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Special Lecture
Cardiovascular Disease in the Post-Genomic Era
Victor J. Dzau, M.D.
Brigham & Women's Hospital
Harvard Medical School
Boston, Massachusetts
  • Genomic Variation
  • Genomic Application
  • Physiologic Genomics
  • Phenotypic Expression
  • Gene Discovery Using Gene Analysis
  • Closing


  • The potential of genomics in cardiovascular disease was illustrated by the case of four brothers with hypertrophic cardiomyopathy, three of whom succumbed to sudden cardiac death. Linkage analysis performed by Seidman and colleagues at Brigham & Women's Hospital localized a mutation to a specific region of the chromosome. Since this mutation was identified in the surviving brother, an implantable cardioverter defibrillator was implanted and the patient is alive and well.

    The greatest challenge is to understand the genetic basis of polygenic complex diseases, such as heart disease and hypertension. The lack of powerful tools to date has been a limitation in this challenge. The use of single nucleotide polymorphism (SNP), made possible by the information and sequencing from the Human Genome Project, provides greater frequency and fidelity than that gained with the technique of sequence length polymorphism, and represents a major step forward. The sequencing technology made possible through the Human Genome Project is an important advancement, along with the preliminary map of the human genome.





    Genomic Variation


    The concept of genomic variation, that is, that within a genome there is a high amount of sequence variation, is the most important contribution. This sequence variation determines many of the differences between people, determines susceptibility to environmental changes and disease, among other factors. Functional or physiological genomics will be important for investigators to understand the function of many newly-discovered genes, their physiology and their role in disease. Comparative genomics across different species will allow understanding of the evolution of life, inheritance, and disease development. Computational biology will be needed, as well as discussion about the social, legal and ethical implications of genomics.

    The single nucleotide polymorphism is the most common genetic variation, and although it may not always have functional consequences (dependent on its location) it serves as an important marker for DNA and thus for the ability to map genes for disease. There are three million SNPs, and within each gene there may be several hundred SNPs, allowing a greater chance to find genes for disease and moves study from pedigree and sib pair to population study. Thus, it is thought that the use of SNPs and population studies will allow for identifying the genes responsible for common diseases, such as diabetes, hypertension, and other cardiovascular diseases. The SNP map, reaching its completion by the SNP Consortium supported by 13 countries, will contain all the SNPs in the genome and will facilitate research.

    A microarray chip is a single microchip that can hold the genetic sequences of specific genes important for disease. Using DNA from a patient and the microarray chip, it will be possible to quickly assess whether a variation or mutation for a specific disease occurs within the patient's own genome. Such chips will be available for both research and patient management.

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    Genomic Application


    The application of genomic information to disease to understand the etiology, natural history, select treatment and predict outcomes. For example, in hypertrophic cardiomyopathy, the work by Seidman and colleagues has shown that regardless of the phenotype, there is tremendous variation in natural history depending on genotype. Specific mutations are predictive of sudden cardiac death, which can direct therapy and impact patient outcomes. Various mutations in hypertrophic cardiomyopathy predict a range of different natural histories. Genomic information can influence treatment outcome, as shown by a study in patients with neuroblastoma. It was demonstrated that patients with a loss of a specific allele had a worse prognosis in response to chemotherapy compared to those who maintain the allele region.

    In the arena of drug therapy, by understanding the target gene sequence variation it will be possible to understand individual responses to drug. A disease gene can affect disease pathways by influencing genes secondarily activated in response to drugs. Thus, by understanding differential responses at the transcription and cellular level to a single drug or treatment intervention, it could be possible to determine efficacy and safety. Metabolism genes that determine metabolism of drugs will predict pharmacokinetics and safety. Many studies in different fields have demonstrated the ability of pharmacogenetic predictors, DNA polymorphisms, to predict treatment outcome. However, the sensitivity and specificity remains to be shown. A study that looked at the cholesterol ester transfer protein, which transfers cholesterol ester from HDL to LDL, showed that patients with a specific polymorphism responded as well to pravastatin treatment as those patients without the polymorphism, compared to those with the polymorphism and no statin treatment whose disease progressed. Pharmacogenomics will improve efficacy, allow for personalized therapy, improved safety, better selection for clinical trials and improved cost management. The redevelopment of drugs that have failed will also be possible.
    Molecular gene profiling will provide the ability to segment patients into responders, non-responders, severe versus mild phenotype, disease remission versus recurrence, and likely to have a side effect or not.

    The tools available to study genes are sequencing and SNPs for genotyping; transcriptional profiling for mRNA expression; protein or proteomics by mass spectrometry; 2D elelectrophoresis and rapid frequencing; and clinical trials, population studies and model organisms for disease physiology study. These will allow genotype-to-phenotype correlation and provide a variety of opportunities for molecular pattern recognition, and ultimately the identification of individual genes and their roles in health and disease.

