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Human Molecular Genetics Advance Access originally published online on June 19, 2006
Human Molecular Genetics 2006 15(15):2313-2323; doi:10.1093/hmg/ddl157
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Mapping genetic modulators of amyloid plaque deposition in TgCRND8 transgenic mice

Giovanna Sebastiani1,*, Pascale Krzywkowski1, Sherri Dudal1, Mathilde Yu1, Julie Paquette1, Danielle Malo2,3, Francine Gervais1,{dagger} and Patrick Tremblay1,{ddagger}

1 Neurochem Inc., 275 Armand-Frappier Blvd., Laval, Canada H7V 4A7, 2 Department of Experimental Medicine and 3 Department of Human Genetics, McGill University, Montreal, Canada H3A 1B1

* To whom correspondence should be addressed. Tel: +1 4506804500; Fax: +1 4506804506; Email: gsebastiani{at}neurochem.com

Received February 28, 2006; Revised April 24, 2006; Accepted June 15, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Alzheimer's disease (AD) is a complex disorder for which various in vivo models exist. The TgCRND8 mouse, transgenic for the human amyloid precursor protein, is an aggressive early onset model of brain amyloid deposition. Preliminary studies revealed that when the transgene is expressed on an A/J genetic background, these mice not only survive longer but also deposit less parenchymal amyloid-ß (Aß) peptides as compared to those on a C57BL/6 background. We performed a genome-wide study of an F2 intercross between TgCRND8 on an A/J background and C57BL/6 mice, to identify genetic modulators of amyloid accumulation and deposition. We identified four highly significant QTLs that together account for 55% of the phenotypic variance in the number of plaques (Thioflavin S). QTLs were found on the distal part of chromosome 4 with an LOD score of 8.1 at D4Mit251, on chromosome 11 with an LOD score of 5.5 at D11Mit242, on chromosome 9 with an LOD score of 5.0 at D9Mit336 and on the proximal part of chromosome 8 with an LOD score of 4.5 at D8Mit223. A/J alleles at these loci are protective and all decreased the amount of Aß deposition. Interestingly, the QTL on chromosome 11 is also significantly linked to the levels of brain Aß42 and Aß40. Although these QTLs do not control the levels of plasmatic Aß, other regions on chromosomes 1 and 6 show significant linkage. Further characterization of these QTL regions may lead to the identification of genes involved in the pathogenesis of AD.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Alzheimer's disease (AD), the leading cause of senile dementia, is characterized by a gradual decline of cognitive functions together with the appearance of amyloid deposits, neurofibrillary tangles, and neuronal cell loss in the brain. Amyloid-ß (Aß) is a key culprit in the pathogenesis of AD (1). Although it has been well accepted that an increased concentration of Aß peptides results in the formation of cerebral amyloid plaques, mounting evidence indicates that Aß accumulation occurs prior to and may be the root cause of the other histopathological abnormalities associated with AD (2). In addition, soluble metastable oligomeric forms of Aß distinct from protofibrils and fibrils have also been implicated in early synaptic damage that ultimately results in neuronal loss (3).

Although the majority of AD cases are sporadic, genetic studies in families with early-onset AD have revealed disease-causing mutations in three proteins: amyloid precursor protein (APP) on chromosome 21, Presenilin 1 and 2, respectively, on chromosome 14 and 1 (4–7). Mutations found within these genes all favor the processing of the APP protein resulting in an increased production of Aß. In addition, linkage studies revealed the presence of an AD susceptibility locus on chromosome 19 in late onset AD patients (8). The {varepsilon}4 allele of apolipoprotein E was shown to account for this linkage and carriers present a 4-fold increased risk of developing AD (9–11). Collectively, these four genes account for less than 50% of the genetic variance seen in AD (12). Over the past 10 years, there has been a concerted effort to identify additional genes involved in the development of this disease either by performing genome-wide linkage studies or by performing candidate gene-specific association studies using cohorts of AD patients and their relatives (reviewed in 13). Although many loci have been examined, functional gene polymorphisms affecting the onset or progression of AD have not been identified yet (14), but it is anticipated that additional genes that remain to be identified will not account for such large effects.

Much of our understanding of the molecular mechanisms at work in the Alzheimer's brain has come from studying transgenic mouse models developed to mimic the human disease. Each model recapitulates some of the neuropathological changes and cognitive deficits characteristic of AD. Interestingly, a few groups have described that similar to AD in humans, the genetic variability found among the different inbred mouse strains can account for differences observed in various models overexpressing variants of human APP. Carlson et al. (15) specifically demonstrated that in FVB or C57BL/6 (B6) genetic backgrounds, APP overexpression is lethal at an early age, whereas the same level of APP overexpression in outbred mice results only in amyloid plaque accumulation. More recently, using a genome-wide study in Tg2576 mice, modifier genes determining susceptibility to the lethal effects of the APP transgene have been mapped (16). Similarly, in a YAC-based transgenic APP model, the level of APP processing was found to be dependent on the genetic background (17).

