Dataset Information

Quantification of in vivo progenitor mutation accrual with ultra-low error rate and minimal input DNA using SIP-HAVA-seq.

ABSTRACT: Assaying in vivo accrual of DNA damage and DNA mutations by stem cells and pinpointing sources of damage and mutations would further our understanding of aging and carcinogenesis. Two main hurdles must be overcome. First, in vivo mutation rates are orders of magnitude lower than raw sequencing error rates. Second, stem cells are vastly outnumbered by differentiated cells, which have a higher mutation rate-quantification of stem cell DNA damage and DNA mutations is thus best performed from small, well-defined cell populations. Here we report a mutation detection technique, based on the "duplex sequencing" principle, with an error rate below ?10-10 and that can start from as little as 50 pg DNA. We validate this technique, which we call SIP-HAVA-seq, by characterizing Caenorhabditis elegans germline stem cell mutation accrual and asking how mating affects that accrual. We find that a moderate mating-induced increase in cell cycling correlates with a dramatic increase in accrual of mutations. Intriguingly, these mutations consist chiefly of deletions in nonexpressed genes. This contrasts with results derived from mutation accumulation lines and suggests that mutation spectrum and genome distribution change with replicative age, chronological age, cell differentiation state, and/or overall worm physiological state. We also identify single-stranded gaps as plausible deletion precursors, providing a starting point to identify the molecular mechanisms of mutagenesis that are most active. SIP-HAVA-seq provides the first direct, genome-wide measurements of in vivo mutation accrual in stem cells and will enable further characterization of underlying mechanisms and their dependence on age and cell state.

SUBMITTER: Taylor PH

PROVIDER: S-EPMC5088601 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Quantification of in vivo progenitor mutation accrual with ultra-low error rate and minimal input DNA using SIP-HAVA-seq.

Taylor Pete H PH Cinquin Amanda A Cinquin Olivier O

Genome research 20161019 11

Assaying in vivo accrual of DNA damage and DNA mutations by stem cells and pinpointing sources of damage and mutations would further our understanding of aging and carcinogenesis. Two main hurdles must be overcome. First, in vivo mutation rates are orders of magnitude lower than raw sequencing error rates. Second, stem cells are vastly outnumbered by differentiated cells, which have a higher mutation rate-quantification of stem cell DNA damage and DNA mutations is thus best performed from small, ...[more]

PMID: 27803194

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Project description:BackgroundRapid progress in high-throughput sequencing (HTS) and the development of novel library preparation methods have improved the sensitivity of detecting mutations in heterogeneous samples, specifically in high-depth (> 500×) clinical applications. However, HTS methods are bounded by their technical and theoretical limitations and sequencing errors cannot be completely eliminated. Comprehensive quantification of the background noise can highlight both the efficiency and the limitations of any HTS methodology, and help differentiate true mutations at low abundance from artifacts.ResultsWe introduce MERIT (Mutation Error Rate Inference Toolkit), designed for in-depth quantification of erroneous substitutions and small insertions and deletions. MERIT incorporates an all-inclusive variant caller and considers genomic context, including the nucleotides immediately at 5 'and 3 ', thereby establishing error rates for 96 possible substitutions as well as four single-base and 16 double-base indels. We applied MERIT to ultra-deep sequencing data (1,300,000 ×) obtained from the amplification of multiple clinically relevant loci, and showed a significant relationship between error rates and genomic contexts. In addition to observing significant difference between transversion and transition rates, we identified variations of more than 100-fold within each error type at high sequencing depths. For instance, T >G transversions in trinucleotide GTCs occurred 133.5 ± 65.9 more often than those in ATAs. Similarly, C >T transitions in GCGs were observed at 73.8 ± 10.5 higher rate than those in TCTs. We also devised an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of expected true mutations. Our analyses showed that increasing sequencing depth might improve sensitivity for detecting some mutations based on their genomic context. For example, T >G rate of error in GTCs did not change when sequenced beyond 10,000 ×; in contrast, T >G rate in TTAs consistently improved even at above 500,000 ×.ConclusionsOur results demonstrate significant variation in nucleotide misincorporation rates, and suggest that genomic context should be considered for comprehensive profiling of specimen-specific and sequencing artifacts in high-depth assays. This data provide strong evidence against assigning a single allele frequency threshold to call mutations, for it can result in substantial false positive as well as false negative variants, with important clinical consequences.

Dataset Information

Quantification of in vivo progenitor mutation accrual with ultra-low error rate and minimal input DNA using SIP-HAVA-seq.

Publications

Quantification of in vivo progenitor mutation accrual with ultra-low error rate and minimal input DNA using SIP-HAVA-seq.

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