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Physiol. Genomics 32: 45-57, 2007. First published September 18, 2007; doi:10.1152/physiolgenomics.00015.2007
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Received 12 January 2007; accepted in final form 12 September 2007.
Physiological Genomics 32:45-57 (2007)
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Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm

Shantel Weinsheimer 1, Guy M. Lenk 1, Monique van der Voet 1, Susan Land 1,2,5, Antti Ronkainen 6, Irina Alafuzoff 7, Helena Kuivaniemi 1,3 and Gerard Tromp 1,4

1 Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit
2 Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit
3 Department of Surgery, Wayne State University School of Medicine, Detroit
4 Department of Neurology, Wayne State University School of Medicine, Detroit
5 Applied Genomics Technology Center, Wayne State University, Detroit, Michigan
6 Department of Neurosurgery, Kuopio University, Kuopio, Finland
7 Department of Clinical Medicine, Neurology, and Pathology, Kuopio University, Kuopio, Finland


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Intracranial aneurysm (IA) is a complex genetic disease for which, to date, 10 loci have been identified by linkage. Identification of the risk-conferring genes in the loci has proven difficult, since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and nonaneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK, and Notch signaling pathways ranked high. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.

pathway analysis; adherens junction; Notch signaling; MAPK signaling


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
EACH YEAR, ABOUT 27,000 INDIVIDUALS in the United States suffer a rupture of an intracranial aneurysm (IA) (24). IA is a complex disease with both environmental and genetic components, and first-degree relatives of patients with IA have a higher risk of developing an aneurysm (23, 26). Several approaches have been used to identify factors that contribute to a genetic predisposition to IA including DNA linkage and genetic association studies.

DNA linkage analyses suggest that genetic factors on chromosomes 1, 2, 5, 7, 11, 14, 17, 19, and X contribute to IA (Table 1). Onda et al. (18) found three loci with suggestive linkage for IA on chromosomes 5q22–q31, 14q22, and 7q11 in a genome-wide linkage study in the Japanese population, including intracranial berry aneurysm-1, known as the ANIB1 locus in Online Mendelian Inheritance in Man on chromosome 7q11.2. Olson et al. (17) detected two regions of suggestive linkage: one on chromosome 19q13 and the other on the X chromosome (17). A follow-up study (28) of the 19q13 locus using additional Finnish samples and covariate affected-relative-pair, as well as model-based, linkage analyses indicated that the most likely location for a gene predisposing to IA mapped to a region at chromosome 19q13.3 that spans 6.6 cM, designated the ANIB2 locus. More recently, another genome-wide linkage analysis of 29 Japanese families revealed linkage to 3 chromosomal regions including chromosomes 17cen, Xp22, and 19q13 (32). A follow-up study of nine of these Japanese IA families confirmed chromosome 19q13.3 as a susceptibility locus for IA, which overlaps with the Finnish susceptibility locus for IA (15). Farnham et al. (7) studied 13 Utah families with IA and confirmed linkage to chromosome 7q11 (7).


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  		Table 1. Distribution of expressed genes across IA loci

 
Four genome-wide linkage studies were performed in single large pedigrees. A Dutch consanguineous family was used to map a susceptibility locus for IA to chromosome 2p13 (20), although it was subsequently reported that the locus was an artefact because of misclassification of several individuals (22). One North American kindred showed significant linkage to chromosome 1p36.13–p34.3, known as the ANIB3 locus, under a dominant model with high penetrance, suggesting a Mendelian form of IA (16). Ozturk et al. (19) performed single nucleotide polymorphism genotyping for genome-wide linkage analysis in two kindreds, a Colombian and a North American family. These analyses provide additional support for linkage to chromosomes 11q and 14q, as previously reported in Japanese IA-affected sib pairs. Recently, another susceptibility locus, ANIB4, mapping to chromosome 5p15.2–p14.3 was identified in a French Canadian family with 12 affected individuals (29).

