Copper induces the accumulation of amyloid-beta in the brain ...

Mol Cell Toxicol (2013) 9:57-66 DOI 10.1007/s13273-013-0009-0

ORIGINAL PAPER

Copper induces the accumulation of amyloid-beta in the brain Dong-Kyeong Kim1, Ji-Won Song1, Jung-Duck Park1 & Byung-Sun Choi1 Received: 21 December 2012 / Accepted: 22 February 2013 �The Korean Society of Toxicogenomics and Toxicoproteomics and Springer 2013

Abstract Accumulation of amyloid beta protein

(Aβ) plays a major role in the etiology of Alzheimer’s disease (AD). Aβ is generated from the cleavage of amyloid precursor protein (APP) by beta-site APPcleaving enzyme 1 (BACE1). There are two factors that reduce of Aβ accumulation in the brain; degradation by peptidases such as neprilysin (NEP) and clearance via two transporters. The low-density lipoprotein receptor related protein 1 (LRP1) is the major transporter that clears Aβ from brain to blood and the receptor for advanced glycation end products (RAGE) is a receptor that transports Aβ from blood to brain. Copper (Cu) has been postulated to play a role in the pathogenesis of AD, especially involved in Aβ aggregation and toxicity. According to a recent study, Cu(II) could reduce Aβ clearance from the brain in cholesterol-fed rabbits. However, the critical mechanism is unclear. This study was purposed to demonstrate whether Cu (II) would alter accumulation of Aβ in brain. We treated 25 and 50 μM CuSO4 for 48-hour in the well-defined neurodevelopmental cell line (PC12), rat choroidal epithelial cell line (Z310), and rat brain endothelial cell line (RBE4) to estimate the effects on Cu(II) exposure in the brain. Cu(II) increased the levels of Aβ(40) and Aβ(42) in the PC12 cell medium in a dose-dependent manner compared with control. The mRNA and protein expression levels of APP and BACE1, which play an important role in Aβ generation, were increased in the PC12 cells exposed to Cu(II). NEP expression levels in mRNA and protein were decreased in a dose-dependent manner in PC12 cells treated with Cu(II). In the RBE4 cells, Cu(II) decreased LRP1 levels and increas-

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Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea Correspondence and requests for materials should be addressed to B. S. Choi ( [email protected])

ed RAGE levels in mRNA and protein compared with control. Moreover, Cu(II) decreased the clearance of Aβ using the blood-brain barrier (BBB) transport study. However, in the Z310 cells, Cu(II) didn’t change the levels of LRP1 and RAGE in mRNA and protein. These results implied that Cu(II) increased Aβ accumulation in the brain by increasing Aβ production but decreasing Aβ degradation in the brain parenchyma and interfering with clearance of Aβ via the BBB. Keywords Amyloid beta protein, Amyloid precursor protein (APP), Beta-site APP-cleaving enzyme 1 (BACE1), Blood-brain barrier (BBB), Blood-CSF barrier (BCB), Copper, Neprilysin (NEP), Low-density lipoprotein receptor related protein 1 (LRP1), Receptor for advanced glycation end products (RAGE) Alzheimer’s disease (AD) is the most common form of dementia in the elderly. AD patients develop memory loss, altered personality, behavior change, and reduced executive function, caused by synaptic loss and death of neurons1. A clear mechanism of AD pathogenesis is not known. However, epidemiological studies suggest that a low education level, history of head trauma and high-calorie intake increase the risk of AD1,2. Especially specific absorption of metals such as copper (Cu) and iron (Fe) from the diet may increase the risk of AD3. Moreover, there are consistent features regarding the development of “plaques”4. Accumulation of plaques is well known as a major factor of pathogenesis in AD5. Plaques are accumulated in extracellular space by aggregates of amyloid β-protein (Aβ)1,6 and the amount of Aβ in the cerebrospinal fluid (CSF) is a major hallmark of AD pathogenesis7,8. Aβ is a small peptide (~4.5 kDa) made up of 40-42 amino acids derived from amyloid precursor protein (APP) by beta-site APP-cleaving enzyme 1 (BACE1)9. Increased levels of Aβ in the

