Assembly of an Active Enzyme by the Linkage of Two Protein Modules
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Resolution of Carbon Metabolism and Sulfur-Oxidation Pathways of Metallosphaera Cuprina Ar-4 Via Comparative Proteomics
JOURNAL OF PROTEOMICS 109 (2014) 276– 289 Available online at www.sciencedirect.com ScienceDirect www.elsevier.com/locate/jprot Resolution of carbon metabolism and sulfur-oxidation pathways of Metallosphaera cuprina Ar-4 via comparative proteomics Cheng-Ying Jianga, Li-Jun Liua, Xu Guoa, Xiao-Yan Youa, Shuang-Jiang Liua,c,⁎, Ansgar Poetschb,⁎⁎ aState Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, PR China bPlant Biochemistry, Ruhr University Bochum, Bochum, Germany cEnvrionmental Microbiology and Biotechnology Research Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing, PR China ARTICLE INFO ABSTRACT Article history: Metallosphaera cuprina is able to grow either heterotrophically on organics or autotrophically Received 16 March 2014 on CO2 with reduced sulfur compounds as electron donor. These traits endowed the species Accepted 6 July 2014 desirable for application in biomining. In order to obtain a global overview of physiological Available online 14 July 2014 adaptations on the proteome level, proteomes of cytoplasmic and membrane fractions from cells grown autotrophically on CO2 plus sulfur or heterotrophically on yeast extract Keywords: were compared. 169 proteins were found to change their abundance depending on growth Quantitative proteomics condition. The proteins with increased abundance under autotrophic growth displayed Bioleaching candidate enzymes/proteins of M. cuprina for fixing CO2 through the previously identified Autotrophy 3-hydroxypropionate/4-hydroxybutyrate cycle and for oxidizing elemental sulfur as energy Heterotrophy source. The main enzymes/proteins involved in semi- and non-phosphorylating Entner– Industrial microbiology Doudoroff (ED) pathway and TCA cycle were less abundant under autotrophic growth. Also Extremophile some transporter proteins and proteins of amino acid metabolism changed their abundances, suggesting pivotal roles for growth under the respective conditions. -
Supplementary Information
Supplementary information (a) (b) Figure S1. Resistant (a) and sensitive (b) gene scores plotted against subsystems involved in cell regulation. The small circles represent the individual hits and the large circles represent the mean of each subsystem. Each individual score signifies the mean of 12 trials – three biological and four technical. The p-value was calculated as a two-tailed t-test and significance was determined using the Benjamini-Hochberg procedure; false discovery rate was selected to be 0.1. Plots constructed using Pathway Tools, Omics Dashboard. Figure S2. Connectivity map displaying the predicted functional associations between the silver-resistant gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S3. Connectivity map displaying the predicted functional associations between the silver-sensitive gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S4. Metabolic overview of the pathways in Escherichia coli. The pathways involved in silver-resistance are coloured according to respective normalized score. Each individual score represents the mean of 12 trials – three biological and four technical. Amino acid – upward pointing triangle, carbohydrate – square, proteins – diamond, purines – vertical ellipse, cofactor – downward pointing triangle, tRNA – tee, and other – circle. -
Supplementary Informations SI2. Supplementary Table 1
Supplementary Informations SI2. Supplementary Table 1. M9, soil, and rhizosphere media composition. LB in Compound Name Exchange Reaction LB in soil LBin M9 rhizosphere H2O EX_cpd00001_e0 -15 -15 -10 O2 EX_cpd00007_e0 -15 -15 -10 Phosphate EX_cpd00009_e0 -15 -15 -10 CO2 EX_cpd00011_e0 -15 -15 0 Ammonia EX_cpd00013_e0 -7.5 -7.5 -10 L-glutamate EX_cpd00023_e0 0 -0.0283302 0 D-glucose EX_cpd00027_e0 -0.61972444 -0.04098397 0 Mn2 EX_cpd00030_e0 -15 -15 -10 Glycine EX_cpd00033_e0 -0.0068175 -0.00693094 0 Zn2 EX_cpd00034_e0 -15 -15 -10 L-alanine EX_cpd00035_e0 -0.02780553 -0.00823049 0 Succinate EX_cpd00036_e0 -0.0056245 -0.12240603 0 L-lysine EX_cpd00039_e0 0 -10 0 L-aspartate EX_cpd00041_e0 0 -0.03205557 0 Sulfate EX_cpd00048_e0 -15 -15 -10 L-arginine EX_cpd00051_e0 -0.0068175 -0.00948672 0 L-serine EX_cpd00054_e0 0 -0.01004986 0 Cu2+ EX_cpd00058_e0 -15 -15 -10 Ca2+ EX_cpd00063_e0 -15 -100 -10 L-ornithine EX_cpd00064_e0 -0.0068175 -0.00831712 0 H+ EX_cpd00067_e0 -15 -15 -10 L-tyrosine EX_cpd00069_e0 -0.0068175 -0.00233919 0 Sucrose EX_cpd00076_e0 0 -0.02049199 0 L-cysteine EX_cpd00084_e0 -0.0068175 0 0 Cl- EX_cpd00099_e0 -15 -15 -10 Glycerol EX_cpd00100_e0 0 0 -10 Biotin EX_cpd00104_e0 -15 -15 0 D-ribose EX_cpd00105_e0 -0.01862144 0 0 L-leucine EX_cpd00107_e0 -0.03596182 -0.00303228 0 D-galactose EX_cpd00108_e0 -0.25290619 -0.18317325 0 L-histidine EX_cpd00119_e0 -0.0068175 -0.00506825 0 L-proline EX_cpd00129_e0 -0.01102953 0 0 L-malate EX_cpd00130_e0 -0.03649016 -0.79413596 0 D-mannose EX_cpd00138_e0 -0.2540567 -0.05436649 0 Co2 EX_cpd00149_e0 -
Supplementary Information
Supplementary Information Laboratory cultivation of acidophilic nanoorganisms. Physiological and bioinformatic dissection of a stable laboratory co-culture. Susanne Krause [1], Andreas Bremges [3,4], Philipp C. Münch [3,5], Alice C. McHardy [3] and Johannes Gescher* [1,2] [1] Department of Applied Biology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany [2] Institute for Biological Interfaces, Karlsruhe Institute of Technology (KIT), Eggenstein- Leopoldshafen, Germany [3] Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany [4] German Center for Infection Research (DZIF), partner site Hannover-Braunschweig, Braunschweig, Germany [5] Max von Pettenkofer-Institute of Hygiene and Medical Microbiology, Ludwig-Maximilians- University of Munich, Munich, Germany SI Materials and Methods Quantification of cells using quantitative PCR The target sequences were amplified by PCR from genomic DNA of the enrichment cultures using primers see table S8. The amplified fragments contained overlapping regions as well as a BamHI site at the 5’ end and a SacI site at the 3’ end. Using the overlaps, the fragments were combined and cloned via the added restrictions sites into plasmid pAH95 1. Integration in the genome of E. coli DH5alphaZ1 was conducted as described before 1. For standard curve design, serial dilutions of E. coli DH5alphaZ1 cells containing the merged 23S target sequences of the ARMAN und Thermoplasmatales enrichments were prepared and cells were counted in a Neubauer counting chamber (Marienfeld, Lauda-Königshofen, Germany). DNA of the dilution series as well as of enrichment-cultures was extracted using the innuSPEED soil DNA kit (Analytic Jena, Jena, Germany) with minor modifications to the manufacturer´s instructions. -