51
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Yazdani M, Elgstøen KBP, Rootwelt H, Shahdadfar A, Utheim ØA, Utheim TP. Tear Metabolomics in Dry Eye Disease: A Review. Int J Mol Sci 2019; 20:E3755. [PMID: 31374809 PMCID: PMC6695908 DOI: 10.3390/ijms20153755] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 07/26/2019] [Accepted: 07/30/2019] [Indexed: 12/13/2022] Open
Abstract
Dry eye disease (DED) is a multifactorial syndrome that can be caused by alteration in the quality or quantity of the precorneal tear film. It is considered one of the most common ocular conditions leading patients to seek eye care. The current method for diagnostic evaluations and follow-up examinations of DED is a combination of clinical signs and symptoms determined by clinical tests and questionnaires, respectively. The application of powerful omics technologies has opened new avenues toward analysis of subjects in health and disease. Metabolomics is a new emerging and complementary research discipline to all modern omics in the comprehensive analysis of biological systems. The identification of distinct metabolites and integrated metabolic profiles in patients can potentially inform clinicians at an early stage or during monitoring of disease progression, enhancing diagnosis, prognosis, and the choice of therapy. In ophthalmology, metabolomics has gained considerable attention over the past decade but very limited such studies have been reported on DED. This paper aims to review the application of tear metabolomics in DED.
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Affiliation(s)
- Mazyar Yazdani
- Department of Medical Biochemistry, Oslo University Hospital, Ullevål, 0450 Oslo, Norway.
- Center for Eye Research, Department of Ophthalmology, Oslo University Hospital, Ullevål, 0450 Oslo, Norway.
- The Norwegian Dry Eye Clinic, 0366 Oslo, Norway.
| | | | - Helge Rootwelt
- Department of Medical Biochemistry, Oslo University Hospital, 0027 Oslo, Norway
| | - Aboulghassem Shahdadfar
- Center for Eye Research, Department of Ophthalmology, Oslo University Hospital, Ullevål, 0450 Oslo, Norway
| | | | - Tor Paaske Utheim
- Department of Medical Biochemistry, Oslo University Hospital, Ullevål, 0450 Oslo, Norway
- The Norwegian Dry Eye Clinic, 0366 Oslo, Norway
- Department of Plastic and Reconstructive Surgery, Oslo University Hospital, 0450 Oslo, Norway
- Department of Maxillofacial Surgery, Oslo University Hospital, 0450 Oslo, Norway
- Department of Ophthalmology, Vestre Viken Hospital Trust, 3019 Drammen, Norway
- Department of Ophthalmology, Stavanger University Hospital, 4011 Stavanger, Norway
- Department of Clinical Medicine, Faculty of Medicine, University of Bergen, 5020 Bergen, Norway
- Department of Ophthalmology, Sørlandet Hospital Arendal, 4604 Arendal, Norway
- Department of Life Sciences and Health, Oslo Metropolitan University, 0130 Oslo, Norway
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52
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Kennedy E, Arcadia CE, Geiser J, Weber PM, Rose C, Rubenstein BM, Rosenstein JK. Encoding information in synthetic metabolomes. PLoS One 2019; 14:e0217364. [PMID: 31269053 PMCID: PMC6608926 DOI: 10.1371/journal.pone.0217364] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 05/10/2019] [Indexed: 12/17/2022] Open
Abstract
Biomolecular information systems offer exciting potential advantages and opportunities to complement conventional semiconductor technologies. Much attention has been paid to information-encoding polymers, but small molecules also play important roles in biochemical information systems. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of small-molecule postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic mixtures of metabolites. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which can be decoded with accuracy exceeding 99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into some of the benefits and limitations of small-molecule chemical information systems.
