Metabolomics and Lipidomics Contributions to Type 1 Diabetes Research F
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Expanding the Molecular Landscape of Exercise Biology
H OH metabolites OH Review Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology Mehdi R. Belhaj 1, Nathan G. Lawler 2,3 and Nolan J. Hoffman 1,* 1 Exercise and Nutrition Research Program, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne 3000, Australia; [email protected] 2 Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Murdoch, Perth 6150, Australia; [email protected] 3 School of Health and Medical Sciences, Edith Cowan University, Joondalup 6027, Australia * Correspondence: [email protected]; Tel.: +61-3-9230-8277 Abstract: Dynamic changes in circulating and tissue metabolites and lipids occur in response to exercise-induced cellular and whole-body energy demands to maintain metabolic homeostasis. The metabolome and lipidome in a given biological system provides a molecular snapshot of these rapid and complex metabolic perturbations. The application of metabolomics and lipidomics to map the metabolic responses to an acute bout of aerobic/endurance or resistance exercise has dramatically expanded over the past decade thanks to major analytical advancements, with most exercise-related studies to date focused on analyzing human biofluids and tissues. Experimental and analytical considerations, as well as complementary studies using animal model systems, are warranted to help overcome challenges associated with large human interindividual variability and decipher the breadth of molecular mechanisms underlying the metabolic health-promoting effects of exercise. In this review, we provide a guide for exercise researchers regarding analytical techniques and experimental workflows commonly used in metabolomics and lipidomics. Furthermore, we discuss Citation: Belhaj, M.R.; Lawler, N.G.; advancements in human and mammalian exercise research utilizing metabolomic and lipidomic Hoffman, N.J. -
Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions
H OH metabolites OH Review Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions Lorraine Smith 1, Joran Villaret-Cazadamont 1, Sandrine P. Claus 2 ,Cécile Canlet 1, Hervé Guillou 1 , Nicolas J. Cabaton 1 and Sandrine Ellero-Simatos 1,* 1 Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France; [email protected] (L.S.); [email protected] (J.V.-C.); [email protected] (C.C.); [email protected] (H.G.); [email protected] (N.J.C.) 2 LNC Therapeutics, 17 place de la Bourse, 33076 Bordeaux, France; [email protected] * Correspondence: [email protected] Received: 11 February 2020; Accepted: 7 March 2020; Published: 12 March 2020 Abstract: Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells). -
S41598-018-25035-1.Pdf
www.nature.com/scientificreports OPEN An Innovative Approach for The Integration of Proteomics and Metabolomics Data In Severe Received: 23 October 2017 Accepted: 9 April 2018 Septic Shock Patients Stratifed for Published: xx xx xxxx Mortality Alice Cambiaghi1, Ramón Díaz2, Julia Bauzá Martinez2, Antonia Odena2, Laura Brunelli3, Pietro Caironi4,5, Serge Masson3, Giuseppe Baselli1, Giuseppe Ristagno 3, Luciano Gattinoni6, Eliandre de Oliveira2, Roberta Pastorelli3 & Manuela Ferrario 1 In this work, we examined plasma metabolome, proteome and clinical features in patients with severe septic shock enrolled in the multicenter ALBIOS study. The objective was to identify changes in the levels of metabolites involved in septic shock progression and to integrate this information with the variation occurring in proteins and clinical data. Mass spectrometry-based targeted metabolomics and untargeted proteomics allowed us to quantify absolute metabolites concentration and relative proteins abundance. We computed the ratio D7/D1 to take into account their variation from day 1 (D1) to day 7 (D7) after shock diagnosis. Patients were divided into two groups according to 28-day mortality. Three diferent elastic net logistic regression models were built: one on metabolites only, one on metabolites and proteins and one to integrate metabolomics and proteomics data with clinical parameters. Linear discriminant analysis and Partial least squares Discriminant Analysis were also implemented. All the obtained models correctly classifed the observations in the testing set. By looking at the variable importance (VIP) and the selected features, the integration of metabolomics with proteomics data showed the importance of circulating lipids and coagulation cascade in septic shock progression, thus capturing a further layer of biological information complementary to metabolomics information. -