The TgCRND8 mouse model is an aggressive early onset model of brain amyloidosis (18). These mice express, under the control of the hamster Prion protein promoter, the human APP gene harboring both the Swedish (K670N, M671L) and Indiana (V717F) familial AD mutations. This leads to elevated levels of Aß (especially Aß42) within the brain. Diffuse and compact amyloid plaques associated with an inflammatory reaction appear as early as 10 weeks of age (19), followed by progressive decline in cognitive function (18). As seen in the other models of amyloid deposition, the genetic background of TgCRND8 mice also plays a role in determining the effects of transgene overexpression (18). Our work describes the effects of the A/J genetic background on the Aß levels and plaque deposition in TgCRND8 mice. We observe differences not only in survival but also in the levels of amyloid deposition as compared to the mice on a B6 background. We performed a genome-wide linkage study to identify genes that modify the levels of Aß accumulation and deposition and report the identification of four QTLs on chromosomes 4, 11, 9 and 8, influencing to various extents the level of Aß peptides and their deposition into amyloid plaques in TgCRND8 animals.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Effect of genetic background in TgCRND8 mice
The survival of TgCRND8 mice is clearly affected by genetic background (18). In our laboratory, the TgCRND8 mice were maintained on a mixed genetic background composed predominantly of FVB. To explore whether allelic variants issued from different inbred strains would alter the phenotype produced by the expression of the TgCRND8 transgene, we generated different strains of TgCRND8 mice by backcrossing to either B6 or A/J inbred mice. As can be seen in Figure 1A, mice that carry ~75% A/J alleles in their genetic make-up show a much improved survival rate as compared to those carrying an 83% B6 genetic background. At 20 weeks of age, only 26% of mice with a B6 background survive as compared to 86% of mice carrying A/J alleles (Fig. 1A). The majority of TgCRND8 mice with an A/J background survive longer than 52 weeks. In addition, Aß deposition begins slightly later in these mice and occurs to a lesser extent than in TgCRND8 mice with a predominantly B6 genetic background. The number of amyloid plaques detected in the neocortical regions of the brain by 6E10-immunohistochemical staining is significantly different (P<0.01) beginning at 13 weeks of age where 1.4±0.2 and 3.2±0.3 plaques per mm2 are seen in TgCRND8 mice with an A/J or B6 genetic background, respectively (Fig. 1B). This difference in amyloid deposition is further accentuated at 16 weeks of age (4.7±0.4 versus 6.7±0.3, P<0.001). Similarly, the amyloid burden is significantly lower at both 13 and 16 weeks of age in TgCRND8 mice with A/J alleles (Fig. 1C). As the level of human APP brain mRNA measured by real-time RT-PCR is comparable in both strains of mice from 8 to 16 weeks of age (unpublished data), we conclude that the A/J inbred mouse carries gene variants that decrease amyloid deposition in TgCRND8 mice.


Figure 1571
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  		Figure 1. TgCRND8 mice with an A/J genetic background survive longer and deposit less cortical amyloid compared with those carrying mostly B6 alleles. (A) Kaplan–Meier survival plots for TgCRND8 mice with different genetic backgrounds: (solid line) A/J, n=76, or (dotted line) B6, n=55. Mice were monitored for survival once weaned at 3 weeks of age. (B and C) Groups of mice (n=3–4) were sacrificed at 8, 13 and 16 weeks of age and brains collected for immunohistochemical analysis with the Aß1–17-specific antibody 6E10. Black bars represent TgCRND8 mice on a B6 genetic background and grey bars represent TgCRND8 mice with an A/J background. The data are expressed as the mean±SEM. In (B), the number of plaques that accumulate in the neocortical brain region normalized per mm2 is depicted. In (C), the difference in amyloid burden is shown. The Mann–Whitney rank sum test or the Student's t-test was used to compare the number of plaques and amyloid burden measured in both backgrounds. *P<0.05, **P<0.01, ***P<0.001.

