Metabolic Pathway Analysis 2017
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Metabolic Network Structure Determines Key Aspects of Functionality and Regulation
letters to nature .............................................................. representative substrates feeding into different parts of metabolism (Table 1). The total number of elementary modes for given Metabolic network structure conditions is here used as a quantitative measure of the degrees of determines key aspects of freedom11, that is, of flexibility. Glucose, for example, can be used in approximately 45 times more different ways than acetate, corre- functionality and regulation sponding to biological intuition. Flux mode number thus directly relates network structure to function. An empty set implies that no Jo¨rg Stelling*, Steffen Klamt*, Katja Bettenbrock*, Stefan Schuster† steady-state flux distribution fulfilling the specifications exists, & Ernst Dieter Gilles* hence predicting an inviable phenotype. For instance, anaerobic use of any of the four substrates except glucose is impossible without * Max Planck Institute for Dynamics of Complex Technical Systems, additional terminal electron acceptors. D-39106 Magdeburg, Germany In particular, we analyse the ability to grow or not to grow of † Max Delbru¨ck Center for Molecular Medicine, D-13092 Berlin, Germany mutants carrying deletions in single genes. For this purpose, the ............................................................................................................................................................................. number of flux modes for a mutant Di using substrate S is The relationship between structure, function and regulation in k 1–3 determined -
The Self-Inhibitory Nature of Metabolic Networks and Its Alleviation Through Compartmentalization
ARTICLE Received 30 Oct 2016 | Accepted 23 May 2017 | Published 10 Jul 2017 DOI: 10.1038/ncomms16018 OPEN The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization Mohammad Tauqeer Alam1,2, Viridiana Olin-Sandoval1,3, Anna Stincone1,w, Markus A. Keller1,4, Aleksej Zelezniak1,5,6, Ben F. Luisi1 & Markus Ralser1,5 Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism. 1 Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. 2 Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. 3 Department of Food Science and Technology, Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Tlalpan, 14080 Mexico City, Mexico. -
Thiamin Biofortification of Crops
Thiamin biofortification of crops Aymeric Goyera,b a Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97330, United States of America b Hermiston Agricultural Research and Extension Center, Oregon State University, Hermiston, OR 97838, United States of America Corresponding author: Aymeric Goyer [email protected] 1 Abstract Thiamin is essential for human health. While plants are the ultimate source of thiamin in most human diets, staple foods like white rice have low thiamin content. Therefore, populations whose diets are mainly based on low-thiamin staple crops suffer from thiamin deficiency. Biofortification of rice grain by engineering the thiamin biosynthesis pathway has recently been attempted, with up to fivefold increase in thiamin content in unpolished seeds. However, polished seeds that retain only the starchy endosperm had similar thiamin content than that of non-engineered plants. Various factors such as limited supply of precursors, limited activity of thiamin biosynthetic enzymes, dependence on maternal tissues to supply thiamin, or lack of thiamin stabilizing proteins may have hindered thiamin increase in the endosperm. Introduction Thiamin (vitamin B1), in its diphosphate form (ThDP), functions as a cofactor for key enzymes of carbohydrates and amino acid metabolism in all living organisms [1,2]. While plants, fungi and bacteria can synthesize thiamin de novo, humans cannot and must obtain it from food. Although plant foods are a major source of thiamin, some staple crops like rice contain relatively low amounts of thiamin. Industrialized milling of the rice grain that removes the outer layers of the seed, i.e. the pericarp, testa, nucellus, and aleurone layer, as well as the embryo (Figure 1), further depletes the grain’s thiamin content [3]. -
