Max Planck Institute

of Molecular Plant Physiology

http://www.mpimp-golm.mpg.de

Nature 433, 6th January 2005, p.12

.. journey southwest from Berlin to Golm, a small village near Potsdam, is a 90-minute train trip to the end of the world …

………. outside Potsdam the only view from the window is farmland stretching to the horizon, until an ultra-modern glass building looms out of the fog.

Thank You

Structure of MPI-MP

Horizontal structure -

with independence for groups led by (time-limited) young scientists

3 departments

comprising 12 departmental research groups mostly led by young scientists, all now qualified as ‚Research Group Leader‘

2 independent junior research groups, funded by the MPG

3 central infrastructure groups (expression profiling, metabolite profiling, bioinformatics) 3 service units (plant growth, microscopy, biophysics)

2 university guest groups + 2 GoFORSYS guest groups

Personnel 2010

Women in science:

- 52 % of the PhD students

- 34 % of the post-docs

- 18 % of the group leaders

Internationality:

- 39 % of the PhD students

- 65 % of the post-docs

- 24 % of the group leaders

Directors & Group Leaders

5%5%

PostDocs

PhD Students

Admin.service

33%33%

26%26%

12%12%

19%19%

5%5%

Students -masters -working

Scientific service

Internationality

Internationalityis atmosphere and environment,

not just counting heads

Internationality:

- 39 % of the PhD students

- 65 % of the post-docs

- 24 % of the group leaders

- 33% of the Directors

32 different countries …

Poland (20)

South America (12), esp. Brazil, Argentina, Chile, Mexico

China (11)

India (7), Nepal (4)

Australia (3), New Zealand (2)

Macedonia (3)

English is the official institute language

- all talks and seminars

- all information, operating instructions safety and other papers

- the new institute intranet / sharepoint- work contracts

Temporary accommodation on Campus, or in Golm

Help where possible with the authorities

The main biological question

What determines plant growth and composition?

Developmental regulation- Meristem activity - Cell cycle- Cell growth

Biophysics- Water movement - Cell expansion

Whole plant allocation - leaf area /unit biomass- root volume /unit biomass

Young leaves

RootsNutrientsWater

Light

CO2

FlowersSeeds

MetabolismMetabolism

and the biogenesis and use of and the biogenesis and use of machinery that is needed to machinery that is needed to

turn metabolites into biomass turn metabolites into biomass e.g. chloroplasts, ribosomes, cellulose synthasee.g. chloroplasts, ribosomes, cellulose synthase

A complex integrated processMany entry points for research

Our entry point

…….. using systems approaches.. using systems approaches

Genetically differing plants - Natural Diversity- Gene Technology nuclear plastome

Environmental conditions

Basic understanding, Biotechnology, Breeding, Biomarkers

Evolution of Research Strategies

Bioinformatics

Huge amounts of data

Many different sorts of data

Time seriesMultiple steady states

Systems Biology

Rigorous conceptual analysis

Multilevel AnalysisMolecular traits

Metabolic traits

Physiological traits

Integrative traits

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

Data acquisition DTD – GC/TOF: high throughput, high quality

~ 200 primary metabolites

robotic derivatisation & full extract injection:no fractionation, no cross contamination, reproducibility <10%RSD

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 45.000rt0

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

allowedchemical elements: C = 30

H = 50N = 5O = 10P = 5S = 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

Organicphase

Aequousphase

UPLC (C18) -MS(sec. Metabolites)

neg. mode

pos. mode

GC-MS AnalysisDerivatisation(prim. Metabolites)

20%

80%

UPLC (C8) -MS(Lipids)

neg. mode

pos. mode

plant tissue

13CO2

15N Ammonium nitrate

MeOH:MTBE:H2O

Thermo Exactive(Orbitrap)

