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Metabolomics by Magnetic Resonance: From Molecules to Man John Griffiths, Yuen-Li Chung, Helen Troy Cancer Research UK Biomedical Magnetic Resonance Research.

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Presentation on theme: "Metabolomics by Magnetic Resonance: From Molecules to Man John Griffiths, Yuen-Li Chung, Helen Troy Cancer Research UK Biomedical Magnetic Resonance Research."— Presentation transcript:

1 Metabolomics by Magnetic Resonance: From Molecules to Man John Griffiths, Yuen-Li Chung, Helen Troy Cancer Research UK Biomedical Magnetic Resonance Research Group Department of Basic Medical Sciences, St George’s Hospital Medical School, London SW17 ORE, UK

2 Metabolomics by NMR NMR-based methods offer quick, simple ways for studying the metabolome. For instance, 1 H NMR can be used ex vivo to obtain metabolic profiles from genetically manipulated cells or biopsies from patients or experimental animals. Soluble metabolites can be extracted with perchloric acid, and lipids with chloroform/methanol. Solid specimens can be studied by MAS-NMR. A unique advantage is that by MRS we can also study some of the same metabolites in vivo.

3 Metabolites detected in cancer by NMR

4 Medical Uses of Metabolomics We use metabolic profiling to study –Genetically modified cells, –Tissues from genetically modified organisms, –Biopsies from patients. –Actions of novel drugs Our general strategy is to take a metabolic profile from the abnormal cell or tissue and compare it with the corresponding metabolic profile of “wild type” cells, control organisms or tissues. This method is even more effective if the corresponding transcriptomic or proteomic profiles are also available. Loss of PKC δ Alters Cardiac Metabolism. Mayr, Chung et al, Am J Physiol Heart Circ Physiol 287: H937-H945 (2004) Ischaemic Preconditioning Exaggerates Cardiac Damage in PKCδ Knockout mice. Mayr, Chung et al, Am J Physiol Heart Circ Physiol 287: H946-H956 (2004). A Proteomic & Metabolomic Analysis of Vascular Smooth Muscle Cells: a Role for PKC δ. Circulation, Circulation Research 94: e87-e96 (2004). Vascular proteomics: Linking proteomic and metabolomic changes. Mayr, M, Mayr, U. Chung, Y-L, Yin, X, Griffiths, JR. Proteomics 4: (2004).

5 Metabolic Profiling of Anticancer Drug Mechanisms We have also used metabolic profiling by NMR methods, both in vivo and ex vivo, to study novel anticancer drugs: –Magnetic Resonance Spectroscopic pharmacodynamic markers of Hsp90 inhibitor, 17-allylamino-17-demethoxygeldanamycin (17AAG) in human colon cancer models. Chung et al., J Natl Cancer Inst. 95: (2003). –Non-invasive measurements of capecitabine metabolism in bladder tumors over-expressing thymidine phosphorylase by 19F MRS. Chung et al., Clinical Cancer Research 10: (2004). –Tumor dose response to the vascular disrupting agent, 5,6- dimethylxanthenone-4-acetic acid, using in vivo magnetic resonance spectroscopy. McPhail, et al. Clin Cancer Res 11: (2005).

6 The Role of HIF in the Tumour Metabolome It has been known since Otto Warburg’s work in the 1920s that cancer cells have an abnormal metabolome. In particular, they rely more on glycolysis for energy metabolism. Glycolytic enzyme and glucose transporter expression are induced by the HIF (Hypoxia Inducible Factor) pathway. HIF-1, the main cellular O 2 detector, is upregulated in cancer cells by two mechanisms. Some carcinonogenic mechanisms constitutively induce HIF-1 Most cancers also become hypoxic, activating HIF-1

7 Angio- genesis & Vascular Function: VEGF PDGF HIF-1β HIF-1α Hypoxia Transcription: VEGF, PDGF, glycolytic enzymes, GLUT-1, CA IX, NOS HIF-1 Metabolism LDH, CA9, pH, Glut-1 The role of HIF-1 in cancer NUCLEUS

8 HIF-1 β Deficiency We set out to study the metabolomes of Hepa c4 cancer cells which are deficient in the HIF-1β subunit (also known as ARNT). We assumed that as they could not form the HIF-1 dimer they would be unable to upregulate their glycolytic pathways. Surprisingly, these HIF-1 β deficient cancer cells had only 20% of normal [ATP]. 1 H NMR metabolic profiles of tumour extracts showed significant changes in several other metabolites.