Identification of multiple susceptibility loci suggests genetic heterogeneity for IA; however, it can reasonably be hypothesized that there are a limited number of pathways that contribute to the phenotype and that genetic (locus) heterogeneity can therefore be utilized to identify the pathway(s). Another hypothesis is that genes responsible for diseases that affect particular tissues are likely to be expressed in that tissue, i.e., genes not expressed, either in diseased or nondiseased tissue, may be excluded from the candidate list. Microarray technology offers a high-throughput approach to determine the expression of thousands of positional candidate genes in diseased and nondiseased tissues. Integration of gene expression information and pathway information together with genetic mapping data will help to prioritize positional candidate genes. We used microarrays to analyze gene expression in human IAs and contralateral nonaneurysmal intracranial arteries (IVs) as well as intracranial arteries from individuals without IA and integrated this information with published genetic mapping data to identify positional and functional candidate genes for IA.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Tissue samples, RNA isolation, and microarray analysis.
Intracranial arteries from a total of 12 Caucasian individuals with IA were obtained postmortem from autopsies performed within 24 h of death at the University of Kuopio, Finland. Specimens were collected from ruptured or unruptured IAs and from the contralateral vessel of the same patients to represent IVs (Table 2). The causes of death for the four patients with unruptured aneurysms were as follows: accidental death due to trauma, alcoholism, heart disease, and hypertensive heart disease. The arteries were dissected, placed in RNAlater solution (Ambion, Austin, Texas), and stored frozen at –80°C (Fig. 1). Homogenized IAs or nonaneurysmal IVs from these same patients were pooled into groups of three to five individuals with similar clinical characteristics including artery type, rupture status (i.e., ruptured IA or unruptured IA), and sex (Table 2). Additional controls included IVs from non-IA individuals of African American decent that were obtained postmortem from autopsies performed within 24 h of death (in Detroit, MI) (1). Larger segments were available (n = 5 samples from 4 different individuals), and they were analyzed individually (Table 2).


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  		Table 2. Tissue samples used for microarray analysis

 

Figure 1
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  		Fig. 1. Outline of the study. We integrated global gene expression profiles of intracranial arteries with genetic mapping data for intracranial aneurysm (IA) to prioritize candidate genes.

 
Total RNA was isolated from blood vessels as previously described (27). IAs and IVs from individuals with IA were analyzed using Affymetrix microarrays before commercial availability of the Illumina platform. Total RNA (300 ng) was amplified one round using the MessageAmp II aRNA Kit according to the manual (version 0404; Ambion). Amplified RNA (10 µg) was reverse transcribed into cDNA and then in vitro transcribed and biotin labeled to produce cRNA, which was subsequently hybridized to Affymetrix HG_U133 Plus 2.0 microarrays according to the Affymetrix GeneChip Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA). The arrays were scanned using the GeneArray scanner (Affymetrix). Image analysis was performed with MicroArray Suite 5.0 software. Affymetrix HG_U133 Plus 2.0 microarrays contain 54,675 probes that represent 19,221 distinct and nonpseudo- genes. Affymetrix data were analyzed with the use of GeneSpring version 7.2 (Silicon Genetics), Bioconductor, and R (version 2.3.0), an open source language and environment for statistical computing and graphics (8, 9), and the data were preprocessed by applying a sequence-specific expression model known as gcrma (robust multiarray analysis with adjustment for GC content of probes), which is a perfect-match probe method (31). Adjusted microarray data were then subjected to an expression filter based on adjusted probe intensity levels to determine the set of genes expressed in either IAs or IVs. A gcrma expression value ≥3.91 was chosen as the threshold, resulting in the same number of genes that were identified in the Illumina gene expression experiments when the detection level was set to >0.99 (see below).

