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PROTEOMICS OF OBESITY

Genomics on Obesity Toulouse 7-8 June 2007. PROTEOMICS OF OBESITY. Jennifer RIEUSSET (jennifer .rieusset@univ-lyon1.fr). UMR INSERM 870 / INRA 1235 Régulations métaboliques, nutrition et diabètes Hubert VIDAL Lyon. CONTENTS. Slide. What is proteomics ? 3 - 8

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PROTEOMICS OF OBESITY

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  1. Genomics on Obesity Toulouse 7-8 June 2007 PROTEOMICS OF OBESITY Jennifer RIEUSSET (jennifer .rieusset@univ-lyon1.fr) UMR INSERM 870 / INRA 1235 Régulations métaboliques, nutrition et diabètes Hubert VIDAL Lyon

  2. CONTENTS Slide • What is proteomics ? 3 - 8 • Why do it ? 9 - 10 • How is it done ? 11 - 24 • Application of proteomics to obesity 25 – 49 • Acknowledgements 50 • Abbreviations used 51 Genomics on Obesity, Toulouse, 7-8 June 2007

  3. OVERVIEW • What is proteomics ? • Why do it ? • How is it done ? • Application of proteomics to obesity Genomics on Obesity, Toulouse, 7-8 June 2007

  4. DEFINITIONS • PROTEOME « The analysis of the entire PROTEin complement expressed by a genOME, or by a cell or tissue type. » Wasinger VC et al. Electrophoresis 16 (1995) • PROTEOMICS Study of the proteins expressed by a genome in a biological sample (organism, organ, biological fluids), at a given point in time, in a given situation. Genomics on Obesity, Toulouse, 7-8 June 2007

  5. Dynamics and protein concentration range Functional Proteins DNA mRNA Proteins Genome Transcriptome Proteome Post-translational modifications Transcription Translation Human: ~ 30 000 genes ~300 000 transcripts ~ 3 000 000 proteins Genomics on Obesity, Toulouse, 7-8 June 2007

  6. Diverse properties of proteins Proteomics is a particularly rich source of biological information • complex • dynamic • PTMs Genomics on Obesity, Toulouse, 7-8 June 2007

  7. Complexity of proteomes Same genome Different proteomes Genomics on Obesity, Toulouse, 7-8 June 2007

  8. Control modified Applications of proteomics • Systematic proteome description • Functional proteomics • Differential analysis (biological markers) • Cell map proteomics (organelles) • protein/protein or protein/drug interactions • Post-translational modifications Multiprotein complexes affinity purification Genomics on Obesity, Toulouse, 7-8 June 2007

  9. OVERVIEW • What is proteomics ? • Why do it ? • How is it done ? • Application of proteomics to obesity Genomics on Obesity, Toulouse, 7-8 June 2007

  10. Why do proteomics ? • mRNA expression analysis does not always reflect the expression level • of proteins • Biological samples such as CSF, serum, urine etc. are not suitable for • mRNA expression analysis • It focuses on gene products – the active agents in cells/tissues/organisms • Analyse the modifications of proteins that are not apparent from DNA • sequence (i.e. post-translational modifications) • Analyse the location of proteins Genomics on Obesity, Toulouse, 7-8 June 2007

  11. OVERVIEW • What is proteomic ? • Why do it ? • How is it done ? • Application of proteomics to obesity Genomics on Obesity, Toulouse, 7-8 June 2007

  12. Proteomics workflow • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional analysis Genomics on Obesity, Toulouse, 7-8 June 2007

  13. Sample preparation Conditions sufficiently denaturing to solubilize a maximum of proteins, to dissociate all the complexes, to maintain them in solution and avoid all chemical modifications of protein subunits. • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis chaotrope Agents Reducing Agents DTT (65 mM) Dithiothreitol DTE ( 65 mM) Dithioerythreitol TBP (2mM) Tributyl phosphine Urea (5-8M) Thiourea (2M) Non ionic Detergents Ampholytes CHAPS (2-4%) SB 3-10 (2%) ASB-14 (1%) SDS <0,25% IPG buffer pH 3-10 0,5-2% Genomics on Obesity, Toulouse, 7-8 June 2007

