Metabolomic Platform at HMGU metaP · 1 Metabolomic Platform at HMGU metaP Jerzy Adamski Helmholtz Zentrum München (HMGU) German Research Center for Environmental Health Institute

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Metabolomic Platform at HMGU

metaP

Jerzy Adamski

Helmholtz Zentrum München (HMGU)

German Research Center for Environmental Health

Institute of Experimental Genetics

Genome Analysis Center

Metabolomic Platform (metaP)

Ingolstaedter Landstrasse 1

D-85764 Neuherberg

Germany

Voice: +49-89-3187-3155 (Prof. Jerzy Adamski, Head)

+49-89-3187-3231 (Dr. Cornelia Prehn, Metabolic Laboratory

Head)

+49-89-3187-3722 (Julia Henrichs, Team assistance)

Fax: +49-89-3187-3225

Email: [email protected]

[email protected]

[email protected]

URL: http://www.helmholtz-muenchen.de/gac-metabolomics

Contents

Description of HMGU................................................................ 2 Portfolio ................................................................................. 4 Methods ................................................................................. 4

SOP Example for human plasma samples ........................... 5 Relevant Publications ............................................................... 6 Annex A ................................................................................. 8 Abbreviations used for metabolites ...........................................15

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Description of HMGU The Helmholtz Zentrum München, National Research Center for

Environmental Health (HMGU), is a federally funded research center

located in Neuherberg/Munich, Germany. Multidisciplinary research

of the HMGU is focused on activities related to the protection of

man and his environment as well as the utilisation of scientific and

technical knowledge to improve health care.

Genome Analysis Center and Institute for Bioinformatics and

Systems Biology (IBIS) jointly support participating laboratory

(metaP, for Metabolomic Platform). It comprises experts in the

biochemical, analytical and bioinformatics fields. The targeted

quantitative metabomic profiling (FDA-validated kit) is based on the

pioneering work by BIOCRATES Life Sciences (www.biocrates.at).

We are equipped with state-of-the-art liquid handling and extraction

robotics (Hamilton Microlab Star) and a high performance mass

spectrometry instruments (API 4000 Q-Trap). Access to versatile

post-equipment data processing is implemented.

Professor Jerzy Adamski ([email protected]) is

Head of Genome Analysis Center (GAC) and the Metabolomic

Plattform (MetaP). The GAC promotes high throughput research in

genomic, metabolomic and proteomic mechanisms of the

development and progression of complex diseases in man. Several

human multifactorial diseases are associated with abnormal

metabolism of sterols, lipids and fatty acids. Dr. Adamskis interests

are to identify the factors, both at the genomic and metabolic

levels, responsible for the pathogenesis of diseases. The strategy is

based on translational approaches that bridge basic research with

clinical application. He participates in the EU-project PROPATH.

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Associate Professor Thomas Illig ([email protected]) is

Head of the group “Molecular Epidemiology” of the HMGU. He has a

longstanding experience in molecular and genetic epidemiology. He

is in the advisory board of the federal government for metabolic

diseases. Dr. Illig co-organised large population based and disease

related epidemiological studies (e.g. KORA). One main focus is the

analysis of cardiovascular diseases as well as of diabetes. Dr. Illig is

principle investigator of subprojects in the German National

Genome Research Net. He participates in EU-projects GABRIEL and

NUTRIMENTHE.

Associate Professor Philippe Schmitt-Kopplin (schmitt-

[email protected]) is group leader with a research

focus on capillary separation techniques (CE, GC, LC) coupled to

mass spectrometry, Fourier transform ion cyclotron mass

spectrometry (FT/ICR-MS), multidimensional magnetic resonance

spectroscopy (NMR), all applied in metabolomic studies. His

research efforts are focused on the development of new and

powerful research tools enabling the targeted and non targeted

analysis of complex mixtures.

Professor Karsten Suhre ([email protected])

is Professor for Bioinformatics at the Ludwig-Maximilians-University

and Head of the Department for Systematic Genome Analysis within

the Institute for Bioinformatics and System Biology (IBIS) at HMGU.

His personal interest is in genetically determined human

metabotypes and their link to complex disease. Metabolomic studies

in animal models, such as mice and bovine are used in complement

to studies in a human population. He recently established MassTRIX

service (http://masstrix.org) identifying chemical compounds from

mass spectrometry analyses in their genomic context on KEGG

pathway maps.

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Portfolio Targeted analysis of metabolites with high throughput quantitative

mass spectrometry is a new and versatile tool for comprehensive

phenotype analyses of large populations. MetaP platform is

designed to extensively characterize metabolic pathways affected in

early-onset and late development disease. The classes of analytes

include (but are not limited to) lipids, sugars, and amino acids. We

quantify 163 different metabolites in human serum and animal

tissue samples (metabolites are described in Annex A). The analytes

give clues both to identity and cross-talk of affected pathways in

early forms of diseases. Several complementary approaches are

possible: (i) bridge the gap between phenotypic observations and

clinical outcome by metabolomics data (ii) characterisation of the

metabolic states in tissue samples from human and animal models.

Methods All sample processing should follow SOP as provided from HMGU

(see example below). Tissues and body fluids should be portioned

and snap-frozen as soon as possible after collection.

Human or animal plasma (50µL) or tissue (100mg) is requested for

a single assay. Samples will be processed in a fully automated

manner in multiwell plates using Hamilton robotics station.

