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CN ONG, FG Xu, L Zou, Y Liu, Department of Epidemiology & Public Health

Metabolomics: A Novel Platform for Environmental Health Investigations?. CN ONG, FG Xu, L Zou, Y Liu, Department of Epidemiology & Public Health National University of Singapore & Singapore-MIT Alliance on Research and Technology Toxicology programme, Life Sciences Institute

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CN ONG, FG Xu, L Zou, Y Liu, Department of Epidemiology & Public Health

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  1. Metabolomics: A Novel Platform for Environmental Health Investigations? CN ONG, FG Xu, L Zou, Y Liu, Department of Epidemiology & Public Health National University of Singapore & Singapore-MIT Alliance on Research and Technology Toxicology programme, Life Sciences Institute National University of Singapore

  2. Building Blocks of Life Marth, JD Nature Cell Biology10, 1015 (2008)

  3. Bio-nomics Family Genomics Transcriptomics Proteomics Phenotypes Functions DNA Metabolomics RNA Proteins • Sugars/Carbohydrates • Fats/lipids • Nucleotides • Amino Acids Metabolites Genomics and proteomics tell you what might happen, but metabolomics tells you what actually did happen! Bill Lasley, University of California, Davis

  4. Classification of Metabolomics Metabonomics Metabolomics Metabolic profiling Metabolic fingerprinting Metabolite target analysis Metabolome Sample classification by rapid, global analysis Specific metabolites Group of compounds or metabolites in cell/system or biofluids Quantification of set of metabolites present in a cell/sample Xenobiotica 1999,29: 1181; Nature 2008, 455:2054

  5. Metabolomics-related papers published between 1999-2009 (ISIS web of science) Xu et al., Trends Anal. Chem. 2010, 29: 269

  6. General Workflow of Metabolomics Time course Dose response Exogenous Bio-systems Toxins, Drugs Environmental etc Analytical Approaches Endogenous • Exposure assessment • Bio-monitoring • Disease diagnostics Metabolic pathways Databases Mol. Biosyst. 2009, 5: 288; J. Proteome. Res. 2009, 8: 5657; 2009, 8: 352.

  7. Examples • Zebrafish- liver • Smoking - urine • Chronic kidney disease- urine • Cataract - urine • Colorectal cancer - tissues

  8. (B) 4 3 13 (A) 5 4 2 12 7 6 6 12 5 1 5 3 9 9 8 13 10 7 2 11 11 1 10 8 ppm ppm 1H NMR (800 MHz) profile of (A) Male and (B) Female Zebrafish Livers OPLS (GC/MS) Ong ES et al Mol. Biosystems, 2009;5:288-98.

  9. Urine of Non-smokers vs Smokers- OPLS (LC/MS) 168 cases 75 smokers 93 non-smokers

  10. Urine of Chronic kidney disease (CKD) OPLS (LC/MS) Control (48) CKD (48)

  11. Urine samples - Cataract OPLS (LC/MS) Cataract (48) Control (48)

  12. Most common cancer among males in Singapore, 18.9%, and second common among females, 14.8% 10% for males and females in US Interplay of environment and genetic factors Preventable –diet and lifestyle Colorectal Cancer (CRC) • Precursor of ~90% of CRC is the adenomatous polyp • Adenoma to carcinoma: 7 to 10 yrs

  13. Objective We used metabolomics approach to test the hypothesis that tissues at different stages of cancer development would exhibit distinct metabolic profile. The profiles of tumor, adenomatous polyps and adjacent matched normal mucosa from 26 CRC patients using; (1) 1H NMR (2) GC/MS, and (3) LC/MS

  14. A complex step-wise series of changes in cellular proliferation and differentiation Colorectal cancer (CRC) arises as the consequence of the progressive accumulation of biomolecular alterations that lead to changes from normal colonic epithelial cells to adenoma and to adenocarcinomas.

  15. Experimental Procedure Search database for metabolites matching Samples Mucosa, Polyps, Tumors Freeze-dried Normalization of raw data Extraction Chloroform/Methanol 3:1(v/v) Statistical analysis PCA score plot Reconstitute in 50 µL ethyl acetate Reconstitute in 50 µL Acetonitrile Metabolite Profiling & pathway analysis BSTFA derivatization GC/MS Analysis LC/MS Analysis Peak identification & Confirmation

  16. Comparison of GC-MS total ion chromatograms of CRC tissues from mucosa (A), polyp (B) and tumor (C) of a patient

  17. PCA scores plots by GC/MS

  18. PCA scores plots by NMR

  19. Lipids 6 * 5 * * Amino acids * * 4 * * Phosphoric acid * * 3 * * * * * * * * * * * * * * * * * Inosine * * * 2 * * * * * * * * * * * * Inositol 1 - Uric acid Glucose myo Fold Change 0 Lysine Uridine Proline Xanthine Tyrosine -1 Vitamin C Oleamide Glutamine Adenosine Methionine Cholesterol ?-Sitosterol * Hypoxanthine Phenylalanine Oleic acid(C18:1) * Lauric acid(C12:0) Glycerol 1-stearate Stearic acid(C18:0) Glycerol 1-palmitate Glycerol 2-palmitate Myristic acid(C14:0) Linoleic acid(C18:2) Palmitic acid(C16:0) Behenic acid(C22:0) * Margaric acid(C17:0) Arachidic acid(C20:0) Inositol monophosphate -2 Arachidonic acid(C20:4) Adenosine monophosphate Glycerol 1-(9-octadecenoate) -3 * -4 * -5 Carbo, nucleotides, etc. n=26 : P<0.001 Polyps/Mucosa ＊ Tumors/Mucosa Metabolic Profiling by GC/MS *