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Affiliation(s)
- Eamonn Kennedy
- School of Engineering, Brown University, Providence, RI, United States of America
| | | | - Joseph Geiser
- Department of Chemistry, Brown University, Providence, RI, United States of America
| | - Peter M. Weber
- Department of Chemistry, Brown University, Providence, RI, United States of America
| | - Christopher Rose
- School of Engineering, Brown University, Providence, RI, United States of America
| | - Brenda M. Rubenstein
- Department of Chemistry, Brown University, Providence, RI, United States of America
| | - Jacob K. Rosenstein
- School of Engineering, Brown University, Providence, RI, United States of America
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53
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Ash JR, Kuenemann MA, Rotroff D, Motsinger-Reif A, Fourches D. Cheminformatics approach to exploring and modeling trait-associated metabolite profiles. J Cheminform 2019; 11:43. [PMID: 31236709 PMCID: PMC6591908 DOI: 10.1186/s13321-019-0366-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 06/17/2019] [Indexed: 12/17/2022] Open
Abstract
Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers. ![]()
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Affiliation(s)
- Jeremy R Ash
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Melaine A Kuenemann
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Daniel Rotroff
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Alison Motsinger-Reif
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA. .,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
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54
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Pinu FR, Goldansaz SA, Jaine J. Translational Metabolomics: Current Challenges and Future Opportunities. Metabolites 2019; 9:E108. [PMID: 31174372 PMCID: PMC6631405 DOI: 10.3390/metabo9060108] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/04/2019] [Accepted: 06/04/2019] [Indexed: 02/06/2023] Open
Abstract
Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.
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Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research, Private Bag 92169, Auckland 1142, New Zealand.
| | - Seyed Ali Goldansaz
- Department of Agriculture, Food and Nutritional Sciences, University of Alberta, Edmonton, AB T6G 2P5, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
| | - Jacob Jaine
- Analytica Laboratories Ltd., Ruakura Research Centre, Hamilton 3216, New Zealand.
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55
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Chaleckis R, Ohashi K, Meister I, Naz S, Wheelock CE. Metabolomic Analysis of Yeast and Human Cells: Latest Advances and Challenges. Methods Mol Biol 2019; 2049:233-245. [PMID: 31602615 DOI: 10.1007/978-1-4939-9736-7_14] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Liquid chromatography-mass spectrometry (LC-MS) based nontargeted metabolomics has been applied to a wide range of biological samples and can provide information on thousands of compounds. However, reliable identification of the compounds remains a challenge affecting result interpretation. In this protocol, we describe comparable yeast cell and whole blood metabolome sample preparation for extracting similar compound groups, and we present a LC-MS method using the all ion fragmentation (AIF) approach for the purposes of increasing accuracy in metabolite annotation. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information.
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Affiliation(s)
- Romanas Chaleckis
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Japan.
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
| | - Kazuto Ohashi
- Institute for Molecular and Cellular Regulation, Gunma University, Maebashi, Japan
| | - Isabel Meister
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Japan
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Shama Naz
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Craig E Wheelock
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Japan
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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56
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Laíns I, Gantner M, Murinello S, Lasky-Su JA, Miller JW, Friedlander M, Husain D. Metabolomics in the study of retinal health and disease. Prog Retin Eye Res 2018; 69:57-79. [PMID: 30423446 DOI: 10.1016/j.preteyeres.2018.11.002] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 10/06/2018] [Accepted: 11/07/2018] [Indexed: 02/06/2023]
Abstract
Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.
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Affiliation(s)
- Inês Laíns
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, 02114, United States; Faculty of Medicine, University of Coimbra, 3000 Coimbra, Portugal.
| | - Mari Gantner
- Lowy Medical Research Institute, La Jolla, CA, 92037, United States; Scripps Research Institute, La Jolla, CA, 92037, United States.
| | - Salome Murinello
- Lowy Medical Research Institute, La Jolla, CA, 92037, United States; Scripps Research Institute, La Jolla, CA, 92037, United States.
| | - Jessica A Lasky-Su
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, United States.
| | - Joan W Miller
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, 02114, United States.
| | - Martin Friedlander
- Lowy Medical Research Institute, La Jolla, CA, 92037, United States; Scripps Research Institute, La Jolla, CA, 92037, United States.
| | - Deeba Husain
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, 02114, United States.
