Metabolites

12 pages, 852 KiB

Open AccessArticle

Changes in the Elemental and Metabolite Profile of Wheat Phloem Sap during Grain Filling Indicate a Dynamic between Plant Maturity and Time of Day

by Lachlan J. Palmer^*

and James C. R. Stangoulis

Biological Sciences, College of Science and Engineering, Flinders University, Bedford Park 5042, Australia

Metabolites 2018, 8(3), 53; https://doi.org/10.3390/metabo8030053 - 19 Sep 2018

Cited by 16 | Viewed by 3855

Abstract

The long distance transport of Fe and Zn in the phloem sap of wheat (Triticum aestivum L.) is the key route for seed supply, due to wheat having a xylem discontinuity. To date, our knowledge is limited on Fe and Zn homeostasis [...] Read more.

The long distance transport of Fe and Zn in the phloem sap of wheat (Triticum aestivum L.) is the key route for seed supply, due to wheat having a xylem discontinuity. To date, our knowledge is limited on Fe and Zn homeostasis in the phloem sap during the reproductive and grain filling stages. With the use of aphid stylectomy to collect samples of phloem sap, we explored maturity and morning versus afternoon (within-day) changes in nutrient and metabolite profiles. Phloem exudate was collected from a wheat breeding line, SAMNYT16, at three times during the grain filling period and at both midday and mid-afternoon. There were significant changes in the concentration of Mg, K, Fe and Zn during the course of grain loading and there were also significant within-day differences for Fe and K concentrations in the phloem exudate during the early phases of grain development. We found that, for K and Fe, there was an increase of 1.1- and 1.4-fold, respectively, for samples taken prior to midday to those from mid-afternoon. There was also a significant decrease in K, Fe and Zn phloem sap concentration of 1.5-, 1.4- and 1.1-fold, respectively, from the start of peak grain loading to the end of grain loading. Of the 79 metabolites detected within samples of phloem exudate, 43 had significant maturity differences and 38 had significant within-day variability. Glutamine was found to increase by 3.3–5.9-fold from midday to mid-afternoon and citric acid was found to decrease by 1.6-fold from the start of grain loading to the end of grain loading. These two metabolites are of interest as they can complex metal ions and may play a role in long distance transport of metal ions. The work presented here gives further insight into the complex composition of the phloem sap and variability that can occur during the day and also with increasing maturity. Full article

(This article belongs to the Special Issue Plant, Food and Nutritional Metabolomics for Health Enhancement)

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9 pages, 1954 KiB

Open AccessReview

High-Throughput Direct Mass Spectrometry-Based Metabolomics to Characterize Metabolite Fingerprints Associated with Alzheimer’s Disease Pathogenesis

by Raúl González-Domínguez^1,2,3,*

, Ana Sayago^1,2 and Ángeles Fernández-Recamales^1,2

¹ Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain

² International Campus of Excellence ceiA3, University of Huelva, 21007 Huelva, Spain

³ Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028 Barcelona, Spain

Metabolites 2018, 8(3), 52; https://doi.org/10.3390/metabo8030052 - 18 Sep 2018

Cited by 20 | Viewed by 4842

Abstract

Direct mass spectrometry-based metabolomics has been widely employed in recent years to characterize the metabolic alterations underlying Alzheimer’s disease development and progression. This high-throughput approach presents great potential for fast and simultaneous fingerprinting of a vast number of metabolites, which can be applied [...] Read more.