  14. Sample preparation • Lysis procedure • Protease inhibitors • Removal of interfering substances • Nucleic acids • lipids • salts • insoluble materials… • Precipitation • Fractionation • subcellular • differential • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis Genomics on Obesity, Toulouse, 7-8 June 2007

  15. Protein separation • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis • Gel-based proteomics • 1D or 2D electrophoresis … • Mass spectrometry driven proteomics • Chromatography • ICAT … • Protein arrays Genomics on Obesity, Toulouse, 7-8 June 2007

  16. 2D electrophoresis The most widely used technical approach • 1D: separation based on the pI of proteins • 2D: separation based on the molecular weight • of proteins • Several visualization/detection possibilities => Up to 10 000 protein spots/gel Genomics on Obesity, Toulouse, 7-8 June 2007

  17. First dimension: IPG strip IPG: Immobolized pH gradients Copolymerisation of the pH gradient with the acrylamide matrix on a plastic film • Size : • Width : 3mm • Depth: 5mm • Length: 7, 11, 13, 18 et 24 cm Best resolution and reproducibility • pH scale: • large : 7 pH units (3-10, 3-10NL) • narrow : 3-4 pH units (3-7, 4-7, 6-9, 6-11) • micro : 1 pH unit (3,5-4,5, 4-5, 4,5-5,5, 5-6, 5,5-6,5) Large scale Narrow scale: Increase loading capacities and resolution of proteins Genomics on Obesity, Toulouse, 7-8 June 2007

  18. First dimension :Isoelectric focusing (IEF) IPGphor (Amersham) • Programming : • Voltage (0-10000V, step-n-hold, gradient) • 50 µA/strip • Vh • temperature: 15-20°C Genomics on Obesity, Toulouse, 7-8 June 2007

  19. Equilibration Tris-HCl 50 mM pH 8,8, Urée 6M, Glycérol 30%, SDS 2% + DTT 125 mM during10 min + iodoacétamide 125 mM during 10 min Genomics on Obesity, Toulouse, 7-8 June 2007

  20. 7,1 8,8 8,8 8,8 7,1 3,7 3,7 8,4 8,4 7,1 3,9 3,9 3,9 8,4 5,3 5,3 pH 3 pH 10 8,4 Poids moléculaires 7,1 5,3 3,7 SDS-PAGE Criterion cell and precast gels (BioRad) Genomics on Obesity, Toulouse, 7-8 June 2007

  21. Protein detection • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis Sensibility 100 ng 200 pg 1 ng 250 pg 1pg Linearity low low high high high Methods Comassie blue Silver Nitrate Fluorescence Fluorescent labelling Radiolabelling Genomics on Obesity, Toulouse, 7-8 June 2007

  22. Protein detection Imagescanner (Amersham) • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis Image Master 2D Platinum (Amersham) Proteins are automatically detected, background is corrected, spot density is quantified and spots are matched between up to 100 gels Genomics on Obesity, Toulouse, 7-8 June 2007

  23. Protein identification • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis • MS identification of proteins after quantitative analysis • by 2DE • Peptide mass fingerprinting (MALDI MS) • Sequence based identification (MS/MS) • Identification and quantitation using MS • Labelling samples for quantitative analysis • Identification of post-translational modifications Genomics on Obesity, Toulouse, 7-8 June 2007

  24. Validation • Validation • by Western-blot • by ELISA • by activity measurements … • Functional analysis • Overexpression • siRNA … • Sample preparation • Protein separation • Protein detection • Protein identification • Validation and functional • analysis Genomics on Obesity, Toulouse, 7-8 June 2007

  25. OVERVIEW • What is proteomics ? • Why do it ? • How is it done ? • Application of proteomics to obesity Genomics on Obesity, Toulouse, 7-8 June 2007

  26. OBESITY Glucose homeostasis requires the coordinated actions of various organs Genomics on Obesity, Toulouse, 7-8 June 2007

