Combined Metabolome and Transcriptome Analysis Reveals Key Components of Complete Desiccation Tolerance in an Anhydrobiotic Insect
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PLGG1, a Plastidic Glycolate Glycerate Transporter, Is Required for Photorespiration and Defines a Unique Class of Metabolite Transporters
PLGG1, a plastidic glycolate glycerate transporter, is required for photorespiration and defines a unique class of metabolite transporters Thea R. Picka,1, Andrea Bräutigama,1, Matthias A. Schulza, Toshihiro Obatab, Alisdair R. Fernieb, and Andreas P. M. Webera,2 aInstitute of Plant Biochemistry, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany; and bMax-Planck Institute for Molecular Plant Physiology, Department of Molecular Physiology, 14476 Potsdam-Golm, Germany Edited by Wolf B. Frommer, Carnegie Institution for Science, Stanford, CA, and accepted by the Editorial Board January 8, 2013 (received for review September 4, 2012) Photorespiratory carbon flux reaches up to a third of photosyn- (PGLP). Glycolate is exported from the chloroplasts to the per- thetic flux, thus contributes massively to the global carbon cycle. oxisomes, where it is oxidized to glyoxylate by glycolate oxidase The pathway recycles glycolate-2-phosphate, the most abundant (GOX) and transaminated to glycine by Ser:glyoxylate and Glu: byproduct of RubisCO reactions. This oxygenation reaction of glyoxylate aminotransferase (SGT and GGT, respectively). Glycine RubisCO and subsequent photorespiration significantly limit the leaves the peroxisomes and enters the mitochondria, where two biomass gains of many crop plants. Although photorespiration is molecules of glycine are deaminated and decarboxylated by the a compartmentalized process with enzymatic reactions in the glycine decarboxylase complex (GDC) and serine hydroxymethyl- chloroplast, the peroxisomes, the mitochondria, and the cytosol, transferase (SHMT) to form one molecule each of serine, ammo- nia, and carbon dioxide. Serine is exported from the mitochondria no transporter required for the core photorespiratory cycle has to the peroxisomes, where it is predominantly converted to glyc- been identified at the molecular level to date. -
METABOLIC EVOLUTION in GALDIERIA SULPHURARIA By
METABOLIC EVOLUTION IN GALDIERIA SULPHURARIA By CHAD M. TERNES Bachelor of Science in Botany Oklahoma State University Stillwater, Oklahoma 2009 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY May, 2015 METABOLIC EVOLUTION IN GALDIERIA SUPHURARIA Dissertation Approved: Dr. Gerald Schoenknecht Dissertation Adviser Dr. David Meinke Dr. Andrew Doust Dr. Patricia Canaan ii Name: CHAD M. TERNES Date of Degree: MAY, 2015 Title of Study: METABOLIC EVOLUTION IN GALDIERIA SULPHURARIA Major Field: PLANT SCIENCE Abstract: The thermoacidophilic, unicellular, red alga Galdieria sulphuraria possesses characteristics, including salt and heavy metal tolerance, unsurpassed by any other alga. Like most plastid bearing eukaryotes, G. sulphuraria can grow photoautotrophically. Additionally, it can also grow solely as a heterotroph, which results in the cessation of photosynthetic pigment biosynthesis. The ability to grow heterotrophically is likely correlated with G. sulphuraria ’s broad capacity for carbon metabolism, which rivals that of fungi. Annotation of the metabolic pathways encoded by the genome of G. sulphuraria revealed several pathways that are uncharacteristic for plants and algae, even red algae. Phylogenetic analyses of the enzymes underlying the metabolic pathways suggest multiple instances of horizontal gene transfer, in addition to endosymbiotic gene transfer and conservation through ancestry. Although some metabolic pathways as a whole appear to be retained through ancestry, genes encoding individual enzymes within a pathway were substituted by genes that were acquired horizontally from other domains of life. Thus, metabolic pathways in G. sulphuraria appear to be composed of a ‘metabolic patchwork’, underscored by a mosaic of genes resulting from multiple evolutionary processes. -
