2009 ASMS Metabolomics Workshop: Current Topics in Metabolomics

1. Co-chairs: Eric Milgram and Anders Nordstrom Philadelphia, PA Tuesday, June 2, 2009 2009 ASMS Metabolomics Workshop: Current Topics in Metabolomics eric.milgram@metabolomics.us anders.nordstrom@ki.se

2. Workshop Purpose • Give practical perspectives on how metabolomics is being used today. • Highlight the challenges of the technique via an interactive audience/panel discussion. eric.milgram@metabolomics.us anders.nordstrom@ki.se

3. Workshop Format • Part 1 – Eric/Anders • Administrative • 2009 Metabolomics Survey Results • Part 2 – Interactive – Discussion Leaders • Metabolomics Tech: hardware and software • Metabolite Identification • Assigning Biological Significance eric.milgram@metabolomics.us anders.nordstrom@ki.se

4. Mass Spectrometry Applications to the Clinical Laboratory ‘10 • February 6 - 10, 2010 • Hyatt Mission Bay, San Diego CA • Two Days of Short Courses • Three Days of Presentations • Exhibits, Posters and Sponsored Lunches • Topic Areas • Biodefense / Environmental - Regulations and Standards • Disease Marker - Small Molecule Analytes • Inborn Errors in Metabolism (e.g., Vitamins, Steroids, Thyroid) • Metabolomics - Tissue Analysis by MALDI • Molecular Diagnostics - Trace Metals • (e.g., Infectious Disease) - New Advances • - Proteins / Proteomics • www.msacl.org • Contact: dherold@ucsd.edu • Student, Postdoc and Junior Faculty Travel Fellowships Available

5. Request for Workshop Coordinators for 2010/2011 • Volunteers? • Nominations? • Use feedback form http://MetabolomicsSurvey.com/ eric.milgram@metabolomics.us anders.nordstrom@ki.se

6. Survey Results In order to download 2009 Workshop survey results, complete workshop feedback form before Thursday, June 4 at 5 PM. http://MetabolomicsSurvey.com eric.milgram@metabolomics.us anders.nordstrom@ki.se

7. Workshop Feedback Link http://MetabolomicsSurvey.com eric.milgram@metabolomics.us anders.nordstrom@ki.se

8. Link to Workshop Survey Results eric.milgram@metabolomics.us anders.nordstrom@ki.se

9. 2009 Workshop Survey Results eric.milgram@metabolomics.us anders.nordstrom@ki.se

10. Organization Type Non-Profit 2% GOV 5% Academic 57% For Profit 36% eric.milgram@metabolomics.us anders.nordstrom@ki.se

11. How long have you or your current research group been practicing or applying metabolomics? 61 59 43 36 13 0-2 yrs 2-4 yrs 5-7 yrs >=7 yrs N/A eric.milgram@metabolomics.us anders.nordstrom@ki.se

12. 9-Other; 2% 8-Data acquisition/throughput; 3% 7-Validation/Utility Studies; 5% 6-Statistical analysis; 5% 1-Identification of metabolites; 35% 5-No opinion; 6% 4-Sample preparation; 8% 3-Data processing/reduction; 14% 2-Assigning biological significance; 22% Bottleneck Summary eric.milgram@metabolomics.us anders.nordstrom@ki.se

13. Technique Summary 173 87 25 21 9 4 LC/MS GC/MS CE/MS NMR Other LC/NMR eric.milgram@metabolomics.us anders.nordstrom@ki.se

14. MS Type Summary 130 100 61 41 40 33 17 3 2 ToF IonTrap 1-Quad N/A Sector QqQ Orbitrap FTICR Other eric.milgram@metabolomics.us anders.nordstrom@ki.se

15. Software Category Summary N/A 7% (24) Proprietary 39% (124) Open Source 25% (83) In-house produced 29% (96) eric.milgram@metabolomics.us anders.nordstrom@ki.se

16. Software/DB Summary eric.milgram@metabolomics.us anders.nordstrom@ki.se

17. Biocrates MarkerIDQ ChromaToF GeneData GeneSpring Golm DB Lipid Search MarkerView MassBank* MassFragmenter MassProfiler MMCD MZmine Shimadzu MetID SIDMS SIEVE Software Write-In Candidates eric.milgram@metabolomics.us anders.nordstrom@ki.se

18. eric.milgram@metabolomics.us anders.nordstrom@ki.se

19. Survey Summary • Reported Bottlenecks • Metabolite Identification • Assignment of Biological Significance • Data processing/reduction • Instrumentation • ToF’s dominate • Triple-quads unexpectedly high • Software • Wide diversity of software packages and databases in use. • No widely accepted workflow solution available yet. eric.milgram@metabolomics.us anders.nordstrom@ki.se

20. Introduce Discussion Leaders eric.milgram@metabolomics.us anders.nordstrom@ki.se

21. Metabolomics Technologies – Hardware/Software • Steve Fischer • Senior Applications Chemist • Agilent Technologies • John Shockcor • Director of Metabolic Profiling Business Development • Waters Corporation eric.milgram@metabolomics.us anders.nordstrom@ki.se

