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Max Planck Institute of Molecular Plant Physiology. http://www.mpimp-golm.mpg.de. Nature 433, 6 th 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 … - PowerPoint PPT Presentation
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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