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Human Molecular Genetics Advance Access originally published online on June 1, 2006
Human Molecular Genetics 2006 15(13):2170-2182; doi:10.1093/hmg/ddl142
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Dynamin-binding protein gene on chromosome 10q is associated with late-onset Alzheimer's disease

Ryozo Kuwano1,2,*, Akinori Miyashita1,2, Hiroyuki Arai3, Takashi Asada4, Masaki Imagawa5, Mikio Shoji6, Susumu Higuchi7, Katsuya Urakami8, Akiyoshi Kakita9, Hitoshi Takahashi9, Tamao Tsukie1, Shinichi Toyabe10, Kohei Akazawa10, Ichiro Kanazawa11, Yasuo Ihara12 and The Japanese Genetic Study Consortium for Alzheimer's Disease

1 Genome Science Branch, Center for Bioresource-Based Researches, Brain Research Institute, Niigata University, Niigata 951-8585, Japan, 2 Center for Transdisciplinary Research, Niigata University, Niigata 951-8585, Japan, 3 Department of Geriatric and Complementary Medicine, Advanced Research Center for Asian Traditional Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan, 4 Department of Psychiatry, Institute of Clinical Medicine, University of Tsukuba, Tsukuba 305-8575, Japan, 5 Department of Neuropsychiatry, Imagawa Clinic, Fukushima-ku, Osaka 553-0003, Japan, 6 Department of Neurology, Neuroscience and Biophysiological Science, Okayama University Graduate School of Medicine and Dentistry, Okayama 700-8558, Japan, 7 Division of Clinical Research, Kurihama Alcoholism Center, National Hospital Organization, Yokosuka 239-0841, Japan, 8 Department of Biological Regulation, Section of Environment and Health Science, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan, 9 Department of Pathology and the Resource Branch for Brain Disease, Brain Research Institute, Niigata University, Niigata 951-8585, Japan, 10 Department of Medical Informatics, Niigata University, Niigata 951-8520, Japan, 11 National Center for Neurology and Psychiatry, Kodaira 187-8502, Japan and 12 Department of Neuropathology, Faculty of Medicine, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan

* To whom correspondence should be addresssed: Tel: +81 25 227 2343; fax: +81 25 227 0793; Email: ryosun{at}gene.med.niigata-u.ac.jp

Received April 25, 2006; Accepted May 24, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
The apolipoprotein E (APOE) gene has been consistently shown to be a major genetic risk factor; however, all cases of Alzheimer's disease (AD) cannot be attributed to the {varepsilon}4 variant of APOE, because about half of AD patients have the APOE-{varepsilon}3*3 genotype. To identify an additional genetic risk factor(s), we performed large-scale single nucleotide polymorphism (SNP)-based association analysis of 1526 late-onset AD patients and 1666 control subjects in a Japanese population. We prepared two independent sets consisting of exploratory and validation samples, respectively, with only the APOE-{varepsilon}3*3 genotype, and first carried out genotyping for the exploratory set with 1206 SNPs in the region between 60 and 107 Mb on chromosome 10q that is implicated by linkage studies as containing an AD susceptibility locus. Thirty-five SNPs that showed significant values (P<0.01) were followed-up to detect any association with the validation samples. Finally, six SNPs exhibited replicated significant associations (P=0.000035–0.00048) on meta-analysis of both sets. These SNPs were clustered in a locus spanning 220 kb at genomic position 101 Mb, and three of the six SNPs were located in the dynamin-binding protein (DNMBP) gene. Quantitative real-time RT–PCR analysis demonstrated that neuropathologically confirmed AD brains exhibit a significant reduction of DNMBP mRNA compared with age-matched ones (P<0.0169). Thus, we confirmed the association of DNMBP with AD individuals with the APOE-{varepsilon}3*3 genotype or lacking the {varepsilon}4 allele, and DNMBP may be one of the susceptibility genes for AD.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
Alzheimer's disease (AD) is neuropathologically characterized by a loss of synapses, extracellular deposition of amyloid ß-protein (Aß), intracellular formation of neurofibrillary tangles and neuronal cell death. AD is thought to be a multifactorial disease probably caused by complicated interactions between genetic and environmental factors. The apolipoprotein E (APOE) gene located on chromosome 19q13.2 was the first identified genetic risk factor associated with late-onset AD (LOAD) (1,2). To date only the APOE-{varepsilon}4 allele has been universally recognized as a major risk factor for LOAD and also as being associated with lowering of the age at onset (AAO) (3). However, about 50% of AD patients do not carry the APOE-{varepsilon}4 allele, and only 20% of AD patients and <10% of the variance in AAO appear to be explained by the APOE-{varepsilon}4 allele (4,5). The sibling recurrence risk ratio ({lambda}s) (6) in AD was estimated to be 3.5–5 based on epidemiological data. Assuming that APOE was the only genetic risk factor, gene-specific {lambda}gs (APOE) was estimated to be 1.7–2.5. Because {lambda}s>{lambda}gs (APOE), there should be an additional genetic risk factor(s) for AD (7,8). The identification of an additional genetic risk factor(s) would greatly facilitate our understanding of the neuropathological findings, the clinical manifestations and the varying responses to drugs.

