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Originally published In Press as doi:10.1074/jbc.M701078200 on June 4, 2007

J. Biol. Chem., Vol. 282, Issue 33, 23818-23828, August 17, 2007
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Accelerating Amyloid-beta Fibrillization Reduces Oligomer Levels and Functional Deficits in Alzheimer Disease Mouse Models*Formula

Irene H. Cheng{ddagger}§, Kimberly Scearce-Levie{ddagger}§, Justin Legleiter{ddagger}§, Jorge J. Palop{ddagger}§, Hilary Gerstein{ddagger}, Nga Bien-Ly{ddagger}, Jukka Puoliväli{ddagger}§1, Sylvain Lesné, Karen H. Ashe, Paul J. Muchowski{ddagger}§||, and Lennart Mucke{ddagger}§2

From the {ddagger}Gladstone Institute of Neurological Disease, San Francisco, California 94158, the Departments of §Neurology and ||Biochemistry and Biophysics, University of California, San Francisco, California 94143, and the Department of Neurology, University of Minnesota Medical School, Minneapolis, Minnesota 55455

Received for publication, February 5, 2007 , and in revised form, May 30, 2007.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Many proteins suspected of causing neurodegenerative diseases exist in diverse assembly states. For most, it is unclear whether shifts from one state to another would be helpful or harmful. We used mutagenesis to change the assembly state of Alzheimer disease (AD)-associated amyloid-beta (Abeta) peptides. In vitro, the "Arctic" mutation (AbetaE22G) accelerated Abeta fibrillization but decreased the abundance of nonfibrillar Abeta assemblies, compared with wild-type Abeta. In human amyloid precursor protein (hAPP) transgenic mice carrying mutations adjacent to Abeta that increase Abeta production, addition of the Arctic mutation markedly enhanced the formation of neuritic amyloid plaques but reduced the relative abundance of a specific nonfibrillar Abeta assembly (Abeta*56). Mice overexpressing Arctic mutant or wild-type Abeta had similar behavioral and neuronal deficits when they were matched for Abeta*56 levels but had vastly different plaque loads. Thus, Abeta*56 is a likelier determinant of functional deficits in hAPP mice than fibrillar Abeta deposits. Therapeutic interventions that reduce Abeta fibrils at the cost of augmenting nonfibrillar Abeta assemblies could be harmful.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Alzheimer disease (AD)3 and many other neurodegenerative disorders are associated with the accumulation of abnormal protein assemblies in the central nervous system (CNS). Much evidence suggests that this association reflects a causal relationship in which the abnormal proteins actually trigger the neuronal dysfunction and degeneration that characterize these conditions (1–3). The prevalence of AD and other neurodegenerative proteinopathies is increasing rapidly around the world, most likely because of their age dependence, the increasing longevity of many populations, and the lack of effective strategies for treatment and prevention (4–6). This alarming trend underlines the need to better understand the relationship between the accumulation of abnormal proteins in the CNS and the decline of neurological function.

This relationship has been difficult to analyze in depth because proteins associated with neurodegenerative disorders can exist in diverse assembly states, and distinct assemblies can differ markedly in pathogenic potential. For example, the amyloid-beta (Abeta) peptide, which seems to play a causal role in AD, can exist as monomers, low molecular weight oligomers (such as dimers and trimers), larger globular oligomers (such as Abeta*56, Abeta-derived diffusible ligands, amylospheroids, and globulomers), amyloid pores, protofibrils, fibrils, and amyloid plaques that contain densely packed Abeta fibrils and a large number of other molecules and cellular elements (7–15). Which of these structures contributes most critically to neurological decline in AD is a matter of active study and debate that has important implications for therapeutic interventions. Studies of transgenic mice with neuronal expression of human amyloid precursor proteins (hAPP), from which Abeta is released by proteolytic cleavage, suggest that nonfibrillar Abeta assemblies are more critical than amyloid plaques in the pathogenesis of AD-related neuronal dysfunction and memory deficits (15–20). However, there also is evidence for a pathogenic role of plaques and plaque-associated neuritic dystrophy (21–24).

Shifting Abeta from one assembly state to another may have profound effects on the pathogenesis of AD. Such shifts can result from genetic alterations and pharmacological interventions that change the amino acid composition of Abeta. For example, increasing the abundance of Abeta1–42 relative to shorter Abeta species promotes aggregation of Abeta monomers and plaque formation (25), whereas increasing the relative abundance of Abeta1–40 appears to counteract these processes (18, 26, 27). Mutations within the Abeta sequence that cause AD or related conditions also have marked effects on the assembly of Abeta (28).

The current study focuses on a familial AD-linked point mutation within Abeta (E22G, "Arctic mutation") that accelerates the aggregation of Abeta into protofibrils and fibrils in vitro (29–33). We investigated by atomic force microscopy (AFM) how the Arctic mutation affects the balance between fibrillar and nonfibrillar Abeta assemblies in cell-free conditions and compared its effect on the relative abundance of neuritic amyloid plaques and nonfibrillar Abeta assemblies in brain tissues of hAPP mice. Lastly, we examined whether behavioral deficits and depletions of synaptic activity-related proteins in these mice relate more closely to plaques or nonfibrillar Abeta assemblies.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
RESULTS
 DISCUSSION
 REFERENCES
 
Ex Situ AFM—Wild-type (wt) and Arctic mutant synthetic Abeta1–42 (Biopeptide, San Diego, CA) were dissolved in 100% 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP; Sigma) to 0.5 mg/ml and dried under vacuum. The HFIP-treated peptides were resuspended to 5 mg/ml in anhydrous dimethyl sulfoxide and incubated for 1 h at 37 °C. This stock solution was diluted in phosphate-buffered saline to a final concentration of 100 µg/ml and agitated at 1000 rpm at 37 °C. An aliquot from each sample was taken at various time intervals for immediate deposition. Abeta*56 was purified from forebrains of Tg2576 mice by immunoaffinity chromatography followed by size-exclusion chromatography as described (15). Three 5-µl samples of each incubation solution were deposited on freshly cleaved mica (SPI, West Chester, PA) and allowed to sit for 30 s. The substrate was washed twice with 100 µl of ultra-pure water, and the sample was then dried under a gentle stream of air. Tapping-mode AFM in air was performed with a Nanoscope IIIa system (Veeco, Santa Barbara, CA) equipped with a vertical-engage E scanner. Standard etched silicon probes (nominal spring constant, 50 N/m; resonance frequency, 300 kHz) were used. Typically, the following imaging parameters were used: drive amplitude of 150–500 kHz with set points of 0.8–1.0 V, scan frequencies of 4–5 Hz, image resolution of 512 by 512 points, and scan sizes of 3–12 µm. Custom software was used for AFM image analysis (35). Volume measurements were corrected for lateral tip contributions as described (36). For image quantification, nonfibrillar Abeta assemblies (oligomers and protofibrils) were defined as any structure with a height of 1–6 nm and a width to length (aspect) ratio smaller than 2.5. Fibrillar Abeta assemblies (fibrils and larger aggregates) were defined as any structure with a height larger than 6 nm and an aspect ratio of 2.5 or above. The ratio of nonfibrillar to fibrillar Abeta in Fig. 1D was calculated from the percent area occupied by each type of Abeta assembly as defined above.

