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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
Building Blocks of Life
Marth, JD
Nature Cell Biology 10, 1015 (2008)
Bio-nomics Family
Genomics
Transcriptomics
Proteomics
DNA
Phenotypes
Functions
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
Classification of Metabolomics
Metabolomics
Metabonomics
Metabolite
target analysis
Metabolic
profiling
Metabolic
fingerprinting
Metabolome
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
Sample
classification
by rapid,
global analysis
Metabolomics-related papers published between
1999-2009 (ISIS web of science)
Xu et al., Trends Anal. Chem. 2010, 29: 269
General Workflow of Metabolomics
Time course
Dose response
Exogenous
Toxins, Drugs
Environmental
etc
Bio-systems
m*
p*
t*
8
7
6
5
4
3
2
1
t[2]
Analytical Approaches
0
-1
-2
-3
-4
-5
-6
-7
-8
-10
Endogenous
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
R2X[2] = 0.166547
SIMCA-P+ 11 - 9/2/2008 5:16:56 PM
•Exposure assessment
•Bio-monitoring
•Disease diagnostics
Metabolic pathways
Databases
Mol. Biosyst. 2009, 5: 288; J. Proteome. Res. 2009, 8: 5657; 2009, 8: 352.
9
t[1]
R2X[1] = 0.203804
Examples
•
•
•
•
•
Zebrafish- liver
Smoking - urine
Chronic kidney disease- urine
Cataract - urine
Colorectal cancer - tissues
(A)
5
4
20000000
6
51
2
3
9
1
3
7
2
1
1
10000000
1
10000000
1
0
5.0
ppm (t1)
8
0
4.0
3.0
2.0
1.0
pp
m
7
(B)
4
1
3
1
2
7
3
6
5000000
2
5
6
1
5
9
4
3
8
10
2
11
1
0
5.0
ppm (t1)
4.0
3.0
2.0
1.0
pp
m
0
-1
-2
-3
1H NMR (800 MHz) profile of
(A) Male and (B) Female
-4
-5
Zebrafish Livers OPLS (GC/MS)
-6
-7
-7
-6
-5
-4
-3
-2
-1
0
M*
Ong ES et al Mol. Biosystems, 2009;5:288-98.
1
2
F*
3
4
5
6
SIMCA-P+ 11 - 3/5/2010 1:30:01 PM
7
Urine of Non-smokers vs Smokers- OPLS
(LC/MS)
75 smokers
168 cases
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
-8
93 non-smokers
-9
-10
-4
-3
-2
-1
0
NC*
1
SC*
2
3
SIMCA-P+ 11 - 3/5/2010 1:24:23 PM
4
Urine of Chronic kidney disease (CKD)
OPLS (LC/MS)
10
0
Control (48)
-10
-10
-9
-8
-7
-6
CKD (48)
-5
-4
-3
-2
-1
CN*
0
1
2
CKD*
3
4
5
6
7
8
9
SIMCA-P+ 11 - 3/10/2010 3:25:38 PM
10
Urine samples - Cataract
OPLS (LC/MS)
10
0
Cataract (48)
-10
-8
-7
-6
-5
-4
-3
-2
-1
CN*
0
1
Control (48)
2
CAT*
3
4
5
6
7
SIMCA-P+ 11 - 3/10/2010 3:32:14 PM
8
Colorectal Cancer (CRC)
• 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
•Precursor of ~90% of CRC is the
adenomatous polyp
•Adenoma to carcinoma:
7 to 10 yrs
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
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.