On availability of the Sentrix Human-6 Whole Genome Expression BeadChips (Sentrix Human WG-6; Illumina, San Diego, CA), we evaluated gene expression in control arteries (IVs) from individuals without IA as follows. Total RNA (150 ng) extracted from arteries was reverse transcribed and cRNA prepared, and 1.5 µg of biotinylated cRNA were hybridized to Sentrix Human WG-6 arrays, washed, and scanned on the BeadArray Reader, as described by Illumina at http://www.illumina.com. Illumina BeadChips contain 47,293 gene targets, representing 18,025 distinct RefSeq genes that are not pseudogenes. Gene expression data were analyzed using BeadStudio version 1.5.0.34 software (Illumina). Array data were fit to a cubic spline in BeadStudio, and the expressed gene targets were identified using a detection level ≥0.99. We used two different microarray platforms for gene expression analysis for two main reasons: 1) the Illumina Human-6 Whole Genome Expression BeadChips became available only after we had performed the initial Affymetrix microarray analysis for the samples from individuals with IA, and 2) the Illumina platform requires less RNA and offers an independent gene expression measurement to validate the results of the Affymetrix analyses. We analyzed 5 samples or sample pools on each platform for a total of 10 independent array experiments. In this way, the use of two different microarray platforms may complicate the analysis, but it serves as a validation of the Affymetrix experiments.

To compare gene expression in arteries from IA individuals (aneurysmal arteries and contralateral control arteries) with arteries from non-IA individuals (IVs), we combined the Affymetrix and Illumina data by identifying the genes represented on both platforms for use as a common reference gene set. The most recent annotations for both platforms were used. The annotations for the HG_U133 Plus 2.0 (version na23; annotated on July 11, 2007) were obtained from Affymetrix (http://www.affymetric.com/). To generate the present annotation for the Illumina chip, we compared the probe sequences with the National Center for Biotechnology Information RefSeq (Release 24, dated July 10, 2007) sequences using stand-alone basic local alignment search tool (BLAST; http://www.ncbi.nlm.nih.gov; ftp://ftp.ncbi.nih.gov/blast) with parameters ensuring near-perfect matches. The matches were evaluated to ensure that the best match was unique, i.e., no secondary matches with one or two mismatches or gaps. There were 16,538 distinct genes with unambiguous gene IDs that were not pseudogenes in common between the Affymetrix (n = 19,221) and Illumina (n = 18,025) platforms. For IA expression data to be combined from both platform analyses, the 20,708 distinct genes that were not pseudogenes probed for on either platform were grouped into distinct classes corresponding to no expression, expression on one platform only, and expression on both platforms and were assigned expression confidence scores as shown in Table 3. The raw microarray data sets (series no. GSE6551) can be accessed at the Gene Expression Omnibus website (http://www.ncbi.nlm.nih.gov/geo).


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  		Table 3. Gene expression classes and scores

 
The Human Investigation Committee of Wayne State University and the Ethics Committee of the University of Kuopio approved this study.

Real-time quantitative RT-PCR.
We performed real-time quantitative RT-PCR (qRT-PCR) using validated TaqMan assays available from Applied Biosystems for six IA candidate genes: calpain, small subunit-1 (CAPNS1); serine protease inhibitor, Kunitz type, 2 (SPINT2); nuclear factor of {kappa}-light polypeptide gene enhancer in B-cells inhibitor, beta (NFKBIB); p21 (CDKN1A)-activated kinase-4 (PAK4); zinc finger protein-36, C3H type, (ZFP36); and latent transforming growth factor-β binding protein-4 (LTBP4). Eight pools of intracranial artery specimens of different parts of the intracranial vessels originating from 11 individuals were evaluated as described in Supplemental Table S1 (supplemental data are available at the online version of this article).

The qRT-PCR assays were performed using 50 ng of artery RNA at the Applied Genomics Technology Center of Wayne State University. cDNA synthesis and quantification of gene expression were carried out as previously described (27). The messenger RNAs for the six genes were quantified using the delta crossover threshold ({Delta}Ct) method, on an average of three replicates, which provides the difference between the Ct values of the target gene and that of the reference gene. Samples were standardized with respect to 18S ribosomal RNA TaqMan probes. We calculated the correlation between the Illumina gene expression data and the RT-PCR data using the Pearson correlation statistic.

