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Circulation. 1997;96:4146-4203

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(Circulation. 1997;96:4146-4203.)
© 1997 American Heart Association, Inc.

Articles

A Genome-Based Resource for Molecular Cardiovascular Medicine

Toward a Compendium of Cardiovascular Genes

David M. Hwang, BSc; Adam A. Dempsey, BSc; Ruo-Xiang Wang, MD, PhD; Mojgan Rezvani, MSc; J. David Barrans; MHSc; Ken-Shwo Dai, MSc; Hui-Yuan Wang, MD; Hong Ma, MD; Eva Cukerman, MSc; Yu-Qing Liu, MD; Jian-Ren Gu, MD; Jing-Hui Zhang, PhD; Stephen K. W. Tsui, PhD; Mary M. Y. Waye, PhD; Kwok-Pui Fung, PhD; Cheuk-Yu Lee, PhD; ; Choong-Chin Liew, PhD

From the Cardiac Gene Unit, Departments of Laboratory Medicine and Pathobiology and Medicine, Centre for Cardiovascular Research, The Toronto Hospital, University of Toronto, Ontario, Canada (D.M.H., A.A.D., R.X.W., M.R., J.D.B., K.-S.D., H.-Y.W., H.M., E.C., C.-Y.L., C.-C.L.); China National Center for Biotechnology Development, STC, Beijing, China (Y.-Q.L., J.-R.G.); National Center for Biotechnology Information, Bethesda, Md (J.-H.Z.); and the Department of Biochemistry, Chinese University of Hong Kong, Shatin (S.K.W.T., M.M.Y.W., K.-P.F., C.-Y.L., C.-C.L.). D.M. Hwang and A.A. Dempsey contributed equally to this work.

Correspondence to C.C. Liew, Banting Institute, University of Toronto, 100 College St, Toronto, Ontario M5G 1L5, Canada. E-mail liewcc{at}tcgu.med.utoronto.ca


*    Abstract
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 *Abstract
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Background Large-scale partial sequencing of cDNA libraries to generate expressed sequence tags (ESTs) is an effective means of discovering novel genes and characterizing transcription patterns in different tissues. To catalogue the identities and expression levels of genes in the cardiovascular system, we initiated large-scale sequencing and analysis of human cardiac cDNA libraries.

Methods and Results Using automated DNA sequencing, we generated 43 285 ESTs from human heart cDNA libraries. An additional 41 619 ESTs were retrieved from public databases, for a total of 84 904 ESTs representing more than 26 million nucleotides of raw cDNA sequence data from 13 independent cardiovascular system–based cDNA libraries. Of these, 55% matched to known genes in the Genbank/EMBL/DDBJ databases, 33% matched only to other ESTs, and 12% did not match to any known sequences (designated cardiovascular system–based ESTs, or CVbESTs). ESTs that matched to known genes were classified according to function, allowing for detection of differences in general transcription patterns between various tissues and developmental stages of the cardiovascular system. In silico Northern analysis of known gene matches identified widely expressed cardiovascular genes as well as genes putatively exhibiting greater tissue specificity or developmental stage specificity. More detailed analysis identified 48 genes potentially overexpressed in cardiac hypertrophy, at least 10 of which were previously documented as differentially expressed. Computer-based chromosomal localizations of 1048 cardiac ESTs were performed to further assist in the search for disease-related genes.

Conclusions These data represent the most extensive compilation of cardiovascular gene expression information to date. They further demonstrate the untapped potential of genome research for investigating questions related to cardiovascular biology and represent a first-generation genome-based resource for molecular cardiovascular medicine.


Key Words: cardiomyopathy • cardiovascular diseases • cDNA library • expressed sequence tags • heart failure • human genome project • hypertrophy


*    Introduction
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 up arrowAbstract
 *Introduction
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 down arrowResults
 down arrowDiscussion
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The complexity of the human cardiovascular system is derived from its multicomponent constituency, with the heart at its center and the vascular network at its periphery. These further reduce to a diversity of cell types that function in concert to generate normal cardiovascular function, including muscle cells involved in producing and maintaining pulsatile flow, neuroendocrine cells involved in control and regulation, endothelial cells lining the vascular tree, and matrix secretory cells responsible for the deposition and upkeep of fibrous and elastic components of the system.