Genome-wide linkage studies on LOAD have provided informative data on putative susceptibility genes on several chromosomes (9–15). Chromosomes 1, 9, 10, 12, 21 and X have linkage peaks that have also been observed in APOE-{varepsilon}4 negative sib pairs (9,10). There are several candidate genes that are near the chromosome 10 linkage peaks and that are thought to be implicated in LOAD, including the catenin (cadherin-associated protein) alpha 3 (CTNNA3) (16,17), the insulin-degrading enzyme (IDE) (11,18–21), the urokinase-plasminogen activator (PLAU) (22–24) and the glutathione S-transferase omega-1 and 2 (GSTO1 and GSTO2) (25–27) genes. However, the association results regarding these candidate genes have not been consistently replicated.

To determine whether or not there are additional genes causing susceptibility to APOE-{varepsilon}4 negative LOAD, we screened the region between 60 and 107 Mb on chromosome 10q with 1206 single nucleotide polymorphisms (SNPs). We found that the gene encoding dynamin-binding protein (DNMBP) (28) was significantly associated with LOAD lacking the APOE-{varepsilon}4 allele. DNMBP was discovered as a novel scaffold protein that brings the dynamin and actin regulatory proteins together and is concentrated at synapses in the brain. To date expression of its gene in the brain has not been demonstrated. In this study quantitative real-time RT–PCR analysis clearly showed that neuropathologically confirmed LOAD brains contain significantly reduced levels of DNMBP mRNA compared with those in age-matched controls. On microarray analysis, the gene expression related to synaptic vesicle trafficking was found to be decrease in the frontal cortex of AD patients (29). In addition, Aß induces a decrease in the dynamin I level at synaptic sites in rat cultured hippocampal neurons (30). In view of the fact that synaptic dysfunction precedes Aß deposition in the brains of AD patients (31), our observations raise the possibility that DNMBP, as a risk factor, might play a predominant role in the early stage of LOAD with APOE-{varepsilon}3*3 or lacking the APOE-{varepsilon}4 allele.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
Genetic screening
Although the APOE-{varepsilon}4 allele is a strong genetic risk factor for LOAD, about half of the Japanese LOAD patients (46%) have the APOE-{varepsilon}3*3 genotype (Table 1). To identify an additional susceptibility gene(s) for LOAD with the APOE-{varepsilon}3 allele, we performed SNP-based two-step screening. This two-step procedure was used to examine an exploratory sample set to find significant markers and to confirm their significance with a validation sample set. Both APOE-{varepsilon}4 positive and negative subjects show linkage peaks for chromosomes 1, 9, 10, 12, 21 and X (9,10). In this study, we screened a wide region of chromosome 10q showing linkage peaks with a relatively high density of SNP markers. With this strategy, we selected 1322 SNPs (Supplementary Material, Tables S1 and S2) in the region from 60 to 107 Mb on chromosome 10q, of which 1206 were polymorphic, finding no significant deviation (P>0.05) from the Hardy–Weinberg equilibrium (HWE) in a Japanese population.


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  		Table 1. Summary of overall subjects collected by JGSCAD

 
Using these 1206 SNPs, we first scanned an exploratory sample set. In the first-step screening, 131 of the 1206 SNPs showed allelic P-values of <0.05, and 35 of them showed more significant values (allelic P<0.01). To determine whether or not these markers exhibit associations, we genotyped these apparently significant SNPs in another sample set (validation samples), because a large number of SNPs probably exhibit false-positive associations. Replication in both exploratory and validation sample sets strongly suggests a true association of particular SNPs with LOAD. As a result, six of the above 35 SNPs (rs911541, rs3740066, rs11190302, rs11190305, C_11214959_10 and rs3740058) exhibited replicable associations (allelic P<0.05), which remained significant on Mantel–Haenszel meta-analysis of the two sample sets (P=0.00003485–0.0004757) (Table 2). These P-values remained at significant levels even after Bonferroni's correction with 35 tests (P=0.001220–0.01665).


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  		Table 2. Statistics for six replicated SNPs found on the two-step screening involving APOE-{varepsilon}3*3

 
The six significant SNPs were located in a region spanning ~220 kb between 101.4 and 101.6 Mb on chromosome 10q (Table 3 and Fig. 1). This associated region contains five genes: the ectonucleoside triphosphate diphosphohydrolase 7 (ENTPD7), the cytochrome c oxidase subunit 15 (COX15), the cutC copper transporter homolog (Escherichia coli) (CUTC), the ATP-binding cassette sub-family C member 2 (ABCC2) and DNMBP genes. SNP rs911541, lying about 171 kb apart from rs3740066, is located in the third intron of ENTPD7, which consists of 13 exons. Five SNPs, i.e. rs3740066, rs11190302, rs11190305, C_11214959_10 and rs3740058, are clustered in an ~51.8 kb region including the 3' flanking regions of both ABCC2 and DNMBP (Fig. 1). SNP rs3740066 is a synonymous SNP (ATC->ATT, Ile1324Ile) as to exon 28 in ABCC2 that comprises 32 exons. SNP rs11190302 is an intergenic SNP between ABCC2 and DNMBP, lying about 7.0 kb from rs11190305 in DNMBP. The other three SNPs, i.e. rs11190305, C_11214959_10 and rs3740058, spanning about 16.1 kb have been mapped to DNMBP, which consists of 17 exons. SNP rs11190305 is a non-synonymous SNP (TGT->TGG, Cys1413Trp) as to exon 16. Both C_11214959_10 and rs3740058 are intronic SNPs, the former being in intron 11 and the latter in intron 10.


