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Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
Masters Thesis Defense
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Masters Thesis Defense

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My Masters Thesis Defense in Computational Science at San Diego State University (SDSU). Thesis title: "Microarray Analysis of the Effects of Rosiglitazone on Gene Expression in Neonatal Rat …

My Masters Thesis Defense in Computational Science at San Diego State University (SDSU). Thesis title: "Microarray Analysis of the Effects of Rosiglitazone on Gene Expression in Neonatal Rat Ventricular Myocytes", Fall 2009.

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  • 1. Microarray Analysis of the Effects of Rosiglitazone on Gene Expression in Neonatal Rat Ventricular Myocytes Elliot Kleiman San Diego State University Masters Thesis Defense in Computational Science September 17, 2009
  • 2. Outline1 Introduction Illumina BeadArray technology2 Materials & Methods Data Analysis3 Results Differential expression KEGG pathway analysis Gene ontology analysis4 Discussion5 Acknowledgements Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 2 / 41
  • 3. Outline1 Introduction Illumina BeadArray technology2 Materials & Methods Data Analysis3 Results Differential expression KEGG pathway analysis Gene ontology analysis4 Discussion5 Acknowledgements Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 2 / 41
  • 4. Outline1 Introduction Illumina BeadArray technology2 Materials & Methods Data Analysis3 Results Differential expression KEGG pathway analysis Gene ontology analysis4 Discussion5 Acknowledgements Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 2 / 41
  • 5. Outline1 Introduction Illumina BeadArray technology2 Materials & Methods Data Analysis3 Results Differential expression KEGG pathway analysis Gene ontology analysis4 Discussion5 Acknowledgements Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 2 / 41
  • 6. Outline1 Introduction Illumina BeadArray technology2 Materials & Methods Data Analysis3 Results Differential expression KEGG pathway analysis Gene ontology analysis4 Discussion5 Acknowledgements Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 2 / 41
  • 7. Diabetes What is it? How many people are affected? Cardiovascular complications Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 3 / 41
  • 8. Diabetes What is it? How many people are affected? Cardiovascular complications Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 3 / 41
  • 9. Diabetes What is it? How many people are affected? Cardiovascular complications Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 3 / 41
  • 10. Rosiglitazone Prescription drug which lowers blood sugar levels Avandia®(1999, GlaxoSmithKline), U.S. patent 2012 Controversial drug Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 4 / 41
  • 11. Rosiglitazone Prescription drug which lowers blood sugar levels Avandia®(1999, GlaxoSmithKline), U.S. patent 2012 Controversial drug Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 4 / 41
  • 12. Rosiglitazone Prescription drug which lowers blood sugar levels Avandia®(1999, GlaxoSmithKline), U.S. patent 2012 Controversial drug Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 4 / 41
  • 13. Previous work Shah et al. 2002 (M.Sci Biology, SDSU) found that Rosiglitazone: Improves cardiac contractility by enhancing cytosolic calcium removal Increases SERCA2 mRNA, protein, and promoter activity Increases NFκB promoter and IL-6 protein secretion Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 5 / 41
  • 14. Previous work Shah et al. 2002 (M.Sci Biology, SDSU) found that Rosiglitazone: Improves cardiac contractility by enhancing cytosolic calcium removal Increases SERCA2 mRNA, protein, and promoter activity Increases NFκB promoter and IL-6 protein secretion Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 5 / 41