Functional classification of genes.
We identified genes expressed in aneurysmal or control intracranial arteries that map to all 10 reported IA susceptibility loci (Fig. 1 and Table 1). These expressed genes were analyzed for membership in specific Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways, using the WebGestalt resource (web-based gene set analysis toolkit, available at http://bioinfo.vanderbilt.edu/webgestalt) (33). Taking into consideration the gene expression data, we developed a likelihood-based score to rank pathways for involvement in the pathogenesis of IA, weighted exponentially by the number of loci

Formula 1(1)

Formula 2(2)

Formula 3(3)
(See supplemental data.) The computation allows for locus-specific weights to reflect the a priori confidence in a specific locus as well as the use of weights for gene expression. We weighted our gene expression based on our expression confidence scores (Table 3). Ranking of the pathway scores in ascending order gives an indication of which pathway is most likely to be involved in IA relative to other pathways. The individual genes in that pathway, which are located in the candidate intervals, then become functional positional candidates. These analyses were carried out with genes belonging to the functional category hsa04nnn that correspond to biological function pathways and include cell signaling and cellular processes.

We also investigated the interrelatedness of the pathways, as they are not entirely separate from one another. The KEGG pathway database includes multiple references to other KEGG pathways, as illustrated by a rectangle with rounded corners in the pathway image (see Fig. 3). We manually extracted this interconnectedness information from the KEGG pathways and illustrated the interactions using a connecting line between two related pathways. CytoScape software (http://www.cytoscape.org) was used to generate the framework for an interaction map of the KEGG biological pathways (25). Final images were created in Adobe Illustrator (version 12.0.1, Adobe Systems).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Genes expressed in intracranial arteries.
To identify genes that may be involved in the molecular pathogenesis of IA, we performed gene expression profiling to find genes expressed in intracranial arteries or aneurysmal arteries. We combined data from two gene expression platforms: Affymetrix HG_U133 Plus 2.0 GeneChips (n = 5) and Illumina WG-6 BeadChips (n = 5). Genes that were represented on either platform and not pseudogenes (20,708) were scored according to the platform in which they were identified as being expressed. Twenty-one percent (4,250) of the genes were identified as expressed on both platforms (class 5 genes; Table 3), 3% (635) were probed and detected on one platform only (class 4 or 3), 25% (5,281) were expressed on one platform (class 2 or 1), and 52% (10,989) of the genes were not expressed on either platform (class 0). The 10,989 genes with a expression class of 0 can therefore be excluded as candidate genes for IA, as they are not expressed in nonaneurysmal or aneurysmal intracranial arteries. Expression class 5 genes are regarded as having the highest confidence in expression, as they were expressed on both microarray platforms.

An independent set of eight pools of RNA samples (see Supplemental Table S1) was used for validation of gene expression with RT-PCR. The Pearson correlation between the Illumina microarray expression data and the RT-PCR expression data for the six genes analyzed was 0.963, indicating that the two gene expression measures are in good agreement. Specifically, this positive correlation coefficient suggests that there is a linear relationship between the two sets of gene expression data. The RT-PCR results for these six genes validated their intracranial artery expression, which was detected by both Affymetrix and Illumina microarray platforms.

The difficulty of obtaining IA samples, the need to amplify them, and the fact that the data were generated on two platforms precluded effective differential expression analysis.