All-in-One-Extraction Procedure combined with isotope labelling

500 550 600 650 700 750 800 850m/z

0

50

1000

50

100

Rel

ativ

e A

bund

ance

0

50

100756.55457

814.60767716.56818591.49866 877.62915554.51471 628.50470798.69470

856.74835819.67371756.70197628.62268587.62531 716.56122519.43634757.55157

779.53314 816.60785717.56573591.49915 878.62500

NL: 3.73E712c_s_pos_1#2315 RT: 9.20 AV: 1 T: FTMS {1,1} + p ESI Full ms [100.00-1500.00]

NL: 2.02E713c_s_pos_1#2605 RT: 9.20 AV: 1 T: FTMS {1,1} + p ESI Full ms [100.00-1500.00]

NL: 6.57E715n_s_pos_1#2643 RT: 9.19 AV: 1 T: FTMS {1,1} + p ESI Full ms [100.00-1500.00]

235 hitsm/z 756.55457

8 hits

C42H84ON2P4 C42H84O3N2P2SC42H79O8NP C42H74O3N7SC42H72O6N6 C42H84O3P2S2

C42H82O2N3S3 C42H80O5N2S2

C42

N1

1 hit C42H79O8NP

C 0 100

H 0 200

O 0 20

N 0 10

S 0 10

P 0 10

15N Labelled Sample

13C Labelled Sample

12C Labelled Sample

Isotope labeling — Annotation

Metabolic Profiling• allows a rapid and simple discrimination

between genotypes, developmental and environmental stages ( fingerprinting)

• allows functional analysis of genes with respect to their influence on metabolic composition

• is an indispensible level in systems approaches

• allows identification of biomarkers

Why (Metabol)Omics?

DNA RNA Protein Metabolite

Transcriptomics

Metabolomics

Complex phenotype

ProteomicsGenomics

Why (Metabol)Omics?

• Growth and performance of any biological system are to a large extent (if not totally) driven by its metabolic activity

• Metabolites are the last level of the realization of genetic information

• This level is most near to the complex phenotype ( in a linear thinking which is of course not correct)

Why (Metabol)Omics?

• Biosynthesis (and degradation) of metabolites is characterized by multiple chemical transformations

• A + B -> C + D

• This means that metabolism by principle represents a network

Why (Metabol)Omics?

• Most phenotypes are due to both linear and epistatic contributions

• Genetic markers at first approximation can only represent linear contributions

• Metabolites due to their inherent network characteristic represent the action and interaction of many gene products

• They should thus have the potential to mirror also epistatic interactions

Metabolomics and Diagnostics

• Prediction of plant biomass

• Discrimination of cancer vs non-cancer tissues

• Patient categorization (personalized medicine)

• Prediction of type 2 diabetes

• Metabolomics as a measure in wine production

Biomass prediction of field grown corn plants

• 300 corn inbred lines representing 3 maturity groups were grown on two field sites

• Leaves from 10 plants of each genotype and plot were harvested four weeks after germination ( leaf size : approx. 5 cm)

• Metabolic profiles were run for each genotype

• Biomass and flowering time were modeled using the metabolite data following a random forest approach

Discrimination of maturity groups in corn

Metabolic profiles have a high diagnostic power for complex traits

such as flowering time and early and late biomass

Division of the entire data set into a training set and a test set allows the prediction of biomass

Metabolomics and Diagnostics

• Prediction of plant biomass

• Discrimination of cancer vs non-cancer tissues

• Patient categorization (personalized medicine)

• Prediction of type 2 diabetes

• Metabolomics as a measure in wine production

Metabolic profiling allows assigment of normal and clear cell kidney carcinoma

class assignments

Control (normal)

Clear-cell RCC

RCC (other)

Control (normal)

66 0 0

Clear-cell RCC

1 60 0

RCC (other)

2 2 1

Metabolic Signature in CSF of Depressive Patients Accurately Predicts Antidepressant Treatment Response

