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Human Molecular Genetics Advance Access originally published online on July 6, 2006
Human Molecular Genetics 2006 15(16):2446-2456; doi:10.1093/hmg/ddl167
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A functional polymorphism within plasminogen activator urokinase (PLAU) is associated with Alzheimer's disease

Matthias Riemenschneider1,*, Lidija Konta1, Patricia Friedrich1, Sandra Schwarz1, Kevin Taddei5,13, Frauke Neff2, Alessandro Padovani6, Heike Kölsch7, Simon M. Laws1,13, Norman Klopp8, Heike Bickeböller9, Stefan Wagenpfeil3, Jakob C. Mueller1,3,14, Albert Rosenberger9, Janine Diehl-Schmid4, Silvana Archetti6, Nicola Lautenschlager10, Barbara Borroni6, Ulrich Müller11, Thomas Illig8, Reinhard Heun7, Rupert Egensperger12, Jürgen Schlegel2, Hans Förstl4, Ralph N. Martins5,13 the German Sib-Pair Study Group, Alexander Kurz4

1 Neurochemistry and Neurogenetics Laboratory, Department of Psychiatry and Psychotherapy, 2 Institute of Pathology, 3 Department of Medical Statistics and Epidemiology, 4 Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), Ismaningerstr. 22, 81675 Munich, Germany, 5 Sir James McCusker Alzheimer's Disease Research Unit, Hollywood Private Hospital, Perth, Western Australia, 6 Clinica Neurologica, Università degli Studi di Brescia, Brescia, Italy, 7 Department of Psychiatry and Psychotherapy, Friedrich Wilhelms Universität, Bonn, Germany, 8 Institute of Epidemiology, GSF, München-Neuherberg, Germany, 9 Department of Genetic Epidemiology, University of Göttingen, Germany, 10 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia, 11 Institute of Human Genetics, University of Giessen, Germany, 12 Institute of Neuropathology, University Hospital Muenster, Germany, 13 Alzheimer's and Aging, School of Biomedical and Sports Science, Edith Cowan University, Joondalup, Australia and 14 Hertie-Institute for Clinical Brain Research, Tuebingen, Germany

* To whom correspondence should be addressed: Tel: +49 8941404214; Fax: +49 8941404888; Email: m.riemenschneider{at}lrz.tu-muenchen.de

Received March 22, 2006; Accepted July 1, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
A number of susceptibility loci for Alzheimer's disease (AD) have been identified including a region on Chromosome 10q21–q22. Within this region the plasminogen activator urokinase gene (PLAU) was considered as a reasonable candidate from its functional implication in plasmin generation, a serine protease capable of degrading beta-Amyloid (Aß) protein. We screened 56 single nucleotide polymorphisms (SNPs) around PLAU using 1751 individuals from four independent case–control samples (Munich, N=679; Bonn N=282; Brescia (Italy) N=219; Perth (Australia) N=557 and one discordant sib-pair sample (Munich N=622). In brain tissue samples of neuropathologically confirmed cases with AD (N=33) we analyzed plaque counts according to the risk allele. We identified that one functional exonic SNP (rs2227564) is associated with development of AD using the four independent case–control samples (Munich, P=0.02; Bonn, P=0.005; Brescia (Italy), P=0.001; Perth (Australia), P=0.03) and the discordant sib-pair sample (P=0.001). In brain tissue, from neuropathologically confirmed cases with AD, we identified significantly higher plaque counts in carriers of the risk allele (N=6; 60.3±16.9) compared with non-carriers (N=9; 26.3±8.8; P=0.007). This study provides compelling evidence of a genetic and functional involvement of a common PLAU variant into the pathogenesis of AD. Further functional investigations are warranted to elucidate the specific role of PLAU, respectively, PLAU variants in the metabolism of Aß proteins.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
Alzheimer's disease (AD) is a common, genetically complex neurodegenerative disorder that causes a progressive decline of cognitive abilities. The spectrum of factors that contribute risk to AD is wide and ranges from rare mutations causing an autosomal-dominant early-onset form of AD, to susceptibility genes and further to epigenetic and non-genetic risk factors, such as advanced age. Abundant evidence suggests that elevated amounts of the small, highly amyloidogenic protein beta-amyloid (Aß), which is mainly composed of 40–42 amino acids (Aß40, Aß42) contributes to the neurodegenerative process in AD through its neurotoxic properties (1). While an increased production of Aß peptides has been demonstrated for the rare autosomal dominant forms of AD due to mutations within the amyloid precursor protein gene (APP; OMIM*104760) or the presenilins (PSEN1, OMIM*104311; PSEN2, OMIM*600759), a decreased degradation of Aß proteins may contribute to the sporadic forms of the disease. Ongoing research identified several proteases capable of degrading Aß proteins. Among these the insulin degrading enzyme (IDE, OMIM*146680), plasmin and activators of the plasmin system are of particular interest, as they may represent reasonable candidate genes conferring risk to the development of AD.