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brain of AD patients may occur through several processes, including overproduction of Aβ in the brain, down-regulation of enzyme for Aβ degradation and/or a disrupted clearance of Aβ out of brain by the transport system on the brain barrier10. Cu is known to be a critical factor of AD pathogenesis11-13, as it is involved in Aβ aggregation and toxicity14. BACE1 has a high affinity with Cu(I) in C-terminal domain15 and APP also has Cu(II)-binding domain at residues 135-15516,17, where bound Cu(II) is reduced to Cu(I)18,19. In APP-knock-out mice, Cu levels were elevated in the brain and liver20. Thus, APP and BACE1 have Cu binding domains, and need Cu in brain for activation. Cu(II) treated neuronal cells were found to have an increase of APP production at the cell surface15,21. The produced Aβ is degraded by proteases such as neprilysin (NEP)22. Some studies suggest that AD patients’ NEP level in brain is lower than control23,24. In previous in vitro study, Cu(II) down-regulates NEP activity through degradation of NEP25. Aβ levels in the brain reflect the balance between rates of generation, degradation and clearance. To reduce Aβ-associated toxicity and damage, it has to be removed efficiently from the brain. Blood-to-brain influx or efflux have to pass two barriers. The bloodbrain barrier (BBB), which is mainly composed of tightly connected cerebral capillary endothelial, separates the blood from the brain interstitial fluid (ISF)26,27. The other barrier is the blood-CSF barrier (BCB), between the blood and the CSF, located in the choroid plexus26,28. These pathways are important for Aβ transport, since aggregated Aβ does not diffuse via capillaries27. Several studies suggest that Aβ accumulation

in the brain is age-related and AD is an age-related inability to clear Aβ from the brain29,30. Two major receptors; the low-density lipoprotein receptor-related protein 1 (LRP1) and advanced glycation end products (RAGE) on the BBB and/or BCB play a key role in controlling Aβ levels in ISF. LRP1 is thought to be a primary transporter of Aβ across the BBB out of the brain, while RAGE is a key transporter protein for Aβ influx in ISF10,31. Brain endothelial mRNA expression of LRP1 decreases with age and was found to be lower in brain tissue from AD mouse models and from AD patients than controls32-34, resulting in Aβ accumulation in the ISF35. Some have suggested that Cu(II) in the drinking water lead to the inhibition of Aβ clearance from the brain35,36. Therefore, this study was designed to test the hypothesis that Cu(II) treatment affects Aβ generation, degradation and clearance from brain with PC12, RBE4 and Z310 cell line. Aβ(40) and Aβ(42) in Cu(II)-treated PC12 cell culture medium

When PC12 cells were treated with 25 and 50 μM Cu (II) for 48-hour, Aβ(40) in culture medium increased 121.0 (P⁄0.05) and 166.0% (P⁄0.01) respectively, and Aβ(42) increased 120.1% (P⁄0.01) compared with controls in 50 μM Cu(II)-treated PC12 cell culture medium (Figure 1). mRNA and protein expression levels of APP and BACE1 in PC12 cells

APP mRNA expression levels in PC12 cells treated with 25 and 50 μM Cu(II) for 48-hour were significant-

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Figure 1. Quantification of Aβ(40) levels (A) and Aβ(42) levels (B) in PC12 cells medium after copper treatment at 25 and 50 μM for 48-hour by sandwich ELISA; *P⁄0.05 and **P⁄0.01 versus control.

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Figure 2. Effect of copper exposure on APP and BACE1 expression of mRNA and protein in PC12 cells; (A) APP mRNA expression on copper treated PC12 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (B-1) APP protein levels in PC12 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (B-2) APP protein levels were quantified and adjusted by actin. (C) BACE1 mRNA expression on copper treated PC12 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (D-1) BACE1 protein levels in PC12 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (D-2) BACE1 protein levels were quantified and adjusted by actin. All of data were expressed as percentages of the control cell levels. Data are expressed as mean±SEM; *P⁄0.05 and **P⁄0.01 versus control.

