Metabolomics - LMU

Metabolomics

Gabi Kastenmüller

Helmholtz Zentrum München Institute of Bioinformatics and Systems Biology

([email protected])

Munich, 17.02.16

What is metabolomics?

Metabolomics

= analysis of metabolomes

Metabolome

= complete set

… of all small-molecules (<1000 Da)

… found within a biological system

(… at a specific time

… under specific conditions)

=> Detection and quantitative measurement of (ideally) all small molecules (= metabolites) in a biological system

Metabonomics

= “the quantitative measurement of the

dynamic multiparametric metabolic

response of living systems to

pathophysiological stimuli or genetic

modification“ (Wikipedia)

What is metabolomics?

Genomics

Transcriptomics

Proteomics

Metabolomics

20000-30000 genes

splicing variants*genes

protein modifications*transcripts

~2500 (+~3500 food +~1200 drugs)

DNA

mRNA

… complete set of “genes”

… complete set of transcripts

… complete set of proteins

…. complete set of metabolites

© KEGG

Why another –omics?

Signaling

Energy

Building Blocks

Xenobiotics

Metabolites are the true end points of most biological processes

Genetic

Regulatory

Environmental

Facto

rs

Fatty acids Sugars

ATP

Hormones

Amino acids

Neurotransmitter

Drugs

Nucleotides

Phospholipids

Food

© Metabolon

How can we measure all these metabolites?

EXPERIMENTAL BACKGROUND

SAMPLE COLLECTION

Sample types

• Cerebrospinal Fluid

• Peritoneal Fluid

• Saliva

• Sweat

• Tears

• Feces

• Breath air/condensate

• Blood

• Urine

• Further body fluids

Sample types

• Liver

• Kidney

• Muscle

• Brain

• Fat

• Blood

• Urine

• Further body fluids

• Tissue

• Cell cultures

• Plant extracts

Sample collection

• Plasma

• Serum

• Spots

• Additives (EDTA, Citrate, Heparin)

• Storage (N2, -80°C, -20°C, 4°C, RT)

• Venous, Capillary, Arterious

• Blood

Things to think about BEFORE sample collection

• Is there any established metabolomics method for the sample

type („matrix)?

• Additives can disturb the measurement (e.g. DNA stabilisors).

• Reactions go on at room temperature

=> standard operating procedures (SOPs) to ensure comparability

• Lab differences might be large

=> cases/controls from all sites

Discuss study design with collaborators for analytics and data analysis!

Sample collection …

… what you loose here

… you will never see (again)!

METABOLITE DETECTION & QUANTIFICATION

Targeted vs non-targeted approaches

Targeted

Preselected set of metab. signals

• Pros: Known identity; better quant.;

• Cons: No new metabolites

Routine

Non-targeted

All metab. signals that can be detected

• Pros: New/Unknown metabolites

• Cons: Difficult metabolite identification;

less reliable quant

Discovery

Technologies for high-throughput metabolomics

Mass spectrometry (MS) Nuclear magnetic resonance (NMR)

NUCLEAR MAGNETIC RESONANCE (NMR)

H

1H, 2H

Li

7Li

Na

23Na

Be

9Be

Mg

25Mg

K

39K

Ca

43Ca

Rb

87Rb

Sr

87Sr

Cs

133Cs

Ba

137Ba

Sc

45Sc

Ti

49Ti

V

50V

Cr

53Cr

Mn

55Mn

Co

59Co

Ni

61Ni

Cu

63Cu

Zn

67Zn

Ga

71Ga

Ge

73Ge

As

75As

Y

89Y

Zr

91Zr

Nb

93Nb

Mo

95Mo

Ru

101Ru

Pd

105Pd

In

115In

Sb

121Sb

Ln

138Ln

Hf

179Hf

Ta

181Ta

Re

187Re

Ir

193Ir

Au

197Au

Bi

209Bi

Br

81Br

Kr

83Kr

I

127I

B

11B

O

17O

Al

27Al

S

33S

Ne

21Ne

Cl

35Cl

Ar

He

3He

C

13C

N

15N

F

19F

Si

29Si

P

31P

Fe

57Fe

Se

77Se

Te

125Te

Xe

129Xe

Sn

119Sn

Pb

207Pb

Tl

205Tl

Po

At

Rn

Hg

199Hg

Pt

195Pt

Rh

103Rh

Os

187Os

W

183W

Tc

Ag

107Ag

Cd

113Cd

Fr

Ra

Ac

Pr

141Pr

Nd

143Nd

Sm

147Sm

Eu

153Eu

Tb

159Tb

Er

167Er

Tm

169Tm

Yb

171Yb

No

Pm

Ce

Gd

157Gd

Dy

163Dy

Ho

165Ho

Lu

175Lu

Md

Fm

Es

Cf

Bk

Cm

Am

Pa

Th

U

235U

Pu

Np

Lr

nuclei with NMR active isotop

nuclei with I=1/2 isotop

NMR

Bo

w z

m

m

w

N

S

S

N

DE = g h Bo = h n

10 12 8 6 4 2 B0

[T]

