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Max-Planck-Institut of Molecular Pflant Physiology, Golm. MPIMP-Golm: Organisation. Founded 1994, following reunification, on a shared site with 2 further MPI‘s, a Fraeunhofer Institute and the Science/Maths Faculty of Potsdam University.

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slide1

Max-Planck-Institut of

Molecular Pflant Physiology, Golm

mpimp golm organisation
MPIMP-Golm: Organisation

Founded 1994, following reunification, on a shared site with 2 further MPI‘s,

a Fraeunhofer Institute and the Science/Maths Faculty of PotsdamUniversity

Actually operates as a Institute with ca. 25 independent research groups, many led by young scientists on time-limited contracts

  • Junior Research Groups
  • - Mike Udvardi
  • Marcus Pauly
  • Infrastructure groups
  • - Biophysics
  • - Transcript Profiling
  • Bioinformatics

Guest Groups

- MPG Professorship (Schmidt)

- Biofutura Group (Krämer)

University Guest Groups

- Müller-Röber

- Altmann

mpi f r molekulare pflanzenphysiologie mission

Aufnahme

Biosynthese

Transport

Verteilung

Speicherung

Speicher-

Struktur-

Signal-

von

Substanzen

mit

MPI für Molekulare Pflanzenphysiologie: Mission

Widmungsbereich :

F

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P

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slide4

Founding Mission: to follow an integrated research approach to solve basic questions in plant physiology,which combines methods from genetics, molecular biology, chemistry and physiks

Analysis

Genotypes

Environment

Creategenetic diversity,

grow it in defined environmental conditions

and subject it tobroad phenotyping

genetic diversity f ed into the phenotyping machine

Genetic diversity

Genetic diversity fed into the phenotyping machine

defined environments

broad phenotyping

growing in is subjected to

Single

Biased

Alter expression

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

Hight throughput unbiased antisense to identify key genes

Systematic production of knock-outs using T-tagging

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

TILLING (in development).

Many

Alter protein

Unbiased

Environment

Genotypes

Analysis

phenotyping platform

Genetic diversity

Phenotyping platform

defined environments

broad phenotyping

growing in is subjected to

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

Real time RT-PCR platform to analyse 1400 transcription factors

Hight throughput unbiased antisense to identify key genes

Systematic production of knock-outs using T-tagging

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

TILLING (in development).

Environment

Genotypes

Analysis

Expression profiling

  • Arrays
  • Commercial systems
  • e.g., Affymetrix array
  • for >22,000 genes
  • In-house arrays
  • e.g. tomato, Lotus
phenotyping platform1

Genetic diversity

Phenotyping platform

defined environments

broad phenotyping

growing in is subjected to

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

Hight throughput unbiased antisense to identify key genes

Systematic production of knock-outs using T-tagging

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

TILLING (in development).

Environment

Genotypes

Analysis

Expression profiling

Proteomics, Enzyme profiling

phenotyping platform2

Genetic diversity

Phenotyping platform

defined environments

broad phenotyping

growing in is subjected to

amino acids

alcohols

hexoses

orgP

disacch.

trisaccharides

glyosylated met.

amines

hydroxy acids

sugar alc

GC/TOF – ca 100 knowns and 500 unknowns

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

Hight throughput unbiased antisense to identify key genes

Systematic production of knock-outs using T-tagging

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

TILLING (in development).

Environment

Genotypes

Analysis

Expression profiling

Proteomics,

Enzyme profiling

Metabolite profiling

Plus:

GC-Electrospray

LC Electrospray

LC-triplequad

Robotised enzymic assays

phenotyping platform3

Genetic diversity

Phenotyping platform

defined environments

broad phenotyping

growing in is subjected to

0,006

*

Ara

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

0,005

*

Xyl

Xyl

0,004

Glc

Glc

Glc

Glc

Ac

0,003

Relative Intensity

0,002

Hight throughput unbiased antisense to identify key genes

*

*

*

0,001

*

Xyl

Xyl

*

*

*

Glc

Glc

Glc

Glc

Systematic production of knock-outs using T-tagging

0

Ac

700

800

900

1000

1100

1200

1300

Enzyme digestionMALDI-TOF

m/z

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

TILLING (in development).

Environment

Genotypes

Analysis

Phenotyping platform

Expression profiling

Proteomics,

Enzyme profiling

Metabolite profiling

Cell wall profiling

phenotyping platform4

Genetic diversity

Phenotyping platform

defined environments

broad phenotyping

growing in is subjected to

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

Hight throughput unbiased antisense to identify key genes

Systematic production of knock-outs using T-tagging

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

TILLING (in development).

