Application of NMR and MS based Application of NMR and MS based Metabolomics Metabolomics

in Natural Product Sciencein Natural Product Science

Choi, Hyung-KyoonChoi, Hyung-Kyoonhykychoi@cau.ac.krhykychoi@cau.ac.kr

College of Pharmacy, Chung-Ang UniversityCollege of Pharmacy, Chung-Ang UniversityRepublic of Korea Republic of Korea

February, 2010February, 2010

‘omics’ technology

Genomics

(30,000 genome)

Transcriptomics

Proteomics

Metabolomics

‘omics’ technologyGenomics Transcriptomics Proteomics Metabolomics

Target material

Gene, chromosome (genetic code)

mRNA

(genetic code)

Protein (function of the protein)

Low molecular weight metabolites

MW 100,000-120,000

100,000-120,000 5,000-20,000 100- 5000

Characteristics

Context independent

Context dependent

Context dependent

Context dependent

Analysis Mapping, sequencing

Sequencing Separation, characterization

Separation, characterization

Methods DNA sequencer

Hybridization 2D gel, Maldi TOF

NMR, MS, GC, LC

Human 30,000 genome >109? ~2,500

○ ○ MetabolomeMetabolome - total low molecular weight compounds in biofluid, - total low molecular weight compounds in biofluid, cells, cells,

and tissue in living organism and tissue in living organism

MetabolomicsMetabolomicsMetabolomicsMetabolomics

○○ MetabolomicsMetabolomics

- - comparative and non-targeted analysiscomparative and non-targeted analysis of metabolome of metabolome

using various analytical methods 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 Pros Cons

NMRNMR Robustness and Robustness and reproducibilityreproducibility

Metabolite Metabolite overlappingoverlapping

GC-MSGC-MSGC X GC TOFGC X GC TOF

Excellent sensitivityExcellent sensitivity Need to derivatizeNeed to derivatize

LC-MSLC-MS Excellent sensitivityExcellent sensitivity Lower reproducibility Lower reproducibility than GCthan GC

Tools for metabolomicsTools for metabolomicsTools for metabolomicsTools for metabolomics

•Identification: Assignment of the unknown compounds

•Higher Resolution, Sensitivity, and Reproducibility •High throughput: Automated data processing Sample preparation time Large sample series

Analytical challenges for metabolomicsAnalytical challenges for metabolomics

MS RI UV NMR ESI-MS NMR FT-IR RamanMS

Metabolite target analysis Metabolite profilingAnd metabolomics

Metabolite fingerprinting

Derivatization

Isolated (specific)metabolite fraction

HPLC

GC

Derivatization

Metabolite fraction

Cell extract

Whole cell Biofluid

Sample pre-fractionation (clean-up)

MS

CE

Cru

de

extrac

t

○ ○ MetabolomeMetabolome - total low molecular weight compounds in biofluid, - total low molecular weight compounds in biofluid, cells, cells,

and tissue in living organism and tissue in living organism

MetabolomicsMetabolomicsMetabolomicsMetabolomics

○○ MetabolomicsMetabolomics

- - comparative and non-targeted analysiscomparative and non-targeted analysis of metabolome of metabolome

using various analytical methods using various analytical methods

Tools Pros Cons

NMRNMR Robustness and Robustness and reproducibilityreproducibility

Metabolite Metabolite overlappingoverlapping

GC-MSGC-MSGC X GC TOFGC X GC TOF

Excellent sensitivityExcellent sensitivity Need to derivatizeNeed to derivatize

LC-MSLC-MS Excellent sensitivityExcellent sensitivity Lower reproducibility Lower reproducibility than GCthan GC

Tools for metabolomicsTools for metabolomicsTools for metabolomicsTools for metabolomics

○ ○ Principal component analysis (PCA)Principal component analysis (PCA)

- - Oldest and most widely used non-supervised

multivariate statistical technique

- Reduce the dimension of the original data set

Statistical methods (1)Statistical methods (1)Statistical methods (1)Statistical methods (1)

○ ○ Partial least squares-discriminant analysis (PLS-DA)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)Statistical methods (2)Statistical methods (2)Statistical methods (2)

○○ Partial least squares-regression (PLS-R)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

* There was no difference in CHCl3 fractions.

Wild leaf

CSA leaf

Wild vein

CSA vein

PC1 (51.4%)

-20 -10 0 10 20

PC

2 (3

8.2%

)

-20

-10

0

10

20

WNL leafWIL leafWSL leafCNL leafCIL leafCSL leafWNL veinWIL veinWSL veinCNL veinCIL veinCSL vein

* W: wild type plant, C: transgenic plant NL: non-inoculated leaf, IL: inoculated leaf, SL: systemic leaf

PC1 and PC2 scores of MeOH/water fractionPC1 and PC2 scores of MeOH/water fraction

Loading plot of all 1H-NMR signalsLoading plot of all 1H-NMR signals

-0.100

-0.050

0.000

0.050

0.100

0.150

-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

PC

2

PC1

Chlorogenic acid

Sucrose

Glucose

Malic acid

Alanine

SAG

SA

00112233445566778899

5.86.06.26.46.66.87.07.27.47.67.88.08.28.48.68.89.0

(a)