Previous genome-wide linkage studies in patients with late-onset AD have identified several regions with considerable linkage, (2–7) which strongly suggests the existence of additional susceptibility genes beside apolipoprotein E (APOE), the only unequivocally confirmed risk gene of AD so far (8,9). One broad linkage region on Chromosome 10q [10q21–q24; multipoint LOD score (MLS) {approx}4] has gained increasing attention due to the linkage to this region being repeatedly demonstrated in independent patient samples (10,11) and in addition, by a linkage study using elevated Aß42 levels in plasma as a quantitative trait (12). The genetic basis of this linkage regions has yet to be determined; however, there is increasing evidence from functional and genetic studies to suggest that two proteases, the plasminogen activator urokinase (PLAU, OMIM*191840) located at the middle and insulin degrading enzyme (IDE) at the distal end of the Chromosome 10 linkage region are strong functional and positional candidate genes. PLAU encodes the urokinase-type plasmin activator (uPA), a serine protease, which activates plasmin from its inactive precursor plasminogen after binding to its receptor (uPAR, OMIM*173391). In the context of AD-related pathological mechanisms it has been shown that Aß induces the expression of PLAU and its receptor (PLAUR) (13,14) and that uPA was found to inhibit Aß-mediated neurotoxicity and fibrillogenesis via activation of plasmin, a serine protease capable of degrading aggregated and soluble forms of Aß proteins (15,16). Besides its capability to degrade Aß peptides directly, plasmin has also been shown to enhance the non-amyloidogenic processing of APP by {alpha}-secretase cleavage (17).

Thus, several groups have investigated genetic variations in PLAU for association with AD (18–21) and AD-related endophenotypes, such as plasma Aß42 levels (22), cerebrospinal fluid (CSF) Aß42 levels and cognitive performance as assessed by the Mini Mental State Examination (MMSE) (23). However, the results of these genetic studies remain controversial, with some studies unable to find any association with AD (19–21) and others, which demonstrated significant association with AD (18,22), plasma Aß42 concentrations (22), CSF Aß42 concentrations and MMSE (23). The situation of PLAU appears to be even more puzzling, as Myers et al. (19) excluded PLAU as being the major locus underlying their linkage signal, whereas Ertekin-Taner (22) identified a substantial contribution of PLAU to their QTL-plasma Aß42 signal and Finckh et al. (18) published a risk association to the opposite allele compared with Ertekin-Taner et al. (22).

In an attempt to clarify the discrepancy of association results, we applied a whole gene approach (24) and conducted a detailed analysis of the PLAU gene locus by genotyping 56 single nucleotide polymorphisms (SNPs) spanning a genomic region of ~300 kB around the PLAU gene using a large case–control sample from Germany. The most promising association signal of a coding polymorphism at Exon 6 was successfully replicated using three independent case–control samples and one discordant sib-pair sample. Another coding polymorphism was identified by sequencing, but excluded as a risk variant through genotyping. Finally, possible functional consequences on the cerebral plaque load were investigated using human brain samples with AD.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
The initial genetic and statistical analyses to determine the LD structure, the single marker and haplotype association signals, as well as the sliding window approach were performed using the Munich cohort.