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    Physiologic Genomics


    Physiologic genomics encompasses translating genomics and proteomics into functional biology, defining functions of novel gene proteins, characterizing novel roles for known genes, discovering new biological pathways and facilitating translational research efforts. Angiotensin is a good example of characterizing novel roles for known genes. A number of different actions for angiotensin have been discovered, and other novel functions could be discovered through physiologic genomics. The important question when doing this sort of study is under what conditions are the functions recruited, which involves the complexity of protein-protein interaction and environmental cues. The tools and techniques available in physiologic genomics are gene transfer knockouts of expression, model organisms, protein-protein interaction, mRNA expression, and computational biology, among others.

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    Phenotypic Expression


    Messenger RNA mediates phenotypic expression. In a single normal phenotype, environmental factors interacting with genetic factors can cause perturbation to the phenotype and lead to changes in gene expression and translation and can result in new transcription proteins, which can mediate disease phenotype. Study of gene expression, therefore, can inform understanding of disease pathways and understanding of genes by the company they keep—the tissue in which it is expressed, the disease conditions in which it is expressed. Blocking expression can prove the function of genes.

    One approach in this regard would be to understand all the genes expressed in the cardiovascular system. Dzau and colleagues have sequenced eight cDNA library and of the genes in the human myocardium and catalogued their express sequence tag (EST) to allow molecular cloning of the cDNA. The cDNA library can be placed into a bioinformatics platform to create a microarray to look at what genes are being expressed, under what conditions, and the differentially-expressed genes. Functional genomics can thus be explored and translated into quantitative genomics using real-time PCR and other approaches to look at the level of gene expression in various diseases.

    In the human heart, throughout any condition, about 25,000 of 35,000 genes may be expressed. This permits the creation of a custom-built chip to study any condition for human heart disease if it is possible to obtain the tissue. The CardioChip has been designed for this purpose. The human genome sequence and the cDNA library provides for placing known DNA sequences, which can be oligonucleotides to cDNA, on a microarray chip and then immobilized. With this approach, it is possible to look at 11,000 cDNAs with one chip simultaneously. Microarray analysis can then allow exploration of what genes are expressed in certain conditions, for example heart failure or hypertrophic cardiomyopathy. One such analysis of genes upregulated by greater than two-fold in the failing heart identified natriuretic peptide (validating the approach), heat shock protein, insulin-like growth factor, cytokine-induced nuclear protein, and a variety of unknown genes, providing the opportunity to discover new genes that may be involved in heart failure. Such new genes can be manipulated in animal studies to validate whether they can produce a phenotype of heart failure.

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    Gene Discovery Using Gene Analysis


    Gene analysis can be used to identify genes involved with disease progression and those with disease reduction. Therapy can then be directed at enhancing the expression of genes that are beneficial and at reducing the expression of genes that are deleterious. In cardiovascular disease, a homologue of angiotensin converting enzyme was identified by genomic sequencing in a human heart failure myocardial library. Determining whether overexpression of this homologue is involved with disease formation or modulation is important.

    In Tangier's disease, using a combination of linkage analysis and gene expression analysis, it was demonstrated that ABC-1 is abnormally expressed and results in low HDL levels in those patients. Investigators are looking at whether there are variations of ABC-1 in human atherosclerosis and whether ABC-1 abnormality is a target for therapy. CD-36, a fatty acid transporter, has been shown using linkage analysis and expression profiling to be elevated in insulin resistance.

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    Closing


    Genomics can be applied to understand pathophysiology, understand and predict natural history, and predict treatment outcome. Novel therapeutics, gene-based, antisense, oligonucleotide, transcriptional factor decoy, ribozyme or recombinatory chemistry, can be developed based on this knowledge. In terms of genetic therapies, in vivo and ex vivo gene therapy, cell-based therapy, somatic cells, autologous, progenitor or stem cells, and the use of embryonic stem cells can be imagined. This provides a larger armamentarium to treat cardiovascular disease. In coronary artery disease, a variety of conditions are amenable for such molecular and genetic therapeutics based on the information derived from genome and research in genome sciences: angiogenesis, bypass vascular graft, restenosis, regional vascular and myocardial protection, and systemic risk factors.

    Genomics will truly bring together many different fields. Cardiovascular medicine will be very different in the future. Bench research, including cell biology, biochemistry and physiology will allow understanding of genes and function. Clinical investigation will validate the function and disease, and will involve patient-oriented research looking at disease and function links and population studies to validate the observations made at the individual patient level. Gene mapping and discovery will allow identification of new genes, which then feeds this bi-directional cycle. Cardiovascular medicine in the post-genome era will be shaped by this bi-directional circle. Physician-scientists will play an important role in every aspect of this cycle but, importantly, will play a role in trying to bring this information to patient care.

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