(b)

IS

5

9

8

3

2

1

10

4

Fig. 1

67

w 1. Leu2. Lactate3. Ala4. Acetic acid5. Choline6. Gly7. Val8. Tyr9. Phe10. Formic acid

-0.02

0.00

0.02

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08

PC

3 (

9.1

%)

PC1 (51.1%)

RT

RM

CT, CM

NT, NM

Fig 1

NMR and antioxidative activity analysis

Mature and immature fruit

Peel and flesh

Metabolomic profiling and prediction model development of Citrus Fruit using NMR andMVA

Metabolomic profiling and prediction model development of Citrus Fruit using NMR andMVA

Family : Rutaceae

Citrus grandisCitrus grandis Osbeck Osbeck

Mature stageImmature stage

PublicationPublication

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.

Introduction

Experimental DesignExperimental Design

SD Male Rats(n=20, 110-120 g)

Normal diet group(ND, n=10)

Biological Analaysis

visceral fat-pad urine

serum

liver

multivariate statistical analysis

1H-NMR

ND low gainers (n=5)

ND high gainers (n=4)

HFD low gainers (n=5)

HFD high gainers (n=5)

Materials & Methods

Materials & Methods

Animal Handling Procedure & Sample PreparationAnimal Handling Procedure & Sample Preparation

Male 5-week-old SD rat

• Plastic cage

• 21±2 ℃ / 50 ±5%

• 12h light/12h dark

cycle

• Commercial diet

• Measurement of

body

weights (once a

week)

1 week

Normal diet

High-fat diet

(Table 1)

8 weeks

UrineCollection

• Individually in plastic

metabolic cage

• 3 days

(8:00 p.m. – 8:00 a.m.

/

8:00 a.m. – 8:00 p.m.

)

• Measurement of

volume & pH

• Centrifugation

(3,000×g for 10 min)

• Supernatant was

stored

at -70 ℃

3 weeks

BloodCollection

• Overnight fasting

• Blood was drawn

from

the abdominal aorta

• Centrifugation

(1,000×g for 15 min

at 4 ℃) → serum

• Weighing of liver &

visceral fat

ND low

gainers

ND high

gainersHFD low

gainers

HFD high

gainers

Materials & Methods

Biological Analyses & Biological Analyses & 11H-NMR AnalysisH-NMR Analysis

Biological Analyses

1H-NMR Analysis

• Commercial kits

- (Serum / hepatic) total cholesterol, free fatty acids & triglyceride

- (Serum) HDL cholesterol & glucose

• (Serum) LDL ＋ VLDL cholesterol : (total cholesterol － HDL cholesterol)

• 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 AnalysisData 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 ParametersTable 2. Biochemical Parameters

Results

The signals assigned based on comparisons with the chemical shifts of

standard

compounds using the Chenomx NMR software suite (version 5.1, Chenomx,

USA).

Fig. 1. Fig. 1. 11H-NMR spectra and assignment of urine metabolitesH-NMR spectra and assignment of urine metabolites

Results

Fig. 2. PLS-DA score plots of urine metabolites 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.

• 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

Results

Validation of PLS-DA modelsValidation of PLS-DA models

Cross-validation

• Plastic cage

• 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

Permutation testing

Results

Table 4. The VIP values of the compoundsTable 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 metabolitesFig. 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.

Physical constitution

High-fat diet

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.

•Biomarker development Early biomarkers Prognostic biomarkers Diagnostic biomarkers Late biomarkers of diseases such as cancers, diabetes, Alzheimers etc.

Application of Metabolomics (1)

Application of Metabolomics (1)

Pharma perspective on metabolomics

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

• Looking for disease markers

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

Effect of acute dietary standardization on the urinary, plasma, andsalivary metabolomic profiles of healthy humans

Urine

Saliva

Plasma

Marianne et al. Am J Clin Nutr 2006;84:531–9.

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 (2)

Application of Metabolomics (2)

Application of Metabolomics (3)

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. Verpoorte

Leiden Univ.

Dr. Gonzalez NIH/NCI

Dr. Nicholson

Imperial Col.

Dr. Fiehn UC Davis

Dr. Sumner Samuel Roberts Noble

Foundation

Dr. TomitaKeio Univ.

Dr. Kopka Max-Planck

Institute

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 non-

hypothesis led studies Conservatism Lack of well trained scientists

AcknowledgementAcknowledgement

Prof. Rob. Verpoorte, Leiden UniversityDr. Younghae Choi, Leiden UniversityDr. Dae Young Kwon, KFRIProf. Young-Suk Kim, Ewha Womans UniversityProf. Somi Cho, Kim, Cheju National UniversityProf. Taesun Park, Yonsei UniversityProf. Yeon-Soo Cha, Chunbuk National UniversityProf. Jung-Hyun Kim, Chung-Ang University

Ph.D studentsSeung-Ok Yang, Sun-Hee Hyun

MS studentsSo-Hyun Kim, Hee-su Kim, Yujin Kim

What is now proved What is now proved was once only was once only imagined.imagined.

- William Blake- William Blake