Underlying this complexity at the macroscopic level is tremendous complexity at the molecular level. The intricacies of cardiovascular function are determined by a proportion of the 60 000 to 120 000 genes of the human genome,1,2 between 15 000 and 30 000 of which are expressed in any given single cell of the cardiovascular system. Although a significant proportion of these are devoted to the maintenance of basic cellular function and are widely expressed, more restricted expression (eg, spatial, temporal, contextual, or kinetic restriction) of subsets of genes presumably gives rise to the diversity of cell types, tissues, and organs of the system. Thus, changes in gene expression in response to developmental cues drive the growth and differentiation of the heart and blood vessels, resulting in the establishment of normal circulatory function. Further, dynamic interaction of the cardiovascular system with physiological or pathological stimuli also elicits alterations in gene expression, ultimately leading to generation of adaptive responses.

Recent years have seen encouraging progress in understanding the genetic basis of cardiovascular function, development, and disease (for reviews, see References 3 through 103 4 5 6 7 8 9 10 ). However, many questions remain unanswered. For example, the identities of the vast majority of genes expressed in the cardiovascular system, together with their roles in the processes of ontogeny, growth, and normal function, remain unknown. Furthermore, although some inroads have been made in identifying genes involved in monogenic disorders,5 the genetic factors underlying multifactorial disorders and their roles in the pathogenesis of such common conditions as hypertension, atherosclerosis, coronary artery disease, and heart failure remain elusive.

To provide a base to aid in the exploration of some of these unknowns, we initiated a project to characterize gene expression in various developmental and pathological states of the cardiovascular system by use of high-throughput sequencing of randomly selected clones from human heart cDNA libraries to generate "expressed sequence tags" (ESTs).11–14 This approach has proved to be a powerful means of discovering novel genes expressed in a wide variety of human tissues.11–33 Because cDNA libraries are representative of gene transcription in cells or tissues used to construct the library, random sampling and sequencing by the EST approach simultaneously generate gene expression profiles that are useful for detailed genetic-level comparisons of different developmental and pathological states of the cells and tissues of interest.14,17,19 In this report, we present the analysis of 84 904 ESTs from 13 cDNA libraries of the cardiovascular system, representing the most extensive compilation of cardiovascular gene expression information to date, and we discuss the potential impact of this genome-based resource on the field of molecular cardiovascular medicine.


*    Methods
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 *Methods
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RNA Isolation
Total RNA was isolated from human heart samples by the method of Chomczynski and Sacchi.34 Tissues were powdered under liquid nitrogen before homogenization and two rounds of extraction with acidic guanidinium isothiocyanate–phenol-chloroform. The poly(A)+ RNA fraction was isolated by oligo-dT cellulose chromatography (Pharmacia; Stratagene). Purity and RNA integrity were assessed by absorbance at 260/280 nm and by agarose gel electrophoresis.

cDNA Library Construction
Initially, we used nondirectionally cloned human adult and fetal heart cDNA libraries, which were purchased from Clontech. Subsequent to concerns regarding the presence of bacterial or yeast contaminating sequences in some commercial libraries, unidirectional cDNA libraries were constructed with 3 to 5 µg poly(A)+ RNA in a modified Gubler and Hoffman one-tube cDNA synthesis procedure.35,36 All libraries were constructed in the {lambda} ZAP Express vector (Stratagene) according to protocols supplied by the manufacturer, with the exception of one library constructed in the {lambda} gt22A vector.13,14 Briefly, first-strand cDNA was synthesized with an Xho I–oligo(dT) adapter-primer in the presence of 5'-methyl dCTP to protect the synthesized cDNA from subsequent Xho I restriction. After second-strand synthesis and ligation of EcoRI adapters, cDNA was digested with Xho I, generating cDNA flanked by EcoRI sites at the 5'-ends and Xho I sites at the 3'-ends. Digested cDNAs were size-fractionated in Sephacryl S-500 spin columns to recover cDNAs larger than 0.5 kb before ligation into {lambda} ZAP Express vector predigested with EcoRI and Xho I. The resulting {lambda} DNA/cDNA concatomers were packaged by use of Gigapack Gold packaging extracts. After titration, aliquots of primary packaging mix were stored in 7% DMSO at -80°C as primary library stocks, and the remainder was amplified to establish stable library stocks.

