SECONDARY BIOACTIVE METABOLITE GENE CLUSTERS IDENTIFICATION OF ANTICANDIDA-PRODUCING Streptomyces Sp . GMR 22 ISOLATED FROM WANAGAMA FOREST AS REVEALED BY GENOME MINING APPROACH

1Graduate School of Biotechnology, Universitas Gadjah Mada, Barek Utara, Yogyakarta, Indonesia 2Biological Resource Center, Nat Inst of Technology and Evaluation, Nishihara, Shibuya-ku, Tokyo, Japan 3Biotechnology Research Center, The University of Tokyo, Bunkyo-ku, Tokyo, Japan 4Dept of Agricultural Microbiology, Universitas Gadjah Mada, Bulaksumur, Yogyakarta, Indonesia 5Otorhinolaryngology Head and Neck Surgery, Faculty of Medicine, Universitas Gadjah Mada, Sekip, Yogyakarta, Indonesia

Recently, genomic discovery from some Streptomyces can be used as guidance to obtain new bioactive compounds.In addition, biosynthesis gene clusters mining their genomes becomes a key method to accelerate identification and characterization.The antibiotic and secondary metabolite analysis of the shell (antiSMASH) is a web server and stand-alone software, which combines automatic identification of secondary metabolite gene clusters in the genome sequence analysis of a large collection of algorithms-specific compounds (Medema et al., 2011;Weber et al., 2015).For the last two years, antiSMASH became a standard tool for analyzing the genomes of bacteria and fungi for their potential of secondary metabolites production (Blin et al., 2013).In this paper, we reported the results of the genome mining by using AntiSMASH for analyzing genome sequence of GMR22 and compared to the most closed related Streptomyces based on the analysis of whole genome phylogenetic (phylogenomic).

Analysis of whole genome phylogenetic tree of GMR22
Streptomyces sp.GMR22 genome sequence analysis was done by Next Generation Sequencing (NGS) platforms using 454 pyrosequencing technology (454 GS FLX) and HiSeq1000 (Illumina).Whole-genome phylogenetic analysis (phylogenomic) was done by Neighbour-joining algorithm showed genome relationship of Streptomyces sp.GMR22 and 20 complete genome sequences of Streptomyces.Phylogeny tree was created using CV tree with a k-value of 6 (Xu and Hao, 2009) with Bacillus subtilis natto BEST195 as an outgroup and visualized with MEGA 6.06 (Tamura et al., 2013).
Identification, annotation, and analysis of gene cluster involved in the biosynthesis of secondary metabolites and predictions core structure produced done by using antiSMASH 3.0.(Medema et al., 2011;Weber et al., 2015).Available at HTTP://antism ash.secondarymetabolites.org.Prodigal and run secondary metabolite detection on all possible ORFs were chosen in the Gene finding options parameters.

Phylogenomic of GMR22
Genome sequence analysis results showed that the size of the genome of GMR22 was 11,42 Mbp.Based on the phylogenomic analysis showed that GMR22 isolate closely related to Streptomyces bingchenggensis BCW1 and clustered with strains S. violaceusniger Tu 4113, S. rapamycinicus NRRL 5491, and S. davawensis JCM 4913 that have large genome size (9.5-12.7Mbp).

Secondary metabolite biosynthesis gene clusters in Streptomyces sp. GMR22
Secondary metabolism of microbes is a rich source of antibiotics, chemotherapy, insecticides and other high value chemicals.Genome mining group biosynthesis pathway genes coding for these metabolites has been a key methodology for the discovery of new compounds.To determine the gene cluster and bioactive secondary metabolites produced by strain GMR22 were then analyzed with AntiSMASH 3.0 program (Table I).
The results indicated that GMR22 harbored at least 63 gene clusters encoding the biosynthetic pathways of secondary metabolites.It was the highest number of gene clusters had been observed among the members of Streptomyces groups.It was also interesting that more than a third of the genes clusters are polyketide syntethase: 21 gene clusters contained of type I of pure polyketide syntethase (T1pks), 3 gene clusters contained of type II of polyketide syntethase (T2pks), 3 gene clusters contained of hybrid type III of polyketide syntethase, and 1 gene cluster of hybrid type I of polyketide syntethase.
Compared to the closest strain in phylogenomic tree (Figure 1), namely Streptomyces bingchenggensis BCW-1 (Wang et al., 2010), some secondary metabolites have some similarities.AntiSMASH analysis revealed that Streptomyces sp.GMR22 harboring at least 63 bioactive secondary metabolites gene clusters (12 NRPS and 28 PKS), while BCW-1 only has 53 bioactive secondary metabolites gene clusters (16 NRPS and 20 PKS).At least there are 9 of bioactive secondary metabolites produced by both strain, they were ectoin, hopene, actinomycin,, skyllamycin, geldanamycin, desferrioxamine B, echosides, meridamycin and spores pigments.Phylogeny tree was created using CVtree with a k-value of 6 (Xu and Hao, 2009) with Bacillus subtilis natto BEST195 as an out-group and was visualized with MEGA 6.06 (Tamura et al., 2013).Here are some predictable chemical structures of secondary metabolites Streptomyces sp.GMR22 (Figures 2 and 3).The analysis was based on the genetic organization of secondary metabolites.
From figure 2, it can be seen that mostly the secondary metabolites of GMR22 dominated by type I-PKS.Polyketide is a large family of natural products found in bacteria, fungi, and plants.In addition, many clinically important drugs are included in this class, such as tetracycline, daunorubicin, erythromycin, rapamycin, and lovastatin.These compounds were biosynthesized from the precursor acyl-CoA by polyketide synthetase (PKS).Recent literature on polyketide biosynthesis suggests that polyketide synthases have much greater diversity in both mechanism and structure than the current type I, II and III paradigms.These examples serve as an inspiration for searching novel polyketide synthases to give new insights into polyketide biosynthesis and to provide new opportunities for combinatorial biosynthesis (Shen, 2003).
Secondary metabolites were also found in GMR22 with NRPS structure and hybrid NRPS with PKSs or others (Figure 3).However, NRPS structure is less compared to the class of PKSs compounds.Two secondary metabolites that encoded by type III of PKS were found in GMR22 as well.Type III of PKS is commonly found in fungi and plants.In general, type III of PKSs are involved in the biosynthesis of several lipid compounds and variety of secondary metabolite has some interesting characteristics, which does not belong to type III of PKS plants.In addition, many compounds produced by type III of PKS bacteria have significant biological functions in the interests of the pharmaceutical (Katsuyama and Ohnishi, 2012).