LD structure
Within the Munich sample, the LD structure based on 52 out of 53 SNPs (one SNP, rs2227579, was excluded because of deviation from Hardy–Weinberg equilibrium; Fig. 1A), shows that the PLAU region is partially separated by likely historical recombination from the neighboring gene, CAMK2G upstream (between markers hCV27069354 and rs2675675) and completely unlinked to the VCL downstream (between markers rs2688624 and rs4745730). Continuous blocks of relatively high r2 values are observed at the 5' flanking and promoter region of PLAU (from rs2675675 to rs2227552), and from PLAU Exon 11 (rs4065) to the 3'-UTR region (rs2688624). Accordingly, the markers at the PLAU gene form two blocks of high D' values (Block 3 and Block 4) separated by a partial breakpoint located at and including the marker rs2227564 (PLAU Exon 6; Fig. 1B). In addition, there were some scattered long-distance correlations between several markers within the genotyped region between rs2292307 and rs2688624, but these were uncorrelated with the markers at the VCL gene.


Figure 1671
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  		Figure 1. (A) Linkage disequilibrium structure of the extended PLAU gene region including the neighboring genes. (B) Linkage disequilibrium structure of the PLAU gene region. Linkage disequilibrium structure of the PLAU gene region. Pairwise r2 values are color-coded: black, high r2 values; white, low r2 values. The haplotype blocks indicated by high D' values are superimposed. The gene structure of the genomic region is shown on top.

 
Association with AD
Analysis of PLAU SNP alleles in the screening sample from Munich revealed evidence of an allelic association with AD for several markers located at the intergenic region upstream of PLAU, the LD breakpoint region within PLAU around Exon 6, and the 3'-UTR region. Specifically, three genetic markers within PLAU (rs2227562 Intron 5, rs2227564 Exon 6, rs2227568 Exon 8) were found to be associated with AD (Table 1). The risk conferred to AD was associated with the minor T allele for marker rs2227564, but was due to the major allele for both neighboring markers rs2227562 and rs2227568. The latter association pattern may be described by a common risk variant with a frequency of ~86% or, alternatively, by a rare protective variant (frequency ~14%). Both markers were also highly intercorrelated (r2=0.9; D'=0.96), whereas the marker rs2227564 was separated from this correlated cluster (pairwise comparison with rs2227562: D'=0.73, r2=0.036 and rs2227568: D'=0.63, r2=0.025).


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  		Table 1. SNP description, allele distribution and association with AD

 
A more detailed analysis of the association signals within the Munich screening sample revealed differences in the strength of the association according to the disease onset. To further explore this association we separately investigated patients with an early-onset of the disease (EOAD; onset ≤65 years; N=140) and those with late-onset AD (LOAD >65 years; N=282). In the EOAD group, we observed stronger allelic association (P=0.001) and a risk allele (T-allele) dose-dependent increase of the odds ratio (OR) from 1.7 (1.1–2.6) in heterozygous subjects up to 3.0 (1.1–8.0) in homozygous subjects (Table 2). No significant association was obtained in patients with LOAD (Table 1). Concerning both neighboring SNPs, the opposite effect was observed. Both markers show a trend or significant allelic association with LOAD [rs2227562: P=0.04; OR=1.5 (1.01–2.2) and rs2227568: P=0.05; OR=1.47 (0.99–2.1)], whereas no association was present in patients with EOAD (Table 1). Because of the number of SNPs genotyped at this region the genotypic association signals would no longer be significant when adjusting for the multiple comparisons performed.


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  		Table 2. Common PLAU haplotypes, frequencies and associations with AD

 
Two LD blocks which include the associated markers rs2227562 (Block 3; Fig. 1B) and rs2227568 (Block 4; Fig. 1B) were identified at the PLAU region. Within Block 3, three haplotypes are estimated. The most common haplotype shows a non-significant trend for association with AD ({chi}2=3.72; P=0.054; Table 3) and another haplotype with a frequency of 13.4% in cases and 18.1% in controls indicate a significant association ({chi}2=5.7; P=0.017; Table 3). Within Block 4, four haplotpyes are estimated and one haplotype showing a frequency of 12% in cases and 16.8% in controls was significantly associated ({chi}2=6.1; P=0.014; Table 3).


