The SIRM Core aims to: 1. Develop high-throughput profiling of stable isotope labeling patterns in metabolites using mass spectrometry and NMR. 2. Build atom-resolved human metabolic network models and non-steady state flux modeling capabilities. 3. Establish mechanisms of metabolic changes and biomarker regulation associated with drug responses in cardiovascular and neuropsychiatric diseases. The Core will apply stable isotope tracers and isotopomer profiling to network projects studying gene polymorphisms and drug responses. Metabolic flux modeling and databases will help interpret data and generate testable hypotheses.

1. Stable Isotope-Resolved
Metabolomics (SIRM) Core
Teresa W-M. Fan, Richard M. Higashi, Hunter N.B.
Moseley, Michael H. Nantz

2. Specific Aims
Specific Aim 1. To implement and further develop high-information-
throughput (HIT) profiling of stable isotope-labeling patterns in metabolites
using FT-ICR-MS and NMR- the SIRM approach
Specific Aim 2. To build atom-resolved human metabolic network and
chemical moiety-based non-steady state metabolic flux modeling capability.
Specific Aim 3. To establish the mechanisms of metabolic changes and
regulation associated with concentration-based biomarkers of drug response
in cardiovascular and neuropsychiatric diseases discerned from human
subject studies.

3. General Approach
 Stable Isotope-Resolved Metabolomics (SIRM)
 Propagate cells in culture in the presence of 13
C6-glucose, 13
C5/15
N2 Gln
or other labeled metabolic precursors specific for a particular
network project and measure the response to perturbations such as
drugs.
 13
C/15
N2 isotopomer/isotopologue analysis of intracellular and
extracellular metabolites using NMR & MS with minimal sample
preparation.
 Use atom-resolved biochemical network modeling to reconstruct the
relevant metabolic network segments and how they are impacted by
the treatment
 Generate testable hypotheses about the effects of the therapeutic at
the protein and gene levels that can be mechanistically linked to the
functional data in the collaborating projects

4. Example: 13
C-labeling patterns : Krebs cycle
Two turns of the cycle (source = 13
C6-glucose)

5. SIRM: Mass spectrometry
FT-ICR-MS analysis of intact
lipids in mammalian cell
extracts. The simultaneous
attainment of accurate mass
and ultra high-resolution by FT-
ICR-MS enables thousands of
ions from > 250 lipids and their
13
C isotopologues to be
resolved and assigned.
Illustrated is the identification of
phosphadidylinositol (PI) with
C18:0 and C20:4 acyl chains
based on the molecular
formulae of source (M) and acyl
chain fragment ions (from
MS/MS data) deduced from the
corresponding accurate
masses. Also shown is the
resolution of a series of 13
C
isotopologue ions (M+3, M+5,
M+7, etc.) representing multiple
13
C labels in PI. Consequently,
the suppression of de novo fatty
acid biosynthesis from 13
C6-Glc
by selenite treatment is clearly
demonstrated.

6. Chemical biology for targeting
compound classes and enhancing MS
sensitivity
Aminooxy-charged nanoparticles for oxime formation with carbonylated
metabolites (O=C-R). Adds a permanent positive charge, hereby
enhancing positive mode (nano)ES-MS sensitivity

7. SIRM: NMR
13
C6 Glc labels Lactate, Ala, and all
isotopomers of Glu/GSH in cancer cells
(left panel)- complete turn of TCA.
Isotopomer patterns describe the 13
C atom
incorporation at specific positions (right
panel), that can be quantified in quantified
in TOCSY. Such data are critical for
network modeling.
Lane, A.N. & Fan, T. W-M. (2007)

8. Modeling: metabolic modules
Biosynthesis of UDP-GlcNAc
Chemical substructure model representing the possible number of 13
C incorporation from 13
C6-Glc tracer into
UDP-GlcNAc,and biosynthetic pathway from 13
C6-Glc. Uracil derives from glycolysis, the citric acid cycle
(CAC) and pyrimidine biosynthesis. Ribose is made in the pentose phosphate pathway (PPP). The Acetyl
moiety is derived from glycolysis and PDH Right: 13
C isotopologues of UDPGlcNAc. The program GAIMS
determines the populations of the individual species from FT-ICR-MS data after stripping natural abundance
(Moseley (2010)).