Secondary metabolite biosynthesis gene clusters involved in anticancer production
Based on the genome mining with antiSMASH 3.0, GMR22 has gene cluster for Desferrioxamine B which is 100% homologous with the most similar known cluster.Figure 4 shows a comparison of the gene cluster Desferrioxamine B GMR22 with Desferrioxamine B gene cluster of Streptomyces coelicolorA3(2) and Streptomyces griseus sub sp.griseus.
Desferrioxamine B (MIB BGC0000941_c1) is a highly conserved gene cluster in Streptomyces genera (Figure 4).From this Figure, it can be seen that both genes that encode enzymes, as well as location and sequence of genes for GMR22, were similar to the gene cluster in Streptomyces coelicolor A3(2).The gene cluster comprises a set of 4 main enzymes (2 to 5) with essential functions for the siderophore biosynthesis: the gene encoding siderophore biosynthesis of L-2,4diaminobutyrate decarboxylase DesA (2), siderophore biosynthesis protein, monooxygenase DesB (3), siderophoresynthetase small component, and acetyltransferase DesC (4), and siderophoresynthetase component ligase DesD (5).There are 2 addition enzymes that may be also involved, namely: a protein associated with Desferrioxamine E biosynthesis (1) and Desferrioxamine E transporter (6).In GMR22 mostly the size of Desferrioxamine genes are longer than Desferrioxamine genes in Streptomyces coelicolor A3(2).Functional analysis comparison between of gene cluster from GMR22 and strain A3(2) would be an interesting study to prove whether the activities are the same.Desferrioxamine B is an iron chelator, which has a variety of clinical applications for patients with iron overload in terms of improving the quality of life and overall survival.In addition, Desferrioxamin B in the experiment showed potential anticancer in the cells of colon cancer (Salis et al., 2014).

CONCLUSION
Based on the analysis of phylogeny relationship on the whole genome sequence showed that GMR22 was closed related with Streptomyces bingchenggensis BCW1 and clustered with strains of Streptomyces which have large genome size (9.5-12.7Mbp).AntiSMASH analysis revealed that Streptomyces sp.GMR22 harbored at least 63 gene clusters that encode the biosynthetic pathways of secondary metabolites.It was the highest number of gene clusters had been observed among the members of Streptomyces groups, with PKS was predicted as the major groups of the identified gene cluster products.Further studies on Streptomyces sp.GMR22 will provide more insights into natural product biosynthesis potential of related Streptomyces.

Figure 1 .
Figure 1.Analysis of whole-genome phylogenetic tree with Neighbour-joining algorithm showed the relationship of Streptomyces sp.GMR22 and other 20 completed genomic sequences of Streptomyces.Phylogeny tree was created using CVtree with a k-value of 6(Xu and Hao, 2009) with Bacillus subtilis natto BEST195 as an out-group and was visualized with MEGA 6.06(Tamura et al., 2013).

Figure 2 .
Figure 2. The chemical structure of the type 1-PKS predictable core group of secondary metabolites Streptomyces sp.GMR22 based on analysis of genome mining

Table 1 .
Cluster of genes and bioactive secondary metabolites of Streptomyces sp.GMR22 based on the analysis of genome sequences with AntiSMASH 3.0 program cluster 1-14.

Table II .
Cluster of genes and bioactive secondary metabolites of Streptomyces sp.GMR22 based on the analysis of genome sequences with AntiSMASH 3.0 program cluster 15-55

Table 3 .
Cluster of genes and bioactive secondary metabolites of Streptomyces sp.GMR22 based on the analysis of genome sequences with AntiSMASH 3.0 program cluster 56-63.