Metabolite Secretion in Microorganisms: the Theory of Metabolic Overflow Put to the Test
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Glossary - Cellbiology
1 Glossary - Cellbiology Blotting: (Blot Analysis) Widely used biochemical technique for detecting the presence of specific macromolecules (proteins, mRNAs, or DNA sequences) in a mixture. A sample first is separated on an agarose or polyacrylamide gel usually under denaturing conditions; the separated components are transferred (blotting) to a nitrocellulose sheet, which is exposed to a radiolabeled molecule that specifically binds to the macromolecule of interest, and then subjected to autoradiography. Northern B.: mRNAs are detected with a complementary DNA; Southern B.: DNA restriction fragments are detected with complementary nucleotide sequences; Western B.: Proteins are detected by specific antibodies. Cell: The fundamental unit of living organisms. Cells are bounded by a lipid-containing plasma membrane, containing the central nucleus, and the cytoplasm. Cells are generally capable of independent reproduction. More complex cells like Eukaryotes have various compartments (organelles) where special tasks essential for the survival of the cell take place. Cytoplasm: Viscous contents of a cell that are contained within the plasma membrane but, in eukaryotic cells, outside the nucleus. The part of the cytoplasm not contained in any organelle is called the Cytosol. Cytoskeleton: (Gk. ) Three dimensional network of fibrous elements, allowing precisely regulated movements of cell parts, transport organelles, and help to maintain a cell’s shape. • Actin filament: (Microfilaments) Ubiquitous eukaryotic cytoskeletal proteins (one end is attached to the cell-cortex) of two “twisted“ actin monomers; are important in the structural support and movement of cells. Each actin filament (F-actin) consists of two strands of globular subunits (G-Actin) wrapped around each other to form a polarized unit (high ionic cytoplasm lead to the formation of AF, whereas low ion-concentration disassembles AF). -
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. -
Epigenetic Modulating Chemicals Significantly Affect the Virulence
G C A T T A C G G C A T genes Article Epigenetic Modulating Chemicals Significantly Affect the Virulence and Genetic Characteristics of the Bacterial Plant Pathogen Xanthomonas campestris pv. campestris Miroslav Baránek 1,* , Viera Kováˇcová 2 , Filip Gazdík 1 , Milan Špetík 1 , Aleš Eichmeier 1 , Joanna Puławska 3 and KateˇrinaBaránková 1 1 Mendeleum—Institute of Genetics, Faculty of Horticulture, Mendel University in Brno, 69144 Lednice, Czech Republic; fi[email protected] (F.G.); [email protected] (M.Š.); [email protected] (A.E.); [email protected] (K.B.) 2 Institute for Biological Physics, University of Cologne, 50923 Köln, Germany; [email protected] 3 Department of Phytopathology, Research Institute of Horticulture, 96-100 Skierniewice, Poland; [email protected] * Correspondence: [email protected]; Tel.: +420-519367311 Abstract: Epigenetics is the study of heritable alterations in phenotypes that are not caused by changes in DNA sequence. In the present study, we characterized the genetic and phenotypic alterations of the bacterial plant pathogen Xanthomonas campestris pv. campestris (Xcc) under different treatments with several epigenetic modulating chemicals. The use of DNA demethylating chemicals unambiguously caused a durable decrease in Xcc bacterial virulence, even after its reisolation from Citation: Baránek, M.; Kováˇcová,V.; infected plants. The first-time use of chemicals to modify the activity of sirtuins also showed Gazdík, F.; Špetík, M.; Eichmeier, A.; some noticeable results in terms of increasing bacterial virulence, but this effect was not typically Puławska, J.; Baránková, K. stable. Changes in treated strains were also confirmed by using methylation sensitive amplification Epigenetic Modulating Chemicals (MSAP), but with respect to registered SNPs induction, it was necessary to consider their contribution Significantly Affect the Virulence and to the observed polymorphism. -
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. -
Biology Work Through the Tasks on the Following Pages