A Metabolomics Approach to Pharmacotherapy Personalization
Journal of Personalized Medicine Review A Metabolomics Approach to Pharmacotherapy Personalization Elena E. Balashova *, Dmitry L. Maslov and Petr G. Lokhov Institute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia; [email protected] (D.L.M.); [email protected] (P.G.L.) * Correspondence: [email protected] Received: 29 June 2018; Accepted: 3 September 2018; Published: 5 September 2018 Abstract: The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of “omics” technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided. Keywords: pharmacometabolomics; metabolomics; pharmacogenomics; therapeutic drug monitoring; personalized medicine; mass spectrometry 1. Introduction The uniformity of the drug response or low inter-individual differences in drug response are commonly accepted tenets in the field of medicine. Almost all drugs are prescribed on the basis of this statement. This approach can be described as treatment of the “average patient” by “the average pill” or “one size fits all”. However, clinicians have long observed that the actual effectiveness of the pharmacotherapy may be variable. -
The Metabolomic Paradigm of Pharmacogenomics in Complex
ics: O om pe ol n b A a c t c e e M s s Cacabelos, Metabolomics 2012, 2:5 Metabolomics: Open Access DOI: 10.4172/2153-0769.1000e119 ISSN: 2153-0769 Editorial Open Access The Metabolomic Paradigm of Pharmacogenomics in Complex Disorders Ramón Cacabelos1,2* 1EuroEspes Biomedical Research Center, Institute for CNS Disorders and Genomic Medicine, EuroEspes Chair of Biotechnology and Genomics, 15165-Bergondo, Corunna, Spain 2President, World Association of Genomic Medicine, Spain Metabolomics represents the networking organization of multiple pathogenic genes usually converge in metabolomic networks leading biochemical pathways leading to a physiological function in living to specific pathogenic cascades responsible for disease phenotypes. organisms. The frontier between health and disease is likely to be the The genomics of the mechanism of action of drugs has so far been result of a fine-tuning equilibrium or disequilibrium, respectively, neglected by the scientific community, and consequently less than 5% between the genomic-transcriptomic-proteomic-metabolomic cascade of FDA-approved drugs, with a pharmacologically-defined mechanism and environmental factors and/or epigenetic phenomena. In recent of action, have been studied in order to evaluate whether mutations times, diverse metabolomic studies have emerged in medical science in the genes encoding receptors or enzymes may affect efficacy and to explain physiological and pathogenic events in several disciplines, safety issues. Pleiotropic genes, involved in multiple pathogenic events, -
Model-Based Integration of Genomics and Metabolomics Reveals SNP Functionality in Mycobacterium Tuberculosis
Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis Ove Øyåsa,b,1, Sonia Borrellc,d,1, Andrej Traunerc,d,1, Michael Zimmermanne, Julia Feldmannc,d, Thomas Liphardta,b, Sebastien Gagneuxc,d, Jörg Stellinga,b, Uwe Sauere, and Mattia Zampierie,2 aDepartment of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; bSIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; cDepartment of Medical Parasitoloy and Infection Biology, Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; dUniversity of Basel, 4058 Basel, Switzerland; and eInstitute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland Edited by Ralph R. Isberg, Tufts University School of Medicine, Boston, MA, and approved March 2, 2020 (received for review September 12, 2019) Human tuberculosis is caused by members of the Mycobacterium infection of macrophages (29–32). Beyond analyses of individual tuberculosis complex (MTBC) that vary in virulence and transmis- laboratory strains, however, no systematic characterization and sibility. While genome-wide association studies have uncovered comparative analysis of intrinsic metabolic differences across several mutations conferring drug resistance, much less is known human-adapted MTBC clinical strains has been performed. about the factors underlying other bacterial phenotypes. Variation If the metabolic and other phenotypic diversity between in the outcome of tuberculosis infection and diseases has been MTBC strains contributes to and modulates pathogenicity, an attributed primarily to patient and environmental factors, but obvious question is: Which elements of the limited genetic di- recent evidence indicates an additional role for the genetic diver- versity in the MTBC are responsible for phenotypic strain di- sity among MTBC clinical strains. -
