Discovery of the Tyrobetaine Natural Products and Their Biosynthetic Gene Cluster via Metabologenomics.
Discovery of the Tyrobetaine Natural Products and Their Biosynthetic Gene Cluster via Metabologenomics.
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DOI：
10.1021/acschembio.7b01089
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发表时间：
2018-04-20
影响因子：
4
通讯作者：
Metcalf WW
中科院分区：
文献类型：
--
作者：
Parkinson EI;Tryon JH;Goering AW;Ju KS;McClure RA;Kemball JD;Zhukovsky S;Labeda DP;Thomson RJ;Kelleher NL;Metcalf WW
天然产物（NPs）是药物的丰富来源，但由于再发现率高，传统的发现方法往往不成功。用于天然产物发现的遗传学方法很有前景，但由于难以识别独特的生物合成基因簇（BGCs）以及基因表达不佳，进展缓慢。我们之前开发了代谢组学 - 基因组学方法，该方法结合基因组学和代谢组学数据来发现新的天然产物及其生物合成基因簇。在此，我们将代谢组学 - 基因组学与分子网络相结合，发现了一类新型的天然产物——酪甜菜碱：具有不寻常的三甲基铵酪氨酸残基的非核糖体肽。利用代谢组学 - 基因组学和计算结构预测数据确定了这类不寻常化合物的生物合成基因簇。异源表达证实了该生物合成基因簇，并表明了一种形成三甲基铵的不寻常机制。总体而言，酪甜菜碱的发现显示了代谢组学 - 基因组学与分子网络以及计算结构预测相结合在识别有趣的生物合成反应和新型天然产物方面的巨大潜力。
Natural products (NPs) are a rich source of medicines, but traditional discovery methods are often unsuccessful due to high rates of rediscovery. Genetic approaches for NP discovery are promising, but progress has been slow due to the difficulty of identifying unique biosynthetic gene clusters (BGCs) and poor gene expression. We previously developed the metabologenomics method, which combines genomic and metabolomic data to discover new NPs and their BGCs. Here, we utilize metabologenomics in combination with molecular networking to discover a novel class of NPs, the tyrobetaines: nonribosomal peptides with an unusual trimethylammonium tyrosine residue. The BGC for this unusual class of compounds was identified using metabologenomics and computational structure prediction data. Heterologous expression confirmed the BGC and suggests an unusual mechanism for trimethylammonium formation. Overall, the discovery of the tyrobetaines shows the great potential of metabologenomics combined with molecular networking and computational structure prediction for identifying interesting biosynthetic reactions and novel NPs.
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影响因子：
15
作者：
Forseth, Ry R.;Fox, Ellen M.;Chung, DaWoon;Howlett, Barbara J.;Keller, Nancy P.;Schroeder, Frank C.
通讯作者：
Schroeder, Frank C.
影响因子：
--
作者：
Duncan KR;Crüsemann M;Lechner A;Sarkar A;Li J;Ziemert N;Wang M;Bandeira N;Moore BS;Dorrestein PC;Jensen PR
通讯作者：
Jensen PR
影响因子：
5.1
作者：
Ciminiello, P;Dell'Aversano, C;Pansini, M
通讯作者：
Pansini, M
影响因子：
16.6
作者：
Johnston CW;Skinnider MA;Wyatt MA;Li X;Ranieri MR;Yang L;Zechel DL;Ma B;Magarvey NA
通讯作者：
Magarvey NA
影响因子：
4
作者：
Henke, Matthew T.;Soukup, Alexandra A.;Goering, Anthony W.;McClure, Ryan A.;Thomson, Regan J.;Keller, Nancy P.;Kelleher, Neil L.
通讯作者：
Kelleher, Neil L.