 
Identification of modifiers of amyloid deposition in TgCRND8 mice
A genome scan approach was used to identify genes within the A/J inbred strain that contribute to decreasing the level of Aß accumulation and deposition in the brains of TgCRND8 mice. TgCRND8 mice carrying 75% A/J alleles were further backcrossed two times to the A/J strain to produce mice carrying a 94% A/J genetic background and are referred to as A.TgCRND8 mice. A.TgCRND8 were then crossed to B6 mice to generate F1 hybrids, which were subsequently intercrossed to produce (A.TgCRND8 x B6)F2 progeny. Of the ~400 mice generated, 201 that carried the human APP695(Swedish and Indiana) transgene were used in the QTL analysis. The mice were sacrificed at 21 weeks of age and their brains were collected for analysis. One hemisphere was analyzed by staining for amyloid deposition using both Thioflavin S (ThioS; compact plaques) and 6E10 (Aß-immunoreactive plaques). For each of these markers, the number of plaques and the amyloid burden were tabulated and served as quantitative traits for QTL mapping. To accommodate the genotyping platform, only 178 of the 201 (A.TgCRND8 x B6)F2 intercross mice were genotyped with 80 microsatellite markers distributed at ~20 cM intervals across the genome. Following this analysis, additional markers were added in areas containing QTLs for a total of 96 markers. Eventhough the mice were not explicitly examined for the presence of residual C3H alleles, the genotyping data revealed that C3H alleles have been retained by every single mouse at marker D7Mit83. This is consistent with the insertion of the transgene on the proximal part of chromosome 7 more specifically within a C3H chromosome. As the exact location of the putative insertion site and the size of the congenic interval have not been determined, our ability to detect modifier loci within this region of chromosome 7 is reduced. Analysis was performed with MapManagerQTXb19 (20). The threshold levels for significance were determined empirically by running 10 000 permutations of the data and correspond to LOD scores of 1.4, 2.7 and 4.3 for suggestive, significant or highly significant linkage. As can be seen in Table 1, the presence of four QTLs showing highly significant linkage to plaque number was revealed. The number of plaques found in the brains of 21-week-old F2 mice was most strongly linked to the D4Mit251 marker on chromosome 4 with a peak LOD score of 8.1 (P<0.00001). This locus accounts for 19% of the phenotypic variance linked to the number of plaques. Three other QTLs, each accounting for 12–13% of the variation, were found on chromosome 11 (D11Mit242, LOD score 5.5, P<0.00001), chromosome 9 (D9Mit336, LOD score 5.0, P=0.00001) and chromosome 8 (D8Mit223, LOD score 4.5, P=0.00003). A/J alleles at these loci decreased the number of plaques in the brains of the (A.TgCRND8 x B6)F2 progeny as compared to mice homozygous for the B6 alleles as can be seen in Figure 2. If we examine the different alleles for these QTLs in pair wise combinations, it becomes evident that the effects of the QTLs are additive (Fig. 3). For example, as demonstrated in Figure 3A, mice that are homozygous for the A/J alleles at D4Mit251 and D11Mit242 (n=12) have an average of 8.8±0.7 plaques as compared to 16.7±0.8 plaques in B6 homozygotes (n=15). About 55% of the variance in the number of plaques can be explained if the effects of the four QTLs are combined. This difference in the number of plaques is clearly seen when comparing mice with either A/J alleles or B6 alleles at these four proposed modifier genes. D4Mit251A/J/A/J–D11Mit242A/J/A/J–D9Mit336A/J/A/J–D8Mit223A/J/A/J mice have fewer ThioS-positive or 6E10-immunoreactive amyloid plaques (Fig. 4A, C and E) than D4Mit251B6/B6–D11Mit242B6/B6–D9Mit336B6/B6–D8Mit223B6/B6 (Fig. 4B, D and F). Not surprisingly, these QTLs also control amyloid burden correlates with the actual number of plaques. Interestingly, the significance level for the four QTLs is somewhat modified as can be seen on the interval mapping curves in Figure 5 and the marker regression analysis in Table 1. When looking specifically at amyloid burden, the most important QTL becomes the one on chromosome 9 with a peak LOD score of 7.1 (P<0.00001) at D9Mit336. Figure 6 demonstrates the allele effects for each of these QTLs on amyloid burden. Overall, the amyloid burden found in the brains of mice carrying A/J alleles at these modifier genes was clearly lower than in mice carrying B6 alleles. Although a fourth QTL on chromosome 8 was found to play a role in determining the number of plaques, it did not reach the level of high significance seen for the other QTL controlling amyoid burden in this panel of mice (Fig. 5D).


Figure 1572
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  		Figure 2. QTLs on chromosomes 4, 11, 9 and 8 affect plaque deposition. The (A.TgCRND8xB6)F2 progeny were divided into groups corresponding to their genotypes at (A) D4Mit251, (B) D11Mit242, (C) D9Mit336 and (D) D8Mit223. AA represents mice that are homozygous for the A/J allele, AB represents mice that are heterozygous and BB represents mice that are homozygous for the B6 alleles.

 


Figure 1573
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  		Figure 3. Additive effects of multiple QTLs on plaque deposition. The F2 progeny were subdivided into groups depending on their genotypes for combinations of two QTLs at a time. Mice that are homozygous for the A/J alleles at both QTLs (AA/AA) are compared with mice that are homozygous for the B6 alleles (BB/BB). The QTLs combinations are (A) D4Mit251/D11Mit242, (B) D4Mit251/D9Mit336, (C) D4Mit251/D8Mit223, (D) D11Mit242/D9Mit336, (E) D11Mit242/D8Mit223 and (F) D9Mit336/D8Mit223.

 


Figure 1574
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  		Figure 4. Representative photomicrographs of brain sections from a mouse with a D4Mit251A/J/A/J–D11Mit242A/J/A/J–D9Mit336A/J/A/J–D8Mit223A/J/A/J genotype (A, C and E) and a mouse with a D4Mit251B6/B6–D11Mit242B6/B6–D9Mit336B6/B6–D8Mit223B6/B6 genotype (B, D and F) showing fibrillar deposition (ThioS staining, A–D) and total amyloid deposition (6E10 immunohistochemistry, E and F). (C) and (D) are a higher magnification of the sections shown in (A) and (B); (E) and (F) are taken from adjacent sections from the same animals. (A) Scale bar=1 mm; (B) scale bar=500 µm.

 


Figure 1575
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  		Figure 5. QTLs controlling various brain amyloid accumulation and deposition phenotypes in (A.TgCRND8xB6)F2 mice. The interval mapping function of MapmanagerQTXb19 was used to generate {chi}2 plots for each of the seven brain amyloid phenotypes. The {chi}2 is plotted against the position on the chromosome beginning with the most proximal marker examined. The markers tested for each chromosome are seen. The dotted line represents a {chi}2 of 19.8, which indicates highly significant linkage. The homologous chromosomal segments from the human genome corresponding to the areas of significant linkage are indicated above the curves. (A) Chromosome 4, (B) chromosome 9, (C) chromosome 11 and (D) chromosome 8.

 


Figure 1576
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  		Figure 6. QTLs on chromosomes 11, 4 and 9 affect amyloid burden. The (A.TgCRND8xB6)F2 mice were divided into groups corresponding to their genotypes at (A) D9Mit336, (B) D11Mit242 and (C) D4Mit251. AA represents mice that are homozygous for the A/J allele, AB represents mice that are heterozygous and BB represents mice that are homozygous for the B6 alleles.

