Principles of cDNA Microarrays as Applied in Heart Failure Research

Sara Arab, Mansoor Husain, and Peter Liu 4.1

The Clinical Problem

Symptomatic heart failure (HF) affects 4.7 million patients in the US with approximately 550,000 new cases of heart failure identified annually [1-3] . Proportionally, there are 428,000 patients in Canada, incurring $ 8 billion in annual hospital costs alone. This burden expected to double in the next 2 decades [4-6]. Other forms of cardiovascular disease are plateauing, but the incidence of heart failure is increasing. The one year mortality rate is between 25-40% [5]. This is partly the consequence of our success in treating myocardial infarction and sudden deaths, and partly due to the aging population.

Meanwhile, cardiac dysfunction often occurs in the patient long before symptoms manifest. Thus the best opportunity to curb the tide of heart failure is likely presented by intervention early in the disease process [7, 8]. What is required therefore is a concerted effort to identify the underlying pathogenesis of the disease, and the timely application of this information for the development of early prognostic indicators and therapies.

The Need for a New Paradigm

Currently, only three classes of agents: angiotensin converting enzyme inhibitors, beta blockers and aldosterone antagonists can modify the disease process and alter the natural history of heart failure [6, 7, 9]. These agents can improve survival, reduce hospitalization and enhance quality of life in HF patients. However, many challenges remain, because the one year mortality is still high in the presence of state of art therapy, and increasing numbers of patients in severe heart failure have a tremendously compromised quality of life. One such challenge is the need to find new targets for the treatment of HF. The traditional approaches have been limited to biochemical analysis of peripheral blood or myocardial biopsies. Many of the targets to date have been selected by reference to other fields of investigations, including nephrology, hypertension and cancer biology. Other targets deriv-

Proteomic and Genomic Analysis of Cardiovascular Disease. Edited by Jennifer E. van Eyk, Michael J. Dunn

Copyright © 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 3-527-30596-3

ing from previous hypothesis driven research have unfortunately failed to deliver in recent investigations. Agents that have been studied modulate calcium sensitivity or calcium release, cytokine inhibitors, vasopeptidase inhibitors as well as en-dothelin antagonists.

With the birth of microarray technology, and the near completion of the human genome [10], we now have an unprecedented opportunity to supplement the hypothesis driven approach with so called "knowledge driven" approach to science, such that genome based discovery takes place side by side with the traditional routes of discovery. Microarrays let us to generate new knowledge without requir-ering a prior known paradigm. For example, recently, Dr. Josef Penninger and Peter Backx from our Centre have discovered the function of a novel angiotensin converting enzyme (ACE2). This is partially derived from in silico analysis of genome and microarray databases, illustrating the power of this novel approach to discovery [11].

The Potential Role of the Microarray

Microarray technology has the singular power to provide a system based description of a global change of state in specific experimental conditions. This capapabil-ity is a great advantage over traditional molecular approaches that expire a single pathway in a complex network of interacting biological activities (Fig. 4.1). The older approaches metaphorically shine a single beam of light in a darkroom in order to determine the dynamic contents of the room. Microarray allows a system-wide or modular approach to analysis of change in an experimental condition, akin to turning on of multiple lighting systems to illuminate our dark room.

The first time investigator may find, that the information derived from microar-ray technology is overwhelming and difficult to comprehend. However, as experience accumulates, and the bioinformatics tools become more sophisticated, the strength of the method to decode changing patterns of gene expression becomes more apparent. When the experiments are done carefully, the data can be extremely consistent from experiment to experiment. This consistently adds a level of robustness to the observations beyond that previously available with technologies to study single molecular target techniques.

As the information database grows signals are more easily differentiated from the noise and the patterns emerge across different model systems. Such capabilities give us insight to the key regulators of a number of these systems. The ability of the bioinformatics system to provide this important insight is continuing to improve, thus making the additional experiments aimed at deciphering the function for novel gene targets more meaningful.

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