What is metabolomics /metabonomics?

1. What is metabolomics/metabonomics? Jules Griffin Department of Biochemistry, University of Cambridge

2. An overview • Some definitions • A brief overview of key literature • Given the subsequent talks this will focus on non-cancer related applications • Phenotyping yeast by NMR • GC-MS and plant phenotypes • CAD and screening patients using blood plasma • Future directions

3. What’s in a name? Metabonomics “…measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification…” Nicholson et al., 1999 Metabolomics “...the complete set of metabolites/low-molecular-weight intermediates, which are context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism…” Oliver2002

4. Systems Biology and the rise of the “-omes” • Genomics • Study of genes – the only -ome which is not context dependent • Transcriptomics • All the mRNA in a cell/tissue/organism • Proteomics • All the proteins in a cell/tissue/organism • Metabonomics/Metabolomics • All the metabolites in a cell/tissue/organism

5. The challenges of metabolomics Conc. Range 109 • How many metabolites? • Just considering one class there are a huge number of permutations • 40 common fatty acids • 40 FA acyl CoA • 64000 TAGs • 120 1-, 2-, 3- MAG • 4800 1,2-, 1,3-, 2,3- DAGs • Total = 69000 Global profiles NMR GC-MS LC-MS Custom assays Polarity Log -6 to 14 Mass < 1500 amu From a talk by J van der Greef

6. Open or Closed? • Open analysis • An analysis of the total detectable content of the sample (e.g. an NMR spectrum of urine) • Primarily used for the detection of novel entities • Closed analysis • An analysis focused onto a specific molecule or molecules (e.g. measurement of a specific m/z) • Used for the measurement of known variables for a model

7. A procedure for Metabonomics • Measure small molecule concentrations through a global approach • Use pattern recognition to define metabolism in a multidimensional space • Define a metabolic phenotype (metabotype) • Use this information to determine an end point (e.g. drug toxicity, disease state) or use to data mine another –omic technology.

8. Global Profiling Tools • NMR spectroscopy • Solution state, solid state, in vivo • High throughput • Relatively robust • GC- and LC-Mass spectroscopy • More analytically sensitive • Potentially truly global • Problems with ionisation though? • Coulombic arrays • FT-IR spectroscopy • TLC • Metabolite arrays • Used to monitor E.Coli strains • Use biochemical assays

9. Key Publications Examples Showing The Potential Of Metabolomics

10. Genomics v. Metabonomics Listening to Silent Phenotypes • Standard way to phenotype yeast strains is to see how rapidly a strain grows on a given substrate mixture • If the mutation does not alter the rate of growth it is said to be a silent mutation • Can we use metabolomics to distinguish these silent phenotypes? • Can we cluster similar genes together? • Yeast was the first eukaryote to be sequenced • Mutants for the 6000 genes in yeast can now be taken from banks such as EUROFAN • Suggests we can completely phenotype all the genes in yeast • Have a significant impact on human disease through comparison of gene sequence/similarities Raamsdonk et al. (2001) Nat. Biotechnol. 19, 45-50.

11. FANCY - Functional ANalysis by Co-responses in Yeast • 1H NMR spectroscopy to study the metabolic changes induced in the different yeast strains • Metabolic perturbations can be used to classify strains • clusters mutants from similar deletions together. • Two mutants involving 6-phosphofructo-2-kinase, and oxidative phosphorylation made up two clusters PLS Component 3 PLS Component 2 Raamsdonk et al. (2001) Nat. Biotechnol. 19, 45-50.

12. GC/MS and plant metabolomics • Huge challenge • plant genomes contain 20,000-50,000 genes, • currently 50,000 metabolites identified • number set to rise ~200,000 • Current plant metabolomics uses metabolic profiling through GC-MS of plant extracts. Fiehn O et al. 2000 Nature Biotechnology, 18, 1157-1161.

13. GC/MS and plant metabolomics • Fiehn et al. - GC-MS quantifies 326 distinct compounds in Arabidopsis thaliana leaf extracts • chemical structure to half of these. • PCA separates 4 genotypes • GC-TOF-MS now detected & characterised ~1000 metabolites. • Since used these data bases to identify metabolic cliques Fiehn O et al. 2000 Nature Biotechnology, 18, 1157-1161.

14. Predicting Coronary Artery Disease In Humans • Predict the occurrence and severity of coronary artery disease using blood plasma. • Models could be built that distinguish the different disease groups • e.g. NCA vs. single vessel, single vessel vs. double vessel • Disease presence and severity can be predicted • Such systems may produce significant financial savings • angiography, currently the gold standard for diagnosis. Brindle JT et al., 2002. Nat Med. 8(12), 1439-45.