ly increased 124.0 (P⁄0.01) and 124.6% (P⁄0.01) respectively compared with control (Figure 2A). Also, APP protein expression of PC12 cells treated with 50 μM Cu(II) for 48-hour increased 228.8% (P⁄0.01) compared to control (Figure 2C). mRNA expression of BACE1 in PC12 cells treated with 50 μM Cu(II) for 48-hour increased by 114.2% (P⁄0.05) versus control (Figure 2B), and protein levels of BACE1 in PC12 cells treated with 50 μM Cu(II) for 48-hour increased signi-

ficantly at 327.9% (P⁄0.05) compared to control (Figure 2D). mRNA and protein expression levels of NEP in PC12 cells

After treatment of 25 and 50 μM Cu(II) for 48-hour, mRNA levels of NEP decreased 56.6 (P⁄0.01) and 34.1% (P⁄0.01) respectively comparison to control

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Figure 3. Effect of copper exposure on NEP expression of mRNA and protein in PC12 cells; (A) NEP mRNA expression on copper treated PC12 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (B-1) NEP protein levels in PC12 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (B-2) NEP protein levels were quantified and adjusted by actin. All of data were expressed as percentages of the control cell levels. Data are expressed as mean±SEM; *P⁄0.05 and **P⁄0.01 versus control.

(Figure 3A). Protein levels of NEP after Cu(II) treatment also decreased significantly 32.6 (P⁄0.01) and 18.8% (P⁄0.01) respectively (Figure 3B).

(P⁄0.05; Cu(II): 21.9±0.48 μg/mL, control: 23.8± 0.44 μg/mL) in comparison to controls respectively (Figure 6).

mRNA and protein levels of LRP1 and RAGE in RBE4 and Z310 cells

Discussion

After Cu(II) treatment with 25 and 50 μM for 48-hour, mRNA levels of LRP1 decreased 84.0 (P⁄0.05) and 83.3% (P⁄0.05) over controls in RBE4 cells respectively (Figure 4A). Protein levels of LRP1 also significantly decreased 68.9% (P⁄0.05) in 50 μM Cu(II)treated RBE4 cells (Figure 4B). 25 and 50 μM Cu(II) treatment induced 122.5 (P⁄0.05) and 125.5% (P⁄ 0.05) increase of mRNA levels of RAGE in RBE4 cells respectively compared to control (Figure 4C). Protein levels of RAGE increased 229.5% (P⁄0.05) over control in 50 μM Cu(II)-treated RBE4 cells (Figure 4D). However, after Z310 cells were treated with 25 and 50 μM Cu(II) for 48-hour, mRNA and protein expression levels of LRP1 and RAGE in Z310 cells did not differ significantly from their control (Figure 5). Transport of Aβ using in BBB model system

After Cu(II) treatment with 50 μM for 2, 3, 6 and 12 h, Aβ clearance via BBB was decreased 85.8% (P⁄ 0.01; Cu(II): 4.7±0.08 μg/mL, control: 5.5±0.03 μg/ mL), 88.8% (P⁄0.01; Cu(II): 7.0±0.15 μg/mL, control: 7.9±0.10 μg/mL), 87.2% (P⁄0.05; Cu(II): 15.3 ±0.30 μg/mL, control: 17.5±0.57 μg/mL) and 92.1%

Extracellular accumulation of Aβ is a major part of the AD pathogenesis37,38. Many different forms of Aβ, such as Aβ(1-40) and Aβ(1-42), have been studied36. Aβ(1-40) especially is the major form of Aβ species and Aβ(1-42) is a minor Aβ species, but Aβ(1-42) has the highest propensity for aggregation and fibrillogenesis, and is heavily enriched in plaque11,39,40. The causes of Aβ elevation extracellularly are unknown, although many researchers suspect that essential metals including Cu, Fe, and zinc (Zn) contribute to the pathogenesis of AD13,41. Especially Cu metabolism is the center of attention on AD pathogenesis12. The levels of Aβ in the brain are regulated by the balanced between its generation, degradation and clearance from the brain. However, how Cu(II) affects the balance of Aβ deposits by the change of APP, BACE1, NEP, LRP1 and RAGE levels are not clear. In this study, we investigated the effects of Cu(II) on the regulation of Aβ generation, degradation and clearance using PC12, RBE4, Z310 cell lines and BBB in vitro model. In previous study, Cu concentrations in CSF and plasma were 0.28 (0.20-0.55) and 18.88 (12.75-53.50)