Tanja Gärtner, Joachim Selbig, Abdelhalim Larhlimi, Patrick Giavalisco, Gareth Catchpole, Christian Namendorf, Lothar Willmitzer, Manfred Uhr,

Florian Holsboer

• Classification performance in strict cross validation

• Accuracy 88 %• Sensitivity 82 %• Specificity 93 %• False-discovery rate 8 %

Predicting Fasting Plasma Glucose Level Developmentusing Human Metabolic Profiles

Manuela Hische, Abdelhalim Larhlimi, Gareth S Catchpole, Andreas FH Pfeiffer, LotharWillmitzer, Joachim Selbig & Joachim Spranger

Charite Berlin, Berlin, University of Potsdam,

Max-Planck-Institute for Molecular Plant Physiology, Potsdam, German Institute of Human Nutrition, Potsdam

Results of Classification: Established risk markers are: gender, waist circumference, BMI, age and baseline fasting glucose levels.Variables Specificity Sensitivity AccuracyMetabolites 0.70 0.67 0.68Established markers 0.56 0.50 0.53Metabolites + Established markers 0.70 0.67 0.68

Metabolomics and Diagnostics

• Prediction of plant biomass

• Discrimination of cancer vs non-cancer tissues

• Patient categorization (personalized medicine)

• Prediction of type 2 diabetes

• Metabolomics as a measure in wine production

32

The wine market is highly fragmented with respect to quality criteria and assurance

This leads to uncertainty concerning quality, origin, year and variety (authenticity)

The problem is rooted in the absence of any objective technology

Problems

33

The sum of its compounds reflects history and determines taste and quality of each

wine

The sum of its compounds is equivalent to its metabolic composition

Some first principles :

3434

Wine samples

Sample preparation

Data Analysis & Interpretation• Relative quantitative sample comparisons • Determine statistical significance• Identify metabolite(s) of interest

5

Biomarkers

Data Analysis & Interpretation/own system• Identification of masses • Potential BioMarkers for discrimination of wine samples

Sample analysis

UPLC/LTQ-Orbitrap

RT: 0.00 - 15.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14Time (min)

0

20

40

60

80

1000

20

40

60

80

1000

20

40

60

80

100

Re

lative

Ab

un

da

nce 0

20

40

60

80

1003.30

6.896.00

5.323.50 7.914.83

6.08 7.824.61 8.518.871.33 10.207.261.15 1.93 2.66 11.09 11.78 12.45 14.14

6.91

3.30

5.336.024.83 7.72 8.503.49 6.10

1.32 10.204.191.24 8.883.10 14.4312.3111.061.69 12.73

4.61

6.01 7.923.32

5.32 7.083.50 8.506.89 9.704.521.33 9.511.711.15 3.11 11.32 14.4712.2110.89 12.51

6.90

5.325.99

6.093.33

7.934.61 6.458.511.331.26 3.09 4.35 8.872.09 9.08 10.20 11.11 12.04 12.69 14.350.84

NL:9.47E6Base Peak MS R_A_Carm_05_4_neg

NL:1.10E7Base Peak MS r_ct_05_2_neg

NL:1.08E7Base Peak MS r_a_syrah_05_4_neg

NL:7.98E6Base Peak MS v_ta_m_06_2_neg

Metabolic Mass spectrum

The workflow

3535

Cultivar Identification

a CM ME SYCS

i

iiiii

iv

vvi

b

CSCM ME SY

ii

i

iv

v

iii

vi

3636

Vineyard Discrimination

Vineyard CT

Vineyard Vasco

Vineyard VSP

From the same country

37

Vintage ( production year) discrimination

Year 2004

Year 2005Year 2006

3838

LDA of Wines (Negative Mod) LDA of Wines (Positive Mod)

Quality Discrimination

Metabolomics and Diagnostics

• Prediction of plant biomass

• Discrimination of cancer vs non-cancer tissues

• Patient categorization (personalized medicine)

• Prediction of type 2 diabetes

• Metabolomics as a measure in wine production