In Situ AFM—In situ AFM experiments were performed with a Nanoscope IIIa MultiMode scanning probe microscope equipped with a fluid cell and a vertical engage E-scanner. Images were taken with a V-shaped oxide-sharpened silicon nitride cantilever with a nominal spring constant of 0.5 N/m. Scan rates were set at 1–3 Hz with cantilever drive frequencies of ~8–10 kHz. Freshly cleaved mica substrates were used, and phosphate-buffered saline was added to the cell in 35-µl aliquots by the hanging-drop method. Once the cantilever had successfully engaged the surface, a 15-µl aliquot of freshly made 1000 µg/ml Abeta-Wt or Abeta-Arctic was injected into the fluid cell, resulting in a final concentration of 300 µg/ml.

Experimental Animals—Artic-mutant hAPP (ARC) lines were generated in C57BL/6N mice (Harlan, Indianapolis, IN) and crossed onto the C57BL/6J background (Jackson Laboratory, Bar Harbor, ME) for 6–8 generations (34). The J20 line was generated in C57BL/6 x DBA/2 hybrids (18) and crossed onto the C57BL/6J background for more than 10 generations. Most results were compared with results in nontransgenic (NTG) littermates from each line to control for any line-specific effects. Unless indicated otherwise, mice were analyzed at 3–4 months of age. The studies were approved by the Institutional Animal Care and Use Committee of the University of California, San Francisco, and conducted in compliance with the National Institutes of Health's "Guide for the Care and Use of Laboratory Animals."

Behavioral Tests—Two independent, genotype- and line-balanced cohorts of singly housed male mice and NTG littermate controls from lines J20, ARC6, and ARC48 (n = 9–13/group) were tested in the sequence of elevated plus maze, open field, Y maze, and Morris water maze. The experimenter was blind to line and genotype during testing.

Open Field—Activity in the open field was tested with the automated Flex-Field/Open Field Photobeam Activity System (San Diego Instruments, San Diego, CA). The system consisted of two identical clear plastic chambers (41 x 41 x 38 cm), a PAS control box, a PC interface board, and a microcomputer for recording and analysis of data. Two sensor frames, each consisting of a 16 x 16 photobeam array at 1.5 cm and 6 cm above the bottom of the cage, were used to detect movements in the horizontal and vertical planes. The test was initiated by placing the mouse in the center of the arena. Horizontal beam breaks (ambulatory moves) in the arena were counted over 15 min. The arena was cleaned and dried after each test.

Y-Maze—The Y-maze was constructed of black plastic walls (10 cm high). It consisted of three compartments (10 cm x 10 cm) connected with 4 cm x 5 cm passages. The mouse was placed in one of the compartments and allowed to move freely for 6 min. An arm entry was manually recorded when all four paws entered the compartment. After each test, the maze was thoroughly cleaned.

Elevated Plus Maze—The elevated plus-shaped maze consisted of two open arms and two closed arms equipped with rows of infrared photobeams (Hamilton-Kinder, Poway, CA). Mice were habituated to dim lighting in the testing room for 30 min and then were placed individually at the center of the apparatus and allowed to explore for 10 min. The time spent and distance traveled in each of the arms were recorded by infrared beam breaks. After each mouse was tested, the apparatus was thoroughly cleaned.

Morris Water Maze—The water maze consisted of a pool (122 cm in diameter) containing opaque water (18 °C) and a platform (14 cm in diameter) submerged 1.5 cm under the water. For cued training sessions (days 1–3), the platform was marked with a visible beacon, and the mice were trained to locate the platform over six sessions (two per day, 4 h apart) each with two trials. The platform location was changed for each session. Hidden platform training (days 4–8) consisted of 10 sessions (two per day, 4 h apart), each with three trials. The platform location remained constant in the hidden platform sessions, and the entry points were changed semirandomly between trials. The maximum trial time was 60 s. Mice that failed to find the platform were led to it and placed on it for 15 s. A day after the last hidden platform training session, a probe trial was conducted by removing the platform and allowing mice to search in the pool for 1 min. Time to reach the platform, time in target quadrant, platform crossings, path length, and swim speed were recorded with an EthoVision video tracking system (Noldus, Netherlands).

Immunoblotting and Immunohistochemistry—Mice were sacrificed 2 days after behavioral testing, and their brains were cut in half sagittally. One half was fixed for 48 h in 4% paraformaldehyde for immunohistochemical analysis, and the other was frozen in dry ice for biochemical analysis. To measure total Abeta1-x and Abeta1–42, snap-frozen forebrains were homogenized in 5 M guanidine buffer, and human Abeta peptides were quantitated by ELISA as described (18). Protein fractionation and Abeta*56 detection in hAPP mice were performed as described (15). Briefly, total proteins (100 µg) from the radioimmune precipitation assay buffer (RIPA) soluble fraction were separated on 10.5–20% Tricine gels or 4–12% Bis-Tris gels and transferred to nitrocellulose membrane (0.2 µm pore size, Bio-Rad). Biotinylated 6E10 antibody (1:1000, Signet, Dedham, MA) and ExtrAvidin (1:5000, Sigma) were used for Western blotting. Immunohistochemistry was performed as described (20) on floating 30-µm sliding microtome sections with anti-Abeta (3D6, 1:500, Elan Pharmaceuticals, South San Francisco, CA), anti-APP (8E5, 1:1000, Elan Pharmaceuticals), anti-Fos (Ab-5, 1:10,000, Calbiochem) or anti-calbindin (1:15,000; Swant, Bellinzona, Switzerland) antibodies. Diaminobenzidine was used as the chromagen. Images were acquired with a digital camera (Axiocam, Carl Zeiss). Densitometric quantification of calbindin immunoreactivity was performed with Bioquant (20). For double-labeling of amyloid plaques and dystrophic neurites, floating sections were stained with monoclonal anti-hAPP antibody (8E5, 1:1000, Elan Pharmaceuticals), mounted on glass slides, and stained with 0.015% thioflavin-S. Images were collected with a confocal microscope (Bio-Rad).

Statistical Analysis—Statistical analyses were performed with SPSS11 (SPSS, Chicago, IL) or Statview (SAS Institute, Cary, NC). Quantitative data are reported as mean ± S.E. Pairs of means were compared by unpaired two-tailed t test and multiple means by ANOVA and Tukey-Kramer posthoc test. Learning curves in the water maze were analyzed by repeated-measures ANOVA. Differences in survival curves were assessed by Kaplan-Meier analysis. Correlations were examined by simple regression analysis.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
DISCUSSION
 REFERENCES
 
The Arctic Mutation Markedly Increases the Ratio of Fibrillar to Nonfibrillar Abeta in Vitro—Whereas it is well established that Abeta-Arctic aggregates more quickly than Abeta-Wt (29, 32), the specific aggregates formed and their ratios have, to our knowledge, not yet been analyzed by AFM. This technique provides quantitative three-dimensional morphological information unavailable with other approaches, such as analytical ultracentrifugation or dye-binding experiments. We first used in situ AFM to monitor the early events of synthetic Abeta1–42 assembly in real time under cell-free conditions as described (45).

At a solution concentration of 62.5 µM, Abeta-Wt assembled predominantly into globular oligomers, which first appeared after 10 min and continuously increased in number during a 2-h incubation period (Fig. 1A, upper panel and Movie S1 in Supplemental Data). At the same concentration, Abeta-Arctic assembled into short rod-shaped protofibrils in less than 1 h (Fig. 1A, lower panel and Movie S2 in Supplemental Data), in agreement with previous reports that the Arctic mutation accelerates the formation of Abeta protofibrils (29–32). The globular Abeta-Wt oligomers were 4–5 nm in height, mobile on mica, and coalesced into extended aggregates with segmented morphologies and often high curvature (Fig. S1, A and B in Supplemental Data), consistent with earlier findings (45). In contrast, Abeta-Arctic protofibrils were 4.5–5.5 nm in height, had a more rigid morphology, were often highly branched, and formed highly ordered arrays along the crystallographic lattice of the mica surface (Fig. S1, C and D in Supplemental Data). Abeta-Arctic protofibrils did not grow from an obvious oligomeric precursor on the mica surface, although we cannot exclude the possibility that these structures may have formed transiently in solution before deposition.