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Figure 4. Effect of copper exposure on LRP1 and RAGE expression of mRNA and protein in RBE4 cells; (A) LRP1 mRNA expression on copper treated RBE4 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (B-1) LRP1 protein levels in RBE4 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (B-2) LRP1 protein levels were quantified and adjusted by actin. (C) RAGE mRNA expression on copper treated RBE4 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (D-1) RAGE protein levels in RBE4 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (D-2) RAGE protein levels were quantified and adjusted by actin. All of data were expressed as percentages of the control cell levels. Data are expressed as mean±SEM; *P⁄0.05 and **P⁄0.01 versus control.

μM respectively in control subjects and Cu concentrations in CSF and plasma of AD patients were 0.28 (0.14-1.72) and 18.88 (12.43-34.62) μM, respectively42. Extracellular plaques in AD patients’ brain were demonstrated high concentration of Cu (~400 μM) compared with the normal brain43. Moreover, in vitro studies used high concentration of Cu (25-100 μM) for regulating of Aβ44,45. Also, a recent study found that treatment with 100 μM Cu(II) for 12 and 24-hour on PC12 cells had minor effects on cell viability46, and we evaluated the viability of Cu(II) treated PC12 cells by MTT assay. We did not observe the effects on the viability of PC12

cells until 50 μM Cu(II) for 48-hour (data not shown). We also investigated the interference of Cu(II) on Aβ levels using Aβ ELISA Kit, but we didn’t find any interference (data not shown). Therefore, we used the levels of 25 and 50 μM Cu(II) for 48-hour. Aβ is made from APP by BACE19, and is degraded by NEP22. In this study, we found that Cu(II) leads to increase APP and BACE1 levels, but decrease NEP levels in PC12 cells. Thus, Aβ levels in Cu(II)-treated PC12 cell culture medium were increased by increasing Aβ generation but decreasing Aβ degradation in PC12 cells.

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Figure 5. Effect of copper exposure on LRP1 and RAGE expression of mRNA and protein in Z310 cells; (A) LRP1 mRNA expression on copper treated Z310 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (B-1) LRP1 protein levels in Z310 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (B-2) LRP1 protein levels were quantified and adjusted by actin. (C) RAGE mRNA expression on copper treated Z310 cells at 0, 25 and 50 μM for 48-hour by real time RT-PCR adjusted by Bestkeeper. (D-1) RAGE protein levels in Z310 cells at 0, 25 and 50 μM for 48-hour by western blotting (top panel) and actin was used as a control (bottom panel). (D-2) RAGE protein levels were quantified and adjusted by actin. All of data were expressed as percentages of the control cell levels. Data are expressed as mean±SEM; *P⁄0.05 and **P⁄0.01 versus control.

Moreover, a key factor of extracellular Aβ accumulation is Aβ clearance by transporter via BBB and/or BCB28,32. LRP1 is the major transporter from brain to blood, while RAGE is the major transporter from blood to brain on BBB and/or BCB10,31. Many studies investigated the role of the Aβ clearance on BBB. However there is insufficient research on the role of the BCB in Aβ clearance. We compared the effect of Cu(II) on BBB and BCB in Aβ clearance using RBE4 and Z310 cells respectively. We found that Cu(II) decreased the LRP1 levels but increased the RAGE levels in RBE4 cells and we confirmed these results by Aβ transport

study using BBB in vitro model system. Aβ clearance via BBB is decreased in Cu(II)-treated BBB model compared with non-treated BBB model. However, in Z310 cells, we didn’t find any changes of Cu(II) on LRP1 and RAGE levels. In summary, we found a direct effect of Cu(II) on Aβ generation, degradation and clearance. Cu(II) increased the accumulation of Aβ by increasing Aβ generation, but decreasing Aβ degradation in PC12 cells and reducing clearance of Aβ via BBB in vitro model system. Moreover, the more important barrier of the clearance of Aβ from brain is the BBB rather than the


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Extracellular accumulation of Aβ(40) and Aβ(42) in PC12 cells medium following Cu exposure was quantified using Human/Rat β Amyloid (40) ELISA kit (WAKO, Japan) and Human/Rat β Amyloid (42) ELISA kit (High-Sensitive, WAKO, Japan). After Cu treatment at 0, 25 and 50 μM for 48-hour, Aβ levels were quantified. Each well was corrected by 0-hour Aβ levels respectively.