  27. Proteomics of obesity Schmid GM, Converset V, Walter N, Sennitt MV, Leung KY, Byers H, Ward M, Hochstrasser DF, Cawthorne MA, Sanchez JC. Effect of high-fat diet on the expression of proteins in muscle, adipose tissues, and liver of C57BL/6 mice. Proteomics. 4:2270-82, 2004. Sanchez JC, Converset V, Nolan A, Schmid G, Wang S, Heller M, Sennitt MV, Hochstrasser DF, Cawthorne MA. Effect of rosiglitazone on the differential expression of obesity and insulin resistance associated proteins in lep/lep mice. Proteomics. 3:1500-20, 2003. Budde P, Schulte I, Appel A, Neitz S, Kellmann M, Tammen H, Hess R, Rose Peptidomics biomarker discovery in mouse models of obesity and type 2 diabetes. Comb Chem High Throughput Screen. 8:775-81, 2005. Hittel DS, Hathout Y, Hoffman EP, Houmard JA.Proteome analysis of skeletal muscle from obese and morbidly obese women. Diabetes. 54:1283-8, 2005. DeLany JP, Floyd ZE, Zvonic S, Smith A, Gravois A, Reiners E, Wu X, Kilroy G, Lefevre M, Gimble JM. Proteomic analysis of primary cultures of human adipose-derived stem cells: modulation by Adipogenesis. Mol Cell Proteomics. 4:731-40, 2005. Xu A, Wang Y, Xu JY, Stejskal D, Tam S, Zhang J, Wat NM, Wong WK, Lam KS. Adipocyte fatty acid-binding protein is a plasma biomarker closely associated with obesity and metabolic syndrome. Clin Chem. 52:405-13, 2006. Genomics on Obesity, Toulouse, 7-8 June 2007

  28. Proteomics of obesity Hittel DS et al.Proteome analysis of skeletal muscle from obese and morbidly obese women. Diabetes. 54:1283-8, 2005. Genomics on Obesity, Toulouse, 7-8 June 2007

  29. OBESITY Chronic elevation of NEFAs inhibits insulin action in skeletal muscle Insulin Genomics on Obesity, Toulouse, 7-8 June 2007

  30. OBESITY Genomics on Obesity, Toulouse, 7-8 June 2007

  31. Mitochondrial dysfunction Potential mechanism by which mitochondrial dysfunction induces insulin resistance in skeletal muscle Lowell BB et al. (2005) Science 307, 384-387. Genomics on Obesity, Toulouse, 7-8 June 2007

  32. Skeletal muscle - 4% of muscle mass - Variation with function of muscles, type of fibers, physical activity and age. - 2 populations of mitochondria: Subsarcolemmal mitochondria Intermyofibrillar mitochondria Genomics on Obesity, Toulouse, 7-8 June 2007

  33. Mitochondrial proteome Human genome: 30 000-40 000 genes Mitochondria ~ 1500 proteins

  34. Mitochondrial dysfunction Stockage TG Fatty acids Fatty acids Signaling - Acyl-CoA Glucose Glucose ß-oxydation Altered mitochondrial structure, biogenesis and function in skeletal muscle of HFD mice PGC-1a Target gene nucleus CO2 O2 Identify differentially expressed proteins in skeletal muscle of SD and HFD-fed mice (after 4 and 16 weeks of diet) 2D gels of mitochondrial proteins Genomics on Obesity, Toulouse, 7-8 June 2007

  35. Sample preparation 2 times of diet (4 and 16 weeks) pI 3 10 6 SD mice 6 HFD mice Gastrocnemius muscle pI 3 10 Purification of mitochondria 7M Urea, 2M thiourea, 1% ASB14, 2mM TBP, 0.2% IPG buffer, BB Protein solubilization Genomics on Obesity, Toulouse, 7-8 June 2007

  36. pI 3 10 SD pI 3 10 kDa kDa HFD 205 205 80 80 45 45 30 30 21 21 14 14 6.5 6.5 2D electrophoresis 6 mt samples SD 6 mt samples HFD pI 3 10 1 mt sample SD 1 mt sample HFD Strip pH 3-10NL 20 µg mt proteins Active rehydration (50V) Focalisation: 22 250 V.h. 6 strip SD 6 strip HFD IEF 6 2D gels SD 6 2D gels HFD SDS-PAGE gels: 8-16% Silver nitrate staining pI 3 Genomics on Obesity, Toulouse, 7-8 June 2007

  37. Data analysis • Format • Resolution (dpi) • depth (8-16 bit) • Artefacts Image acquisition (300 dpi) 10 Image analysis ImageMaster 2D Platinum Identification by LC-MS/MS Proteomic platform of Rhônes-Alpes Region Jerome Garin, CEA Grenoble Genomics on Obesity, Toulouse, 7-8 June 2007