M. Tomita T. Nishioka (Eds.) Metabolomics the Frontier of Systems Biology
M. Tomita T. Nishioka (Eds.) Metabolomics The Frontier of Systems Biology M. Tomita, T. Nishioka (Eds.) Metabolomics The Frontier of Systems Biology With 112 Figures, Including 4 in Color Springer Masaru Tomita, Ph.D. Professor and Director Institute for Advanced Biosciences Keio University Tsuruoka 997-0035, Japan Takaaki Nishioka, Ph.D. Professor Graduate School of Agricuhure Kyoto University Kyoto 606-8502, Japan This book is based on the Japanese original, M. Tomita, T. Nishioka (Eds.), Metabolome Kenkyu no Saizensen, Springer-Verlag Tokyo, 2003. Library of Congress Control Number: 2005928331 ISBN 4-431-25121-9 Springer-Verlag Tokyo Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in other ways, and storage in data banks. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Product liability: The publishers cannot guarantee the accuracy of any information about dosage and application contained in this book. In every individual case the user must check such information by consulting the relevant literature. Springer is a part of Springer Science+Business Media springeronline.com © Springer-Vertag Tokyo 2005 Printed in Japan Typesetting: Camera-ready by the editor. Printing and binding: Nikkei Printing, Japan Printed on acid-free paper Preface The aim of this book is to review metabolomics research. -
Lipidomics and Metabolomics Service Gain Deeper Insights Into Exosomes
EXOSOMES LIPIDOMICS AND METABOLOMICS SERVICE GAIN DEEPER INSIGHTS INTO EXOSOMES SYSTEMBIO.COM/LIPIDOMICS HIGHLIGHTS What can lipidomics of exosomes tell you? n Discover novel circulating biomarkers Lipids are an important part of cellular physiology, and are increasingly being recognized for their importance in exosome biology as well. Exosomes were recently shown to have the highest lipid- n Learn more about exosome biology to-protein ratio of all classes of extracellular vesicles (1), with lipid content that both differs from n Send us your sample and receive data the parent cell the vesicles are shed from (2) and also changes as exosomes undergo a variety of in 4 - 6 weeks physiological processes (3). These unique lipid profiles can serve as novel circulating biomarkers, and recent evidence suggests that specific lipid species carried by exosomes can also modulate Service Overview the function of recipient cells (4). Whether you’re interested in With so much information revealed by lipid content, lipidomics studies of exosomes are a great way to identify lipid-based biomarkers and for understanding vesicle biogenesis and function (5). circulating biomarker discovery, basic exosome research, or other TUMOR MICROENVIRONMENT exosome-related studies, SBI’s CANCER CELLS CAFs EXOSOMES Exosome Lipidomics & Metabolomics Service helps you quickly and efficiently get the most information from your exosomes. Simply send Exosomes affect metabolism of cancer us your sample or purified exosomes cells A recent study by Zhao, et al, (6) and we’ll send back a report with demonstrated that exosomes from putative identifications, mass/charge patient-derived cancer-associated fibroblasts (CAFs) can reprogram EXOSOME ratios, and differential analysis (if UPTAKE the cellular machinery in cancer requested). -
Lipidomics at the Interface of Structure and Function in Systems Biology
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Chemistry & Biology Crosstalk Lipidomics at the Interface of Structure and Function in Systems Biology Richard W. Gross1,2,3,4,* and Xianlin Han1,2 1Division of Bioorganic Chemistry and Molecular Pharmacology 2Departments of Medicine 3Developmental Biology Washington University School of Medicine, St. Louis, MO 63110, USA 4Department of Chemistry, Washington University, St. Louis, MO 63105, USA *Correspondence: [email protected] DOI 10.1016/j.chembiol.2011.01.014 Cells, tissues, and biological fluids contain a diverse repertoire of many tens of thousands of structurally distinct lipids that play multiple roles in cellular signaling, bioenergetics, and membrane structure and func- tion. In an era where lipid-related disease states predominate, lipidomics has assumed a prominent role in systems biology through its unique ability to directly identify functional alterations in multiple lipid metabolic and signaling networks. The development of shotgun lipidomics has led to the facile accrual of high density information on alterations in the lipidome mediating physiologic cellular adaptation during health and path- ologic alterations during disease. Through both targeted and nontargeted investigations, lipidomics has already revealed the chemical mechanisms underlying many lipid-related disease states. The Multiple Roles of Lipids (Zhang et al., 2002; Breen et al., 2005). Cantley, 2006; Chiang et al., 2006; Simon in Cellular -
