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      Expanding the Described Metabolome of the Marine Cyanobacterium Moorea producens JHB through Orthogonal Natural Products Workflows

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          Abstract

          Moorea producens JHB, a Jamaican strain of tropical filamentous marine cyanobacteria, has been extensively studied by traditional natural products techniques. These previous bioassay and structure guided isolations led to the discovery of two exciting classes of natural products, hectochlorin ( 1) and jamaicamides A ( 2) and B ( 3). In the current study, mass spectrometry-based ‘molecular networking’ was used to visualize the metabolome of Moorea producens JHB, and both guided and enhanced the isolation workflow, revealing additional metabolites in these compound classes. Further, we developed additional insight into the metabolic capabilities of this strain by genome sequencing analysis, which subsequently led to the isolation of a compound unrelated to the jamaicamide and hectochlorin families. Another approach involved stimulation of the biosynthesis of a minor jamaicamide metabolite by cultivation in modified media, and provided insights about the underlying biosynthetic machinery as well as preliminary structure-activity information within this structure class. This study demonstrated that these orthogonal approaches are complementary and enrich secondary metabolomic coverage even in an extensively studied bacterial strain.

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          Most cited references33

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          antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers

          Microbial secondary metabolites are a potent source of antibiotics and other pharmaceuticals. Genome mining of their biosynthetic gene clusters has become a key method to accelerate their identification and characterization. In 2011, we developed antiSMASH, a web-based analysis platform that automates this process. Here, we present the highly improved antiSMASH 2.0 release, available at http://antismash.secondarymetabolites.org/. For the new version, antiSMASH was entirely re-designed using a plug-and-play concept that allows easy integration of novel predictor or output modules. antiSMASH 2.0 now supports input of multiple related sequences simultaneously (multi-FASTA/GenBank/EMBL), which allows the analysis of draft genomes comprising multiple contigs. Moreover, direct analysis of protein sequences is now possible. antiSMASH 2.0 has also been equipped with the capacity to detect additional classes of secondary metabolites, including oligosaccharide antibiotics, phenazines, thiopeptides, homo-serine lactones, phosphonates and furans. The algorithm for predicting the core structure of the cluster end product is now also covering lantipeptides, in addition to polyketides and non-ribosomal peptides. The antiSMASH ClusterBlast functionality has been extended to identify sub-clusters involved in the biosynthesis of specific chemical building blocks. The new features currently make antiSMASH 2.0 the most comprehensive resource for identifying and analyzing novel secondary metabolite biosynthetic pathways in microorganisms.
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            Overview of bacterial expression systems for heterologous protein production: from molecular and biochemical fundamentals to commercial systems.

            Kay Terpe (2006)
            During the proteomics period, the growth in the use of recombinant proteins has increased greatly in the recent years. Bacterial systems remain most attractive due to low cost, high productivity, and rapid use. However, the rational choice of the adequate promoter system and host for a specific protein of interest remains difficult. This review gives an overview of the most commonly used systems: As hosts, Bacillus brevis, Bacillus megaterium, Bacillus subtilis, Caulobacter crescentus, other strains, and, most importantly, Escherichia coli BL21 and E. coli K12 and their derivatives are presented. On the promoter side, the main features of the l-arabinose inducible araBAD promoter (PBAD), the lac promoter, the l-rhamnose inducible rhaP BAD promoter, the T7 RNA polymerase promoter, the trc and tac promoter, the lambda phage promoter p L , and the anhydrotetracycline-inducible tetA promoter/operator are summarized.
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              NRPSpredictor2—a web server for predicting NRPS adenylation domain specificity

              The products of many bacterial non-ribosomal peptide synthetases (NRPS) are highly important secondary metabolites, including vancomycin and other antibiotics. The ability to predict substrate specificity of newly detected NRPS Adenylation (A-) domains by genome sequencing efforts is of great importance to identify and annotate new gene clusters that produce secondary metabolites. Prediction of A-domain specificity based on the sequence alone can be achieved through sequence signatures or, more accurately, through machine learning methods. We present an improved predictor, based on previous work (NRPSpredictor), that predicts A-domain specificity using Support Vector Machines on four hierarchical levels, ranging from gross physicochemical properties of an A-domain’s substrates down to single amino acid substrates. The three more general levels are predicted with an F-measure better than 0.89 and the most detailed level with an average F-measure of 0.80. We also modeled the applicability domain of our predictor to estimate for new A-domains whether they lie in the applicability domain. Finally, since there are also NRPS that play an important role in natural products chemistry of fungi, such as peptaibols and cephalosporins, we added a predictor for fungal A-domains, which predicts gross physicochemical properties with an F-measure of 0.84. The service is available at http://nrps.informatik.uni-tuebingen.de/.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 July 2015
                2015
                : 10
                : 7
                : e0133297
                Affiliations
                [1 ]Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, United States
                [2 ]Department of Biology, William Paterson University, Wayne, New Jersey, 07470, United States of America
                [3 ]Department of Pharmacology, Creighton University School of Medicine, Omaha, Nebraska, 68178, United States of America
                [4 ]Department of Biology, California State University San Marcos, San Marcos, California, 92078, United States of America
                [5 ]Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, 194021, Russia
                [6 ]Department of Mathematics and Mechanics, St. Petersburg State University, St. Petersburg, 194021, Russia
                [7 ]Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, 194021, Russia
                [8 ]Life Sciences Institute and Department of Medical Chemistry, University of Michigan, Ann Arbor, Michigan, 48109, United States of America
                [9 ]Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, 92093, United States of America
                [10 ]Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, 92093, United States of America
                CEA-Saclay, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PDB TFM LG PCD WHG. Performed the experiments: PDB EAM SM SD AK LG. Analyzed the data: PDB EAM SM AK LG WHG. Contributed reagents/materials/analysis tools: AK DHS TFM LG PCD WHG. Wrote the paper: PDB EAM TFM LG WHG.

                Article
                PONE-D-15-09239
                10.1371/journal.pone.0133297
                4519256
                26222584
                57860048-4615-4d2a-a653-141467260cf6
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 2 March 2015
                : 25 June 2015
                Page count
                Figures: 9, Tables: 2, Pages: 23
                Funding
                This work was supported by National Institutes of Health grant NS053398 (to WHG and TFM), National Institutes of Health grant CA100851 (to WHG), National Institutes of Health grant CA108874 (to WHG, LG, and DHS), National Institutes of Health grant GM107550 (to WHG, LG, and PCD), and Russian Science Foundation grant 14-50-069 (to AK).
                Categories
                Research Article
                Custom metadata
                NMR and MS data for structural elucidation are found within the paper and its Supporting Information files. All gene sequence data are available from GenBank with the accession numbers KP860346-48. All mass spectrometry data used in forming the Molecular Networks are available from Dryad (datadryad.org) under DOI: 10.5061/dryad.2k6nr.

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