Syed Ghulam Musharraf

1. Metabolomics and Cancer Research Syed Ghulam Musharraf Dr. Panjwani Center for Molecular Medicine and Drug Research, International Centre for Chemical and Biological Sciences (ICCBS) University of Karachi, Karachi-75270 E mail: musharraf1977@yahoo.com

2. The Omic Sciences: side by side comparison Journal of Surgical Oncology Volume 103,Issue 5, pages 451-459, 28, 2011

3. Yearly Increase in Metabolomics Publications

4. Metabolomics is…. • Metabolomics is the comparative analysis of endogenous metabolites found in biological samples: • Compare two or more biological groups • Find and identify potential biomarkers • Look for biomarkers of toxicology • Understand biological pathways • Discover new metabolites • Metabolites are the by-products of metabolism • Range of physiochemical properties • Classes: Amino acids, lipids, fatty acid, organic acids, sugars

5. Classification of Endogenous Metabolite Analysis Metabolome analysis Metabolite target analysis Metabolite profiling Metabolomics Metabolic fingerprinting group of related compounds or metabolites in specific metabolic pathways specific metabolites all metabolites present in a cell/sample Sample classification by rapid, global analysis Plant Molecular Biology 2002, 48, (155- 171).

6. Metabolomics in Oncology • Potential applications of metabolomics in the field of cancer research: • Early diagnosis • Cancer Staging • Refining tumor characterization • Predictive biomarkers of cancer • Personalized drug discovery

7. Some Examples from Published Data Metabolomic profiling of B16 melanoma (top) and 3LL pulmonary carcinoma tumors (bottom) showing variations in multiple metabolites before (red) and after (blue) chloroethylnitrosurea treatment. *, P <0.05; **, P < 0.01; ***, P < 0.001. Cancer Research 2004, 64, 4270–4276.

8. Examples of Key Metabolite Differences Key Cancer Types Healthy Controls/ Benign Disease vs Malignancy Journal of Surgical Oncology Volume 103,Issue 5, 451-459, 2010

9. Challenges in Metabolomics Study • Number of samples to analyze (for proper statistical treatment of the data) • Metabolites have a wide range of molecular weights and large variations in concentration • The metabolome is much more dynamic than proteome and genome, which makes the metabolome more time sensitive • Detection • Identification and quantification • Efficient and unbiased separation of analytes

10.   A General Methodology for Metabolomics Study

11. Yearly Increase in Metabolomics Publications

12. Mass Spectrometry Turbo molecular pumps High Vacuum System Ion source Mass Analyzer Data System Inlet Detector Magnetic Sector Electrostatic Sector Quadrupole Iontrap Time-of-Flight FT-ICR EI CI FAB ESI MALDI

13. Most commonly used methods for Metabolomics

14. GC-MS: A tool for high-throughput phytochemicalsanalysis

15. GC-MS: A tool for high-throughput phytochemicalsanalysis Mass Spectrometers as a GC Detector

16. GC-MS: A tool for high-throughput phytochemicalsanalysis Methods in GC-MS

17. GC-MS: A tool for high-throughput phytochemicalsanalysis

18. GC-MS: A tool for high-throughput phytochemicalsanalysis Normal Operation

19. GC-MS: A tool for high-throughput phytochemicalsanalysis Two Different Chromatogram:

20. GC-MS: A tool for high-throughput phytochemicalsanalysis Use of Extracted Ions: GC-MS: A tool for high-throughput phytochemicalsanalysis

21. GC-MS: A tool for high-throughput phytochemicalsanalysis Data Refinement:

22. GC-MS: A tool for high-throughput phytochemicalsanalysis Data Refinement:

23. GC-MS: A tool for high-throughput phytochemicalsanalysis Data Refinement:

24. GC-MS: A tool for high-throughput phytochemicalsanalysis Data Refinement:

25. GC-MS: A tool for high-throughput phytochemicalsanalysis Data Refinement:

26. GC-MS: A tool for high-throughput phytochemicalsanalysis Data Refinement:

27. GC-MS: A tool for high-throughput phytochemicalsanalysis • AMDIS = • Automated • Mass Spectral • Deconvolution and • Identification • System • Developed by NIST (National Institute of Standards and Technology) in USA • An automated mass spectrometric data analysis software

28. GC-MS: A tool for high-throughput phytochemicalsanalysis

29. GC-MS: A tool for high-throughput phytochemicalsanalysis Important points need to consider:

30. GC-MS analysis of Plant extract AlO (Oil-01)

31. GC-MS analysis of Plant extract

32. GC-MS analysis of Plant extract

33. GC-MS analysis of Plant extract 10.26= (-)-β-Pinene

34. GC-MS analysis of Plant extract

35. GC-MS analysis of Plant extract ALO (Oil-01) Fraction of ALO (Oil-02)

36. GC-MS analysis of Plant extract

37. GC-MS • Advancement in the sample preparation • Advancement in GC system

38. 1-D SDS-PAGE Analysis Male Plasma Female Plasma Smoker Plasma Cancer Plasma Electrophoretic Conditions: 0.2µL Crude Plasma , NuPAGE 12 % precast gel , MES SDS Running Buffer. 200 volts, 90-120 mA , colloidal blue staining solution 38

