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Global Profiling of Lysine Acetylation in Borrelia burgdorferi B31 Reveals Its Role in Central Metabolism

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      Abstract

      The post-translational modification of proteins has been shown to be extremely important in prokaryotes. Using a highly sensitive mass spectrometry-based proteomics approach, we have characterized the acetylome of B. burgdorferi. As previously reported for other bacteria, a relatively low number (5%) of the potential genome-encoded proteins of B. burgdorferi were acetylated. Of these, the vast majority were involved in central metabolism and cellular information processing (transcription, translation, etc.). Interestingly, these critical cell functions were targeted during both ML (mid-log) and S (stationary) phases of growth. However, acetylation of target proteins in ML phase was limited to single lysine residues while these same proteins were acetylated at multiple sites during S phase. To determine the acetyl donor in B. burgdorferi, we used mutants that targeted the sole acetate metabolic/anabolic pathway in B. burgdorferi (lipid I synthesis). B. burgdorferi strains B31-A3, B31-A3 ΔackA (acetyl-P- and acetyl-CoA-) and B31-A3 Δpta (acetyl-P+ and acetyl-CoA-) were grown to S phase and the acetylation profiles were analyzed. While only two proteins were acetylated in the ΔackA mutant, 140 proteins were acetylated in the Δpta mutant suggesting that acetyl-P was the primary acetyl donor in B. burgdorferi. Using specific enzymatic assays, we were able to demonstrate that hyperacetylation of proteins in S phase appeared to play a role in decreasing the enzymatic activity of at least two glycolytic proteins. Currently, we hypothesize that acetylation is used to modulate enzyme activities during different stages of growth. This strategy would allow the bacteria to post-translationally stimulate the activity of key glycolytic enzymes by deacetylation rather than expending excessive energy synthesizing new proteins. This would be an appealing, low-energy strategy for a bacterium with limited metabolic capabilities. Future work focuses on identifying potential protein deacetylase(s) to complete our understanding of this important biological process.

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      Most cited references 81

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      KEGG: kyoto encyclopedia of genes and genomes.

       S. Goto,  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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        VMD: Visual molecular dynamics

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          STRING v10: protein–protein interaction networks, integrated over the tree of life

          The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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            Author and article information

            Affiliations
            1Laboratory of Bacteriology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health , Hamilton, MT, United States
            2CNRS UMR 6270 Polymères, Biopolymères, Surfaces Laboratory, Université de Rouen , Mont-Saint-Aignan, France
            3PISSARO Proteomic Facility, Institut de Recherche et d’Innovation Biomédicale , Mont-Saint-Aignan, France
            4Department of Chemistry and Biochemistry, Middlebury College , Middlebury, VT, United States
            Author notes

            Edited by: Catherine Ayn Brissette, University of North Dakota, United States

            Reviewed by: Azad Eshghi, UVic Genome BC Protein Centre, Canada; Dan Drecktrah, University of Montana, United States

            *Correspondence: Frank C. Gherardini, fgherardini@ 123456niaid.nih.gov

            †Present address: Sébastien Bontemps-Gallo, Center for Infection and Immunity of Lille, U1019-UMR8204, Institut Pasteur de Lille, Inserm, Université of Lille, CNRS, Lille, France

            This article was submitted to Microbial Physiology and Metabolism, a section of the journal Frontiers in Microbiology

            Contributors
            Journal
            Front Microbiol
            Front Microbiol
            Front. Microbiol.
            Frontiers in Microbiology
            Frontiers Media S.A.
            1664-302X
            31 August 2018
            2018
            : 9
            6127242
            10.3389/fmicb.2018.02036
            Copyright © 2018 Bontemps-Gallo, Gaviard, Richards, Kentache, Raffel, Lawrence, Schindler, Lovelace, Dulebohn, Cluss, Hardouin and Gherardini.

            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.

            Counts
            Figures: 7, Tables: 1, Equations: 0, References: 81, Pages: 15, Words: 0
            Funding
            Funded by: Division of Intramural Research, National Institute of Allergy and Infectious Diseases 10.13039/100006492
            Categories
            Microbiology
            Original Research

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