Large-Scale Sequencing of cDNA Inserts
Protocols for large-scale PCR-based sequencing of {lambda} gt11 and gt22A libraries have been described previously11–14 and are outlined in Fig 1Down. These protocols made use of phage plaques as the direct source of cDNA clones, thus eliminating problems of bias due to in vivo excision associated with most other EST projects. For {lambda} ZAP Express libraries, phage plaques were plated at low density (<500 pfu/150-mm plate) onto Escherichia coli XL1-blue MRF' lawns, with IPTG/X-gal for color selection. Plaques were picked into 75 µL suspension medium buffer. Phage eluates (5 µL) were used for PCR reactions (50 µL final volume) in the presence of 125 µmol/L of each dNTP (Pharmacia), 10 pmol each of modified T3 (5'-GCCAAGCTC GAAATTAACCCTCACTAAAGGG-3') and T7 (5'-CCAGT GAATTGTAATACGACTCACTATAGGGCG-3') primers, and 2 U of Taq DNA polymerase (Pharmacia). PCR samples were incubated at 95°C for 5 minutes, followed by 28 cycles of amplification (94°C, 45 seconds; 55°C, 30 seconds; 72°C, 3 minutes) and a terminal isothermal extension (72°C, 3 minutes) in a DNA Thermal Cycler (Perkin-Elmer). After agarose gel electrophoresis to assess purity and concentration, PCR products (100 to 150 ng) were used directly, without further purification, for DNA sequencing reactions using Taq Cyclist (Stratagene) or Amplicycle (Perkin-Elmer) cycle sequencing kits and 5 pmol of a dye-labeled (fluorescein or Cy5) modified T3 primer (5'-GAAATTAACCCTCACTAAAGGG-3'). Reactions were incubated at 94°C for 2 minutes, followed by linear amplification (94°C, 30 seconds; 50°C, 15 seconds; 72°C, 1 minute for 20 cycles and 94°C, 30 seconds; 72°C, 1 minute for 15 cycles), then stopped by addition of 0.5 vol loading buffer (95% formamide, 20 mmol/L EDTA, 10 mg/mL blue dextran). Sequencing reactions were electrophoresed with A.L.F. and A.L.F. Express DNA sequencers (Pharmacia).



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  		Figure 1. Overview of methodology.

Data Acquisition and Analysis
EST sequence data from other tissues of the cardiovascular system were retrieved by various strategies from the Genbank/EMBL/DDBJ databases at the National Center for Biotechnology Information (NCBI). When possible, entire data sets from individual libraries were isolated and retrieved by text string searching. Failing this, individual accession numbers for each library were obtained from field-restricted dbEST queries, and sequences were retrieved with Batch Entrez. Sequences from the Genome Sequencing Center at Washington University24 were obtained by retrieving Genbank accessions for each data set from the Genome Sequencing Center worldwide web site (URL: http://genome.wustl.edu/est/esthmpg.html) and retrieving the corresponding sequences with Batch Entrez.

Sequence similarity searching of all ESTs against the nonredundant Genbank/EMBL/DDBJ and dbEST databases (Release 98.0) were performed with the BLAST algorithm37,38 on a Pentium Pro200 Solaris x86 platform (Micron Electronics). Assignments of putative identities for ESTs exhibiting matches to known genes or to other ESTs required a minimum value of P=10-10 and nucleotide sequence identity >95%. Gene homologues were defined as those ESTs that had a value of P<10-10 but that exhibited <95% sequence identity to a known gene. Relative gene expression frequencies were computed by summing the number of ESTs matching to that particular gene or its equivalent (ie, a single gene can have more than one entry in the GenBank database) and dividing the sum by the total number of ESTs matching to known genes. Functional assignments of ESTs with known gene matches were made according to categories described in Adams et al,19 with minor modifications, with the assistance of the Genome Directory19 (search engine, http://www.tigr.org/tdb/hgi/hgi.html), UniGene (http://www.ncbi.nlm.nih.gov/UniGene/index.html), and Entrez and PubMed at the NCBI (http://www.ncbi.nlm.nih.gov/Entrez/ and http://www.ncbi.nlm.nih.gov/PubMed/medline.html, respectively). Genes represented by at least one EST in each tissue and developmental stage (ie, aorta, endothelial cell, adult heart, and fetal heart) were considered to be widely expressed in the cardiovascular system. Genes for which EST frequency was at least three times higher in a specific tissue or stage than in other tissues were considered highly expressed in that tissue or stage.