Characteristics of IA positional candidate genes.
The genome-wide mRNA expression data provide means for prioritizing candidate genes for IA from all reported linkage regions (i.e., the most plausible candidates for IA). So far, 10 loci have been linked to IA, containing a total of 1,858 distinct and nonpseudo- genes (Table 1, Supplemental Table S2). The candidate intervals for chromosomes 7q11 and 19q13 have been identified in two or more studies. In addition, the candidate regions on chromosome 14q overlap by 56 genes, and those of chromosome Xp22 overlap by 44 genes. The 1,858 genes for IA include 335 LOC (hypothetical protein in which orthologs have not yet been determined), 49 FLJ (Japanese database full-length human cDNA clone), 18 MGC (mammalian gene collection clone), 9 KIAA [human novel large (>4 kb) cDNA identified in the Human Unidentified Gene-Encoded (HUGE) protein database (http://www.kazusa.or.jp/huge)], 66 open reading frame (ORF), and 7 DFKZP (cDNA produced by Deutsches Krebsforschungszentrum) genes as well as 36 noncoding RNAs, leaving 1,338 genes with known function (not hypothetical) as candidates for IA (Table 4). The Affymetrix and Illumina microarray platforms probed for 1,561 (84%) of the 1,858 positional IA candidate genes, including 1,298 (97%) of the 1,338 genes with known function. Altogether, 810 (52%) of the 1,561 probed genes were expressed in aneurysmal or control intracranial arteries including 727 (56%) of the 1,298 IA candidate genes with known function (see Supplemental Table S2). Table 1 describes the distribution of the expressed genes across all IA-linked regions, in which linked loci on the same chromosomal region were combined. The number of potential candidate genes per linkage interval ranged from 35 on the chromosome 11q24–q25 interval to 572 on the chromosome 19q13 interval. The coverage on the microarray platforms was best for the chromosome 19q13 interval, with 91.8% of the genes being represented on the arrays, and worst for the chromosome 5p15.2–p14.3 interval, with only 49.1% coverage. The coverage for known genes was good to excellent, ranging from 89% for chromosome 5q22–q31 to 100% for chromosomes 2p13, 5p15.2–p14.3, and 11q24–q25, with the coverage for all other loci being >97%. The average proportion of genes in all intervals that were expressed with high confidence (class 5) was 17% (9–28%) for all genes and 19% (9–34%) for probed genes, increasing to 23% (12–41%) for known genes. The lowest proportion of expressed genes was in the chromosome 11q24–q25 interval and the highest in the chromosome 2p13 interval (see Table 1).


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  		Table 4. Summary of positional IA candidate genes

 
Integration of genetic mapping and expression data to identify functional pathways in IA.
Only ~22% (177 genes) of the 810 expressed IA genes have KEGG pathway annotations. These include 93 genes involved in 45 biological function pathways (hsa04nnn), where pathways were represented on at least 1 locus, and 92 genes involved in 32 biological function pathways represented on at least 2 loci. The pathway membership of the 177 expressed and annotated linked IA genes (Table 4) was determined for each pathway and for each of the 15 chromosomal regions (10 distinct), where pathways were represented on at least 2 loci (n = 32 KEGG pathways; Table 5, Fig. 2). We grouped the pathways into five categories: neurological, signaling, immunological, cell-cell adhesion, and basal cell processes. Figure 2 and Supplemental Table S3 show that the pathways are highly interrelated, with many genes having membership in more than one biological function pathway. The locus on chromosome 2p13 recently was shown to be an artefact; this was due largely to misclassification (22), and it was eliminated from further analyses.


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  		Table 5. Most significant KEGG biological pathways in IA

 

Figure 2
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  		Fig. 2. Interaction map of Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways for IA. Pathway annotations (nodes) were available for 177 of the 810 expressed positional candidate genes. Presence or absence of pathway genes in linked loci is indicated by dark pie chart sections in nodes for pathways with genes on at least 2 loci (n = 32 KEGG pathways; n = 92 genes). Node diameters are proportional to pathway size (in genes), node circumference color indicates pathway category, and colored edges for nodes in the center of the interaction map differentiate pathways with connections to multiple pathway categories. Pathway abbreviations are shown in Table 5. See EXPERIMENTAL PROCEDURES for details on how the interactions were identified.

 
All pathways were ranked according to the likelihood-based score that accounts for the number of genes expressed and number of linked loci with genes mapping to a particular pathway, and in which the number of linked loci contributes substantially to the score. We considered two ways to rank the pathways: 1) pathways weighted by expressed gene count, where all genes with nominal expression are ranked equally, and 2) pathways weighted by a confidence score in the expression of the gene based on whether the gene was expressed on one or both microarray platforms (Table 5). With the use of this approach, the adherens junction and MAPK signaling pathway had the highest likelihood-based scores (Table 5). There were altogether 83 expressed positional candidate genes in the top 20 biological pathways (Table 6); 29 (35%) of them belonged to more than one pathway.


