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      C-Diazeniumdiolate Graminine in the Siderophore Gramibactin Is Photoreactive and Originates from Arginine

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      ACS Chemical Biology
      American Chemical Society

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          Abstract

          Siderophores are synthesized by microbes to facilitate iron acquisition required for growth. Catecholate, hydroxamate, and α-hydroxycarboxylate groups comprise well-established ligands coordinating Fe(III) in siderophores. Recently, a C-type diazeniumdiolate ligand in the newly identified amino acid graminine (Gra) was found in the siderophore gramibactin (Gbt) produced by Paraburkholderia graminis DSM 17151. The N–N bond in the diazeniumdiolate is a distinguishing feature of Gra, yet the origin and reactivity of this C-type diazeniumdiolate group has remained elusive until now. Here, we identify l-arginine as the direct precursor to l-Gra through the isotopic labeling of l-Arg, l-ornithine, and l-citrulline. Furthermore, these isotopic labeling studies establish that the N–N bond in Gra must be formed between the N δ and N ω of the guanidinium group in l-Arg. We also show the diazeniumdiolate groups in apo-Gbt are photoreactive, with loss of nitric oxide (NO) and H + from each d-Gra yielding E/ Z oxime isomers in the photoproduct. With the loss of Gbt’s ability to chelate Fe(III) upon exposure to UV light, our results hint at this siderophore playing a larger ecological role. Not only are NO and oximes important in plant biology for communication and defense, but so too are NO-releasing compounds and oximes attractive in medicinal applications.

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          Most cited references43

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          KEGG: kyoto encyclopedia of genes and genomes.

          M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            Toward understanding the origin and evolution of cellular organisms

            In this era of high‐throughput biology, bioinformatics has become a major discipline for making sense out of large‐scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high‐throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.
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              KEGG: integrating viruses and cellular organisms

              Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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                Author and article information

                Journal
                ACS Chem Biol
                ACS Chem Biol
                cb
                acbcct
                ACS Chemical Biology
                American Chemical Society
                1554-8929
                1554-8937
                10 November 2022
                18 November 2022
                : 17
                : 11
                : 3140-3147
                Affiliations
                [1]Department of Chemistry & Biochemistry, University of California , Santa Barbara, California 93106-9510, United States
                Author notes
                Author information
                https://orcid.org/0000-0001-5836-3195
                https://orcid.org/0000-0002-3525-7864
                Article
                10.1021/acschembio.2c00593
                9679993
                36354305
                8514a718-f24d-4652-8292-c14eee2e6d6c
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 25 July 2022
                : 24 October 2022
                Funding
                Funded by: National Science Foundation, doi 10.13039/100000165;
                Award ID: CHE-2108596
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                cb2c00593
                cb2c00593

                Biochemistry
                Biochemistry

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