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57
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Paris D, Maniscalco M, Motta A. Nuclear magnetic resonance-based metabolomics in respiratory medicine. Eur Respir J 2018; 52:13993003.01107-2018. [PMID: 30115612 DOI: 10.1183/13993003.01107-2018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 08/05/2018] [Indexed: 12/17/2022]
Affiliation(s)
- Debora Paris
- Institute of Biomolecular Chemistry, National Research Council, Pozzuoli (Naples), Italy.,Both authors contributed equally
| | - Mauro Maniscalco
- Pulmonary Rehabilitation Unit, ICS Maugeri SpA, IRCCS, Telese Terme (Benevento), Italy.,Both authors contributed equally
| | - Andrea Motta
- Institute of Biomolecular Chemistry, National Research Council, Pozzuoli (Naples), Italy
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58
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Mussap M, Zaffanello M, Fanos V. Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism. ANNALS OF TRANSLATIONAL MEDICINE 2018; 6:338. [PMID: 30306077 DOI: 10.21037/atm.2018.09.18] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.
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Affiliation(s)
- Michele Mussap
- Laboratory Medicine, Department of Surgical Sciences, University of Cagliari, Cagliari, Italy
| | - Marco Zaffanello
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, Verona, Italy
| | - Vassilios Fanos
- Department of Surgical Sciences, Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Cagliari, Italy
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59
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Tugizimana F, Mhlongo MI, Piater LA, Dubery IA. Metabolomics in Plant Priming Research: The Way Forward? Int J Mol Sci 2018; 19:ijms19061759. [PMID: 29899301 PMCID: PMC6032392 DOI: 10.3390/ijms19061759] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/02/2018] [Accepted: 06/04/2018] [Indexed: 12/26/2022] Open
Abstract
A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.
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Affiliation(s)
- Fidele Tugizimana
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
| | - Msizi I Mhlongo
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
| | - Lizelle A Piater
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
| | - Ian A Dubery
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
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60
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Wohlgemuth R. Horizons of Systems Biocatalysis and Renaissance of Metabolite Synthesis. Biotechnol J 2018; 13:e1700620. [DOI: 10.1002/biot.201700620] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 04/26/2018] [Indexed: 12/12/2022]
Affiliation(s)
- Roland Wohlgemuth
- European Federation of Biotechnology; Section on Applied Biocatalysis (ESAB); Theodor-Heuss-Allee 25,Frankfurt am Main 60486 Germany
- Sigma-Aldrich; Member of Merck Group; Industriestrasse 25,Buchs 9470 Switzerland
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61
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Kerley RN, McCarthy C, Kell DB, Kenny LC. The potential therapeutic effects of ergothioneine in pre-eclampsia. Free Radic Biol Med 2018; 117:145-157. [PMID: 29284116 DOI: 10.1016/j.freeradbiomed.2017.12.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 12/21/2017] [Accepted: 12/22/2017] [Indexed: 12/21/2022]
Abstract
Ergothioneine (ERG), is a water-soluble amino acid that is derived entirely from dietary sources. It has received much attention as a therapeutic agent due to its anti-oxidant properties, and there are claims of preferential accumulation within high oxidative stress organs. Pre-eclampsia, a condition accompanied by increased oxidative stress, is one of the leading causes of maternal morbidity and mortality. Despite intense research efforts, its aetiologies remain somewhat unclear and there are still no effective treatment options. Clinical trials of the anti-oxidants vitamin C and vitamin E have proven largely ineffective with little improvement in clinical outcome or even a negative response. This could be explained in part by their inability to permeate the plasma and mitochondrial membranes and scavenge mitochondria-derived superoxide species, and for the former by the fact that it is actually a pro-oxidant in the presence of unliganded iron. ERG accumulates within tissues through the action of a specific organic cation transporter, SLC22A4 (previously referred to as OCTN1), which is possibly also expressed in mammalian mitochondria. Mitochondrial dysfunction has been implicated in a variety of vascular diseases including pre-eclampsia. This review discusses the use of ERG as a possibly mitochondrial-targeted anti-oxidant, focusing on its physical properties, potential mechanisms of action, safety profile and administration in relation to pregnancies complicated by pre-eclampsia.
