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: [email protected]
Journal of Surgical Oncology
Volume 103,Issue 5, pages 451-459, 28, 2011
Metabolite target analysis
group of related compounds or metabolites in specific metabolic pathways
all metabolites present in a cell/sample
Sample classification by rapid, global analysis
Plant Molecular Biology 2002, 48, (155- 171).
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.
Key Cancer Types
Healthy Controls/ Benign
Disease vs Malignancy
Journal of Surgical Oncology
Volume 103,Issue 5, 451-459, 2010
Turbo molecular pumps
High Vacuum System
Mass Spectrometers as a GC Detector
Methods in GC-MS
Two Different Chromatogram:
Use of Extracted Ions:
GC-MS: A tool for high-throughput phytochemicalsanalysis
Important points need to consider:
Fraction of ALO (Oil-02)
0.2µL Crude Plasma , NuPAGE 12 % precast gel ,
MES SDS Running Buffer.
200 volts, 90-120 mA , colloidal blue staining solution
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
and Pooling Strategy
HMPG1-G4 (all samples)
HFPG1-G4 (all samples)
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
GC/MS Total Ion Chromatogram (TIC) of Healthy Pakistani Plasma-Pool (HPP-P) by different fractionation techniques
An example of a well-aligned metabolite feature detected from all 30 runs
Each bar represents standard deviation of the average of three independent run in each method.
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.
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
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.
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).
The percentage of metabolites identified by each fractionation techniques by NIST and Fiehn GC/MS data base
The intensities of some of the compounds through NIST and Fiehn data base are shown by a heat map pattern
Metabolite features observed in the different methods represented the same or different metabolites were evaluated through venn diagram
2D C-18 based fractionation recovered a large number of the metabolite so it is compared with all the method.
For this comparison, compounds with retention time difference less than 0.02 mints in RTL method consider as same metabolite.
from each fractionation technique