 


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  		Table 1. Markers linked to plaque deposition

 
QTL on chromosome 11 also controls soluble Aß42 and soluble Aß40 levels in the brain
The same panel of 201 (A.TgCRND8 x B6)F2 mice was also phenotyped for Aß levels in the brain. For each mouse, the left hemisphere was homogenized, fractionated into soluble and insoluble fractions and analyzed for Aß40 and Aß42 content by ELISA. QTL analysis was performed for each of these phenotypes. Whereas no highly significant QTL was found to be linked to the levels of insoluble Aß40, the other phenotypes were partially controlled by a QTL on chromosome 11. As seen in Table 2, the levels of both soluble Aß40 and Aß42 showed linkage with peak LOD scores of 4.9 and 7.2, respectively, at D11Mit242. The level of insoluble Aß42 is also linked to this chromosomal region but the maximal LOD score is found at D11Mit213, which is ~20 cM distal to D11Mit242, which may be indicative of a distinct QTL. As expected, A/J alleles at each of these QTL regions result in decreased levels of Aß. For example, F2 progeny that are homozygous for the B6 alleles at D11Mit242 have 2.05±0.07 µg of soluble Aß42 and 0.27±0.02 µg of soluble Aß40 per gram of brain as compared to mice that harbor A/J alleles at this locus, which have 1.51±0.05 µg of soluble Aß42 and 0.18±0.01 µg of soluble Aß40 per gram of brain. Mice that are heterozygous have on average 1.82±0.06 µg of soluble Aß42 and 0.22±0.01 µg of soluble Aß40 per gram of brain indicative of co-dominant inheritance. As seen above, this QTL was also found to be linked to amyloid deposition. No additional markers were significantly linked (LOD score >2.7) to the levels of Aß in our study, but this does not exclude the possibility that other small-effect loci exist.


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  		Table 2. Markers linked to brain Aß levels

 
Plasma Aß levels are not controlled by the same modifiers identified for the brain amyloid phenotypes
In addition to the parameters that were examined in the brain, we were also interested in determining if these same modifier genes were also involved in controlling the levels of Aß found in the periphery. Plasma samples were isolated from our intercross progeny and sandwich ELISA used to quantify the level of Aß40 and Aß42. The QTLs on chromosomes 4, 11, 9 and 8 were not detected. As can be seen in Table 3, plasma Aß42 was linked to markers on chromosome 6, whereas plasma Aß40 was significantly linked to chromosomes 6 and 1.


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  		Table 3. Markers demonstrating linkage to plasma Aß levels

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
It is widely accepted that genetic make-up plays a significant role in the development of AD. After age, genetic constitution is the most important risk factor in AD. As revealed by the number of studies published in recent years (14), the hunt to identify genes involved in AD is intense. By far, the most useful tool in our understanding of AD has been the different mouse models created to reproduce particular features of the disease. This work shows that the genetic heterogeneity found in different inbred lines of mice can be used to modulate the progression of some features of AD pathology, a phenomenon reminiscent of what has been observed in humans. Thereby, these inbred lines of mice can be used to identify genetic modifiers that affect the development of AD pathology. In this report, we demonstrate that the A/J genetic background imparts to the TgCRND8 mice a reduced propensity to accumulate and deposit Aß in the brain. Using a genome scan approach, we have identified loci on chromosomes 4, 11, 9 and 8 that modulate these phenotypes.

Early on, it became evident that genetic background was important in determining the survival rate of TgCRND8 mice. Whereas TgCRND8 mice on FVB or 129SvEv/Tac backgrounds were quite susceptible to the overexpression of hAPP, the introduction of C3H genetic material resulted in an improved survival rate (18). Interestingly, in other transgenic APP models, the 129S6 genetic background imparts resistance to APP overexpression (16). In our colony, breeding TgCRND8 mice onto the A/J background generated mice that are completely resistant to the early lethal effects of APP overexpression, with survival rates approaching those of wild-type mice. These observations indicate that the A/J strain carries gene variants, which could provide resistance to the toxic effects of elevated Aß levels. Interestingly, over 95% of the (A.TgCRND8xB6)F2 intercross mice produced for our genome scan analysis survived to 21 weeks of age; it was therefore impossible to use age of death as a quantitative trait to map ‘survival’ genes from the A/J inbred strain. These findings are consistant with the presence of multiple susceptibility genes originating from the FVB or B6 strains necessary to induce premature death of TgCRND8 mice and are supported by recently published data in the Tg2576 model indicating that at least two genes exist on chromosomes 14 and 9 accounting for ~25% of the variation in survival time (16).

