A method for accurate prediction of the size of secondary metabolite clusters in Aspergillus nidulans.

Ref ID: 15769

Author:

Mikael R. Andersen1, Jakob B. Nielsen1,
Mia Zachariassen1, Tilde J. Hansen1, Kristian F. Nielsen1, and Uffe H. Mortensen1

Author address:

Center for Microbial Biotechnology, Technical University of
Denmark, Denmark.

Full conference title:

26th Fungal Genetics Conference

Date: 15 March 2014

Abstract:

Fungal secondary metabolites (SMs) are receiving increasing interest due to their role as bioactives, ranging from antibiotics over cholesterol-lowering
drugs to food toxins. The identification of SMs and their biosynthetic gene clusters are thus a major topic of interest. Identifying these genes is a tedious
and time-consuming affair, with the standard method requiring the knockout of genes on both sides of putative SM synthases. Furthermore, one does not
know the number of genes in the cluster and thereby extent of this work before starting the experiment. In this work, we present an algorithm for prediction
of the size of SM clusters in Aspergillus nidulans. The method is based on an gene expression catalog of >60 transcriptome experiments, using a diverse
set of strains, media, carbon sources, and solid/liquid cultivations. Furthermore, the method is independent of the quality of annotation. Application of
the algorithm has allowed the accurate prediction of the number of included genes in well-characterized gene clusters. including the 25 genes of the
sterigmatocystin cluster and the emericellamide cluster (4 genes). The method has provided strong predictions of unknown clusters, some of which we
have verified experimentally and identified the corresponding metabolites.

Abstract Number: NULL

Conference Year: 2011

Link to conference website: NULL

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