Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters
Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters
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DOI：
10.1038/s42003-019-0333-6
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发表时间：
2019-02-28
影响因子：
5.9
通讯作者：
Breitling，Rainer
中科院分区：
文献类型：
--
作者：
Del Carratore，Francesco;Zych，Konrad;Breitling，Rainer
负责产生细菌特异性代谢物的生物合成机制由称为生物合成基因簇（BGC）的物理聚集的基因组编码。许多BGC的实验表征导致BGC内的基因亚群的阐明，共同负责在不同的遗传背景下相同的生物合成功能。我们开发了一种无监督的统计方法，能够以系统和自动化的方式成功地检测大量预测BGC中的模块（假定的功能子簇）。我们的方法证实了多个已知的亚群，证明了其效率和灵敏度。此外，由此产生的大量新定义的模块提供了新的见解，这些模块遗传实体的流行和推定的生物合成作用。对数百个共同进化的基因组的自动化和无偏见的鉴定是高价值化合物的发现和生物合成工程的重要突破。
The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative functional subclusters) within an extensive set of predicted BGCs in a systematic and automated manner. Multiple already known subclusters were confirmed by our method, proving its efficiency and sensitivity. In addition, the resulting large collection of newly defined modules provides new insights into the prevalence and putative biosynthetic role of these modular genetic entities. The automated and unbiased identification of hundreds of co-evolving group of genes is an essential breakthrough for the discovery and biosynthetic engineering of high-value compounds.