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Affiliation(s)
- Robert N Kerley
- Irish Centre for Fetal and Neonatal Translational Research (INFANT), Cork University Maternity Hospital, Cork, Ireland.
| | - Cathal McCarthy
- Irish Centre for Fetal and Neonatal Translational Research (INFANT), Cork University Maternity Hospital, Cork, Ireland
| | - Douglas B Kell
- School of Chemistry and The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK.
| | - Louise C Kenny
- Irish Centre for Fetal and Neonatal Translational Research (INFANT), Cork University Maternity Hospital, Cork, Ireland.
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62
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Tugizimana F, Steenkamp PA, Piater LA, Dubery IA. Mass spectrometry in untargeted liquid chromatography/mass spectrometry metabolomics: Electrospray ionisation parameters and global coverage of the metabolome. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2018; 32:121-132. [PMID: 28990281 DOI: 10.1002/rcm.8010] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 09/25/2017] [Accepted: 09/28/2017] [Indexed: 06/07/2023]
Abstract
RATIONALE Liquid chromatography coupled to mass spectrometry (LC/MS) is a dominant analytical platform in metabolomics, because of the high sensitivity and resolution, thus enabling large-scale coverage of metabolomes. Correspondingly, electrospray ionisation (ESI) is the favoured ionisation method in untargeted LC/MS metabolomics given the ability to produce large numbers of ions. In the workflow of LC/ESI-MS metabolomics, maximising the ionisation efficiency over a wide mass range is inevitably an essential and determining step, subsequently defining the extent of coverage of the metabolome under investigation. Thus in this study, electronic factors related to the functioning of the ESI source, namely the capillary and sample cone voltages, were explored to investigate the influence on the data acquired in metabolomic investigations. METHODS Hydromethanolic samples from an untargeted study (sorghum plants responding dynamically to fungal infection) were analysed on a high-resolution/definition LC/ESI-MS system. Here the capillary and sample cone voltages of the ZSpray™ ESI source were varied between 1.5-3.0 kV and 10.0-40.0 V, respectively. The acquired data were processed with MarkerLynx™ software and analysed using central composite design response surface methodology and chemometric approaches (principal component analysis and orthogonal projection latent structures-discriminant analysis). RESULTS The results evidently demonstrate that both capillary and sampling cone voltages not only significantly influence the recorded MS signals with regard to the number and abundance of features, but also the overall structure of the collected data. This consequently impacts on the information extracted from the data and thus affects coverage of the metabolome. CONCLUSIONS The observations postulate in that, untargeted LC/MS metabolomics, 'what you see is what you ionise'. Although there is convergence of collected data under different ESI conditions, the nuances observed indicate that the exploration of different ion source settings could be the best trade-off in expanding and maximising the metabolome coverage in untargeted metabolomic experiments.
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Affiliation(s)
- Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa
| | - Paul A Steenkamp
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa
| | - Lizelle A Piater
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa
| | - Ian A Dubery
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa
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63
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Kenny LC, Kell DB. Immunological Tolerance, Pregnancy, and Preeclampsia: The Roles of Semen Microbes and the Father. Front Med (Lausanne) 2018; 4:239. [PMID: 29354635 PMCID: PMC5758600 DOI: 10.3389/fmed.2017.00239] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 12/12/2017] [Indexed: 12/18/2022] Open
Abstract
Although it is widely considered, in many cases, to involve two separable stages (poor placentation followed by oxidative stress/inflammation), the precise originating causes of preeclampsia (PE) remain elusive. We have previously brought together some of the considerable evidence that a (dormant) microbial component is commonly a significant part of its etiology. However, apart from recognizing, consistent with this view, that the many inflammatory markers of PE are also increased in infection, we had little to say about immunity, whether innate or adaptive. In addition, we focused on the gut, oral and female urinary tract microbiomes as the main sources of the infection. We here marshall further evidence for an infectious component in PE, focusing on the immunological tolerance characteristic of pregnancy, and the well-established fact that increased exposure to the father's semen assists this immunological tolerance. As well as these benefits, however, semen is not sterile, microbial tolerance mechanisms may exist, and we also review the evidence that semen may be responsible for inoculating the developing conceptus (and maybe the placenta) with microbes, not all of which are benign. It is suggested that when they are not, this may be a significant cause of PE. A variety of epidemiological and other evidence is entirely consistent with this, not least correlations between semen infection, infertility and PE. Our view also leads to a series of other, testable predictions. Overall, we argue for a significant paternal role in the development of PE through microbial infection of the mother via insemination.
