutation type, which is approximately the same as the mutation frequencies induced by mutagens; the variation in the background error frequency between samples and analyses was very small, enabling the detection of minor changes in mutation frequencies caused by chemical exposures (Supplementary Fig. S3), a relatively high error frequency was observed for the G:C > T:A mutation call
Summary
Cancer genomics studies have uncovered somatic mutations in cancer genomes and revealed their roles in cancer development[1]. Mutations induced by exposure to mutagens have primarily been evaluated by short term carcinogenicity studies, the bacterial Ames test system[10] Such tests provide mutational information based on six mutation types (C > A, C > G, C > T, T > A, T > C, and T > G) and are useful for predicting mutagenicity in humans; they could not identify sufficient mutations to achieve high-resolution spectrum analyses, or to clarify trinucleotide signatures. This study demonstrated the utility of bacterial experiments for analysis of trinucleotide signatures that could be extrapolated to the human genome; as in the analyses using mammalian cells discussed above, these approaches require sampling and sequencing of multiple single cells to obtain mutational signatures at sufficient resolution These approaches are expensive, when applied to analyses of a number of mutagens; rather than isolating single cells, an alternative approach could be the identification of mutations based on information of single reads or read pairs derived from each DNA molecule. We confirm the similarity of the resulting mutation patterns with those observed in historical bacterial studies and human cancer, and assess the applicability of our method for mutagenicity testing and the potential for extrapolation of the generated results to humans
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