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  		Table 6. Expressed positional candidate genes with membership in the 20 most significant KEGG biological pathways in IA

 
The adherens junction pathway includes 77 genes, of which 59 were identified as expressed in aneurysm or control arteries, 37 of which were at high confidence (Fig. 3 and Supplemental Table S4). Eight of the 77 adherens junction pathway genes map to 5 distinct IA linkage intervals. Six of these genes were expressed at high confidence: ACTN4, CDC42, EGFR, NLK, PVRL2, and TCF7. Two were expressed at lower confidence: CTNNA1 and WASF2.


Figure 3
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  		Fig. 3. Adherens junction pathway (hsa04520) modified from KEGG pathway. Protein symbols were replaced by gene symbols to reflect gene-centric data. For details on the scoring system, see EXPERIMENTAL PROCEDURES and Table 3. Supplemental Table S4 provides details on the expression of each gene in this pathway.

 
The MAPK pathway includes 256 genes, 165 of which were expressed, with 68 at high confidence (Supplemental Table S5). Thirty genes map to 13 IA linkage intervals, of which 8 are distinct. Eighteen of the 30 genes were expressed, 7 at high confidence: CDC42, EGFR, NLK, RPS6KA3, STMN1, TAOK1, and TGFB3. Eleven were expressed at low confidence: CD14, FOS, MAP2K3, MAP3K6, MAP3K10, MAP4K1, MAPK7, NF1, PPP3R1, PPP5C, and RRAS.

The Notch pathway was among the top biologically relevant pathways and includes 47 genes, of which 40 were identified as expressed in aneurysm or control arteries, 19 of which at high confidence (Fig. 4 and Supplemental Table S6). Eight of the 47 Notch pathway genes are located in IA loci and can therefore be considered IA positional candidate genes, including 6 that were identified as expressed in IA or control arteries, DTX2, SNW1, HDAC1, PSENEN, NUMB, and NUMBL, and two that were not detected as expressed, DLL3 and PSEN1. Three genes were expressed at high confidence: HDAC1, NUMB, and SNW1.


Figure 4
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  		Fig. 4. Notch signaling pathway (hsa04330) modified from KEGG pathway. Protein symbols were replaced by gene symbols to reflect gene-centric data. For details on the scoring system, see EXPERIMENTAL PROCEDURES and Table 3. Supplemental Table S6 provides details on the expression of each gene in this pathway.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The global gene expression data together with previous genetic mapping data for IA gave us the tools to identify the positional candidate genes that are expressed in intracranial arteries. A gene involved in disease may influence the concentration of protein isoforms or protein functionality or efficiency or alter the protein responsiveness to environmental factors that signal for gene expression (30). The genes identified as expressed are involved in several biological pathways including adherens junction, MAPK signaling, focal adhesion, regulation of actin cytoskeleton, calcium signaling, and Notch signaling. Such pathways regulate important processes such as cell growth, tissue remodeling, inflammation, and wound healing, processes that likely contribute to the pathophysiology of IA. Here we discuss in some detail three signaling pathways, adherens junction, MAPK, and Notch signaling, that are functionally relevant to the pathogenesis of IA.

Adherens junction.
The most significant biological pathway identified for IA was the adherens junction pathway. Adherens junctions are complex dynamic structures that include both adhesive and signaling molecules that maintain contact inhibition of endothelial cell growth and vascular permeability to inflammatory cells and solutes and are required for the organization of new vessels in angiogenesis (3). In vascular endothelial cells, adherens junctions are important structures that maintain the integrity of the vessel wall and are also involved in endothelium remodeling in various physiological and pathological processes (14). Adherens junction proteins can transfer intracellular signals to β-catenin, which has the ability to translocate to the nucleus and associate with transcription factors to regulate the expression of genes involved in endothelial cell growth, differentiation, and apoptosis (3).