Figure 1
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  		FIGURE 1.
The Arctic mutation increases fibril formation and decreases the nonfibrillar/fibrillar Abeta1–42 ratio in vitro. A and B, self-assembly of synthetic Abeta-Wt or Abeta-Arctic peptides was monitored by AFM under in situ (A) or ex situ (B) cell-free conditions. Color brightness corresponds to relative aggregate height. A, freshly prepared monomer solutions (62.5 µM in phosphate-buffered saline) were injected at t = 0 directly into the liquid cell of the AFM and imaged continuously (see Movies S1 and S2 in Supplemental Data). As early as 60 min, Abeta-Arctic formed extended aggregates distinctly different from the globular Abeta-Wt aggregates. B, monomer solutions (20.8 µM in phosphate-buffered saline) were incubated in tubes at 37 °C with agitation, and then imaged by AFM at the times indicated. After 1 h of incubation, both solutions contained mostly globular oligomeric Abeta. At 24 h, Abeta-Wt was predominately nonfibrillar, and Abeta-Arctic had mature fibril morphologies and large amorphous aggregates. Fibrils and large amorphous aggregates appeared in the Abeta-Wt samples only after 72 h of incubation. Scale bars represent 1 µm(A) and 500 nm (B). C and D, numbers of small nonfibrillar Abeta assemblies (C) and ratios of nonfibrillar to fibrillar Abeta assemblies (D) at each time were determined from 15 images (9–25 µm2 each) from three independent ex situ experiments. Ratios were calculated from the percent surface area occupied by nonfibrillar versus fibrillar Abeta assemblies as defined under "Experimental Procedures." *, p < 0.05; **, p < 0.01; ***, p < 0.001 versus Abeta-Wt.

 


Figure 2
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  		FIGURE 2.
Expression of hAPP and Abeta in transgenic brain. A, diagram of the PDGF-hAPP transgene. Elements are not to scale. The PDGF beta-chain promoter directs an alternatively spliced hAPP minigene (67). B–D, forebrain lysates of 3–4-month-old mice were analyzed by Western blotting to determine relative levels of hAPP (B) or by ELISA to determine levels of guanidine hydrochloride (GuHCl)-soluble Abeta1-x and Abeta1–42 (C), and Abeta1–42/Abeta1-x ratios (D). Mean hAPP levels in J20 mice were defined as 1.0. n = 8–9/group. ***, p < 0.001 versus J20.

 


Figure 3
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  		FIGURE 3.
Accelerated plaque formation in ARC lines. A–D, hippocampal plaques were assessed in hAPP mice from lines J20, ARC6, and ARC48 at 3–4 (A and C) and 10–12 (B and D) months of age. A, Abeta deposits were detected by immunostaining with the 3D6 antibody. B, mature plaques were detected by thioflavin-S staining (green) and plaque-associated dystrophic neurites with the 8E5 antibody (red). C and D, Plaque loads were determined by measuring the percent area covered by 3D6-immunoreactive Abeta deposits (C) and by counting the number of neuritic plaques double-labeled with thioflavin-S and 8E5 (D). E, levels of reactive astrocytosis in the dentate gyrus of 3–4-month-old mice were determined by densitometric analysis of GFAP immunoreactivity. n = 8–10/group. **, p < 0.01; ***, p < 0.001 versus J20.

 
To quantify the levels of various structural assemblies of Abeta that accumulate over longer periods of time, Abeta samples at lower concentration (20.8 µM) were allowed to aggregate in test tubes at 37 °C before ex situ analysis by AFM (35). The majority of Abeta-Wt assemblies that appeared within 48 h of incubation were globular oligomers (1–2.5 nm in height) and short rod-shaped protofibrils (3–5 nm in height), whereas elongated fibrils and larger aggregates that were taller than 6 nm in height appeared only after 72 h (Fig. 1B, upper panel and Fig. S2, A and B in Supplemental Data). Under the same conditions, Abeta-Arctic remained in small nonfibrillar states at 1 h but had already assembled into fibrils and larger aggregates by 24 h (Fig. 1B, lower panel and Fig. S2, C and D in Supplemental Data). These results differ from the original report of the Arctic mutation (29), which concluded that it does not affect the Abeta fibrillization rate. Differences in experimental conditions that may explain this discrepancy include the use of Abeta1–42 instead of Abeta1–40 and of AFM instead of size exclusion chromatography in the current study. Our AFM findings are consistent with other studies demonstrating that the Arctic mutation accelerates fibril formation (30–33, 46). Notably, after 24 h of incubation, samples containing Abeta-Arctic had significantly fewer nonfibrillar assemblies (Fig. 1C) and lower ratios of nonfibrillar to fibrillar assemblies than samples containing Abeta-Wt (Fig. 1, C and D).

Effects of the Arctic Mutation on Plaque Load and Abeta*56 Levels in Vivo—To examine the effect of the Arctic mutation on Abeta assembly in vivo, we studied three lines of hAPP transgenic mice that produce human Abeta-Wt (line J20) or Abeta-Arctic (lines ARC6 and ARC48) (Fig. 2A). The Swedish and Indiana familial AD mutations within hAPP sequences flanking Abeta (Fig. 2A) were introduced into all three lines to maximize production of the pathogenic Abeta1–42 species. Because J20 mice carry only these mutations, their transgene-derived Abeta has the sequence of wild-type human Abeta. Because both ARC lines in addition carry the Arctic mutation, which resides in the middle of Abeta, they produce Arctic-mutant human Abeta.

The levels of hAPP in the forebrain were lower in ARC6 mice and higher in ARC48 mice than in J20 mice (Fig. 2B). At 3–4 months of age, levels of guanidine-soluble Abeta1-x and Abeta1–42 in the forebrain were comparable in J20 and ARC6 mice and much higher in ARC48 mice (Fig. 2C). The Abeta1–42/Abeta1-x ratios were similar in all three lines (Fig. 2D).

An analysis of plaque formation in the hippocampus confirmed and extended our previous observations (34). At 3–4 months, hippocampal Abeta deposition was undetectable or minimal in J20 mice, moderate in ARC6 mice, and prominent in ARC48 mice (Fig. 3, A and C). Only ARC48 mice showed reactive astrocytosis (Fig. 3E). At 10–12 months, thioflavin-S-positive mature plaques with dystrophic neurites were present in all three lines (Fig. 3B) but were much more abundant in the ARC lines than in line J20 (Fig. 3D).


Figure 4
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  		FIGURE 4.
Levels of Abeta*56 and other hAPP products at 3–4 months of age. A–E, Western blot analysis was used to detect APP metabolites and Abeta assemblies in membrane-enriched fractions of forebrain lysates. A, representative Western blot probed with the anti-Abeta3-8 antibody 6E10. Identified bands are labeled on the left of the blot; arrowheads indicate nonspecific signals. B–E, densitometry was used to quantify Western blot signals for Abeta*56 (B), beta-CTF (C), Abeta*56/APP (D), and Abeta*56/beta-CTF ratios (E). Mean levels in J20 mice were defined as 1.0. n = 16–18/group. ***, p < 0.001 versus J20.

