Transcript PowerPoint Template

Application of NMR and MS based
Metabolomics
in Natural Product Science
February, 2010
Choi, Hyung-Kyoon
[email protected]
College of Pharmacy, Chung-Ang University
Republic of Korea
‘omics’ technology

Genomics
(30,000 genome)

Transcriptomics

Proteomics

Metabolomics
‘omics’ technology
Genomics
Transcriptomics Proteomics
Metabolomics
Target
material
Gene,
chromosome
(genetic code)
mRNA
(genetic code)
Protein
(function of
the protein)
Low
molecular
weight
metabolites
MW
100,000120,000
100,000-120,000
5,000-20,000
100- 5000
Charact
eristics
Context
independent
Context
dependent
Context
dependent
Context
dependent
Analysis Mapping,
sequencing
Sequencing
Separation,
Separation,
characterizat characterizati
ion
on
Methods DNA
sequencer
Hybridization
2D gel, Maldi
TOF
NMR, MS, GC,
LC
>109?
~2,500
Human
30,000
genome
Metabolomics
○ Metabolome
- total low molecular weight compounds in biofluid, cells,
and tissue in living organism
○ Metabolomics
- comparative and non-targeted analysis of metabolome
using various analytical methods
General flow for metabolomics
1. Is there a difference between samples?
2. What is the difference between samples?
3. What is the reason of difference?
Tools for metabolomics
Tools
Pros
Cons
NMR
Robustness and
reproducibility
Metabolite
overlapping
GC-MS
Excellent sensitivity
Need to derivatize
Excellent sensitivity
Lower reproducibility
than GC
GC X GC TOF
LC-MS
Analytical challenges for metabolomics
•Identification: Assignment of the unknown
compounds
•Higher Resolution, Sensitivity, and Reproducibility
•High throughput: Automated data processing
Sample preparation time
Large sample series
Whole cell
Sample pre-fractionation
(clean-up)
Biofluid
Cell extract
Metabolite fraction
Crude extract
Isolated (specific)
metabolite fraction
Derivatization
GC
Derivatization
CE
HPLC
MS
MS
MS
RI
UV
NMR
ESI-MS
Metabolite target analysis Metabolite profiling
And metabolomics
NMR
FT-IR
Raman
Metabolite fingerprinting
Metabolomics
○ Metabolome
- total low molecular weight compounds in biofluid, cells,
and tissue in living organism
○ Metabolomics
- comparative and non-targeted analysis of metabolome
using various analytical methods
Tools for metabolomics
Tools
Pros
Cons
NMR
Robustness and
reproducibility
Metabolite
overlapping
GC-MS
Excellent sensitivity
Need to derivatize
Excellent sensitivity
Lower reproducibility
than GC
GC X GC TOF
LC-MS
Statistical methods (1)
○ Principal component analysis (PCA)
- Oldest and most widely used non-supervised
multivariate statistical technique
- Reduce the dimension of the original data set
○ Partial least squares-discriminant analysis (PLS-DA)
- Supervised method rendering class to each sample
- Clearer differentiation of each class and easier
investigation of marker compounds
Statistical methods (2)
○ Partial least squares-regression (PLS-R)
- Correlate the X variables (eg. NMR spectra data)
with Y variables (eg. Antioxidative activity)
- Prediction model can be developed
Timeline of major plant metabolomics papers
NMR spectra of tobacco in 50%
MeOH fraction
Wild leaf
CSA leaf
Wild vein
CSA vein
* There was no difference in CHCl3 fractions.
PC1 and PC2 scores of MeOH/water fraction
20
WNL leaf
WIL leaf
WSL leaf
CNL leaf
CIL leaf
CSL leaf
WNL vein
WIL vein
WSL vein
CNL vein
CIL vein
CSL vein
PC2 (38.2%)
10
0
-10
-20
-20
-10
0
10
20
PC1 (51.4%)
* W: wild type plant, C: transgenic plant
NL: non-inoculated leaf, IL: inoculated leaf, SL: systemic leaf
Loading plot of all 1H-NMR signals
Sucrose
0.150
Glucose
Chlorogenic acid
0.100
PC2
0.050
0.000
Alanine
SA
-0.050
SAG
Malic acid
-0.100
-0.140 -0.120 -0.100 -0.080 -0.060 -0.040 -0.020 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140
PC1
w
IS
(a)
2
5
6
7
9
(b)
8
7
6
5
4
4
3
2
3
1
1
9
10
8
9.0 8.8 8.6 8.4 8.2 8.0 7.8 7.6 7.4 7.2 7.0 6.8 6.6 6.4 6.2
Fig. 1
0
6.0 5.8
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Leu
Lactate
Ala
Acetic acid
Choline
Gly
Val
Tyr
Phe
Formic acid
PC3 (9.1%)
0.02
RT
NT, NM
0.00
RM
CT, CM
-0.02
-0.08
-0.06
-0.04
-0.02
0.00
0.02
PC1 (51.1%)
0.04
0.06
0.08
Fig 1
Metabolomic profiling and prediction model
development of Citrus Fruit using NMR and
MVA