Biology Key skills developed in this work: • Locating, selecting, organising and presenting information from a variety of sources • Translating information from one form to another • Manipulating numerical and other data • Using information to identify patterns, report trends and draw conclusions • Giving reasoned explanations for phenomena, patterns and relationships Research work: https://www.futurelearn.com/subjects/science-engineering-and-maths-courses/biology-and- biotechnology Website links: https://www.cambridgeinternational.org/programmes-and-qualifications/cambridge- international-as-and-a-level-biology-9700/ https://www.senecalearning.com/ https://www.thestudentroom.co.uk/a-level/ https://www.s-cool.co.uk/a-level/biology Appropriate additional reading: • The Selfish Gene by Richard Dawkins • Silent Spring by Rachel Carson • I Contain Multitudes by Ed Yong • • Nature.com • Newscientist.com • Scientificamerican.com Work through the tasks on the following pages Tasks to complete: A: Examination Questions Units of measurement 1) Complete the diagram below to show: names of the units of measurement, unit symbols, and mathematical operations for converting between units. 2) Complete the table below to show the corresponding values in nanometres, micrometres and millimetres for the measurements given in each row. The first row has been completed for you. Add in the correct unit symbols for each answer you give. Nanometre Micrometre Millimetre 5 0.005 0.000005 1 1 1 3 7 0.5 Magnification and Resolution 1) Define the following terms: Term Definition Magnification Resolution 2) Visible light has a wavelength of 400-700 nm. Calculate the best resolution achievable with a light microscope? Show your working out: 3) The diagram below shows the general structure of a plant cell when viewed under and electron microscope. -
Basic Histology (23 Questions): Oral Histology (16 Questions
Board Question Breakdown (Anatomic Sciences section) The Anatomic Sciences portion of part I of the Dental Board exams consists of 100 test items. They are broken up into the following distribution: Gross Anatomy (50 questions): Head - 28 questions broken down in this fashion: - Oral cavity - 6 questions - Extraoral structures - 12 questions - Osteology - 6 questions - TMJ and muscles of mastication - 4 questions Neck - 5 questions Upper Limb - 3 questions Thoracic cavity - 5 questions Abdominopelvic cavity - 2 questions Neuroanatomy (CNS, ANS +) - 7 questions Basic Histology (23 questions): Ultrastructure (cell organelles) - 4 questions Basic tissues - 4 questions Bone, cartilage & joints - 3 questions Lymphatic & circulatory systems - 3 questions Endocrine system - 2 questions Respiratory system - 1 question Gastrointestinal system - 3 questions Genitouirinary systems - (reproductive & urinary) 2 questions Integument - 1 question Oral Histology (16 questions): Tooth & supporting structures - 9 questions Soft oral tissues (including dentin) - 5 questions Temporomandibular joint - 2 questions Developmental Biology (11 questions): Osteogenesis (bone formation) - 2 questions Tooth development, eruption & movement - 4 questions General embryology - 2 questions 2 National Board Part 1: Review questions for histology/oral histology (Answers follow at the end) 1. Normally most of the circulating white blood cells are a. basophilic leukocytes b. monocytes c. lymphocytes d. eosinophilic leukocytes e. neutrophilic leukocytes 2. Blood platelets are products of a. osteoclasts b. basophils c. red blood cells d. plasma cells e. megakaryocytes 3. Bacteria are frequently ingested by a. neutrophilic leukocytes b. basophilic leukocytes c. mast cells d. small lymphocytes e. fibrocytes 4. It is believed that worn out red cells are normally destroyed in the spleen by a. neutrophils b. -
Cell Secretion and Membrane Fusion: Highly Significant Phenomena in the Life of a Cell
DISCOVERIES 2014, Jul -Sep, 2(3): e30 DOI: 10.15190/d.2014.22 Cell Secretion and Membrane Fusion EDITORIAL Cell secretion and membrane fusion: highly significant phenomena in the life of a cell Mircea Leabu 1,2,3,*, Garth L. Nicolson 4,* 1University of Medicine and Pharmacy “Carol Davila”, Department of Cellular and Molecular Medicine, 8, Eroilor Sanitari Blvd., 050474, Bucharest, Romania 2 “Victor Babes” National Institute of Pathology, 99101, Splaiul Independentei, 050096, Bucharest, Romania 3University of Bucharest, Research Center for Applied Ethics, 204, Splaiul Independentei, 060024, Bucharest, Romania 4Department of Molecular Pathology, Institute for Molecular Medicine, Huntington Beach, California, 92647 USA *Corresponding authors: Mircea Leabu, PhD , “Victor Babes” National Institute of Pathology, 99-101, Splaiul Independentei, 050096, Bucharest, Romania; E-mail: [email protected]; Garth L. Nicolson, Ph.D , The Institute for Molecular Medicine, P.O. Box 9355, S. Laguna Beach, CA 92652 USA. Email: [email protected] Submitted: Sept. 10, 2014; Revised: Sept. 14, 2014; Accepted: Sept. 17, 2014; Published: Sept. 18, 2014; Citation : Leabu M, Nicolson GL. Cell Secretion and membrane fusion: highly significant phenomena in the life of a cell. Discoveries 2014, Jul-Sep; 2(3): e30. DOI: 10.15190/d.2014.22 Keywords : cell secretion, membrane fusion, every cell’s existence, and they must be very well porosome, exosomes, electron microscopy, cancer, coordinated and controlled. Membrane trafficking, mathematical approach, secretory vesicle, science which involves vesicular budding of the source history membrane, directed transport and eventually fusion with the target membrane is a very specific process. All of these processes depend, in particular, on Introduction basic principals of biological membrane structure Is there any cell that does not secrete something and dynamics, a topic that was reviewed recently in necessary for maintenance of the organism? this journal 1. -
