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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]

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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]


The Omic Sciences: side by side comparison

Journal of Surgical Oncology

Volume 103,Issue 5, pages 451-459, 28, 2011



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


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).


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


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.


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


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




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




GC-MS: A tool for high-throughput phytochemicalsanalysis

Mass Spectrometers as a GC Detector





GC-MS: A tool for high-throughput phytochemicalsanalysis

Two Different Chromatogram:


GC-MS: A tool for high-throughput phytochemicalsanalysis

Use of Extracted Ions:

GC-MS: A tool for high-throughput phytochemicalsanalysis








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



GC-MS: A tool for high-throughput phytochemicalsanalysis

Important points need to consider:





GC-MS analysis of Plant extract

10.26= (-)-β-Pinene



GC-MS analysis of Plant extract

ALO (Oil-01)

Fraction of ALO (Oil-02)



GC-MS

  • Advancement in the sample preparation

  • Advancement in GC system


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


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


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



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.


Method Reproducibility Plasma-Pool (HPP-P) by different fractionation techniques

An example of a well-aligned metabolite feature detected from all 30 runs


Reproducibility of Four Selected Compounds in Each Method Plasma-Pool (HPP-P) by different fractionation techniques

Each bar represents standard deviation of the average of three independent run in each method.


Clustering of Fractionation Techniques Plasma-Pool (HPP-P) by different 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.


Comparative Analysis between Male and Female Plasma Samples Plasma-Pool (HPP-P) by different fractionation techniques

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


Comparative Analysis between Male and Female Plasma Samples Plasma-Pool (HPP-P) by different fractionation techniques

  • 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.


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).


Metabolite Identification 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).

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


The intensities of some of the compounds through NIST and Fiehn data base are shown by a heat map pattern


Venn Diagram of Metabolite Features 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.


Percent distribution of different classes Fiehn data base are shown by a heat map pattern

from each fractionation technique


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