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. -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
The Metabolism of Subcutaneous Adipose Tissue in the Immediate Postnatal Period of Human Newborns
Pediat. Res. 6: 211-218 (1972) Adipose tissue glucose metabolism /3-hydroxyacyl-CoA dehydrogenase neonates fatty acid catabolism phosphofructokinase The Metabolism of Subcutaneous Adipose Tissue in the Immediate Postnatal Period of Human Newborns. 2. Developmental Changes in the Metabolism of 14C-(U)-D-Glucose and in Enzyme Activities of Phosphofructo- kinase (PFK; EC. 2.7.1.11) and /3-Hydroxyacyl-CoA Dehydro- genase (HAD; EC. 1.1.1.35) M. NOVAK1351, E. MONKUS, H. WOLF, AND U. STAVE Department of Pediatrics, University of Miami School of Medicine, Miami, Florida, USA, Staedtische Kinderklinik, Kassel, West Germany, and Fels Research Institute, Yellow Springs, Ohio, USA Extract Changes in the in vitro metabolism of subcutaneous adipose tissue have been compared in normal human newborns from 2 hr to 2 weeks of age. A group of healthy adult volunteers was also included. Samples were obtained by using a needle biopsy tech- nique. More of the isotope from uC-(U)-D-glucose was incorporated into triglyc- erides (P < 0.05) and also oxidized by suspensions of adipose cells from infants 2-3 hr of age than in older infants (P < 0.01). The ratio of radioactivity in carbon dioxide to radioactivity in triglyceride was also significantly greater in 2- to 3-hr-old infants than in older neonates (P < 0.05). Thin layer chromatography of the total lipid ex- tract showed the greatest amount of radioactivity in the triglycerides, a small amount in 1,3-digiycerides and 1,2-diglycerides, and a trace in fatty acids and monogiyc- erides. These findings were compared with the developmental changes in two key enzymes: phosphofructokinase (PFK), which represents the glycolytic pathway, and (3-hydroxyacyl-coenzyme A (GoA) dehydrogenase (HAD), which is involved in the P oxidation of fatty acids. -
Chapter 12 Slides
11/15/17 CHAPTER 12: Carbohydrates: Structure and Function OUTLINE • 12.1 Role of Carbohydrates • 12.2 Monosaccharides • 12.3 Complex Carbohydrates • 12.4 Carbohydrate Catabolism • 12.5 Oligosaccharides as Cell Markers CHAPTER 12: Carbohydrates: Structure and Function WHAT ARE CARBOHYDRATES? • Glucose and its derivatives are carbohydrates: Ø Carbohydrates are simple organic molecules that have a shared basic chemical Formula: Cn(H2O)n Ø The name “carbo + hydrate” represents that Fact that they are made from CO2 and H2O by photosynthesis • About halF oF all earth’s solid carbon is Found in two polymers of glucose found in plants: Ø Starch = major energy storage molecule Ø Cellulose = major structural component oF the plant cell wall (aka. “fiber”) CHAPTER 12: Carbohydrates: Structure and Function THE SIMPLEST CARBOHYDRATES • Monosaccharides are carbohydrates that cannot be hydrolyZed into simpler carbohydrates: Ø These are the Fundamental building blocks For all other carbohydrates (oFten called “simple sugars”) Ø All have Formulas of based on the basic pattern: Cn(H2O)n • Monosaccharides have speciFic Functional groups: 1. An aldehyde OR a ketone (not both!) 2. Several (two or more) alcohol (-OH) groups 1 11/15/17 CHAPTER 12: Carbohydrates: Structure and Function STRUCTURE & NOMENCLATURE OF MONOSACCHARIDES • Monosaccharides are classiFied by two features: 1. Length of their main carbon chain (utilize standard IUPAC naming For # oF carbons) 2. Whether they contain an aldehyde or ketone group • Names always end with –ose • Two common hexoses: -