Direct mass spectrometry-based metabolomics has been widely employed in recent years to characterize the metabolic alterations underlying Alzheimer’s disease development and progression. This high-throughput approach presents great potential for fast and simultaneous fingerprinting of a vast number of metabolites, which can be applied to multiple biological matrices including serum/plasma, urine, cerebrospinal fluid and tissues. In this review article, we present the main advantages and drawbacks of metabolomics based on direct mass spectrometry compared with conventional analytical techniques, and provide a comprehensive revision of the literature on the use of these tools in the investigation of Alzheimer’s disease. Full article

(This article belongs to the Special Issue Metabolomics in Neurodegenerative Disease)

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14 pages, 3363 KiB

Open AccessArticle

Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas

by Clément Frainay¹

, Emma L. Schymanski^2,3

, Steffen Neumann^4,5

, Benjamin Merlet¹, Reza M. Salek⁶

, Fabien Jourdan^1,*

and Oscar Yanes^7,8,*

¹ Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France

² Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland

³ Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg

⁴ Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany

⁵ German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher Platz 5e, 04103 Leipzig, Germany

⁶ The International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon CEDEX 08, France

⁷ Metabolomics Platform, IISPV, Department of Electronic Engineering, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, 43007 Tarragona, Spain

⁸ Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Monforte de Lemos 3-5, 28029 Madrid, Spain

Metabolites 2018, 8(3), 51; https://doi.org/10.3390/metabo8030051 - 15 Sep 2018

Cited by 49 | Viewed by 10534

Abstract

The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. [...] Read more.

The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future. Full article

(This article belongs to the Special Issue Metabolomics Data Processing and Data Analysis—Current Best Practices)

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20 pages, 1630 KiB

Open AccessArticle

Potential Metabolomic Linkage in Blood between Parkinson’s Disease and Traumatic Brain Injury

by Massimo S. Fiandaca^1,2,3,*

, Thomas J. Gross^1,3, Thomas M. Johnson⁴, Michele T. Hu^5,6, Samuel Evetts⁵, Richard Wade-Martins⁷, Kian Merchant-Borna⁸

, Jeffrey Bazarian⁸, Amrita K. Cheema^9,10, Mark Mapstone¹ and Howard J. Federoff^1,*

¹ Translational Laboratory and Biorepository, Department of Neurology, University of California Irvine School of Medicine, Irvine, CA 92697-3910, USA

² Department of Neurological Surgery, University of California Irvine School of Medicine, Irvine, CA 92697-3910, USA

³ Department of Anatomy & Neurobiology, University of California Irvine School of Medicine, Irvine, CA 92697-3910, USA

⁴ Intrepid Spirit Concussion Recovery Center, Naval Medical Center Camp Lejeune, Jacksonville, NC 28540, USA

⁵ Nuffield Department of Clinical Neurosciences, University of Oxford, 01865 Oxford, UK

⁶ Department of Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford 01865, UK

⁷ Department of Physiology, Anatomy and Genetics, Oxford Parkinson’s Disease Centre, University of Oxford, Oxford 01865, UK

⁸ Department of Emergency Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY 14604, USA

⁹ Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20001, USA

¹⁰ Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20001, USA

Metabolites 2018, 8(3), 50; https://doi.org/10.3390/metabo8030050 - 7 Sep 2018

Cited by 22 | Viewed by 6233

Abstract

The etiologic basis for sporadic forms of neurodegenerative diseases has been elusive but likely represents the product of genetic predisposition and various environmental factors. Specific gene-environment interactions have become more salient owing, in part, to the elucidation of epigenetic mechanisms and their impact [...] Read more.

The etiologic basis for sporadic forms of neurodegenerative diseases has been elusive but likely represents the product of genetic predisposition and various environmental factors. Specific gene-environment interactions have become more salient owing, in part, to the elucidation of epigenetic mechanisms and their impact on health and disease. The linkage between traumatic brain injury (TBI) and Parkinson’s disease (PD) is one such association that currently lacks a mechanistic basis. Herein, we present preliminary blood-based metabolomic evidence in support of potential association between TBI and PD. Using untargeted and targeted high-performance liquid chromatography-mass spectrometry we identified metabolomic biomarker profiles in a cohort of symptomatic mild TBI (mTBI) subjects (n = 75) 3–12 months following injury (subacute) and TBI controls (n = 20), and a PD cohort with known PD (n = 20) or PD dementia (PDD) (n = 20) and PD controls (n = 20). Surprisingly, blood glutamic acid levels in both the subacute mTBI (increased) and PD/PDD (decreased) groups were notably altered from control levels. The observed changes in blood glutamic acid levels in mTBI and PD/PDD are discussed in relation to other metabolite profiling studies. Should our preliminary results be replicated in comparable metabolomic investigations of TBI and PD cohorts, they may contribute to an “excitotoxic” linkage between TBI and PD/PDD. Full article