15. Future directions A wish list from current research….

16. Future directions I: Rapid Phenotyping • As part of a large scale mutagenesis program at the MRC Mammalian Genetics unit, Harwell • Harwell Mutagenesis program • Use N-ethyl n-nirosourea to induce mutations • Aim to generate 100 F1 progeny of mutagenised animals per week! • Mice with interesting phenotypes will then be posted for the wider research community • Need a high through put phenotyping tool to correlate with the genotype information PCA of 160 urine samples from a diabetic mouse model (dbdb mouse maintained at MRC Harwell). Class 1 – Male Wild Type/Heterozygous; Class 2 - Male Homozygous; Class 3 - Female WT/Heterozygous; Class 4 - Female Homozygous.

17. Future directions II • Improvements in metabolomic technology • Cryoprobes • LC-NMR • LC-MS, GC-MS • HRMAS • Integration of these approaches

18. Female Day Male Day DIURNAL DIFFERENCE Female Night Male Night GENDER DIFFERENCE Rat Urine baseline study: A Combined NMR and LC-MS study

19. Female Night Female Day GENDER DIFFERENCE Male Day Male Night DIURNAL DIFFERENCE LC/MS

20. LC/MS Male Male Female Female

21. Tentative assignment of 333.24: Ovarian steroid hormone such as 17a-hydroxypregnenolone or isomer thereof 315.24 = single water loss 297.22 = double water loss 355.26 = Na adduct; ~372 = K adduct 817.5935 = ? Male Male Female Female Results: LC/MS

22. 5 6 1 3 4 2 day 8 day 6 day 4 day 2 day 0 STEAM Increase in PUFAs during PCD • Used a combined MRI, MRS, HRMAS and high res NMR approach to following PCD in tumours (see Prof Kauppinen’s talk) • During PCD lipids increase in intensity for both saturated and unsaturated resonances •  5.3, 2.8  3-fold •  1.3 2-fold • This increase in PUFAs also detected in T2 hyperintensive core of tumours • Lipid changes associated both with PCD and cell debris region Entire region Hyper-intensive region

23. Metabolomics and transcriptomics – Fatty liver disease • Non-alcoholic steatohepatisis is a common feature of the Metabolic Syndrome & toxic reactions to pharmacological drugs. • Orotic acid supplementation induces fatty liver • disruption of Apo proteins production? • Applied a genomic, proteomic and metabolomics approach to the problem Griffin et al., Physiol Genomics 2004

24. A simple system, but polygenic challenge 10 m m m m m m m m m m 0 m m m m m m m m m m m -10 m m m -20 -10 0 10 20 PC 1 Increased lipids & PtdChol, decreased glucose • Two rat strains used • Wistar - classically used - Out bred strain • Kyoto - prone to fatty liver accumulation -In bred • Comparable to pharmacogenomics Wistar Kyoto Wistar Kyoto Griffin et al., Physiol Genomics 2004

25. ATP ADP NAD NADH Sn-Glycerol 3-phosphate Glycerol Glycolysis Adenosine Uricase Sarcosine Dimethylglycine Betaine Glycerol 3 phosphate acyltransferase Glycolate Glucose Glycogen phosphorylase Betaine aldehyde 1-Acylglycerol 3-phosphate TMAO Glycogen Glyoxylate Glycine Creatine Deposition of hepatic lipid triglyceride Fatty acid synthesis: ATP citrate lyase Acyl carrier protein domain of fatty acid synthetase Stearyl-CoA Desaturase -Oxidation -hydroxy-butyrate Fatty acid Coenzyme A ligase Transport ApoB Apo C III OROTIC ACID Choline TMA Serine 1,2-diacylglycerol phosphate UTP Phosphocholine CTP Phosphoserine 1,2 Diacylglycerol PP 1,2 Diacylglycerol Phosphoserine aminotransferase CDP-choline CMP 3P-Hydroxypyruvate Triacylglycerol Phosphatidylcholine D-3-phosphoglycerate dehydrogenase Phosphatidyserine & Phosphatidylethanolamine 3P-D glycerate Griffin et al., Physiol Genomics 2004

26. NMR derived metabolic profiles ideal for phenotyping animals FANCY in yeast Can we mine information from a number of mouse models simultaneously (PIMP)? Development of metabolomic databases MIAME for metabolomics? Future Directions III

27. Acknowledgements • JLG Group • Helen Atherton • Melanie Gulston • Mark Hodson • Oliver Jones • Mahon Maguire • Michael Pears • Denis Rubtsov • Reza Salek • Jeff Troke • MRC Harwell • Steve Brown • Michael Cheeseman • Tertius Hough • GlaxoSmithKline • Brian Sweatman • John Haselden • Andy Nicholls • Sue Connor • Imperial College • James Scott • Jeremy Nicholson • University of Birmingham • Risto Kauppinen • Waters • John Shockcor