  • 15. Previous work Shah et al. 2002 (M.Sci Biology, SDSU) found that Rosiglitazone: Improves cardiac contractility by enhancing cytosolic calcium removal Increases SERCA2 mRNA, protein, and promoter activity Increases NFκB promoter and IL-6 protein secretion Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 5 / 41
  • 16. Previous work Shah et al. 2002 (M.Sci Biology, SDSU) found that Rosiglitazone: Improves cardiac contractility by enhancing cytosolic calcium removal Increases SERCA2 mRNA, protein, and promoter activity Increases NFκB promoter and IL-6 protein secretion Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 5 / 41
  • 17. Current thesis work Are there other genes affected by rosiglitazone in addition to SERCA2? Can we: identify these genes? determine their functional relationships? classify these genes as early or late responders over time? How to implement these objectives? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 6 / 41
  • 18. Current thesis work Are there other genes affected by rosiglitazone in addition to SERCA2? Can we: identify these genes? determine their functional relationships? classify these genes as early or late responders over time? How to implement these objectives? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 6 / 41
  • 19. Current thesis work Are there other genes affected by rosiglitazone in addition to SERCA2? Can we: identify these genes? determine their functional relationships? classify these genes as early or late responders over time? How to implement these objectives? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 6 / 41
  • 20. Current thesis work Are there other genes affected by rosiglitazone in addition to SERCA2? Can we: identify these genes? determine their functional relationships? classify these genes as early or late responders over time? How to implement these objectives? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 6 / 41
  • 21. Current thesis work Are there other genes affected by rosiglitazone in addition to SERCA2? Can we: identify these genes? determine their functional relationships? classify these genes as early or late responders over time? How to implement these objectives? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 6 / 41
  • 22. Current thesis work Are there other genes affected by rosiglitazone in addition to SERCA2? Can we: identify these genes? determine their functional relationships? classify these genes as early or late responders over time? How to implement these objectives? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 6 / 41
  • 23. Gene expression primer Replication Genes DNA Transcription (RNA synthesis) Gene Expression RNA Translation (Protein synthesis) Phenotype PROTEIN Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 7 / 41
  • 24. Experimental approach DNA microarrays, useful why? because one can measure the gene expression levels of thousands of genes simultaneously because measuring the levels of mRNA is easier than measuring levels of proteins because mRNA is a good surrogate marker for protein (or is it?) because when you don’t have a hypothesis, microarrays can help you find one Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 8 / 41
  • 25. Experimental approach DNA microarrays, useful why? because one can measure the gene expression levels of thousands of genes simultaneously because measuring the levels of mRNA is easier than measuring levels of proteins because mRNA is a good surrogate marker for protein (or is it?) because when you don’t have a hypothesis, microarrays can help you find one Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 8 / 41
  • 26. Experimental approach DNA microarrays, useful why? because one can measure the gene expression levels of thousands of genes simultaneously because measuring the levels of mRNA is easier than measuring levels of proteins because mRNA is a good surrogate marker for protein (or is it?) because when you don’t have a hypothesis, microarrays can help you find one Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 8 / 41
