The Bioanalysis Glossary
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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. -
Designing Biological Systems
Downloaded from genesdev.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Designing biological systems David A. Drubin,1 Jeffrey C. Way,2 and Pamela A. Silver1,3 1Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA; 2EMD Lexigen Research Center, Billerica, Massachusetts 01821, USA The design of artificial biological systems and the under- chemical systems that mimic living systems, especially standing of their natural counterparts are key objectives in regard to replication and Darwinian selection. This of the emerging discipline of synthetic biology. Toward review will primarily focus on accomplishments of the both ends, research in synthetic biology has primarily first strategy; however, readers are referred to several fine focused on the construction of simple devices, such as reviews on the generation of artificial, life-emulating transcription-based oscillators and switches. Construc- systems (Szostak et al. 2001; Benner 2003; Benner and tion of such devices should provide us with insight on Sismour 2005). the design of natural systems, indicating whether our According to the synthetic biology paradigm, a syn- understanding is complete or whether there are still gaps thetically programmed cell should be composed of many in our knowledge. Construction of simple biological sys- subsystems that operate successfully as a result of ex- tems may also lay the groundwork for the construction tensive characterization and educated design. System of more complex systems that have practical utility. To construction is the product of iterative cycles of com- realize its full potential, biological systems design bor- puter modeling, biological assembly, and testing. In this rows from the allied fields of protein design and meta- way, much from the field of systems biology can be ap- bolic engineering. -
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. -
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. -
The NEURONS and NEURAL SYSTEM: a 21St CENTURY PARADIGM
The NEURONS and NEURAL SYSTEM: a 21st CENTURY PARADIGM This material is excerpted from the full β-version of the text. The final printed version will be more concise due to further editing and economical constraints. A Table of Contents and an index are located at the end of this paper. A few citations have yet to be defined and are indicated by “xxx.” James T. Fulton Neural Concepts [email protected] July 19, 2015 Copyright 2011 James T. Fulton 2 Neurons & the Nervous System 4 The Architectures of Neural Systems1 [xxx review cogn computation paper and incorporate into this chapter ] [xxx expand section 4.4.4 as a key area of importance ] [xxx Text and semantics needs a lot of work ] Don’t believe everything you think. Anonymous bumper sticker You must not fool yourself, and you are the easiest person to fool Richard Feynman I am never content until I have constructed a model of what I am studying. If I succeed in making one, I understand; otherwise, I do not. William Thomson (Lord Kelvin) It is the models that tell us whether we understand a process and where the uncertainties remain. Bridgeman, 2000 4.1 Background [xxx chapter is a hodge-podge at this time 29 Aug 11 ] The animal kingdom shares a common neurological architecture that is ramified in a specific species in accordance with its station in the phylogenic tree and the ecological domain. This ramification includes not only replication of existing features but further augmentation of the system using new and/or modified features. -
The Device Approach to Emergent Properties
arXiv:1801.05452, Version 3 Asking Biological Questions of Physical Systems: the Device Approach to Emergent Properties Bob Eisenberg Department of Applied Mathematics Illinois Institute of Technology USA Department of Physiology and Biophysics Rush University USA [email protected] January 17, 2018 9/23/2021 9:14 AM Abstract Life occurs in concentrated ‘Ringer Solutions’ derived from seawater that Lesser Blum studied for most of his life. As we worked together, Lesser and I realized that the questions asked of those solutions were quite different in biology from those in the physical chemistry he knew. Biology is inherited. Information is passed by handfuls of atoms in the genetic code. A few atoms in the proteins built from the code change macroscopic function. Indeed, a few atoms often control biological function in the same sense that a gas pedal controls the speed of a car. Biological questions then are most productive when they are asked in the context of evolution. What function does a system perform? How is the system built to perform that function? What forces are used to perform that function? How are the modules that perform functions connected to make the machinery of life. Physiologists have shown that much of life is a nested hierarchy of devices, one on top of another, linking atomic ions in concentrated solutions to current flow through proteins, current flow to voltage signals, voltage signals to changes in current flow, all connected to make a regenerative system that allows electrical action potentials to move meters, under the control of a few atoms. -
