introduction to metabolomics thomas m. o’connell, ph.d. unc metabolomics laboratory definitions of...

Introduction to MetabolomicsIntroduction to Metabolomics

Thomas M. O’Connell, Ph.D. UNC Metabolomics Laboratory

• Definitions of Metabolomics

• Analytical Instrumentation– Nuclear Magnetic Resonance– Mass Spectrometry

• Multivariate statistical Analysis– Principal Component Analysis

• Applications to Toxicology

From Genotype to PhenotypeFrom Genotype to Phenotype

GENOMEGENOMETRANSCRIPTOMETRANSCRIPTOME

PROTEOMEPROTEOME METABOLOMEMETABOLOME

Mostly unknown Mostly known

Dettmer et al., MS Reviews, 26, 51, 2007

Metabolomics is the Most Closely Metabolomics is the Most Closely Related to PhenotypeRelated to Phenotype

Studying the Whole MetabolomeStudying the Whole Metabolome

CH2OP

CHOH

CH2O-

3-phosphoglyceric

acid dehydrogenase

CH2OP

CO

CH2O-

Focused analysis of a single metabolic pathwayFocused analysis of a single metabolic pathway

Unbiased analysis of the entire metabolomeUnbiased analysis of the entire metabolome

Identification of BiosignaturesIdentification of Biosignatures

Nature Rev Drug Disc, 1, 153, (2002)

Some DefinitionsSome Definitions

Typical Size Range of MetabolitesTypical Size Range of Metabolites

Douglas B. Kell, Curr Opin Microbiol. 7, 296, 2004

Development of NMR and MS in MetabolomicsDevelopment of NMR and MS in Metabolomics

PubMed references for title/abstract search on “metabolomics OR metabonomics” with NMR or MS

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Metabolomics

Metabolomics & MS

Metabolomics & NMR

Main Analytical Approaches to MetabolomicsMain Analytical Approaches to Metabolomics

MS

Chromatography

LC/MS

GC/MS

CE/MS

NMR

LC/NMR

Off-linehyphenation

NMR

LC/UV

GC/MS

LC/MS

M (10-6)

nM (10-9)

pM (10-12)

fM (10-15)

Range of Tools Required to Cover the Entire MetabolomeRange of Tools Required to Cover the Entire Metabolome

Adapted from Sumner, LW, et al., Phytochem, 62, 817,2003

Inlet

Ionization

Mass Analyzer

Mass Sorting (filtering)

Ion Detector

Detection

Ion Source

• Solid• Liquid• Vapor

Detect ionsForm ions

(charged molecules)Sort Ions by Mass (m/z)

1330 1340 1350

100

75

50

25

0

Mass Spectrum

Acquiring a Mass SpectrumAcquiring a Mass Spectrum

All compounds must be ionized, but ionization efficiency is variable with different compounds

High voltage applied to metal sheath (~4 kV)

Sample Inlet Nozzle(Lower Voltage)

Charged droplets

++

++++

++

++++

++

++++ +++

+++ +++

+++ +

++

+

+

+

+

+++

+++

+++

MH+

MH3+

MH2+

Pressure = 1 atmInner tube diam. = 100 um

Sample in solution

N2

N2 gas

Partialvacuum

Electrospray ionization:

Ion Sources make ions from sample molecules

Typical MS SpectraTypical MS Spectra

Features of GC/MS MetabolomicsFeatures of GC/MS Metabolomics• Useful for volatiles or compounds that can be derivatized to volatile

compounds (derivatization often required)• Ideal for long chain compounds e.g. FFA, acyl carnitines, etc• More stable and reproducible than LC/MS• Most advanced metabolomics libraries• Standards are typically required for positive identification• Inexpensive technology

Experiment

Library match

• Chromatography can be tailored to specific chemical classes• Various MS analyzers can be coupled e.g. triple quad, TOF, ion trap each with it’s own

advantages in speed, resolution and sensitivity.• Very high mass accuracy available with TOF instruments (< 2ppm)• Variable ionization efficiencies and matrix suppression leads to poor quantitation w/out

standards• Excellent for targetted metabolomics, more challenging for global “unbiased” profiling• Q-TOF can acquire high res data + MS/MS for fragmentation analyses• Libraries are available but suffer from inconsistent retention times in the LC front end.

Features of LC/MS MetabolomicsFeatures of LC/MS Metabolomics

The NMR PhenomenonThe NMR Phenomenon(Hydrogen nuclei act like little magnets)(Hydrogen nuclei act like little magnets)

Hydrogen nuclei out and about Hydrogen nuclei in a magnetic field

RF

pulse

Aligned with the big magnetic field

Precessionbased on magnetic

environment& detection

Excited statetransverse to the field

The NMR ExperimentThe NMR Experiment

detector

The Chemical ShiftThe Chemical Shift

Different hydrogen atoms (gray) are in unique Different hydrogen atoms (gray) are in unique chemicalchemical and and magneticmagnetic environments environments

This results in different precession frequencies and This results in different precession frequencies and distinct spectral features.distinct spectral features.

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12,13

3

2

0.51.01.52.02.53.03.54.04.55.05.56.06.57.07.58.08.59.0

5,96,8

The NMR Spectrum of IbuprofenThe NMR Spectrum of Ibuprofen

O

OH

CH3

CH3

CH3

3

2

5

9

6

8

10

1112

13

What types of samples can we look at?What types of samples can we look at?