(This article belongs to the Special Issue Metabolomics in Neurodegenerative Disease)

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15 pages, 1778 KiB

Open AccessArticle

Untargeted Metabolomics Analysis of Eggplant (Solanum melongena L.) Fruit and Its Correlation to Fruit Morphologies

by Abu Hanifah¹

, Awang Maharijaya^1,2, Sastia P. Putri³, Walter A. Laviña⁴ and Sobir^1,2,*

¹ Department of Agronomy and Horticulture, Bogor Agricultural University, Jl. Meranti, IPB Dramaga Campus Bogor, West Java 16680, Indonesia

² Center for Tropical Horticulture Studies, Bogor Agricultural University, Bogor 16680, Indonesia

³ Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

⁴ Microbiology Division, Institute of Biological Sciences, University of the Philippines Los Baños, Los Baños, Laguna 4031, Philippines

Metabolites 2018, 8(3), 49; https://doi.org/10.3390/metabo8030049 - 1 Sep 2018

Cited by 36 | Viewed by 7698

Abstract

Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. [...] Read more.

Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. To gain insight on the diversity of eggplant metabolites, twenty-one eggplant accessions were analyzed by untargeted metabolomics using GC-MS and LC-MS. The dataset of eggplant fruit morphologies, and metabolites specific to different eggplant fruit accessions were used for correlation analysis. Untargeted metabolomics analysis using LC-MS and GC-MS was able to detect 136 and 207 peaks, respectively. Fifty-one (51) metabolites from the LC-MS analysis and 207 metabolites from the GC-MS analysis were putatively identified, which included alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. Spearman correlation analysis revealed that 14 fruit morphologies were correlated with several metabolites. This information will be very useful for the development of strategies for eggplant breeding. Full article

(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics and Its Applications)

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11 pages, 1300 KiB

Open AccessArticle

The Energetic Viability of Δ¹-Piperideine Dimerization in Lysine-derived Alkaloid Biosynthesis

by Hajime Sato^1,2

, Masanobu Uchiyama^2,3

, Kazuki Saito^1,4

and Mami Yamazaki^1,*

¹ Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan

² Cluster of Pioneering Research (CPR), Advanced Elements Chemistry Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

³ Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

⁴ RIKEN Center for Sustainable Resource Science (Yokohama campus), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan

Metabolites 2018, 8(3), 48; https://doi.org/10.3390/metabo8030048 - 31 Aug 2018

Cited by 13 | Viewed by 5797

Abstract

Lys-derived alkaloids widely distributed in plant kingdom have received considerable attention and have been intensively studied; however, little is known about their biosynthetic mechanisms. In terms of the skeleton formation, for example, of quinolizidine alkaloid biosynthesis, only the very first two steps have [...] Read more.

Lys-derived alkaloids widely distributed in plant kingdom have received considerable attention and have been intensively studied; however, little is known about their biosynthetic mechanisms. In terms of the skeleton formation, for example, of quinolizidine alkaloid biosynthesis, only the very first two steps have been identified and the later steps remain unknown. In addition, there is no available information on the number of enzymes and reactions required for their skeletal construction. The involvement of the

Δ^{1}

-piperideine dimerization has been proposed for some of the Lys-derived alkaloid biosyntheses, but no enzymes for this dimerization reaction have been reported to date; moreover, it is not clear whether this dimerization reaction proceeds spontaneously or enzymatically. In this study, the energetic viability of the

Δ^{1}

-piperideine dimerizations under neutral and acidic conditions was assessed using the density functional theory computations. In addition, a similar type of reaction in the dipiperidine indole alkaloid, nitramidine, biosynthesis was also investigated. Our findings will be useful to narrow down the candidate genes involved in the Lys-derived alkaloid biosynthesis. Full article