Figure 1421
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  		Figure 1. Genomic position of an associated locus. Vertical lines indicate the SNPs used in this study: six significantly associated SNPs, rs911541, rs3740066, rs11190302, rs11190305, C_11214959_10 and rs3740058, are indicated by the long-labeled vertical lines. These SNPs are located in a genomic region in which five genes, ENTPD7, COX15, CUTC, ABCC2 and DNMBP, are clustered, spanning ~350 kb. The asterisked vertical lines in the 5' region of DNMBP show the SNPs additionally designed and analyzed after real-time RT–PCR experiments. Horizontal arrows within open boxes indicate the transcription orientations of individual genes. The mapping position of each SNP is according to dbSNP build 125 on NCBI build 35. CPN1, gene encoding carboxypeptidase-N-polypeptide 1, 50 kDa.

 


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  		Table 3. Summary of associated SNPs

 
Stratified analysis with entire samples
As the next step, the significantly replicated markers were tested by stratified analysis using entire samples with all APOE-genotypes, i.e. in 1526 LOAD samples and 1666 control samples (Table 4). It became clear that the six SNPs were strongly associated with APOE-{varepsilon}4 negative LOAD (sample set Negative-{varepsilon}4: range of allelic P=0.00005699–0.001164), whereas none of the six SNPs showed any significant association with APOE-{varepsilon}4 positive LOAD at all (sample set Positive-{varepsilon}4: range of allelic P=0.7271–0.988). We cannot exclude the possibility that the lack of significance may be due to the small sample size, because the APOE-{varepsilon}4 allele is fairly rare in controls (frequency 0.0915) in comparison with in LOAD (frequency 0.3027).


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  		Table 4. Allelic association of six SNPs in sample sets stratified as to the APOE genotype

 
Linkage disequilibrium and case–control haplotype analyses
To further characterize these significantly associated SNPs detected on the two-step screening, linkage disequilibrium (LD) and case–control haplotype analyses were performed (Tables 5 and 6). Pair-wise LD measures, |D'|, are given in Table 5 for LOAD and control subjects with the APOE-{varepsilon}3*3 genotype. We found a strong correlation between the six SNPs and confirmed that the LD block was highly structured in the associated locus identified here. There was no difference in the LD structure between LOAD and control subjects.


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  		Table 5. LD measures, |D'|, for six SNPs associated with LOAD

 


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  		Table 6. Case–control haplotype analysis

 
In this LD block, five common haplotypes were inferred with the expectation–maximization (EM) algorithm in four sample sets examined: [H1]A-C-C-T-G-G, [H2]G-T-T-G-C-A, [G3]G-T-T-G-C-G, [H4]A-T-T-G-C-A and [H5]A-C-T-G-C-A (Table 6). Haplotype H1, composed of all major alleles of the six SNPs, exhibited the highest frequency (range 0.7118–0.7681) and haplotype H5 the lowest (range 0.0087–0.0188). Haplotype H2 consisted of all minor alleles. For case–control haplotype analysis, the haplotype frequencies in LOAD subjects were compared with those in controls. In the Positive-{varepsilon}4 sample set, no haplotypes exhibited a significant difference. Global tests also did not give statistically significant results (P=0.7872, permutation P=0.8388). However, regarding All, Negative-{varepsilon}4 and {varepsilon}3*3, two haplotypes, H1 and H2, showed significant association. The most significant difference between these two haplotypes was observed for the sample set composed of only APOE-{varepsilon}3*3 subjects: H1, P=0.0001958 and permutation P=0.0001; H2, P=0.00006021 and permutation P<0.0001. The frequency of the most common haplotype, H1, was significantly decreased in LOAD, whereas that of the H2 haplotype comprising all minor alleles was significantly increased in LOAD. In contrast, the other three haplotypes, H3, H4 and H5, exhibited no association in any sample set. These findings do not appear to be more significant than the results for the individual SNP, which may be due to the fact that the six SNPs are in one strong LD block and have very similar minor allele frequencies.

Expression of DNMBP in the human brain
As DNMBP was found to be genetically associated with LOAD, we measured the expression levels of DNMBP in autopsy-confirmed LOAD brains using quantitative real-time RT–PCR (Fig. 2). A summary of the brains examined is given in Supplementary Material, Table S3. The mean age at death for LOAD was 76.1±5.5 years, which was not significantly higher (P=0.0725) than that for controls (72.7±6.1 years). To select an appropriate internal standard for the normalization of DNMBP mRNA levels, we evaluated, using the TaqMan® Human Endogenous Control Plate described under Materials and Methods, the expression levels of endogenous housekeeping genes in eight brains consisting of four LOAD ones and four control ones. The transcripts of ß-glucuronidase (GUSB) and 18S rRNA genes were found to exhibit little variation within the eight brains (data not shown).