Upregulated Kynurenine Pathway Enzymes in Aortic Atherosclerotic Aneurysm: Macrophage Kynureninase Downregulates Inflammation
The official journal of the Japan Atherosclerosis Society and the Asian Pacific Society of Atherosclerosis and Vascular Diseases Original Article J Atheroscler Thromb, 2021; 28: 000-000. http://doi.org/10.5551/jat.58248 Upregulated Kynurenine Pathway Enzymes in Aortic Atherosclerotic Aneurysm: Macrophage Kynureninase Downregulates Inflammation Masanori Nishimura1, 2, Atsushi Yamashita2, Yunosuke Matsuura3, Junichi Okutsu4, Aiko Fukahori4, Tsuyoshi Hirata4, Tomohiro Nishizawa5, Hirohito Ishii1, Kazunari Maekawa2, Eriko Nakamura2, Kazuo Kitamura3, Kunihide Nakamura1 and Yujiro Asada2 1Division of Cardiovascular Surgery, Department of Surgery, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan 2Department of Pathology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan 3Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan 4Translational Research Department, Daiichi Sankyo RD Novare Co., Ltd., Tokyo, Japan 5Specialty Medicine Research Laboratories I, Daiichi Sankyo Co., Ltd., Tokyo, Japan Aims: Inflammation and hypertension contribute to the progression of atherosclerotic aneurysm in the aorta. Vascular cell metabolism is regarded to modulate atherogenesis, but the metabolic alterations that occur in ath- erosclerotic aneurysm remain unknown. The present study aimed to identify metabolic pathways and metabo- lites in aneurysmal walls and examine their roles in atherogenesis. Methods: Gene expression using microarray and metabolite levels in the early atherosclerotic lesions and aneu- rysmal walls obtained from 42 patients undergoing aortic surgery were investigated (early lesion n=11, aneu- rysm n=35) and capillary electrophoresis–time-of-flight mass spectrometry (early lesion n=14, aneurysm n=38). Using immunohistochemistry, the protein expression and localization of the identified factors were examined (early lesion n=11, non-aneurysmal advanced lesion n=8, aneurysm n=11). -
HIV-1 Envelope Mimicry of Host Enzyme Kynureninase Does Not Disrupt Tryptophan Metabolism
HIV-1 Envelope Mimicry of Host Enzyme Kynureninase Does Not Disrupt Tryptophan Metabolism This information is current as Todd Bradley, Guang Yang, Olga Ilkayeva, T. Matt Holl, of September 29, 2021. Ruijun Zhang, Jinsong Zhang, Sampa Santra, Christopher B. Fox, Steve G. Reed, Robert Parks, Cindy M. Bowman, Hilary Bouton-Verville, Laura L. Sutherland, Richard M. Scearce, Nathan Vandergrift, Thomas B. Kepler, M. Anthony Moody, Hua-Xin Liao, S. Munir Alam, Roger McLendon, Jeffrey I. Everitt, Christopher B. Newgard, Downloaded from Laurent Verkoczy, Garnett Kelsoe and Barton F. Haynes J Immunol 2016; 197:4663-4673; Prepublished online 14 November 2016; doi: 10.4049/jimmunol.1601484 http://www.jimmunol.org/content/197/12/4663 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2016/11/12/jimmunol.160148 Material 4.DCSupplemental References This article cites 67 articles, 31 of which you can access for free at: http://www.jimmunol.org/content/197/12/4663.full#ref-list-1 by guest on September 29, 2021 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented. -
Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice