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Affiliation(s)
- Louise C. Kenny
- The Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
- Department of Obstetrics and Gynecology, University College Cork, Cork, Ireland
- Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Douglas B. Kell
- School of Chemistry, The University of Manchester, Manchester, United Kingdom
- The Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
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64
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Chaleckis R, Naz S, Meister I, Wheelock CE. LC-MS-Based Metabolomics of Biofluids Using All-Ion Fragmentation (AIF) Acquisition. Methods Mol Biol 2018; 1730:45-58. [PMID: 29363064 DOI: 10.1007/978-1-4939-7592-1_3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The field of liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compounds in biological samples. However, compound identification remains a major challenge, which is crucial in interpreting the biological function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an in-house spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative standard deviation between ion ratios of a metabolite in a biofluid vs. its analytical standard is used as an additional metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compounds. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information.
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Affiliation(s)
- Romanas Chaleckis
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, Gunma, Japan
| | - Shama Naz
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Isabel Meister
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, Gunma, Japan
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, Gunma, Japan.
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65
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Guitton Y, Tremblay-Franco M, Le Corguillé G, Martin JF, Pétéra M, Roger-Mele P, Delabrière A, Goulitquer S, Monsoor M, Duperier C, Canlet C, Servien R, Tardivel P, Caron C, Giacomoni F, Thévenot EA. Create, run, share, publish, and reference your LC–MS, FIA–MS, GC–MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. Int J Biochem Cell Biol 2017; 93:89-101. [DOI: 10.1016/j.biocel.2017.07.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Revised: 06/14/2017] [Accepted: 07/10/2017] [Indexed: 12/11/2022]
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66
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van Rijswijk M, Beirnaert C, Caron C, Cascante M, Dominguez V, Dunn WB, Ebbels TMD, Giacomoni F, Gonzalez-Beltran A, Hankemeier T, Haug K, Izquierdo-Garcia JL, Jimenez RC, Jourdan F, Kale N, Klapa MI, Kohlbacher O, Koort K, Kultima K, Le Corguillé G, Moreno P, Moschonas NK, Neumann S, O'Donovan C, Reczko M, Rocca-Serra P, Rosato A, Salek RM, Sansone SA, Satagopam V, Schober D, Shimmo R, Spicer RA, Spjuth O, Thévenot EA, Viant MR, Weber RJM, Willighagen EL, Zanetti G, Steinbeck C. The future of metabolomics in ELIXIR. F1000Res 2017; 6. [PMID: 29043062 PMCID: PMC5627583 DOI: 10.12688/f1000research.12342.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/31/2017] [Indexed: 01/11/2023] Open
Abstract
Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
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Affiliation(s)
- Merlijn van Rijswijk
- ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands.,Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands
| | - Charlie Beirnaert
- ADReM, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, 2020, Belgium
| | - Christophe Caron
- ELIXIR-FR, French Institute of Bioinformatics, Gif-sur-Yvette, F-91198, France
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Victoria Dominguez
- ELIXIR-FR, French Institute of Bioinformatics, Gif-sur-Yvette, F-91198, France
| | - Warwick B Dunn
- School of Biosciences, Phenome Centre Birmingham and Birmingham Metabolomics Training Centre, University of Birmingham, Birmingham, B15 2TT, UK
| | - Timothy M D Ebbels
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, SW7 2AZ, UK
| | - Franck Giacomoni
- INRA, UNH, Human Nutrition Unit, PFEM, Metabolism Exploration Platform, MetaboHUB-Clermont, Clermont Auvergne University, Clermont-Ferrand, F-63000, France
| | | | - Thomas Hankemeier
- Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands.,Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2300 RA, Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | - Jose L Izquierdo-Garcia
- Centro Nacional Investigaciones Cardiovasculares, Madrid, 28029, Spain.,CIBER de Enfermedades Respiratorias, Madrid, 28029 , Spain
| | | | - Fabien Jourdan
