Comparative and non-targeted analysis of metabolome using various analytical methods -Choi, Hyung-Kyoon, presentation February 2010 at CPMB, TNAU

1. Application of NMR and MS based
Metabolomics
in Natural Product Science
February, 2010
Choi, Hyung-Kyoon
hykychoi@cau.ac.kr
College of Pharmacy, Chung-Ang University
Republic of Korea

2. Metabolomics
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

3. Tools for metabolomics
Tools for metabolomics
Tools Pros Cons
Robustness and Metabolite
NMR
reproducibility overlapping
GC-MS Excellent sensitivity Need to derivatize
GC X GC TOF
Lower reproducibility
LC-MS Excellent sensitivity
than GC

4. Statistical methods (1)
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

5. Statistical methods (2)
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

6. Schematic overview of NMR –based
metabolomics
Holmes et al. (2006) Planta medica 72:771-785

8. Timeline of major plant metabolomics papers

9. NMR spectra of tobacco in 50%
MeOH fraction
Wild leaf
CSA leaf
Wild vein
CSA vein
* There was no difference in CHCl3 fractions.

10. PC1 and PC2 scores of MeOH/water fraction
PC1 and PC2 scores of MeOH/water fraction
20
WNL leaf
10 WIL leaf
WSL leaf
CNL leaf
PC2 (38.2%)
CIL leaf
CSL leaf
0 WNL vein
WIL vein
WSL vein
CNL vein
CIL vein
-10 CSL vein
-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

11. Loadingplot of all 1H-NMR signals
Loading plot of all 1H-NMR signals
Sucrose
0.150
Glucose Chlorogenic acid
0.100
0.050
PC2
0.000
Alanine
-0.050 SA
Malic acid SAG
-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

13. w IS 1. Leu
(a) 2. Lactate
2 3. Ala
4. Acetic acid
5 5. Choline
6. Gly
6 3 7. Val
7 4 1
8. Tyr
9 8 7 6 5 4 3 2 1 0
9. Phe
10. Formic acid
9
(b) 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 6.0 5.8
Fig. 1

14. 0.02 RT
PC3 (9.1%)
NT, NM
0.00
RM
-0.02 CT, CM
-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08
PC1 (51.1%)

16. Fig 1

17. Metabolomic profiling and prediction model
Metabolomic profiling and prediction model
development of Citrus Fruit using NMR and
development of Citrus Fruit using NMR and
MVA
MVA
 NMR and antioxidative activity analysis
 Mature and immature fruit
 Peel and flesh

19. Citrus grandis Osbeck
Family : Rutaceae
Immature stage Mature stage

24. Application of Metabolomics (1)
Application of Metabolomics (1)
•Biomarker development
 Early biomarkers
 Prognostic biomarkers
 Diagnostic biomarkers
 Late biomarkers of diseases
such as cancers, diabetes, Alzheimers etc.

25. Pharma perspective on metabolomics
• Looking for disease markers
Disease Conventional Ideal scenario Animal Metabolic
biomarker model profiling tools
Diabetes Increased Earlier marker High fat diet Lipid-MS,
plasma/urinary pre-disease mice NMR/MS
glucose onset profiling
Atherosclerosis Lipoprotein Earlier marker Watanabe Lipid-MS,
profiles pre-disease rabbits NMR/MS
onset profiling
Alzheimer Cognitive Markers of PS1 mice NMR/MS
function test disease onset, profiling
progression
Schizophrenia Behavioural Markers of Coloboma NMR/MS
test disease onset, mice profiling
progression

26. Consideration for Right Samples!
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

27. 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.

28. Application of Metabolomics (2)
Application of Metabolomics (2)
 Enhanced production of useful secondary metabolites by
M/O, plant cell and tissue culture
 Metabolic engineering 의 기초자료로 metabolomics 이
용
 stress 에 의해 유도된 metabolic change 의 monitoring
 대사경로 중 rate-limiting step 의 규명
 유전체 기능 연구 (functional genomics)
 외래유전자 도입에 의해 유발된 metabolic changes 의
규명
 knockout mutation 에 의한 metabolic effects 의 규명

29. Application of Metabolomics (3)
Application of Metabolomics (3)
• 천연물 신약개발 : 지표성분 탐색 , 원료 및제품 표준
화
elucidation of bioactivity correlated biomarker
 약용식물 개체별 원산지 구분
 천연물 함유 제품의 quality control
(batch to batch variation)
• 건강기능식품의 efficacy 조사
 dietary effects

31. Publication

32. 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.

33. Materials & Methods
Experimental Design
SD Male Rats
(n=20, 110-120 g)
Normal diet group
(ND, n=10)
ND low gainers ND high gainers HFD low gainers HFD high gainers
(n=5) (n=4) (n=5) (n=5)
visceral fat-pad urine
serum 1
H-NMR
liver
multivariate
statistical
Biological analysis
Analaysis

34. Results
Table 2. Biochemical Parameters

35. 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).

36. 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.

37. 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
• Plastic cage testing
Permutation
• 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

38. 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.

39. Results
Fig. 4. Intensity of the metabolites
 Normalized relative to the creatinine
l cons titution intensity
Physica
 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,
t diet
High-fa phenylacetylglycine, pyruvate, lactate,
and citrate differed significantly
between ND low gainers and ND high
gainers/HFD high gainers.

40. VIP in Metabolomics
Dr.
Nicholson
Imperial Dr.
Col.
   Verpoorte
Leiden
Dr.
Gonzalez
Univ. Dr. Tomita NIH/NCI
 Keio Univ.
   
Dr. Kopka
Max-Planck
Institute
Dr. Fiehn Dr. Sumner
UC Davis Samuel
Roberts
Noble
Foundation

41. SWOT of Metabolomics
Strength Weakness
 Robust and stable  Analytical sensitivity
analytical platforms  Analytical dynamic range
 Minimally invasive  Complexity of data sets
 Real biological endpoint  High capital cost
 Whole system integration
Oppurtinities Threats
 Much experience from  Skepticism of non-
mammalian system studies hypothesis led studies
(e.g. pathways)  Conservatism
 Potential of multi-omics  Lack of well trained scientists
integration
 Web-based diagnotics

42. 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

43. What is now proved
was once only
imagined.
- William Blake