9.  Stable isotope-labeled tracers and isotopomer/isotopologue
profiling are indispensible for tracing metabolic networks at the
atomic level. These will be applied to network projects including:
1. Functional consequences of gene polymorphism associated
with differential anti-depression drug response in human subjects
(PI Dr. Weinshilboum).
2. Altered biochemical pathways and differential SSRI response
mechanism in depression rodent model (PI Dr. Sanacora).
3. Altered cellular pathways and regulation of lipid metabolism in
relation to statin efficacy and gene polymorphism (PI Dr. Krause).
Network Applications

10. Making Sense of the data: Metabolic Flux
Modeling and Databases
Biochemical understanding of the pathology needs close
collaborations with the projects and other cores for:
1.Optimal experimental design
2.Database tools such as HumanCyc, atom-resolved network
modeling (http://www.metabolome.jp/software;
http://humancyc.org/; http://www.hmdb.ca/)
3.Flux modeling
4.The core will work closely with the
Informatics core (S. Subramanian, B. Palsson),
Analytical core (O. Fiehn, T. Hankemeier)
Database core (S. Subramanian, R. Pietrobon, D. Wishart)

11. Bibliography
Arita, M. (2003) In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary
metabolism. Genome Research, 13: 2455-2466.
Fan, T. W-M., Lane, A.N. & Higashi, R.M., (2004) The Promise of Metabolomics in Cancer Molecular Therapeutics.
Current Opin. Molec. Ther. 6:584-592
Fan, T. W-M., Bandura, L.L., Lane, A.N. & Higashi, R.M., (2006) “ Integrating Genomics and Metabolomics for
Probing Se Anticancer Mechanisms” Drug Metabolism Reviews 38, 1-25
Fan, T.W-M. & Lane, A.N. (2008) Structure-based profiling of Metabolites and Isotopomers by NMR, Prog.
NMR Spectrosc. 52: 69-117
Fan, T.W-M., Lane, A.N., Higashi, R.M., Farag, M.A., Gao, H., Bousamra, M. & Miller, D.M. (2009) Altered Regulation
of Metabolic Pathways in Human Lung Cancer Discerned by 13
C Stable Isotope-Resolved Metabolomics
(SIRM). Molecular Cancer. 8:41
Fan, T. W-M. Yuan, P., Lane, A.N., Higashi, R.M. Wang, Y., Hamidi, A., Zhou, R., Xavier Guitart-Navarro, X., Chen, G.,
Manji, H.K., Kaddurah-Daouk, R. (2010) Stable Isotope Resolved Metabolomic Analysis of
Lithium Effects on Glial-Neuronal Interactions. Metabolomics 6, 165 – 179.
Lane, A.N. & Fan, T. W-M. (2007) Determination of positional isotopomers in metabolites. Metabolomics 3: 79-86
Lane, A.N., Fan, T-W-M. & Higashi, R.M. (2008) “Isotopomer-based metabolomic analysis by NMR and mass
spectrometry". Methods in Cell Biology, 84: 541-588.
Lane, A.N., Fan, T. W-M., Xie, X. Moseley, H.N. & Higashi, R.M. (2009) Stable isotope analysis of lipid
biosynthesis by high resolution mass spectrometry and NMR. Anal. Chim. Acta. 651: 201-208
Moseley, H.N.B. (2010) Correcting for the effects of natural abundance in stable isotope resolved metabolomics
experiments involving ultra-high resolution mass spectrometry. BMC Bioinformatics 11:139
Selivanov, V.A., et al., (2006). Software for dynamic analysis of tracer-based metabolomic data: estimation of
metabolic fluxes and their statistical analysis Bioinformatics 22: 2806-2812.
Sumner, L.W., Amberg, A., Barrett, D., Beger, R., Beale, M.H., Daykin, C. Fan, T. W-M., Fiehn, O., Goodacre, R.,
Griffin, J.L., Hardy, N., Higashi, R.M., Kopka, J., Lindon, J.C., Lane, A.N., Marriott, P., Nicholls, A.W., Reily, M.D.,
Viant, M.
(2007) Proposed Minimum Reporting Standards for Chemical Analysis. Metabolomics. 3, 211-221