Strain Prioritization for Natural Product Discovery by a High-Throughput Real-Time PCR Method
Author(s) -
Fransiskus Hindra,
Tingting Huang,
Dong Yang,
Jeffrey D. Rudolf,
Pengfei Xie,
Guangbo Xie,
Qihui Teng,
Jeremy R. Lohman,
Xiangcheng Zhu,
Yong Huang,
LiXing Zhao,
Yi Jiang,
Yanwen Duan,
Ben Shen
Publication year - 2014
Publication title -
journal of natural products
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.976
H-Index - 139
eISSN - 1520-6025
pISSN - 0163-3864
DOI - 10.1021/np5006168
Subject(s) - natural product , prioritization , drug discovery , computational biology , natural product research , product (mathematics) , genome , strain (injury) , throughput , chemical space , computer science , biology , biochemical engineering , gene , bioinformatics , engineering , pharmacognosy , genetics , mathematics , biological activity , management science , biochemistry , telecommunications , geometry , anatomy , in vitro , wireless
Natural products offer unmatched chemical and structural diversity compared to other small-molecule libraries, but traditional natural product discovery programs are not sustainable, demanding too much time, effort, and resources. Here we report a strain prioritization method for natural product discovery. Central to the method is the application of real-time PCR, targeting genes characteristic to the biosynthetic machinery of natural products with distinct scaffolds in a high-throughput format. The practicality and effectiveness of the method were showcased by prioritizing 1911 actinomycete strains for diterpenoid discovery. A total of 488 potential diterpenoid producers were identified, among which six were confirmed as platensimycin and platencin dual producers and one as a viguiepinol and oxaloterpin producer. While the method as described is most appropriate to prioritize strains for discovering specific natural products, variations of this method should be applicable to the discovery of other classes of natural products. Applications of genome sequencing and genome mining to the high-priority strains could essentially eliminate the chance elements from traditional discovery programs and fundamentally change how natural products are discovered.
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