Figure 1422
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  		Figure 2. Expression levels of DNMBP in post mortem brain tissues. Real-time RT–PCR assaying was carried out to determine the steady-state mRNA level of DNMBP by using the standard curve method recommended by the manufacturer. DNMBP expression was normalized as to the concentration of total RNA (A) used for first-strand cDNA synthesis and the amounts of GUSB (B) and 18S rRNA (C) expression. Values are means±SD. P-values were computed with the two-sided Student's t-test. Relative expression of DNMBP in LOAD patients and controls was examined with a dominant model (D–F) by means of two-way ANOVA.

 
After this preliminary experiment, we increased the samples to 41 and assessed the correlation between the expression of DNMBP, GUSB and 18S rRNA and age and gender. Neither age- nor gender-dependent significant differences between LOAD patients and controls were observed in the levels of transcripts of DNMBP, GUSB and 18S rRNA (data not shown). Normalization relative to the quantity of total RNA revealed statistically significant differences between LOAD patients and controls (Fig. 2A, P=0.0003). As can be seen in Figure 2B and C, there was a significant reduction in the DNMBP mRNA levels in AD brains compared with that in age-matched controls following normalization as to either GUSB (P=0.0002) or 18S rRNA (P=0.0169).

To address whether or not the expression differences between LOAD and control brains are due to genetic variability in DNMBP, we first genotyped three associated SNPs (rs11190305, C_11214959_10 and rs3740058) using 41 brain tissue specimens. There were no minor-allele homozygotes in the control brains (Supplementary Material, Table S4), therefore we compared the expression levels of DNMBP in total samples with a dominant model (minor-allele homozygotes+heterozygotes versus major-allele homozygotes) (Fig. 2D–F). Two-way ANOVA with the genotype and case–controls as independent variables showed the significant effects of the diagnosis and genotypes examined in this study: rs11190305, P=0.0190; C_11214959_10, P=0.0234 and rs3740058, P=0.0205. However, no significant interactions of DNMBP expression, SNPs and diagnosis were observed (Fig. 2D–F).

To determine whether or not the reduced expression of DNMBP mRNA was caused by genetic variation including the 5' upstream region of DNMBP, we genotyped the exploratory sample set with eight additional SNPs (indicated by asterisked vertical lines in Figure 1). We did not detect any association of these eight SNPs with LOAD (range of allelic P, 0.00526–0.875).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
This paper describes an attempt to identify an additional susceptibility gene(s) for LOAD with a high density of SNPs in two independent APOE-{varepsilon}3*3 sample sets and to verify the finding, if any, in the entire samples. Like many complex diseases, LOAD is caused by interactions between environmental and genetic factors. As genetic risk factors for complex diseases are thought to be of lower penetrance and heterogeneous, the replication of genetic risk factors in well-characterized case–control samples is critical for validating the markers associated with a complex disease. A strong association between the APOE-{varepsilon}4 allele and LOAD has been repeatedly reported by a number of groups. However, as about 50% of AD patients do not carry the APOE-{varepsilon}4 allele, the remaining AD cases must be attributed to other risk factors or environmental factors. Thus, to identify a genetic risk factor(s) other than the APOE-{varepsilon}4 allele, we prepared two independent sample sets, exploratory and validation ones, from only APOE-{varepsilon}3*3 subjects. Our strategy was to scan a broad region on chromosome 10q with a high density of SNPs in the exploratory samples and to follow-up the significant initial markers in the other set (validation samples) and then to examine the significant replicated markers in the entire samples consisting of all APOE genotypes. This is similar to the strategy that was adopted for genome-wide (32) or chromosome-wide scanning (33,34). Using this strategy, Grupe et al. (34) performed a chromosome 10-specific association study involving 1412 gene-based SNPs. They found one marker, rs498055 (97,344,904 bp), located in a gene homologous to RPS3A (LOC439999) significantly associated with LOAD.

In a case–control association study involving 1206 SNPs on chromosome 10q, we found a novel locus in which six SNPs, rs911541, rs3740066, rs11190302, rs11190305, C_11214959_10 and rs3740058, were significantly associated with APOE-{varepsilon}3*3-LOAD (Table 2) but not with APOE-{varepsilon}4-LOAD (Table 4). No significant interaction was observed between the APOE-{varepsilon}4 allele and the SNPs identified here by logistic regression analysis (data not shown). However, as APOE-{varepsilon}4 allele positive samples are fairly rare among controls, we cannot exclude the possibility that the lack of significance in the APOE-{varepsilon}4 positive group is due to the small sample size. Further analyses are necessary to determine the interaction of the six SNPs and APOE genotypes in a sufficient number of controls.