8 7 6 5 4 3 2 1 0Chemical Shift (ppm)

Human serum


Human bronchoalveolar lavage fluid


Human CSF

Typical Urine NMR Spectrum Typical Urine NMR Spectrum

9 8 7 6 5 4 3 2 1 0Chemical Shift (ppm)

Hundreds/thousands of peaks corresponding to hundreds/thousands

of metabolites

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5Chemical Shift (ppm)

Features of NMRFeatures of NMR

• High structural information content

• Very high intra/inter-lab reproducibility

• Inherently quantitative (no need for authentic standards)

• Minimal sample processing required

• Non-destructive

• Expensive instrumentation• Relatively low sensitivity (typically >M

concentrations required)• Spectral crowding can hinder interpretation• Long chain aliphatics are challenging (e.g.

fatty acids)

PROS

CONS

Throughput of NMRThroughput of NMR

Tube based robot w/ 100 sample capacity

Urine & serum spectra require 10-30min

Total sample volume = 550ml

Vial & well plates delivered via fluidics

Sample volumes of 5-10ml

Optimal for highly sample limited or concentrated samples

Analytical Considerations NMR MS

Sensitivity

Reproducibility – w/in lab

Reproducibility – across labs

Quantitation

Sample Prep Requirements

Sample Analysis Automation

Versatility

Selectivity

Non-selectivity

Comparison of NMR vs MS for MetabonomicsComparison of NMR vs MS for Metabonomics

Taken from D.G. Robertson, Toxicological Sciences, 85, 809, 2005

Metabolomics Involves Many SamplesMetabolomics Involves Many Samples

Looking for subtle differences in many spectra requires some data reduction/simplification

NONAME00


The Need for Multivariate Statistical Analysis The Need for Multivariate Statistical Analysis

• There are 10s, 100s or even 1000s of samples• Each spectrum (MS or NMR) can contain hundreds or

thousands of signals• Metabolic perturbations may be very subtle and effect a

small number of the observed metabolites• How do we find the needle metabolites in the biofluid

haystack

Principal Component Analysis Principal Component Analysis

• People can only visualize in 3 dimensions

• Each variable can be considered another dimension

• Generally there is a great deal of correlated and redundant data

• PCA finds combinations of variables (factors) that explain the major variation in the data.

Example of PCA: international drinking habits

liquor wine beer life expec CHD rateFrance 2.5 63.5 40.1 78 61.1

Italy 0.9 58 25.1 78 94.1Switzerland 1.7 46 65 78 106.4

Australia 1.2 15.7 102.1 78 173Great Britain 1.5 12.2 100 77 199.7

US 2 8.9 87.8 76 176Russia 3.8 2.7 17.1 69 373.6

Czech Repub 1 1.7 140 73 283.7Japan 2.1 1 55 79 34.7Mexico 0.8 0.2 50.4 73 36.4

Picking out trends in the data

France

Italy

Switz

erla

nd

Austra

lia

Gre

at B

ritai

nUS

Russia

Czech

Repu

b

Japan

Mex

ico

liquorwine

beerlife expec

CHD rate0

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400liquor

wine

beer

life expec

CHD rate

How does drinking relate to CHD?

How does drinking relate to life expectancy?

If you want to live longest what should you drink?

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Principal Component Number

Cu

mu

lati

ve V

aria

nce

Cap

ture

d (

%)

Eigenvalues for Wine

Capture the Variance w/ Fewer Variables (factors)

-4 -3 -2 -1 0 1 2-2.5

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2.5

Scores on PC 1 (46.03%)

Sco

res

on

PC

2 (

32.1

1%)

France

Italy

Switz

Austra Brit

U.S.A.

Russia

Czech

Japan

Mexico

Samples/Scores Plot of Wine

Which Countries are Most Similar?

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-0.6

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Loadings on PC 1 (46.03%)

Lo

adin

gs

on

PC

2 (

32.1

1%)

Liquor

Wine

Beer

LifeEx HeartD

Variables/Loadings Plot for Wine

How are the Variables Correlated?

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Scores on PC 1 (46.03%)

Sco

res

on

PC

2 (

32.1

1%)

France

Italy

Switz

Austra Brit

U.S.A.

Russia

Czech

Japan

Mexico

Samples/Scores Plot of Wine

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Loadings on PC 1 (46.03%)

Lo

adin

gs

on

PC

2 (

32.1

1%)

Liquor

Wine

Beer

LifeEx HeartD

Variables/Loadings Plot for Wine

Trends in the Scores Plot are Explained by the Trends in the Scores Plot are Explained by the Corresponding Variables in the Loadings PlotCorresponding Variables in the Loadings Plot

““Binning” the NMR SpectrumBinning” the NMR Spectrum

5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0Chemical Shift (ppm)

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Nor

mal

ized

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nsity

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Standard spectrum Binned spectrum

Data ReductionData Reduction

Variables (chemical shift bins)

Sa

mp

les

Each sample is described by ~ 200 variablesReduce the data by capturing the variance with combinations of variables