To identify differentially expressed genes in cardiac hypertrophy, EST frequencies from two independent cDNA libraries from hypertrophic myocardium were compared with frequencies expected on the basis of observed frequencies in other heart cDNA libraries used for this study (except normalized libraries, because normalization changes relative frequencies within the library). To minimize the effects of stochastic library amplification events on the identification of such genes, only genes represented by ESTs in both hypertrophic libraries were selected. Poisson probabilities for observing at least the observed number of ESTs in each library were calculated. Combined Poisson probabilities for observed frequencies of each gene in both libraries were calculated by multiplying individual probabilities. Genes with values of P<.10 for both libraries and combined P<.01 were considered to be strong candidates for differential expression. Genes with combined P<.01 but with one individual value of P>.10 were designated good candidates for differential expression, whereas genes with combined value of .01<P<.05 were designated weak candidates for differential expression.

Cardiovascular gene chromosome transcript maps were generated from 22 623 heart ESTs generated by our laboratory. Briefly, the ESTs were combined with all entries in the UniGene database and processed by use of a sequence clustering algorithm. Those that belonged to a cluster were isolated as one group. A second group was compiled consisting of statistically significant matches to database entries after BLAST searches were performed against the Genbank databases (W=12, E=1.0x10-6). Chromosome locations of the cardiovascular ESTs were determined from the integrated genome mapping and sequencing data compiled at the NCBI (Zhang and Ostell, unpublished; http://www.ncbi.nlm.nih.gov/Entrez/Genome/org.html) based on significant sequence similarity between members of either group and sequences on the integrated map.

Sequence deposition in dbEST39 was by the standard flat file submission format. Further clone information is available through NCBI or at the Cardiac Gene Unit world-wide web site (URL: http://www.tcgu.med.utoronto.ca). Clones are available on request, as detailed in GenBank reports.


*    Results
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 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
 *Results
down arrowDiscussion
 down arrowReferences
 
Large-Scale cDNA Sequencing and EST Acquisition
A total of 43 285 ESTs were generated in our laboratories from six different human cardiac cDNA libraries representing several developmental stages and disease states (Table 1DownA). An additional 41 619 ESTs from seven cardiovascular tissues or cell types were obtained through dbEST (Table 1B), for a total of 84 904 ESTs representing 26 million nucleotides of raw cDNA sequence data. In the entire cardiovascular EST data set, 55% represented sequences with significant identity to known sequences in the nonredundant nucleotide and peptide databases, and 33% matched to other ESTs in dbEST but not to any known gene sequences. The remaining 12% were novel transcripts exhibiting no similarity to any known sequences and were designated CVbESTs. Proportions for individual cDNA libraries varied widely. The fraction of known gene matches ranged from 18% for an aorta library (T. Fujiwara) to 78% for an endothelial cell library (Stratagene), whereas the proportion of matches to other ESTs ranged from 17% (hypertrophic heart) to 72% (aorta). The lowest level of novel transcripts was observed in the endothelial cell library (2%) and the highest in an adult heart library (Clontech, 25%) sequenced in our laboratories (Table 1Down).


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  		Table 1. Summary of EST Data

Known Gene Expression in the Cardiovascular System
General
The composition of known genes represented by ESTs was widely variable between libraries. Although most transcripts corresponding to known genes were nuclear-encoded, mitochondrial transcripts were observed in every library (with the exception of an aorta library [T. Fujiwara]), ranging from a low of 0.1% of total ESTs in a normalized fetal heart library (NbHH19 weeks, M.B. Soares) to a high of 29% in a hypertrophic heart library (data not shown). Ribosomal proteins also formed a major class of transcripts in most libraries (1% to 10% of total ESTs), most dramatically in the endothelial cell library, where together they constituted 25% of all transcripts. Sequences corresponding to repetitive elements ranged from 0.8% (aorta, Clontech) to 14% (Atrium, Genethon).