We have successfully identified highly significant QTLs on chromosomes 4, 11, 9 and 8 for which A/J alleles lead to a less severe amyloidogenic state. Interestingly, while the QTL on chromosome 11 is implicated in controlling both the accumulation of the various Aß peptides and their subsequent deposition into plaques, the QTLs on chromosomes 4, 8 and 9 show highly significant linkage only to the plaque deposition phenotypes (number of plaques and amyloid burden) but not Aß levels. These observations may be consistent with the QTL on chromosome 11 being specific to the development of AD, while the QTLs on chromosomes 4, 8 and 9 being involved in mechanisms underlying formation and deposition of amyloid, a process that may be relevant to all amyloid diseases. In other words, it is possible to speculate that the gene or genes underlying the chromosome 11 QTL may be involved in the production or clearance or transport of Aß peptides that are directly and specifically involved in the development of AD. In contrast, the other QTLs may have a greater impact on proteins such as proteoglycans or apolipoproteins or even metals, which are an integral part in the formation of various types of amyloid. In fact, this observation is supported by results of a recent report, as the QTL that was identified on chromosome 4 corresponds to the same genomic region where an A/J amyloidosis modifier has recently been mapped using a senile ApoAII amyloidosis model (21). Alternatively, this inability to link the QTLs on chromosomes 4, 8 and 9 with the total levels of brain Aß40 and Aß42 peptides as measured by ELISA may reflect the fact that the homogenates used for the quantification comprised the entire brain hemisphere, whereas histological analysis of the amyloid plaques was carried out only on three sections of the brain. In other words, the level of Aß deposition is not uniform throughout the brain. In fact, marker regression analysis using plaque number for each brain section independently reveals that the levels of significance of the four QTLs vary. Finally, as can be seen in Figure 5, whereas the QTLs on chromosomes 4 and 9 do not reach the threshold level for high significance, there is some indication that these may very well also control the levels of the different Aß peptides within the brain.

The QTL on chromosome 11 is significantly linked to all of the brain phenotypes examined except for the level of insoluble Aß40 found within the brain. The genomic region covered by this QTL is large covering ~49 Mb. The individual phenotypes observed indicate that there may be more than one gene underlying this QTL. More specifically, the levels of insoluble Aß42 found within the brain seem to be linked to the region distal to D11Mit242 and bordered by D11Mit213. Conversely, the levels of soluble Aß40, number of plaques and amyloid burden appear to be governed by the region between D11Mit236 and D11Mit36. Finally, the levels of soluble Aß42 are significantly linked to the entire interval. This large genomic interval contains ~840 genes. A survey of the publicly available databases reveals a few known genes with coding region SNPs resulting in amino acid differences between A/J and B6 genomes: serine carboxypeptidase 1 (Scpep1), ATP-binding cassette, subfamily C (CFTR/MRP), member 3 (Abcc3), ring finger protein 130 (Rnf130) or cold autoinflammatory syndrome 1 homolog (Cias1). Further studies will be required to determine the role of these genes in modulating the extent of Aß accumulation.

The QTLs on chromosomes 4, 8 and 9 are all involved in the process of plaque deposition possibly determining the actual number of plaques or controlling the size of the plaques that develop. By comparing the level of significance of each loci for either phenotype, the QTL on chromosome 4 appears to contribute more to the mechanisms underlying initial formation or seeding of the plaque, whereas the QTL on chromosome 9 may contribute more to the control of plaque growth following the initial seeding. In contrast, the QTL on chromosome 8 displays comparable levels of significance with respect to the number of plaques and amyloid burden and may be central to both mechanisms.

The region covered by the QTL on chromosome 4 is 24 Mb long and includes ~350 genes. It is homologous to human chromosome 1p, which has been linked to AD in human populations (22–24). As stated above, this chromosomal segment has been previously described as modifying the formation of amyloid in an ApoAII model of amyloidosis (21) and perlecan (Hspg2) is proposed as a candidate gene within this region. Perlecan is a basement membrane proteoglycan that codeposits with amyloid and as such remains an interesting candidate (25). The QTL on chromosome 9 is quite large covering over 50 Mb, making it quite difficult to propose candidate genes within the interval. The proximal part of this interval was recently implicated by genome-wide analysis in susceptibility to the lethal effects of APP trangene overexpression in the Tg2576 human APP mouse (16). The QTL on chromosome 8 encompasses a rather small genomic region of ~9 Mb and includes 67 genes. The syntenic region in the human genome has not yet been linked to AD. Interestingly, Phinney et al. (26) have described a mutation within the ATPase7b transporter, which is located in the proximal part of chromosome 8 that when crossed to the TgCRND8 mouse results in the deposition of fewer amyloid plaques. Further analysis of the A/J and B6 alleles of this transporter may reveal functional differences. Of note, is the presence of a coding region SNP in the A/J and B6 alleles of the Wrn gene that results in an amino acid substitution. This gene codes for a protein that is mutated in Werner syndrome, a disease that is characterized by premature aging (27).

In light of recent evidence describing the importance of vascular Aß as a seed in the formation of amyloid plaques, it will be interesting to determine whether the QTLs described here also influence vascular deposition of Aß in this model. Characterization of candidate genes in these QTL regions may allow for the identification of novel genes involved in amyloid production and deposition. Such genes may help to explain the mechanisms at play in the development of AD in humans. More importantly, these genes or genomic regions can be a part of pharmacogenomic screens that will help to differentiate AD patients with regards to the course of their disease and perhaps the response to potential therapies that will be available in the not-so-distant future.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Mice
The TgCRND8 [official designation B6C3-Tg(APPSwInd)8Dwst] model expresses a double mutant of the human APP695 gene (Swedish K670M671->NL and Indiana V717->F) driven from a hamster Prion promoter (18). These mice were originally produced by microinjection of (C3H x B6) x B6 oocytes. The founder was maintained by first backcrossing for two generations to C3H mice and subsequently to FVB for three additional generations. The incipient congenic strain used to generate the panel of mice for QTL mapping was produced by backcrossing for four generations to the A/J strain and is refered to as A.TgCRND8. The genetic background of the A.TgCRND8 mice is composed of ~94% A/J, 5.5% FVB and ~0.5% C3H. All mice were maintained in an animal facility under the conditions specified by the Canadian Council on Animal Care.