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  		Table 3. PLAU rs2227564 allele and genotype frequencies in case–control and discordant sib-pair samples

 
Using a sliding window approach for three marker associations, we identified two regions showing evidence for a table-wide significant association. The proximal signal was obtained at the intergenic region within window 1 (Haplotype 1.3; adjusted P=0.024) and a trend for window 2 (Haplotype 2.3; adjusted P=0.051; Supplementary Table, S1). The strongest, second signal was observed at the breakpoint for window 11 (marker combinations rs2227552, rs2227562, rs2227564) and window 12 (marker combination rs2227562, rs2227564, rs2227568; Supplementary Table, S1). Two haplotypes with frequencies of 12.5% in cases and 17.9% in controls (Haplotype 11.3; adjusted P=0.057) and 0.7% in cases and 3% in controls (Haplotype 11.4; adjusted P=0.009) showed a trend or table-wide significant association (1000 permutations). In addition, the haplotypic combination 12.3 of window 12 with frequencies of 11.3% in cases and 16.7% in controls was also significantly associated (adjusted P=0.048).

Sequencing
To identify additional SNPs which are in LD with the functional rs2227564 polymorphism, we sequenced the complete Exon 6 of the PLAU gene including both exon/intron boundaries using an independent sample from Munich consisting of 80 patients with AD and 80 controls. By sequencing we identified one additional functional, but rare variation within Exon 6 (1811 C/T His->Tyr), 23 bp upstream of the rs2227564 (1788 C/T; Leu->Pro) polymorphism (Fig. 2). Subsequent genotyping of this novel polymorphism in the Munich sample did not reveal any evidence of an association with AD or patients with EOAD (data not shown).


Figure 1672
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  		Figure 2. Sequencing chromatogram of the PLAU Exon 6 region. Identification of SNPs at the PLAU Exon 6 region. One additional coding SNP was identified by sequencing of genomic DNA in 80 patients with AD and 80 controls (320 alleles).

 
Replication
To replicate our initial genetic results we investigated the rs2227564 polymorphism in three independent case–control series from Bonn (Germany), Brescia (Italy), Perth (Australia) and in addition applied a family-based approach using a discordant sib-pair sample from Germany.

Regarding the case–control collectives we observed a significant allelic association for all series and further obtained a T-allele dose-dependent increase of the OR in the sample from Bonn and a similar trend in the series from Brescia, which just failed to reach significance in homozygous carriers of the T allele, probably because of the limited sample size (Table 2). In the Australian series, this effect was, likewise to the Munich sample, only present in patients with a younger onset of the disease. Because of a relatively small number of patients with EOAD, we divided this sample into two equal-sized subgroups defined by the cohort median age of 80 years (Table 2).

As a final step of our replication procedure, we applied a family-based strategy and analyzed the PLAU rs2227564 polymorphism in a discordant sib-pair sample from Germany. For the statistical analysis we applied the S-TDT (25) and obtained a highly significant over-representation of the risk-allele in the affected group (P=0.001; Table 2).

In addition, no significant interaction between the rs2227564 polymorphism and the APOE{epsilon}4 carrier status was observed in the larger series from Munich and Perth (P>0.05).

Functional analyses
To corroborate our genetic data, we extended our analysis to histopathologically confirmed brain samples of subjects with AD (N=33) and investigated possible effects of the PLAU rs2227564 polymorphism on cerebral plaque counts, as one characteristic feature of AD associated with Aß metabolism. The brain samples used for this analysis are presented in Table 4. In a first approach we restricted our analysis to patients lacking the APOE{epsilon}4 allele because of its known effect on plaque pathology. We found significantly higher plaque counts in temporal cortices of patients carrying the PLAU rs2227564 T allele (60.3±16.9; N=6) compared with patients without the rs2227564 T allele (26.3±8.8; N=9; P=0.007) (Table 4, Figs 3 and 4). In the complete histopathological sample (N=33), we then determined the influence of the APOE{epsilon}4 and PLAU rs2227564 T allele on cerebral plaque count by linear regression analysis, using age and sex as covariates. As expected, we found a significant effect for presence of the APOE {epsilon}4 allele (P=0.0069), but also a weaker independent effect for the PLAU rs2227564 T allele (P=0.015). Age and sex, however, did not contribute to the number of plaques.


Figure 1673
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  		Figure 3. Plaque counts according to presence or absence of the PLAU s2227564 T and the APOE{epsilon}4 alleles. Scatter of temporal plaque counts with mean and 95%CI. Grouping with number of samples according to presence or absence of the PLAU rs2227564 T allele and the APOE{epsilon}4 allele. P-value for group differences using the two-sided Mann–Whitney U test as indicated.