9 Creatine Choline Phosphocholine Taurine Glycerophosphocholine Glycine Betaine Hepa c4 (HIF-1ß deficient) Tumour Extract (% of wild-type concentration, where indicated) Hepa WT (wild-type) Tumour Extract These results suggested a hypothesis to explain the low (20% of normal) [ATP] in the HIF-1ß deficient tumours 32% 40% 36% 49%

10 Glucose Glut 1 & 3 Glycolysis Lactic Acid Lactate - H+H+ Membrane Phospholipids SerineGlycine Phosphocholine Choline Betaine Phosphodiesters ATP Synthesis ATP HIF + + Hepa WT (wild-type) Tumour Oncogenesis ↑ ↑ O2O2 ↑

11 Glucose Glut 1 & 3 Glycolysis Lactic Acid Lactate - H+H+ Membrane Phospholipids SerineGlycine Phosphocholine Choline Betaine Phosphodiesters ATP Synthesis ATP HIF ↑ + + Hepa WT (wild-type) Tumour HIF activation enhances expression of glucose transporters and glycolytic enzymes, increasing glycolytic flux

12 Glucose Glut 1 & 3 Glycolysis Lactic Acid Lactate - H+H+ Membrane Phospholipids SerineGlycine Phosphocholine Choline Betaine Phosphodiesters ATP Synthesis ATP HIF activation enhances expression of glucose transporters and glycolytic enzymes, increasing glycolytic flux HIF ↑ + + Hepa WT (wild-type) Tumour

13 Glucose Glut 1 & 3 Glycolysis Lactic Acid Lactate - H+H+ Membrane Phospholipids SerineGlycine Phosphocholine Choline Betaine Phosphodiesters ATP Synthesis ATP Some of the glycolytic metabolites are bled off for cell growth, including serine which is converted to glycine and used for synthesis of new ATP molecules Hepa WT (wild-type) Tumour

14 Glucose Glut 1 & 3 Glycolysis Lactic Acid Lactate - H+H+ Membrane Phospholipids SerineGlycine Phosphocholine Choline Betaine Phosphodiesters ATP Synthesis ATP In HIF-1β deficient tumours the glycolytic pathway is not upregulated so the normal source of anabolic precursors is not available Instead, the low concentrations of phosphocholine, choline betaine and glycine suggest that an alternative pathway is being used and the pools of its intermediates are being drained Other evidence (Koutcher et al) suggests that 20% of normal [ATP] is the minimum for a cancer cell to be viable HIF-1ß deficient Tumour 32%40%36%49% 20%

15 Was Our Original Hypothesis Correct? We had originally assumed (based on numerous published studies) that HIF-1β deficient c4 tumours would be unable to upregulate glycolysis. We therefore attributed their deficiency of ATP to lack of glycolytic precursors for purine synthesis (Griffiths et al., Cancer Research, 62: , 2002). However, cultured c4 cells (which failed to form active HIF complex) produced the same amount of lactate and other glycolytic products as WT cells.

16 Normal Lactate Output from Cultured c4 Cells WT c4 Cells cultured under oxic conditions, 95% O 2, 5% CO 2 Cells cultured for 18 hours under hypoxic conditions, 1% O 2, 5% CO 2, 94% N 2 WT c4

17 Time (s) SUV Cps/vox/  Ci [ 18 F]FDG PET Studies on WT and c4 Tumours in mice (n=4-6) WT c4 When Dr Eric Aboagye performed 18 FDG PET studies for us, the HIF- deficient c4 tumours took up the glucose analogue at the same rate as the normal WT tumours.

18 Early studies on small c4 tumours (Maxwell et al, PNAS 94: 8104, 1997) reported slower growth rates than in WT tumours. The larger tumours that we grew for our 31 P MRS studies in vivo showed a more complex pattern. Initially our c4 tumours grew slowly, but after 3 weeks they started secreting VEGF and grew faster than WT tumours. Growth Rate Studies in WT and c4 Tumours Hepa WT Hepa c4

19 Conclusions Despite their defective HIF pathway, cultured c4 cells took up glucose and produced lactate at normal rates, so their glycolytic flux was normal. They could even upregulate glycolysis in hypoxic conditions. Large Hepa-c4 tumours had normal glycolytic enzyme, glucose transporter and VEGF expression. After 3 weeks they grew even faster than normal tumours. Nevertheless their [ATP] was 20% of normal, suggesting a defect in purine synthesis. Perhaps HIF-deficient tumours eventually express another transcription factor that upregulates glycolysis and VEGF secretion, accelerating growth, but which fails to upregulate ATP synthesis.