E

I=-1/2

I=+1/2

DE=100 MHz DE=300 MHz DE=500 MHz

NMR

Metabolite fingerprint

0 1 2 3 4 ppm

H C

H

H

C

O

O C

H

H

C

H

H

H

0.98 ppm 2.03 ppm 4.12 ppm

NMR spectrum for a plasma sample

Metabolite identification using spectra library

Quantification

0 1 2 3 4 ppm

H C

H

H

C

O

O C

H

H

C

H

H

HJ coupling

Integral

0.98 ppm 2.03 ppm 4.12 ppm

1H NMR Spectrum

Technologies for high-throughput metabolomics

Mass spectrometry (MS) Nuclear magnetic resonance (NMR)

LC or GC-

Sample

preparation

Chromato-

graphy

Sample preparation

Homogenized sample

• Extraction (e.g. solvent extraction with MeOH)

• Addition of standards (for QC and quantification)

• Derivatisation

=> Depending on the extraction method, different metabolite classes may be analyzed

Sample preparation

Separation techniques

• Liquid chromatography

liquid mobile phase; solid stationary phase

• Gas chromatography

carrier gas mobile phase; liquid stationary phase

• Capillary Electrophoresis

=> Reduces complexity by introducing a temporal dimension (elution/retention time)

Separation techniques

4 5 6 7 8 9 10 11 12 13 14 Time (min)

4.01 14.43

5.84

4.38 10.66

8.46

10.18

11.76 4.55 6.52 6.73 7.74

9.34 11.79

11.03

13.05 9.47 7.50 11.21 5.34

12.89 3.17 13.30

8.01

Chromatogram

MASS SPECTROMETRY (MS)

Mass spectrometry

Principle: Separation/Identification of molecules by mass (precisely: mass/charge)

3.1 g 5.4 g

396.7 g/mol 180.1 g/mol

Don’t be afraid of abbreviations

EI

ESI

MALDI

APCI

FTICR

Triple Quad

TOF

Ion Trap

Orbitrap

HPLC

UPLC

GC

FIA

Separation Ionisation Mass

detection

Mass spectrometry

Taylor et al., 2005

Possible molecular formulas:

C6H12O6

Which molecule is it?

Fragmentation

3.1 g 5.4 g

180.1 g/mol

e.g. by collision with gas molecules

Relative abundance of isotopes

The ratio of peaks containing 79Br and its isotope

81Br (100/98) confirms the presence of bromine in

the compound.

http://www.chem.arizona.edu/massspec/intro_html/intro.html

4 5 6 7 8 9 10 11 12 13 14 Time (min)

4.01 14.43 5.84

4.38 10.66

8.46

10.18 11.76 4.55 6.52 6.73 7.74 9.34 11.79 11.03

13.05 9.47 7.50 11.21 5.34 12.89 3.17 13.30

8.01

Chromatogram

LC-MS spectra for a sample

3.17 min

MS Scan

MS/MS Fragmentation

4 5 6 7 8 9 10 11 12 13 14 Time (min)

4.01 14.43 5.84

4.38 10.66

8.46

10.18 11.76 4.55 6.52 6.73 7.74 9.34 11.79 11.03

13.05 9.47 7.50 11.21 5.34 12.89 3.17 13.30

8.01

Chromatogram 3.17 min

MS/MS Fragmentation

Time

Inte

nsity

LC-MS spectra for a sample

120.1

103.1

93.1

50 61 72 83 94 105 116 127

20

0

40

60

80

100

MS/MS 120.1

1.88 1.90 1.92 1.94 1.96 1.98 2.00 2.02 2.04 2.06 2.08 2.10 2.12 2.14 2.16 2.18

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100 RT: 2.02 ± 0.05 min

Time

Rela

tiv

e A

bu

nd

an

ce

166.1

50 148 246 344 442 540 638 736

0

20

40

60

80

100

m/z

Rela

tiv

e A

bu

nd

an

ce

166.1

120.1 330.8

Retention MS Mass Profile (m/z)