The six SNPs are located in a locus between 101.4 and 101.7 Mb, which is far from D10S1225 (64.4 Mb) in the strong linkage region described by several groups. Two peaks were found for chromosome 10q when a sample was stratified as to APOE genotype on genome-wide linkage analysis (9,10). There was a major peak around 80 cM and another small peak was observed at marker D10S1265 (102.6 Mb) near the six SNPs. The linkage was found in different conditions: in familial AD (9–11,13,15), with plasma Aß levels as an intermediate quantitative trait (12) and with AAO in AD (14). In this broad region, candidate genes were presumed to be as follows: CTNNA3 (16,17), PLAU (22–24), IDE (18–21), GTSO1 and GTSO2 (25–27). CTNNA3 was identified in high plasma Aß42 pedigrees, and both IDE and PLAU are suggested to be involved in the degradation of Aß. We also measured the plasma Aß40 and Aß42 levels in the samples to determine whether or not the SNP genotype influences the plasma Aß level. We did not observe any relationship between the six SNPs identified in this study and the plasma Aß level (data not shown). Li et al. (14) analyzed transcripts in the hippocampus and reported decreased expression of GSTO1 and GSTO2 in the region between D10S1239 (98.9 Mb) and D10S1237 (116.1 Mb). We genotyped 167 SNPs in CTNNA3 (67.35–69.10 Mb), five SNPs in PLAU (75.33–75.35 Mb), 19 SNPs in IDE (94.20–94.33 Mb), two SNPs in GSTSO1 (106.00–106.02 Mb) and five SNPs in GTSO2 (106.01–106.05 Mb) (Supplementary Material, Table S1) in the exploratory samples, but found no association with LOAD. Recently, Grupe et al. (34) reported that SNP rs498055 (97,344,904 bp), a locus for the RPS3A homolog, is associated with LOAD as described above. The SNPs neighboring the RPS3A homolog locus, rs526928 (97,324,281 bp), rs496641 (97,347,389 bp) and rs749049 (97,366,086 bp), were genotyped, but no association was detected for our exploratory sample set. Thus, none of the SNPs in the above-described candidate genes was significantly associated with LOAD with the APOE-{varepsilon}3*3 genotype in the Japanese population examined here. However, it is still possible that this finding is due to the ethnic difference.

The novel association locus found in this study contains five genes, ENTPD7, COX15, CUTC, ABCC2 and DNMBP (Fig. 1). SNP rs911541 occurs in intron 3 of ENTPD7, also known as LAPL1, which encodes apylase with an intracellular catalytic domain (35). ENTPD7 exhibits 71% similarity to LALP70 (36), a lysosomal/autophagolysosomal membrane protein, suggesting that ENTPD7 is also located in a lysosomal/autophagic compartment, but its physiological function is unclear. The SNP rs3740066 (ATC->ATT, Ile1324Ile) is in exon 28 of ABCC2, which is a member of the ATP-binding cassette transporter superfamily that transports various molecules across extra- and intracellular membranes. ABCC2 is expressed predominantly in the liver but was undetectable in human brain on immunocytochemistry (37). The SNP rs11190302 is located in the intergenic region between ABCC2 and DNMBP. Three SNPs, rs11190305, C_11214959_10 and rs3740058, are present at a high density in the 3' region of DNMBP: rs11190305 causes a non-synonymous exchange (TGT->TGG, Cys1413Trp). Taking these data together, we focussed on DNMBP, although ENTPD7 and ABCC2 may also influence the pathogenesis of AD. This is the first description of a significant association between a DNMBP polymorphism and the risk of LOAD with the APOE-{varepsilon}3*3 genotype or lacking the APOE-{varepsilon}4 allele.

DNMBP binds to dynamin selectively through four N-terminal Src homology-3 (SH3) domains. GTPase dynamin is an essential component for vesicle formation in receptor-mediated endocytosis, synaptic vesicle recycling, caveolae internalization and possibly vesicle trafficking in and out of the Golgi complex (38,39). DNMBP also binds to several actin regulatory proteins including direct binding partners, i.e. N-WASP (neuronal Wiskott–Aldrich syndrome protein) and Ena (Enabled)/VASP (vasodilator-stimulated phosphoprotein), via two SH3 domains at the C-terminus. The DH domain in the middle of DNMBP is involved in the activation of Cdc42. The molecule promotes F-actin nucleation and/or recruitment within cells (28). N-WASP, which acts as a key molecule for fillopodium formation through Cdc42 activation (40), is increased in the AD brain and may be involved in aberrant neuronal sprouting (41). DNMBP is co-localized with synapse-enriched proteins, amphiphysin-1 and dynamin-1 (28). The BAR domain of amphiphysin-1 is required for the triggering of dynamin GTPase activity and fission of the endocytic pit (42). Amphiphysin-knockout mice have defects in synaptic vesicle recycling and major learning deficits (43). The polymorphism of T/G in rs11190305 corresponds to an amino acid change of cysteine to tryptophan (Cys1413Trp). The Cys1413Trp mutation occurs at a position between the two SH3 domains at the C-terminus of DNMBP. It is possible that this amino acid change leads to a conformational alteration, and subsequently affects the interactions with binding partners, although further experiments are necessary to confirm this. To find a new variation, we sequenced all exons, exon–intron boundaries and an about 200 bp 5' upstream region of DNMBP in 92 LOAD patients, but found no polymorphism in these sequenced regions (data not shown).