Functional Categorization
ESTs matching to known genes (excluding repetitive elements and probable microbial contaminant sequences) were catalogued into seven general categories (cell division, cell signaling/cell communication, cell structure/motility, cell/organism defense, gene/protein expression, metabolism, and unclassified) based on the putative functions of the known genes, as described by Adams et al19 (summarized in Table 2Down; see Appendix 1 for alphabetical listing of all genes). Two subcategories were added under cell structure/motility, namely, contractile proteins and vesicular transport.


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  		Table 2. Functional Distribution of Known Genes in the Cardiovascular System: Overview

In total, up to 4575 unique known genes were represented in the data set (Table 2Up). In concordance with the results observed by Adams et al,19 the largest class of genes represented those involved in gene and protein expression (24% of all genes represented). This was followed by genes involved in cell signaling/cell communication (18%), metabolism (16%), cell structure/motility (10%), cell/organism defense (7%), and cell division (6%). Genes lacking enough information to be classified constituted the remaining 20%. A more detailed breakdown by cDNA library (Table 3Down) found that in general, the fractions of genes devoted to these cellular functions did not deviate significantly from the overall average, except in the case of the endothelial cell library, which had slightly more genes devoted to gene/protein expression and slightly fewer for cell structure/motility, and in the case of several heart libraries that exhibited a greater proportion of genes involved in cell structure and motility.


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  		Table 3. Functional Distribution of Known Genes in the Cardiovascular System by Library

Although the proportions of unique genes involved in each function were relatively uniform between cDNA libraries, striking differences existed between actual levels of gene expression (Table 4Down). For example, endothelial cells exhibited elevated expression of genes involved in gene and protein expression (52%) and relatively depressed levels of metabolic (12%) and cell structure/motility (8%) genes compared with all other tissues, most likely reflective of rapid proliferation in culture. As previously described,14 the fetal heart consistently exhibited higher expression of genes involved in gene and protein expression and lower expression of cell structure/motility genes than the adult heart (22% to 33% versus 14% to 18% and 12% to 17% versus 22% to 25%, respectively). Interestingly, the expression of cell structure/motility genes in adult hypertrophic hearts (14% to 16%) was also significantly diminished compared with normal adult hearts (22% to 25%), whereas expression of transcripts involved in cell/organism defense was slightly increased in both hypertrophic heart libraries (7% to 8%) compared with all other cardiac libraries (4% to 6%), with the exception of the two normalized libraries (7% and 11%).


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  		Table 4. Relative Levels of Gene Expression in the Cardiovascular System

In Silico Northern Analysis
Relative frequencies of known ESTs for each gene were computed, and frequencies were represented by differing intensities to generate an "in silico Northern blot" of known genes of the cardiovascular system (Table 5Down). As for conventional Northern blots, higher frequencies were represented by darker intensities and lower frequencies by lighter intensities, allowing for convenient assessment of the relative abundance of large numbers of transcripts in different tissues and in different developmental and pathological states of the cardiovascular system.


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  		Table 5. In Silico Northern Analysis of Known Cardiovascular Genes

Several transcripts were widely expressed throughout most tissues and developmental stages (Table 6Down), although none were uniformly observed in all libraries, probably because of the small sample sizes of several of the libraries used in the analysis (Table 1Up). A large proportion of these widely expressed genes are involved in basic cellular functions such as protein synthesis (eg, ribosomal proteins), energy generation (eg, cytochromes and ATP synthases), and maintenance of cell and tissue structure (eg, cytoskeletal and extracellular matrix proteins). A number of genes also appeared to be more restricted in their distribution, being highly expressed only in specific tissue types or developmental states (Table 7Down). Several of these were not unexpected, on the basis of known gene and organ functions (eg, plasminogen activator inhibitor and platelet–endothelial cell adhesion molecule in endothelial cells, smooth muscle caldesmon in aorta, myosin genes in adult heart, and {gamma}-globin in fetal heart). Others (eg, SDF5 isologue in aorta, G protein–activated potassium channel in atrium, and osteoblast specific factor in fetal heart) may warrant further verification as to their tissue specificity.