Computational Modeling of Genetic and Biochemical Networks. Edited by James M. Bower and Hamid Bolouri Computational Molecular Biology, a Bradford Book, the MIT Press, Cambridge
BRIEFINGS IN BIOINFORMATICS. VOL 7. NO 2. 204 ^206 doi:10.1093/bib/bbl001 Advance Access publication March 9, 2006 Book Review Computational Modeling of Genetic stochastic modelling are inevitable due to the small and Biochemical Networks number of molecules in a cell [1]. These intrinsic Edited by James M. Bower and noise effects have been measured in gene expression Hamid Bolouri using fluorescent probes [2, 3]. Chapter 3, ‘A Logical Model of cis-Regulatory Control in Eukaryotic Computational Molecular Biology, Downloaded from https://academic.oup.com/bib/article/7/2/204/304421 by guest on 23 September 2021 A Bradford Book, The MIT Press, Systems’ by Chiou-Hwa Yuh and others, builds Cambridge, Massachusetts; 2004; up on the theoretical framework introduced in ISBN: 0 262 52423 6; Paperback; 390pp.; Chapter 1 and presents a detailed characterization of £22.95/$35.00. a developmental biology gene network with a large number of regulatory factors. Chapters 1–3 deal with individual gene regulations. Eric Mjolsness intro- ‘Computational Modeling of Genetic and duces and reviews computational techniques, such as Biochemical Networks’ arose from a graduate neural network approaches, to study gene networks course taught by the editors in the California in Chapter 4 ‘Trainable Gene Regulation Networks Institute of Technology in 1998. The aim of the with Application to Drosophila Pattern Formation’. book is to provide instruction in the application of Special emphasis is made in the activity patterns modelling techniques in molecular and cell biology during the fruit-fly development. High-throughput to graduate students and postdoctoral researchers. experimental assays play a major role in the current It is also intended as a primer in the subject for shift from reductionist to systems biology approa- both theoretical and experimental biologists. -
Characterization of the Heterooligomeric Red-Type Rubisco Activase from Red Algae
Characterization of the heterooligomeric red-type rubisco activase from red algae Nitin Loganathana, Yi-Chin Candace Tsaia, and Oliver Mueller-Cajara,1 aSchool of Biological Sciences, Nanyang Technological University, Singapore 637551 Edited by Sue Wickner, National Cancer Institute, National Institutes of Health, Bethesda, MD, and approved October 28, 2016 (received for review July 2, 2016) The photosynthetic CO2-fixing enzyme ribulose 1,5-bisphosphate car- Three distantly related classes of Rcas (green, red, and CbbQO boxylase/oxygenase (rubisco) is inhibited by nonproductive binding types) have been identified so far (13–16). They all belong to the + of its substrate ribulose-1,5-bisphosphate (RuBP) and other sugar superfamily of AAA (ATPases associated with various cellular phosphates. Reactivation requires ATP-hydrolysis–powered remodel- activities) proteins (17) and function as ring-shaped hexamers that ing of the inhibited complexes by diverse molecular chaperones couple the energy of ATP hydrolysis to rubisco remodeling. known as rubisco activases (Rcas). Eukaryotic phytoplankton of the CbbQO requires one adaptor protein CbbO to associate with the red plastid lineage contain so-called red-type rubiscos, some of which AAA hexamer CbbQ6 to function (15). Common themes in the have been shown to possess superior kinetic properties to green-type activation mechanism are emerging (such as manipulation of rubiscos found in higher plants. These organisms are known to en- the large subunit C terminus for red-type Rca and CbbQO), al- code multiple homologs of CbbX, the α-proteobacterial red-type acti- though clear differences are also apparent (3, 12, 15). vase. Here we show that the gene products of two cbbX genes Following the primary endosymbiotic event, the green plastid encoded by the nuclear and plastid genomes of the red algae lineage toward green algae and plants retained the green-type form Cyanidioschyzon merolae are nonfunctional in isolation, but together IB rubisco from the cyanobacterial ancestor. -
Mathematical Models for Explaining the Warburg Effect: a Review Focussed on ATP and Biomass Production