  20. 6 5 4 3 2 1 0 Lysine Uridine Proline Xanthine Tyrosine -1 Vitamin C Oleamide Glutamine Adenosine Methionine Cholesterol ?-Sitosterol Hypoxanthine Phenylalanine Oleic acid(C18:1) Lauric acid(C12:0) Glycerol 1-stearate Stearic acid(C18:0) Glycerol 1-palmitate Glycerol 2-palmitate Myristic acid(C14:0) Linoleic acid(C18:2) Palmitic acid(C16:0) Behenic acid(C22:0) Margaric acid(C17:0) Arachidic acid(C20:0) -2 Arachidonic acid(C20:4) Adenosine monophosphate Glycerol 1-(9-octadecenoate) -3 -4 -5 Metabolic Profiling by GC/MS Lipids * * * * Amino acids * * * * Phosphoric acid * * * * * * * * * * * * * * * * * * * Inosine * * * * * * * * * * * * * * * Inositol - Uric acid Glucose myo Fold Change * * * Inositol monophosphate * * Carbo, nucleotides, etc. n=26 : P<0.001 Polyps/Mucosa ＊ Tumors/Mucosa

  21. Otto Warburg 1931 Nobel Prize “For his discovery of the nature and mode of action of the respiratory enzyme” Cancer was caused by altered metabolism - deranged energy processing - in the cell (1930) http://nobelprize.org JNCI 2004;96:1805-6

  22. Nature Rev Cancer 2004;4:891-9 Low malignancy High Malignancy

  23. Lipids 6 * 5 * * Amino acids * * 4 * * Phosphoric acid * * 3 * * * * * * * * * * * * * * * * * Inosine * * * 2 * * * * * * * * * * * * Inositol 1 - Uric acid Glucose myo Fold Change 0 Lysine Uridine Proline Xanthine Tyrosine -1 Vitamin C Oleamide Glutamine Adenosine Methionine Cholesterol ?-Sitosterol * Hypoxanthine Phenylalanine Oleic acid(C18:1) * Lauric acid(C12:0) Glycerol 1-stearate Stearic acid(C18:0) Glycerol 1-palmitate Glycerol 2-palmitate Myristic acid(C14:0) Linoleic acid(C18:2) Palmitic acid(C16:0) Behenic acid(C22:0) * Margaric acid(C17:0) Arachidic acid(C20:0) Inositol monophosphate -2 Arachidonic acid(C20:4) Adenosine monophosphate Glycerol 1-(9-octadecenoate) -3 * -4 * -5 Carbo, nucleotides, etc. n=26 : P<0.001 Polyps/Mucosa ＊ Tumors/Mucosa Metabolic Profiling by GC/MS * *

  24. Purine metabolism DNA/RNA ATP AMP Adenosine Inosine Normal tissue De novo synthesis Cancerous cells Salvage pathway IMP H2O2 H2O＋O2 H2O2 H2O＋O2 Xanthine oxireductase Xanthine oxireductase Hypoxanthine Xanthine Uric acid

  25. Metabolic Profiling by LC/MS Others Carnitines 5 * Eicosapentanoic acid (EPA, C20:5) 4 Oleic acid (C18:1) α-Linolenic acid (C18:3) γ-Linolenic acid (C18:3) * Myristic acid (C14:0) 3 Stearic acid (C18:0) * * LPC C16:0 Glycerophosphocholine Myristamide * 2 * * Deoxycholic aicd * Citramalic acid Stearamide Oleamide 1 Fold Change 0 Betaine Choline Carnitine - 1 LPC C20:4 LPC C18:1 LPC C18:2 LPC C18:0 Palmitamide Acetylcarnitine Palmitoylcarnitine Linoleic acid (C18:2) Palmitic acid (C16:0) Unknown, m/z=426.3 - 2 Arachidonic acid (C20:4) cis-11-Eicosenoic acid (C20:1) = Docosahexaenoic acid (DHA, C22:6) - 3 - 31 - 32 * Phospholipids Amides Fatty Acids Lipids Polyps/Mucosa * n = 26 : p<0.001 Tumors/Mucosa

  26. Three stages are rather different in metabolomes, although polyps and carcinoma show overlapping profiles. Increase in aa - provide reservoir for bioactivities. Enhanced glucose consumption - switching to glycolytic pathways in cancerous tissues. High concentrations of carnitines, FAs and other lipids - requirements for biosynthesis of cell membrane of rapidly dividing cells. Nucleotide purine salvage pathway – growth advantage. Significant decrease of 2nd bile acid in carcinoma -abnormal metabolic function for adaptation? Main Findings

  27. CONCLUSION Metabolomics has promise in helping us dissect a metabolome. As a platform for biomarker discovery -early days. Integrating genomic, proteomic and metabolomic data in a system approach will be interesting and challenging.

  28. Acknowledgements ONG Eng Shi, XU Fungguo, LIU Ying- EPH, NUS ZOU Li - SMART LI Shao Xia, GAO Yen Hong - China CDC CHEAH Peh Yean, EU Kong Weng - Colorectal Surgery Department, SGH Life Sciences Inst., NUS Centre for Environmental Health, YLLSOM, NUS. Singapore MIT Alliance on Research & Technology Department of Chemistry, NUS All patients participated in this study

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