Transcript Stable Isotopes-Resolved Metabolomics (SIRM) Core
Stable Isotope-Resolved Metabolomics (SIRM) Core
Teresa W-M. Fan, Richard M. Higashi, Hunter N.B. Moseley, Michael H. Nantz
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.
General Approach
Stable Isotope-Resolved Metabolomics (SIRM) Propagate cells in culture in the presence of 13 C 6 -glucose, 13 C 5 / 15 N 2 Gln or other labeled metabolic precursors specific for a particular network project and measure the response to perturbations such as drugs.
13 C/ 15 N 2 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
Example:
13
C-labeling patterns : Krebs cycle Two turns of the cycle (source =
13
C
6
-glucose)
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 C 6 -Glc by selenite treatment is clearly demonstrated.
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
SIRM: NMR
13 C 6 Glc
labels Lactate, Ala, and all isotopomers of Glu/GSH in cancer cells (left panel)- complete turn of TCA.
Isotopomer patterns describe the network modeling.
13 C atom incorporation at specific positions (right panel), that can be quantified in quantified in TOCSY. Such data are critical for Lane, A.N. & Fan, T. W-M. (2007 )
Modeling: metabolic modules
Biosynthesis of UDP-GlcNAc
Chemical substructure model representing the possible number of 13 C incorporation from 13 C 6 -Glc tracer into UDP-GlcNAc,and biosynthetic pathway from 13 C 6 Glc .
U
racil derives from glycolysis, the citric acid cycle (CAC) and pyrimidine biosynthesis.
R
ibose is made in the pentose phosphate pathway (PPP). The
A
cetyl 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)).
Network Applications
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).
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)
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