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  		Table 6. Widely Expressed Genes in the Cardiovascular System


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  		Table 7. Highly Expressed Genes in Specific Tissues of the Cardiovascular System

Analysis of Known Gene Expression in Disease
Cardiac Hypertrophy
In silico Northern analysis was used to identify genes potentially overexpressed in cardiac hypertrophy compared with normal myocardium. In total, 69 genes were represented by at least one EST in both hypertrophic cDNA libraries. Of these, 23 were identified to be strong candidates for high expression in cardiac hypertrophy, including mitochondrial genome transcripts (which were counted as a single entity), myosin light chain-2, brain natriuretic peptide, desmin, heat shock protein 70, and superoxide dismutase (Table 8DownA). Another 11 were good candidates, including atrial natriuretic factor, {alpha}-skeletal muscle tropomyosin, and {alpha}-cardiac actin (Table 8B), whereas an additional 14 were identified as weak candidates for differential expression (Table 8C). The remaining 21 genes exhibited combined values of P>.05 and were therefore not identified as differentially expressed (data not shown).


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  		Table 8. Differential Gene Expression in Cardiac Hypertrophy

Similar analyses were performed to identify genes exhibiting diminished expression in cardiac hypertrophy compared with normal myocardium by identifying genes highly expressed in other libraries that were absent from both hypertrophic libraries. These analyses identified only three genes achieving statistical significance: atrial myosin light chain-2 (P=1.5x10-7), {gamma}-globin (P=.0004), and {alpha}-tubulin (P=.02).

Identification of Disease Genes in the Cardiovascular EST Data Set: Database Search and Chromosome Localization
A large number of known disease genes were identified in the cardiovascular EST data set. These include genes identified by biochemical and positional strategies for a variety of cardiovascular system–related (Table 9A) and noncardiovascular (Table 9B) disorders. To assist in the search for new candidates for cardiovascular genetic disorders, 22 623 human heart ESTs generated in our laboratory were analyzed for chromosome map locations. With the protocols described in the methods section, 19 158 ESTs were matched to entries in the GenBank databases; of these, 1048 were localized to chromosomes (Fig 2Down; see Appendix 2 for complete listing).



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  		Figure 2. Pages 4-10. Chromosome map locations of cardiac ESTs. A total of 1048 ESTs from human cardiac cDNA libraries were assigned to chromosomal loci on the basis of sequence identity to genes or ESTs of known map location. Presented are locus names of matching genes or accession numbers of mapped ESTs. A complete listing of Genbank accession numbers of ESTs used in this analysis, together with matching genes and map locations, is given in Appendix 2.


*    Discussion
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 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
 up arrowResults
 *Discussion
down arrowReferences
 
The application of molecular biological techniques to the study of cardiovascular biology has proved to be a powerful means of exploring the molecular mechanisms underlying cardiovascular function, development, and disease. Although these methods have traditionally focused on the discovery and characterization of single genes or of relatively limited combinations of genes, recent developments in human genome research have led to the rapid discovery of thousands of previously unknown genes and increasingly to the ability to analyze large numbers of genes simultaneously. However, although ESTs and related genome-based data represent an important resource for the Human Genome Project, their vast potential for molecular medicine remains largely untapped.

Previously, we reported the development and application of an efficient protocol for the large-scale sequencing of human cardiac cDNA libraries11–14 and demonstrated the utility of EST data for studying the genetics of cardiac development.14 In this report, we present results from the analysis in our laboratories of an additional 38 000 ESTs from six human heart cDNA libraries and compile detailed expression data for known genes of the cardiovascular system from these and all other ESTs derived from cardiovascular system–based cDNA libraries currently in dbEST. Preliminary analysis found that 12% of cardiovascular ESTs represented novel transcripts (CVbESTs), although this number appeared to be tissue and/or library dependent (Table 1Up). Nevertheless, these numbers suggest that EST generation remains a reasonable method for the discovery of novel genes of the cardiovascular system, despite the 850 000 human ESTs currently available in dbEST.

Functional categorization of ESTs with known gene matches highlighted general differences in gene expression between different tissues and developmental states of the cardiovascular system. Previous work established that fetal heart exhibited fewer transcripts representing contractile proteins and more transcripts representing signal transduction and cell regulatory proteins than adult heart, consistent with a less differentiated, rapidly growing phenotype.14 The data presented in this paper (Table 4Up) strengthen these findings by verifying them in a larger number of independent libraries (although it should be noted that the categories used in this study were slightly different from those used previously).14 They further suggest that global changes in gene expression are occurring in the adult hypertrophic heart, for example, in the decreased expression of cell structure/motility genes, perhaps reminiscent of a switch to more embryonic patterns of gene expression, and in the increased expression of genes involved in cell and organ defense.