120.1

50 65 80 95 110 125 140 155

20

0

40

60

80

100

170

146.1

MS/MS 166.1

73 104 135 166 197 228 259

166.1

103.1

93.1

20

0

40

60

80

100

MS/MS 330.8

Fragmentation Spectra (EI or MS/MS)

Molecular ion, adducts, multimers, in-source fragments, isotopes

Metabolite fingerprint

quant. ion

Fingerprints Library

automated

matching

Suggested matching metabolites

Metabolite identification with spectra library

Forward-Fit

Matches everything in the component to the library

Reverse-Fit

Matches the library entry to the component

Fit 120.1

103.1

93.1

50 61 72 83 94 105 116 127

20

0

40

60

80

100

MS/MS 120.1 lib.

120.1

103.1

50 61 72 83 94 105 116 127

20

0

40

60

80

100

MS/MS 120.1 exp.

93.1 71.8

Automated matching

Fingerprints Library

automated

matching

Suggested matching metabolites

Metabolite identification with spectra library

“Knowns”

manual

curation

“Known unknowns”

manual

curation

?

Quantification

RT: 2.02

Area: 120089

m/z: 120.14

Time

RT: 2.02

Area: 40112

m/z: 121.15

Internal standard (isotope labled)

DATA ANALYSIS

Raw metabolomics data

LC or GC-MS NMR

fragmentation spectrum

mass

retention time


mass

retention time


mass

retention time


mass

retention time

Four steps

• Raw data processing

peak detection, peak alignment, peak integration, identification of

metabolites, …

• Primary data analysis (QC):

outlier detection, normalization (batch effects, dilution), missing value

handling/imputing…

• Statistical analysis:

univariate/multivariate hypothesis tests,

supervised/unsupervised machine learning (classification/clustering), …

• Bioinformatic analysis:

biological context, network analyses, data integration

Raw data analysis tools

metlin.scripps.edu/xcms/

www.metaboanalyst.ca

Batman (NMR) batman.r-forge.r-project.org/

Raw data analysis

Targeted

Preselected set of metab. signals

Non-targeted

All metab. signals that can be detected

…

…

…

…

…

…

…

…

…

…

…

…

Sample

Ident. metabolites Peak list

Metabolomics Data

Sample phenotypes Metabolite concentrations

… including batch, collection

date, etc

Four steps


peak detection, peak alignment, peak integration, identification of metabolites, …









server

Quality control

PCA

Primary data analysis (exploratory)

metap.helmholtz-muenchen.de/metap2

Distributions

Four steps











Results from statistical analysis

e.g. cases vs. control:

Fumarate

Arginine

Citrulline

Ornithine

Glutamine

Urea

Aspartate

N-acetylglutamate

…

….

Four steps











Approach 1: Mapping results onto pathway maps

Fumarate

Arginine

But: mapping problem

Gaps because not all metabolites are measured

Measured and map metabolites do not match

exactly

No mapping of unknown metabolites

Approach 2: Reconstruction of networks from data

Metabolomics data Metabolic network

Sample

Reconstruction of metabolic networks using correlations

Krumsiek et al.,

BMC Systems Biology, 2011

Problem: indirect effects

Problem: indirect effects

Eliminating indirect effects: partial correlation

Krumsiek et al.,


Reconstruction of metabolic networks using partial correlation networks (=GGMs)

Krumsiek et al.,


+

Metabolic databases

Krumsiek et al.,

PLoS Genet., 2012

Reconstructions by GGMs: Closer inspection

APPLICATIONS & AIMS

Applications & Aims of Metabolomics

• „Biomarkers“ discovery

• Diagnosis

• Response on therapy

• Stratification => Personalized Medicine

• Pathomechanistic insights

• Preclinical drug testing

disease

healthy

Applications & Aims of Metabolomics

• Biomarkers discovery

• Diagnosis

• Response on therapy

• Stratification => Personalized Medicine

• Pathomechanistic insights

• Preclinical drug testing

Diagnostic ‘urine charts’ were widely used from the Middle Ages onwards

These charts linked the colors, smells and tastes of urine to various medical conditions.

© Nicholson & Lindon

So what is new?