 


Figure 1674
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  		Figure 4. Immunohistochemistry of plaques according to the PLAU rs2227564 alleles. Comparison of Aß load of the temporal cortex in two cases lacking the APOE{epsilon}4 allele with (A) and without (B) the PLAU rs2227564 T allele. Immunohistochemistry with monoclonal Aß antibody (DAKO; 6F/3D; 1:100), peroxidase/DAB. Scale bar: 500 µm.

 


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  		Table 4. Description of brain samples used for analysis of cerebral plaque counts, description of brain samples and plaque counts of the temporal cerebral cortex

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
PLAU represents a reasonable functional and positional candidate gene as it is located under a linkage region of AD on Chromosome 10q and functionally is involved in the degradation of Aß via plasmin activation.

In an attempt to elucidate the genetic contribution of PLAU to the development of AD and to further understand the conflicting results of several association studies on PLAU, we analyzed a large case–control sample on a high-density SNP map using a whole gene approach (24) and analyzed the LD structure of the genomic region. Using a sliding window approach, we fine-mapped the associated region. The strongest single marker association conferring risk to the disease, particular EOAD was observed for the functional rs2227564 polymorphism, which is located at an LD breakpoint within PLAU Exon 6. Interestingly, the strength of this association increased in patients with a younger onset of the disease, particularly in patients with EOAD where we observed a T-allele dose-dependent increase of the ORs, whereas other, weaker association signals appeared in patients with LOAD. Sequencing of PLAU Exon 6 identified a new functional polymorphism, which did not contribute to the observed association. As the rs2227564 polymorphism showed the strongest association signal and because of its functional nature we replicated this SNP in three independent case–control series from Bonn, Brescia and Perth and confirmed our initial results. All series showed an allelic association and a T-allele dose-dependent increase of the ORs. In the Australian series the association appeared, likewise to the Munich sample, to be stronger in patients with a younger onset of the disease. To reduce the possibility of a spurious association finding due to hidden stratification we included a discordant sib-pair sample from Germany, which also showed a significant over-representation of the risk allele in the affected group. Because of the hypothesized functional involvement of PLAU in Aß catabolism, we finally examined possible effects of the PLAU rs2227564 T-allele on cerebral plaque counts using post-mortem brain material of subjects with neuropathological confirmed AD. Brain samples with the rs2227564 T-allele showed significantly higher plaque counts compared with non-carriers, and this effect was independent from the presence of the APOE{epsilon}4 allele.

Taken together our results provide considerable evidence of a genetic and functional involvement of PLAU polymorphisms, respectively, of a yet untyped causal variant in LD, into the pathogenesis of AD.

However, six articles investigating a possible association of PLAU polymorphisms with AD and related endophenotypes, such as plasma Aß42 concentrations and cognitive performance (MMSE) were published so far. In summary, three of these articles by Myers et al. (19), Papassotiropoulos et al. (20) and Bagnoli et al. (21) failed to identify a significant association for the rs2227564 polymorphism with AD. While Ertekin-Taner et al. (22), reported a similar association to our study, Finckh et al. (18) observed a risk association to the opposite allele.

On the first view, these discrepant results do not substantially support a major role of PLAU as a new susceptibility gene of Alzheimer's disease, but rather may reflect the typical situation often observed in genetically complex diseases. Typical reasons for the discrepancies are well known and include factors such as statistical over-interpretation of the first study, small sample sizes, genetic and phenotypic heterogeneity of the analyzed trait, population differences in allele frequencies and correlation structures among genetic markers, drawbacks in study design and differences regarding ethnic background and population sub-structures (e.g. different age structures in patients).

