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(Circulation Research. 2000;86:919.)
© 2000 American Heart Association, Inc.

Editorials

Leading the Way Using Microarray

A More Comprehensive Approach for Discovery of Gene Expression Patterns

Maha Abdellatif

From the Department of Medicine, Baylor College of Medicine, Houston, Tex.

Correspondence to Maha Abdellatif, Molecular Cardiology Unit, Section of Cardiology, Department of Medicine, Baylor College of Medicine, One Baylor Plaza, Room 506C, Houston, TX 77030.

Key Words: DNA microarray • differential expression • myocardial infarction • gene chip

	Introduction

 
Cells react to various stimuli by modulating their biological functions through selective changes in the activities of their constituent proteins. These changes are rendered by secondary modifications, translocation, or interaction with other molecules or cofactors or by altering a protein’s concentration. The latter event occurs via regulated modifications in the rate of gene transcription, RNA translation, or degradation of RNA or protein. In the premicroarray era, the number of genes that were subject to differential expression was seriously underestimated; this is demonstrated in the study by Stanton et al¹ in this issue of Circulation Research. Using microarray technology, Stanton et al identified more than 700 genes whose expression was altered during myocardial infarction. Expression profiling is not merely a descriptive method; disclosure of the temporal and spatial changes in gene expression provides insight into cellular functions and underlying mechanisms in disease pathogenesis.

DNA microarrays, or gene chips, are usually comprised of micron-range–sized spots of genomic DNA, cDNA, or oligonucleotides arrayed on a glass slide. They are used for a wide scope of applications, including sequencing, detection of mutations or polymorphisms, identification of drug targets, and gene expression profiling (reviewed by Wilgenbus and Lichter² ). The latter application has gained wide use for monitoring differences in gene expression patterns in normal versus pathological conditions. Different clustering methods have been devised for the management of the large number of data points obtained by this method. One such method, demonstrated by Eisen et al³ and also used by Stanton et al,¹ relies . . . [Full Text of this Article]

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