In addition to observation of global patterns of gene transcription, EST data were used for more detailed large-scale monitoring of single gene expression in various tissues of the cardiovascular system and in different developmental and pathological states of the heart. Cross-comparisons between cDNA libraries identified a number of genes that were widely expressed throughout the tissues of the cardiovascular system (Table 6Up), as well as several genes more restricted in their patterns of expression (Table 7Up). Perhaps the most striking example, however, was seen in the identification of potentially differentially expressed genes in hypertrophic cardiomyopathy using multiple-tissue in silico Northern analysis. In this approach, expression frequencies of individual genes in the normal heart were estimated from the number of ESTs matching to those genes in normal heart cDNA libraries. Expected frequencies of gene expression in the hypertrophic heart were calculated on the basis of observed gene frequencies in the normal cardiac libraries. Statistically significant deviations from expected frequency values identified potentially differentially expressed genes.

To identify genes differentially expressed in cardiac hypertrophy, a small sample of ESTs was generated from each of two independent hypertrophic heart cDNA libraries (1089 ESTs and 474 ESTs, respectively; see Table 1Up). To minimize the effects of stochastic clone amplification events during cDNA library construction and of variations in gene expression levels between individuals, only genes represented by at least one EST in both hypertrophic libraries were selected. Potentially differentially expressed genes were grouped into three categories based on Poisson probabilities, as described above (Table 8Up). At least 10 genes identified in this manner as either strong candidates or potential candidates for differentially expressed genes have previously been demonstrated to be elevated or involved in cardiac hypertrophy, including atrial natriuretic factor,40–42 brain natriuretic factor,42 myosin light chain-2,43 desmin,44,45 heat shock protein 70,46 superoxide dismutase,47,48 {alpha}-cardiac actin,49,50 ß-actin,51 and {alpha}-tropomyosin.52,53 Further, the ADP/ATP translocator, identified as a weak candidate, is known to be activated in severe cardiac hypertrophy and congestive heart failure.54 Two sarcomeric proteins also identified as weak candidates have been well characterized as being involved in the pathogenesis of hypertrophic cardiomyopathy, ie, ß-myosin heavy chain55,56 and cardiac troponin T.52,53

In addition to the genes previously known to be involved in cardiac hypertrophy, this approach also suggests several new genes to be differentially regulated in hypertrophy. A number of these were involved in energy or high-energy phosphate metabolism (myoglobin, mitochondrial genome transcripts, muscle and mitochondrial isoforms of creatine kinase, pyruvate dehydrogenase {alpha}-subunit, NADH dehydrogenase subunit ND2, cytochrome c oxidase subunit VIIc, and ATPase coupling factor 6 subunit), which might be anticipated, given known alterations in substrate delivery and energy metabolism in the hypertrophied heart. Several others were involved in protein synthesis (ribosomal protein S28) or in regulating protein turnover, either in the intracellular ({alpha}-B-crystallin, ubiquitin, 26S proteosome subunit p31) or the extracellular (thrombin inhibitor, tissue inhibitor of metalloproteinase-3) compartments. Although there is no direct evidence in the literature for differential regulation of these specific genes, thyroxine-induced hypertrophy was found to increase rates of protein synthesis and protein degradation in rabbit hearts,57 and the remodeling of the cardiac interstitium during hypertrophy is well documented, suggesting a possible role for these and other related genes in such processes. Also of some interest was the identification of prostaglandin D synthases as strong candidates for differential expression. Given that prostaglandin D2 has been shown to have positive inotropic effects on rodent hearts58,59 and that prostaglandin F2 but not prostaglandin D2 induces cardiomyocyte hypertrophy and cardiac growth,60 further investigation into whether differential regulation of these and other prostaglandin synthases is occurring and into what role these may play in cardiac hypertrophy and heart failure appears to be warranted.

