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      A Scalable Platform to Identify Fungal Secondary Metabolites and Their Gene Clusters

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          Abstract

          The genomes of filamentous fungi contain up to ~90 biosynthetic gene clusters (BGCs), encoding diverse secondary metabolites, an enormous reservoir of untapped chemical potential. However, recalcitrant genetics, cryptic expression, and unculturability prevent the systematic exploitation of these gene clusters and harvesting of their products. With heterologous expression of fungal BGCs largely limited to expression of single or partial clusters, we established a scalable process for expression of large numbers of full-length gene clusters, called FAC-MS. Using Fungal Artificial Chromosomes (FACs) with Metabolomic Scoring (MS) we screened 56 secondary metabolite BGCs from diverse fungal species for expression in A. nidulans. Fifteen new metabolites were discovered and confidently assigned to their BGCs. A new macrolactone, valactamide A, and its hybrid PKS-NRPS gene cluster were characterized extensively using this integrated platform. Regularizing access to fungal secondary metabolites at an unprecedented scale stands to revitalize drug discovery platforms with renewable sources of natural products.

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          Most cited references39

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          CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

          Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. © 2011 American Chemical Society
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            SMURF: Genomic mapping of fungal secondary metabolite clusters.

            Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters. 2010 Elsevier Inc. All rights reserved.
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              Recombineering: a powerful new tool for mouse functional genomics.

              Highly efficient phage-based Escherichia coli homologous recombination systems have recently been developed that enable genomic DNA in bacterial artificial chromosomes to be modified and subcloned, without the need for restriction enzymes or DNA ligases. This new form of chromosome engineering, termed recombinogenic engineering or recombineering, is efficient and greatly decreases the time it takes to create transgenic mouse models by traditional means. Recombineering also facilitates many kinds of genomic experiment that have otherwise been difficult to carry out, and should enhance functional genomic studies by providing better mouse models and a more refined genetic analysis of the mouse genome.
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                Author and article information

                Journal
                101231976
                32624
                Nat Chem Biol
                Nat. Chem. Biol.
                Nature chemical biology
                1552-4450
                1552-4469
                21 March 2017
                12 June 2017
                August 2017
                12 December 2017
                : 13
                : 8
                : 895-901
                Affiliations
                [1 ]Department of Chemistry, Northwestern University, Evanston, Illinois, USA
                [2 ]Department of Medical Microbiology and Immunology and Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA
                [3 ]Intact Genomics, Inc. St Louis, Missouri, USA
                [4 ]Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
                [5 ]Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA
                [6 ]Center for Forest Mycology Research, Northern Research Station, US Forest Service, Madison, WI, 53726
                Author notes
                Correspondence should be addressed to C.C.W. ( cwu@ 123456intactgenomics.com ), N.P.K. ( npkeller@ 123456wisc.edu ), or N.L.K. ( n-kelleher@ 123456northwestern.edu )
                [7]

                These authors contributed equally to this work.

                Article
                NIHMS859941
                10.1038/nchembio.2408
                5577364
                28604695
                3dc1923e-2571-401f-895f-64bd37635657

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                Biochemistry
                Biochemistry

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