(ppm) File Name [0.50 .. 0.53] [0.53 .. 0.59] [0.59 .. 0.61] [0.61 .. 0.63] [0.63 .. 0.66] [0.66 .. 0.69] [0.69 .. 0.72] [0.72 .. 0.75] [0.75 .. 0.79] [0.79 .. 0.81] [0.81 .. 0.83] [0.83 .. 0.85] [0.85 .. 0.91] 3 s01_d01 2.04 4.20 1.88 1.60 2.17 2.08 2.42 2.64 2.83 1.80 1.76 2.01 9.754 s01_d03 2.15 4.38 1.95 1.68 2.25 2.19 2.51 2.69 2.81 1.82 1.81 2.00 9.625 s01_d05 2.37 4.81 2.18 1.85 2.47 2.38 2.75 2.88 3.05 1.98 1.95 2.10 9.366 s01_d07 2.47 4.97 2.26 1.91 2.54 2.45 2.83 3.03 3.18 2.01 1.98 2.13 8.917 s02_d01 2.12 4.42 1.94 1.66 2.24 2.15 2.52 2.70 2.85 1.86 1.83 2.15 10.268 s02_d03 2.29 4.73 2.15 1.81 2.42 2.35 2.71 2.93 3.04 1.99 2.01 2.25 10.069 s02_d05 2.36 4.85 2.19 1.87 2.46 2.37 2.76 2.91 3.04 1.98 1.95 2.11 9.0510 s02_d07 2.41 4.86 2.20 1.89 2.50 2.40 2.80 2.94 3.07 1.99 1.97 2.14 8.8811 s03_d01 2.39 4.88 2.22 1.88 2.52 2.48 2.87 3.22 3.56 2.20 2.17 2.58 11.1112 s03_d03 2.41 4.88 2.22 1.89 2.58 2.48 2.89 3.19 3.45 2.15 2.12 2.47 10.6213 s03_d05 2.43 4.92 2.24 1.91 2.56 2.47 2.88 3.17 3.38 2.14 2.06 2.32 9.4114 s03_d07 2.38 4.83 2.20 1.87 2.50 2.43 2.83 3.18 3.41 2.09 2.02 2.26 9.4215 s04_d01 2.43 5.00 2.21 1.89 2.57 2.51 2.89 3.22 3.49 2.21 2.16 2.54 11.1816 s04_d03 2.53 5.20 2.35 2.00 2.67 2.57 3.05 3.36 3.61 2.27 2.21 2.51 10.7917 s04_d05 2.85 5.58 2.51 2.12 2.94 2.84 3.28 3.45 3.63 2.31 2.27 2.43 8.8418 s04_d07 2.74 5.49 2.45 2.11 2.83 2.79 3.17 3.45 3.63 2.32 2.24 2.46 9.8519 s05_d01 2.24 4.71 2.14 1.82 2.44 2.43 2.78 3.06 3.48 2.20 2.09 2.52 11.4320 s05_d03 2.35 4.85 2.22 1.89 2.51 2.57 2.96 3.25 3.56 2.26 2.19 2.52 10.6821 s05_d05 2.44 4.98 2.27 1.93 2.59 2.52 2.88 3.09 3.28 2.13 2.05 2.26 9.0422 s05_d07 2.42 4.91 2.21 1.92 2.55 2.49 2.88 3.11 3.35 2.14 2.12 2.19 9.4823 s06_d01 2.77 5.63 2.51 2.15 2.94 2.86 3.31 3.58 3.73 2.39 2.42 2.77 11.4924 s06_d03 2.89 5.92 2.66 2.30 3.04 2.98 3.47 3.63 3.82 2.50 2.50 2.71 10.8425 s06_d05 3.09 6.24 2.80 2.41 3.18 3.12 3.57 3.78 3.97 2.59 2.55 2.76 10.6026 s06_d07 3.01 6.05 2.71 2.34 3.11 3.01 3.50 3.69 3.89 2.53 2.50 2.75 10.6727 s07_d01 2.89 5.81 2.58 2.23 3.02 2.94 3.36 3.66 3.85 2.51 2.49 2.75 11.3828 s07_d03 2.79 5.71 2.53 2.22 2.91 2.83 3.29 3.55 3.78 2.43 2.42 2.70 11.0229 s07_d05 3.00 6.05 2.71 2.31 3.08 3.01 3.47 3.73 3.91 2.53 2.46 2.70 10.3830 s07_d07 2.87 5.85 2.62 2.27 2.99 2.89 3.39 3.60 3.73 2.46 2.46 2.57 10.2231 s08_d01 2.04 4.21 1.86 1.66 2.19 2.13 2.48 2.77 2.99 1.91 1.89 2.16 10.8632 s08_d03 2.35 4.82 2.19 1.89 2.55 2.49 2.89 3.30 3.54 2.22 2.23 2.48 11.5033 s08_d05 2.60 5.27 2.41 2.07 2.75 2.68 3.09 3.34 3.51 2.25 2.25 2.40 9.8034 s08_d07 2.39 4.88 2.24 1.89 2.54 2.48 2.87 3.11 3.35 2.15 2.12 2.27 9.9639 s10_d01 2.37 4.84 2.18 1.87 2.53 2.41 2.80 2.99 3.14 2.07 2.07 2.22 10.0140 s10_d03 2.44 5.02 2.30 1.94 2.63 2.55 2.94 3.11 3.26 2.12 2.13 2.21 10.0741 s10_d05 2.76 5.57 2.55 2.14 2.89 2.78 3.16 3.42 3.54 2.29 2.21 2.33 9.0542 s10_d07 2.70 5.46 2.49 2.10 2.79 2.73 3.19 3.38 3.50 2.23 2.19 2.33 9.0743 s11_d01 2.28 4.72 2.10 1.79 2.44 2.40 2.82 3.00 3.19 2.05 2.03 2.39 10.4544 s11_d03 2.54 5.20 2.39 1.98 2.66 2.62 3.09 3.25 3.44 2.21 2.18 2.46 10.20

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-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

PC

2

PC 1

ControlEthanol

SIMCA-P 11 - 11/29/2006 5:06:46 PM

Principal Component AnalysisPrincipal Component Analysis

Principal components are composed of linear combinations of variables that best describe the variance in the data

Finding Critical Bins for Finding Critical Bins for Biomarker IdentificationBiomarker Identification

-0.1

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0.1

0.2

0.3

-0.2 -0.1 -0.0 0.1 0.2 0.3

p[2]

p[1]

Rusyn_all_integ.M1 (PCA-X)p[Comp. 1]/p[Comp. 2]Colored according to model terms

R2X[1] = 0.903587 R2X[2] = 0.0421382

[9.40 .. 9[9.38 .. 9[9.35 .. 9[9.33 .. 9[9.31 .. 9[9.25 .. 9[9.23 .. 9[9.21 .. 9[9.19 .. 9[9.13 .. 9[9.11 .. 9[9.09 .. 9[9.03 .. 9[8.99 .. 9[8.93 .. 8[8.90 .. 8

[8.85 .. 8[8.83 .. 8[8.78 .. 8[8.74 .. 8[8.70 .. 8[8.68 .. 8[8.63 .. 8

[8.60 .. 8[8.58 .. 8[8.52 .. 8[8.47 .. 8[8.44 .. 8[8.42 .. 8[8.40 .. 8[8.37 .. 8[8.32 .. 8[8.26 .. 8[8.23 .. 8[8.18 .. 8

[8.16 .. 8[8.13 .. 8

[8.07 .. 8[8.01 .. 8[7.95 .. 8[7.93 .. 7[7.91 .. 7[7.85 .. 7[7.81 .. 7[7.79 .. 7[7.76 .. 7[7.71 .. 7

