摘要
利用微生物的基因组信息预测其合成特定天然产物的潜能,进而进行新化合物分离纯化和结构鉴定的基因组挖掘技术,已经成为国内外研究的热点,并在多种细菌和真菌的天然产物发现中得到成功应用。本文综述了基因组挖掘技术的最新进展,包括生物信息分析和结构预测、基因组指导的天然产物的发现、沉默基因的激活和异源表达技术等,以及我国学者开发的转录组挖掘技术,并重点综述了影像质谱技术在基因组挖掘中的应用。目前对海洋放线菌进行基因组挖掘的研究还比较少,而基因组挖掘技术的发展,将极大地促进对海洋放线菌天然产物的发现和鉴定。未来除了充分挖掘可培养微生物的基因组,对未培养微生物宏基因组的挖掘将进一步深入。此外,除了开发利用基因组中合成天然产物的结构基因和调节基因,还应该充分开发利用其他不同的遗传元件,包括不同转录活性和响应不同环境条件和信号的启动子,以及具有调节作用的RNA等。
Genome mining of natural product biosynthesis employs genome sequences to pre- dict the biosynthetic potential of the producer strains guiding the purification and structure elucidation of novel compounds, and has been successfully applied in natural product discov- ery in both bacterial and fungal systems. In this review, the latest advances of genome mining of marine actinobacteria have been summarized, with emphasis on bioinformatic tools for se- quence analysis and prediction of chemical structures, strategies for isolation of targeted natu- ral products under the guidance of genome sequence information, activation of silent gene clusters and heterologous expression, as well as transcriptome mining developed by domestic scholars. Genome mining based on imaging mass spectrometry (IMS) was addressed as well. Although genome mining of marine actinobacteria has not been fully explored, we believe that genome mining will greatly facilitate natural product research in the near future not only in culturable marine actinobacteria, but also in mining of metagenomes in yet unculturable acti- nobacteria. We also propose that genome mining is not restricted in the identification of new structural and regulatory genes, and it is also important to discover and utilize various other genetic elements, including promoters with different strengths and sequences that response to various environmental conditions and signals, as well as small regulator RNA molecules.
出处
《微生物学通报》
CAS
CSCD
北大核心
2013年第10期1896-1908,共13页
Microbiology China
基金
韩国Next-Generation BioGreen 21295项目(No.PJ0080932011)
关键词
海洋放线菌
天然产物
基因簇
基因组挖掘
影像质谱
Marine actinobacteria, Natural products, Gene cluster, Genome mining, Imagingmass spectrometry