Regarding the correlation structures, there is evidence that the LD structure of PLAU differs substantially among population samples. The finding of a LD breakpoint at PLAU Exon 6 in the German population is supported by a previous study which examined block boundary shifts in a number of genes, including PLAU in several European populations (26). This breakpoint has not been observed in two larger studies from the US which genotyped five (22), respectively, seven SNPs (19) within PLAU. Even between European and German samples, the probability for LD block boundaries around markers rs2633306/rs2688610 and rs2227564 (markers 14 and 22 in figure 2 of Mueller et al.) (26) are highly variable. Such differences in LD-block structures including boundary shifts are expected among different populations and may help to limit association signals to smaller regions (26). Moreover, the low r2 values at the associated region of PLAU rs2227562 (Intron 5), rs2227564 (Exon 6) and rs2227568 (Exon 8) and some scattered high r2 values among non-neighboring markers, may indicate a long and independent mutational history of the individual markers. Recent mutational events may vary in different populations, thus leading to different correlation structures between populations, which complicate replications and comparison of genetic association studies. However, all polymorphisms in correlation with our tested coding SNP have to be considered as equally likely causal candidates. There is a large block (~300 kb) of scattered correlated SNPs between two indicated recombination hotspots (see HapMap). This block encompasses the following 10 genes: FLJ32658, SEC24C, FUT11, CHCHD1, LOC196752, KIAA0913, NDST2, CAMK2G, C10orf55 and PLAU. On the basis of our results we assume, however, that our association signal maps to the PLAU region, which is supported by relatively high, although variable, probabilities for LD block boundaries between the neighboring genes CAMK2G and PLAU in most European samples [around marker 8/9 (rs2675671/rs2633303) and marker 14 (rs2688611) in figure 2 of Mueller et al.] (26), which in turn corresponds to higher estimated recombination rates in the HapMap data. In addition, the 3' neighboring gene VCL is clearly unlinked to PLAU.

Sample size considerations may apply to the studies of Papassotiropoulos et al. (20) and Finckh et al. (18) who used small sample sizes, which were less than 100 individuals in some of their analyzed samples (e.g. 43 cases and 55 controls from Basel, Switzerland and 94 patients and 30 controls from Brescia, Italy) (18), or 181 cases and 99 controls from Greece (20). Another weak point of both studies is the applied pooling strategy, thereby combining several underpowered case–control series of different ethnic backgrounds and considerable differences of the genotype distributions (upto 10%). In contrast to all other studies, Finckh et al. (18) identified the major allele to confer risk to AD. This is of particular interest as we observed a similar association pattern for both neighboring SNPs (rs2227562 and rs2227568). Although there appears to be large variability of the rs2227564 minor allele frequencies among different populations with a gradient from Northern Europe (30%) to the Southern part (13%) (26), which is accompanied by an increase of the recombination signal in the southern parts, the reason for this observation remains to be elucidated.

An often neglected and underestimated factor that certainly contributes to the large variability of genetic association studies concerns sample differences in the age structures. We also observed only a weak association in our larger series from Munich and Perth, but subsequent analysis of the patient substructure revealed that the risk conferred by rs2227564 appears to be stronger in patients with a younger onset of AD, whereas no effect was present in elderly patients. This age-dependency seems reasonable as it may be hypothesized that the genetic contribution, in general and that of a specific genetic risk factor may vary according to differences regarding the onset of the disease. This fact has been repeatedly demonstrated for APOE, the strongest genetic risk factor of AD so far (27). Therefore, studies using relatively old patient samples, such as Myers et al. (19) (e.g. Mayo series mean age 82.7 years) may have missed to detect the possible association with EOAD.

In contrast, Ertekin-Taner et al. (22) demonstrated genetic results similar to this study in their larger series (MCR) from the US. They observed not only a significant OR of 1.4 (1.018–1.9) for the functional Exon 6 rs2227564 polymorphism, but also significant results for some neighboring markers at Intron 7, Intron 9 and Exon 11.

Moreover, they present functional data of significantly elevated plasma Aß42 levels in carriers of the PLAU risk genotypes, which are believed to reflect a quantitative trait of AD. This result corresponds to our findings of higher cerebral plaque counts in brain samples with the risk allele. In addition, they suggest that the elevation of Aß42 levels may be due to a loss of function of PLAU as aged mice with a knock-out of the PLAU gene also showed substantially elevated Aß42 levels. Further support of a functional involvement of PLAU in the mediation of lethal effects due to APP over-expression comes from transgenic mouse studies (28). Analysis of age at death as a quantitative trait in transgenic animals vulnerable to APP over-expression identified a region on mouse Chromosome 14, which counteracts the lethal effects of APP. This region harbors several sequences with human homologues, including two genes, PLAU and neuregulin 3 (NRG3) on Chromosome 10 (28). As preliminary genotyping data from our group do not support NRG3 as a possible susceptibility gene of AD (data not shown), it remains to be seen whether PLAU alone is responsible for the linkage signal.