 
Recent studies have identified a specific nonfibrillar Abeta assembly (Abeta*56) in brains of hAPP mice from another line (Tg2576) that was closely linked to memory deficits in these mice and caused memory deficits when infused into the brains of NTG rats (15). Western blot analysis detected a band consistent with Abeta*56 in the membrane-enriched fraction of forebrain lysates from 3–4-month-old hAPP mice (Fig. 4A). This fraction also contained full-length hAPP and various APP metabolites, including putative beta-secretase-generated C-terminal APP fragments (beta-CTF) (Fig. 4A). In contrast to their vastly different plaque loads (Fig. 3, A and C), J20 and ARC48 mice had roughly comparable Abeta*56 levels with a trend toward lower levels in ARC48 mice (Fig. 4B). Abeta*56 levels were much lower in the ARC6 line (Fig. 4B). In contrast, full-length hAPP (Fig. 2B) and beta-CTF levels were significantly higher in ARC48 mice than in J20 mice and much lower in ARC6 mice (Fig. 4, A and C), consistent with previous findings (34). Compared with the J20 line, both ARC lines showed similar decreases in the ratio of Abeta*56 to hAPP (Fig. 4D) and the ratio of Abeta*56 to beta-CTF (Fig. 4E).


Figure 5
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  		FIGURE 5.
Ex situ AFM height image and size distribution of wildtype Abeta*56. A, Western blot analysis (6E10 antibody) of wild-type Abeta*56 purified from RIPA-soluble forebrain lysates of Tg2576 mice by immunoaffinity chromatography and size exclusion chromatography as described (15). B, representative 1-µm2 AFM image of Abeta*56 demonstrating ellipsoidal shapes. C and D, analysis of 3243 particles in 26 images (5 x 5 µm). The mode of the height was ~1nm(C), and the volume was 125–175 nm3 (D).

 
Because the morphology of nonfibrillar Abeta assembled in vivo has never been examined, we used ex situ AFM to image Abeta*56 purified directly from brain tissue of hAPP mice (Fig. 5A). Most Abeta*56 particles detected by AFM had a globular ellipsoidal shape (Fig. 5B). Size characterization analysis revealed these aggregates to be ~1 nm in height (Fig. 5C) and 125–175 nm3 in volume (Fig. 5D), after partial correction for the finite shape and size of the AFM tip. This size is similar to that of the small globular oligomers of synthetic Abeta we identified at early stages in our in vitro aggregation experiments (Fig. S2A in Supplemental Data).

Functional Deficits Relate More Closely to Abeta*56 Than to Plaques—To determine if the increased plaque burden and neuritic dystrophy in ARC mice were associated with more severe behavioral deficits, we compared 3–4-month-old hAPP mice and NTG controls in a Morris water maze. Consistent with previous findings in J20 mice (20) and other hAPP lines overexpressing Abeta-Wt (37), J20 mice required more time than NTG mice to learn to escape to a cued platform that was moved to a different location between sessions, although their performance ultimately reached control levels (Fig. 6A). In contrast, ARC6 mice with greater hippocampal plaque loads and even the plaque-laden ARC48 mice had no difficulty learning this task (Fig. 6A).

In the spatial component of the water maze test, mice must use extramaze cues to navigate to a platform hidden in a constant location. J20 mice never learned this task as well as NTG controls (Fig. 6B), consistent with previous observations (20). ARC6 mice performed as well as NTG controls, but the higher expresser ARC48 mice were impaired (Fig. 6B). No significant differences were identified in the learning curves in the cued or spatial component of the test between the NTG mice from these three lines (Fig. 6, A and B).


Figure 6
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  		FIGURE 6.
Association between learning/memory deficits and Abeta*56 levels. A–C, Transgenic (TG) and nontransgenic (NTG) mice from the indicated lines were tested in the Morris water maze at 3–4 months of age. Mice were first trained to locate a cued platform (A) and then a hidden platform (B). Escape latency (sec) and path length (not shown) were used as measures of learning. The first data point in B(0) indicates performance on the first of three trials in session 1. Subsequent points represent average performance in each session. C, mice were tested in a probe trial 16–18 h after the last hidden platform trial. During this test, the platform was removed, and the search patterns of the mice were monitored. n = 9–13/group. *, p < 0.05; **, p < 0.01; ***, p < 0.001 versus NTG. D and E, relationship between probe trial performance in the water maze and levels of Abeta*56 (D) and plaque load (E) at 3–4 months of age. Levels of Abeta*56 in individual mice were normalized to the average levels in transgenic mice from the respective line. n = 12–18/group.

 
A day after the last training session, mice were tested in a probe trial during which search patterns were monitored in the absence of a platform. J20 and ARC48 mice showed less preference for the original target location than NTG controls, whereas ARC6 mice had normal spatial memory retention (Fig. 6C). In both J20 and ARC48 mice, probe trial deficits correlated with Abeta*56 levels (Fig. 6D), but not with plaque loads (Fig. 6E) or with hAPP and beta-CTF levels (Fig. S3 in Supplemental Data).

Water-maze deficits in J20 mice also correlate well with the depletion of calcium- and synaptic activity-related proteins in granule cells of the dentate gyrus (20, 38, 44). The calcium-binding protein calbindin-D28K and the immediate-early gene product Fos are among the most robust and sensitive of these biomarkers. Compared with NTG controls, calbindin (Fig. 7A) and Fos (Fig. 7B) levels in the dentate gyrus were significantly reduced in J20 and ARC48 mice, but not in ARC6 mice.

Other Behavioral Alterations and Premature Death—Several lines of hAPP mice expressing high levels of human Abeta-Wt have increased locomotor activity (37), and similar abnormalities have been observed in rats after disruption of input from the entorhinal cortex to the dentate gyrus (41). To look for this behavioral abnormality in our hAPP lines, we used the open field test and the Y maze. In both tests, J20 mice were significantly more active than NTG controls, whereas ARC6 mice were not (Fig. 8, A and B). ARC48 mice showed a trend toward hyperactivity. J20 mice (38, 39) and other hAPP models (37) also show an abnormal phenotype in the elevated plus maze, which is widely used to assess emotionality (40). In this test, ARC6 mice again behaved normally, whereas J20 mice and ARC48 mice traveled longer distances on the open arms of the maze than NTG controls (Fig. 8C). Performance in the open field and plus maze did not correlate with Abeta*56 or beta-CTF levels (Fig. S4 in Supplemental Data).

Last, hAPP mice expressing high levels of human Abeta-Wt have an increased incidence of premature death, the cause of which remains unknown (42, 43). Significant premature mortality was evident in lines J20 and ARC48, but not in ARC6 (Fig. 8D).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
REFERENCES
 
These results demonstrate a striking dissociation between plaque formation and functional deficits. ARC6 mice had more plaques than J20 mice but essentially no behavioral deficits. To our knowledge, ARC6 is the first hAPP line to show prominent neuritic plaques but normal learning and memory in the Morris water maze. Furthermore, ARC48 mice had markedly greater plaque loads than J20 mice but comparable or less severe functional deficits (Table S1 in Supplemental Data).

We think that these findings can be explained by differences in Abeta*56 levels and by the kinetic model presented in Fig. 9. Levels of pathogenic oligomers, such as Abeta*56, likely depend on many factors, including hAPP levels, production and degradation of Abeta, and sequestration of Abeta monomers or oligomers into mature fibrils. Although hAPP/Abeta levels in ARC6 mice were not high enough to result in the formation of Abeta*56 or in functional deficits, they were high enough for plaque formation to occur, presumably because of the high fibrillogenic propensity imparted by the Arctic mutation. The effective fibrillization of Abeta-Arctic may also explain why Abeta*56/APP ratios were lower in ARC48 than J20 mice. Nevertheless, hAPP/Abeta levels in ARC48 mice were high enough to result in robust levels of Abeta*56 and neuronal dysfunction. In mice overexpressing Abeta-Wt or Abeta-Arctic, behavioral and neuronal deficits were closely related to Abeta*56 levels, but not to plaque loads, consistent with other evidence suggesting that AD-related functional deficits are more likely caused by nonfibrillar Abeta assemblies than by amyloid plaques (1, 15, 16, 19, 47, 48).


