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Presenter Disclosure Information. Seigo Izumo Plenary Session . DISCLOSURE INFORMATION: The following relationships exist related to this presentation: None. Using Genomic Resources to Advance Cardiovascular Medicine. Seigo Izumo, M.D. Beth Israel Deaconess Medical Center

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  1. Presenter Disclosure Information Seigo Izumo Plenary Session DISCLOSURE INFORMATION: The following relationships exist related to this presentation: None

  2. Using Genomic Resources to Advance Cardiovascular Medicine Seigo Izumo, M.D. Beth Israel Deaconess Medical Center Harvard Medical School sizumo@caregroup.harvard.edu www.CardioGenomics.org

  3. Genome Sequencing “Completed” Nematode Drosophila Human Mouse Fish (Fugu) Many Bacteria Yeast Arabidopsis Rice Potato Rat In Progress Rat Chicken Chimpanzee Zebrafish Pig …Many More…

  4. Benefit of Human Genome Project 1. Faster Identification of Candidate Disease Genes: • Far more markers for mapping are • available. • Candidate genes can be rapidly • searched in the genome databases. • Mutational screens of candidate • gene are greatly aided by • information on the gene structure. 2. Faster Cloning of Genes of Interest by Homology Search in Genome Database: • Comparative Genomics • e-Cloning

  5. Drosophila tinman Gene and Its Mouse and Human Homologs 68% 77% Chr. 14 82% Chr. 17 100% Chr. 5

  6. Estimated Number of Genes Bacteria 5,500 Yeast: 6,200 Nematode 20,000** Fly 14,000 Fish 33,000 Plant 24,000 Human 34,000* Mouse 33,000* *Alternative splicing of mRNA may generate >100,000 proteins ** In addition, there are many small non-coding RNAs that have regulatory functions

  7. Functional Genomics Development and application of global (genome- or system-wide) experimental approaches to assess biological functions Modified from P. Hieter & M. Boguski,1997 DNA SNP genotyping is a method of determining variation in genetic sequences. • RNA • Gene expression profiling is the analysis of which genes are active in a particular cell or group of cells • DNA microarrays • Oligonucleotide microarrays • SAGE (Serial Analysis of Gene Expression) • Animal Models • Saturation mutagenesis by ENU • Large-scale Gene Trap and Knock-Out mouse generation • Systemic high-throughput phenotype screening PROTEINS Proteomics is the process of determining which proteins are present in cells and how they interact

  8. PROGRAMS FORGENOMICAPPLICATIONS National Heart, Lung, and Blood Institute Goal To link, on a genomic scale, the resources and tools of the Genome Project to major biological processes and systems involved in cardiovascular, pulmonary, hematologic, and sleep dysfunction.

  9. NHLBI PGA Research Network • SeattleSNPs - Seattle . . • PhysGen - Milwaukee • JAX PGA - Bar Harbor . • CardioGenomics -Boston . • ParaBioSys - Boston . . • HopGenes - Baltimore • BayGenomics • - San Francisco . . • TREX - Rockville • Berkeley PGA • - Berkeley . . • InnateImmunity • - Tucson • Southwestern - Dallas

  10. Tools & Databases PGA Generates a Variety of Data Sets SNPs & Genotyping Animal Models Human Study Mutagenesis Microarrays Proteomics BayGenomic Berkeley-PGA CardioGenomics HopGene InnateImmunity JaxPGA ParaBioSys PhysGen SeattleSNPs Southwestern TREX NHLBI PGA Resource Page: http://pga.lbl.gov/PGA/PGA_inventory.html

  11. Overview of CardioGenomics PGA www.cardiogenomics.org The Goal: To begin linking genes to function, dysfunction, and structural abnormalities of the heart caused by genetic and environmental stimuli in the mouse and humans.