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10 pages, 1231 KiB

Open AccessReview

Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

by Helena U. Zacharias¹, Michael Altenbuchinger² and Wolfram Gronwald^3,*

¹ Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

² Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Am Biopark 9, 93053 Regensburg, Germany

³ Institute of Functional Genomics, University of Regensburg, Am Biopark 9, 93053 Regensburg, Germany

Metabolites 2018, 8(3), 47; https://doi.org/10.3390/metabo8030047 - 28 Aug 2018

Cited by 37 | Viewed by 6336

Abstract

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification [...] Read more.

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization. Full article

(This article belongs to the Section Thematic Reviews)

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17 pages, 2653 KiB

Open AccessArticle

Interlaboratory Coverage Test on Plant Food Bioactive Compounds and Their Metabolites by Mass Spectrometry-Based Untargeted Metabolomics

by Ville Mikael Koistinen^1,*

, Andreia Bento da Silva², László Abrankó³, Dorrain Low⁴

, Rocio Garcia Villalba⁵, Francisco Tomás Barberán⁵

, Rikard Landberg⁶, Otto Savolainen⁶, Inmaculada Alvarez-Acero⁷

, Sonia De Pascual-Teresa⁷

, Christof Van Poucke⁸

, Conceição Almeida², Lucie Petrásková⁹, Kateřina Valentová⁹

, Stephanie Durand⁴, Wiesław Wiczkowski¹⁰

, Dorota Szawara-Nowak¹⁰, Raúl González-Domínguez^11,12

, Rafael Llorach^11,12, Cristina Andrés-Lacueva^11,12, Anna-Marja Aura¹³, Tuulikki Seppänen-Laakso¹³, Kati Hanhineva¹, Claudine Manach⁴ and Maria Rosário Bronze^2,14,15

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¹ Institute of Public Health and Clinical Nutrition, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland

² Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa (ITQB NOVA), Av. da República, 2780-157 Oeiras, Portugal

³ Alkalmazott Kemia Tsz, Elelmiszertudomanyi Kar, Szent István Egyetem, 29-43 Villanyi Street, 1118 Budapest, Hungary

⁴ INRA, Human Nutrition Unit, Université Clermont Auvergne, F63000 Clermont-Ferrand, France

⁵ Department of Food Science and Technology, CEBAS-CSIC, Campus Universitario de Espinardo, edf 25, 30100 Murcia, Spain

⁶ Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden

⁷ Department of Metabolism and Nutrition, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC), Jose Antonio Novais 10, 28040 Madrid, Spain

⁸ Technology and Food Science Department, Flanders Research Institute for Agriculture Fisheries and Food (ILVO), Brusselsesteenweg 370, B-9090 Melle, Belgium

⁹ Laboratory of Biotransformation, Institute of Microbiology of the CAS, Vídeňská 1083, CZ-142 20 Prague, Czechia

¹⁰ Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Tuwima 10, 10-748 Olsztyn, Poland

¹¹ Department of Nutrition, Food Science and Gastronomy, Pharmacy Faculty, University of Barcelona, Av Joan XXIII, s/n, 08028 Barcelona, Spain

¹² CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, 08028 Barcelona, Spain

¹³ VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 VTT, Espoo, Finland

¹⁴ Instituto de Biologia Experimental Tecnológica (iBET), Av. da República, 2780-157 Oeiras, Portugal

¹⁵ Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon (FFULisboa), Avenida Professor Gama Pinto, 1649-003 Lisbon, Portugal

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Metabolites 2018, 8(3), 46; https://doi.org/10.3390/metabo8030046 - 24 Aug 2018

Cited by 22 | Viewed by 7651

Abstract

Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for [...] Read more.

Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for the profiling of these compounds in complex matrices, including plant food materials and biofluids. The aim of this project was to compare the analytical coverage of untargeted metabolomics methods independently developed and employed in various European platforms. In total, 56 chemical standards representing the most common classes of bioactive compounds spread over a wide chemical space were selected and analyzed by the participating platforms (n = 13) using their preferred untargeted method. The results were used to define analytical criteria for a successful analysis of plant food bioactives. Furthermore, they will serve as a basis for an optimized consensus method. Full article

(This article belongs to the Special Issue Plant, Food and Nutritional Metabolomics for Health Enhancement)

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15 pages, 1103 KiB

Open AccessArticle

Night Shift Work Affects Urine Metabolite Profiles of Nurses with Early Chronotype

by Markus Rotter^1,2,3, Stefan Brandmaier^1,2,3, Marcela Covic^1,2,3, Katarzyna Burek⁴, Johannes Hertel⁵, Martina Troll^1,2,3, Erik Bader^1,2,6, Jonathan Adam^1,2,3, Cornelia Prehn⁷, Birgit Rathkolb^3,8,9, Martin Hrabe de Angelis^3,10,11, Hans Jörgen Grabe^5,12, Hannelore Daniel¹³, Thomas Kantermann^14,15

, Volker Harth¹⁶, Thomas Illig^17,18, Dirk Pallapies⁴, Thomas Behrens⁴, Thomas Brüning⁴, Jerzy Adamski^3,7,11

, Heiko Lickert^3,6,19, Sylvia Rabstein⁴ and Rui Wang-Sattler^1,2,3,* Show full author list Hide full author list

¹ Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 München-Neuherberg, Germany

² Institute of Epidemiology, Helmholtz Zentrum München, 85764 München-Neuherberg, Germany

³ German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany

⁴ Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44721 Bochum, Germany

⁵ Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17489 Greifswald, Germany

⁶ Institute Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 München-Neuherberg, Germany

⁷ Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 München-Neuherberg, Germany

⁸ Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, and Center for Innovative Medical Models (CiMM), LMU Munich, 81377 Munich, Germany

⁹ German Mouse Clinic (GMC), Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 München-Neuherberg, Germany

¹⁰ Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 München-Neuherberg, Germany

¹¹ Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85353 Freising, Germany

¹² German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, 17489 Greifswald, Germany

¹³ Molecular Nutrition Unit, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85354 Freising, Germany

¹⁴ University of Applied Sciences for Economics and Management (FOM), 45127 Essen, Germany

¹⁵ SynOpus, 44789 Bochum, Germany

¹⁶ Institute for Occupational Medicine and Maritime Medicine (ZfAM), University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany

¹⁷ Hannover Unified Biobank, Hannover Medical School, 30625, Hannover, Germany

¹⁸ Institute for Human Genetics, Hannover Medical School, Carl-Neuberg-Strasse 1, D-30625 Hanover, Germany

¹⁹ Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 München-Neuherberg, Germany

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Metabolites 2018, 8(3), 45; https://doi.org/10.3390/metabo8030045 - 21 Aug 2018

Cited by 14 | Viewed by 6765

Abstract

Night shift work can have a serious impact on health. Here, we assess whether and how night shift work influences the metabolite profiles, specifically with respect to different chronotype classes. We have recruited 100 women including 68 nurses working both, day shift and [...] Read more.

Night shift work can have a serious impact on health. Here, we assess whether and how night shift work influences the metabolite profiles, specifically with respect to different chronotype classes. We have recruited 100 women including 68 nurses working both, day shift and night shifts for up to 5 consecutive days and collected 3640 spontaneous urine samples. About 424 waking-up urine samples were measured using a targeted metabolomics approach. To account for urine dilution, we applied three methods to normalize the metabolite values: creatinine-, osmolality- and regression-based normalization. Based on linear mixed effect models, we found 31 metabolites significantly (false discovery rate <0.05) affected in nurses working in night shifts. One metabolite, acylcarnitine C10:2, was consistently identified with all three normalization methods. We further observed 11 and 4 metabolites significantly associated with night shift in early and late chronotype classes, respectively. Increased levels of medium- and long chain acylcarnitines indicate a strong impairment of the fatty acid oxidation. Our results show that night shift work influences acylcarnitines and BCAAs, particularly in nurses in the early chronotype class. Women with intermediate and late chronotypes appear to be less affected by night shift work. Full article