[7.66 .. 7[7.61 .. 7

[7.55 .. 7[7.49 .. 7[7.46 .. 7

[7.40 .. 7[7.34 .. 7

[7.29 .. 7

[7.24 .. 7[7.20 .. 7

[7.14 .. 7

[7.12 .. 7

[7.10 .. 7[7.07 .. 7[7.02 .. 7[6.99 .. 7

[6.93 .. 6

[6.88 .. 6[6.83 .. 6

[6.80 .. 6[6.77 .. 6[6.75 .. 6[6.73 .. 6[6.70 .. 6[6.64 .. 6[6.62 .. 6[6.60 .. 6[6.58 .. 6[6.55 .. 6[6.51 .. 6[6.47 .. 6[6.45 .. 6[6.40 .. 6

[4.25 .. 4[4.21 .. 4[4.18 .. 4[4.13 .. 4

[4.07 .. 4[4.02 .. 4

[3.97 .. 4

[3.92 .. 3

[3.86 .. 3

[3.82 .. 3

[3.79 .. 3

[3.74 .. 3

[3.69 .. 3

[3.67 .. 3[3.65 .. 3

[3.63 .. 3

[3.61 .. 3[3.59 .. 3

[3.56 .. 3

[3.54 .. 3[3.48 .. 3

[3.46 .. 3

[3.40 .. 3

[3.37 .. 3

[3.31 .. 3

[3.26 .. 3

[3.24 .. 3

[3.22 .. 3

[3.19 .. 3[3.13 .. 3[3.09 .. 3[3.07 .. 3

[3.02 .. 3

[3.00 .. 3[2.98 .. 3[2.96 .. 2[2.94 .. 2

[2.92 .. 2

[2.87 .. 2 [2.85 .. 2[2.81 .. 2

[2.77 .. 2

[2.71 .. 2[2.69 .. 2

[2.66 .. 2

[2.60 .. 2

[2.55 .. 2[2.51 .. 2[2.46 .. 2[2.43 .. 2

[2.37 .. 2[2.32 .. 2[2.27 .. 2

[2.25 .. 2

[2.20 .. 2[2.18 .. 2

[2.16 .. 2

[2.11 .. 2

[2.08 .. 2

[2.02 .. 2

[2.00 .. 2[1.98 .. 2[1.95 .. 1

[1.90 .. 1

[1.86 .. 1[1.80 .. 1

[1.75 .. 1[1.70 .. 1

[1.66 .. 1

[1.60 .. 1

[1.58 .. 1[1.56 .. 1[1.54 .. 1[1.52 .. 1

[1.47 .. 1

[1.45 .. 1[1.40 .. 1[1.35 .. 1

[1.31 .. 1

[1.27 .. 1

[1.25 .. 1

[1.20 .. 1[1.18 .. 1[1.15 .. 1[1.13 .. 1

[1.10 .. 1[1.07 .. 1[1.03 .. 1

[0.97 .. 1[0.91 .. 0

[0.87 .. 0

[0.84 .. 0[0.82 .. 0[0.80 .. 0

[0.76 .. 0

[0.74 .. 0

[0.68 .. 0

[0.66 .. 0[0.64 .. 0[0.62 .. 0[0.60 .. 0[0.58 .. 0[0.56 .. 0[0.54 .. 0[0.51 .. 0[0.46 .. 0

[0.44 .. 0[0.42 .. 0[0.40 .. 0[0.38 .. 0[0.35 .. 0[0.33 .. 0[0.29 .. 0[0.25 .. 0

SIMCA-P+ 11.5 - 2/7/2008 9:25:21 AM

Higher in EtOH

Higher in Controls

Quantitative Fitting with NMR DatabaseQuantitative Fitting with NMR Database

Tools to Identify BiomarkersTools to Identify Biomarkers

Set of 1D1H spectra

2D spectra1H & 13C

NMRDatabase

1H & 13CPrediction

KEGG Analysis

MetaboliteID

The Human Metabolome DatabaseThe Human Metabolome Database

http://www.metabolomics.ca/

1

2,3

21

5 4

6

7

89

14

11

13

15

17,18,19

20

1612

5

7

17

101

2,3

21

5 4

6

7

89

14

11

13

15

17,18,19

20

1612

5

7

17

10

Metabolite ID with 2D DatasetsMetabolite ID with 2D Datasets11H-H-11H or H or 11H-H-1313C correlation spectra on selected samplesC correlation spectra on selected samples

1 = terminal methyl groups of low density (LDL) and very low density lipoproteins (VLDL). 2 = valine. 3 = leucine. 4 = 3-hydroxybutyrate. 5 = lactate. 6 = methylene protons of LDL and VLDL. 7 = alanine. 8 = methylene protons of C3 of VLDL lipoproteins. 9 = allylic methylenes of lipoproteins. 10 = acetate. 11 = N-acetylated glyoproteins. 12 = methylene protons of C2 of VLDL. 13 = methylene protons between olefinic groups of lipoproteins. 14 = albumin lysyl methylene groups. 15 = phospholipid choline headgroups. 16 = taurine. 17 = glucose. 18 = glycerol. 19 = amino acid Ca protons. 20 = choline. 21 = methylene groups of phosphatidylethanolamines.

