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Immense diversity found in secondary metabolite gene clusters in filamentous fungi and bacteria using comparative genomics
Tammi Vesth , , Jens Frisvad , Ákos Kovács , Lars Jelsbak , Tilmann Weber , Scott E. Baker , Mikael R. Andersen 
1) Department of Bioengineering, Technical University of Denmark, Lyngby, Denmark 2) Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark 3) Joint Bioenergy Institute, Berkeley, CA, USA
Secondary metabolism in microorganisms is defined as non-life essential metabolism such as the production of mycotoxins and antibiotic compounds. This definition has to some extent been obscure as it is hard to define what is essential for the life of a microorganism in natural environments. Who is to say that the secondary metabolite penicillin produced by species of Penicillium is not essential for the life of the fungi. The compounds produced in these reactions are possible antimicrobial, biofuels, food spoilers and other important or valuable compounds. Secondary metabolism is, therefore, the subject of great curiosity for both biological and financial reasons. The number of known secondary metabolite compounds far exceed the number of characterized genes involved in these pathways and the effort required to elucidate the connection between metabolites and genes require time-consuming and expensive chemical characterization. Because of the benefits and challenges in this elucidation much effort has been given to the computational prediction of genes involved in secondary metabolism, with the most prominent software being AntiSMASH (bacteria and fungi) and SMURF (fungi). These methods provide valuable knowledge in the selection of gene targets for further analysis and can increase the speed of gene to compound association tremendously.
Here we present the prediction of secondary metabolism genes clusters and following comparative genomics analysis of species of Penicillium, Aspergillus, Bacillus, and Pseudomonas. We have predicted secondary metabolism in species 20 species from each of these 4 different groups (80 species) using antiSMASH and fungiSMASH and created families of clusters believed to produce similar compounds. The method relies partly on homology of individual genes but also on the analysis shows a tremendous diversity of clusters in all fours groups of species, but also shows that the fungi species have a much higher diversity than the bacteria compared to the general diversity. It is also seen that the number of predicted clusters keeps growing with new species illustrating the immense natural diversity and potential of these compounds. In the construction of cluster families, we test the effect of parameters on the families for bacterial and fungal data. The analysis method presented here illustrates how the prediction of secondary metabolite genes can be used for bacteria and fungi and shows how the methods must be adjusted to the type of microorganism. It also illustrates the vast diversity and potential in bioinformatics in the field of secondary metabolism and the further association of genes to compounds.