- Toxalim, UMR 1331, Université de Toulouse, Toulouse, F-31300, France
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | - Maria I Klapa
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, GR-26504, Greece
| | - Oliver Kohlbacher
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, 72076, Germany.,Department of Computer Science, University of Tübingen, Tübingen, 72076, Germany.,Center for Bioinformatics, University of Tübingen, Tübingen, 72076, Germany
| | - Kairi Koort
- The Centre of Excellence in Neural and Behavioural Sciences, Tallinn, Tallinn, 10120, Estonia.,School of Natural Sciences and Health, Tallinn University, 10120, 10120, Estonia
| | - Kim Kultima
- Department of Medical Sciences, Uppsala University, Uppsala, 752 36, Sweden
| | - Gildas Le Corguillé
- ELIXIR-FR, French Institute of Bioinformatics, Gif-sur-Yvette, F-91198, France.,UPMC, CNRS, FR2424, ABiMS, Station Biologique, Roscoff, F-29680, France
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | - Nicholas K Moschonas
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, GR-26504, Greece.,Department of General Biology, School of Medicine, University of Patras, Patras, GR-26504, Greece
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, 06120, Germany
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | | | - Philippe Rocca-Serra
- Oxford e-Research Centre, Engineering Science Department, University of Oxford, Oxford, OX1 3QG, UK
| | - Antonio Rosato
- Magnetic Resonance Center, Interuniversity Consortium for Magnetic Resonance on MetalloProteins, University of Florence, Florence, 50121, Italy
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Engineering Science Department, University of Oxford, Oxford, OX1 3QG, UK
| | - Venkata Satagopam
- Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Daniel Schober
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, 06120, Germany
| | - Ruth Shimmo
- The Centre of Excellence in Neural and Behavioural Sciences, Tallinn, Tallinn, 10120, Estonia.,School of Natural Sciences and Health, Tallinn University, 10120, 10120, Estonia
| | - Rachel A Spicer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, 752 36, Sweden
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-sur-Yvette, F-91191, France
| | - Mark R Viant
- School of Biosciences, Phenome Centre Birmingham and Birmingham Metabolomics Training Centre, University of Birmingham, Birmingham, B15 2TT, UK
| | - Ralf J M Weber
- School of Biosciences, Phenome Centre Birmingham and Birmingham Metabolomics Training Centre, University of Birmingham, Birmingham, B15 2TT, UK
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, NL-6200, Netherlands
| | - Gianluigi Zanetti
- CRS4, Data Intensive Computing Group, Ed.1 POLARIS, Pula, 09010, Italy
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67
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Pinna M, Manchia M. Preventing aggressive/violent behavior: a role for biomarkers? Biomark Med 2017; 11:701-704. [PMID: 30669857 DOI: 10.2217/bmm-2017-0135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Martina Pinna
- Unit of Psychiatry, Department of Mental Health & Addiction Services, Regional Health Agency, Oristano, Italy.,Section of Neurosciences & Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Mirko Manchia
- Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada.,Section of Psychiatry, Department of Medical Sciences & Public Health, University of Cagliari, Cagliari, Italy
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68
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Naz S, Gallart-Ayala H, Reinke SN, Mathon C, Blankley R, Chaleckis R, Wheelock CE. Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition. Anal Chem 2017. [PMID: 28641411 DOI: 10.1021/acs.analchem.7b00925] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biological context. In the current study, a liquid chromatography (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum, and (iv) product/precursor ion intensity ratios. We constructed an in-house mass spectral library of 408 metabolites containing AMRT and MS/MS spectra information at four collision energies. The percent relative standard deviations between ion ratios of a metabolite in an analytical standard vs sample matrix were used as an additional metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, respectively. We also included a scheme to distinguish coeluting isobaric compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. Our data acquisition strategy enables a simultaneous mixture of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biological information acquired in a metabolomics experiment.
