Genome neighborhood network reveals insights into enediyne biosynthesis and facilitates prediction and prioritization for discovery
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  • 作者:Jeffrey D. Rudolf ; Xiaohui Yan ; Ben Shen
  • 关键词:Enediyne polyketide synthase ; Genome neighborhood network ; Biosynthetic gene cluster ; Genome mining ; Natural products
  • 刊名:Journal of Industrial Microbiology and Biotechnology
  • 出版年:2016
  • 出版时间:March 2016
  • 年:2016
  • 卷:43
  • 期:2-3
  • 页码:261-276
  • 全文大小:7,594 KB
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  • 作者单位:Jeffrey D. Rudolf (1)
    Xiaohui Yan (1)
    Ben Shen (1) (2) (3)

    1. Department of Chemistry, The Scripps Research Institute, Jupiter, FL, 33458, USA
    2. Department of Molecular Therapeutics, The Scripps Research Institute, Jupiter, FL, 33458, USA
    3. Natural Products Library Initiative, The Scripps Research Institute, Jupiter, FL, 33458, USA
  • 刊物类别:Biomedical and Life Sciences
  • 刊物主题:Chemistry
    Biotechnology
    Genetic Engineering
    Biochemistry
    Bioinformatics
    Microbiology
    Microbial Genetics and Genomics
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1476-5535
文摘
The enediynes are one of the most fascinating families of bacterial natural products given their unprecedented molecular architecture and extraordinary cytotoxicity. Enediynes are rare with only 11 structurally characterized members and four additional members isolated in their cycloaromatized form. Recent advances in DNA sequencing have resulted in an explosion of microbial genomes. A virtual survey of the GenBank and JGI genome databases revealed 87 enediyne biosynthetic gene clusters from 78 bacteria strains, implying that enediynes are more common than previously thought. Here we report the construction and analysis of an enediyne genome neighborhood network (GNN) as a high-throughput approach to analyze secondary metabolite gene clusters. Analysis of the enediyne GNN facilitated rapid gene cluster annotation, revealed genetic trends in enediyne biosynthetic gene clusters resulting in a simple prediction scheme to determine 9- versus 10-membered enediyne gene clusters, and supported a genomic-based strain prioritization method for enediyne discovery. Keywords Enediyne polyketide synthase Genome neighborhood network Biosynthetic gene cluster Genome mining Natural products

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