Metabolite Secretion in Microorganisms: the Theory of Metabolic Overflow Put to the Test
Metabolomics (2018) 14:43 https://doi.org/10.1007/s11306-018-1339-7 REVIEW ARTICLE Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test Farhana R. Pinu1 · Ninna Granucci2 · James Daniell2,3 · Ting‑Li Han2 · Sonia Carneiro4 · Isabel Rocha4 · Jens Nielsen5,6 · Silas G. Villas‑Boas2 Received: 10 November 2017 / Accepted: 7 February 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Introduction Microbial cells secrete many metabolites during growth, including important intermediates of the central car- bon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies. Materials and Methods The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow. Conclusions Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabo- lites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering. Keywords Microbial metabolism · Microorganisms · Active efflux · Secretion · Metabolic engineering · Metabolic modeling · Systems biology 1 Introduction Microorganisms have been used for the production of indus- trially relevant compounds for many years. Corynebacte- rium glutamicum and its mutants have been employed for the large-scale industrial production of glutamate, lysine Electronic supplementary material The online version of this and other flavor active amino acids (Hermann and Krämer article (https ://doi.org/10.1007/s1130 6-018-1339-7) contains 1996; Krämer 1994, 2004). -
Systems Biology and Its Relevance to Alcohol Research
Commentary: Systems Biology and Its Relevance to Alcohol Research Q. Max Guo, Ph.D., and Sam Zakhari, Ph.D. Systems biology, a new scientific discipline, aims to study the behavior of a biological organization or process in order to understand the function of a dynamic system. This commentary will put into perspective topics discussed in this issue of Alcohol Research & Health, provide insight into why alcohol-induced disorders exemplify the kinds of conditions for which a systems biological approach would be fruitful, and discuss the opportunities and challenges facing alcohol researchers. KEY WORDS: Alcohol-induced disorders; alcohol research; biomedical research; systems biology; biological systems; mathematical modeling; genomics; epigenomics; transcriptomics; metabolomics; proteomics ntil recently, most biologists’ emerging discipline that deals with, Alcohol Research & Health intend to efforts have been devoted to and takes advantage of, these enormous address. In this commentary, we will Ureducing complex biological amounts of data. Although scientists try to put the topics discussed in this systems to the properties of individual and engineers have applied the concept issue into perspective, provide views molecules. However, with the com of an integrated systemic approach for on the significance of systems biology pleted sequencing of the genomes of years, systems biology has only emerged as a new, distinct discipline to study 1 High-throughput genomics is the study of the structure humans, mice, rats, and many other and function of an organism’s complete genetic content, organisms, technological advances in complex biological systems in the past or genome, using technology that analyzes a large num the fields of high-throughput genomics1 several years. -
Communication Theory in Biological Systems
SUMMARY CDI TYPE I: A Communications Theory Approach to Morphogenesis and Architecture Maintenance The assertion that biological systems are communication networks would draw no rebuke from biologists – the terms signaling, communication, and network are deeply embedded parts of the biology parlance. However, the more profound meanings of information and communication are often overlooked when considering biological systems. Information can be quantified, its flow can be measured and tight bounds exist for its representation and conveyance between transmitters and receivers in a variety of settings. Furthermore, communications theory is about efficient com- munication where energy is at a premium – as is often the case in organisms. But perhaps most important, information theory allows mechanism-blind bounds on decisions and information flow. That is, the physics of a system allows determination of limits that any method of information description, delivery or processing must obey. Thus, rigorous application of communication theory to complex multi-cellular biological sys- tems seems both attractive and obvious as an organizing principle – a way to tease order from the myriad engineering solutions that comprise biological systems. Likewise, study of biological systems – engineering solutions evolved over eons – might yield new communication and com- putation theory. Yet so far, a communications-theoretic approach to multi-cellular biology has received scant, if any, attention. We therefore propose to explore this interdisciplinary intellectual -
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.