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Affiliation(s)
- Shama Naz
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Hector Gallart-Ayala
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Stacey N Reinke
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Caroline Mathon
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | | | - Romanas Chaleckis
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden.,Gunma University Initiative for Advanced Research (GIAR), Gunma University , Gunma, Japan
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden.,Gunma University Initiative for Advanced Research (GIAR), Gunma University , Gunma, Japan
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69
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Lu X, Solmonson A, Lodi A, Nowinski SM, Sentandreu E, Riley CL, Mills EM, Tiziani S. The early metabolomic response of adipose tissue during acute cold exposure in mice. Sci Rep 2017; 7:3455. [PMID: 28615704 PMCID: PMC5471228 DOI: 10.1038/s41598-017-03108-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 04/25/2017] [Indexed: 12/15/2022] Open
Abstract
To maintain core body temperature in cold conditions, mammals activate a complex multi-organ metabolic response for heat production. White adipose tissue (WAT) primarily functions as an energy reservoir, while brown adipose tissue (BAT) is activated during cold exposure to generate heat from nutrients. Both BAT and WAT undergo specific metabolic changes during acute cold exposure. Here, we use an untargeted metabolomics approach to characterize the initial metabolic response to cold exposure in multiple adipose tissue depots in mice. Results demonstrate dramatically distinct metabolic responses during cold exposure in BAT and WAT. Amino acids, nucleotide pathways, and metabolites involved in redox regulation were greatly affected 4 hours post-exposure in BAT, while no polar metabolites were observed to significantly change in WAT depots up to 6 hours post exposure. Lipid metabolism was activated early (2 hours) in both BAT and the subcutaneous WAT depots, with the most striking change being observed in the modulation of diglyceride and monoglyceride levels in BAT. Overall, these data provide a timeline of global thermogenic metabolism in adipose depots during acute cold exposure. We have highlighted differences in visceral and subcutaneous WAT thermogenic metabolism and demonstrate the distinct metabolism of BAT during cold exposure.
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Affiliation(s)
- Xiyuan Lu
- Department of Nutritional Sciences & Dell Pediatric Research Institute, The University of Texas at Austin, 1400, Barbara Jordan Blvd., Austin, TX 78723, USA
| | - Ashley Solmonson
- Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas, Austin, Texas, 78712, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Alessia Lodi
- Department of Nutritional Sciences & Dell Pediatric Research Institute, The University of Texas at Austin, 1400, Barbara Jordan Blvd., Austin, TX 78723, USA
| | - Sara M Nowinski
- Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas, Austin, Texas, 78712, USA
| | - Enrique Sentandreu
- Department of Nutritional Sciences & Dell Pediatric Research Institute, The University of Texas at Austin, 1400, Barbara Jordan Blvd., Austin, TX 78723, USA
| | - Christopher L Riley
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Edward M Mills
- Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas, Austin, Texas, 78712, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Stefano Tiziani
- Department of Nutritional Sciences & Dell Pediatric Research Institute, The University of Texas at Austin, 1400, Barbara Jordan Blvd., Austin, TX 78723, USA.
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, 78712, USA.
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70
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Goodacre R, Kell DB. Commentary on "Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks". FEMS Microbiol Lett 2017; 364:fnx018. [PMID: 28130368 PMCID: PMC5399912 DOI: 10.1093/femsle/fnx018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 01/24/2017] [Indexed: 11/14/2022] Open
Abstract
This is an invited review/commentary by the first and last authors of a paper that was the most cited in FEMS Microbiology Letters for 1996, presently showing in excess of 150 citations at Web of Science, and over 200 at Google Scholar. It was the first paper in which diffuse reflectance absorbance FT-IR spectroscopy was used with a supervised learning method in the form of artificial neural networks, and showed that this combination could succeed in discriminating a series of closely related, clinically relevant, Gram-positive bacterial strains.