Mapping to Pathway DatabasesMapping to Pathway Databases

Targeted MetabolomicsTargeted Metabolomics

Perform quantitative fitting on all critical metabolites and use this data for statistical analysis

Serum Metabolomics Analysis from Binned DataSerum Metabolomics Analysis from Binned Data

-6

-4

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0

2

4

6

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

t[2]

t[1]

tmoc_101_a.M4 (PCA-X)t[Comp. 1]/t[Comp. 2]Colored according to classes in M4

R2X[1] = 0.378319 R2X[2] = 0.267475 Ellipse: Hotelling T2 (0.95)

Class 1Class 2Class 3Class 4

SIMCA-P+ 11.5 - 11/21/2007 3:43:14 AM

MD EtOH+ -+ +- -- +

-6

-4

-2

0

2

4

6

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

t[2]

t[1]

tmoc_101_a.M4 (PCA-X)t[Comp. 1]/t[Comp. 2]Colored according to classes in M4



SIMCA-P+ 11.5 - 11/21/2007 3:43:14 AM

MD EtOH+ -+ +- -- +

MD

Effects of methyl donor rich diet (choline, betaine, folic acid) on high dose ethanol consuption

-40

-30

-20

-10

0

10

20

30

40

-50 -40 -30 -20 -10 0 10 20 30 40 50

t[2]

t[1]

All_conc_targetted.M3 (PCA-X)t[Comp. 1]/t[Comp. 2]Colored according to classes in M3



SIMCA-P+ 11.5 - 2/4/2008 1:26:37 PM

HFD+MD

HFD+MD+EtOH

HFD

HFD+EtOH

Serum Metabolomics Analysis from Targeted Serum Metabolomics Analysis from Targeted Metabolite ProfilesMetabolite Profiles

MD

EtOH

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

p[2]

p[1]

All_conc_targetted.M3 (PCA-X)p[Comp. 1]/p[Comp. 2]Colored according to model terms

R2X[1] = 0.453432 R2X[2] = 0.246049

Betaine

Choline

O-Phosphoc

Methionine

N,N-Dimeth

Acetate

Carnitine

CitrateTrimethyla

Creatine

CreatinineAlanine

GlutamateGlutamine

GlycineLysineThreonine

Valine

glyco-protglyceryl/c

LDL &VLDLlipidslipids1

SIMCA-P+ 11.5 - 2/4/2008 1:31:38 PM

HFD+EtOHHFD+MD+EtOH

Loadings Plot from Targeted Metabolite PCALoadings Plot from Targeted Metabolite PCA

-0.1

0.0

0.1

0.2

-0.1 0.0 0.1 0.2

p[2]

p[1]

All_conc_targetted.M3 (PCA-X)p[Comp. 1]/p[Comp. 2]Colored according to model terms

R2X[1] = 0.453432 R2X[2] = 0.246049

Choline

O-Phosphoc

N,N-Dimeth

Carnitine

CitrateTrimethyla

Creatine

CreatinineAlanine

GlutamateGlutamine

Glycine

LysineThreonine

Valine

glyco-prot

glyceryl/c

LDL &VLDL

lipidslipids1

SIMCA-P+ 11.5 - 2/4/2008 1:32:39 PM

HFD+EtOHHFD+MD+EtOH

HFD+MD HFD

Loadings Plot from Targeted Metabolite PCALoadings Plot from Targeted Metabolite PCA

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Metabolite Correlation Map (>0.75 & < -0.75) Groups 1 and 2

1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Bet

aine

Cho

line

O-P

hosp

hoch

olin

eM

ethi

onin

eN

,N-D

imet

hylg

lyci

neA

ceta

teC

arni

tine

Citr

ate

Glu

cose

Lact

ate

Trim

ethy

lam

ine

Cre

atin

eC

reat

inin

eA

lani

neG

luta

mat

eG

luta

min

eG

lyci

neLy

sine

Thr

eoni

neV

alin

egl

yco-

prot

eins

glyc

eryl

/cho

line

of li

pids

LDL

&V

LDL

lipid

slip

ids

BetaineCholine

O-PhosphocholineMethionine

N,N-DimethylglycineAcetate

CarnitineCitrate

GlucoseLactate

TrimethylamineCreatine

CreatinineAlanine

GlutamateGlutamine

GlycineLysine

ThreonineValine

glyco-proteinsglyceryl/choline of lipids

LDL &VLDLlipidslipids

Metabolite Correlation MappingMetabolite Correlation Mapping

Look for correlated changes in metabolites

to infer same/related pathways


NMR spectra4.0 3.5 3.0 2.5 2.0 1.5 1.0

Chemical Shift (ppm)

High throughput collection

1 [0.50 .. 0.52] 1.1 1.0 1.1 1.0 1.1 1.0 1.1 1.0 1.0 1.1 1.1 1.0 1.1 1.1 1.1 1.1 1.4 1.12 [0.52 .. 0.55] 1.1 1.0 1.1 0.9 1.1 1.0 1.1 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.0 1.4 1.13 [0.55 .. 0.59] 1.9 1.7 1.9 1.7 1.9 1.8 1.9 1.8 1.7 1.9 2.0 1.8 1.9 1.9 1.9 1.9 2.4 1.84 [0.59 .. 0.61] 1.1 0.9 1.0 0.9 1.1 1.0 1.0 1.0 0.9 1.1 1.1 1.0 1.0 1.0 1.1 1.0 1.3 1.05 [0.61 .. 0.64] 1.2 1.0 1.2 1.0 1.2 1.1 1.2 1.1 1.1 1.2 1.2 1.1 1.2 1.2 1.2 1.1 1.5 1.16 [0.64 .. 0.66] 1.0 0.9 1.0 0.9 1.0 1.0 1.0 0.9 0.9 1.0 1.0 0.9 1.0 1.0 1.0 1.0 1.2 1.07 [0.66 .. 0.68] 1.1 0.9 1.1 0.9 1.1 1.0 1.0 1.0 0.9 1.1 1.1 1.0 1.1 1.0 1.1 1.0 1.3 1.08 [0.68 .. 0.70] 1.1 0.9 1.0 0.9 1.1 1.0 1.0 1.0 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.3 1.09 [0.70 .. 0.72] 1.2 1.0 1.1 1.0 1.2 1.1 1.1 1.1 1.0 1.1 1.2 1.1 1.1 1.1 1.2 1.3 1.4 1.1