Pinder, Epiphanie medicorum (1506), Universitätsbibliothek München

Metabolites as Diagnostic Biomarkers

Increase of “molecular resolution”

© www.biocrates.at

Example: Newborn screening

phenylalanine tyrosine

Phenylketonuria (>1 in 25,000)

~40 metabolites (amino acids, carnitines)

tested to identify

inborn errors of metabolism

[Inborn errors of metabolism]

… are merely extreme

examples of variations of

chemical behavior

which are probably

everywhere

present in minor degrees.

A.E. Garrod, Lancet, 1902

Garrod suggested a link between chemical individuality

and predisposition to disease.

THE “NORMAL” HUMAN METABOLOME (HUMET) Krug et al., FASEB, 2012

HuMet: Studying the “normal” human metabolome

• 15 young healthy men:

Metabolome – a snapshot of biochemical state

Metabolome 1

Metabolome 2

Metabolome 3

8 am fasting

9 am after breakfast

4 pm sports

HuMet: Studying the “normal” human metabolome

• 15 young healthy men:

age: 22-33

BMI: 20-25 kg/m2

• Controlled trial over the time course of 4 days

4 nutritional interventions: (fasting, standard meal, OGTT, OLTT)

physical exercise

stress test

HuMet: Study design

8:00 24:00 8:00 16:00

...

8:00 18:00 8:00 18:00

4 weeks

56 blood samples

25 urine samples

32 breath condensate samples

breath air

O

O

O

O

ON

+

OP

O

O

HuMet plasma samples @ metaP

Targeted metabolomics

HNH

2

OH

O

OH

tyrosine 100 µM

163 metabolites

per sample

phosphatidylcholine 20 µM

hexanoylcarnitine 0.05 µM

O

O

OH

O

N+

H

15 individuals x 56 plasma samples

Metabolites levels largely vary during the day and on response to challenges

glucose

acetylcarnitine

Krug et al., FASEB, 2012

sta

ndard

ized c

oncentr

ations

Switching from anabolism to catabolism and back …

… pushing volunteers through the metabolic space

Metabolite levels differ between individuals

Normal ranges!

Metabolic response differs between individuals


Acetyl-

carnitine

Time of day

Extended fasting

Meals

Concentr

ation

Metabolite profiles are individual

Personal metabolomes are stable

• Short-term (days), challenges

plasma, 15 young men, MS-based


• Mid-term (months)

urine, 22 subjects, NMR-based

Assfalg et al., PNAS, 2008

Metabolic individuality


Chua et al., PNAS, 2013

Assfalg et al., PNAS, 2008

Yousri et al., Metabolomics, 2014

Long-term stability (years)?

1999/2001 (S4)

Long-term stability of metabolite profiles?

Non-targeted metabolomics:

212 metabolites

n=818

2006/08 (F4)

7 years

measured in 2010 measured in 2012

M1 M2 M3 …

S1 1.3 5.3 2.3 …

S2 1.9 6.6 2.1

S3

S4

S5

S6

…

M1 M2 M3 …

S1 1.5 4.9 2.0 …

S2 2.0 7.1 1.3

S3

S4

S5

S6

…

Baseline 7-year follow-up

Assessing ranks of “self-correlation”


212 metabolites

90

1 s

ub

jects

High long-term stability of metabolite profiles


40% of subjects: strongest correlation with own profiles in 7y follow-up

95% of subjects: “self” correlation ranked among the 30% strongest

Major changes

in metabolome

Conserved metabolome

Are metabolomes with major changes special?



Individuals with major changes in their metabolome

Reasons for high conservation?

Genetic variation Lifestyle Microbiome

Steve Gschmeissner/SPL

Estimation of heritability

based on twins (n=6000)

(ACE model)

Which fraction of variance

inherited, which due to

common environment?

Median heritability: 25%

Max heritability: 76%

(butyrylcarnitine)

Shin et al., Nature Genet, 2014

Heritability of metabolite levels

M1 M2 M3 …

S1 1.3 5.3 2.3 …

S2 1.9 6.6 2.1

S3

S4

S5

S6

…

M1 M2 M3 …

S1 1.5 4.9 2.0 …

S2 2.0 7.1 1.3

S3

S4

S5

S6

…


pairwise correlations -> rank correlations -> most stable metabolites?

Conservation of metabolites

Yousri et al., Metabolomics, in press

Conservation vs heritability

Associated with sex

Associated with BMI

Associated with age

C4 carnitine

hormones

Can heritability be explained by

common genetic variants?

Partnerships with industry and science

IEG IOEC

IGE AME

Thank you for your attention !

© K. Suhre

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Metabolomics - LMU