Metabolic pathways analysis 2015 1187 Mathematical models for explaining the Warburg effect: a review focussed on ATP and biomass production Stefan Schuster*1, Daniel Boley†, Philip Moller*,¨ Heiko Stark*‡ and Christoph Kaleta§ *Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany †Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A. ‡Institute of Systematic Zoology and Evolutionary Biology, Friedrich Schiller University Jena, Erbertstraße 1, 07737 Jena, Germany §Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, Brunswiker Straße 10, Kiel 24105, Germany Abstract For producing ATP, tumour cells rely on glycolysis leading to lactate to about the same extent as on respiration. Thus, the ATP synthesis flux from glycolysis is considerably higher than in the corresponding healthy cells. This is known as the Warburg effect (named after German biochemist Otto H. Warburg) and also applies to striated muscle cells, activated lymphocytes, microglia, endothelial cells and several other cell types. For similar phenomena in several yeasts and many bacteria, the terms Crabtree effect and overflow metabolism respectively, are used. The Warburg effect is paradoxical at first sight because the molar ATP yield of glycolysis is much lower than that of respiration. Although a straightforward explanation is that glycolysis allows a higher ATP production rate, the question arises why cells do not re-allocate protein to the high-yield pathway of respiration. Mathematical modelling can help explain this phenomenon. Here, we review several models at various scales proposed in the literature for explaining the Warburg effect. These models support the hypothesis that glycolysis allows for a higher proliferation rate due to increased ATP production and precursor supply rates. -
Bacterial Microcompartments: Catalysis- Enhancing Metabolic Modules for Next Generation Metabolic and Biomedical Engineering Henning Kirst1,2 and Cheryl A
Kirst and Kerfeld BMC Biology (2019) 17:79 https://doi.org/10.1186/s12915-019-0691-z QUESTION AND ANSWER Open Access Bacterial microcompartments: catalysis- enhancing metabolic modules for next generation metabolic and biomedical engineering Henning Kirst1,2 and Cheryl A. Kerfeld1,2,3* Abstract What defines the BMC shell? BMCs are defined by the structural proteins that compose Bacterial cells have long been thought to be simple their “membranes”. There are three structural groups of cells with little spatial organization, but recent research shell proteins: BMC-H (Pfam00936), which form hex- has shown that they exhibit a remarkable degree of agonal hexamers [1]; BMC-P (Pfam03319), which from subcellular differentiation. Indeed, bacteria even have pentagonal pentamers [7, 8], and BMC-T (a tandem fu- organelles such as magnetosomes for sensing magnetic sion of the Pfam00936), which subdivide into two types: fields or gas vesicles controlling cell buoyancy. trimers (BMC-TS)[9–12], and stacked dimers of trimers A functionally diverse group of bacterial organelles (BMC-TD)(Fig.1a) [13–15]. Pores, typically at the cyclic are the bacterial microcompartments (BMCs) that axis of symmetry in the hexamers, vary in size (4–7Å in fulfill specialized metabolic needs. Modification and diameter) and charge, thereby contributing to selective reengineering of these BMCs enable innovative permeability (Fig. 1a) [1, 13–19]. It has been shown that approaches for metabolic engineering and some BMC-H proteins are specifically permeable to an- − D nanomedicine. ions like HCO3 [20]. The BMC-T trimers can have gated pores, meaning they have an open and closed con- formation (closed conformation shown in Fig. -
Coupling Between D-3-Phosphoglycerate Dehydrogenase and D-2-Hydroxyglutarate Dehydrogenase Drives Bacterial L-Serine Synthesis
