Accurate prediction of secondary metabolite gene clusters in filamentous fungi
Accurate prediction of secondary metabolite gene clusters in filamentous fungi
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DOI：
10.1073/pnas.1205532110
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发表时间：
2013-01-02
影响因子：
11.1
通讯作者：
Mortensen, Uffe H.
中科院分区：
文献类型：
--
作者：
Andersen, Mikael R.;Nielsen, Jakob B.;Mortensen, Uffe H.
真菌次级代谢产物的生物合成途径目前受到强烈的努力，以阐明这些化合物的遗传基础，由于其在制药和合成生物化学的巨大潜力。优选的方法是有条理的基因缺失，以一次一簇地鉴定关键酶的支持酶。在这项研究中，我们设计并应用了构巢曲霉的DNA表达阵列，结合传统数据，形成一个全面的基因表达纲要。我们应用基于关联的内疚分析来预测在我们的实验条件下活性的58个酶的生物合成簇的程度。与传统数据的比较显示，该方法在16个已知聚类中的13个中是准确的，并且对于其余3个聚类几乎是准确的。此外，我们应用数据聚类方法，识别物理上分离的基因簇（超簇）之间的交叉化学，并通过预测和验证由合成酶AN1242和异戊烯基转移酶AN11080组成的超簇以及识别产物化合物nidulanin A，用遗留数据和实验验证这一点。我们使用A。由于丰富的可用生物化学数据，我们的方法开发和验证需要使用nidulans，但该方法可应用于具有测序和组装基因组的任何真菌，从而支持真菌界中进一步的次级代谢物途径阐明。
Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.