TgCRND8 genotyping
TgCRND8 mice are maintained in a hemizygous state and need to be genotyped for the presence or absence of transgene after each generation of breeding. Hair follicles are collected at weaning, incubated for 7 min at 98°C in 50 mM NaOH and then rapidly cooled on ice. After neutralization with 1 M Tris (pH 7), 2–3 µl are used in a PCR reaction with primers (5'-GTCTACCCTGAACTGCAGATCA-3'; 5'-TTGCACCAGTTCTGGATGGT-3') that amplify both the transgenic human APP and the wild-type mouse App alleles. Due to a difference of four nucleotides in the amplicon between the human APP allele and the endogenous mouse App allele, the melting temperature of the amplicon can be used to discriminate transgenic (80.3°C) from non-transgenic mice (81.7°C).

Human APP mRNA expression
Total brain RNA was extracted with TRIzol reagent (Invitrogen, ON, Canada). The relative quantities of human APP transgene mRNA were determined by real-time RT-PCR using SYBR Green chemistry. The human APP transgene was amplified using oligonucleotide primers: 5'-GCCTGGCCCTGGAGAACT-3'; 5'-GATCCACCATGCGCACATGC-3'. The expression data were normalized to ß-actin (Actb) expression. Actb was amplified using primers: 5'-AGGTCATCACTATTGGCAACG-3' and 5'-CACTTCATGATGGAATTGAATGTAGTT-3'. Data are presented as a ratio of transgene to Actb mRNA.

Brain preparation
Mice were transcardially perfused with saline solution. Brains were dissected out and separated in hemispheres. The right hemisphere was snap-frozen and kept at –80°C for Aß level determination. The left hemisphere was immersed in freshly prepared 4% paraformaldehyde for 3 h at 4°C, and then transferred into 30% sucrose at 4°C. When cryoprotection was achieved (16–48 h), brains were frozen in isopentane at –45°C and subsequently stored at –80°C for histological studies.

Histochemical staining
Forty-micron thick sections were prepared. For ThioS staining, brain sections were mounted on Super Frost slides and allowed to dry before staining with 1% ThioS in water. After differentiation of the ThioS staining, sections were counterstained with hematoxylin for 5 min. For immunohistochemistry, free-floating sections were washed and pretreated to block endogenous peroxidase. Aß immunoreactivity was detected using a 1:1000 dilution of 6E10-biotinylated (Senetek, MA, USA) incubated overnight at 4°C. After amplification with avidin–biotin complex kit (Vector Laboratories, ON, Canada), the reaction was developed by Ni-3,3'-diaminobenzidine/H2O. Three brain sections, taken at levels –2.1, –2.8 and –3.4 mm from the bregma, were analyzed for each mouse (28). A quantitative image analysis of plaque deposition was performed using Image Pro Plus software (Media Cybernetics, MD, USA) for 6E10 and ThioS. Parameters measured include the number of plaques as well as the amyloid burden. The amyloid burden is defined as the percentage of the neocortical area analyzed that is occupied by Thio-S or 6E10-positive plaques.

Quantification of human Aß40 and Aß42 levels
Perfused brains were homogenized in four volumes of ice-cold 50 mM Tris–HCL pH 8.0 containing protease inhibitors. The homogenates were vortexed and centrifuged at 16 000g for 20 min at 4°C. Supernatants were further treated with 8 M and 5 M guanidine–HCL/50 mM Tris–HCL pH 8.0 (1 volume supernatant: 1.7 volumes 8 M guanidine–HCL/50 mM Tris–HCL pH 8.0: 2.7 volumes 5 M guanidine–HCL/50 mM Tris–HCL pH 8.0) and pellets were treated with 7 volumes 5 M guanidine–HCL/50 mM Tris–HCL pH 8.0. Supernatants were identified as soluble fractions and pellets were identified as insoluble fractions. Both soluble and insoluble fractions were frozen, thawed at room temperature, sonicated 15 min at 80°C, cooled at room temperature and frozen on dry ice for 10 min. Fractions were then thawed again, sonicated 10 min at 80°C and cooled at room temperature. The soluble and insoluble fractions were centrifuged at 16 000g for 20 min at 4°C. Supernatants of both fractions were stored at –80°C until Aß quantification. Fractions were thawed and sonicated 5 min at 80°C just before quantification of Aß. The quantification was performed in duplicate using fluorometric ELISA kits specific for Aß40 or Aß42 (BioSource International Inc., Camarillo, CA, USA). Procedures were followed as recommended by the manufacturer. Quantification of the plasma samples was done without any sample preparation.