  • 27. Experimental approach DNA microarrays, useful why? because one can measure the gene expression levels of thousands of genes simultaneously because measuring the levels of mRNA is easier than measuring levels of proteins because mRNA is a good surrogate marker for protein (or is it?) because when you don’t have a hypothesis, microarrays can help you find one Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 8 / 41
  • 28. Experimental approach DNA microarrays, useful why? because one can measure the gene expression levels of thousands of genes simultaneously because measuring the levels of mRNA is easier than measuring levels of proteins because mRNA is a good surrogate marker for protein (or is it?) because when you don’t have a hypothesis, microarrays can help you find one Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 8 / 41
  • 29. Illumina BeadArray technologySource: Illumina.com, Mark Dunning Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 9 / 41
  • 30. Bead design BEAD DESIGN Labelled cRNA Address Probe 29b 50b Gene-speci c probes are concatenated with a short "address sequence."Source: Illumina.com Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 10 / 41
  • 31. Materials & Methods Drug = rosiglitazone Control = dimethylsulfoxide (DMSO) Two samples of ≈100 newborn (neonatal) rats isolated and cultured neonatal rat ventricular myocytes (NRVMs) 48 arrays or 4 Illumina RatRef-12 Expression BeadChips Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 11 / 41
  • 32. Study Design Table: 12×2 Factorial Design Timea (hour) 0b ½ 1 2 4 6 8 12 18 24 36 48 DMSO -c +d + + + + + + + + + + Drug Rosiglitazone - + + + + + + + + + + + DMSO, dimethylsulfoxide. a Exposure time to drug treatment. b Untreated RNA. c No drug administered. d Drug administered. Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 12 / 41
  • 33. Array hybridization layout Sample 1 Sample 2 06/21/07 06/21/07 07/10/07 07/10/07 A R 0.5hr A U A R 0.5hr A U D 48hr B U B D 48hr B U B C D 36hr C R 48hr C D 36hr C R 48hr D 24hr D R 36hr D D 24hr D R 36hr D E D 18hr E R 24hr E D 18hr E R 24hr D12 hr F R 18hr F D12 hr F R 18hr F G D 8hr G R 12hr G D 8hr G R 12hr D 6hr H R 8hr H D 6hr H R 8hr H I D 4hr I R 6hr I D 4hr I R 6hr D 2hr J R 4hr J D 2hr J R 4hr J K D 1hr K R 2hr K D 1hr K R 2hr D 0.5hr L R 1hr L D 0.5hr L R 1hr L 1677718214 1677718210 1677718217 1677718209 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 13 / 41
  • 34. Microarray Experiment Steps 1 Biological Question 2 Design of Experiment 3 Sample Preparation (mRNA extraction) 4 Array Processing 5 Image Analysis 6 Pre-processing of Data (Normalization, Filter) 7 Data Analysis 8 Statistical InferenceSource: Sonia Jain, Ph.D (Microarray Technologies, 2006) Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 14 / 41
  • 35. Data Analysis Data analysis goal: to find an association between treatment condition and gene expression Common gene selection strategies: Fold change Parametric test: two sample t-test Non-parametric tests: rank sum, signed-rank tests ANOVA Permutation or bootstrap resampling . . . zillions of others! Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 15 / 41
  • 36. Data Analysis Data analysis goal: to find an association between treatment condition and gene expression Common gene selection strategies: Fold change Parametric test: two sample t-test Non-parametric tests: rank sum, signed-rank tests ANOVA Permutation or bootstrap resampling . . . zillions of others! Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 15 / 41
  • 37. Data Analysis Data analysis goal: to find an association between treatment condition and gene expression Common gene selection strategies: Fold change Parametric test: two sample t-test Non-parametric tests: rank sum, signed-rank tests ANOVA Permutation or bootstrap resampling . . . zillions of others! Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 15 / 41
