29
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genomewide landscape of gene–metabolome associations in Escherichia coli

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.

          Related collections

          Most cited references35

          • Record: found
          • Abstract: found
          • Article: not found

          Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection

          We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants—the ‘Keio collection'—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The genetic landscape of a cell.

            A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              KEGG for integration and interpretation of large-scale molecular data sets

              Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
                Bookmark

                Author and article information

                Contributors
                sauer@imsb.biol.ethz.ch
                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                16 January 2017
                January 2017
                : 13
                : 1 ( doiID: 10.1002/msb.v13.1 )
                : 907
                Affiliations
                [ 1 ] Institute of Molecular Systems BiologyETH Zürich ZürichSwitzerland
                [ 2 ]Present address: CellzomeGlaxoSmithKline R&D HeidelbergGermany
                Author notes
                [*] [* ]Corresponding author. Tel: +41 44 633 36 72; E‐mail: sauer@ 123456imsb.biol.ethz.ch
                [†]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0000-0001-5006-6874
                http://orcid.org/0000-0002-5923-0770
                http://orcid.org/0000-0003-1271-1021
                Article
                MSB167150
                10.15252/msb.20167150
                5293155
                28093455
                fd1ee4a6-ffa0-435d-942f-6b719966a32c
                © 2017 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 June 2016
                : 13 December 2016
                : 15 December 2016
                Page count
                Figures: 16, Tables: 0, Pages: 12, Words: 9960
                Funding
                Funded by: Eidgenössische Technische Hochschule (ETH) Zürich
                Categories
                Article
                Articles
                Custom metadata
                2.0
                msb167150
                January 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.0.4 mode:remove_FC converted:30.01.2017

                Quantitative & Systems biology
                functional genomics,gwas,interaction network,metabolism,metabolomics,genome-scale & integrative biology,methods & resources

                Comments

                Comment on this article