PNAS PLUS Coupling between D-3-phosphoglycerate dehydrogenase and D-2-hydroxyglutarate dehydrogenase drives bacterial L-serine synthesis Wen Zhanga,b, Manman Zhanga, Chao Gaoa,1, Yipeng Zhanga, Yongsheng Gea, Shiting Guoa, Xiaoting Guoa, Zikang Zhouc, Qiuyuan Liua, Yingxin Zhanga, Cuiqing Maa, Fei Taoc, and Ping Xuc,1 aState Key Laboratory of Microbial Technology, Shandong University, Jinan 250100, People’s Republic of China; bCenter for Gene and Immunotherapy, The Second Hospital of Shandong University, Jinan 250033, People’s Republic of China; and cState Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China Edited by Joshua D. Rabinowitz, Princeton University, Princeton, NJ, and accepted by Editorial Board Member Gregory A. Petsko July 26, 2017 (receivedfor review November 17, 2016) L-Serine biosynthesis, a crucial metabolic process in most domains Certain enzymes exhibit, beyond their classical functions, 2-KG– of life, is initiated by D-3-phosphoglycerate (D-3-PG) dehydrogena- reducing activity for D-2-HG production. These enzymes include tion, a thermodynamically unfavorable reaction catalyzed by D-3-PG hydroxyacid-oxoacid transhydrogenase (HOT) in H. sapiens (20, dehydrogenase (SerA). D-2-Hydroxyglutarate (D-2-HG) is traditionally 21), 4-phospho-D-erythronate dehydrogenase (PdxB) in E. coli (22), viewed as an abnormal metabolite associated with cancer and UDP-N-acetyl-2-amino-2-deoxyglucuronate dehydrogenase (WbpB) neurometabolic disorders. Here, we reveal that bacterial anabo- in Pseudomonas aeruginosa (23), 2-hydroxyacid dehydrogenase lism and catabolism of D-2-HG are involved in L-serine biosynthesis (McyI) in Microcystis aeruginosa (24), and D-3-phosphoglycerate in Pseudomonas stutzeri A1501 and Pseudomonas aeruginosa PAO1. -
German Conference on Bioinformatics 2012
German Conference on Bioinformatics 2012 Jena, 19-22 September 2012 Conference Program Supporters and Sponsors German Conference on Bioinformatics 2012 Welcome Dear GCB 2012 Attendee, it is a pleasure to welcome you in Jena, to the German Conference on Bioinformatics 2012. This annual, international conference provides a forum for the presentation of current re- search in bioinformatics and computational biology. In addition, five satellite workshops place thematic emphasis on diverse aspects of systems biology: “Systems Biology of Aging” organized by J. Sühnel, “Organ-oriented Systems Biology” by D. Driesch and R. Mrowka, “Network Reconstruction and Analysis in Systems Biology” by W. Wiechert and T. Lengauer, “Computational Proteomics and Metabolomics” by S. Böcker, and “Image- based Systems Biology” by M. Figge. This year we will have 10 highlight papers and 11 regular papers presented at the GCB selected out of 39 submissions. GCB 2012 will also feature keynote presentations by six leading scientists: Claude dePamphilis (Pennsylvania State University, University Park, USA), Oliver Fiehn (University of California, Davis, USA), Arndt von Haeseler (Max F. Perutz Laboratories, Vienna, Austria), Tom Kirkwood (Newcastle University, Newcastle, UK), Erik van Nimwegen (University of Basel, Basel, Switzerland), and Ruth Nussinov (National Cancer Institute, Frederick, USA). With the topics of these talks the meeting indeed succeeded in ‘Joining Evolution, Networks, and Algorithms’, according to this year’s conference motto. Additionally, about 95 poster abstracts were accepted for pres- entation. The GCB 2012 was organized by the Jena Centre of Bioinformatics (JCB) in cooperation with the German Society for Chemical Engineering and Biotechnology (DECHEMA) and the Society for Biochemistry and Molecular Biology (GBM). -
Two Novel L2HGDH Mutations Identified in a Rare Chinese Family
Peng et al. BMC Medical Genetics (2018) 19:167 https://doi.org/10.1186/s12881-018-0675-9 CASE REPORT Open Access Two novel L2HGDH mutations identified in a rare Chinese family with L-2- hydroxyglutaric aciduria Wei Peng1,2,3†, Xiu-Wei Ma1,2,3†, Xiao Yang1,2,3, Wan-Qiao Zhang1,2,3, Lei Yan1,2,3, Yong-Xia Wang1,2,3, Xin Liu1,2,3, Yan Wang1,2,3* and Zhi-Chun Feng1,2,3* Abstract Background: L-2-Hydroxyglutaric aciduria (L-2-HGA) is a rare organic aciduria neurometabolic disease that is inherited as an autosomal recessive mode and have a variety of symptoms, such as psychomotor developmental retardation, epilepsy, cerebral symptoms as well as increased concentrations of 2-hydroxyglutarate (2-HG) in the plasma, urine and cerebrospinal fluid. The causative gene of L-2-HGA is L-2-hydroxyglutarate dehydrogenase gene (L2HGDH), which consists of 10 exons. Case presentation: We presented a rare patient primary diagnosis of L-2-HGA based on the clinical symptoms, magnetic resonance imaging (MRI), and gas chromatography-mass spectrometry (GC-MS) results. Mutational analysis of the L2HGDH gene was performed on the L-2-HGA patient and his parents, which revealed two novel mutations in exon 3: a homozygous missense mutation (c.407 A > G, p.K136R) in both the maternal and paternal allele, and a heterozygous frameshift mutation [c.407 A > G, c.408 del G], (p.K136SfsX3) in the paternal allele. The mutation site p.K136R of the protein was located in the pocket of the FAD/NAD(P)-binding domain and predicted to be pathogenic. -