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Figure 6. Amount of Aβ(40) clearance via blood-brain barrier (BBB) in vitro model after copper treatment at 0 and 50 μM for 1, 2, 3, 6 and 12 h; Data are expressed as mean±SEM; *P⁄0.05 and **P⁄0.01 versus controls.

BCB. These results suggest an outline of the combined mechanism of Cu(II) on the Aβ accumulation in brain and explain the role of Cu(II) in amyloidosis in AD.

Materials & Methods Chemicals and reagents

Copper sulfate (CuSO4∙5H2O) was products of KANTO Chemical (Japan). α-MEM : DMEM/F-12, DMEM, DMEM/F-12, RPMI 1640, fetal bovine serum (FBS) and heat-inactivated horse serum were purchased from Welgene (South Korea). All other chemicals used were of analytical grade or molecular biology grade. Cell culture and treatment

Rat pheochromocytoma cells (PC12) and rat glioma cells (C6) were obtained from the Korean cell line bank (KCLB, South Korea). Rat brain endothelial cells (RBE4) and rat choroidal epithelial cells (Z310) were kindly provided by Dr. Wei Zheng (School of Health Sciences, Purdue University). RBE4 cells were grown in a α-MEM : DMEM/F-12 (1 : 1) medium supplemented with 10% (v/v) FBS, 1 ng/mL bFGF (Cell Signaling, MA, USA). Z310 cells were grown in a DMEM medium supplemented with 10% (v/v) FBS. PC12 cells were cultured in RPMI 1640 medium supplemented with 10% (v/v) heat-inactivated horse serum, 5% (v/v) FBS. C6 cells were cultured in DMEM/F-12 medium supplemented with 10 % (v/v) FBS. RBE4 cells, Z310 cells and PC12 cells were treated 25 and 50 μM Cu for 48-hour and all cells were maintained at 37� C in a humidified atmosphere containing 5% CO2.

Quantification of mRNA expression by real time RT-PCR

Total RNA was isolated and purified using RNeasy Plus Mini (Qiagen, Hilden, Germany). cDNA was synthesized by PrimeScriptTM RT reagent Kit (TAKARA, Japan). Real-time RT-PCR using the Mx3005P (Agilent, CA, USA) was used to quantify the mRNA levels. The following primer pairs were used : APP forward primer 5′-AAC ATG TGC GCA TGG TGG A-3′, reverse primer 5′-CAC GGC AGG GAC GTT GTA GA3′; BACE1 forward primer 5′-TGG TGG ACA CGG GCA GTA GTA-3′, reverse primer 5′-TCG GAG GTC TCG GTA TGT ACT GG-3′; NEP forward primer 5′CCG GCC AGA GTA TGC AGT CA-3′, reverse primer 5′-TGC CAT GGA TGC TCC ACT TC-3′; LRP1 forward primer 5′-TGG GTG TCC CGA AAT CTG TT-3′, reverse primer 5′-ACC ACC GCA TTC TTG AAG GA-3′; RAGE forward primer 5′-TTC AGC TGT TGG TTG AGC CTG AA-3′, reverse primer 5′CAC CGG TTT CTG TGA CCC TGA T-3′; beta-actin forward primer 5′-GGA GAT TAC TGC CCT GGC TCC TA-3′, reverse primer 5′-GAC TCA TCG TAC TCC TGC TTG CTG-3′; GAPDH forward primer 5′GGC ACA GTC AAG GCT GAG AAT G-3′, reverse primer 5′-ATG GTG GTG AAG ACG CCA GTA-3′ and 18s rRNA forward primer 5′-AAG TTT CAG CAC ATC CTG CGA GTA-3′, reverse primer 5′-TTG GTG AGG TCA ATG TCT GCT TTC-3′. Semi-nested RT-PCR method was used to quantify the mRNA expression of NEP and RAGE. The mRNA levels of beta-actin, GAPDH and 18s rRNA were determined to normalize the mRNA expression levels of target gene using BestKeeper software47,48. The relative differences in mRNA expression in the Cu-treated cells were calculated and expressed as a relative increase, setting the control at 100%. Western Blotting