IPA Analysis of Metabolomics Data Including Cross-Platform Integration with Transcriptomics Data from a Diabetic Mouse Model
IPA Analysis of Metabolomics Data Including Cross-Platform Integration with Transcriptomics Data from a Diabetic Mouse Model CONTENTS Overview ........................................................................................................................................................................ 3 Introduction ................................................................................................................................................................... 3 Metabolomics Background ........................................................................................................................................ 3 Integration of Metabolomic and Transcriptomic data .............................................................................................. 5 The Ingenuity Platform .............................................................................................................................................. 5 Case Study Background ............................................................................................................................................. 5 Methods ........................................................................................................................................................................ 6 Establishing a Metabolite profile for db/db mice and identification of exploratory biomarkers .............................. 6 IPA-Metabolomics™ Analysis: ................................................................................................................................... -
Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales
life Review Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales Jayan D. M. Senevirathna 1,2,* and Shuichi Asakawa 1 1 Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan; [email protected] 2 Department of Animal Science, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka * Correspondence: [email protected] Abstract: Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identify- ing unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challeng- ing. As an environmental stress, the acoustic radiation has a significant effect on the alteration of Citation: Senevirathna, J.D.M.; Asakawa, S. Multi-Omics Approaches lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics and Radiation on Lipid Metabolism applications. -
Glossary of Terms
GLOSSARY OF TERMS Table of Contents A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z A Amino acids: any of a class of 20 molecules that are combined to form proteins in living things. The sequence of amino acids in a protein and hence protein function are determined by the genetic code. From http://www.geneticalliance.org.uk/glossary.htm#C • The building blocks of proteins, there are 20 different amino acids. From https://www.yourgenome.org/glossary/amino-acid Antisense: Antisense nucleotides are strings of RNA or DNA that are complementary to "sense" strands of nucleotides. They bind to and inactivate these sense strands. They have been used in research, and may become useful for therapy of certain diseases (See Gene silencing). From http://www.encyclopedia.com/topic/Antisense_DNA.aspx. Antisense and RNA interference are referred as gene knockdown technologies: the transcription of the gene is unaffected; however, gene expression, i.e. protein synthesis (translation), is lost because messenger RNA molecules become unstable or inaccessible. Furthermore, RNA interference is based on naturally occurring phenomenon known as Post-Transcriptional Gene Silencing. From http://www.ncbi.nlm.nih.gov/probe/docs/applsilencing/ B Biobank: A biobank is a large, organised collection of samples, usually human, used for research. Biobanks catalogue and store samples using genetic, clinical, and other characteristics such as age, gender, blood type, and ethnicity. Some samples are also categorised according to environmental factors, such as whether the donor had been exposed to some substance that can affect health.