Description of HMGU - Helmholtz Zentrum München

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)
Head)
+49-89-3187-3231 (Dr. Cornelia Prehn, Metabolic Laboratory
+49-89-3187-3722 (Julia Henrichs, Team assistance)
Fax: +49-89-3187-3225
Email: Adamski@helmholtz-muenchen.de
Julia.Henrichs@helmholtz-muenchen.de
Prehn@helmholtz-muenchen.de
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 .......................................... 13
1

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 (Adamski@helmholtz-muenchen.de) 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.
2

Associate Professor Thomas Illig (illig@helmholtz-muenchen.de) 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-
kopplin@helmholtz-muenchen.de) 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 (karsten.suhre@helmholtz-muenchen.de)
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.
3

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.
4

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.
5

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)
6

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
7

Annex A:
Metabolites assayed and limits of assays
Acylcarnitines 1
8

Acylcarnitines 2
Amino Acids
Sugars
9

Glycerophospholipids 1
10

Glycerophospholipids 2
11

Glycerophospholipids 3
Shingolipids
12

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- hydroxylacyloylsphingosylphosphocholine
(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).
13