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      Taxonomic and Metabolic Incongruence in the Ancient Genus Streptomyces

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          Abstract

          The advent of culture independent approaches has greatly facilitated insights into the vast diversity of bacteria and the ecological importance they hold in nature and human health. Recently, metagenomic surveys and other culture-independent methods have begun to describe the distribution and diversity of microbial metabolism across environmental conditions, often using 16S rRNA gene as a marker to group bacteria into taxonomic units. However, the extent to which similarity at the conserved ribosomal 16S gene correlates with different measures of phylogeny, metabolic diversity, and ecologically relevant gene content remains contentious. Here, we examine the relationship between 16S identity, core genome divergence, and metabolic gene content across the ancient and ecologically important genus Streptomyces. We assessed and quantified the high variability of average nucleotide identity (ANI) and ortholog presence/absence within Streptomyces, even in strains identical by 16S. Furthermore, we identified key differences in shared ecologically important characters, such as antibiotic resistance, carbohydrate metabolism, biosynthetic gene clusters (BGCs), and other metabolic hallmarks, within 16S identities commonly treated as the same operational taxonomic units (OTUs). Differences between common phylogenetic measures and metabolite-gene annotations confirmed this incongruence. Our results highlight the metabolic diversity and variability within OTUs and add to the growing body of work suggesting 16S-based studies of Streptomyces fail to resolve important ecological and metabolic characteristics.

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          FLASH: fast length adjustment of short reads to improve genome assemblies.

          Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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            The comprehensive antibiotic resistance database.

            The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.
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              Quantifying community assembly processes and identifying features that impose them.

              Spatial turnover in the composition of biological communities is governed by (ecological) Drift, Selection and Dispersal. Commonly applied statistical tools cannot quantitatively estimate these processes, nor identify abiotic features that impose these processes. For interrogation of subsurface microbial communities distributed across two geologically distinct formations of the unconfined aquifer underlying the Hanford Site in southeastern Washington State, we developed an analytical framework that advances ecological understanding in two primary ways. First, we quantitatively estimate influences of Drift, Selection and Dispersal. Second, ecological patterns are used to characterize measured and unmeasured abiotic variables that impose Selection or that result in low levels of Dispersal. We find that (i) Drift alone consistently governs ∼25% of spatial turnover in community composition; (ii) in deeper, finer-grained sediments, Selection is strong (governing ∼60% of turnover), being imposed by an unmeasured but spatially structured environmental variable; (iii) in shallower, coarser-grained sediments, Selection is weaker (governing ∼30% of turnover), being imposed by vertically and horizontally structured hydrological factors;(iv) low levels of Dispersal can govern nearly 30% of turnover and be caused primarily by spatial isolation resulting from limited exchange between finer and coarser-grain sediments; and (v) highly permeable sediments are associated with high levels of Dispersal that homogenize community composition and govern over 20% of turnover. We further show that our framework provides inferences that cannot be achieved using preexisting approaches, and suggest that their broad application will facilitate a unified understanding of microbial communities.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                20 September 2019
                2019
                : 10
                : 2170
                Affiliations
                [1] 1Department of Plant Pathology, Wisconsin Institute for Discovery, University of Wisconsin-Madison , Madison, WI, United States
                [2] 2Department of Biology, Texas State University , San Marcos, TX, United States
                [3] 3Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Joint Genome Institute , Berkeley, CA, United States
                [4] 4Department of Bacteriology, University of Wisconsin-Madison , Madison, WI, United States
                Author notes

                Edited by: Iain Sutcliffe, Northumbria University, United Kingdom

                Reviewed by: Francisco (Paco) Barona-Gomez, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico; Govind Chandra, John Innes Centre (JIC), United Kingdom

                *Correspondence: Cameron R. Currie, currie@ 123456bact.wisc.edu

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2019.02170
                6763951
                31616394
                c50c0fd2-fcc0-47a5-911b-cabdbc117976
                Copyright © 2019 Chevrette, Carlos-Shanley, Louie, Bowen, Northen and Currie.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 July 2019
                : 04 September 2019
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 55, Pages: 12, Words: 7558
                Funding
                Funded by: National Institutes of Health (NIH) 10.13039/100000002
                Award ID: U19 Al109673
                Award ID: U19 TW009872
                Funded by: NIH National Research Service
                Award ID: T32 GM008505
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                streptomyces,metabolism,16s,phylogenomics,metabolites
                Microbiology & Virology
                streptomyces, metabolism, 16s, phylogenomics, metabolites

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