NMR and antioxidative activity analysis
 Mature and immature fruit
 Peel and flesh
Citrus grandis Osbeck
Family : Rutaceae
Immature stage
Mature stage
Publication
Introduction
 The prevalence of obesity is increasing rapidly worldwide.
 To reduce the associated risks, it is necessary to investigate the causes
of weight gain (e.g., lifestyle and behavior).
 To prevent obesity, early diagnosis and treatment of obesity are
important.
 Obesity studies involving the administration of a high-fat diet (HFD) in
animal models are known to be applicable to human obesity.
Materials & Methods
Experimental Design
SD Male Rats
(n=20, 110-120 g)
Normal diet group
(ND, n=10)
ND low gainers
(n=5)
ND high gainers
(n=4)
visceral fat-pad
serum
High fat diet group
(HFD, n=10)
HFD low gainers
(n=5)
urine
1H-NMR
liver
Biological
Analaysis
multivariate
statistical
analysis
HFD high gainers
(n=5)
Materials & Methods
Animal Handling Procedure & Sample Preparation
Male 5-week-old
SD rat
1 week
• Plastic cage
Urine
Collection
Normal diet
8 weeks
High-fat diet
• Individually in plastic
(Table 1)
• 21±2 ℃ / 50 ±5%
metabolic cage
• 12h light/12h dark cycle
• 3 days
• Commercial diet
(8:00 p.m. – 8:00 a.m. /
Blood
Collection
• Measurement of body
weights (once a week)
8:00 a.m. – 8:00 p.m. )
• Measurement of
volume & pH
• Overnight fasting
• Centrifugation
• Blood was drawn from
(3,000×g for 10 min)
the abdominal aorta
ND low gainers
ND high gainers
HFD high gainers
at -70 ℃
(1,000×g for 15 min
at 4 ℃) → serum
HFD low gainers
• Supernatant was stored
• Centrifugation
• Weighing of liver &
visceral fat
3 weeks
Materials & Methods
Biological Analyses & 1H-NMR Analysis
Biological Analyses
• Commercial kits
- (Serum / hepatic) total cholesterol, free fatty acids & triglyceride
- (Serum) HDL cholesterol & glucose
• (Serum) LDL＋VLDL cholesterol : (total cholesterol －HDL cholesterol)
1H-NMR
Analysis
• Selected urine samples (8:00 p.m. - 8:00 a.m. / 3 days)
• Frozen urine was thawed in a water bath at 40 ℃
• 0.35 ml of each urine was transferred to an e-tube and vortexed for 5 s.
• 0.3 ml aliquot of the urien mixture ＋ 0.2 ml D2O were pipetted into NMR tube.
• NMR (Avance 600 FT-NMR, 600.13 MHz) condition
- Temperature: 298 K, 128 scans, 0.155 Hz/point, pulse width: 4.0 μs (30°), relaxation delay: 2.0 s
- Triple-axis inverse (TXI) cryoprobe
- zgpr as a presaturation pulse sequence for water suppression
Materials & Methods
Data Processing & Multivariate Data Analysis
1H-NMR
spectrum
Binning (δ 0.52 – 10.00)
•Exclude region
- water (δ 4.60 – 4.90)
- urea (δ 5.50 – 6.00)
Multivariate Statistic Analysis
•PLS-DA
• Cross-validation (R2, Q2)
& Permutation testing
• VIP
ANOVA-test
•Bonferroni correction
(p < 0.025)
Results
Table 2. Biochemical Parameters
Results
Fig. 1. 1H-NMR spectra and assignment of urine metabolites
 The signals assigned based on comparisons with the chemical shifts of standard
compounds using the Chenomx NMR software suite (version 5.1, Chenomx, USA).
Results
Fig. 2. PLS-DA score plots of urine metabolites
• The PLS-DA score plot showed a
separation between ND low gainers and ND
high gainers
• Although each rat of the two groups
comsumed the same normal diet, it was
possible to metabolically discriminate rat
groups with different physical constitutions.
• The PLS-DA score plot showed a
separation between ND low gainers and
HFD high gainers
• The various endogenous metabolites
changed in rats comsuming the high-fat
diet.
Results
Validation of PLS-DA models
•Cross-validation
Plastic cage
• R2: the goodness of fit (0<R2<1)
- 1 means perfect fit
• Q2: the goodness of prediction
- >0.5 means good prediction
- >0.9 means excellent prediction
• Permutation
Plastic cage testing
• Provided the statistical significance of the estimated
predicted power of the models
• Comparing R2Y and Q2Y values of original model with
them of re-ordered model
• Valid model
: R2Y intercept <0.3-0.4 & Q2Y intercept <0.05
Results
Table 4. The VIP values of the compounds
Generally, a cutoff for VIP around 0.7-0.8 works well.
 The compounds with VIP>0.75
: influential compounds for separating each samples in PLS-DA models.
Results
Fig. 4. Intensity of the metabolites
 Normalized relative to the creatinine
intensity
 An independent t test (*p < 0.025)
was performed to assess the statistical
significance between each group
 The relative intensities of betaine,
taurine, acetone/acetoacetate,
phenylacetylglycine, pyruvate, lactate,
and citrate differed significantly
between ND low gainers and ND high
gainers/HFD high gainers.
Discussion
 Betaine can prevent and cure cirrhosis in rats and decrease the contents of hepatic
cholesterol and total lipids in rats consuming a high-cholesterol diet.
 Taurine is known to exert insulin-like effects such as accelerating glucose uptake into
tissues and glycogen synthesis in the liver.
 Acetoacetate & acetone were ketone bodies produced when acetyl-CoA derived from
lipid β-oxidation exceeds the capacity of the tricarboxylic acid cycle.
 The precoursors of phenylacetylglycine were preduced by gut bacteria related to the
obesity.
 Pyruvate in urine samples was elevated in an HFD group due to the inhibition of
pyruvate degydrogenase.
 Adipose tissue is an important source of lactate production in vivo.
 The increased provision of FFAs causes an increase in FFA oxidation, resulting in
increasing the concentration of citrate.
Application of Metabolomics (1)
•Biomarker development
 Early biomarkers
 Prognostic biomarkers
 Diagnostic biomarkers
 Late biomarkers of diseases
such as cancers, diabetes, Alzheimers etc.
Pharma perspective on metabolomics
•
Looking for disease markers
Disease
Conventional
biomarker
Ideal scenario
Animal
model
Metabolic
profiling tools
Diabetes
Increased
plasma/urinary
glucose
Earlier marker
pre-disease
onset
High fat diet
mice
Lipid-MS,
NMR/MS
profiling
Atherosclerosis
Lipoprotein
profiles
Earlier marker
pre-disease
onset
Watanabe
rabbits
Lipid-MS,
NMR/MS
profiling
Alzheimer
Cognitive
function test
Markers of
disease onset,
progression
PS1 mice
NMR/MS
profiling
Schizophrenia
Behavioural
test
Markers of
disease onset,
progression
Coloboma
mice
NMR/MS
profiling
Consideration for Right Samples!
• Getting the right sample
- plasma, serum, urine, tissue, saliva
- Correlation with the disease
• Control group
- Gender
- Ethnic
- Age
- Lifestyle
- Nutritional and medical condition
Effect of acute dietary standardization on the urinary, plasma, and
salivary metabolomic profiles of healthy humans
Urine
Saliva
Plasma
Marianne et al. Am J Clin Nutr 2006;84:531–9.
Application of Metabolomics (2)