Thus far, there has been no information about the expression level of DNMBP in the AD brain. In this study, we demonstrated a significant reduction of DNMBP transcripts in the cerebral cortex of autopsy-confirmed AD patients using quantitative real-time RT–PCR (Fig. 2A–C). Although risk alleles of three SNPs, rs11190305 (allele G), C_11214959_10 (allele C) and rs3740058 (allele A), obviously decreased the DNMBP expression level in a dominant model, there were no significant interactions between DNMBP gene expression, genotype variation and diagnosis (Fig. 2D—F). APOE-{varepsilon}4-carrying AD subjects also tended to exhibit decreased levels of DNMBP expression (data not shown). An alternative and attractive interpretation would be that reduced DNMBP expression is caused not only by the SNPs identified here but also by altered expression of other genes. Thus, it is possible that several pathways lead to the reduced DNMBP expression that acts as a risk factor for LOAD.

Recently, Yao et al. (29) described the reduced expression of a group of genes including those of dynamin I and amphiphysin-1, all of which are involved in synaptic vesicle trafficking, in the frontal cortex of AD brains. AD begins with subtle alterations of hippocampal synaptic function prior to Aß deposition followed by frank neuronal degeneration (31). Dynamin I is an important mediator of clathrin-dependent endocytosis and synaptic vesicle recycling. These facts may be consistent with our observation that DNMBP is a genetic risk factor for LOAD. The decrease in the level of DNMBP mRNA might be related to the pathogenesis traced to synapses in the brain of LOAD patients and is probably caused by multiple environmental and genetic factors and their combination.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 APPENDIX
 REFERENCES
 
Subjects
To search for susceptibility genes for LOAD by means of genome-wide screening, the Japanese Genetic Study Consortium for AD (JGSCAD) was organized in 2000, and blood samples were collected. The subject information is summarized in Table 1. Expectedly, the APOE-{varepsilon}4 allele was found to be a highly significant risk factor for LOAD (OR 4.96, 95% CIs 4.22–5.84; p of chi-square test <0.0001). All individuals included in this study were Japanese. Probable AD cases met the criteria of the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders. Controls who had no signs of dementia and lived in an unassisted manner in the local community were also recruited. AAO is here defined as the age at which the family and/or individuals first noted cognitive problems while working or in daily activities. For evaluation of cognitive impairment, the mini-mental state examination (MMSE) was used.

A total of 41 post mortem brains from LOAD patients and control subjects were obtained from the Bioresource Center, Brain Research Institute, Niigata University (Supplementary Material, Table S3). The distribution of the APOE-{varepsilon}4 allele was significantly different between LOAD and control subjects, as expected (OR 5.70, 95% CIs 1.25–25.93, Fisher's exact P=0.0367). Autopsies were performed after a mean post mortem interval of 4.2 h (range 1–22 h). LOAD patients with dementia were neuropathologically characterized based on consensus criteria that included physiologically age-matched densities of senile plaques and neurofibrillary tangles as distinguished from other neurodegenerative disorders, i.e. dementia with Lewy body disease, frontotemporal dementia and Parkinson's disease according to published criteria. Autopsied controls were confirmed to have no diagnosable brain disease.

The present study was approved by the Institutional Review Board of the University of Niigata and by all participating institutes. Informed consent was obtained from all controls and appropriate proxies for patients, and all samples were anonymously analyzed for genotyping.

Marker selection and genotyping
SNP information was obtained from four open databases: NCBI dbSNP (Build 125, http://www.ncbi.nlm.nih.gov/SNP/), International HapMap Project (Rel#20/phaseII on NCBI Build 35 assembly and dbSNP Build 125, http://www.hapmap.org/index.html), Ensemble Human (Version 37 on NCBI Build 35, http://www.ensembl.org/Homo_sapiens/) and Celera myScience (Version R27 g on NCBI Build 35, http://myscience.appliedbiosystems.com/). We selected 1322 SNPs in the region from 60 to 107 Mb on chromosome 10q (Supplementary Material, Tables S1 and S2); mean intermarker distance ±SD, 34.9±87.4 kb; 95% CIs, 30.2–39.6 kb. To examine the genotyping quality of the 1322 SNPs, the HWE test was performed with 337 control subjects (carrying APOE-{varepsilon}3*3 in the exploratory sample set, as shown in Table 1). These SNPs consisted of 29 missense mutations, 27 silent mutations, six SNPs in the 5'-UTR, 29 SNPs in the 3'-UTR, 921 SNPs in introns, 282 SNPs in intergenic regions and 28 SNPs in four loci shared by two different genes (CTNNA3/LRRTM3, CDH23/C10orf54, C10orf55/PLAU and PGAM1/EXOSC1). We used 1206 SNPs that were shown to be actually polymorphic in the Japanese population and showed P>0.05 in the HWE test; mean intermarker distance±SD, 38.3±93.3 kb; 95% CIs, 33.0–43.6 kb.