QTL mapping
A.TgCRND8 and C57BL/6 inbred mice were used to produce 201 F2 progeny. Genomic DNA was extracted from the tails of the F2 progeny. Briefly, a 0.5 cm piece of tail was incubated in lysis buffer (10 mM Tris, 100 mM EDTA, 0.6% SDS, 400 mM NaCl, 0.75 mg/ml proteinase K) at 55°C for 16 h. DNA was precipitated in ethanol and resuspended in water. Some 10 µg/ml dilutions in water were prepared and sent to the McGill University and Genome Quebec Innovation Center for genotyping. A panel of 96 microsatellite markers spaced at ~20 cM intervals was used in this study. Markers were chosen based on their ability to differentiate between B6 and A/J alleles. Of these markers, 55 were also able to differentiate the FVB alleles. To allow the analysis of the data by MapManagerQTX, the mice that carried one FVB allele were scored as heterozygotes (carrying at least one B6 or A/J allele). Whereas no residual FVB alleles were detected at markers D4Mit251 and D8Mi223, the presence of FVB alleles at D9Mit336 and D11Mit242 was seen, but this did not affect the detection of the respective QTLs. The presence of C3H alleles was not considered in the selection of markers as they theoretically comprise less than 0.4% of the genetic make-up of the F2 mice. The complete list of markers used in the analysis is available upon request. All mice were sacrificed at 21 weeks of age. Plasma was collected from each mouse by transcardial puncture for the determination of Aß levels. The brain was isolated following perfusion with isotonic saline solution. Histochemical staining of the left brain hemisphere allowed us to quantify the plaque number and also amyloid burden. The right hemisphere was used to determine Aß levels. For each of these phenotypes, marker regression analysis and interval mapping was performed using MapManager QTXb19 (20). The levels of significance were determined empirically for each individual phenotype by performing 10 000 permutations of the data. The LOD scores corresponding to suggestive (1.4), significant (2.7) and highly significant (4.3) linkage were found to be very similar between phenotypes.

Box-plot graphs
Box plot limit closest to 0 represents the 25th percentile; the limit farthest from 0 represents the 75th percentile. Whiskers above and below the boxes indicate the 90th and 10th percentiles, respectively. Median values are represented by bars within the boxes.


   ACKNOWLEDGEMENTS
 
We thank Dr David Westaway, University of Toronto for providing the TgCRND8 mice. We also wish to acknowledge the excellent technical assistance provided by Caroline Lagacé, Marie-Josée Ouellette, Stéphanie Bastien, as well as the histology and in vivo pharmacology groups. The authors wish to thank Judith Caron for helpful technical advice and Dr Diane Lacombe for help in the final preparation of this manuscript. Dr Danielle Malo is a William Dawson Scholar.

Conflict of Interest statement. This work was carried out at and financially supported by Neurochem Inc. The following authors were employed by Neurochem Inc. while carrying out this work: G.S., P.K., S.D., M.Y., J.P., F.G. and P.T.


   FOOTNOTES
 
{dagger} Present address: Painceptor Pharma Corp., 7150 Albert-Einstein Ave., Suite 100, Saint-Laurent, Canada H4S 2C1. Back

{ddagger} Present address: Bioaxone Therapeutic Inc., 7150 Frederick-Banting Ave., Suite 200, Saint-Laurent, Canada H4S 2C1. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Hardy J. and Selkoe D.J. (2002) The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics. Science 297:353–356.[Abstract/Free Full Text]

  2. Oddo S., Caccamo A., Kitazawa M., Tseng B.P., LaFerla F.M. (2003) Amyloid deposition precedes tangle formation in a triple transgenic model of Alzheimer's disease. Neurobiol. Aging 24:1063–1070.[CrossRef][Web of Science][Medline]

  3. Walsh D.M., Klyubin I., Fadeeva J.V., Cullen W.K., Anwyl R., Wolfe M.S., Rowan M.J., Selkoe D.J. (2002) Naturally secreted oligomers of amyloid beta protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416:535–539.[CrossRef][Medline]

  4. Goate A., Chartier-Harlin M.C., Mullan M., Brown J., Crawford F., Fidani L., Giuffra L., Haynes A., Irving N., James L. (1991) Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature 349:704–706.[CrossRef][Medline]

  5. Sherrington R., Rogaev E.I., Liang Y., Rogaeva E.A., Levesque G., Ikeda K., Chi H., Lin C., Holman K., Tsuda T., et al. (1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer's disease. Nature 375:754–760.[CrossRef][Medline]

  6. Rogaev E.I., Sherrington R., Rogaeva E.A., Levesque G., Ikeda M., Liang Y., Chi H., Lin C., Holman K., Tsuda T., et al. (1995) Familial Alzheimer's disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer's disease type 3 gene. Nature 376:775–778.[CrossRef][Medline]

  7. Levy-Lahad E., Wasco W., Poorkaj P., Romano D.M., Oshima J., Pettingell W.H., Yu C.E., Jondro P.D., Schmidt S.D., Wang K. (1995) Candidate gene for the chromosome 1 familial Alzheimer's disease locus. Science 269:973–977.[Abstract/Free Full Text]

  8. Pericak-Vance M.A., Bebout J.L., Gaskell P.C., Jr P.C., Yamaoka L.H., Hung W.Y., Alberts M.J., Walker A.P., Bartlett R.J., Haynes C.A., Welsh K.A. (1991) Linkage studies in familial Alzheimer disease: evidence for chromosome 19 linkage. Am. J. Hum. Genet. 48:1034–1050.[Web of Science][Medline]

  9. Strittmatter W.J., Saunders A.M., Schmechel D., Pericak-Vance M., Enghild J., Salvesen G.S., Roses A.D. (1993) Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl Acad. Sci. USA 90:1977–1981.[Abstract/Free Full Text]

  10. Schmechel D.E., Saunders A.M., Strittmatter W.J., Crain B.J., Hulette C.M., Joo S.H., Pericak-Vance M.A., Goldgaber D., Roses A.D. (1993) Increased amyloid beta-peptide deposition in cerebral cortex as a consequence of apolipoprotein E genotype in late-onset Alzheimer disease. Proc. Natl Acad. Sci. USA 90:9649–9653.[Abstract/Free Full Text]

