05 December 2013, 16:15
Ernst-Abbe-Platz 2, seminar room 3423

MDM – a Novel Method for Secondary Metabolite Gene Cluster Prediction

Thomas Wolf, Vladimir Shelest, Ekaterina Shelest
(Leibniz Institute for Natural Product Research and Infection Biology – Hans-Knöll-Institute (HKI); Research Group Systems Biology / Bioinformatics)

Co-localization and co-regulation of functionally related genes is a common genomic feature, not only in eukaryotes. Clusters of genes which belong to the same pathway are prevalent in protists, plants and fungi, but can also be found in other organisms. Especially genes involved in the biosynthesis of secondary metabolites (SMs) are often organized in clusters. Although SM gene clusters are relatively well investigated, the majority of regulatory and biochemical details remains still unknown.

The analysis of SM gene clusters is an expensive and time-consuming task in the laboratory. Tools to predict gene clusters in silico already exist, however, todays predictions are rather imprecise and often based on prior knowledge from known clusters. To exactly determine the genes, which belong to a specific cluster, we are working on the so called “motif density method” (MDM). As a novelty, MDM is based on hypothesized cluster specific transcription factor binding sites (csTFBSs), which should occur frequently within the cluster, due to the former mentioned co-regulation, and less frequently outside.

First, MDM extracts all promoter regions of a given genome. Secondly, it predicts putative csTFBS motifs within the promoter regions located in the proximity of cluster backbone genes, for example polyketide synthases or non-ribosomal peptide synthetases. These backbone genes form the basis of SM biosyntheses and are easy to detect in the genome. The most promising motifs are then searched in all promoter regions extracted before. The final step incorporates the calculation of a score, which is based on the variable density of motif occurrences. The set of promoters, respectively genes, with the highest score represents the SM cluster prediction provided by MDM.

¹ corresponding author: ekaterina.shelest@hki-jena.de