Environment

Genotypes

Analysis

Phenotyping platform

Expression profiling

Proteomics,

Enzyme profiling

Metabolite profiling

Cell wall profiling

Subcellular analysis

Single Cell/Tissue analysis

integrate display and analyse the data

Genetic diversity

IntegrateDisplay and Analyse the Data

defined environments

broad phenotyping

growing in is subjected to

Environment

Genotypes

Analysis

Targetted or systematic overexpressionorinhibition of genes in major metabolic paths or other processes–of-interest

Expression profiling

Enzyme profiling

Hight throughput unbiased antisense to identify key genes

Systematic production of knock-outs using T-tagging

Metabolite profiling

Create (Arabidopsis) or collab-orate to gain access to (tomato, maize, pea) introgression lines.

Cell wall profiling

TILLING (in development).

Subcellular analysis

Single Cell/Tissue analysis

complementary ways to interpret and display data
Complementary ways to interpret and display data

Central Bioinformatics Group

Research Service

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

complementary ways to interpret and display data1
Complementary ways to interpret and display data

Interpretation against the background of known pathwaysand processes

Flexible user-driven display package

Curation of e.g. gene assignment

Central Bioinformatics Group

Research Service

Cluster trees

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

‚Wet bench‘Groups with Embedded

Bioinformatics Activities

MapMan O.Thimm & Y.Gibon

Mining and visualisation of novel relations using a large informatics toolbox

Clustering

Machine learning

Mutual information

Neuranl networks

CSB.DB Net Visualisation: A. Luedemann

mpi f r molekulare pflanzenphysiologie mission1

Aufnahme

Biosynthese

Transport

Verteilung

Speicherung

Speicher-

Struktur-

Signal-

von

Substanzen

mit

MPI für Molekulare Pflanzenphysiologie: Mission

main research area :

F

u

n

k

t

i

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P

h

s

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g

y

rational metabolic engineering
‚rational metabolic engineering‘

‚Your favorite‘ Gen bzw. Protein

Trans-

genic Plant

‚Reverse‘ Genetik

Analysis

understanding

directed manipulation of

metabolism and analysis of the plants

..............

biologische systems are networks

Strike

Biologische Systems are networks .....

... Like a undergrond system

antisense inhibition of purf in tab acco
Antisense-Inhibition of purF in Tabacco

pBinAR Atase1 1482 bp

sense

OCS

214 bp

S35

pBin AR

A: Phenotype of the T0 Plants

B: Northern analysis

WTWT WT WT 3 38 60 54 19 17 41 50

determination of the flux control coefficient for every enzyme in a pathway

Sucrose

Cell wall

4

Amyloplast

Sucrose

Starch

Create a set of antisense lines with a progressiveinhibition of expression for each of the enzymes in the pathway, and measure the impact on flux ...

UDP

1

12, 13

ADP

3

Glu

Fru

UDPglc

ADPglc

PPi

PPi

2

11

5

6

UTP

ATP

ATP

F6P

G1P

< 0.1

0.1-0.2

0.2-0.3

0.3-0.4

0.4-0.5

0.5-0.6

0.6-0.7

0.7-0.8

0.8-0.9

0.9-1.0

> 1.0

G1P

10

7

8

9

G6P

F6P

G6P

G6P

8

8

Pi

Glycolysis

3PGA

Pi

ATP

ADP

15

Cytosol &

Mitochondria

14

ATP

TCA-cycle

Respiration

Determination of the Flux Control Coefficient for every enzyme in a pathway:
rational metabolic engineering1
‚rational metabolic engineering‘

Needs knowledge or / and luck

Often didnt work / give the expected result

...............

Assumes can understand complex systems as a sum of the parts

... decided to develop a technology platform

to allow broad phenotyping

and understand the response better

biologische systems are networks1
Biologische systems are networks ......

.. ´shut down each station ...... one after the other

.. and carry out a detailed analysis of the consequences....

slide21

Needs model systems

  • ARABIDOPSIS GENOME:
  • Small and compact

28,000 Genes

complete Genomesequence

December 2000

‚Knock-outs‘ available for almost every gene

t dna insertions to produce k o lin es for every gene

Insertion eines Stücks DNS in den kodierenden Bereich eines Gens

T-DNA insertions to produce k.o.-Lines for every gene

‚Knock-out‘-Mutante

Suche nach sichtbaren oder biochemischen Merkmalen

Analyse des Expressionsmusters dieser Mutanten

Analyse des Metabolitenprofils

Physiologie der Pflanze

Herstellung der Gen-Funktionsbeziehung

slide23

Search for a visible or biochemical/molecular phenotype

using a broad multi-level analytic platform

The technology platform was in place

from single to multi gene traits
From single- to multi-gene traits

Exploit Natural Diversity in Arabidopsis and

Crop plants -. QTL‘s, Association studies, NILS

Genomic disection/functional analysis of responses

cross between a modern breeding line and a wild relation
Cross between a modern breeding line and a wild relation