Likewise to our study, also Papassotiropoulos et al. (20) investigated possible functional effects of the rs2227564 polymorphism in human brain material but failed to find any effects. However, the use of brain samples from subjects without dementia and any neuropathological abnormalities may be the reason for not identifying any effects on the cerebral plaque load. While Myers et al. (19) excluded variations in PLAU to contribute to their linkage region at Chromosome 10q (2,10), Ertekin-Taner et al. (22) demonstrated for the rs2227564 polymorphism, a contribution of 22% to their plasma Aß42 phenotype (22).

In summary, this study provides compelling evidence of a genetic and functional involvement of a common PLAU variant into the pathogenesis of AD. Further functional investigations are required to elucidate the role of PLAU and PLAU polymorphisms in the pathogenesis of AD and the processing of Aß proteins.


   MATERIAL AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIAL AND METHODS
SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
Subjects and material
This study was approved by the review boards of each medical faculty and refers to a total of 2359 Caucasian subjects compromising four independent case–control samples from Munich, Germany (AD, N=422; controls=257); Bonn, Germany (AD=109; controls=173); Brescia, Italy (AD=120; controls=99); Perth, Australia (AD=219; controls=338) and a discordant sib-pair sample from Germany (affected = 251; unaffected = 371). All individuals were recruited from specialists at University Memory Clinics and each control group was matched for geographical location, ethnicity, sex and age and consisted of cognitively healthy individuals. The clinical diagnosis of probable AD was established according to National Institute of Neurological and Communicative disorders and Stroke-Alzheimer's Disease and Related Disorder Association (NINCDS-ADRDA) criteria (29). After informed consent had been obtained, blood samples of each individual were taken by venous puncture. Cognitive performance was assessed using standard neuropsychological tests, such as the Cambridge Cognitive Examination (30) or the Consortium to Establish a Registry for Alzheimer's disease (31) which includes the Mini Mental State Examination (MMSE) (32), which was used as a measure of global cognitive performance in the control groups. Control subjects with an MMSE score below 28 were excluded from further analyses. All patients and controls underwent a thorough psychiatric, neurological and neuropsychological evaluation. The diagnostic work-up also included an informant interview, a chemistry survey and structural brain imaging.

Frozen and paraffin-embedded brain tissue from 33 randomly selected patients (11 male, 22 female; mean age at death 78.5±7.9 years; disease duration 7.5±3.4 years) with clinically and neuropathologically confirmed late-onset AD was obtained from the Departments of Neuropathology at the University of Munich and the Technical University Munich.

The neuropathological diagnosis of AD was performed according to established criteria (33).

Genotyping and sequencing
We genotyped 56 SNPs derived from Public or the Celera human genome databases covering ~299 kb of the PLAU gene region on Chromosome 10 including the upstream located genes calcium/calmodulin-dependent protein kinase II-gamma (CAMK2G; OMIM # 602123 [OMIM] ), N-deacetylase/N-sulfotransferase 2 (NDST2; OMIM # 603268 [OMIM] ), KIAA0913 and downstream Vinculin (VCL; OMIM # 193065 [OMIM] ). A total of 52 SNPs with a median spacing of 6.2'kb showed sufficient genotyping quality (mean call rate above 95%), Hardy–Weinberg equilibrium and a minor allele frequency of more than 0.05 in the controls (Fig. 1). At the PLAU region, we genotyped 24 markers with a mean intermarker distance of 2 kb. The markers and locations are described in detail in Table 1. APOE genotypes were determined by the restriction enzyme approach (34). Genotyping was performed at the Department of Psychiatry, TU Munich and the Genome Analysis Center, GSF, Munich by a primer extension of multiplex polymerase chain reaction products and detection of the allele-specific extension products by matrix-associated laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry (Sequenom, San Diego, USA).

We sequenced genomic DNA (PLAU Exon 6 including the Exon/Intron boundaries at both sides) from 80 patients with AD and 80 cognitively healthy controls using Applied Biosystems BigDye Chemistry according to the recommendations of the manufacturer.

DNA from paraffin-embedded brain tissues was extracted as described (35), whereas standard methodologies were used for frozen brain samples. APOE and PLAU genotypes of brain material were determined using two independent methodologies, the MALDI-TOF and the restriction enzyme approach (34,36).

