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Spectrum of ENU-induced mutations in phenotype-driven and gene-driven screens in the mouse

期刊

ENVIRONMENTAL AND MOLECULAR MUTAGENESIS
卷 48, 期 2, 页码 124-142

出版社

WILEY
DOI: 10.1002/em.20286

关键词

forward genetics; exon; coding sequence; specific locus test

资金

  1. Medical Research Council [MC_U142684172, MC_U142684175, MC_UP_1502/1] Funding Source: Medline
  2. MRC [MC_U142684172, MC_U142684175, MC_UP_1502/1] Funding Source: UKRI

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N-ethyl-N-nitrosourea (ENU) mutagenesis in mice has become a standard tool for (i) increasing the pool of mutants in many areas of biology, (ii) identifying novel genes involved in physiological processes and disease, and (iii) in assisting in assigning functions to genes. ENU is assumed to cause random mutations throughout the mouse genome, but this presumption has never been analyzed. This is a crucial point, especially for large-scale mutagenesis, as a bias would reflect a constraint on identifying possible genetic targets. There is a significant body of published data now available from both phenotype-driven and gene-driven ENU mutagenesis screens in the mouse that can be used to reveal the effectiveness and limitations of an ENU mutagenesis approach. Analysis of the published data is presented in this paper. As expected for a randomly acting mutagen, ENU-induced mutations identified in phenotype-driven screens were in genes that had higher coding sequence length and higher exon number than the average for the mouse genome. Unexpectedly, the data showed that ENU-induced mutations were more likely to be found in genes that had a higher G + C content and neighboring base analysis revealed that the identified ENU mutations were more often directly flanked by G or C nucleotides. ENU mutations from phenotypedriven and gene-driven screens were dominantly A:T to T:A transversions or A:T to G:C transitions. Knowledge of the spectrum of mutations that ENU elicits will assist in positional cloning of ENU-induced mutations by allowing prioritization of candidate genes based on some of their inherent features.

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