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. -
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. -
Ranking Metabolite Sets by Their Activity Levels
H OH metabolites OH Article Ranking Metabolite Sets by Their Activity Levels Karen McLuskey 1,† , Joe Wandy 1,† , Isabel Vincent 2 , Justin J. J. van der Hooft 3 , Simon Rogers 4 , Karl Burgess 5 and Rónán Daly 1,* 1 Glasgow Polyomics, University of Glasgow, Glasgow G61 1QH, UK; [email protected] (K.M.); [email protected] (J.W.) 2 IBioIC, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G1 1XQ, UK; [email protected] 3 Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands; [email protected] 4 School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK; [email protected] 5 Centre for Synthetic and Systems Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JG, UK; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. -
Eukaryotic Cell Structure and Function: (Part 1)
Harriet Wilson, Lecture Notes Bio. Sci. 4 - Microbiology Sierra College Eukaryotic Cell Structure and Function: (Part 1) The science or study of cell structure and function is called cytology; but courses dealing with this topic frequently come under the heading of cell and molecular biology. Cytology has undergone extensive change over time. The term cell (cella = a small room) was first used by Robert Hooke (1665) with reference to an empty space or chamber (like a prison cell). Hooke was observing the cell walls of dead cork cells from the bark of cork oaks, and not living cells. We now know cells are far from empty spaces. According to the cell theory, as articulated by Matthias Schleiden and Theodor Schwann (1839), the cell is the basic unit of structure and function in all, living organisms. When first written, the cell theory indicated that living cells could arise spontaneously through abiogenesis, but experiments conducted by Louis Pasteur and others invalidated this concept. Instead, it is now recognized that all cells arise from preexisting cells, and that they carry hereditary information (DNA) that is passed from one generation to the next through cell division. Cells are currently divided into two types, Prokaryotic and Eukaryotic. The term karyon (karyon = nucleus) appears in both names, and is preceded by either pro, meaning before or eu meaning well or truly. Fossil and molecular evidence indicates that prokaryotic cells evolved first, and that the larger, nucleated cells evolved later. Some of the distinguishing features of these two cell types are outlined below. A typical prokaryotic cell (Before a nucleus): Does not contain a nucleus surrounded by a nuclear membrane or envelope. -
Primary and Secondary Metabolites
PRIMARY AND SECONDARY METABOLITES INRODUCTION Metabolism-Metabolism constituents all the chemical transformations occurring in the cells of living organisms and these transformations are essential for life of an organism. Metabolites-End product of metabolic processes and intermediates formed during metabolic processes is called metabolites. Types of Metabolites Primary Secondary metabolites metabolites Primary metabolites A primary metabolite is a kind of metabolite that is directly involved in normal growth, development, and reproduction. It usually performs a physiological function in the organism (i.e. an intrinsic function). A primary metabolite is typically present in many organisms or cells. It is also referred to as a central metabolite, which has an even more restricted meaning (present in any autonomously growing cell or organism). Some common examples of primary metabolites include: ethanol, lactic acid, and certain amino acids. In higher plants such compounds are often concentrated in seeds and vegetative storage organs and are needed for physiological development because of their role in basic cell metabolism. As a general rule, primary metabolites obtained from higher plants for commercial use are high volume-low value bulk chemicals. They are mainly used as industrial raw materials, foods, or food additives and include products such as vegetable oils, fatty acids (used for making soaps and detergents), and carbohydrates (for example, sucrose, starch, pectin, and cellulose). However, there are exceptions to this rule. For example, myoinositol and ß-carotene are expensive primary metabolites because their extraction, isolation, and purification are difficult. carbohydr -ates hormones proteins Examples Nucleic lipids acids A plant produces primary metabolites that are involved in growth and metabolism.