Max-Planck-Institut of Molecular Pflant Physiology, Golm

1. Max-Planck-Institut of Molecular Pflant Physiology, Golm

2. 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

3. Aufnahme Biosynthese Transport Verteilung Speicherung Speicher- Struktur- Signal- von Substanzen mit MPI für Molekulare Pflanzenphysiologie: Mission Widmungsbereich : F u n k t i o n P h s i o l o g i e

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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 o n P h s i o l o g y

15. ‚rational metabolic engineering‘ ‚Your favorite‘ Gen bzw. Protein Trans- genic Plant ‚Reverse‘ Genetik Analysis understanding directed manipulation of metabolism and analysis of the plants ..............

16. Strike € € € € € € € € € € Biologische Systems are networks ..... ... Like a undergrond system

17. 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

18. 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:

19. ‚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

20. Biologische systems are networks ...... .. ´shut down each station ...... one after the other .. and carry out a detailed analysis of the consequences....

21. Needs model systems • ARABIDOPSIS GENOME: • Small and compact 28,000 Genes complete Genomesequence December 2000 ‚Knock-outs‘ available for almost every gene

22. 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

23. Search for a visible or biochemical/molecular phenotype using a broad multi-level analytic platform The technology platform was in place

24. 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

25. 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

26. 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

27. 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

28. Expanding the paradigm of gene function: from gene knock-outs to allelic diversity Starting up: Natural Diversity, including/especially Arabidopsis ‚ecotypes‘ TILLING Chemical genetics

29. 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

30. 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

31. 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

32. 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

33. Plans for the future Expand the use of natural diversity and NILS Bioinformatics ....... Data integration, Data visualisation