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      Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

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          Abstract

          Background

          Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria.

          Results

          To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp).

          Conclusions

          We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S. oneidensis MR-1. Analysis of correlations in gene expression patterns helps to interpret the reconstructed regulatory network. The inferred regulatory interactions will provide an additional regulatory constrains for an integrated model of metabolism and regulation in S. oneidensis MR-1.

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          Most cited references52

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          MicrobesOnline: an integrated portal for comparative and functional genomics

          Since 2003, MicrobesOnline (http://www.microbesonline.org) has been providing a community resource for comparative and functional genome analysis. The portal includes over 1000 complete genomes of bacteria, archaea and fungi and thousands of expression microarrays from diverse organisms ranging from model organisms such as Escherichia coli and Saccharomyces cerevisiae to environmental microbes such as Desulfovibrio vulgaris and Shewanella oneidensis. To assist in annotating genes and in reconstructing their evolutionary history, MicrobesOnline includes a comparative genome browser based on phylogenetic trees for every gene family as well as a species tree. To identify co-regulated genes, MicrobesOnline can search for genes based on their expression profile, and provides tools for identifying regulatory motifs and seeing if they are conserved. MicrobesOnline also includes fast phylogenetic profile searches, comparative views of metabolic pathways, operon predictions, a workbench for sequence analysis and integration with RegTransBase and other microbial genome resources. The next update of MicrobesOnline will contain significant new functionality, including comparative analysis of metagenomic sequence data. Programmatic access to the database, along with source code and documentation, is available at http://microbesonline.org/programmers.html.
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            RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation

            RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference database offering curated knowledge of the transcriptional regulatory network of Escherichia coli K12, currently the best-known electronically encoded database of the genetic regulatory network of any free-living organism. This paper summarizes the improvements, new biology and new features available in version 6.0. Curation of original literature is, from now on, up to date for every new release. All the objects are supported by their corresponding evidences, now classified as strong or weak. Transcription factors are classified by origin of their effectors and by gene ontology class. We have now computational predictions for σ54 and five different promoter types of the σ70 family, as well as their corresponding −10 and −35 boxes. In addition to those curated from the literature, we added about 300 experimentally mapped promoters coming from our own high-throughput mapping efforts. RegulonDB v.6.0 now expands beyond transcription initiation, including RNA regulatory elements, specifically riboswitches, attenuators and small RNAs, with their known associated targets. The data can be accessed through overviews of correlations about gene regulation. RegulonDB associated original literature, together with more than 4000 curation notes, can now be searched with the Textpresso text mining engine.
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              Information content of binding sites on nucleotide sequences.

              Repressors, polymerases, ribosomes and other macromolecules bind to specific nucleic acid sequences. They can find a binding site only if the sequence has a recognizable pattern. We define a measure of the information (R sequence) in the sequence patterns at binding sites. It allows one to investigate how information is distributed across the sites and to compare one site to another. One can also calculate the amount of information (R frequency) that would be required to locate the sites, given that they occur with some frequency in the genome. Several Escherichia coli binding sites were analyzed using these two independent empirical measurements. The two amounts of information are similar for most of the sites we analyzed. In contrast, bacteriophage T7 RNA polymerase binding sites contain about twice as much information as is necessary for recognition by the T7 polymerase, suggesting that a second protein may bind at T7 promoters. The extra information can be accounted for by a strong symmetry element found at the T7 promoters. This element may be an operator. If this model is correct, these promoters and operators do not share much information. The comparisons between R sequence and R frequency suggest that the information at binding sites is just sufficient for the sites to be distinguished from the rest of the genome.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2011
                15 June 2011
                : 12
                : Suppl 1
                : S3
                Affiliations
                [1 ]Sanford-Burnham Medical Research Institute, La Jolla, California, USA
                [2 ]Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
                [3 ]Lawrence Berkeley National Laboratory, Berkeley, California, USA
                [4 ]Faculty of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia
                [5 ]State Scientific Center GosNIIGenetika, Moscow, Russia
                [6 ]Fellowship for Interpretation of Genomes, Burr Ridge, Illinois, USA
                [7 ]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
                [8 ]Department of Energy Joint Genome Institute, Walnut Creek, California, USA
                Article
                1471-2164-12-S1-S3
                10.1186/1471-2164-12-S1-S3
                3223726
                21810205
                bdc33ecc-1a1d-455e-9abf-636f88816d0c
                Copyright ©2011 Rodionov et al; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research

                Genetics
                Genetics

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