Analysis of cerebral plaque counts and immunohistochemistry
The quantitative analysis of plaques and Aß immunohistochemistry was performed using sections from the medial temporal gyrus according to the methodology described before (37,38). In particular, the number of plaques were counted by an experienced neuropathologist who was blinded for the genotyping results by examination of three slides per case and eight consecutive representative fields from severely affected tissue areas.

Statistics
The pairwise linkage disequilibrium measures, D' and r2, were calculated using the software package Haploview (39). Association between AD affection state and the alleles/genotypes of SNP markers was tested by logistic regression analysis including age of onset/examination and sex as covariates. Corrections for multiple comparisons were considered using a global permutation test (1000 permutations). Multiplicative interaction terms were tested between SNP effects and APOE{epsilon}4 status (present or absent), age and gender by appropriate logistic regression models. Haplotype assignments for each individual were estimated by the EM algorithm and subsequently used for association tests, both implemented in Haploview (39). A sliding window approach was performed to test for all two-, and three-locus haplotype associations. The discordant sib-pair sample was analyzed using the sib transmission/disequilibrium test (S-TDT) (25).

Differences in plaque counts were analyzed according to the presence or absence of the PLAU risk alleles by the two-sided Mann–Whitney U test. An overall analysis of genetic effects on cerebral plaque pathology was estimated by linear regression analysis using the APOE{epsilon}4 allele, PLAU risk allele, duration of the disease and gender as covariates.


   SUPPLEMENTARY MATERIAL
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
APPENDIX
 REFERENCES
 
Supplementary Material is available at HMG Online.


   APPENDIX
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
REFERENCES
 
The German Sib-Pair Study Group: Many data and biomaterials were collected by two recruitment sides of the German Sib-Pair Study Group at the TU-Munich (M. Krömmer, B. Cramer, A. Klimbacher), the GSF, Munich (P. Belcredi) and several centers that participated at this study. The principal investigators and co-investigators were: Bezirkskrankenhaus Gabersee; Professor Dr G. Laux and Dr Eberl; Department of Psychiatry, University of Regensburg, Professor Dr H.E. Klein and Dr Bernd Ibach; Neurologische Klinik Bad Aibling, Professor Dr Eberhard Koenig and Dr Barbara Romero; Bezirkskrankenhaus Bayreuth, Professor Dr Manfred Wolfersdorf and Dr Michael Schüler; Department of Psychiatry, University of Freiburg, Professor Dr Mathias Berger, PD Dr Schmidtke; E. Jost MSc; Bezirkskrankenhaus Augsburg Professor Dr M. Schmauss and Ch. Steber; Krankenhaus München-Neuperlach, Abt. für Akutgeriatrie, Professor Dr R. Heinrich and Dr Britta Wiegele; Bezirkskrankenhaus Werneck PD Dr H.-P. Volz and Dr M. Jähnel; Bezirkskrankenhaus Taufkirchen, Professor Dr M. Dose, Dr Marquard and Dr Bremer; Psychiatrische Klinik Agatharied, Dr N. Braunisch and Dr H. Nickl; Bezirksklinikum Mainkofen, Dr L. Blaha and Dr S. Herpich; Bezirkskrankenhaus Haar, Dr H.W. Dietl; Bezirkskrankenhaus Günzburg, Professor Dr R. Schüttler and PD Dr R. Hess; Bezirkskrankenhaus Landshut, Professor Dr M. Philipp and Dr A. Wermuth.


   ACKNOWLEDGEMENTS
 
We wish to thank all individuals who participated in this study for their contribution and would also like to thank the dementia outpatient unit employees and Tamara Eisele for their help in collecting and processing of samples. This work was funded by the German National Genome Network (NGFN) and the German Ministry for Education and Research; Grant Numbers 01GS0465 to M.R., 01GS0166 to M.R., A.K., U.M., 01GS0116 to H.B. and FE 77443 to T.I. R.N.M. and K.T. were supported by The McCusker Foundation for Alzheimer's Disease Research.

Conflict of Interest statement. None of the authors has had involvements that might raise the question of bias in the work reported or in the conclusions, implications or opinions stated.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIAL AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 

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