  11. Corder E.H., Saunders A.M., Strittmatter W.J., Schmechel D.E., Gaskell P.C., Small G.W., Roses A.D., Haines J.L., Pericak-Vance M.A. (1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 261:921–923.[Abstract/Free Full Text]

  12. Bertram L. and Tanzi R.E. (2005) The genetic epidemiology of neurodegenerative disease. J. Clin. Invest. 115:1449–1457.[CrossRef][Web of Science][Medline]

  13. Bertram L. and Tanzi R.E. (2004) Alzheimer's disease: one disorder, too many genes? Hum. Mol. Genet. 13:R135–R141.[Abstract/Free Full Text]

  14. Bertram L., McQueen M., Mullin K., Blacker D., Tanzi R.E. The AlzGene Database, Alzheimer Research Forum. Available at: http://www.alzgene.org. Accessed 2005.

  15. Carlson G.A., Borchelt D.R., Dake A., Turner S., Danielson V., Coffin J.D., Eckman C., Meiners J., Nilsen S.P., Younkin S.G., Hsiao K.K. (1997) Genetic modification of the phenotypes produced by amyloid precursor protein overexpression in transgenic mice. Hum. Mol. Genet. 6:1951–1959.[Abstract/Free Full Text]

  16. Krezowski J., Knudson D., Ebeling C., Pitstick R., Giri R.K., Schenk D., Westaway D., Younkin L., Younkin S.G., Ashe K.H., Carlson G.A. (2004) Identification of loci determining susceptibility to the lethal effects of amyloid precursor protein transgene overexpression. Hum. Mol. Genet. 13:1989–1997.[Abstract/Free Full Text]

  17. Lehman E.J., Kulnane L.S., Gao Y., Petriello M.C., Pimpis K.M., Younkin L., Dolios G., Wang R., Younkin S.G., Lamb B.T. (2003) Genetic background regulates beta-amyloid precursor protein processing and beta-amyloid deposition in the mouse. Hum. Mol. Genet. 12:2949–2956.[Abstract/Free Full Text]

  18. Chishti M.A., Yang D.S., Janus C., Phinney A.L., Horne P., Pearson J., Strome R., Zuker N., Loukides J., French J., et al. (2001) Early-onset amyloid deposition and cognitive deficits in transgenic mice expressing a double mutant form of amyloid precursor protein 695. J. Biol. Chem. 276:21562–21570.[Abstract/Free Full Text]

  19. Dudal S., Krzywkowski P., Paquette J., Morissette C., Lacombe D., Tremblay P., Gervais F. (2004) Inflammation occurs early during the Abeta deposition process in TgCRND8 mice. Neurobiol. Aging 25:861–871.[CrossRef][Web of Science][Medline]

  20. Manly K.F., Cudmore R.H. Jr, Meer J.M. (2001) Map Manager QTX, cross-platform software for genetic mapping. Mamm. Genome 12:930–932.[CrossRef][Web of Science][Medline]

  21. Guo Z., Mori M., Fu X., Yao J., Xing Y., Korenaga T., Li G., Matsushita T., Hosokawa M., Higuchi K. (2003) Amyloidosis modifier genes in the less amyloidogenic A/J mouse strain. Lab. Invest. 83:1605–1613.[CrossRef][Web of Science]

  22. Myers A., Wavrant De-Vrieze F., Holmans P., Hamshere M., Crook R., Compton D., Marshall H., Meyer D., Shears S., Booth J., et al. (2002) Full genome screen for Alzheimer disease: stage II analysis. Am. J. Med. Genet. 114:235–244.[CrossRef][Web of Science][Medline]

  23. Blacker D., Bertram L., Saunders A.J., Moscarillo T.J., Albert M.S., Wiener H., Perry R.T., Collins J.S., Harrell L.E., Go R.C., et al. (2003) Results of a high-resolution genome screen of 437 Alzheimer's Disease families. Hum. Mol. Genet. 12:23–32.[Abstract/Free Full Text]

  24. Hiltunen M., Mannermaa A., Thompson D., Easton D., Pirskanen M., Helisalmi S., Koivisto A.M., Lehtovirta M., Ryynanen M., Soininen H. (2001) Genome-wide linkage disequilibrium mapping of late-onset Alzheimer's disease in Finland. Neurology 57:1663–1668.[Abstract/Free Full Text]

  25. Castillo G.M., Ngo C., Cummings J., Wight T.N., Snow A.D. (1997) Perlecan binds to the ß-amyloid proteins (Aß) of Alzheimer's disease, accelerates Aß fibril formation, and maintains Aß fibril stability. J. Neurochem. 69:2452–2465.[Web of Science][Medline]

  26. Phinney A.L., Drisaldi B., Schmidt S.D., Lugowski S., Coronado V., Liang Y., Horne P., Yang J., Sekoulidis J., Coomaraswamy J., et al. (2003) In vivo reduction of amyloid-beta by a mutant copper transporter. Proc. Natl Acad. Sci. USA 100:14193–14198.[Abstract/Free Full Text]

  27. Yu C.E., Oshima J., Fu Y.H., Wijsman E.M., Hisama F., Alisch R., Matthews S., Nakura J., Miki T., Ouais S., et al. (1996) Positional cloning of the Werner's syndrome gene. Science 272:258–262.[Abstract]

  28. Paxinos G. and Watson C. (2001) The Rat Brain in Stereotaxic Coordinates (Academic Press, San Diego, CA).


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