39. Comparative Analysis of Healthy, Smoker and Cancer Differentially expressed peaks: Gel view in chromatic mode representing the comparison of average intensity of the individual signature peptide. Lung cancer (red), smokers (green), and nonsmokers (blue) Comparative gel view of the three classes comprises of 120 mass spectra of 40 of each class of nonsmokers, smokers, and lung cancer patients

40. Metabolomics studies: Sample Collection and Pooling Strategy HMPG1-G4 (all samples) HMPG1-G4-P HMP-P HPP-P Pooling 1 Pooling 2 Pooling 3 HFPG1-G4 (all samples) HFPG1-G4-P HFP-P HMP-P (Healthy Male Plasma-Pool), HFP-P (Healthy Female Plasma-Pool), HPP-P (Healthy Pakistani Plasma-Pool) Ping et al., Proteomics 2005, 5, 3442-3453

42. Data Processing and Statistical Analysis

43. GC/MS Total Ion Chromatogram (TIC) of Healthy Pakistani Plasma-Pool (HPP-P) by different fractionation techniques • Key points of this figure: • It is important to emphasize that as this is an average of several thousand metabolite features • 2D-Si, solvent precipitation, 2D-C18, 1D-cation, 1D-anion, 2D-cation and 1D-MWCOT showed RSDs of <15%. • Metabolite features from each spectrum were analyzed by XCMS online software. A metabolite feature was defined as a mass spectral peak in the mass region of m/z 100-1000 with a signal-to-noise ratio exceeding 10:1. • %RSD of each metabolite feature intensity was obtained from three replicates, where Avg. % RSD reports the average value for all detected features within each method. • Using XCMS software, between 1000 to 2000 reproducible metabolite features were detected for each fractionation technique.

44. Method Reproducibility An example of a well-aligned metabolite feature detected from all 30 runs

45. Reproducibility of Four Selected Compounds in Each Method Each bar represents standard deviation of the average of three independent run in each method.

46. Clustering of Fractionation Techniques Key points of this figure: To examine similarities in all 10 methods, GC/MS data was aligned through XCMS than Hierarchical clustering of the fractionation techniques was performed to produce a dendrogram of reproducible features illustrating which techniques produced the similar metabolite features from healthy Pakistani pool plasma Out of total 7,299 metabolite features from all 10 fractionation techniques, overall 52% distinct features were observed by applying different fractionation techniques.

47. Comparative Analysis between Male and Female Plasma Samples At p<10-5 200 metabolite features in 2D-C18 out of 1,076 6 in 1D-anion out of 1,201 39 in 2D-anion out of 1,533 56 in 2D-cation out of 1,441 15 in 2D-Si out of 1,522 16 in 1D-C18 out of 2,287 1 in 1D-MWCOT out of 1,741 176 in 1D-Si out of 3,268 32 in solvent precipitation out of 2,068 No differentiative metabolite in 1D-cation out of 1,010

48. Comparative Analysis between Male and Female Plasma Samples • Key points of this figure: • The optimized GC/MS method was applied for the plasma metabolite profiling of 50 pooled samples from healthy male (HMP-P) and female (HFP-P) which is pooled sample of four different age group in order to minimize age and gender effect using all 10 fractionation techniques. • Welch’s t-test was then employed to detect metabolites that are significantly different in male and female groups. Using a threshold value of p<10-5 Box and whisker plots of metabolite features with threshold value of p<10-5 from 10 fractionation techniques except 1D-cation at p<0.001.

49. The first component of PCA vary approximately 60% in all cases, this may be due to different metabolite characteristics of male and female, which is in context with comparative analysis of male and female but the second and third components have least differences (Figure 6A and 6B). Similarly, when male and female pooled compared with individual male and female samples, respectively still showed first component variation approx 60% (Figure 6C and 6D) while 2nd component variation > 30%, indicated that pooling does not completely nullify the individual effect but may be partially nullify the variance and it enhance the common features which reduce the variance in 2nd component in pooled samples. Comparison of Pooled and Individual Samples Five individual samples are randomly selected and process through 2D-Si and compared with pooled samples through PCA (loading plot). GC/MS data were aligned through XCMS software than all the metabolite features were used to plot on Qlucore version 2.3. Loading plot of A. HPP-P, HMP-P and HFP-P samples (1615 metabolite features) B. HPP-P and individual samples (900 metabolite features) C. HMP-P and individual male samples (1,442 metabolite features) D. HFP-P and individual female samples, (1,903 metabolite feature).

50. Metabolite Identification 64 153 139 77 145 84 130 158 173 155 The percentage of metabolites identified by each fractionation techniques by NIST and Fiehn GC/MS data base