10 [0.72 .. 0.74] 1.1 0.9 1.1 1.1 1.2 1.3 1.0 1.0 0.9 1.1 1.1 1.0 1.1 1.0 1.1 1.4 1.3 1.111 [0.74 .. 0.76] 1.1 0.9 1.1 1.1 1.3 1.5 1.1 1.1 0.9 1.1 1.1 1.0 1.1 1.1 1.1 1.2 1.4 1.112 [0.76 .. 0.78] 1.3 1.1 1.3 1.1 1.4 1.5 1.2 1.2 1.1 1.3 1.3 1.2 1.3 1.2 1.3 1.2 1.5 1.213 [0.78 .. 0.80] 1.3 1.0 1.2 1.1 1.4 1.4 1.1 1.1 1.0 1.2 1.2 1.1 1.3 1.2 1.2 1.2 1.5 1.214 [0.80 .. 0.82] 1.6 1.2 1.4 1.9 2.0 2.2 1.3 1.4 1.1 1.5 1.3 1.2 1.6 1.3 1.4 1.6 1.7 1.415 [0.82 .. 0.88] 12.9 12.0 11.3 16.9 21.4 20.2 9.5 13.1 10.2 11.9 9.6 10.8 12.5 11.1 9.8 8.6 13.2 10.616 [0.88 .. 0.94] 6.9 8.4 6.2 6.7 8.5 6.7 5.4 5.3 5.3 8.2 5.8 6.2 6.4 5.3 6.3 5.4 8.3 6.217 [0.94 .. 0.99] 6.5 7.3 6.3 5.5 6.0 6.2 5.9 5.6 5.8 7.4 6.9 6.9 6.2 5.8 7.3 6.2 9.2 6.418 [0.99 .. 1.05] 5.1 4.9 4.9 3.8 4.4 4.5 4.6 4.3 4.2 5.3 5.0 4.8 4.7 4.5 5.2 4.2 6.4 4.719 [1.05 .. 1.07] 2.1 2.0 2.1 1.4 1.8 1.7 1.9 1.7 1.7 2.3 2.2 2.0 2.0 1.9 2.2 1.6 2.7 1.920 [1.07 .. 1.10] 1.6 1.2 1.5 1.1 1.5 1.5 1.4 1.3 1.1 1.5 1.5 1.2 1.5 1.3 1.6 1.3 1.8 1.421 [1.10 .. 1.12] 1.5 0.9 1.5 1.1 1.5 1.4 1.3 1.3 0.9 1.3 1.3 1.1 1.4 1.2 1.4 1.2 1.6 1.322 [1.12 .. 1.15] 5.0 2.9 4.5 5.1 6.0 8.2 4.2 4.8 3.3 3.7 3.8 3.2 4.4 4.2 4.2 5.1 4.7 4.423 [1.15 .. 1.20] 137.8 84.6 126.5 83.3 76.6 113.7 110.6 128.4 110.0 80.4 83.7 84.8 98.2 110.6 101.1 99.6 107.2 113.424 [1.20 .. 1.22] 6.5 5.4 5.0 2.8 2.8 2.2 4.7 4.2 10.4 6.7 5.6 5.3 4.2 4.9 6.2 2.1 6.9 6.625 [1.22 .. 1.24] 1.9 2.2 1.8 2.8 3.1 3.2 1.7 1.8 2.0 2.0 1.6 2.1 2.0 1.7 1.6 1.6 2.3 1.926 [1.24 .. 1.26] 2.6 2.7 2.3 5.0 6.1 5.8 2.0 2.2 2.0 2.7 2.0 2.3 2.7 2.0 2.0 2.0 2.8 2.327 [1.26 .. 1.30] 6.8 9.1 5.5 12.9 19.8 12.0 4.7 5.8 4.6 9.0 4.8 5.7 7.2 4.9 5.4 4.7 7.7 5.628 [1.30 .. 1.35] 59.0 80.7 57.1 56.6 60.6 59.9 65.7 75.5 61.4 70.6 73.8 74.9 63.7 67.7 68.6 54.6 82.2 60.029 [1.35 .. 1.39] 2.2 4.0 2.2 3.0 2.9 2.5 2.4 2.5 3.2 2.8 2.4 3.4 2.3 2.4 2.1 2.5 3.4 2.430 [1.39 .. 1.43] 2.4 3.1 2.4 2.8 2.5 2.8 2.5 2.7 2.7 2.6 2.5 3.0 2.5 2.5 2.3 2.9 3.4 2.531 [1.43 .. 1.49] 3.8 5.5 3.7 5.6 5.2 5.7 4.0 4.0 4.3 5.1 4.6 5.3 4.3 4.2 4.8 4.0 6.3 4.432 [1.49 .. 1.51] 1.3 1.9 1.3 1.5 1.5 1.8 1.4 1.6 1.5 2.1 1.8 2.4 1.5 1.5 1.5 1.3 2.0 1.433 [1.51 .. 1.56] 5.2 5.6 4.7 7.2 7.9 10.0 5.4 7.9 5.8 5.4 5.3 5.9 5.9 6.5 4.8 4.7 7.0 5.534 [1.56 .. 1.62] 2.8 3.7 2.8 3.3 3.7 3.2 3.1 3.3 3.6 3.3 3.2 3.6 3.0 3.3 3.1 2.8 4.2 3.035 [1.62 .. 1.64] 1.3 1.5 1.3 1.4 1.4 1.5 1.4 1.5 1.5 1.5 1.5 1.6 1.4 1.5 1.4 1.5 1.9 1.436 [1.64 .. 1.66] 1.1 1.2 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.3 1.2 1.2 1.2 1.2 1.6 1.237 [1.66 .. 1.68] 1.1 1.2 1.1 1.2 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.3 1.2 1.2 1.2 1.3 1.6 1.238 [1.68 .. 1.74] 3.8 4.4 3.8 3.7 3.7 4.0 4.0 3.9 3.9 4.5 4.3 4.6 4.1 4.0 4.3 4.1 5.8 4.039 [1.74 .. 1.80] 3.4 3.8 3.4 3.2 3.3 3.4 3.6 3.5 3.6 3.8 3.8 4.0 3.6 3.6 3.7 3.5 4.9 3.540 [1.80 .. 1.82] 1.1 1.2 1.1 1.1 1.1 1.1 1.2 1.2 1.1 1.1 1.2 1.2 1.2 1.2 1.1 1.2 1.5 1.241 [1.82 .. 1.84] 1.0 1.1 1.1 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.2 1.1 1.1 1.1 1.2 1.5 1.142 [1.84 .. 1.86] 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.1 1.2 1.2 1.2 1.1 1.2 1.2 1.5 1.6 1.243 [1.86 .. 1.88] 1.1 1.2 1.2 1.3 1.2 1.2 1.4 1.3 1.2 1.3 1.2 1.3 1.2 1.3 1.3 1.5 1.7 1.344 [1.88 .. 1.94] 4.9 5.1 4.7 4.2 4.4 5.0 4.8 4.3 4.9 5.4 5.2 5.6 5.1 5.0 5.5 4.0 7.2 4.745 [1.94 .. 1.96] 1.5 1.6 1.4 1.3 1.3 1.4 1.3 1.3 1.4 1.5 1.5 1.6 1.4 1.4 1.3 1.3 1.8 1.346 [1.96 .. 1.98] 1.3 1.4 1.3 1.5 1.6 1.6 1.2 1.2 1.2 1.4 1.3 1.4 1.3 1.2 1.2 1.3 1.6 1.247 [1.98 .. 2.00] 1.7 1.8 1.6 2.2 2.6 2.0 1.5 1.5 1.5 1.8 1.5 1.7 1.7 1.5 1.5 1.6 2.0 1.648 [2.00 .. 2.06] 5.2 6.3 4.9 6.3 8.0 5.3 4.5 4.4 4.6 6.0 4.7 5.2 5.0 4.4 4.9 4.6 6.6 4.949 [2.06 .. 2.10] 3.0 3.6 3.0 3.2 3.4 3.3 3.3 3.2 3.1 3.7 3.3 3.5 3.1 3.1 3.5 3.5 4.5 3.550 [2.10 .. 2.13] 2.4 2.8 2.6 2.4 2.5 2.7 2.8 2.7 2.7 3.0 3.0 3.1 2.7 2.7 3.3 2.1 4.0 2.951 [2.13 .. 2.18] 6.0 6.5 5.5 7.3 8.1 11.1 5.9 8.1 6.8 6.4 6.7 7.1 6.5 7.2 6.0 5.2 8.3 5.952 [2.18 .. 2.21] 2.0 2.1 1.8 1.7 1.9 1.8 2.1 2.6 2.4 2.1 2.1 2.3 2.1 2.4 2.0 1.6 2.7 2.153 [2.21 .. 2.26] 4.5 5.4 4.6 4.3 5.0 4.0 4.4 4.1 4.5 5.5 4.6 5.4 4.6 4.6 4.7 3.7 6.4 4.354 [2.26 .. 2.31] 2.9 3.1 2.8 2.6 2.8 2.7 2.8 2.9 3.2 3.1 3.1 3.3 2.8 2.8 2.9 2.7 3.9 3.055 [2.31 .. 2.34] 2.4 2.4 2.2 2.1 2.1 2.2 2.4 2.6 2.8 2.3 2.6 2.6 2.2 2.3 2.2 2.2 3.2 2.656 [2.34 .. 2.36] 1.3 1.4 1.2 1.1 1.2 1.2 1.3 1.3 1.3 1.4 1.4 1.4 1.3 1.3 1.3 1.2 1.8 1.357 [2.36 .. 2.41] 3.4 3.5 3.3 3.2 3.2 3.4 3.4 3.6 3.9 3.5 3.8 3.9 3.2 3.4 3.4 3.5 4.6 3.658 [2.41 .. 2.47] 4.1 4.4 4.2 3.9 4.0 4.4 4.5 4.6 4.6 4.5 4.9 5.0 4.3 4.5 4.9 3.8 6.2 4.659 [2.47 .. 2.52] 3.0 3.2 3.0 2.7 2.8 2.7 3.0 2.9 3.4 3.2 3.4 3.6 2.9 3.2 3.1 2.8 4.0 2.960 [2.52 .. 2.58] 3.0 3.0 3.0 2.8 2.9 2.8 3.1 3.0 3.2 3.2 3.1 3.4 2.9 3.1 3.0 2.9 3.8 3.1