The adherens junction pathway is connected to several biological pathways including MAPK, Wnt, and transforming growth factor-β (TGF-β) signaling, cytokine-cytokine receptor interaction, and the cell adhesion molecule pathway. The interaction of key integral membrane and intracellular proteins to form appropriate endothelial cell-cell junctions is important to maintain vascular homeostasis. Our gene expression analysis indicated that the majority of genes in the adherens junction pathway were expressed at moderate-to-high confidence levels. It is noteworthy that CTNND2, mapping to the IA locus on chromosome 5p, encodes catenin delta-2, a protein that may be involved in the regulation of adhesion molecules in the brain (29). Positional IA candidate genes in the adherens junction pathway, such as the CTNNA1gene encoding catenin alpha-1, may therefore have a specific role in maintaining the integrity of intracranial arteries.

MAPK signaling.
MAPK is one of the most interconnected pathways (hsa04010; Fig. 2, Supplemental Table S3). The three major branches of the MAPK pathway include the classical MAPK pathway, c-Jun NH2-terminal kinase (JNK) and p38 MAPK pathway, and the ERK5 pathway, which involves signaling systems that regulate cell proliferation, differentiation, inflammation, and apoptosis. MAPKs are expressed in multiple vascular cell types including cardiomyocytes, vascular endothelial cells, and vascular smooth muscle cells (VSMCs) and function to regulate cardiovascular signal transduction pathways (12). The MAPKs mediate signals triggered by cytokines, growth factors, and environmental stress including ischemia, shear stress, and vasoactive agents. Studies of MAPK signaling in the cardiovascular system indicate that these cascades regulate vascular endothelial cell permeability, production of cytokines, modulation of vasomotor function, and mediation of reperfusion injury (12).

There are 18 expressed IA candidate genes that play a role in MAPK signaling, including MAPK7, TGFB3, CDC42, NLK, STMN1, PPP3R1, and RPS6KA3 (see Table 6). Additionally, genes encoding heat shock proteins such as HSPA9B and HSPB1 are expressed in IA. Other genes that influence or interact with the MAPK signaling pathway and are expressed in intracranial arteries include EGFR, TGFBR1, TNFRSF1A, CASP2, IL1B, IL1R1, JUN, MAPK8 (JNK), HSPA5, and DUSP10. HSP5A is a positional candidate gene for IA that encodes a heat shock protein involved in the negative regulation of JNK. DUSP10 encodes a dual-specificity MAPK phosphatase that has a principal function in both innate and adaptive immune responses, as it inhibits JNK, which regulates the transcription factor activator protein-1 (AP1), implicated in the controlled expression of many genes involved in the immune response (4, 34).

Recently, TNFRSF13B, located on chromosome 17, was identified as a susceptibility gene for IA (10). It encodes transmembrane activator and calcium modulator ligand interactor (TACI), involved in immunity by mediation of isotype switching in B cells. TNFRSF13B is not known to be one of the TNF receptors in the MAPK signaling pathway; however, a related TNF receptor superfamily member, TNFRSF1A, is involved in the MAPK pathway and is expressed in IA.

Notch signaling.
Several linked genes for IA are part of the Notch signaling pathway (hsa04330; see Table 6) (5), which is involved in the development of cardiovascular structures in mammals and is important for endothelial and smooth muscle cells to form arteries and veins. Its relevance in the adult vascular system is demonstrated by mutations in NOTCH3 that cause cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a vascular degenerative disease that is the most common form of hereditary stroke disorder (11). The underlying pathology involves progressive degeneration of the VSMCs in arteries. Mutations in NOTCH3 cause abnormal accumulation of the Notch3 receptor at the cytoplasmic membrane of VSMCs in intracranial vessels (5). The disruption of the normal anchorage of VSMCs to the extracellular matrix and other nearby cells, in addition to cytoskeletal alterations, seems to trigger the VSMC degeneration observed in CADASIL patients (21). The loss of VSMCs has been shown to lead to endothelial cell abnormalities in tight junctions and gap junctions (6). These cell-cell adhesion pathways are connected to other pathways that function to maintain cell junctions, including adherens junction, cell adhesion molecules, regulation of the actin cytoskeleton, and MAPK and Ca2+ signaling pathways (Fig. 2).