  • 38. Data Analysis Data analysis goal: to find an association between treatment condition and gene expression Common gene selection strategies: Fold change Parametric test: two sample t-test Non-parametric tests: rank sum, signed-rank tests ANOVA Permutation or bootstrap resampling . . . zillions of others! Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 15 / 41
  • 39. Linear models of microarrays (LIMMA)Linear Model log(ygi ) = µg + βgR xRi + βgD xDi + βgR:D xRi xDi + gi (1) Idea: use a linear model to parameterize the effects of drug and time from our factorial designed experimentSource: Smyth, Limma (2004) Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 16 / 41
  • 40. Moderated, bayesian t-testModerated t-statistic 2 2 d0 s0 − dg sg 2 sg = d0 + dg (2) ∗ βg tg = sg ug 2 2 Std.Err used in test-statistic is a weighted average of s0 + sg Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 17 / 41
  • 41. Significant contrasts of interest Table: Numbers of genes regulated during significant exposure times to rosiglitazone vs. DMSO in NRVMs Significant exposure times for rosiglitazone vs. DMSO (hour) 2 4 6 8 12 18 24 36 48 a -1 0 0 0 0 0 0 2 8 9 No. genes regulated 0b 22516 22513 22514 22513 22506 22506 22498 22491 22491 1c 1 4 3 4 11 11 17 18 17 a Numbers of genes down-regulated. b Numbers of genes unchanged. c Numbers of genes up-regulated. Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 18 / 41
  • 42. Differentially expressed genes 1-10 11-20 21-30 31-37 Abca1 Cidea Hmgcs2 RGD1309930 Acaa2 Cyp1b1 Impa2 RGD1310039 Acadv1 Dapp1 Kel RT1-CE15 Acot7 Decr1 LOC501283 Rassf6 Adfp Dpt LOC501396 Retsat Aldh3a2 Ech1 LOC691522 Tap1 Angptl4 Entpd2 Lpcat3 Vipr2 Aqp7 Etfdh Olr472 Arhgdib Grip2 Psmb9 Ccl12 Gusb PtprrAngptl4 and Adfp most consistently expressed (up-regulated) over time course! Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 19 / 41
  • 43. Time course expression profile 4 h q q q Gene q Angptl4 2.5 q q q q Cyp1b1 q Olr472 q Adfp 2.0 Log2 fold change q 1.5 1.0 q q q q q q q q q 0.5 q q q q 0.0 q 0 4 8 12 16 20 24 28 32 36 40 44 48 Time (hour) Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 20 / 41
  • 44. Time course expression profile 36 h 3.0 q q q Gene 2.5 q q q Angptl4 q q q Ech1 q Abca1 Hmgcs2 Acaa2 2.0 Lpcat3 Impa2 Decr1 Adfp Log2 fold change q q 1.5 q q Acot7 q Etfdh q q q Acadvl Retsat Cidea 1.0 q Grip2 Vipr2 q q q q q q q q q Aqp7 q q q q Aldh3a2 0.5 q q q Kel q q q Dapp1 q q q q q q LOC501396 q qq q q q q q LOC691522 q q q q q Ptprr 0.0 q q qq q qqq q qq qq q q Entpd2 q q qq q q q q q q Gusb q q q q q q q Dpt q −0.5 q q q q q q 0 4 8 12 16 20 24 28 32 36 40 44 48 Time (hour) Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 21 / 41
  • 45. Hcl heatmap 4 h 1 −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 dummy.x Angptl4 Adfp Olr472 Cyp1b1 0.5 hour 1 hour 2 hour 4 hour 6 hour 8 hour 12 hour 18 hour 24 hour 36 hour 48 hour Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 22 / 41
  • 46. Hcl heatmap 36 h 1 −2 −1 0 1 2 dummy.x Etfdh Lpcat3 Acadvl Retsat Lpcat3 Decr1 Acot7 Adfp Impa2 Grip2 Aldh3a2 Aqp7 Cidea Vipr2 Hmgcs2 Abca1 Acaa2 Ech1 Kel Dapp1 Ptprr LOC501396 LOC691522 Dpt Gusb Entpd2 Angptl4 0.5 hour 1 hour 2 hour 4 hour 6 hour 8 hour 12 hour 18 hour 24 hour 36 hour 48 hour Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 23 / 41
  • 47. What is a biological pathway?Biological process: The set of all molecules required to perform a biological functionBiological pathway: The set of all molecular interactions that belong to a biological process Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 24 / 41