  12. Microarray technology platforms cDNA microarray High-density oligonucleotide microarray Array preparation Array 2 Array 1 Target preparation

  13. Bioinformatics: Data Organization, and Hypothesis Generation • Several algorithms have already been developed for knowledge discovery and data-mining of RNA expression data sets • Intervention Fold-Differences:DeRisi J, et al. Nat Genet 1996;14(4):457-60. • Self-Organizing Maps:Tamayo P, et al. Proc Natl Acad Sci U S A 1999;96(6):2907-2912. • Phylogenetic-Type Tree Clustering:Eisen MB, Proc Natl Acad Sci U S A 1998;95(25):14863-8. • Nearest Neighbors:Golub TR, Science 1999;286:531-7. • Relevance Networks:Butte AJ, et al. Proc Natl Acad Sci U S A 2000; 97(22):12182-6.

  14. Data Analysis Number of genes that are differentially expressed Pressure overload induced cardiac hypertrophy Aortic banding in FVB wildtype mice analyzed at 1h, 4h, 24h, 48h, 1w, 8w after the intervention p < 0.05 997 genes p < 0.025 331 genes p < 0.01 31 genes Cardiac development, maturation, and aging Benchmark data set of normal FVB wildtype mice analyzed at e12.5, NN, 1w, 4w, 12w, and 1y of age p < 0.05 2986 genes p < 0.025 1842 genes p < 0.01 321 genes

  15. Pressure-overload induced cardiac hypertrophy Identifying immediate early, early, and late events Immediate-early Early Cluster 21 Cluster 4 Cluster 24 7 genes 45 genes 45 genes Late Cluster 29 Cluster 18 11 genes 35 genes

  16. Pressure-overload induced cardiac hypertrophy Cluster analysis of genes that are induced after aortic banding Fibronectin Transgelin / SM22 alpha Sparc / Osteonectin Fibulin 2 Follastin-like Fstl Granulin Leptin receptor Rbp1 Fibrillin I Cathepsin Annexin 3 Procollagen I,III,V,VI,VIII Osteoblast-specific factor 2 Serpin b6Calponin 2 ESTs: 6 Cluster 2

  17. How to….. link phenotypic to gene expression data HW/BW ratio is highly correlated to BNP expression Dataset: Pressure overload hypertrophy Normalized Values Time Post-OP

  18. How to….. link phenotypic to gene expression data Finding genes with high positive correlation to BNP Dataset: Pressure overload hypertrophy Normalized Values Time Post-OP

  19. How to….. link phenotypic to gene expression data Finding genes with high positive correlation to BNP Dataset: Pressure overload hypertrophy Normalized Values Time Post-OP

  20. Analyze Array Data • PGA tools ______ _______Data Analysis Software___________________ MarC-V: Microarray Calculation and Visualization http://pga.swmed.edu http://www.tigr.org http://www.cardiogenomics.org) _____ __________Annotation tools________________________ http://www.unchip.org http://gladstone-genome.ucsf.edu/ http://www.tigr.org

  21. Last Updated Oct. 11th

  22. A A A A A A A A A C C C C C C C Most Variants Change a Single DNA Base:Single Nucleotide Polymorphism (“SNP”) T T G G T G Person 1 T A T C G C G T A C A G Person 2 T T G T G T G Person 3 T T G T G T G Person 4

  23. Many Common Variants have been Identified 1,400,000 SNPs earlier this year Now over 2,000,000 SNPs in public databases The SNP Consortium Human Genome Project EST overlaps PGA and other efforts Nature 409:928-933 (2001)

  24. List of Genes for SNPs Studies in PGA Last Updated: October 30, 2001

  25. Genomic Association Studies for Disease Genes Discover and catalogue SNPs; construct haplotypes around genes Haplotype# 1 2 3 4 5 …. ...100,000 Test SNPs and haplotypes for association with human diseases/conditions Hypertension Obesity Diabetes Hypertrophy Thrombosis Drug Response Arteriosclerosis Heart Failure

  26. Examination of 40 Genetic Association Studies Published in Lancet, New England Journal of Medicine, JAMA, and British Medical Journal in 1995 7 Methodological Standards of Clinical Epidemiology: Percentage of Paper Meeting Standard: • Reproducibility • Objectivity • Delineation of Case Group • Adequacy of Spectrum in Case Group • Delineation of Comparison Group • Adequacy of Comparison Group • Quantitative Summary of Results 37.5% 32.5% 77.5% 87.5% 70.0% 87.5% 90.0% No. of Standards Passed: Percentage of Papers: 7/7 6/7 5/7 4/7 3/7 2/7 1/7, 0/7 12.5% 25.0% 25.0% 15.0% 15.0% 7.5% 0.0%