Study limitations.
Although gene expression analysis is a valuable experimental tool, KEGG annotation is ~75% incomplete for expressed IA positional candidate genes, a significant limitation. The experiments described here evaluated gene expression as it appears in end-stage IA rather than differential gene expression in the development of aneurysmal arteries compared with control arteries. To determine differential gene expression in the development and rupture of IA, a larger sample set needs to be evaluated, preferably in tissues excised during aneurysm repair surgery. Also, to understand why aneurysms are prone to form at specific locations along the circle of Willis, there is a need for a comprehensive analysis of gene expression of the different intracranial arteries. Additionally, the intracranial arteries evaluated in this study come from two different ethnic groups, Finnish Caucasians and African Americans. It is possible that vascular gene expression might differ between populations. Extensive analysis of a larger sample set of arteries from these two populations would be required to quantify such differences.

The intracranial artery tissue samples were obtained at autopsy; thus we cannot completely exclude the possibility of postmortem changes in the analyzed samples. All autopsies were performed within 24 h of death. In addition, the brains used for IV tissue here were also used by Albertson et al. (1) to obtain good-quality RNA from the accumbens for gene expression experiments. Also, our recent microarray expression study on abdominal aortic aneurysms, described in Lenk et al. (13), showed that high-quality RNA can be obtained from autopsies. Our approach may have missed genes involved in IA pathogenesis, because expression of causative genes may occur only at certain times in development and not in adult tissues, although this is unlikely, since most IAs develop later in life. Technical problems (e.g., so-called nonperforming probes) with the microarray probes representing some genes have been noted previously (2). This is unlikely to influence these results, since two different microarray platforms were used, and they agree extensively (see also Lenk et al., Ref. 13). There could have been a loss of transcripts in the RNA isolation or amplification process. Nevertheless, the microarray data provide useful qualitative gene expression information that assists in the identification of functional candidate genes involved in the formation or rupture of IA, or both. These data will also be useful for studies of other cerebrovascular diseases.

In summary, the 10 IA susceptibility loci contain 810 expressed positional candidate genes. These genes function in biological pathways such as adherens junction, MAPK signaling, and Notch signaling that may be involved in the pathogenesis of IA. This work demonstrates a method for integrating global gene expression profiles of nonaneurysmal and aneurysmal intracranial arteries with genetic mapping data to prioritize positional candidate genes for further study.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This project was funded in part by a grant from the National Institute of Neurological Disorders and Stroke (NS-034395 to G. Tromp). S. Weinsheimer (0410051Z) and G. M. Lenk (0510063Z and 0710099Z) were recipients of predoctoral fellowships from the American Heart Association, Greater Midwest Affiliate.


   ACKNOWLEDGMENTS
 
We thank Daniel Lott and Sara McNorton (Applied Genomics Technology Center at Wayne State University) for work on microarray experiments, Kristine Barker and Dr. Michael Bannon (Dept. of Pharmacology at Wayne State University) for the contribution of collecting intracranial artery specimens, and Katariina Helin (Kuopio University) for assistance in the collection of IA samples.

Present addresses: S. Weinsheimer, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA; and M. van der Voet, Dept. of Developmental Biology, Utrecht University, Utrecht, The Netherlands.


   FOOTNOTES
 
Address for reprint requests and other correspondence: G. Tromp, Center for Molecular Medicine and Genetics, Wayne State Univ. School of Medicine, 3309 Gordon H. Scott Hall of Basic Medical Sciences, 540 East Canfield Ave., Detroit, MI 48201 (e-mail: gerard.tromp{at}sanger.med.wayne.edu).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

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