  • 48. Overrepresented KEGG pathways PPAR signaling Fatty acid metabolism Synthesis and degradation of ketone bodies Valine, leucine, and isoleucine degradation Butanoate metabolism Bile acid metabolism ATP binding cassette transporters, generalbiol. pathway theme: fatty acid and lipid metabolism and mitochondrial energy transfer Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 25 / 41
  • 49. PPAR signaling 48 h Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 26 / 41
  • 50. Fatty acid metabolism 48 h Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 27 / 41
  • 51. Gene ontology, what is it? structured vocabulary for describing genes and gene products molecular function (what it does) biological process (how it contributes) cellular component (where it does it) Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 28 / 41
  • 52. Hypergeometric testing test of association between two categories of interest (equivalent to Fisher’s Exact test) used to assess the over-representation of GO terms how many genes in the universe(array) are annotated at a given term? how many of those are also in the set of interesting genes? Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 29 / 41
  • 53. Hypergeometric testing case exampleUniverse = 1000 genes, 400 are DE, GO term has 40 annotationsWhat is the Prob that 10 of the 40 genes in GO term are also in the setof DE? DE DE Total In GO term 10 30 40 On Array 390 570 960 Total 400 600 1000Falcon, GOstats, 2007 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 30 / 41
  • 54. Hypergeometric random variablee.g., sampling balls from an urn model without replacement each trialis dependent on the previous oneHypergeometric random variable k N−k y n−y P(y) = N nwhere N = population size k = number of population successes n =sample size y = number of sample successesfrom prev slide we would have, 400 600 10 30 P(10) = 1000 = 0.99 40 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 31 / 41
  • 55. Overrepresented GO terms Biological process Cellular component Molecular function primary metabolic lipid particle catalytic activity process mitochondrion electron carrier activity lipid metabolic process mitochondral membrane transferase activity cellular lipid metabolic mitochondral inner transferring acyl groups process membrane acyltransferase activity oxidation reduction nuclear oxidoreductase activity response to drug envelope-endoplasmic reticulum networkgene ontology theme: energetic and metabolism activities Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 32 / 41
  • 56. Induced GOBP 8 h q p < 0.01 1525 1260 6695 q p >= 0.01 q None from gene list 8514 1005 3066 1259 8203 6126 6631 1568 0192 1004 2981 3069 6461 6125 2787 6694 8299 5909 1944 9887 1346 0191 6915 3067 5003 0271 6066 9752 6951 8202 6720 8610 7584 5908 8513 8646 0154 3086 1336 2501 1093 8523 7165 2607 3933 6082 6950 4249 4255 9216 4070 3434 1667 9915 6869 8731 9653 8869 0790 8219 0793 8519 0794 7154 6043 4237 9058 9222 6629 0033 2493 9725 1666 9991 0876 6810 7275 8856 5009 6265 0789 9987 4238 2221 9719 6950 9605 3036 1234 2501 2502 5007 8152 0896 1179 8150 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 33 / 41
  • 57. Angptl4 & Adfp Angptl4, Angiopoietin-like protein 4 Adfp, Adipose differentiation protein up-regulated 3 to 7 fold up-regulated 1.5 to 1.7 fold potent inhibitor of LPL plays a key role in formation of lipid plays key role in modulating cardiac droplets substrate metabolism lipid droplet associated protein decreases TG delivery to heart for FA β adipocyte differentiation oxidation responsible for increase in subcutaneous tissue mass observed in rosiglitazone Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 34 / 41
  • 58. Lipid & energy metabolism in cardiomyocytes Lipoprotein secretion VLDL Albumin Chylomicrons LDL TG FFA TG TG FATP CD36 FFA sis LACS TG synthe TG Intermembrane Mitochondria Fatty Acyl-CoA space lipolysis Outer membrane CPT-I Carnitine Acylcarnitine Inner membrane CPT-II CACT FADH FAD + Acyl-CoA Coenzyme A Enoyl-CoA UCP2, LCAD UCP3 Enoyl-CoA H 2O Energy uncoupling hydratase 3-OH-acyl-CoA NAD + HAD NAD+H + CO 2 ATP TCA cycle n 3-ketoacyl-CoA ai CoA-SH ch ry to Thiolase ira Acyl-CoA + Acetyl-CoA sp re n t ro ec ElYang & Li 2007 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 35 / 41