Total protein was isolated in cells using Protein Extraction Solution (NP40) (ELPIS, South Korea). Protein concentration was determined with the Bradford Assay (BIO-RAD, CA, USA). Equal amounts of protein (25

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μg) were subjected to SDS-PAGE on a 10% polyacrylamide gel and transferred to PVDF membrane (GE Healthcare, UK). The primary antibodies used were APP (1 : 200, rabbit polyclonal IgG (Santa Cruz, CA, USA)); LRP1 (1 : 200, goat polyclonal IgG (Santa Cruz, CA, USA)); NEP (1 : 50, mouse monoclonal IgG (Santa Cruz, CA, USA)); RAGE (1 : 100, rabbit polyclonal IgG (Santa Cruz, CA, USA)). Membranes were developed using PowerOpti-ECL Western blotting Detection reagent (Animal Genetics, South Korea); the exposure time varied from 10 sec to several seconds depending on signal strength by LAS-1000 (Fujifilm, Japan). We measured the density of each band used UN-SCAN-IT (Silk Scientific Corporation, UT, USA) BBB transport model

To establish the BBB transport model, we used a transwell permeable supports composed of two chambers separated by an insert with a polyester membrane (Corning, NY, USA). Before cell seeding, each membrane was coated by collagen (Sigma, MO, USA). C6 cells were plated on the underside of the membrane insert, and incubated for 4-hour to allow cell attachment. Then RBE4 cells were seeded on the membrane with 800 μL medium in inner chamber. BBB model was cultured in RBE4 cell medium. TEER measurement

To validate the BBB transport model system, we measure transepithelial electric resistance (TEER) using an epithelial voltmeter (Millicell-ERS, Millipore, MA, USA). When the TEER value reached 180-190 ohm ㆍcm2, we used this two-chamber transport model system. Transport study

Aliquots (800 μL) of culture medium containing 0 and 50 μM CuSO4∙5H2O were added to the inserts. All of the outer compartments contained 1.2 mL culture medium that same levels of Cu. After 24 hours, all medium was removed. Aliquots (1.2 mL) of culture medium containing 83 μg/mL FAM-Labeled β-Amyloid (1-40) (ANASPEC, CA, USA) without Cu were added to the outer chamber. The inner chamber contained 800 μL culture medium without Cu and FAMLabeled β-Amyloid (1-40). After changing medium, aliquots (25 μL) of medium were taken from the upper compartment at 0 and 1, 2, 3, 6 and 12 hours. Aβ 1-40 levels in the outer chamber culture medium were assessed using a fluorescence spectrophotometer (Kontron, CA, USA).


Statistics

All data were expressed as a mean±SEM. Statistical analyses of the differences between groups were carried out by one-way analysis of variance (ANOVA), followed by a post hoc multiple comparison with Duncan test, and Student’s t test by using PASW statistics package for Windows program (version 18.0). The differences than control were considered significant if the p value was less than 0.05 and 0.01. Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 20110012890).