Figure 7
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  		FIGURE 7.
Depletion of synaptic activity-related proteins in the dentate gyrus. Coronal brain sections of 3–4-month-old mice from the indicated lines were immunostained for calbindin or Fos. A, relative calbindin levels were determined by densitometric analysis of the molecular layer (arrow) from two sections per mouse. B, Fos-positive granule cells in the dentate gyrus were counted in every 10th serial section throughout the rostrocaudal extent of the hippocampus. For both measures, the mean level in NTG mice was defined as 1.0. n = 9–13/group. ***, p < 0.001 versus NTG.

 
Although APP and CTFs are biologically active and can have cytotoxic properties (56–61), their levels did not correlate with the severity of behavioral deficits in our mouse models, making them less likely culprits than nonfibrillar Abeta assemblies, such as Abeta*56. Interestingly, Abeta*56 levels correlated with water-maze deficits but not with activity measurements in the open field and plus maze, suggesting that Abeta*56 may affect learning and memory more than other behavioral domains. In addition, Abeta*56 levels may be higher in brain regions responsible for learning and memory than in brain regions that govern activity in the open field and plus maze. Because available methods are not yet sensitive enough to detect Abeta*56 in very small tissue samples, technological advances will likely be required to assess these possibilities experimentally.

By AFM analysis, Abeta*56 isolated from hAPP mouse brains was ~1 nm in height and 125–175 nm3 in volume and was similar in size to early-stage synthetic oligomers detected in our AFM analysis but smaller than most synthetic Abeta oligomers and protofibrils reported by others (8, 62, 63). Assuming that the calculated volume of an Abeta monomer folded into a two-stranded beta-sheet is ~10 nm3 (64) and that Abeta*56 is a dodecamer, as suggested by its apparent molecular weight (15), a perfectly packed Abeta*56 should have a volume of ~120 nm3. Because the finite size of the AFM tip convolutes the lateral dimensions of each particle, resulting in exaggerated volume measurements, we applied geometric methods to partially compensate for this convolution in our quantitative analysis. Because this correction protocol underestimates lateral tip contributions (36), it is not surprising that the Abeta*56 volumes we measured were slightly larger than the theoretical values. Although our results do not exclude the possibility that larger oligomers and protofibrils are also pathogenic, they suggest that small globular oligomers assembled in vitro more closely resemble Abeta oligomers that can cause memory deficits in vivo.

Because many factors can influence Abeta aggregation, our in vitro experiments were performed as close to physiological conditions as possible in regards to temperature, ionic strength, and pH. Because surface chemistry can influence Abeta aggregation during in situ AFM imaging (45), all quantitative results were obtained by ex situ AFM on Abeta aggregated in solution in the absence of mica. We used mica for in situ experiments because Abeta-Wt aggregates that form on mica closely resemble those formed under "free solution" conditions (45). Interestingly, protofibrillar structures formed on mica during in situ AFM differed markedly between Abeta-Wt and Abeta-Arctic. As noted in previous studies (45), Abeta aggregation observed by in situ AFM is highly dependent on the surface chemistry of the substrate. When exposed to aqueous solutions, the surface of mica bears a negative charge, similar to the surface of cell membranes containing anionic phospholipids. The E22G mutation reduces the net charge of Abeta-Arctic compared with Abeta-Wt, which may result in differential interactions of these peptides with anionic surfaces both in vitro and in vivo.


Figure 8
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  		FIGURE 8.
Locomotor activity and premature mortality in hAPP lines. A–C, locomotor activity in mice from the indicated lines was assessed at 3–4 months of age. A, in the open field, total activity (beam breaks) was monitored during 15 min of exploration. B, in the Y maze, total arm entries were counted for 6 min. C, in the elevated plus maze, the percent distance traveled in the open arms relative to the total distance traveled was monitored during 10 min of exploration. n = 9–13/group. **, p < 0.01; ***, p < 0.001 versus NTG. D, the cumulative proportion of survival in 220–310 mice per genotype (440–610 mice per line) was recorded between birth and 16 months of age.

 
A number of comments are in order about the relation of our models to human carriers of the Arctic mutation. First and foremost, our ARC48 line demonstrates that Abeta-Arctic can result in the formation of Abeta*56 as well as in the development of cognitive deficits, which is perfectly consistent with the fact that the Arctic mutation causes familial AD in humans. However, human carriers are typically heterozygotes, expressing both Abeta-Wt and Abeta-Arctic. In vitro, nonfibrillar Abeta assemblies persist longer and fibril formation occurs later in mixtures of Abeta-Arctic and Abeta-Wt than in samples containing only Abeta-Arctic (30). Although our ARC lines express wild-type murine APP and Abeta, the only human Abeta produced at high levels in their brains is Abeta-Arctic. In addition, people with the Arctic mutation do not have the Swedish and Indiana mutations, which were incorporated in our ARC lines to increase Abeta1–42 production to levels that are readily detectable and likely to yield pathological alterations within a short time. Because Abeta1–42 is more fibrillogenic than Abeta1–40 (25, 65), the high Abeta1–42/Abeta1-x ratios in our ARC lines may have synergized with the fibrillogenic effects of the Arctic mutation. Furthermore, wild-type Abeta1–40 can prevent aggregation and deposition of Abeta1–42 (18, 26, 27). Thus, the presence of Abeta-Wt, in combination with potentially lower Abeta1–42/Abeta1–40 ratios, in the brains of human carriers may promote the accumulation of nonfibrillar Abeta assemblies and worsen neurological deficits relative to our ARC models.

Because the pathogenicity of Abeta is linked so tightly to its assembly state, it is difficult, if not impossible, to determine if the Arctic mutation affects the pathogenicity of Abeta independently of its effects on Abeta assembly. How one interprets our results in this regard depends on the relative pathogenic impact of oligomers versus plaques. If plaques were the main cause of functional deficits, the comparable levels of deficits in lines J20 and ARC48 would suggest that plaques containing Abeta-Arctic are much less pathogenic than those containing Abeta-Wt, as the plaque burden was so much greater in the ARC48 line. If oligomers are the main cause of functional deficits, the comparable levels of Abeta*56 and of behavioral and neuronal deficits in lines J20 and ARC48 suggest that Abeta*56 containing Abeta-Arctic or Abeta-Wt are equally pathogenic. Because J20 mice develop functional deficits before plaque formation, we favor the latter conclusion.

In some in vitro studies (31, 46, 66), but not others (62, 68), Abeta-Arctic appeared to be more toxic than Abeta-Wt. We find it difficult to relate these findings to our results in transgenic mice, because synthetic Abeta preparations with different assembly states were used in the in vitro studies, and synthetic Abeta assemblies differ from in vivo-generated Abeta assemblies in biological activity even in tissue culture models (69). What is more, the formation of Abeta*56 in brain tissues is age-dependent, and this Abeta assembly does not appear to occur even in primary cortical cultures (15).