Multifunctional Enzymes and Pathway Modelling
115 ESCEC, Oct. 5th - 8th 2003, Rüdesheim, Germany MULTIFUNCTIONAL ENZYMES AND PATHWAY MODELLING 1,* 2 STEFAN SCHUSTER AND IONELA ZEVEDEI-OANCEA 1Friedrich Schiller University Jena, Faculty of Biology and Pharmaceutics, Section of Bioinformatics, Ernst-Abbe-Platz 2, D-07743 Jena, Germany 2Humboldt University Berlin, Section of Theoretical Biophysics, Invalidenstr. 42, D-10115 Berlin, Germany E-Mail: *[email protected] Received: 10th March 2004 / Published: 1st October 2004 ABSTRACT The analysis of network properties of metabolic systems has recently attracted increasing interest. While enzymes are usually considered to be specific catalysts, many enzymes in living cells are characterized by broad substrate specificity. Here we discuss some aspects of the treatment of such multifunctional enzymes in metabolic pathway analysis, for example, their suitable representation. The fact that the choice of independent functions of multifunctional enzymes is non- unique is explained. We comment on the annotation of such enzymes in metabolic databases and give some suggestions to improve this. We then explain the proper definition of metabolic pathways (elementary flux modes) for systems involving multifunctional enzymes and discuss some ontological problems. INTRODUCTION Metabolic pathway analysis has become a widely used tool in biochemical modelling [1-5]. It is instrumental in metabolic engineering [6,7] and functional genomics [8,9]. The analysis is based on the decomposition of metabolic networks into their smallest functional entities - the metabolic pathways. One of its major advantages is that it does not require any knowledge of kinetic parameters. It only uses the stoichiometric coefficients and information about the directionality of enzymatic reactions. Published in „Experimental Standard Conditions of Enzyme Characterizations“, M.G. -
ISGSB 2016 Will Be Devoted to the Memory of One of the Founders 7
General Information General Information Venue ISGSB Organisers 2 016 Friedrich-Schiller-University of Jena Stefan Schuster ISGSB JENA Auditorium maximum Department of Bioinformatics • FSU Jena Fürstengraben 1 Dominik Driesch 07743 Jena, Germany BioControl Jena GmbH INTERNATIONAL STUDY GROUP Oliver Ebenhöh Date Institute for Quantitative and Theoretical Biology FOR SYSTEMS BIOLOGY 4–7 October 2016 HHU Düsseldorf Marc Thilo Figge Conference Website Applied Systems Biology • HKI Jena www.isgsb2016.de Pu Li Institute for Automation and Systems Engineering CALL FOR ABSTRACTS Conference Chair TU Ilmenau Stefan Schuster Manja Marz Friedrich-Schiller-University of Jena RNA Bioinformatics and High Throughput Analysis • FSU Jena Department of Bioinformatics Günter Theißen Ernst-Abbe-Platz 2 Department of Genetics • FSU Jena 07743 Jena, Germany Johannes Woestemeyer Institute of Microbiology • FSU Jena Conference Organisation Conventus Congressmanagement & Marketing GmbH YSGSB Organisers Anja Kreutzmann Kailash Adhikari Carl-Pulfrich-Strasse 1 Oxford Brookes University 07745 Jena, Germany Mathias Bockwoldt Tel. +49 3641 31 16-357 University of Tromsø Fax +49 3641 31 16-243 Sybille Dühring [email protected] Department of Bioinformatics • FSU Jena © Sergey Nivens - fotlia.com www.conventus.de Sabrina Ellenberger Institute of Microbiology • FSU Jena Industrial Exhibition Alexander Heberle The meeting will be accompanied by a specialised industrial exhi- University Medical Centre Groningen GERMANY bition. Interested companies are invited to request information on Sebastian Henkel 04–07 OCTOBER JENA, the exhibition modalities from the conference organisation through BioControl Jena GmbH 2016 Conventus. Franziska Hufsky RNA Bioinformatics and High Throughput Analysis • FSU Jena Registration James Rowe Further information about registration and fees can be found online Durham University at www.isgsb2016.de soon.