References 1. Mattson, M. P. Pathways towards and away from Alzheimer’s disease. Nature 430:631-639 (2004). 2. Lesne, S. et al. A specific amyloid-beta protein assembly in the brain impairs memory. Nature 440:352-357 (2006). 3. Bush, A. I., Masters, C. L. & Tanzi, R. E. Copper, betaamyloid, and Alzheimer’s disease: tapping a sensitive connection. Proc Natl Acad Sci USA 100:11193-11194 (2003). 4. Huang, X. et al. Trace metal contamination initiates the apparent auto-aggregation, amyloidosis, and oligomerization of Alzheimer’s Abeta peptides. J Biol Inorg Chem 9:954-960 (2004). 5. Mattson, M. P. Gene-diet interactions in brain aging and neurodegenerative disorders. Ann Intern Med 139: 441-444 (2003). 6. Raman, B. et al. Metal ion-dependent effects of clioquinol on the fibril growth of an amyloid {beta} peptide. J Biol Chem 280:16157-16162 (2005). 7. Martorana, A. et al. Cerebrospinal fluid levels of Abeta42 relationship with cholinergic cortical activity in Alzheimer’s disease patients. J Neural Transm. http: //link.springer.com/content/pdf/10.1007%2Fs00702012-0780-4 (2012). 8. Ogomori, K. et al. Beta-protein amyloid is widely distributed in the central nervous system of patients with Alzheimer’s disease. Am J Pathol 134:243-251 (1989). 9. Selkoe, D. J. The cell biology of beta-amyloid precursor protein and presenilin in Alzheimer’s disease. Trends Cell Biol 8:447-453 (1998). 10. Deane, R., Bell, R. D., Sagare, A. & Zlokovic, B. V. Clearance of amyloid-beta peptide across the bloodbrain barrier: implication for therapies in Alzheimer’s disease. CNS Neurol Disord Drug Targets 8:16-30 (2009). 11. Donnelly, P. S., Xiao, Z. & Wedd, A. G. Copper and Alzheimer’s disease. Curr Opin Chem Biol 11:128133 (2007). 12. Hung, Y. H., Bush, A. I. & Cherny, R. A. Copper in


the brain and Alzheimer’s disease. J Biol Inorg Chem 15:61-76 (2010). 13. Lovell, M. A., Robertson, J. D., Teesdale, W. J., Campbell, J. L. & Markesbery, M. R. Copper, iron and zinc in Alzheimer’s disease senile plaques. J Neurol Sci 158:47-52 (1998). 14. Huang, X., Moir, R. D., Tanzi, R. E., Bush, A. I. & Rogers, J. T. Redox-active metals, oxidative stress, and Alzheimer’s disease pathology. Ann N Y Acad Sci 1012:153-163 (2004). 15. Angeletti, B. et al. BACE1 cytoplasmic domain interacts with the copper chaperone for superoxide dismutase-1 and binds copper. J Biol Chem 280:1793017937 (2005). 16. Atwood, C. S. et al. Copper mediates dityrosine crosslinking of Alzheimer’s amyloid-beta. Biochemistry 43:560-568 (2004). 17. Hesse, L., Beher, D., Masters, C. L. & Multhaup, G. The beta A4 amyloid precursor protein binding to copper. FEBS Lett 349:109-116 (1994). 18. Multhaup, G. et al. The amyloid precursor protein of Alzheimer’s disease in the reduction of copper (II) to copper (I). Science 271:1406-1409 (1996). 19. Multhaup, G. et al. Copper-binding amyloid precursor protein undergoes a site-specific fragmentation in the reduction of hydrogen peroxide. Biochemistry 37:72247230 (1998). 20. White, A. R. et al. Copper levels are increased in the cerebral cortex and liver of APP and APLP2 knockout mice. Brain Res 842:439-444 (1999). 21. Acevedo, K. M. et al. Copper promotes the trafficking of the amyloid precursor protein. J Biol Chem 286: 8252-8262 (2011). 22. Shirotani, K. et al. Neprilysin degrades both amyloid beta peptides 1-40 and 1-42 most rapidly and efficiently among thiorphan- and phosphoramidon-sensitive endopeptidases. J Biol Chem 276:21895-21901 (2001). 23. Maruyama, M. et al. Cerebrospinal fluid neprilysin is reduced in prodromal Alzheimer’s disease. Ann Neurol 57:832-842 (2005). 24. Yasojima, K., Akiyama, H., McGeer, E. G. & McGeer, P. L. Reduced neprilysin in high plaque areas of Alzheimer brain: a possible relationship to deficient degradation of beta-amyloid peptide. Neurosci Lett 297: 97-100 (2001). 25. Li, M. et al. Copper downregulates neprilysin activity through modulation of neprilysin degradation. J Alzheimers Dis 19:161-169 (2010). 26. Choi, B. S. & Zheng, W. Copper transport to the brain by the blood-brain barrier and blood-CSF barrier. Brain Res 1248:14-21 (2009). 27. Zlokovic, B. V. et al. Blood-brain barrier transport of circulating Alzheimer’s amyloid beta. Biochem Biophys Res Commun 197:1034-1040 (1993). 28. Behl, M., Zhang, Y., Monnot, A. D., Jiang, W. & Zheng, W. Increased beta-amyloid levels in the choroid plexus following lead exposure and the involvement of low-density lipoprotein receptor protein-1.