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
m*
p*
t*
8
7
6
5
Reconstitute in
50 µL Acetonitrile
3
2
1
t[2]
Reconstitute in
50 µL ethyl acetate
4
0
-1
-2
-3
-4
-5
-6
-7
-8
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
t[1]
R2X[1] = 0.203804
8
7
6
5
4
Chemical Shift (ppm)
3
2
1
0
SIMCA-P+ 11 - 9/2/2008 5:16:56 PM
Metabolite Profiling &
pathway analysis
BSTFA derivatization
GC/MS Analysis
R2X[2] = 0.166547
LC/MS Analysis
Peak identification &
Confirmation
Comparison of GC-MS total ion chromatograms of CRC tissues
from mucosa (A), polyp (B) and tumor (C) of a patient
1.2
1
0.8
0.6
0.4
0.2
0
×107
Glucose
5
0.8
0.6
0.4
0.2
0
7
9
myo-Inositol
A
11
13
15
B
×107
5
1
0.8
0.6
0.4
0.2
0
7
9
11
13
C18:0
15
C
×107
Phosphoric acid
C18:1
Phenylalanine
C18:2
Glutamine
5
7
9
11
C20:4
13
15
m*
p*
t*
8
7
6
5
4
3
2
t[2]
1
0
-1
-2
-3
-4
-5
-6
-7
-8
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
t[1]
R2X[1] = 0.203804
R2X[2] = 0.166547
PCA scores plots by GC/MS
SIMCA-P+ 11 - 9/2/2008 5:16:56 PM
M*
P*
T*
t[2]O
10
0
-10
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
t[1]P
R2X[1] = 0.152012
R2X[2] = 0.348756
PCA scores plots by NMR
SIMCA-P+ 11 - 9/10/2008 10:05:36 AM
*
-1
-2
-4
n=26
-3
＊ : P<0.001
Polyps/Mucosa
-5
Carbo, nucleotides, etc.
Tumors/Mucosa
?-Sitosterol
Cholesterol
Glycerol 1-stearate
Glycerol 1-(9-octadecenoate)
*
*
**
**
**
***
*
**
** *
*
*
* *
*
*
*
*
*
*
*
Glycerol 1-palmitate
Glycerol 2-palmitate
Behenic acid(C22:0)
Arachidonic acid(C20:4)
Arachidic acid(C20:0)
Linoleic acid(C18:2)
Oleic acid(C18:1)
Stearic acid(C18:0)
Margaric acid(C17:0)
Palmitic acid(C16:0)
Myristic acid(C14:0)
Lauric acid(C12:0)
**
**
Amino acids
Oleamide
Uric acid
*
Phosphoric acid
Inosine
6
Vitamin C
*
*
Adenosine monophosphate
Adenosine
Uridine
Xanthine
Hypoxanthine
1
Glucose
myo-Inositol
**
*
**
* *
* *
4
Inositol monophosphate
*
Tyrosine
Lysine
Glutamine
Phenylalanine
2
Methionine
3
Proline
5
*
Fold Change
Metabolic Profiling by GC/MS
Lipids
0
-1
-2
-4
n=26
-3
＊ : P<0.001
Polyps/Mucosa
-5
Carbo, nucleotides, etc.
Tumors/Mucosa
?-Sitosterol
Cholesterol
Glycerol 1-stearate
*
*
*
*
*
*
*
*
*
Glycerol 1-(9-octadecenoate)
**
**
**
***
*
**
** *
*
*
* *
**
**
6
Glycerol 1-palmitate
Glycerol 2-palmitate
Behenic acid(C22:0)
Arachidonic acid(C20:4)
Arachidic acid(C20:0)
Linoleic acid(C18:2)
Oleic acid(C18:1)
Stearic acid(C18:0)
Margaric acid(C17:0)
Palmitic acid(C16:0)
Myristic acid(C14:0)
Lauric acid(C12:0)
Oleamide
Uric acid
*
Phosphoric acid
Inosine
Amino acids
Vitamin C
*
*
Adenosine monophosphate
Adenosine
Uridine
Xanthine
Hypoxanthine
1
Glucose
myo-Inositol
**
*
**
* *
* *
4
Inositol monophosphate
*
Tyrosine
Lysine
Glutamine
Phenylalanine
2
Methionine
3
Proline
5
* *
Fold Change
Metabolic Profiling by GC/MS
Lipids
0
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
Liver
Blood
Glucose
2 NAD+
2 NADH
6 ~P
2 Pyruvate
2 NADH
2 NAD+
2 Lactate
Cancer Cell
Glucose
2 NAD+
2 NADH
2 ~P
2 Pyruvate
2 NADH
2 NAD+
2 Lactate
JNCI 2004;96:1805-6
Nature Rev Cancer
2004;4:891-9
Low
malignancy
High
Malignancy
*
-1
-2
-4
n=26
-3
＊ : P<0.001
Polyps/Mucosa
-5
Carbo, nucleotides, etc.