Genomic DNA was extracted from peripheral blood with a QIAamp® DNA Blood Maxi Kit (Qiagen, Duseldorf, Germany) and examined fluorometrically with a PicoGreen® dsDNA quantification kit (Molecular Probes, CA, USA). SNP genotyping for individual samples was performed with an ABI PRISM® 7900HT instrument using TaqMan technology, and TaqMan SNP Genotyping Assays were purchased from Applied Biosystems (CA, USA).

Sequencing
APOE genotyping of all samples was performed by direct cycle sequencing with an ABI 3100 sequencer and a BigDye® Terminator v3.1 kit (Applied Biosystems) using the following primers: C19APOE001-F (sense 5'-GCCTACAAATCGGAACTGGA-3') and C19APOE001-R (antisense 5'-ACCTGCTCCTTCACCTCGT-3'). All exons and their exon–intron boundaries and the 5' upstream region of DNMBP were sequenced with 20 primer pairs (Supplementary Material, Table S5).

Case–control study
To identify candidate loci in a broad region of chromosome 10q (60–107 Mb on NCBI build 35), two independent sample sets comprising case–control subjects with APOE-{varepsilon}3*3 were constructed (Table 1). The exploratory sample set comprising 363 LOAD patients and 337 control subjects was genotyped, and SNPs showing significant association (allelic P<0.01) were used for further examination using the validation sample set comprising 336 LOAD patients and 372 control subjects. Subsequently, we increased the samples and stratified them as the APOE-{varepsilon}4 carrier status: Negative-{varepsilon}4, APOE-{varepsilon} 2*2, 2*3 and 3*3; Positive-{varepsilon}4, APOE-{varepsilon} 2*4, 3*4 and 4*4; All, all genotypes of APOE. The sample numbers for LOAD patients and controls in All, Negative-{varepsilon}4, {varepsilon}3*3 and Positive-{varepsilon}4 were 1526 and 1666, 749 and 1378, 699 and 1243, and 777 and 288, respectively. To examine the genetic association of multiple SNP combinations, case–control haplotype analysis with significant SNPs was performed using the following sample sets: All, Negative–{varepsilon}4, {varepsilon}3*3 and Positive-{varepsilon}4.

Statistical analysis
Using SNPAlyze ver. 3.2.3 software (DYNACOM, Chiba, Japan; http://www.dynacom.co.jp/index.html.en), we performed the HWE test, single SNP case–control analysis, haplotype estimation based on the EM algorithm, case–control haplotype analysis with 10 000 iterated permutations and calculation of LD measures (|D'|) to elucidate the LD block structure. The Mantel–Haenszel test was performed using Statcel 2 software (OMS, Tokyo, Japan). Evidence of replication, rather than multiple testing corrections, was used to evaluate the significance of associated SNPs (32–34). We carried out the two-sided Student's t-test for comparison of the mean DNMBP expression levels between the AD and control brain tissues, using Prism 4.0 b (GraphPad Software, CA, USA). The effects of variation on gene expression were examined using the two-way ANOVA (Prism 4.0 b) with the genotype and case–controls as independent variables.

Quantitative real-time PCR
Frozen materials (Supplementary Material, Table S3) were prepared on dry ice blocks from 1 cm thick slices of the cerebral cortices. RNA was extracted directly from the frozen preparations with an ISOGEN solution (Nippongene, Tokyo, Japan). The first strand cDNA was synthesized from total RNA (2.0 µg) with SuperScriptsIIITM (Invitrogen, CA, USA) and random hexamers in a total volume of 20 µl according to manufacturer's protocol. The synthesized cDNA solution was diluted 1:20 and then used for quantitative real-time PCR amplification with TaqMan Gene Expression Assays (Applied Biosystems) and an ABI PRISM 7900HT instrument in a total volume of 10 µl according to manufacturer's instructions. Briefly, 2.5 µl of a diluted cDNA solution (corresponding to 12.5 ng of total RNA) was mixed with 5.0 µl of 2xTaqMan Universal PCR Master Mix (Applied Biosystems), 0.5 µl of 20xTaqMan Gene Expression Assay and 2.0 µl of distilled water on a 384-well optical PCR plate. The PCR conditions were: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. All measurements were performed in quadruplicate. The threshold cycle was determined in the linear range and relative gene expression was calculated as the cycle difference. Each measurement of DNMBP mRNA (Celera assay ID, Hs00324375_m1) was normalized to the expression levels of GUSB (Celera assay ID, Hs99999908_m1) and 18S rRNA (Celera assay ID, Hs99999901_s1), which were selected from among the 11 housekeeping genes [acidic ribosomal protein, beta-actin, cyclophilin, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerokinase, beta-2-microglobulin, GUSB, hypoxanthine ribosyl transferase, transcription factor IID (TATA binding protein), transferring receptor genes and 18S rRNA] on a TaqMan Human Endogenous Control Plate (Applied Biosystems) as internal standards.