Metabolite spectrum is designed to monitor the metabolism of

sugars, acylcarnitines, amino acids, glycerophospholipids and

sphingolipids. The resulting dataset will be subject to several levels

of data analyses, starting with metabolite identification and

quantification based on the raw multiplexed MS/MS spectra and the

knowledge of the spiked isotope reference markers. In this step,

BIOCRATES Life Sciences MarkerIDQ™ software shall be used as

provided with the AbsoluteIDQ™ kit.

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In a second step, correlations within the metabolite dataset could

be combined with external biochemical knowledge (e.g. from

metabolic pathway maps, KEGG), using bioinformatics tools

developed specifically for every project at HMGU-IBIS.

Throughput is at present stable at 160 samples a day.

SOP Example for human plasma samples

(Please request the SOP for mouse plasma or other matrices)

Collection and handling of plasma for metabolomics

The AbsoluteIDQ™ kit has been designed for performing targeted

metabolomics using plasma samples. To assure high quality results

some guidelines, which are described in this section, need to be

followed.

Blood samples are directly collected into tubes that contain

anticoagulants. The preferred anticoagulant is EDTA but also

heparin is acceptable. It is not recommended to use citrate!

Alternatively, blood can be drawn with a plastic syringe and is

subsequently transferred into an EDTA coated tube.

Immediately, the samples need to be stored on ice until

centrifugation. Centrifugation should take place as soon as possible.

Suitable spinning conditions would be 10 min at 2000 x g at 4°C.

The resulting plasma is transferred into fresh tubes without carry-

over of any blood cells. Plasma samples need to be frozen in small

portions (200-300 microliters) immediately and stored at -80°C

until further use with the kit.

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Relevant Publications Prehn, C., Ströhle, F., Haller, F., Keller, B., Hrabě de Angelis, M.,

Adamski, J. and Mindnich, R. (2007) A Comparison Of Methods For

Assays Of Steroidogenic Enzymes: New GC/MS Versus HPLC And

TLC. Purdue University Press, West Lafayette, Indiana, USA.

Guo, K., Lukacik, P., Papagrigoriou, E., Meier, M., Lee, W.H.,

Adamski, J. and Oppermann, U. Characterization of Human DHRS6,

an Orphan Short Chain Dehydrogenase/ Reductase Enzyme: a

novel, cytosolic type 2 R-beta-hydroxybutyrate dehydrogenase. J

Biol Chem, 281: 10291-10297 (2006)

Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T,

Wichmann HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A,

Hebebrand J, Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H,

Hirschhorn JN, Laird NM, Lenburg ME, Lange C, Christman MF.A

common genetic variant 10 kb upstream of INSIG2 is associated

with adult and childhood obesity. Science. 312: 279-283 (2006)

Döring A, Gieger C, Mehta D, Gohlke H, Prokisch H, Coassin S,

Fischer G, Henke K, Klopp N, Kronenberg F, Paulweber B, Pfeufer A,

Rosskopf D, Völzke H, Illig T, Meitinger T, Wichmann HE, Meisinger

C. SLC2A9 influences uric acid concentrations with pronounced sex-

specific effects. Nat Genet. (2008) 2008 Apr;40(4):430-6.

Chen, J., X. Zhao, R. Lehmann, J. Fritsche, P. Yin, Ph. Schmitt-

Kopplin, W. Wang, X. Lu, H.U. Häring, E. D. Schleicher, G. Xu,

Strategy for biomarker discovery and identification based on LC-

MSn in metabonomics research. Anal. Chem. 80: 1280-89 (2008)

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Altmaier E, Ramsay SL, Graber A, Mewes HW, Weinberger KM,

Suhre K.: Bioinformatics analysis of targeted metabolomics -

uncovering old and new tales of diabetic mice under medication.

Endocrinology (2008) 149(7):3478-89

K. Suhre, P. Schmitt-Kopplin MassTRIX: Mass TRanslator Into

Pathways, Nucleic Acid Research (2008) 2008 Jul 1;36 (Web Server

issue):W481-4.

Gieger, Ch., L. Geistlinger, E., M. Hrabé de Angelis, F. Kronenberg,

Th. Meitinger, H.-W. Mewes, H.-E. Wichmann, K.M. Weinberger, J.

Adamski, Illig, T., Suhre, K. Genetics meets metabolomics: a

genome-wide association study of metabolite profiles in human

serum. PLOS Genetics, 2008 in press

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Annex A Metabolites assayed and limits of assays

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Abbreviations used for metabolites sugars Hn for nhexose, dH for desoxyhexose UA for uronic acid HNAc for N-acetylglucosamine acylcarnitines (Cx:y, where x denotes the number of carbons in the side chain and y the number of double bonds) sphingomyelins (SMx:y) sphingomyelin derivatives, such as N- hydroxyldicarboacyloylsphingosyl-phosphocholine (SM(OH,COOH)x:y) and N- hydroxylacyloylsphingosyl-phosphocholine (SM (OH)x:y) Glycerophospholipids are further differentiated with respect to the presence of ester (a) and ether (e) bonds in the glycerol moiety, where two letters (aa, ea, or ee) denote that the first as well as the second position of the glycerol unit are bound to a fatty acid residue, while a single letter (a or e) indicates a bond with only one fatty acid residue. E.g. PC_ea_33:1 denotes a plasmalogen phosphatidylcholine with 33 carbons in the two fatty acid side chains and a single double bond in one of them. glycero-phosphatidic acids (PA), glycero-phosphatidylcholines (PC), glycero-phosphatidylethanolamines (PE), phosphatidylglycerols (PG), glycero-phosphatidylinositols (PI) glycerophosphatidylinositol-bisphosphate (PIP2) and - triphosphate (PIP3) glycerophosphatidylserines (PS).