20 Metabolic Profiling Studies on the Role of Succinate and Fumarate in the HIF Pathway Recently we have provided metabolic profiles for two studies that have shown how succinate dehydrogenase and fumarate hydratase can act as oncogenes because of their effect on the HIF pathway. These papers are discussed in a review by Esteban & Maxwell in Nature Med (News and Views) 11: , 2005 –Isaacs et al, Cancer Cell 8: (2005) –Pollard et al, Human Mol. Genet. 14: (2005)

21 HIF-1β (ARNT) HIF-1α Hypoxia Proteasomal Degradation Prolyl hydroxylase TCA Cycle Glucose Uptake Glycolysis + + O2O2 HIF Acts as the Main Cellular O 2 Sensor

22 Fumarate Hydratase as an Oncogene The inherited cancer Hereditary Leiomyomatosis Renal Cell Carcinoma is characterised by germ line mutations in the fumarate hydratase gene. Fumarate hydratase is a well-known “housekeeping” energy metabolism enzyme in the tricarboxylic acid cycle. It catalyses conversion of fumarate to malate. We collaborated in a study (Isaacs et al 2005) which demonstrated that these mutations of fumarate hydratase elevate intracellular fumarate which then upregulates HIF by competitively inhibiting HIF prolyl hydroxylase. NMR metabolic profiling showed that siRNA knockdown of fumarate hydratase in A459 cells elevated fumarate levels and increased glycolysis.

23 siRNA Knockdown of Fumarate Hydratase in Cultured Cells Succinate μmol/g protein Fumarate μmol/g protein Lactate μmol/g protein Glucose μmol/g protein Control FH siRNA

24 siRNA Knockdown of Fumarate Hydratase in Cultured Cells Succinate μmol/g protein Fumarate μmol/g protein Lactate μmol/g protein Glucose μmol/g protein Control * FH siRNA * *Inhibiting fumarate hydratase doubled the cellular fumarate concentration

25 siRNA Knockdown of Fumarate Hydratase in Cultured Cells Succinate μmol/g protein Fumarate μmol/g protein Lactate μmol/g protein Glucose μmol/g protein Control FH siRNA *20.9† Knocking down fumarate hydratase by FH siRNA increased cellular [glucose] 4-6 fold † and [lactate] 60%* - i.e. glucose uptake and glycolysis were upregulated. This and other evidence suggests that fumarate hydratase defects promote cancer by upregulating HIF.

26 Metabolic Profiling of Human Cancers Caused by FH and SDH In another study (Pollard et al, 2005) metabolic profiling was performed on biopsies from patients with: – Hereditary Leiomyomatosis and Renal Cell Cancer Syndrome (HLRCC) which is caused by fumarate hydratase mutations. –Hereditary Paraganglioma and Phaeochromocytoma Syndrome (HPGL) which is caused by mutations of another TCA cycle enzyme, succinate dehydrogenase.

27 Elevated Succinate in Gangliomas With and Without Germline Succinate Dehydrogenase Mutations Succinate μmol/g protein

28 Elevated Succinate in Hereditary Leiomyomatosis and Renal Cell Cancer Syndrome μmol succinate/g protein * Myometrium from an HLCC patient *

29 Elevated Fumarate in Hereditary Leiomyomatosis and Renal Cell Cancer Syndrome μmol fumarate/g protein * Myometrium from an HLCC patient * Thus all these rare hereditary cancers seem to arise from pseudo-hypoxic upregulation of the HIF pathway by TCA Cycle metabolites.

30 HIF-1β (ARNT) HIF-1α Hypoxia Proteasomal Degradation Prolyl hydroxylase TCA Cycle SDH FH Glucose Uptake Glycolysis + + Succinate Fumarate HIF-1α O2O2 Pseudo-hypoxic Oncogenic Mechanisms involving Succinate Dehydrogenase and Fumarate Hydratase

31 Acknowledgements Oxford University –Adrian Harris Hammersmith Hospital –Eric Aboagye Molecular & Population Genetics, St George’s, London –Patrick Pollard –Shirley Hodgson Urologic Oncology, NCI –Jennifer Isaacs –Len Neckers Institute of Cancer Research –Martin Leach –Ian Judson –Paul Workman

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33 Metabolic Profiling by NMR Methods However, NMR metabolic profiles measure only 50 or so metabolites. How can a method that gives such partial information about the metabolome provide useful insights into the phenotypic effects of gene knockouts, anticancer drug actions etc? Kevin Brindle has proposed two explanations: –Pathways intersect so densely that any perturbation rapidly affects the metabolites we can measure. –Because we quantify all the metabolites simultaneously in the same sample, with minimal processing, their relative concentrations are known with great precision.


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