  • 59. Actions of PPARγ in FA trappingSemple, 2006 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 36 / 41
  • 60. Molecular mechanisms of TZDs PPARg Coactivator binding site Coactivator fragment Ligand binding site Transactivation Transrepression TZD TZD Ligands Ligands PPARg PPARg Ligand activation Ligand activation Coactivator PPARg Cofactor recruitment p65 p50 Fos Jun STAT1 STAT3 PPARg RXR X X X PPAR target genes PPRE PPRE NF- kB-RE TRE ISGF-REHannele Yki-Jarvinen, (SDSU) Elliot Kleiman 2004 Microarray Analysis Sept. 17, 2009 37 / 41
  • 61. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 62. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 63. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 64. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 65. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 66. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 67. Limitations Drug No PCR validation No assay for PPARγ levels technician-level variation Limitations of the array technology Sample size of 2 No db/db disease model Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 38 / 41
  • 68. Acknowledgements SDSU UC San Diego Northwestern UC Riverside Paul Paolini Gary Hardiman University Thomas Girke Jose Castillo Roman Sasik Denise Scholtens Peter Salamon Charles Berry Pan Du James Otto Jennifer Lapira Simon Lin Frank Gonzales Lynelle Garnica Kirubel Gebresenbet Magda Nemeth David Torres Evri Linux administration Illumina, Inc. EMD Biosciences Seth Falcon Greg Chandler Andrew Carmen Huda Shubeita Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 39 / 41
  • 69. Non-normalized array data q qqqqqqqqqqqqq qqqqqqqqqqq q q qq q q qqqq qqqqq qqqq qqqq qqqqqqqqqqqqqqqqq q qqq qqq qq qqqqqq q qqqqqqqqqqqqqqqqqq q q q qq qqqqqqqqqqqq q qq q qqqqqqqqqqq qqqqqq qq q qq qq q qqqq qqqqqqqqqqqqqqqq qqq qqqq qqqqqq qqqqqqqqqqqqq qqqqqq qqqqqqqq qq q qq q q q 14 qqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq q q q q q qq q qqqqqq qq q qqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqq q q q qqqqqqqq qqq q q qq qq qqq qq qqqqqqqqqqqq q q q qqqq qqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqq qq qqqqqqqq q qq q qq qq q q q qq qqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qqq qqqq qqqqq q q qqqqqqq qq qq qqqq qqqqqqq q qqq qq qqqqq q qqqq qqqq q q qqq q q q q q qqq q qqqqqqqqq qqqqqqqqqqqqq qqq qqqqqqqqqqqqqqqqqqqq q q q qq q q q qqq q qq qq q q q q q q qqqqqqqqqqqqqqqqqq q qqqqq qq q q qqqqqq qqqqqqqqqqqqqqqqqqqqqqq qqqqqq q q q q q qqq qq qqq qq qqq qqqqqq qqqqqqqqqqqqqqqqqqqq q q qq q q qq qqqqq qqqqqqqqqqqqq qqqqqqqqqqq qqq qqqqqqqqqqqq qq qqqqqqqq qq q q qq qq 12 qqqqq qqqqqqqqqqqqqq q qq qqq q qqqq qqqqqqqqq q q q qqq q qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq q qqqqqqqqq qqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqq qqq qq q qq qqqqq q q q qq qqqqq qq qqq q qq qqq qqq q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq qq qq q q qqqqqqq qqqq q q q qqq q qqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq qqqqq qqq q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqq qqqq log2 intensity qqq qqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqq q q q q q q qq qq qqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqq qqq q q q q q qq q qq q q q q qq q q qqqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q qqq q qqqq qq qqqqqqq qqqqqqq qqqqq qqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qqqqqq qq qqq qqq qqqqqqqqqqqqqqqqqq qqq qq q qqqqqqqqqqqq qqqqqqqqqq qqqqqq qqqqqqqqqqqqqqqqq qqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q qqqq qq qq qqqqqq qqqqq q q qqqq q qq qqqqqqqq qqq qq qqqqqq q q qqq qqqqqq q qq q q qq q qqqq q qqq q qqq qqqqqqqqqqqq qqqqqqqqqq qqqqqqqqqqqqq qqqqqqqqqqqq q qqqqqqqq qqqqqqqqqqqqq q qqq q qqqqqqq qqq qqqqqqqq q qq q qqq 10 qqqqqqqqqqqq qqqqqqqqqq qqqqqqqqqqqq qq qqqqq q qqq q q q q q q q q qqq q qqq q qq q q q q q qqqqqqqq q q qqqqqq q q q q q q q qq q q q q q q q q q 8 6 un2−1 un2−2 R48−2 R36−2 R24−2 R18−2 R12−2 R8−2 R6−2 R4−2 R2−2 R1−2 un1−1 un1−2 R48−1 R36−1 R24−1 R18−1 R12−1 R8−1 R6−1 R4−1 R2−1 R1−1 R.5−1 D48−1 D36−1 D24−1 D18−1 D12−1 D8−1 D6−1 D4−1 D2−1 D1−1 D.5−1 R.5−2 D48−2 D36−2 D24−2 D18−2 D12−2 D8−2 D6−2 D4−2 D2−2 D1−2 D.5−2 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 40 / 41