Data Processing/Reduction

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t[2

]

t[1]SIMCA-P+ 11.5 - 7/11/2007 2:57:58 PM

-0.1

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[3.5

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3

[7.1

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[2.4

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[6.9

0 ..

6

[1.8

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[7.2

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[1.4

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1

[1.9

1 ..

1

[6.9

5 ..

7

[8.4

1 ..

8

Coe

ffCS[

2](re

spon

der (

<1.5

, >2)

)

Var ID (Primary)

2 week APAP phar metab- 100 scaled.M8 (OPLS), OPLSDA- Day 5-6, 1.5 > ALT > 2.0CoeffCS[Last comp.](responder (<1.5, >2))

SIMCA-P+ 11.5 - 7/18/2007 3:46:31 PM

Pathway AnalysisStatistical analysis Metabolite ID

The Overall The Overall MetabolomicsMetabolomics Process Process

The COMET ProjectCOnsortium on MEtabonomic Toxicology

Formed to investigate the utility of metabonomic approaches to the toxicological assessment of drug candidates

Use NMR based methods to categorize the pathologic effects caused by substances with toxic effects

Initially composed of Imperial College, UK and 6 big Pharma companies (BSM, Eli Lilly, Hoffman La Roche, NovoNordisk, Pfizer and Pharmacia.

J. Proteome Research, 6, 4407, 2007

Advantages of Metabolomics to Toxicology

Metabolomic profiling of biofluids is non-invasive and systemic

Compare with transcriptomics or much proteomics which comes from specific tissues – in a tox study which tissue do you look at?

Repeated sampling allow for temporal data which can help define fast & slow responders

Picking a single timepoint can be difficult; e.g. acetaminophen toxicity takes several days to develop?