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22 pages, 5496 KiB

Open AccessArticle

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry

by Jatin Nandania^1,2, Gopal Peddinti^3,4, Alberto Pessia^1,5

, Meri Kokkonen^1,6 and Vidya Velagapudi^1,*

¹ Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland

² Roche Diagnostics GmbH, Nonnenwald 2, 82377 Penzberg, Germany

³ Computational Systems Medicine group, University of Helsinki, 00290 Helsinki, Finland

⁴ Technical Research Center of Finland, P.O. Box 1000, 02044 Espoo, Finland

⁵ Network Pharmacology for Precision Medicine Group, University of Helsinki, 00290 Helsinki, Finland

⁶ Finnish Customs Laboratory, Tekniikantie 13, 02150 Espoo, Finland

Metabolites 2018, 8(3), 44; https://doi.org/10.3390/metabo8030044 - 5 Aug 2018

Cited by 25 | Viewed by 7541

Abstract

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover [...] Read more.

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies. Full article

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10 pages, 1087 KiB

Open AccessFeature PaperArticle

A Multi-Methodological Protocol to Characterize PDO Olive Oils

by Simone Circi¹

, Cinzia Ingallina¹, Silvia Vista¹, Donatella Capitani², Andrea Di Vecchia³, Genesio Leonardi⁴, Giovanni D’Achille⁴, Luigi Centauri⁵, Federica Camin⁶ and Luisa Mannina^1,2,*

¹ Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy

² Istituto di Metodologie Chimiche, CNR, Area della Ricerca di Roma 1, Laboratorio di Risonanza Magnetica “Annalaura Segre”, Via Salaria km 29,300, 00015 Monterotondo, Italy

³ Istituto di Biometeorologia, CNR, Via dei Taurini 19, 00185 Rome, Italy

⁴ Associazione Produttori Olivicoli Latina, via Don Minzoni 1, 04100 Latina, Italy

⁵ Centro Assaggiatori Produzioni Olivicole Latina, via Don Minzoni 1, 04100 Latina, Italy

⁶ Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), via E. Mach 1, 38010 San Michele all’Adige, Italy

Metabolites 2018, 8(3), 43; https://doi.org/10.3390/metabo8030043 - 28 Jul 2018

Cited by 13 | Viewed by 4640

Abstract

An analytical approach including Panel Test, Isotope Ratio Mass Spectrometry (IRMS) and Nuclear Magnetic Resonance (NMR) spectroscopy was proposed to characterize Italian “Colline Pontine” PDO olive oils (40 samples) of two consecutive crop years. Our approach has evidenced the high quality [...] Read more.

An analytical approach including Panel Test, Isotope Ratio Mass Spectrometry (IRMS) and Nuclear Magnetic Resonance (NMR) spectroscopy was proposed to characterize Italian “Colline Pontine” PDO olive oils (40 samples) of two consecutive crop years. Our approach has evidenced the high quality of these olive oils. Only 6 of 40 olive oils samples were defined as “defective” by the official Panel Test due to the detection of negative sensory attributes. The low variability of isotopic data monitored by IRMS confirmed that the olive oil samples all came from a limited geographical area. NMR spectra did not evidence any chemical composition anomaly in the investigated samples. In order to assess the influence of harvesting year over the olive oil chemical composition, the NMR analysis was extended to other 22 olive oil samples of a third harvesting year. NMR data were submitted to two different statistical methods, namely, analysis of variance (ANOVA) and principal component analysis (PCA) allowing olive oils of three consecutive harvesting years to be grouped. Full article

(This article belongs to the Special Issue Metabolomics in Toxicology)

