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      The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2)

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          Abstract

          Individual Streptomyces species have the genetic potential to produce a diverse array of natural products of commercial, medical and veterinary interest. However, these products are often not detectable under laboratory culture conditions. To harness their full biosynthetic potential, it is important to develop a detailed understanding of the regulatory networks that orchestrate their metabolism. Here we integrate nucleotide resolution genome-scale measurements of the transcriptome and translatome of Streptomyces coelicolor, the model antibiotic-producing actinomycete. Our systematic study determines 3,570 transcription start sites and identifies 230 small RNAs and a considerable proportion (∼21%) of leaderless mRNAs; this enables deduction of genome-wide promoter architecture. Ribosome profiling reveals that the translation efficiency of secondary metabolic genes is negatively correlated with transcription and that several key antibiotic regulatory genes are translationally induced at transition growth phase. These findings might facilitate the design of new approaches to antibiotic discovery and development.

          Abstract

          Bacteria of the genus Streptomyces produce a great variety of natural products, the biosynthesis of which is subject to complex regulatory networks. Here the authors present a high-resolution, genome-wide analysis of the transcriptome and translatome of Streptomyces coelicolor under various growth conditions.

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          Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2).

          Streptomyces coelicolor is a representative of the group of soil-dwelling, filamentous bacteria responsible for producing most natural antibiotics used in human and veterinary medicine. Here we report the 8,667,507 base pair linear chromosome of this organism, containing the largest number of genes so far discovered in a bacterium. The 7,825 predicted genes include more than 20 clusters coding for known or predicted secondary metabolites. The genome contains an unprecedented proportion of regulatory genes, predominantly those likely to be involved in responses to external stimuli and stresses, and many duplicated gene sets that may represent 'tissue-specific' isoforms operating in different phases of colonial development, a unique situation for a bacterium. An ancient synteny was revealed between the central 'core' of the chromosome and the whole chromosome of pathogens Mycobacterium tuberculosis and Corynebacterium diphtheriae. The genome sequence will greatly increase our understanding of microbial life in the soil as well as aiding the generation of new drug candidates by genetic engineering.
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            Comprehensive comparative analysis of strand-specific RNA sequencing methods

            Strand-specific, massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in strand-specificity, library complexity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.
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              Molecular regulation of antibiotic biosynthesis in streptomyces.

              Streptomycetes are the most abundant source of antibiotics. Typically, each species produces several antibiotics, with the profile being species specific. Streptomyces coelicolor, the model species, produces at least five different antibiotics. We review the regulation of antibiotic biosynthesis in S. coelicolor and other, nonmodel streptomycetes in the light of recent studies. The biosynthesis of each antibiotic is specified by a large gene cluster, usually including regulatory genes (cluster-situated regulators [CSRs]). These are the main point of connection with a plethora of generally conserved regulatory systems that monitor the organism's physiology, developmental state, population density, and environment to determine the onset and level of production of each antibiotic. Some CSRs may also be sensitive to the levels of different kinds of ligands, including products of the pathway itself, products of other antibiotic pathways in the same organism, and specialized regulatory small molecules such as gamma-butyrolactones. These interactions can result in self-reinforcing feed-forward circuitry and complex cross talk between pathways. The physiological signals and regulatory mechanisms may be of practical importance for the activation of the many cryptic secondary metabolic gene cluster pathways revealed by recent sequencing of numerous Streptomyces genomes.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                02 June 2016
                2016
                : 7
                : 11605
                Affiliations
                [1 ]Department of Biological Sciences, Korea Advanced Institute of Science and Technology , Daejeon 305-701, Korea
                [2 ]KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology , Daejeon 305-701, Korea
                [3 ]School of Chemical and Biological Engineering, Seoul National University , Seoul 151-742, Korea
                [4 ]Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Surrey GU2 7XH, UK
                [5 ]Department of Chemistry and Nano Science, Ewha Womans University , Seoul 120-750, Korea
                [6 ]School of Biological Sciences, Seoul National University , Seoul 151-742, Korea
                [7 ]Intelligent Synthetic Biology Center , Daejeon 305-701, Korea
                Author notes
                [*]

                These authors contributed equally to this work.

                [†]

                Present address: School of Pharmacy and Biomolecular Sciences, University of Brighton, Brighton BN2 4GJ, UK.

                Author information
                http://orcid.org/0000-0001-5286-7771
                http://orcid.org/0000-0003-4788-4184
                Article
                ncomms11605
                10.1038/ncomms11605
                4895711
                27251447
                3e66861c-169a-4f4c-bc18-abdce74de52e
                Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 16 June 2015
                : 12 April 2016
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