Biochemical changes can be detected even w/out histopathological changes – can detect perturbations with sub-toxic doses

Flowchart of analyses used to develop models for classifying

compounds according to toxicity

Sampling Protocol

Urine collected

0-88-24

824487296

120144168

Pre Dose

Dosing

½ euthanized at 48 hrs

½ euthanized a 168 hrs

Similarity Matrix to Identify Compounds with Similar/Related Mechanisms of Toxicity

7 replicates of hydrazine

Acetaminophen studiesSingle dose & repeat dose

Drugs with endocrine disrupting effects

Drugs causing tubular necrosis

Papillary toxins

Correlates the metabolic profiles of the different treatments

J. Proteome Res. 6, 513, 2007

Monitoring the Metabolome and Determining Monitoring the Metabolome and Determining MetabotypesMetabotypes

Metabotype: the probabalistic, multiparametric description of an organism in a given physiological state based on the analysis of its cell types, biofluids or tissues

What is Pharmaco-Metabolomics?What is Pharmaco-Metabolomics?

The prediction of the outcome of a drug or The prediction of the outcome of a drug or xenobiotic intervention in an individual xenobiotic intervention in an individual based on a mathematical model of pre-based on a mathematical model of pre-

intervention metabolite signatures.intervention metabolite signatures.Clayton, et. al, Nature, 440, 1073, 2006

Pharmaco-Metabolomic HypothesisPharmaco-Metabolomic Hypothesis

Genetic & environ. characteristics of

individuals

Inter-subject variation in effects

of drugs

Metabolite profilespre-dose or

pre-adverse event

influ

ence

predictable?

influence

adapted from Clayton, et. al, Nature, 440, 1073, 2006

• 65 rats given 600mg/kg acetaminophen

• pre-dose (-48 to -24hrs) urine collected

• Spectra were correlated to liver damage by histology score

Metabolomics Proof of Principle in RatsMetabolomics Proof of Principle in Rats

necrosisminimal moderate

Predose Metabolome Predicts Predose Metabolome Predicts Gluc/Parent RatioGluc/Parent Ratio

• pre-dose metabolite profiles could predict APAP metabolite ratio

• 12 bins were identified as significantly correlated to the G/P ratio

• top two bins relate to endogenous glucuronides

Studying Liver Toxicity in HumansStudying Liver Toxicity in Humans

Study found that 30-40% of subjects experienced ALT elevations > 3X ULN

Watkins, et al., JAMA, 296 (1), 87, 2006

Pharmaco-Metabonomic HypothesisPharmaco-Metabonomic Hypothesis

Metabolic and other individual

characteristics

Inter-subject variation in effects

of drugs

Metabolite profilespre-dose or

pre-adverse event

influ

ence

predictable?

influence

adapted from Clayton, et. al, Nature, 440, 1073, 2006

Clinical Study Design of Two Week Clinical Study Design of Two Week Human Trial of APAP ToxicityHuman Trial of APAP Toxicity

• 72 healthy volunteers, men & women, age 18-55

• Housed as inpatients for 14 days & given controlled diet

• 3 days on a controlled diet

• 4gm APAP/day (2 500mg tablets 4 times) for 7 days

• Urine and serum collected daily along with standard liver chemistry tests including ALT level

• Urine collected continuously and pooled for 24hr period

Responders and Non-Responders in the Responders and Non-Responders in the Two Week APAP StudyTwo Week APAP Study

Responders: Daily ALT (U/L)

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AL

T (

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Subject 3

Subject 5

Subject 8

Subject 10

Subject 11

Subject 16

Subject 20

Subject 23

Subject 28

Non-Responders: Daily ALT (U/L)

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Subject 13

Subject 19

Subject 26

Subject 30

Subject 32

Subject 33

APAP APAP

Peak ALT levels < 1.5 x baselinePeak ALT levels > 2.0 x baseline

Daily Patient ALT Levels

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Can Metabolomics Distinguish Can Metabolomics Distinguish Responders from Non-Responders?Responders from Non-Responders?

Dosing Days

5 97 11

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Class 1Class 2

SIMCA-P+ 11 - 8/18/2008 10:06:44 AM

PCA Analysis of Responders & Non-responders PCA Analysis of Responders & Non-responders at Days 9-10at Days 9-10

Non-responder

Responder

OPLS Analysis of Responders vs. OPLS Analysis of Responders vs. Non-responders at Days 9-10Non-responders at Days 9-10

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R2X = 0.471Q2 = 0.452

2 components

Non-responder Responder


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Metabonomic Prediction of Hepatotoxicity Metabonomic Prediction of Hepatotoxicity Prior to ALT RisePrior to ALT Rise

5 97 11

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R2X = 0.432Q2 = 0.451

2 components

OPLS Analysis of Responders vs. OPLS Analysis of Responders vs. Non-responders at Days 5-6Non-responders at Days 5-6

Non-responder Responder


0

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T (

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Dosing Days

Pharmaco-Metabonomic Approach for the Pharmaco-Metabonomic Approach for the Hepatotoxicity in HumansHepatotoxicity in Humans

5 97 11

2-Component OPLS Model Statistics2-Component OPLS Model Statistics

0

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Days 9-10 Days 5-6 Days 2-3

R2X

Q2

Pre-dose Metabolite Models Were Much WeakerPre-dose Metabolite Models Were Much Weaker

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t[1]PSIMCA-P+ 11 - 8/28/2008 4:59:21 PM

Days 9-10

Days 5-6

Days 2-3

Early Intervention Pharmaco-metabonomics Early Intervention Pharmaco-metabonomics

• Pre-dose profiles may not be predictive in many cases• Early doses may evoke the predictive metabolic phenotype

pre-dose dosingearly intervention monitoring

met

abo

lic

ph

eno

typ

e

responder

non-responder

Presentation of classical phenotypic changeDosing Start

Future Directions for MetabolomicsFuture Directions for Metabolomics

• Integrate multiple platforms for increased coverage of the metabolome

• Expand robust libraries of metabolites• Combined targeted and global profiles• Integrate with other omics datasets

(genomics, transcriptomics, proteomics)• Improve software tools to translate omics

findings into biochemical pathway knowledge

introduction to metabolomics thomas m. o’connell, ph.d. unc metabolomics laboratory definitions of...

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