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利用基因簇网络和遗传去重复化揭示次生代谢产物的进化和生物合成。

Uncovering secondary metabolite evolution and biosynthesis using gene cluster networks and genetic dereplication.

机构信息

Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800, Kongens Lyngby, Denmark.

The Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, DK-2800, Kongens Lyngby, Denmark.

出版信息

Sci Rep. 2018 Dec 18;8(1):17957. doi: 10.1038/s41598-018-36561-3.

DOI:10.1038/s41598-018-36561-3
PMID:30560908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6298953/
Abstract

The increased interest in secondary metabolites (SMs) has driven a number of genome sequencing projects to elucidate their biosynthetic pathways. As a result, studies revealed that the number of secondary metabolite gene clusters (SMGCs) greatly outnumbers detected compounds, challenging current methods to dereplicate and categorize this amount of gene clusters on a larger scale. Here, we present an automated workflow for the genetic dereplication and analysis of secondary metabolism genes in fungi. Focusing on the secondary metabolite rich genus Aspergillus, we categorize SMGCs across genomes into SMGC families using network analysis. Our method elucidates the diversity and dynamics of secondary metabolism in section Nigri, showing that SMGC diversity within the section has the same magnitude as within the genus. Using our genome analysis we were able to predict the gene cluster responsible for biosynthesis of malformin, a potentiator of anti-cancer drugs, in 18 strains. To proof the general validity of our predictions, we developed genetic engineering tools in Aspergillus brasiliensis and subsequently verified the genes for biosynthesis of malformin.

摘要

人们对次生代谢产物（SMs）的兴趣日益浓厚，这促使许多基因组测序项目阐明其生物合成途径。结果表明，次生代谢产物基因簇（SMGCs）的数量远远超过已检测到的化合物，这对当前在更大规模上对这些基因簇进行去冗余和分类的方法提出了挑战。在这里，我们提出了一种用于真菌次生代谢基因遗传去冗余和分析的自动化工作流程。本研究以富含次生代谢产物的曲霉属为重点，使用网络分析将基因组中的 SMGC 分类为 SMGC 家族。我们的方法阐明了 Nigri 节中次生代谢的多样性和动态，表明该节中 SMGC 的多样性与属内的多样性相当。我们利用基因组分析能够预测 18 株菌中负责合成抑瘤素的基因簇，抑瘤素是一种抗癌药物的增效剂。为了证明我们预测的普遍有效性，我们在粗糙脉孢菌中开发了遗传工程工具，随后验证了抑瘤素生物合成的基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/6dda1d5c776d/41598_2018_36561_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/c1b24507b934/41598_2018_36561_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/ca2fdba431ba/41598_2018_36561_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/be6b5be36073/41598_2018_36561_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/bd243a39c625/41598_2018_36561_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/6dda1d5c776d/41598_2018_36561_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/c1b24507b934/41598_2018_36561_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/ca2fdba431ba/41598_2018_36561_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/be6b5be36073/41598_2018_36561_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/bd243a39c625/41598_2018_36561_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47ab/6298953/6dda1d5c776d/41598_2018_36561_Fig5_HTML.jpg

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