pharmaceutics Review Mechanisms of CYP450 Inhibition: Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice Malavika Deodhar 1, Sweilem B Al Rihani 1 , Meghan J. Arwood 1, Lucy Darakjian 1, Pamela Dow 1 , Jacques Turgeon 1,2 and Veronique Michaud 1,2,* 1 Tabula Rasa HealthCare Precision Pharmacotherapy Research and Development Institute, Orlando, FL 32827, USA; [email protected] (M.D.); [email protected] (S.B.A.R.); [email protected] (M.J.A.); [email protected] (L.D.); [email protected] (P.D.); [email protected] (J.T.) 2 Faculty of Pharmacy, Université de Montréal, Montreal, QC H3C 3J7, Canada * Correspondence: [email protected]; Tel.: +1-856-938-8697 Received: 5 August 2020; Accepted: 31 August 2020; Published: 4 September 2020 Abstract: In an ageing society, polypharmacy has become a major public health and economic issue. Overuse of medications, especially in patients with chronic diseases, carries major health risks. One common consequence of polypharmacy is the increased emergence of adverse drug events, mainly from drug–drug interactions. The majority of currently available drugs are metabolized by CYP450 enzymes. Interactions due to shared CYP450-mediated metabolic pathways for two or more drugs are frequent, especially through reversible or irreversible CYP450 inhibition. The magnitude of these interactions depends on several factors, including varying affinity and concentration of substrates, time delay between the administration of the drugs, and mechanisms of CYP450 inhibition. Various types of CYP450 inhibition (competitive, non-competitive, mechanism-based) have been observed clinically, and interactions of these types require a distinct clinical management strategy. This review focuses on mechanism-based inhibition, which occurs when a substrate forms a reactive intermediate, creating a stable enzyme–intermediate complex that irreversibly reduces enzyme activity. -
BIOCHEMISTRY of TRYPTOPHAN in HEALTH and DISEASE Contents
Molec. Aspects Med. Vol. 6, pp. 101-197, 1982 0098-2997/82/020101-97548.50/0 Printed in Great Britain. All rights reserved. Copyright © Pergamon Press Ltd. BIOCHEMISTRY OF TRYPTOPHAN IN HEALTH AND DISEASE David A. Bender Courtauld Institute of Biochemistry, The Middlesex Hospital Medical School, London WIP 7PN, U.K. Contents Chapter 1 THE DISCOVERY OF TRYPTOPHAN, ITS PHYSIOLOGICAL SIGNIFICANCE AND METABOLIC FATES 103 Tryptophan and glucose metabolism 105 Xanthurenic acid and insulin 105 The glucose tolerance factor 106 Inhibition of gluconeogenesis by tryptophan metabolites i07 Metabolic fates of tryptophan 108 Protein synthesis 108 Oxidative metabolism Ii0 5-Hydroxyindole synthesis 111 Intestinal bacterial metabolism iii Chapter 2 THE 5-HYDROXYINDOLE PATHWAY OF TRYPTOPHAN METABOLISM; SEROTONIN AND OTHER CENTRALLY ACTIVE TRYPTOPHAN METABOLITES 112 Tryptophan 5-hydroxylase 112 Inhibition of tryptophan hydroxylase and the carcinoid syndrome 116 Aromatic amino acid decarboxylase 118 The specificity of aromatic amino acid decarboxylase 120 Tryptophan metabolism in the pineal gland 121 Monoamine oxidase 124 The uptake of tryptophan into the brain 124 The binding of tryptophan to serum albumin 127 Competition for uptake by other neutral amino acids 129 Changes in tryptophan metabolism in response to food intake 129 Tryptophan uptake into the brain in liver failure 131 Sleep and tryptophan metabolism 134 101 102 D.A. Bender Tryptophan and serotonin in psychiatric disorders 135 Affective disorders 136 Evidence for a deficit of serotonin or tryptophan -
Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling
H OH metabolites OH Article Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling Nhung Pham , Ruben G. A. van Heck † , Jesse C. J. van Dam , Peter J. Schaap , Edoardo Saccenti and Maria Suarez-Diez * Laboratory of Systems and Synthetic Biology; Wageningen University & Research, 6708 WE, Wageningen, The Netherlands; [email protected] (N.P.); [email protected] (R.G.A.v.H.); [email protected] (J.C.J.v.D.); [email protected] (P.J.S.); [email protected] (E.S.) * Correspondence: [email protected] † Current address: EDP Patent Attorneys, 6708 WH, Wageningen, The Netherlands. Received: 28 December 2018; Accepted: 31 January 2019; Published: 6 February 2019 Abstract: Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models.