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


   APPENDIX
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 APPENDIX
REFERENCES
 
The members of JGSCAD who participated in the collection of blood samples from AD patients and controls were as follows. All the authors of this paper, Akihiko Nunomura, MD, and Shigeru Chiba, MD, Department of Psychiatry and Neurology, Asahikawa Medical College, Asahikawa; Satoshi Takahashi, MD, Department of Neurology, Iwate Medical University, Morioka; Naoki Tomita, MD, Department of Geriatric and Complementary Medicine, Tohoku University Graduate School of Medicine, Sendai; Jyunzo Ito, MD, Alpine Kawasaki, Kawasaki, Miyagi; Haruo Hanyu, MD, Department of Geriatric Medicine, Tokyo Medical University, Tokyo; Hideo Kimura, PhD, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira; Shin Kitamura, MD, Second Department of Internal Medicine, Nippon Medical School, Tokyo; Hitoshi Shinotoh, MD, Asahi Hospital for Neurological Disease, Chiba; Hiroyuki Iwamoto, MD, Department of Neurology, Hatsuishi Hospital, Kashiwa; Masahiko Takahashi, MD, Department of Old Age Psychiatry and Memory Clinic, Tokyo Metropolitan Geriatric Medical; Yasuo Harigaya, MD, Department of Neurology, Maebashi Red Cross Hospital, Gunma; Masaki Ikeda, MD, and Masakuni Amari, MD, Department of Neurology, Gunma University Graduate School of Medicine, Maebashi; Takeo Takahashi, MD, Ina Neurological Hospital, Ina; Ryoichi Nakano, MD, and Masatoyo Nishizawa, MD, Department of Neurology, Brain Research Institute, Niigata University, Niigata; Masaichi Suga, MD, Higashi Niigata Hospital, Niigata; Makoto Hasegawa, MD, Niigata Shin-ai Hospital, Niigata; Yasuhiro Kawase, MD, Kawase Neurology Clinic, Sanjo; Kenichi Honda, MD, Honda Hospital, Uonuma; Toshiro Kumanishi, MD, and Yukiyosi Takeuchi, MD, Niigata Longevity Research Institute, Shibata; Atsushi Ishikawa, MD, Department of Neurology, Brain Disease Center, Agano Hospital, Agano; Masahiro Morita, MD, Department of Psychiatry, Mishima Hospital, Mishima; Fumihito Yoshii, MD, Department of Neurology, Tokai University School of Medicine, Isehara; Hiroyasu Akatsu, MD, and Kenji Kosaka, MD, Choju Medical Institute, Fukushimura Hospital, Toyohashi; Masahito Yamada, MD, and Tsuyoshi Hamaguchi, MD, Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Science, Kanazawa; Satoshi Masuzugawa, MD, Department of Neurology, Mie Prefectural Shima Hospital, Shima; Etsuro Matsubara, MD, and Takeshi Kawarabayashi, MD, Department of Neurology, Okayama University Graduate School of Medicine and Dentistry, Okayama; Takeo Takao, MD, and Nobuko Ota, Kurashiki Heisei Hospital, Kurashiki; Ken Sasaki, MD, Yoshikatsu Fujisawa, MD, and Kenji Nakata, MD, Kinoko Espoir Hospital, Kasaoka; Ken Watanabe, MD, Watanabe Hospital, Tottori; Yosuke Wakutani, MD, and Kenji Nakashima, MD, Department of Neurology, Institute of Neurological Science, Tottori University, Yonago; Toshiyuki Hayabara, MD, Iwaki Hospital, Kagawa; Terumi Ooya, Town Office, Mizuho, Shimane; Mitsuo Takahashi, MD, Department of Clinical Pharmacology, Fukuoka University, Fukuoka; Tatsuo Yamada, MD, Fifth Department of Internal Medicine, Fukuoka University, Fukuoka; Taihei Miyakawa, MD, Labour Welfare Corporation Kumamoto Rosai Hospital, Yatsushiro; Eiichiro Uyama, MD, Department of Neurology, Graduate School of Medical Science, Kumamoto University, Kumamoto; Takefumi Yuzuriha, MD, Department of Psychiatry, National Hospital Organization Hizen Psychiatric Center, Sefuri; Ryuji Nakagawa, MD, Shizushi Yoshimoto, MD, and Kayoko Serikawa, MD, Ureshino-Onsen Hospital, Saga.


   ACKNOWLEDGEMENTS
 
We thank Professor Shoji Tsuji, Department of Neurology, and Professor Katsushi Tokunaga, Department of Human Genetics, University of Tokyo, for the critical discussion about human molecular genetics. We also thank N. Takei, K. Horigome, A. Kitamura, M. Hirose, Y. Satoh, A. Hirokawa, T. Hosino, S. Yanagihara, K. Takadono, M. Saitoh and N. Yahata for the technical assistance. This study was supported in part by a Grant-in-Aid for Scientific Research on Priority Areas (C)—Advanced Brain Science Project—from the Ministry of Education, Culture, Sports, Science and Technology, Japan (Y.I.).

Conflict of Interest statement. None declared.


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

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