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Affiliation(s)
- Royston Goodacre
- School of Chemistry, The University of Manchester, Manchester, Manchester M1 7DN, UK
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, Manchester M1 7DN, UK
- Centre for the Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131 Princess St, Manchester M1 7DN, UK
| | - Douglas B. Kell
- School of Chemistry, The University of Manchester, Manchester, Manchester M1 7DN, UK
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, Manchester M1 7DN, UK
- Centre for the Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131 Princess St, Manchester M1 7DN, UK
- Corresponding author: School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK. Tel: 1613064492; E-mail:
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71
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Abstract
One of the fundamental traits of immune cells in rheumatoid arthritis (RA) is their ability to proliferate, a property shared with the joint-resident cells that form the synovial pannus. The building of biomass imposes high demands for energy and biosynthetic precursors, implicating metabolic control as a basic disease mechanism. During preclinical RA, when autoreactive T cells expand and immunological tolerance is broken, the main sites of disease are the secondary lymphoid tissues. Naive CD4+ T cells from patients with RA have a distinct metabolic signature, characterized by dampened glycolysis, low ATP levels and enhanced shunting of glucose into the pentose phosphate pathway. Equipped with high levels of NADPH and depleted of intracellular reactive oxygen species, such T cells hyperproliferate and acquire proinflammatory effector functions. During clinical RA, immune cells coexist with stromal cells in the acidic milieu of the inflamed joint. This microenvironment is rich in metabolic intermediates that are released into the extracellular space to shape cell-cell communication and the functional activity of tissue-resident cells. Increasing awareness of how metabolites regulate signalling pathways, guide post-translational modifications and condition the tissue microenvironment will help to connect environmental factors with the pathogenic behaviour of T cells in RA.
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72
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Aguilar-Mogas A, Sales-Pardo M, Navarro M, Guimerà R, Yanes O. iMet: A Network-Based Computational Tool To Assist in the Annotation of Metabolites from Tandem Mass Spectra. Anal Chem 2017; 89:3474-3482. [DOI: 10.1021/acs.analchem.6b04512] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Antoni Aguilar-Mogas
- Departament
d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
| | - Marta Sales-Pardo
- Departament
d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
| | - Miriam Navarro
- Metabolomics
Platform, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Monforte de Lemos 35, 28029 Madrid, Spain
| | - Roger Guimerà
- Departament
d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
| | - Oscar Yanes
- Metabolomics
Platform, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Monforte de Lemos 35, 28029 Madrid, Spain
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73
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Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: The future of metabolomics in a personalized world. NEW HORIZONS IN TRANSLATIONAL MEDICINE 2017; 3:294-305. [PMID: 29094062 PMCID: PMC5653644 DOI: 10.1016/j.nhtm.2017.06.001] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 06/02/2017] [Accepted: 06/02/2017] [Indexed: 02/07/2023]
Abstract
Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.
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Affiliation(s)
| | | | - Royston Goodacre
- Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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74
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Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C. Navigating freely-available software tools for metabolomics analysis. Metabolomics 2017; 13:106. [PMID: 28890673 PMCID: PMC5550549 DOI: 10.1007/s11306-017-1242-7] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 07/25/2017] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. OBJECTIVES To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. METHODS The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for. RESULTS A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary. CONCLUSION This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.
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Affiliation(s)
- Rachel Spicer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Reza M. Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Daniel Cañueto
- Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007 Tarragona, Catalonia Spain
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
- Friedrich-Schiller-University Jena, Lessingstr. 8, Jena, 07743 Germany
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75
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Kell DB, Kenny LC. A Dormant Microbial Component in the Development of Preeclampsia. Front Med (Lausanne) 2016; 3:60. [PMID: 27965958 PMCID: PMC5126693 DOI: 10.3389/fmed.2016.00060] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 11/04/2016] [Indexed: 12/12/2022] Open
Abstract
Preeclampsia (PE) is a complex, multisystem disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is, in fact, microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, and urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of "preeclampsia" that we assessed has, in fact, also been shown to be raised in response to infection. An infectious component to PE fulfills the Bradford Hill criteria for ascribing a disease to an environmental cause and suggests a number of treatments, some of which have, in fact, been shown to be successful. PE was classically referred to as endotoxemia or toxemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one.
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Affiliation(s)
- Douglas B. Kell
- School of Chemistry, The University of Manchester, Manchester, UK
- The Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of Manchester, Manchester, UK
- *Correspondence: Douglas B. Kell,
| | - Louise C. Kenny
- The Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
- Department of Obstetrics and Gynecology, University College Cork, Cork, Ireland
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