  27. Many associations have been reported for type 2 diabetes, but none have been confirmed in multiple samples and with comprehensive controls. • This paper evaluated 16 published genetic associations to type 2 diabetes using family-based design to control for population stratification, and replication samples to increase power. By Analyzing over 3,000 individuals, only one polymorphism (PPARgPro12Ala) could be confirmed.

  28. Paradigm Shift in Genetic Research in Post-Genomic Era Modified From Peltonen and McKusick, 2001

  29. Acknowledgement CardioGenomics PGA www.cardiogenomics.org Martina Schinke JuHan Kim Joanna Brownstein Atul Butte Issac Kohane Emelia Benjamin Christopher O’Donnell Daniel Levy Joel Hirschhorn Eric Lander Jon Seidman Christine Seidman Tetsuo Shioi Steve DePalma Lauren Riggi Daniel Chen PGA (National) www.nhlbi.nih.gov/resources/pga Howard Jacob (PhysGen) Susan Old (NHLBI) Julie Messenger (PhysGen) Leslie Reinlib (NHLBI) Edward Rubin (Berkeley PGA)

  30. sizumo@caregroup.harvard.edu Questions? About the CardioGenomics program: For CardioGenomics web and data access issues: jbrownst@caregroup.harvard.edu www.cardiogenomics.org

  31. sizumo@caregroup.harvard.edu Questions? About the CardioGenomics program: For CardioGenomics web and data access issues: jbrownst@caregroup.harvard.edu www.cardiogenomics.org

  32. Comparison of Human and Mouse Genome Led to Identification of a New Apolipoprotein Gene (ApoA5)

  33. Over Expression of ApoA5 in Transgenic Mice Reduces Serum Triglyceride Level and Knock-Out of ApoA5 Gene Cause Hypertriglyceridemia in Mice Triglyceride Cholesterol Triglyceride Cholesterol Over Expression of Human ApoA5 Knock-Out of ApoA5

  34. Single Nucleotide Polymorphisms (SNPs) in ApoA5 Gene in Human are Associated wih Plasma Triglyceride Level in Two Independent Cohorts Clinical Implication: ApoA5 Gene Polymorphism may be a New Genetic Risk Factor for Coronary Artery Disease

  35. New Mutant Animals to be Generated by PGA

  36. Angiotensin II JAK/ STAT Mechanical stress Phenylephrine (alpha adrenergic) Fhl1 0.94, 0.90 Endothelin-1 MAPK TGF-b induced protein TGF-b Fhl2 -0.67 ERK ? c-Jun CTGF CT-1 0.74 oncostatin M receptor p38 0.83 WD repeat protein CREB Cardiac hypertrophy Ras 0.78 rhoC Raf-1 kinase hypertrophy GATA-4 Rac1 YY-1 IL-1 Ddah2 0.86 CARP 0.89, 0.82 BNP 1.0 NO ? c-fos CD53 0.83 Cardiac alpha actin Skeletal alpha actin 0.76 Cardiac troponin C Beta adrenergic ANP Calpactin 1 0.88, 0.88 cGMP Shear stress TIMP-1 0.86 ODC ? Spermidine synthase 0.76 cAMP TNF 0.69 Sparc 0.69 Antizyme Serpine1 0.82 Spp1 0.82 Antizyme inhibitor 0.86 diruresis Disintegrin & metalloproteinase 0.70 Cardiac Remodelling natriuresis vasodilation

  37. . NHLBI PGA Microarray Data Resources . . . . . . . . . • BayGenomics • Berkeley PGA • CardioGenomics 54 • HopGenes 86 • JAX PGA (TREX) • ParaBioSys • PhysGen (TREX) • Southwestern 1 • TREX 78

  38. Inheritance of Monogenic and Complex (Multifactorial) Disorders Peltonen and McKusick, 2001

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