Despite the preliminary nature of these data, the fact that a large number of genes identified as candidates for differential expression by the in silico method correspond to genes actually known to be differentially expressed suggests that a combined Poisson probability cutoff of P<.05 is appropriate for screening and that this method holds tremendous potential for genome-wide searching for novel genes involved in cardiovascular disorders such as cardiac hypertrophy and heart failure, even with relatively small EST data sets (1089 ESTs and 474 ESTs in the two hypertrophic libraries). Nevertheless, increased numbers of ESTs from both normal and diseased tissues would undoubtedly increase the sensitivity of this approach, especially for transcripts normally expressed at very low levels and for detection of transcripts that might be underexpressed in disease. It should also be noted that the analyses presented here included only ESTs with known gene matches. Extension of this strategy to ESTs without known gene matches presents more of a challenge because, in the absence of full-length gene sequences, it is not always apparent whether two ESTs represent two different genes or nonoverlapping segments of the same gene.

Progress toward the tagging and eventual sequencing of the entire set of human genes suggests that other similar genome-based methods such as serial analysis of gene expression61,62 or differential hybridization of arrayed cDNA clones63–65 will become increasingly informative and powerful for such analyses in coming years. However, the observation that 12% of cardiovascular ESTs do not match to any known sequences suggests that a significant proportion of human genes may remain untagged despite the large number of ESTs currently available and hence that EST generation will continue to play a key role in large-scale gene discovery and expression studies.

The utility of EST information for the identification of novel genes involved in disease was further demonstrated by the number of known disease genes for both cardiovascular and noncardiovascular disorders represented in the cardiovascular EST data set (Table 9Down), the implication being that a substantial number of currently unknown disease genes are also likely to be represented within the data set. Large-scale mapping of ESTs to their chromosomal loci66 should expedite the search for novel disease genes by positional candidate approaches67,68 in which ESTs mapping to a known disease locus serve as candidate genes for that disorder. Compilation of map data for cardiovascular ESTs (Fig 2Up, Appendix 2) should ultimately allow for correlation of expression data with map location and enhance the usefulness of this resource for the identification of genes associated with cardiovascular disease susceptibility loci.


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  		Table 9. Known Disease Genes Represented in the Cardiovascular EST Dataset

Future Directions
Exploitation of the wealth of information generated by genome research holds exciting prospects for the field of cardiovascular science. Although an extensive database of human cardiac ESTs is rapidly being compiled, ESTs from other human cardiovascular tissues or cell types or from diseased specimens remain relatively limited. Moreover, cardiovascular EST data from most other model organisms widely used for cardiovascular research (eg, rat, mouse, chicken, frog, zebrafish) are almost nonexistent. Continued generation of ESTs from such specimens, coupled with application of other strategies such as serial analysis of gene expression and differential hybridization, is therefore essential.

At the same time, as increasing numbers of genes are identified, increasing emphasis needs to be placed on functional analysis of newly discovered genes in the cardiovascular system and on developing strategies to maximize extraction of useful information from the large body of raw data currently available. The construction of cardiovascular gene databases at different stages of development and pathology, coupled with integration of information gleaned from large-scale mapping, expression, and functional analyses, will establish an invaluable resource for future genetic studies of cardiovascular function and cast light on the complex genetic mechanisms underlying disease, development, and evolution of the cardiovascular system.


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  		Table 9A. Continued


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  		Table 10. Appendix 1. Alphabetical Listing of Known Cardiovascular Genes, With Functional Categorization


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  		Table 11. Appendix 1. Continued


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  		Table 12. Appendix 2. Summary of Accession Numbers for Mapped Expressed Sequence Tags (ESTs), Together With the Locus Identifier for the Corresponding Gene or EST Match and Chromosomal Locus


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  		Table 13. Appendix 2. Continued


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  		Table 14. Appendix 2. Continued


*    Acknowledgments
 
The Cardiac Gene Unit was established in memory of Nigel M.S. Martin. This work was supported by The Canadian Genome Analysis and Technology Program, The Medical Research Council of Canada, The Heart and Stroke Foundation of Ontario, Spectral Diagnostics, Inc, and the Research Grants Council of Hong Kong (CUHK 418/95M, CUHK 205/96M). D.M.H. is a recipient of a Medical Research Council of Canada Studentship. M.R. is a recipient of a Heart and Stroke Foundation of Ontario Traineeship. K.-S.D. is a recipient of a University of Toronto Graduate Studies Award for International Students. S.K.W.T. is a recipient of a Postdoctoral Fellowship from the Chinese University of Hong Kong.

Received April 17, 1997; revision received July 16, 1997; accepted August 1, 1997.


*    References
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