Accurate prediction of secondary metabolite gene clusters in filamentous fungi
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 supporti...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 110; no. 1; pp. E99 - E107 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
02.01.2013
National Acad Sciences |
| Series | PNAS Plus |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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. 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. [PUBLICATION ABSTRACT] 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. In summary, our present findings can immediately support an area of intense focus within fungal biology, namely, the identification of gene clusters involved in biosynthesis of bioactive metabolites, by providing targets and predictions for gene clusters for the important model fungus A. nidulans . Further, in the short term, they provide a general method for rapid prediction of gene clusters in other fungi. This will assist in the identification of biosynthetic genes for a given SM, which can support pathway elucidation in general and is of particular interest for known and potential bioactive compounds. This method can be applied directly to the many fungal species for which large amounts of legacy data exist. Further, we showed that the gene expression profiles of key genes can be used to predict gene clusters located on different chromosomes involved in the biosynthesis of the same class of compounds (cross-chemistry). Our analysis showed a high degree of coordinated expression between biosynthetic gene clusters, which, in some cases, suggests cross-chemistry between clusters. For example, we used gene deletions and chemical analysis of deletion mutants efficiently to determine two gene clusters on separate chromosomes involved in producing the same family of compounds. We further confirmed the interaction of these gene clusters by structural elucidation of the main compound, a prenylated nonribosomal cyclopeptide called nidulanin A. In the present study, we collected and expanded a compendium of gene expression data for a model fungus, Aspergillus nidulans , to encompass >40 samples from a diverse set of conditions. We combined the expression profiles with the chromosomal location of the genes to identify colocalized and coregulated genes. Using a statistical method, we identified the member genes of biosynthetic clusters around predicted and known SM synthases. Here, we predicted the members of 58 gene clusters and validated these predictions through comparison with 16 known clusters (see example in Fig. P1 ). We constructed additional gene deletion strains to investigate further the accuracy of predictions and to compare the findings with the findings of previous studies, as well as to account for changes in gene annotation over time. Our analysis showed overall accuracy of the predictions. The efficiency of the method depends on the number and diversity of the sampling conditions included in gene expression analysis. This diversity should at least include different growth media and liquid as well as solid-state cultivation. The method is immediately applicable to any fungal species with legacy gene expression data and a sequenced genome. Filamentous fungi are particularly interesting as sources of SMs. Despite their relatively small genomes (30–40 Mb), microbial fungi contain more than 40 different genes catalyzing the biosynthesis of SMs. The number of different compounds produced by each fungus can exceed the number of genes by many times. This increased diversity is due to the highly modular mode of the biosynthesis of SMs, which involves different classes of polymer backbones being modified by a plethora of tailoring enzymes, such as (de)hydratases, oxygenases, hydrolases, and methylases. Secondary (nongrowth associated) metabolites (SMs) are chemical entities found primarily in plants, fungi, and microbes. SMs comprise molecules such as hormones, antibiotics, and toxins and provide abundant sources of pharmaceuticals ( 1 ). Here, we describe methods for predicting and identifying the genes of microbial fungi responsible for the abundant biosynthesis of SMs. These methods are capable of accelerating elucidation of the important SM biosynthetic pathways and should benefit development of pharmaceuticals and synthetic biochemistry ( 2 ). |
| Author | Andreas Klitgaard Jakob B. Nielsen Thomas O. Larsen Lene H. Blicher Uffe H. Mortensen Lene M. Petersen Mia Zachariasen Kristian F. Nielsen Tilde J. Hansen Mikael R. Andersen Charlotte H. Gotfredsen |
| Author_xml | – sequence: 1 givenname: Mikael R. surname: Andersen fullname: Andersen, Mikael R. organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 2 givenname: Jakob B. surname: Nielsen fullname: Nielsen, Jakob B. organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 3 givenname: Andreas surname: Klitgaard fullname: Klitgaard, Andreas organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 4 givenname: Lene M. surname: Petersen fullname: Petersen, Lene M. organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 5 givenname: Mia surname: Zachariasen fullname: Zachariasen, Mia organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 6 givenname: Tilde J. surname: Hansen fullname: Hansen, Tilde J. organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 7 givenname: Lene H. surname: Blicher fullname: Blicher, Lene H. organization: DTU Multi-Assay Core, Department of Systems Biology, and – sequence: 8 givenname: Charlotte H. surname: Gotfredsen fullname: Gotfredsen, Charlotte H. organization: Department of Chemistry, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark – sequence: 9 givenname: Thomas O. surname: Larsen fullname: Larsen, Thomas O. organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 10 givenname: Kristian F. surname: Nielsen fullname: Nielsen, Kristian F. organization: Center for Microbial Biotechnology, Department of Systems Biology – sequence: 11 givenname: Uffe H. surname: Mortensen fullname: Mortensen, Uffe H. organization: Center for Microbial Biotechnology, Department of Systems Biology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23248299$$D View this record in MEDLINE/PubMed |
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| Notes | http://dx.doi.org/10.1073/pnas.1205532110 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 Author contributions: M.R.A., J.B.N., L.M.P., C.H.G., T.O.L., K.F.N., and U.H.M. designed research; M.R.A., J.B.N., A.K., L.M.P., M.Z., T.J.H., L.H.B., C.H.G., and K.F.N. performed research; M.R.A. and K.F.N. contributed new reagents/analytic tools; M.R.A., J.B.N., A.K., L.M.P., T.J.H., C.H.G., T.O.L., K.F.N., and U.H.M. analyzed data; and M.R.A., J.B.N., A.K., L.M.P., C.H.G., K.F.N., and U.H.M. wrote the paper. Edited by Jerrold Meinwald, Cornell University, Ithaca, NY, and approved November 19, 2012 (received for review August 20, 2012) |
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| SubjectTerms | Aspergillus nidulans Aspergillus nidulans - genetics biochemical pathways Biochemistry Biological Sciences Biosynthesis Biosynthetic Pathways - genetics Cluster Analysis DNA Enzymes Fungi Gene expression Gene Expression Profiling - methods Gene Expression Regulation, Fungal - genetics Metabolites Metabolome - genetics Microarray Analysis - methods multigene family Multigene Family - genetics PNAS Plus Polyketide Synthases - genetics prediction Secondary metabolites Tandem Mass Spectrometry Xanthines - chemistry Xanthines - isolation & purification |
| Title | Accurate prediction of secondary metabolite gene clusters in filamentous fungi |
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