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      Secondary Metabolism in the Gill Microbiota of Shipworms (Teredinidae) as Revealed by Comparison of Metagenomes and Nearly Complete Symbiont Genomes

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          Abstract

          We define a system in which the major symbionts that are important to host biology and to the production of secondary metabolites can be cultivated. We show that symbiotic bacteria that are critical to host nutrition and lifestyle also have an immense capacity to produce a multitude of diverse and likely novel bioactive secondary metabolites that could lead to the discovery of drugs and that these pathways are found within shipworm gills. We propose that, by shaping associated microbial communities within the host, the compounds support the ability of shipworms to degrade wood in marine environments. Because these symbionts can be cultivated and genetically manipulated, they provide a powerful model for understanding how secondary metabolism impacts microbial symbiosis.

          ABSTRACT

          Shipworms play critical roles in recycling wood in the sea. Symbiotic bacteria supply enzymes that the organisms need for nutrition and wood degradation. Some of these bacteria have been grown in pure culture and have the capacity to make many secondary metabolites. However, little is known about whether such secondary metabolite pathways are represented in the symbiont communities within their hosts. In addition, little has been reported about the patterns of host-symbiont co-occurrence. Here, we collected shipworms from the United States, the Philippines, and Brazil and cultivated symbiotic bacteria from their gills. We analyzed sequences from 22 shipworm gill metagenomes from seven shipworm species and from 23 cultivated symbiont isolates. Using (meta)genome sequencing, we demonstrate that the cultivated isolates represent all the major bacterial symbiont species and strains in shipworm gills. We show that the bacterial symbionts are distributed among shipworm hosts in consistent, predictable patterns. The symbiotic bacteria harbor many gene cluster families (GCFs) for biosynthesis of bioactive secondary metabolites, only <5% of which match previously described biosynthetic pathways. Because we were able to cultivate the symbionts and to sequence their genomes, we can definitively enumerate the biosynthetic pathways in these symbiont communities, showing that ∼150 of ∼200 total biosynthetic gene clusters (BGCs) present in the animal gill metagenomes are represented in our culture collection. Shipworm symbionts occur in suites that differ predictably across a wide taxonomic and geographic range of host species and collectively constitute an immense resource for the discovery of new biosynthetic pathways corresponding to bioactive secondary metabolites.

          IMPORTANCE We define a system in which the major symbionts that are important to host biology and to the production of secondary metabolites can be cultivated. We show that symbiotic bacteria that are critical to host nutrition and lifestyle also have an immense capacity to produce a multitude of diverse and likely novel bioactive secondary metabolites that could lead to the discovery of drugs and that these pathways are found within shipworm gills. We propose that, by shaping associated microbial communities within the host, the compounds support the ability of shipworms to degrade wood in marine environments. Because these symbionts can be cultivated and genetically manipulated, they provide a powerful model for understanding how secondary metabolism impacts microbial symbiosis.

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication

            Abstract Summary We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 min, with rich information such as pseudogenes, translation exceptions and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future. Availability and implementation The software is implemented in Python 3 and runs in both Python 2.7 and 3.4—on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/. Contact yn@nig.ac.jp Supplementary information Supplementary data are available at Bioinformatics online.
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              antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

              Abstract Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msys
                msys
                mSystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                30 June 2020
                May-Jun 2020
                : 5
                : 3
                : e00261-20
                Affiliations
                [a ]Ocean Genome Legacy Center, Department of Marine and Environmental Science, Northeastern University, Nahant, Massachusetts, USA
                [b ]The Marine Science Institute, University of the Philippines Diliman, Quezon City, Philippines
                [c ]Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah, USA
                [d ]Bioinformatic and Microbial Ecology Laboratory—BIOME, Federal University of Bahia, Salvador, Bahia, Brazil
                [e ]Drug Research and Development Center, Department of Physiology and Pharmacology, Federal University of Ceará, Ceará, Brazil
                [f ]Philippine Genome Center, University of the Philippines Diliman, Quezon City, Philippines
                [g ]Institute of Marine Science, School of Biological Sciences, University of Portsmouth, Portsmouth, United Kingdom
                University of Vienna
                Author notes
                Address correspondence to Eric W. Schmidt, ews1@ 123456utah.edu , or Margo G. Haygood, mhaygood@ 123456ucsd.edu .

                Marvin A. Altamia and Zhenjian Lin contributed equally to this article. Author order was determined alphabetically.

                Citation Altamia MA, Lin Z, Trindade-Silva AE, Uy ID, Shipway JR, Wilke DV, Concepcion GP, Distel DL, Schmidt EW, Haygood MG. 2020. Secondary metabolism in the gill microbiota of shipworms (Teredinidae) as revealed by comparison of metagenomes and nearly complete symbiont genomes. mSystems 5:e00261-20. https://doi.org/10.1128/mSystems.00261-20.

                Author information
                https://orcid.org/0000-0001-5277-9544
                Article
                mSystems00261-20
                10.1128/mSystems.00261-20
                7329324
                32606027
                1279a8c4-c56c-47ae-9724-3d7783323337
                Copyright © 2020 Altamia et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 1 April 2020
                : 16 June 2020
                Page count
                supplementary-material: 10, Figures: 9, Tables: 1, Equations: 0, References: 66, Pages: 18, Words: 11856
                Funding
                Funded by: National Council of Technological and Scientific Development (CNPq);
                Award ID: 473030/2013-6
                Award Recipient :
                Funded by: Coordination for the Improvement of Higher Education Personnel (CAPES);
                Award ID: 400764/2014-8
                Award Recipient :
                Funded by: HHS | National Institutes of Health (NIH), https://doi.org/10.13039/100000002;
                Award ID: U19TW008163
                Award Recipient : Award Recipient : Award Recipient : Award Recipient : Award Recipient : Award Recipient : Award Recipient : Award Recipient :
                Funded by: National Science Foundation (NSF), https://doi.org/10.13039/100000001;
                Award ID: IOS 1442759
                Award Recipient : Award Recipient : Award Recipient :
                Funded by: DOC | National Oceanic and Atmospheric Administration (NOAA), https://doi.org/10.13039/100000192;
                Award ID: NA190AR0110303
                Award Recipient : Award Recipient : Award Recipient : Award Recipient :
                Categories
                Research Article
                Host-Microbe Biology
                Custom metadata
                May/June 2020

                biosynthesis,metagenomics
                biosynthesis, metagenomics

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