Bioen Workshop on Metabolomics of Sugarcane

FAPESP, Sao Paulo, 07 December 2009

Lothar Willmitzer

Max-Planck-Institut für Molekulare Pflanzenphysiologie

14476 Potsdam-Golm, Germany

Biomass and Metabolomics at the

MPI for Molecular Plant Physiology

The Max Planck Society

Founded 1948is a research organization supposed to work complementary to Universities

78 Institutes

276 Departments (led by Max Planck Directors)

12,200 employees (include. 4,200 scientists)

+ 9,600 junior scientists

Budget: 1.3 BEuro = ≤2% of total R&D in Germany

government + 16 states (1:1)

“Knowledge must precede application”

our research activities are

(Max Planck)

- focused on basic research

- curiosity-based and hypothesis-driven

- open to but not focused on applications

17 Nobel prizes since 1948

2007 Chemistry: Gerd Ertl

2005 Physics: Theodor Hänsch

1995 Chemistry: Paul Crutzen

1995 Medicine: Christiane Nüsslein-Volhard

1991 Medicine: Erwin NeherBert Sakmann

1988 Chemistry: Johann Deisenhofer Robert HuberHartmut Michel

1986 Physics: Ernst Ruska

1985 Physics: Klaus von Klitzing

1984 Medicine: Georges Köhler

1973 Medicine: Konrad Lorenz

1967 Chemistry: Manfred Eigen

1964 Medicine: Feodor Lynen

1963 Chemistry: Karl Ziegler

1954 Physics: Walter Bothe

Commercial success = Technology transfer

„Max Planck Innovation GmbH“

since 1979: 2,025 patents filed

1,186 licence contracts

154 M€ patent licences

60 Start Up companies

Potsdam-Golm Campus harbors

University Potsdam, Fraunhofer Research

Institutes and Max-Planck Institutes

MPI für Molekulare Pflanzenphysiologie

Structure of the MPI-MP

• 3 departments hosting 12 research groups

mostly led by young scientists on limited

contracts

• 2 independent Max-Planck research

groups

• 3 infrastructure groups

• 2 university guest groups

• 2 Systems Biology guest groups

Structure of the MPI-MP

• Approx 350 employees

• 90+ post-doc´s and 90+ PhD students

• More than 50% coming from abroad covering 25+ countries

• Working language is english

Biological Processes studied at the MPI-MP

Plant Energetics

(Photosynthesis, Respiration)

Abiotic stress

(temperature, nutrients)

Metabolism and Growth

Cell wall biosynthesis

Biomass

Genotyp

es

Environment Analy

sis

Create genetic diversity,

grow it in defined environmental conditions

and subject it to broad molecular phenotyping

Genotypes Environment

Analysis

RNA analysis (arrays, RT-PCR)

Proteomics facility ( LC-MS, 2D)

Enzyme HTP platform

Single cell analysis

Metabolite analysis

Subcellular analysisTransgenics

Natural Variation

Arabidopsis, tomato,rice

Data Mining

(Bioinformatics)

Metabolomics

• The metabolome comprises all small molecules

present in a given biological system

• Metabolomics aims at the quantitative

determination of all small molecules

• The metabolome contains molecules hugely

varying in three parameters : concentration,

structure and chemical behaviour

• In contrast to RNA and protein metabolites are

non-linear to the DNA

Metabolomics - Challenges

• Increase the fraction of molecules we see..

• Annotate more of them …

• Quantitate them ( relative and absolute) ..

• Discriminate a true metabolite from a contaminant …

• Make sure that what we measure reflects the situation in the cell …. Sampling

• subcellular distribution, labelling ( fluxomics) ….

Metabolomics - Extraction

• Metabolomics aims at a complete

coverage of all small molecules

• Thus as little prefractionation as possible

is applied

• We start from a simple

methanol/chloroform/water extract which is

exposed to three different platforms

robotic derivatisation & full extract injection:

GC-MS : the workhorse for 100 – 150 metabolites

mostly from primary metabolism

Acquired on 09-Dec-1998 at 12:59:56

7.500 10.000 12.500 15.000 17.500 20.000 22.500 25.000 27.500 30.000 32.500 35.000 37.500 40.000 42.500 45.000

rt0

100

%

Scan EI+

T IC

1.20e8

8343AO01

„What Is This Peak?“

LTQ FT Ultra

• Resolution

– > 1 000 000

• Mass Range

– m/z 50-2000

• Dynamic Range

– 1 000

0.26 ppm

+ 0.000045.93 ppm

- 0.00103

102 ppm

- 0.017

Mass Accuracy

Phenylalanine [M+H+]+

[M+H+]+ Mass 181.07066

allowed

chemical elements: C = 30

H = 50

N = 5

O = 10

P = 5

S = 5

500 ppm = 0.09085 Da Error 268 predicted Formulas

100 ppm = 0.01817 Da Error 54 predicted Formulas

10 ppm = 0.00181 Da Error 6 predicted Formulas

1 ppm = 0.00018 Da Error 1 predicted Formula C6H12O6

Formula calculation is depending on mass accuracy and resolution

Arabidopsis 12CO2 Arabidopsis 13CO2

•UPLC FT-ICR MS

•Peak Extraction

•DB Search

12C chemical formula

and retention time

13C chemical formula

and retention time

Matched 12C/13C formulas

with RT tolerance 0.05 minunambiguous

Annotated formula

ambiguous

•Isotope pattern

•MS/MS analysisunambiguous

Metabolite Extraction

MeOH:CHCl3:H2O

Whole metabolome 13CO2 labelling strategy

Parallel analysis of 12/13C labelled compounds allows

discrimination of biological from non-biological material

and annotation of number of carbon atomsx:\ag willmitzer\...\at_13c_pos1 10/7/2008 10:23:16 PM

RT: 0.00 - 13.01

0 1 2 3 4 5 6 7 8 9 10 11 12 13

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50

60

70

80

90

100 NL:1.62E7

Base Peak MS At_12C_pos1

NL:1.57E7

Base Peak MS at_13c_pos1

13C

12C382.14236

383.14574 384.13859

382.14225

383.14599384.13865

X:\AG Willmitzer\...\At_12C_pos1 10/7/2008 8:41:49 PM

382.0 382.5 383.0 383.5 384.0

m/z

0

10

20

30

40

50

60

70

80

90

1000

10

20

30

40

50

60

70

80

90

100 NL: 7.52E6

At_12C_pos1#582 RT: 8.21 AV: 1 T: FTMS + p NSI Full ms [100.00-1300.00]

NL: 1.56E6

at_13c_pos1#591 RT: 8.27 AV: 1 T: FTMS + p NSI Full ms [100.00-1300.00]

7.27min8.23min

X:\AG Willmitzer\...\At_12C_pos1 10/7/2008 8:41:49 PM

730 735 740 745 750 755 760 765 770 775 780 785

m/z

0

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1000

10

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100 NL: 9.74E6

At_12C_pos1#515 RT: 7.33 AV: 1 T: FTMS + p NSI Full ms [100.00-1300.00]

NL: 8.58E6

at_13c_pos1#514 RT: 7.30 AV: 1 T: FTMS + p NSI Full ms [100.00-1300.00]

13C

12C

C33

741.22238

774.33350

Sample 1 Sample 2 Sample 3 13CO2 Sample

•Metabolite Extraction

•Spike fixed amount (1:1)•UPLC FT-ICR MS•Peak Extraction

•Spectra Alignment•Differential Peak detection

•DB Search

1:2 1:4 1:20

Sample 1

1:1

A

B

1:1 1:1 1:1 1:2 1:2 1:2 1:4 1:4 1:4 1:20 1:20 1:20

05

1015

2025

3035

12C/13C dilution

Mixing Ratio 12C/13C

Ratio

13C/1

2C

Relative quantitation via spiking with a 13C labeled metabolome

IRMPD

ESI neg

100 200 300 400 500 600 700 800 900 1000

m/z

0

10

20

30

40

50

60

70

80

90

100

Re

l. A

bu

nd

an

ce [%

]

301.03397

609.14234

C15H9O7

C27H28O16

636.23571

13C27H28O16316.08536

13C15H9O7

[M-H]-

m/z 318m/z 303

MS/MS for elucidation of structures

chloroform

phase

aqueous

phase

UPLC separation

UPLC peak detection,

peak lists extraction

and differential database

searches

Compound annotation

and result interpretation

Arabidopsis12CO2 grown

Arabidopsis13CO2 grown

Metabolite Extraction

MeOHl:CHCl3:H2O

neg.

mode

Lipids

pos.

mode

pos.

mode

neg.

mode

UPLC fractionation

Strategic Overview of the Platform

GC-TOF

FT-ICR-MS

Summary Metabolomics

• Starting from a MeOH-H2O/CHCl3 extract

three platforms allow :

• analysis of 150 primary metabolites (GC-TOF)

