Licht ins Dunkel BLACKBOX

Transcription

1 Licht ins Dunkel BLACKBOX 1

2 Hypothesis-driven Research Hypothesis 1 Hypothesis 2 Hypothesis 1 Hypothesis 2 Hypothesis Focus 3 3 Hypothesis 3 Hypothesis 4 Hypothesis 5 2

3 Light into the Blackbox Answers to Questions we have asked 3

4 Vision Look at the whole Picture OME GARTEN DER LÜSTE (bosch) Hieronymus Bosch, The garden of earthly delights 4

5 The whole Picture Viewing with OMICS more Answers -- but to Questions we have never asked (we are confused) (and the pixel quality is worse) (and it is expensive) GARTEN DER LÜSTE (bosch) Hieronymus Bosch, The garden of earthly delights 5

6 OMICS for YOU (Christoph Aufricht, Wien) OMICS is the Analysis of the OMEs GENOME TRANSCRIPTOME PROTEOME METABOLOME 1. Country Map View from the Helicopter 2. Some Sightseeing 3. Survey/Looking back PS: I will not talk about Genomics. 6

7 What is the potential Clinical Use for OMICS? Personalized Medicine Risikprofile Individualized Medication Individual Profiling Populations profiling Moleculare Epidemiology Identification of novel therapeutic Targets New Drugs Molecular Targets Side effects BIOMICS Transcriptomics Proteomics Metabolomics Bioinformatics ome -wide Associations New Biomarkers Better Understanding of Pathomechanisms Translational Science ome wide Associations new (=unexpected) Results Modified from Nature 2008, 455,

8 Different OMICS in our Research static Genome dynamic Gene expression Transcriptomics Microarrays RNA expression mirna Environmental effects Protein expression Proteomics 2DGE + MS volatile Metabolism Metabolomics Liquid chromatogr. MALDI-TOF modified from: Du, 2001, Computational Proteomics and Metabolomics Introduction 8

9 Different OMICS in our Research static Genome dynamic Gene expression Transcriptomics Microarrays RNA expression mirna Environmental effects Protein expression Proteomics 2DGE + MS volatile Metabolism Metabolomics Liquid chromatogr. MALDI-TOF modified from: Du, 2001, Computational Proteomics and Metabolomics Introduction 9

10 Transcriptomics Workflow am Beispiel MicroArray Cell population A Cell population B RNA extraction A A B Quantify pixel intensities. B Reverse transcription A A B Overlay images B Klenow label incorporation Sample A labelled with cy5 dye Sample B labelled with cy3 dye Red increase of Cy5 sample transcripts Bioinformatics cy5 Green Scan increase of Cy3 sample transcripts channel Yellow equal abundance Scan cy3 channel Hybridisation Washing

11 A Combined Transcriptome and Bioinformatics Approach to Unilateral Ureteral Obstructive Uropathy in the Fetal Sheep Model Springer A et al. J. Urol 2012 Transcriptomics Bioinformatics 11

12 PROTEOMICS (the Queen of OMICS) static Genome dynamic Gene expression Transcriptomics Microarrays RNA expression mirna Environmental effects Protein expression Proteomics 2DGE + MS volatile Metabolism Metabolomics Liquid chromatogr. MALDI-TOF modified from: Du, 2001, Computational Proteomics and Metabolomics Introduction 12

13 Proteomics = Integrative Approach towards Research Stress Adapted from K. Kratochwill

14 Proteomics = Integrative Approach towards Research Stress Lechner et al., J Proteome Res Adapted from K. Kratochwill

15 Proteomics = Integrative Approach towards Research Stress 15

16 Concept of Inadequate Stress Responses in PD Kratochwill et al., Am J Pathol Open&Unbiased Reduced Cytoprotection Metabolic Stress&Toxicity Inflammation Hypothesis- Generating 16

17 Metabolomics Hold your Breath... static Genome dynamic Gene expression Transcriptomics Microarrays RNA expression mirna Environmental effects Protein expression Proteomics 2DGE + MS volatile Metabolism Metabolomics Liquid chromatogr. MALDI-TOF modified from: Du, 2001, Computational Proteomics and Metabolomics Introduction 17

18 Measuring Metabolites in PD Effluents ( Profiling ) Mass Spectrum of Metabolites in PD Effluent from PET Samples of Patients of the PD-protec Study In Cooperation with D. Kasper, STW Screening Program 18

19 Fitting Metabolite Profiles into Pathways Trying to understand life without knowledge of biochemical network would be like trying to understand Shakespeare without knowledge of English grammar. 19

20 Looking back und next Goals ( Biomarker ) Genome What could happen. Transcriptome What seems to happen. Hypothesis-Driven (Focused) Research Approach (Open & Unbiased) Hypothesis-Generating Proteome What makes it happen. Metabolome What has happened and is happening. 20

21 Does OMICS change everything in Research? Experiments Time Gene expression Time Protein expression Time Metabolite profil Relevant Questions & Hypothesis From (Raw)data to Information New Findings, Model adequat? Analysis, Interpretation Kowledge New 21

22 Thank You! Universitätsklinik Kinder&Jugendheilkunde Arnold Pollak Kinderdialyse Wien Klaus Arbeiter Michael Böhm Steffi Dufek Dagmar Csaicsich Thomas Müller Krisztina Rusai Das Pflegeteam Rob Beelen Piet ter Wee Margot Schilte Anna-Spiegel Lab Klaus Kratochwill Rebecca Herzog Anton Lichtenauer Lilian Kuster Silvia Tarantino Katarszina Bialas Monika Sabler Anja Wagner Elisabeth H. Salzer Konstantin Bergmeister Markus Süß Abt. Kinderchirurgie Alex Springer Forschungslabor David Kasper Klinisches Institut Pathologie Heinz Regele Nickolaus Wick Katalin Nagy Zentrum für Biomedizinische Forschung Helga Bergmeister Universitätsklinik Chirurgie Rudolf Öhler Andreas Spittler Universitätsklinik Innere Medizin III Andreas Vychytil Walter H. Hörl Das Pflegeteam KKS Cornelia Krabacher Marion Gerlach Verena Wächter Johannes Pleiner Universitätsklinik Dermatologie Marion Gröger Andreas Rizzi Bernd Mayer Paul Perco Achim Jörres Thorsten Bender Oliver Eickelberg Scott Van Why Gunilla Thulin Michael Kashgarian R. Beelen C. Aufricht J. Witowski M. Fischbach N. Topley C.P. Schmitt M. Lopez-Cabrera A. Jörres P. Rutherford 22