  • 70. Normalized array data qqqqqqqqqqqqq q qqqqqqqqqqqqqqqqqqqq qq qqqqqqqq q q q q q q qqqqqqqqqq qq q q q q qq qq qq qqqq q qqqqq q qqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqq q qqqqq qq q qqqqqqqqqqqqqqqqqqq qqqqq qqqqqqq qqqqqqqqqqqqqq qq qqqq qqqq qq q q q q qq q 3.8 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqq qqqqqqqq qqqqqqqqqq q qq q qqq q q qq qqqqqqqqqq q qqqqqqq qqqqqq q q BeadChip qqqq qqqqq qqqqq q q qq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqq qqqqqqq q q q qq qq qqqq qqq q qqq q q q q q q qqqq q q qqqqq qqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqq qqq qqq qqqqqq qqq q qq 1 qqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqq qqq q qqq q q q q q q q qqqqqqqqqq qq qq q q q qqqq q q q 2 qq q q q qqq qqqqqqqqqqqqqqq qq q qqqqqqqqqqqqq q qqqqqq q q 3 qqqqqqqq qq qq qq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq q q qqqqq q q qqqqq q qqqq qqqqqqqqqqqqqq qqq qqqqqq qq qqqqqq qqq qqqqqqq qq q qq qq q qqq qq q 4 3.6 qqqqq q q q q qq qq q qqqq q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq qqqqqqqqqqqqqq qqqq qqqqqqqqqqqqqqqq qqqqqqqqqq q q q q q qqqqqqqqq qqqqqqqq qqqqqqqqqq qqqqqqqqqqqqqqqqqq q q qq q qqqq qq qqq q q qq q qqqqq qqqqqqqqqqq qqqqqqqqqq qqqqqqq qqqqqqq q q q q q qq qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q q qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqq q qqq qqqq qqqq q q q qq qq qqqqq qqq qqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qqqqqqqq qqq qq qqqqqqq qqqqqqqqqqqqqqq qqqqqqq q qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq qq q qq qqqqqqqqq q qq q Log2 intensity qq q q q q q qqq qqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqq qqqq q qq qq q qq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqq q qqq q q qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq qq q q qqqqqqqqqq qq q q qqqqq q qqqq qqqqqq qqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqq q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q q qq q q q q qqq 3.4 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqq q qqqq qqqqq qq qq qqqq qqqq qqqqqq q qq qqqqqqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqq q q qq q q q q q q qq qqqqqqqqqqqqq q q qqq qqqqqq qqqqqqqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq q q q qqq qq q qqqqq q qqq qqqqq q qqqq qqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q qqqqqqqq qqq q qq qqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q q q qqq q q qqqq qqqqqq qq qqqqqqqqqq qqqqq qqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q q q qqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q q qq q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq qq qqqqq q qq qqq q q qqqqqqqqqqqqq qqq qqq qqqqqqqqqqqq qq q qqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq q q qqqq q qqqqqqq q q q q q q q q qqqqqqqqqqqq qqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqq q qq q qq q q q qqqqqqq q qqqqqqqqqqqq qqq q qqqqqqq qq q 3.2 q qqq q q q qq q q q q q qq q 3.0 2.8 un2−1 un2−2 R48−2 R36−2 R24−2 R18−2 R12−2 R8−2 R6−2 R4−2 R2−2 R1−2 un1−1 un1−2 R48−1 R36−1 R24−1 R18−1 R12−1 R8−1 R6−1 R4−1 R2−1 R1−1 R.5−1 D48−1 D36−1 D24−1 D18−1 D12−1 D8−1 D6−1 D4−1 D2−1 D1−1 D.5−1 R.5−2 D48−2 D36−2 D24−2 D18−2 D12−2 D8−2 D6−2 D4−2 D2−2 D1−2 D.5−2 Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 41 / 41

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