• analysis of 500 – 1000 secondary metabolites

(UPLC-FT-ICR-MS)

• analysis of about 140 complex lipids

Metabolomics : Applications

• Metabolomics and Genetics : Annotation of gene function

• Metabolomics and Diagnostics : Discrimination/Identification/Prediction of states

• Metabolomics and Systems Biology : Integration of different data sets

Metabolomics and Genetics :

Annotation of gene function using KO

populations and overexpressors

Metabolic Genomics: From Gene to

Function - Directly

Arabidopsis thalianaThe “domestic pet” about 27.000 Genes

Genetic blueprint known

Small

Grows rapidly

Large progenyFunctional

Analysis

Plant

Collections

Metabolic AnalysesPerformance

Metabolic Genomics at metanomics: Process Overview

Transformants B

Transfer of

additional Genes

“Gene.........................

Donors”:

Transformants A

Knockout

Wild Type

Fig.4

1 20.2

X-Fold ratio in comparison

to Wild Type control

Plant

Lines

Metabolites

Metabolomics and Diagnostics :

Discrimination/Identification/Prediction

of states

Metabolic profiling applied to two ecotypes

and one mutant each

Col-2 wild type dgd1 mutant C24 wild type sdd1 mutant

Arabidopsis Profiling: PCA Cluster Analysis

Factor1

Factor4 Factor2

C24 sdd

C24 WT

Col2 dgd1

Col2 WT

Medical applications of Metabolomics

• Differentiation of diabetics/nondiabetics

• Diagnostics of cancer and non-cancer tissues (kidney cancer)

• Prediction of health risk ( myocardial infarct)

• Differentiation of responder/non-responder (antidepressiva)

Separation of diabetic and nondiabetic

male and female patients

Metabolic profiling allows assigment of

normal and clear cell kidney carcinoma

Prediction of myocardial infarction by metabolic signature.

0 20 40 60 80 100

100-Specificity

100

80

60

40

20

0

Se

nsitiv

ity

Standard Model and five metabolites

Standard Model

Age, Sex and five metabolites

0 20 40 60 80 100

100-Specificity

100

80

60

40

20

0

Se

nsitiv

ity

Standard Model and five metabolites

Standard Model

Age, Sex and five metabolites

Standard Model and five metabolites

Standard Model

Age, Sex and five metabolites

Prediction was estimated by calculation of receiver-operating-curves (ROC). Prediction of a model including age, sex and the here identified five

metabolites (red ROC; AUC 0.844) was not inferior to a standard model including all established risk factors (age, sex, smoking, education, alcohol

consumption, physical activity, hormone replacement therapy, BMI, hypertension, diabetes, HDL/total-cholesterol-ratio and C-reactive protein) (blue

ROC; AUC 0.86; p=0.385). A model with all established risk factors (standard model) and the five metabolites (AUC 0.89) outperformed the

standard model alone (p=0.033).

Medical applications of Metabolomics

• Differentiation of diabetics/nondiabetics

• Diagnostics of cancer and non-cancer tissues (kidney cancer)

• Prediction of health risk ( myocardial infarct)

• Differentiation of responder/non-responder (antidepressiva)

Metabolomics and systems biology:

Network analysis and integration

with transcript data

stress

M T

M T

M T

M TM TM T

Optical D

ensity 6

00nm

time

Adaptation phase

Heat

Cold

Oxidative stress

Glucose-lactose shift

control

E. coli response towards different stresses on the transcriptome and metabolome

Network construction by correlation

analysis (metabolite A vs B)

B B B

AAA

Cold stress

Heat stress

Lactose shift

Oxidative stress

control growth

overlap

Superimposition of stable networks

and metabolic pathways

Differential network properties identify

candidate molecules

Canonical correlation analysis allows identification

of transcripts correlating with metabolite changes

Coordinated expression of

transcripts and metabolites

Conclusion

• Metabolomics is rapidly developing into

mature technology already now covering

several thousand small molecules

• Combined with genetic diversity it allows

the high-throughput annotation of gene

function

• Probably due to its vicinity to the ultimate

phenotype it has a surprisingly high

predictive power

• It is an integral part of systems biology

Thank you for your attention!

Patrick Giavalisco

Bettina Seiwert

Aenne Eckardt

Szymon Josefcuk

Jedrzey Szymanski

Alvaro Inostroza

Sebastian Klie

Zoran Nikoloski Goforsys

Joachim Selbig UPThanks to FAPESP for support!