Enhanced production of useful secondary metabolites by
M/O, plant cell and tissue culture
 Use of Metabolomics as a tool for Metabolic engineering
 monitoring of stress-induced metabolic change

Functional genomics
 Elucidation of metabolic changes induced by foreign gene
 Elucidation of metabolic effects by knockout mutation
Application of Metabolomics (3)
• Investigation of bioactivity related biomarker compounds
• Standardization of medicinal resources and products
• Differentiation of medicinal resources according to origins
• Quality control of batch to batch variation of products
containing natural compounds
• Investigation of efficacy and toxicity of medicinal resources
VIP in Metabolomics
Dr.
Nicholson
Imperial Col.
Dr.
Verpoorte
   Leiden
Univ.


Dr.
Gonzalez
NIH/NCI
Dr. Tomita
Keio Univ.


Dr. Kopka
Max-Planck
Institute
Dr. Fiehn
UC Davis
Dr. Sumner
Samuel
Roberts
Noble
Foundation
SWOT of Metabolomics
Strength
 Robust and stable
analytical platforms
 Minimally invasive
 Real biological endpoint
 Whole system integration
Weakness
 Analytical sensitivity
 Analytical dynamic range
 Complexity of data sets
 High capital cost
Oppurtinities
 Much experience from
mammalian system studies
(e.g. pathways)
 Potential of multi-omics
integration
 Web-based diagnotics
Threats
 Skepticism of nonhypothesis led studies
 Conservatism
 Lack of well trained scientists
Acknowledgement
Prof. Rob. Verpoorte, Leiden University
Dr. Younghae Choi, Leiden University
Dr. Dae Young Kwon, KFRI
Prof. Young-Suk Kim, Ewha Womans University
Prof. Somi Cho, Kim, Cheju National University
Prof. Taesun Park, Yonsei University
Prof. Yeon-Soo Cha, Chunbuk National University
Prof. Jung-Hyun Kim, Chung-Ang University
Ph.D students
Seung-Ok Yang, Sun-Hee Hyun
MS students
So-Hyun Kim, Hee-su Kim, Yujin Kim
What is now proved
was once only imagined.
- William Blake