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19 pages, 2667 KiB

Open AccessArticle

Metabolomics Discovers Early-Response Metabolic Biomarkers that Can Predict Chronic Reproductive Fitness in Individual Daphnia magna

by Nadine S. Taylor, Alex Gavin and Mark R. Viant^*

School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

Metabolites 2018, 8(3), 42; https://doi.org/10.3390/metabo8030042 - 23 Jul 2018

Cited by 40 | Viewed by 6157

Abstract

Chemical risk assessment remains entrenched in chronic toxicity tests that set safety thresholds based on animal pathology or fitness. Chronic tests are resource expensive and lack mechanistic insight. Discovering a chemical’s mode-of-action can in principle provide predictive molecular biomarkers for a toxicity endpoint. [...] Read more.

Chemical risk assessment remains entrenched in chronic toxicity tests that set safety thresholds based on animal pathology or fitness. Chronic tests are resource expensive and lack mechanistic insight. Discovering a chemical’s mode-of-action can in principle provide predictive molecular biomarkers for a toxicity endpoint. Furthermore, since molecular perturbations precede pathology, early-response molecular biomarkers may enable shorter, more resource efficient testing that can predict chronic animal fitness. This study applied untargeted metabolomics to attempt to discover early-response metabolic biomarkers that can predict reproductive fitness of Daphnia magna, an internationally-recognized test species. First, we measured the reproductive toxicities of cadmium, 2,4-dinitrophenol and propranolol to individual Daphnia in 21-day OECD toxicity tests, then measured the metabolic profiles of these animals using mass spectrometry. Multivariate regression successfully discovered putative metabolic biomarkers that strongly predict reproductive impairment by each chemical, and for all chemicals combined. The non-chemical-specific metabolic biomarkers were then applied to metabolite data from Daphnia 24-h acute toxicity tests and correctly predicted that significant decreases in reproductive fitness would occur if these animals were exposed to cadmium, 2,4-dinitrophenol or propranolol for 21 days. While the applicability of these findings is limited to three chemicals, they provide proof-of-principle that early-response metabolic biomarkers of chronic animal fitness can be discovered for regulatory toxicity testing. Full article

(This article belongs to the Special Issue Metabolomics in Toxicology)

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14 pages, 2671 KiB

Open AccessCommunication

Unbiased Lipidomic Profiling of Triple-Negative Breast Cancer Tissues Reveals the Association of Sphingomyelin Levels with Patient Disease-Free Survival

by Preeti Purwaha^1,†, Franklin Gu^1,2,†, Danthasinghe Waduge Badrajee Piyarathna¹, Theckelnaycke Rajendiran³, Anindita Ravindran¹, Angela R. Omilian⁴, Sao Jiralerspong¹, Gokul Das⁴

, Carl Morrison⁴, Christine Ambrosone⁴, Cristian Coarfa¹, Nagireddy Putluri¹ and Arun Sreekumar^1,2,*

¹ Alkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA

² Verna and Mars McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA

³ Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA

⁴ Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA

^† These authors contributed equally to this work.

Metabolites 2018, 8(3), 41; https://doi.org/10.3390/metabo8030041 - 13 Jul 2018

Cited by 42 | Viewed by 6920

Abstract

The reprogramming of lipid metabolism is a hallmark of many cancers that has been shown to promote breast cancer progression. While several lipid signatures associated with breast cancer aggressiveness have been identified, a comprehensive lipidomic analysis specifically targeting the triple-negative subtype of breast [...] Read more.

The reprogramming of lipid metabolism is a hallmark of many cancers that has been shown to promote breast cancer progression. While several lipid signatures associated with breast cancer aggressiveness have been identified, a comprehensive lipidomic analysis specifically targeting the triple-negative subtype of breast cancer (TNBC) may be required to identify novel biomarkers and therapeutic targets for this most aggressive subtype of breast cancer that still lacks effective therapies. In this current study, our global LC-MS-based lipidomics platform was able to measure 684 named lipids across 15 lipid classes in 70 TNBC tumors. Multivariate survival analysis found that higher levels of sphingomyelins were significantly associated with better disease-free survival in TNBC patients. Furthermore, analysis of publicly available gene expression datasets identified that decreased production of ceramides and increased accumulation of sphingoid base intermediates by metabolic enzymes were associated with better survival outcomes in TNBC patients. Our LC-MS lipidomics profiling of TNBC tumors has, for the first time, identified sphingomyelins as a potential prognostic marker and implicated enzymes involved in sphingolipid metabolism as candidate therapeutic targets that warrant further investigation. Full article