Figure 9
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  		FIGURE 9.
Hypothetical relationship between different Abeta assemblies and neuronal deficits. The numbers of Abeta monomers at the top roughly correspond to the levels of soluble Abeta detected in the indicated lines at 2–4 weeks of age, when none of them has amyloid deposits (34). Abeta monomers can aggregate into oligomers or protofibrils. However, it is not clear if oligomerization is an intermediate step in the formation of protofibrils and fibrils (on pathway) or the end point of an alternative assembly process (off pathway). Abeta monomers may also be added directly to growing fibrils without traversing an oligomeric or protofibrillar stage. It is unknown if, under natural conditions, oligomers, protofibrils, or fibrils ever spontaneously dissociate after they have formed. Abeta oligomers are more closely related to neuronal dysfunction and cognitive deficits than fibrils and plaques. If particularly pathogenic oligomers are not on the pathway to fibril formation, accelerating the formation of protofibrils and fibrils may divert a potentially limiting pool of monomers away from oligomer formation. If oligomers are on pathway, accelerating the formation of protofibrils and fibrils may sequester them more rapidly into higher order structures. In either case, the result would be a decrease in the bioactive pool of oligomeric Abeta assemblies. At low monomer levels (as in line ARC6), steady-state levels of oligomers may be too low to elicit functional deficits. At high monomer levels (as in lines ARC48 and J20), steady-state levels of oligomers may reach a critical threshold at which neuronal function becomes impaired, independently of whether Abeta carries the Arctic mutation or not.

 
We detected Abeta*56 in both the membrane-enriched fraction (Fig. 4A) and the extracellular-enriched fraction of forebrain lysates (data not shown). Furthermore, memory deficits in the water maze correlated more strongly with Abeta*56 in the membrane-enriched fraction than in the extracellular-enriched fraction (data not shown). Whether Abeta*56 in the membrane-enriched fraction reflects interactions of this oligomer with the outside or inside of the surface membrane or with membranes of intracellular compartments remains to be determined. While low molecular weight Abeta oligomers have been detected in lysates of APP-expressing CHO cells (7PA2) and primary neurons, Abeta*56 was undetectable in both systems (15), suggesting that this oligomer forms extracellularly.

Although our study was not designed to assess diagnostic procedures or therapeutic interventions, it highlights the dissociation between plaques and neurological deficits, as well as the impact of altering Abeta fibrillization in vivo. Both aspects have potentially important clinical implications. There is justifiable excitement in the field about plaque detection with radiopharmaceutical probes in the brains of live patients, an approach that is being included as an outcome measure in an increasing number of clinical trials (70, 71). Notably, diverse factors can alter the aggregation or deposition of Abeta after it is produced (39, 72–76), which could make the relationship between Abeta production and plaque load more complex and less predictable than is desirable from a clinical trials perspective. In addition, the results of the current study suggest that the relationship between Abeta levels and cognitive decline may depend on the balance between fibrillar and nonfibrillar Abeta assemblies, which could be influenced by endogenous factors as well as drug treatments.

Our study also raises some concerns about therapeutic efforts to block or reverse the formation of Abeta fibrils. The risk/benefit ratio of this approach might critically depend on the extent to which it also diminishes the pool of pathogenic Abeta oligomers. Within the obvious constraints of mouse-to-human extrapolations, our data caution against any strategies that decrease Abeta fibrils at the cost of augmenting pathogenic Abeta oligomers. They also raise the possibility that promoting fibril formation in ways that bypass oligomer formation or rapidly sequester oligomers into more inert fibrils might be of therapeutic benefit. Additional studies are needed to further test these hypotheses.


   FOOTNOTES
 
* This work was supported in part by National Institutes of Health Grants AG011385, AG022074, and AG023501 (to L. M.), NS33249 (to K. H. A.), and a Facilities Grant (RR 18928-01) from the National Institutes of Health National Center for Research Resources. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

Formula The on-line version of this article (available at http://www.jbc.org) contains supplemental Table S1, Figs. S1–S4, and Movies S1 and S2. Back

1 Present address: Cerebricon Ltd., 70211 Kuopio, Finland. Back

2 To whom correspondence should be addressed: 1650 Owens St., San Francisco, CA 94158. Tel.: 415-734-2504; Fax: 415-355-0131; E-mail: lmucke{at}gladstone.ucsf.edu.

3 The abbreviations used are: AD, Alzheimer disease; Abeta, amyloid-beta; hAPP, human amyloid precursor protein; CNS, central nervous system; AFM, atomic force microscopy; NTG, nontransgenic; TG, transgenic; beta-CTF, beta-secretase-generated C-terminal APP fragment; Tricine, N-[2-hydroxy-1,1-bis(hydroxymethyl)ethyl]glycine; ANOVA, analysis of variance; GuHCl, guanidine hydrochloride. Back


   ACKNOWLEDGMENTS
 
We thank J. Weissman for providing access to his AFM; G.-Q. Yu, F. Yan, X. Wang, D. Kim and H. Solanoy for technical assistance; J. Carroll, C. Goodfellow, and T. Roberts for preparation of graphics; G. Howard and S. Ordway for editorial review; and D. McPherson and L. Manuntag for administrative assistance.