22. Metabolite Identification • Annie Evans • Senior R&D Scientist • Metabolon • Bill Wikoff • Research Associate • The Scripps Research Institute eric.milgram@metabolomics.us anders.nordstrom@ki.se

23. Assigning Biological Significance • Chris Beecher • Professor • University of Michigan, Ann Arbor • Michigan Center for Translational Pathology • Tsutomu Masujima • Professor • Hiroshima University • Molecular Medicine & Devices Lab. eric.milgram@metabolomics.us anders.nordstrom@ki.se

24. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression Arun Sreekumar, Laila M. Poisson, Thekkelnaycke M. Rajendiran, Amjad P. Khan, Qi Cao, Jindan Yu, Bharathi Laxman, Rohit Mehra, Robert J. Lonigro, Yong Li, Mukesh K. Nyati, Aarif Ahsan, Shanker Kalyana-Sundaram, Bo Han, Xuhong Cao, Jaeman Byun, Gilbert S.Omenn, Debashis Ghosh, Subramaniam Pennathur, Danny C. Alexander1, Alvin Berger2, Jeffrey R. Shuster1, John T. Wei, Sooryanarayana Varambally, Christopher Beecher2 & Arul M. Chinnaiyan ABSTRACT Multiple, complex molecular events characterize cancer development and progression1,2. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we profiled more than 1,126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity. Published February 12, 2009 in Nature, vol 457, 910-914.

25. 2009 ASMS Metabolomics Workshop Metabolomics Technologies: hardware and software eric.milgram@metabolomics.us anders.nordstrom@ki.se

26. Targeted Metabolomics Hardware Tandem Quads Tof/QTof Software Quantitative Packages Statistical Packages Visualization Tools eric.milgram@metabolomics.us anders.nordstrom@ki.se

27. Non-Targeted Metabolomics Hardware Nominal Mass Tandem Quads Ion-Traps Accurate Mass Tof/Qtof OrbiTrap Software Statistical packages Visualization tools Database tools eric.milgram@metabolomics.us anders.nordstrom@ki.se

28. Discussion Questions eric.milgram@metabolomics.us anders.nordstrom@ki.se • Targeted vs Non-Targeted analysis? • What is your biggest challenge/problem? • What improvements need to be made to hardware? • What improvements need to be made to software?

29. Compound Identification in Metabolomics ? • How to streamline routine compound identification ?  • Next generation of metabolomics databases ? • Utility of ultrahigh mass accuracy / resolution data ? • How useful are alternatives to CID and multi-step fragmentation ? • Can we progress towards de novo structure determination using MS methods alone ? • How feasible is preparative scale up for compound purification and ID by NMR ? • Are publication standards needed for compound identification in metabolomics ? • What new technologies or methods will emerge in the next few years ? Annie Evans Metabolon aevans@metabolon.com ASMS Metabolomics Workshop Tuesday June 2nd 2009 William Wikoff The Scripps Research Institute billw@scripps.edu

30. Chris Beecher and Tsutomu Masujima Assignment of Biological Significance eric.milgram@metabolomics.us anders.nordstrom@ki.se

31. Data Generation • Know your system • Understand your system’s variances • Noise reduction & reproducibility • Power curves • Experiment design • Factorial designs • QA/QC (Standards) • Recovery standards • Derivatization (or chemistry) standards • Injection standards eric.milgram@metabolomics.us anders.nordstrom@ki.se

32. Data Interpretation • Standardization • Normalization • Median centered • Z-transformation • (Obs-MeanC) / StdDevC • Statistics vs. Data-mining • Statistics • Independent observations • False discovery • Data-mining • Random Forest • Non-negative factorization • Partition • Singular Value Decomposition eric.milgram@metabolomics.us anders.nordstrom@ki.se

33. We can improve highway safety in the USA by Importing More Fresh Lemons from Mexico eric.milgram@metabolomics.us anders.nordstrom@ki.se

34. Workshop Feedback Linkhttp://MetabolomicsSurvey.com eric.milgram@metabolomics.us anders.nordstrom@ki.se

35. Auxillary Slides eric.milgram@metabolomics.us anders.nordstrom@ki.se

36. Obstacles to Biological Relevance: Expedience vs Relevance? • Surrogate Model Systems • Cell lines • What is a proper control? • Are mono-layer cells biochemically representative of an organ? • Animals (non-humans) • Interspecies differences? • Human Subjects? • Intersubject variability: age, race, gender, diet, … • Sample collection constraints? • Fresh vs banked samples? eric.milgram@metabolomics.us anders.nordstrom@ki.se

37. Obstacles to Biological Relevance: Experimental Challenges? • Process Artifacts • Contaminants? • Chemical Stability • NADPH+ vs NADP • Prostaglandins  isoprostanes • Normalization strategies for urine and tissue? • False Discovery • How can you minimize your chances of being “Fooled by Randomness?” eric.milgram@metabolomics.us anders.nordstrom@ki.se