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16 pages, 3179 KiB

Open AccessArticle

Stable Isotope-Resolved Metabolomics Shows Metabolic Resistance to Anti-Cancer Selenite in 3D Spheroids versus 2D Cell Cultures

by Teresa W.-M. Fan^1,*,†, Salim S. El-Amouri^1,†, Jessica K. A. Macedo¹

, Qing Jun Wang², Huan Song¹

, Teresa Cassel¹ and Andrew N. Lane¹

¹ Center for Environmental and Systems Biochemistry, Markey Cancer Center, and Depart of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA

² Department of Ophthalmology and Visual Sciences, University of Kentucky, Lexington, KY 40506, USA

^† These authors contributed equally to this work.

Metabolites 2018, 8(3), 40; https://doi.org/10.3390/metabo8030040 - 10 Jul 2018

Cited by 41 | Viewed by 8148

Abstract

Conventional two-dimensional (2D) cell cultures are grown on rigid plastic substrates with unrealistic concentration gradients of O₂, nutrients, and treatment agents. More importantly, 2D cultures lack cell–cell and cell–extracellular matrix (ECM) interactions, which are critical for regulating cell behavior and functions. [...] Read more.

Conventional two-dimensional (2D) cell cultures are grown on rigid plastic substrates with unrealistic concentration gradients of O₂, nutrients, and treatment agents. More importantly, 2D cultures lack cell–cell and cell–extracellular matrix (ECM) interactions, which are critical for regulating cell behavior and functions. There are several three-dimensional (3D) cell culture systems such as Matrigel, hydrogels, micropatterned plates, and hanging drop that overcome these drawbacks but they suffer from technical challenges including long spheroid formation times, difficult handling for high throughput assays, and/or matrix contamination for metabolic studies. Magnetic 3D bioprinting (M3DB) can circumvent these issues by utilizing nanoparticles that enable spheroid formation and growth via magnetizing cells. M3DB spheroids have been shown to emulate tissue and tumor microenvironments while exhibiting higher resistance to toxic agents than their 2D counterparts. It is, however, unclear if and how such 3D systems impact cellular metabolic networks, which may determine altered toxic responses in cells. We employed a Stable Isotope-Resolved Metabolomics (SIRM) approach with ¹³C₆-glucose as tracer to map central metabolic networks both in 2D cells and M3DB spheroids formed from lung (A549) and pancreatic (PANC1) adenocarcinoma cells without or with an anti-cancer agent (sodium selenite). We found that the extent of ¹³C-label incorporation into metabolites of glycolysis, the Krebs cycle, the pentose phosphate pathway, and purine/pyrimidine nucleotide synthesis was largely comparable between 2D and M3DB culture systems for both cell lines. The exceptions were the reduced capacity for de novo synthesis of pyrimidine and sugar nucleotides in M3DB than 2D cultures of A549 and PANC1 cells as well as the presence of gluconeogenic activity in M3DB spheroids of PANC1 cells but not in the 2D counterpart. More strikingly, selenite induced much less perturbation of these pathways in the spheroids relative to the 2D counterparts in both cell lines, which is consistent with the corresponding lesser effects on morphology and growth. Thus, the increased resistance of cancer cell spheroids to selenite may be linked to the reduced capacity of selenite to perturb these metabolic pathways necessary for growth and survival. Full article

(This article belongs to the Special Issue Selected Papers from 2nd International Electronic Conference on Metabolomics)

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Metabolites, Volume 8, Issue 3 (September 2018) – 14 articles

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