Tumors/Mucosa
?-Sitosterol
Cholesterol
Glycerol 1-stearate
Glycerol 1-(9-octadecenoate)
*
*
**
**
**
***
*
**
** *
*
*
* *
*
*
*
*
*
*
*
*
*
Glycerol 1-palmitate
Glycerol 2-palmitate
Behenic acid(C22:0)
Arachidonic acid(C20:4)
Arachidic acid(C20:0)
Linoleic acid(C18:2)
Oleic acid(C18:1)
Stearic acid(C18:0)
Margaric acid(C17:0)
Palmitic acid(C16:0)
Myristic acid(C14:0)
**
*
Lauric acid(C12:0)
Uric acid
*
Phosphoric acid
Inosine
Amino acids
Oleamide
*
6
Vitamin C
* *
Adenosine monophosphate
Adenosine
Uridine
Xanthine
Hypoxanthine
1
Glucose
myo-Inositol
*
*
**
* *
* *
4
Inositol monophosphate
*
Tyrosine
Lysine
Glutamine
Phenylalanine
2
Methionine
3
Proline
5
*
Fold Change
Metabolic Profiling by GC/MS
Lipids
0
Purine metabolism
N
DNA/RNA
ATP
N
N
N
O
NH2
NH2
N
N
N
N
N
N
AMP
N
ribose
ribose
ribose-P
NH
Adenosine
Inosine
O
N
Normal tissue
De novo synthesis
NH
N
Cancerous cells
Salvage pathway
N
ribose-P
IMP
O
O
H
N
H2O2
NH
O
N
H
N
H
Uric acid
H2O＋O2
O Xanthine
oxireductase
N
N
H
O
NH
N
H
Xanthine
O
H2O2
H2O＋O2
Xanthine
oxireductase
N
N
H
NH
N
Hypoxanthine
Fold Change
0
-1
Carnitine
-3
n = 26
*: p<0.001
Phospholipids
Amides
Polyps/Mucosa
- 31
- 32
Docosahexaenoic acid (DHA, C22:6)
Deoxycholic aicd
Citramalic acid
**
Fatty Acids
Lipids
Tumors/Mucosa
=
*
Unknown, m/z=426.3
Eicosapentanoic acid (EPA, C20:5)
*
Arachidonic acid (C20:4)
cis-11-Eicosenoic acid (C20:1)
γ-Linolenic acid (C18:3)
α-Linolenic acid (C18:3)
Oleic acid (C18:1)
Stearic acid (C18:0)
Myristic acid (C14:0)
Stearamide
Oleamide
Myristamide
Carnitines
Linoleic acid (C18:2)
Palmitic acid (C16:0)
LPC C16:0
*
Palmitamide
1
LPC C20:4
*
LPC C18:2
*
LPC C18:1
*
Glycerophosphocholine
4
LPC C18:0
3
Choline
2
Palmitoylcarnitine
5
Acetylcarnitine
-2
Betaine
Metabolic Profiling by LC/MS
Others
*
Main Findings
1.
2.
Three stages are rather different in metabolomes,
although polyps and carcinoma show overlapping
profiles.
Increase in aa - provide reservoir for bioactivities.
3.
Enhanced glucose consumption - switching to glycolytic
pathways in cancerous tissues.
4.
High concentrations of carnitines, FAs and other lipids requirements for biosynthesis of cell membrane of
rapidly dividing cells.
5.
Nucleotide purine salvage pathway – growth advantage.
6.
Significant decrease of 2nd bile acid in carcinoma abnormal metabolic function for adaptation?
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.

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