   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Walsh, D. M., and Selkoe, D. J. (2004) Neuron 44, 181–193[CrossRef][Medline] [Order article via Infotrieve]
  2. Tanzi, R., and Bertram, L. (2005) Cell 120, 545–555[CrossRef][Medline] [Order article via Infotrieve]
  3. Skovronsky, D. M., Lee, V. M.-Y., and Trojanowski, J. Q. (2006) Annu. Rev. Pathol. Mech. Dis. 1, 151–170[CrossRef]
  4. Hebert, L., Scherr, P., Bienias, J., Bennett, D., and Evans, D. (2003) Arch. Neurol. 60, 1119–1122[Abstract/Free Full Text]
  5. Ferri, C. P., Prince, M., Brayne, C., Brodaty, H., Fratiglioni, L., Ganguli, M., Hall, K., Hasegawa, K., Hendrie, H., Huang, Y., Jorm, A., Mathers, C., Menezes, P. R., Rimmer, E., and Scazufca, M. (2005) Lancet 366, 2112–2117[CrossRef][Medline] [Order article via Infotrieve]
  6. Lleó, A., Greenberg, S. M., and Growdon, J. H. (2006) Annu. Rev. Med. 57, 513–533[CrossRef][Medline] [Order article via Infotrieve]
  7. Roher, A. E., Chaney, M. O., Kuo, Y.-M., Webster, S. D., Stine, W. B., Haverkamp, L. J., Woods, A. S., Cotter, R. J., Tuohy, J. M., Krafft, G. A., Bonnell, B. S., and Emmerling, M. R. (1996) J. Biol. Chem. 271, 20631–20635[Abstract/Free Full Text]
  8. Lambert, M. P., Barlow, A. K., Chromy, B. A., Edwards, C., Freed, R., Liosatos, M., Morgan, T. E., Rozovsky, I., Trommer, B., Viola, K. L., Wals, P., Zhang, C., Finch, C. E., Krafft, G. A., and Klein, W. L. (1998) Proc. Natl. Acad. Sci. U. S. A. 95, 6448–6453[Abstract/Free Full Text]
  9. Hartley, D. M., Walsh, D. M., Ye, C. P., Diehl, T., Vasquez, S., Vassilev, P. M., Teplow, D. B., and Selkoe, D. J. (1999) J. Neurosci. 19, 8876–8884[Abstract/Free Full Text]
  10. Walsh, D. M., Tseng, B. P., Rydel, R. E., Podlisny, M. B., and Selkoe, D. J. (2000) Biochemistry 39, 10831–10839[CrossRef][Medline] [Order article via Infotrieve]
  11. Bitan, G., Lomakin, A., and Teplow, D. B. (2001) J. Biol. Chem. 276, 35176–35184[Abstract/Free Full Text]
  12. Lashuel, H. A., Hartley, D., Petre, B. M., Walz, T., and Lansbury, P. T., Jr. (2002) Nature 418, 291
  13. Hoshi, M., Sato, M., Matsumoto, S., Noguchi, A., Yasutake, K., Yoshida, N., and Sato, K. (2003) Proc. Natl. Acad. Sci. U. S. A. 100, 6370–6375[Abstract/Free Full Text]
  14. Barghorn, S., Nimmrich, V., Striebinger, A., Krantz, C., Keller, P., Janson, B., Bahr, M., Schmidt, M., Bitner, R. S., Harlan, J., Barlow, E., Ebert, U., and Hillen, H. (2005) J. Neurochem. 95, 834–847[CrossRef][Medline] [Order article via Infotrieve]
  15. Lesné, S., MT, K., Kotilinek, L., Kayed, R., Glabe, C. G., Yang, A., Gallagher, M., and Ashe, K. H. (2006) Nature 440, 352–357[CrossRef][Medline] [Order article via Infotrieve]
  16. Hsia, A., Masliah, E., McConlogue, L., Yu, G., Tatsuno, G., Hu, K., Kholodenko, D., Malenka, R. C., Nicoll, R. A., and Mucke, L. (1999) Proc. Natl. Acad. Sci. U. S. A. 96, 3228–3233[Abstract/Free Full Text]
  17. Holcomb, L. A., Gordon, M. N., Jantzen, P., Hsiao, K., Duff, K., and Morgan, D. (1999) Behav. Genet. 29, 177–185[CrossRef][Medline] [Order article via Infotrieve]
  18. Mucke, L., Masliah, E., Yu, G.-Q., Mallory, M., Rockenstein, E. M., Tatsuno, G., Hu, K., Kholodenko, D., Johnson-Wood, K., and McConlogue, L. (2000) J. Neurosci. 20, 4050–4058[Abstract/Free Full Text]
  19. Westerman, M. A., Cooper-Blacketer, D., Mariash, A., Kotilinek, L., Kawarabayashi, T., Younkin, L. H., Carlson, G. A., Younkin, S. G., and Ashe, K. H. (2002) J. Neurosci. 22, 1858–1867[Abstract/Free Full Text]
  20. Palop, J. J., Jones, B., Kekonius, L., Chin, J., Yu, G.-Q., Raber, J., Masliah, E., and Mucke, L. (2003) Proc. Natl. Acad. Sci. U. S. A. 100, 9572–9577[Abstract/Free Full Text]
  21. Cummings, B. J., Pike, C. J., Shankle, R., and Cotman, C. W. (1996) Neurobiol. Aging 17, 921–933[CrossRef][Medline] [Order article via Infotrieve]
  22. Knowles, R. B., Gomez-Isla, T., and Hyman, B. T. (1998) J. Neuropathol. Exp. Neurol. 57, 1122–1130[Medline] [Order article via Infotrieve]
  23. Tsai, J., Grutzendler, J., Duff, K., and Gan, W. B. (2004) Nat. Neurosci. 7, 1181–1183[CrossRef][Medline] [Order article via Infotrieve]
  24. Stern, E. A., Bacskai, B. J., Hickey, G. A., Attenello, F. J., Lombardo, J. A., and Hyman, B. T. (2004) J. Neurosci. 24, 4535–4540[Abstract/Free Full Text]
  25. Younkin, S. G. (1995) Ann. Neurol. 37, 287–288[CrossRef][Medline] [Order article via Infotrieve]
  26. Snyder, S. W., Ladror, U. S., Wade, W. S., Wang, G. T., Barrett, L. W., Matayoshi, E. D., Huffaker, H. J., Kraft, G. A., and Holzman, T. F. (1994) Biophys. J. 67, 1216–1228[Medline] [Order article via Infotrieve]
  27. Kim, J., Onstead, L., Randle, S., Price, R., Smithson, L., Zwizinski, C., Dickson, D. W., Golde, T., and McGowan, E. (2007) J. Neurosci. 27, 627–633[Abstract/Free Full Text]
  28. Chiti, F., Stefani, M., Taddei, N., Ramponi, G., and Dobson, C. M. (2003) Nature 424, 805–808[CrossRef][Medline] [Order article via Infotrieve]
  29. Nilsberth, C., Westlind-Danielsson, A., Eckman, C. B., Condron, M. M., Axelman, K., Forsell, C., Stenh, C., Luthman, J., Teplow, D. B., Younkin, S. G., Naslund, J., and Lannfelt, L. (2001) Nat. Neurosci. 4, 887–893[CrossRef][Medline] [Order article via Infotrieve]
  30. Lashuel, H. A., Hartley, D. M., Petre, B. M., Wall, J. S., Simon, M. N., Walz, T., Lansbury, J., and Peter, T. (2003) J. Mol. Biol. 332, 795–808[CrossRef][Medline] [Order article via Infotrieve]
  31. Murakami, K., Irie, K., Morimoto, A., Ohigashi, H., Shindo, M., Nagao, M., Shimizu, T., and Shirasawa, T. (2003) J. Biol. Chem. 278, 46179–46187[Abstract/Free Full Text]
  32. Johansson, A.-S., Berglind-Dehlin, F., Karlsson, G., Edwards, K., Gellerfors, P., and Lannfelt, L. (2006) FEBS J. 273, 2618–2630[CrossRef][Medline] [Order article via Infotrieve]
  33. Hori, Y., Hashimoto, T., Wakutani, Y., Urakami, K., Nakashima, K., Condron, M. M., Tsubuki, S., Saido, T. C., Teplow, D. B., and Iwatsubo, T. (2006) J. Biol. Chem. 282, 4916–4923[CrossRef][Medline] [Order article via Infotrieve]
  34. Cheng, I., Palop, J., Esposito, L., Bien-Ly, N., Yan, F., and Mucke, L. (2004) Nat. Med. 10, 1190–1192[CrossRef][Medline] [Order article via Infotrieve]
  35. Legleiter, J., Czilli, D. L., Gitter, B., DeMattos, R. B., Holtzman, D. M., and Kowalewski, T. (2004) J. Mol. Biol. 335, 997–1006[CrossRef][Medline] [Order article via Infotrieve]