  38. Data analysis • Visualizing and calibrating gels • Detecting spots • (intensity, volume) • Matching spots • Verification of match • Intra-class analysis • Statistical tests • Kolmogorov, Wilcoxon, T-test Image acquisition (300 dpi) 10 Image analysis ImageMaster 2D Platinum Identification by LC-MS/MS Proteomic platform of Rhônes-Alpes Region Jerome Garin, CEA Grenoble Genomics on Obesity, Toulouse, 7-8 June 2007

  39. Relative intensity (u. a.) Data analysis Molecules Ionisation:MALDI, ESI, … Image acquisition (300 dpi) Analyser TOF, Q, B, IT … m/z 10 Detection Image analysis ImageMaster 2D Platinum Identification by LC-MS/MS Spectrum Proteomic platform of Rhônes-Alpes Region Jerome Garin, CEA Grenoble Genomics on Obesity, Toulouse, 7-8 June 2007

  40. Analyse of mRNA and protein expression levels mRNA=Prot (46%) mRNA≠Prot (54%) PTMs ? mRNA: Real-time RT-PCR Protein: 2D electrophoresis

  41. PROTEOMIC APPROACH 2D Electrophoresis ProteomLab PF 2D 1st dimensionHPLCSeparation based on pI 2nd dimensionHPLCSeparation based on hydrophobicity 1st dimensionIsoelectric focalisation Separation based on pI 2nd dimension SDS-PAGE gel Separation based on molecular weight Identification of proteins by mass spectrometry (Proteomic plateform of Rhône-Alpes Region - JéromeGarin, CEA, Grenoble)

  42. C B A D E F 2D electrophoresis 330 matched proteins on 12 gels 18 dysregulated proteins (17 up, 1 down) A B C D E F STZ STZ STZ+INS STZ+INS 4324 4333 4329 4370 4367 4390 4432 4405 4348 4182 4168 4062 4123 4133 4129 4271 5307 4378

  43. PROTEOMLAB PF2D Genomics on Obesity, Toulouse, 7-8 June 2007

  44. PF2D: 32KARA B Gradient (pH8-4) A Genomics on Obesity, Toulouse, 7-8 June 2007

  45. PF2D: ProteoVue Hydrophobicity profile of the proteins with pI 4.96-5.2 pH Washing A pH Gradient B Hydrophobicity Temps de rétention (min) Wells 10 B 11 A 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 9 Fractions Genomics on Obesity, Toulouse, 7-8 June 2007

  46. PF2D: DeltaVue Hydrophobicity profile for the proteins with pI 6. 32-6.6 Hydrophobicity profile for the proteins with pI 6. 32-6.6 pI Hydrophobicité STZ STZ + INS STZ + INS Differential STZ Genomics on Obesity, Toulouse, 7-8 June 2007

  47. STZ pI 8.05-8.18 pI 8.08-8.21 DO 214nm STZ + INS Sample preparation 1 mg mt proteins Mitochondria purification from gastrocnemius muscle STZ ± INS 6 mice/group n=2 49 mitochondrial proteins are regulated by insulin treatment : 43 and 6 Identification by mass spectrometry Genomics on Obesity, Toulouse, 7-8 June 2007

  48. Strategy for identification of proteins PF2D (no MS/MS) Elution Purification Differential analysis Excision of bands 1D gel and silver nitrate staining MALDI-TOF Genomics on Obesity, Toulouse, 7-8 June 2007

  49. Limits of each proteomic approach 2D-E 2D-LC • time consuming • reproductibility • staining • proteins of high MW • hydrophobic proteins • low quantity proteins • higher number of proteins • quantity of sample (1-5 mg) • columns/buffers • differential analysis • mass spectrometry • several proteins in a fraction Genomics on Obesity, Toulouse, 7-8 June 2007

  50. Acknowledgements CEA Grenoble Jérome Garin UMR INSERM U449/INRA U1235 Jennifer Rieusset Charlotte Bonnard Hubert Vidal IFR 62 Laennec Simone Peyrol Annabelle Bouchardon CRNH RA Martine Laville Genomics on Obesity, Toulouse, 7-8 June 2007

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