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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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Presenter Disclosure Information

Seigo Izumo

Plenary Session

DISCLOSURE INFORMATION:

The following relationships exist related to this presentation:

None


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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


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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…


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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


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Drosophila tinman Gene and Its Mouse and Human Homologs

68%

77%

Chr. 14

82%

Chr. 17

100%

Chr. 5


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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


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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


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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.


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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


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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


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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.


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Microarray technology platforms

cDNA microarray

High-density oligonucleotide microarray

Array preparation

Array 2

Array 1

Target preparation


Bioinformatics data organization and hypothesis generation l.jpg
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.


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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


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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


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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


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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


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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


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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


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______ _______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



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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


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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)


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List of Genes for SNPs Studies in PGA

Last Updated: October 30, 2001


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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


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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%


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  • 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.


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Paradigm Shift in Genetic Research in Post-Genomic Era but none have been confirmed in multiple samples and with comprehensive controls.

Modified From Peltonen and McKusick, 2001


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Acknowledgement but none have been confirmed in multiple samples and with comprehensive controls.

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)


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sizumo@caregroup.harvard.edu but none have been confirmed in multiple samples and with comprehensive controls.

Questions?

About the CardioGenomics program:

For CardioGenomics web and data access issues:

jbrownst@caregroup.harvard.edu

www.cardiogenomics.org


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sizumo@caregroup.harvard.edu but none have been confirmed in multiple samples and with comprehensive controls.

Questions?

About the CardioGenomics program:

For CardioGenomics web and data access issues:

jbrownst@caregroup.harvard.edu

www.cardiogenomics.org



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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


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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


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New Mutant Animals to be Generated by PGA Human are Associated wih Plasma Triglyceride Level in Two Independent Cohorts


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Angiotensin II Human are Associated wih Plasma Triglyceride Level in Two Independent Cohorts

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


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. Human are Associated wih Plasma Triglyceride Level in Two Independent Cohorts

NHLBI PGA Microarray Data Resources

.

.

.

.

.

.

.

.

.

  • BayGenomics

  • Berkeley PGA

  • CardioGenomics 54

  • HopGenes 86

  • JAX PGA (TREX)

  • ParaBioSys

  • PhysGen (TREX)

  • Southwestern 1

  • TREX 78


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Inheritance of Monogenic and Complex (Multifactorial) Disorders

Peltonen and McKusick, 2001