  36. Legleiter, J., DeMattos, R. B., Holtzman, D. M., and Kowalewski, T. (2004) J. Colloid Interface Sci. 278, 96–106[CrossRef][Medline] [Order article via Infotrieve]
  37. Kobayashi, D. T., and Chen, K. S. (2005) Genes Brain and Behav. 4, 173–196[CrossRef]
  38. Chin, J., Palop, J. J., Puoliväli, J., Massaro, C., Bien-Ly, N., Gerstein, H., Scearce-Levie, K., Masliah, E., and Mucke, L. (2005) J. Neurosci. 25, 9694–9703[Abstract/Free Full Text]
  39. Esposito, L., Raber, J., Kekonius, L., Fan, Y., Yu, G.-Q., Bien-Ly, N., Puoliväli, J., Scearce-Levie, K., Masliah, E., and Mucke, L. (2006) J. Neurosci. 26, 5167–5179[Abstract/Free Full Text]
  40. Belzung, C., and Griebel, G. (2001) Behav. Brain Res. 125, 141–149[CrossRef][Medline] [Order article via Infotrieve]
  41. Steward, O., Loesche, J., and Horton, W. C. (1977) Brain Res. Bull. 2, 41–48[CrossRef][Medline] [Order article via Infotrieve]
  42. Hsiao, K. K., Borchelt, D. R., Olson, K., Johannsdottir, R., Kitt, C., Yunis, W., Xu, S., Eckman, C., Younkin, S., Price, D., Iadecola, C., Clark, H. B., and Carlson, G. (1995) Neuron 15, 1203–1218[CrossRef][Medline] [Order article via Infotrieve]
  43. Chin, J., Palop, J. J., Yu, G.-Q., Kojima, N., Masliah, E., and Mucke, L. (2004) J. Neurosci. 24, 4692–4697[Abstract/Free Full Text]
  44. Palop, J. J., Chin, J., Bien-Ly, N., Massaro, C., Yeung, B. Z., Yu, G.-Q., and Mucke, L. (2005) J. Neurosci. 25, 9686–9693[Abstract/Free Full Text]
  45. Kowalewski, T., and Holtzman, D. M. (1999) Proc. Natl. Acad. Sci. U. S. A. 96, 3688–3693[Abstract/Free Full Text]
  46. Whalen, B. M., Selkoe, D. J., and Hartley, D. M. (2005) Neurobiol. Dis. 20, 254–266[CrossRef][Medline] [Order article via Infotrieve]
  47. Holcomb, L., Gordon, M. N., McGowan, E., Yu, X., Benkovic, S., Jantzen, P., Wright, K., Saad, I., Mueller, R., Morgan, D., Sanders, S., Zehr, C., O'Campo, K., Hardy, J., Prada, C. M., Eckman, C., Younkin, S., Hsiao, K., and Duff, K. (1998) Nat. Med. 4, 97–100[CrossRef][Medline] [Order article via Infotrieve]
  48. Klein, W. L., Krafft, G. A., and Finch, C. E. (2001) Trends Neurosci. 24, 219–224[CrossRef][Medline] [Order article via Infotrieve]
  49. Deleted in proof.
  50. Deleted in proof.
  51. Deleted in proof.
  52. Deleted in proof.
  53. Deleted in proof.
  54. Deleted in proof.
  55. Deleted in proof.
  56. Rockenstein, E., Mante, M., Alford, M., Adame, A., Crews, L., Hashimoto, M., Esposito, L., Mucke, L., and Masliah, E. (2005) J. Biol. Chem. 280, 32957–32967[Abstract/Free Full Text]
  57. Kerr, M. L., and Small, D. H. (2005) J. Neurosci. Res. 80, 151–159[CrossRef][Medline] [Order article via Infotrieve]
  58. Galvan, V., Gorostiza, O. F., Banwait, S., Ataie, M., Logvinova, A. V., Sitaraman, S., Carlson, E., Sagi, S. A., Chevallier, N., Jin, K., Greenberg, D. A., and Bredesen, D. E. (2006) Proc. Natl. Acad. Sci. U. S. A. 103, 7130–7135[Abstract/Free Full Text]
  59. Zheng, H., and Koo, E. H. (2006) Mol. Neurodegener. 1, 5[CrossRef][Medline] [Order article via Infotrieve]
  60. Lee, K. W., Im, J. Y., Song, J. S., Lee, S. H., Lee, H. J., Ha, H. Y., Koh, J. Y., Gwag, B. J., Yang, S. D., Paik, S. G., and Han, P. L. (2006) Neurobiol. Dis. 22, 10–24[CrossRef][Medline] [Order article via Infotrieve]
  61. Neve, R. L., and McPhie, D. L. (2007) Biochim. Biophys. Acta 1772, 430–437[Medline] [Order article via Infotrieve]
  62. Dahlgren, K. N., Manelli, A. M., Stine, W. B., Jr., Baker, L. K., Krafft, G. A., and LaDu, M. J. (2002) J. Biol. Chem. 277, 32046–32053[Abstract/Free Full Text]
  63. Harper, J. D., and Lansbury, P. T. J. (1997) Annu. Rev. Biochem. 66, 385–407[CrossRef][Medline] [Order article via Infotrieve]
  64. Yang, D. S., Yip, C. M., Huang, T. H. J., Chakrabartty, A., and Fraser, P. E. (1999) J. Biol. Chem. 274, 32970–32974[Abstract/Free Full Text]
  65. Burdick, D., Soreghan, B., Kwon, M., Kosmoski, J., Knauer, M., Henschen, A., Yates, J., Cotman, C., and Glabe, C. (1992) J. Biol. Chem. 267, 546–554[Abstract/Free Full Text]
  66. Klyubin, I., Walsh, D. M., Cullen, W. K., Fadeeva, J. V., Anwyl, R., Selkoe, D. J., and Rowan, M. J. (2004) Eur. J. Neurosci. 19, 2839–2846[CrossRef][Medline] [Order article via Infotrieve]
  67. Rockenstein, E. M., McConlogue, L., Tan, H., Gordon, M., Power, M., Masliah, E., and Mucke, L. (1995) J. Biol. Chem. 270, 28257–28267[Abstract/Free Full Text]
  68. Ji, Z. S., Mullendorff, K., Cheng, I. H., Miranda, R. D., Huang, Y., and Mahley, R. W. (2006) J. Biol. Chem. 281, 2683–2692[Abstract/Free Full Text]
  69. Wang, Q., Walsh, D. M., Rowan, M. J., Selkoe, D. J., and Anwyl, R. (2004) J. Neurosci. 24, 3370–3378[Abstract/Free Full Text]
  70. Klunk, W. E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D. P., Bergstrom, M., Savitcheva, I., Huang, G. F., Estrada, S., Ausen, B., Debnath, M. L., Barletta, J., Price, J. C., Sandell, J., Lopresti, B. J., Wall, A., Koivisto, P., Antoni, G., Mathis, C. A., and Langstrom, B. (2004) Ann. Neurol. 55, 306–319[CrossRef][Medline] [Order article via Infotrieve]
  71. Buckner, R. L., Snyder, A. Z., Shannon, B. J., LaRossa, G., Sachs, R., Fotenos, A. F., Sheline, Y. I., Klunk, W. E., Mathis, C. A., Morris, J. C., and Mintun, M. A. (2005) J. Neurosci. 25, 7709–7717[Abstract/Free Full Text]
  72. Mucke, L., Yu, G.-Q., McConlogue, L., Rockenstein, E. M., Abraham, C. R., and Masliah, E. (2000) Am. J. Pathol. 157, 2003–2010[Abstract/Free Full Text]
  73. Wyss-Coray, T., Lin, C., Yan, F., Yu, G.-Q., Rohde, M., McConlogue, L., Masliah, E., and Mucke, L. (2001) Nat. Med. 7, 612–618[CrossRef][Medline] [Order article via Infotrieve]
  74. Cherny, R. A., Atwood, C. S., Xilinas, M. E., Gray, D. N., Jones, W. D., McLean, C. A., Barnham, K. J., Volitakis, I., Fraser, F. W., Kim, Y. S., Huang, X. D., Goldstein, L. E., Moir, R. D., Lim, J. T., Beyreuther, K., Zheng, H., Tanzi, R. E., Masters, C. L., and Bush, A. I. (2001) Neuron 30, 665–676[CrossRef][Medline] [Order article via Infotrieve]
  75. Fagan, A. M., Watson, M., Parsadanian, M., Bales, K. R., Paul, S. M., and Holtzman, D. M. (2002) Neurobiol. Dis. 9, 305–318[CrossRef][Medline] [Order article via Infotrieve]
  76. DeMattos, R. B., Cirrito, J. R., Parsadanian, M., May, P. C., O'Dell, M. A., Taylor, J. W., Harmony, J. A. K., Aronow, B. J., Bales, K. R., Paul, S. M., and Holtzman, D. M. (2004) Neuron 41, 193–202[CrossRef][Medline] [Order article via Infotrieve]

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