Lycopersicon pennellii x Lycopersicon esculentum

‚Introgression‘ Lines-

Each contains a small section of the genome from the wild relation

slide26

chromosome 9

+GP39

GP39

9-A

-GP39

(CT225A)

8.9

+TG254

IL9-1-2

TG254

5.5

9-B

TG18

IL9-1

3.6

TG9

+TG9

4.0

-CT143

CT143

9-C

-CT143

(CT283A)

6.9

IL9-1-3

+TG223A

TG223A

9-D

IL9-2-5

10.1

(TG225,TG10)

+GP263

-CT32

CT32

4.7

9-E

IL9-2

CP44

1.1

CD32A,CT215A,CT284B

5.3

+CD32A

-TG591B

TG568

(TG3A,CD8,CT215B,CT215C)

3.3

GP125A

9-F

TG79

0.0

TG207,CT17,TG486,TG589,TG640,Tm2a

TG101,TG291

PC6

CT208

-CT208

CT208.,CT235,TG79,TG415,CT17,CD3,TG591B

TG390

+TG390

4.0

9-G

IL9-2-6

-TG551

TG551

(CT279,TG35,CT183,TG558,TG409)

2.8

+TG404

TG404

9-H

(TG144)

1.9

TG186,CT236

+TG186

2.0

TG429

1.0

(Est-2)

-TG348

TG348,TG347

3.0

TG248

9-I

1.0

GP94B

2.0

CT74,CT177

1.7

-GP129

GP129

2.2

+CT198

CT198

3.9

(GP123A)

CT218

3.8

IL9-3

IL9-3-2

TG421

9-J

IL9-3-1

(TG8, Nr)

5.5

TG424

5.9

GP101, CHS4

+GP101

(CT96)

4.5

-CT112A

CT112A

(CT210)

2.2

TG328,GP41,TG591A

1.6

CT71

9-K

2.7

CT220

+CT220

analysis of complex traits by profiling and bio informatics
Analysis of complex traits by profiling and bio-informatics

Database of all genes, identified by locus and organised into hierachical functional categories

Commercial 22K Affymetrix Arrays

D

I

S

P

L

A

Y

Real Time RT-PCR for >1200 transcription factors

2D peptide chrom-atography/MS to

monitor proteins

Biased metabolite measurements to define the experimental system

Enzyme assays (ELISA, robot)

Metabolite profiles

In sets that make sense and can be digested by the user

expanding the paradigm of gene function from gene knock outs to allelic diversity
Expanding the paradigm of gene function: from gene knock-outs to allelic diversity

Starting up:

Natural Diversity, including/especially

Arabidopsis ‚ecotypes‘

TILLING

Chemical genetics

main problems
Main Problems

Lack of - Inadequate annotation of genes

knowledge - Poor knowledge about plant function

Complexity - Subcellullar organisation/dynamics

of the system - Multicellular organism

- Need to approach many systems (CROPS!!)

Inadequate - Massive underfunding relative to

Funding the complexity of the system

- Fragmentation of funding between countries

- Lack of acceptance of plant biotech

most succesful applications most critical failures
Most succesful applications / most critical failures

Creating data is becoming easier and easier

With some exceptions

- Quantitative proteomics

- Fluxes

- Dealing with spatial and temporal dynamics

- Quantifying ‚whole plant‘ phenotypes

The bottleneck is the interpretation of the data

In plants: special problem is to identify systems that balance

simplicity/prior knowledge with high academic importance

special technologies challenges
Special technologies & challenges

Establishing a wide range of phenotyping platforms

Multiplexed RT-PCR

Mutliple platforms for metabolite profiling

Enzyme Profiling

Driving the bioinformatics by the need to intepret your own data sets

Integrate bioinformatics into the ‚wet bench‘ groups

Keep the applications at an appropriate complexity level

Trying to integrate data at different levels

interactions with industry government
Interactions with industry / government
  • Government
  • Major role in the German plant genomics program (GABI).
  • with many projects, host the SCC (Coordinating Office).
  • Informal contacts and advice
  • Spend (waste?) a lot of time trying to influence the
  • political and public debate on plant biotechnology
  • Industry
  • Many interactions and collaborations, in governnment-
  • funded and in bilateral projects
  • Two firms founded – one centered around systems biology
  • approaches
plans for the future
Plans for the future

Expand the use of natural diversity and NILS

Bioinformatics .......

Data integration,

Data visualisation