65

Toxicol Appl Pharmacol 240:245-254 (2009). 29. Rubenstein, E. Relationship of senescence of cerebrospinal fluid circulatory system to dementias of the aged. Lancet 351:283-285 (1998). 30. Selkoe, D. J. Toward a comprehensive theory for Alzheimer’s disease. Hypothesis: Alzheimer’s disease is caused by the cerebral accumulation and cytotoxicity of amyloid beta-protein. Ann N Y Acad Sci 924:17-25 (2000). 31. Shibata, M. et al. Clearance of Alzheimer’s amyloidss(1-40) peptide from brain by LDL receptor-related protein-1 at the blood-brain barrier. J Clin Invest 106: 1489-1499 (2000). 32. Donahue, J. E. et al. RAGE, LRP-1, and amyloid-beta protein in Alzheimer’s disease. Acta Neuropathol 112: 405-415 (2006). 33. Miller, M. C. et al. Hippocampal RAGE immunoreactivity in early and advanced Alzheimer’s disease. Brain Res 1230:273-280 (2008). 34. Yan, S. D. et al. RAGE and amyloid-beta peptide neurotoxicity in Alzheimer’s disease. Nature 382:685-691 (1996). 35. Sparks, D. L. & Schreurs, B. G. Trace amounts of copper in water induce beta-amyloid plaques and learning deficits in a rabbit model of Alzheimer’s disease. Proc Natl Acad Sci USA 100:11065-11069 (2003). 36. Selkoe, D. J. & Schenk, D. Alzheimer’s disease: molecular understanding predicts amyloid-based therapeutics. Annu Rev Pharmacol Toxicol 43:545-584 (2003). 37. Selkoe, D. J. Clearing the brain’s amyloid cobwebs. Neuron 32:177-180 (2001). 38. Zlokovic, B. V. The blood-brain barrier in health and chronic neurodegenerative disorders. Neuron 57:178201 (2008). 39. Prelli, F. et al. Different processing of Alzheimer’s beta-protein precursor in the vessel wall of patients with hereditary cerebral hemorrhage with amyloidosisDutch type. Biochem Biophys Res Commun 151:11501155 (1988). 40. Seubert, P. et al. Isolation and quantification of soluble Alzheimer’s beta-peptide from biological fluids. Nature 359:325-327 (1992). 41. Rivera-Mancia, S. et al. The transition metals copper and iron in neurodegenerative diseases. Chem Biol Interact 186:184-199 (2010). 42. Gerhardsson, L., Lundh, T., Minthon, L. & Londos, E. Metal concentrations in plasma and cerebrospinal fluid in patients with Alzheimer’s disease. Dement Geriatr Cogn Disord 25:508-515 (2008). 43. Lovell, M. A., Robertson, J. D., Teesdale, W. J., Campbell, J. L. & Markesbery, W. R. Copper, iron and zinc in Alzheimer’s disease senile plaques. J Neurol Sci 158:47-52 (1998). 44. Lin, R. et al. Exposure to metal ions regulates mRNA levels of APP and BACE1 in PC12 cells: blockage by curcumin. Neurosci Lett 440:344-347 (2008). 45. Varela-Nallar, L. et al. Induction of cellular prion protein gene expression by copper in neurons. Am J Phy-

66

siol Cell Physiol 290:C271-281 (2006). 46. Lin, R. et al. Exposure to metal ions regulates mRNA levels of APP and BACE1 in PC12 cells: blockage by curcumin. Neurosci Lett 440:344-347 (2008). 47. Andersen, C. L., Jensen, J. L. & Orntoft, T. F. Normalization of real-time quantitative reverse transcriptionPCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to


bladder and colon cancer data sets. Cancer Res 64: 5245-5250 (2004). 48. Pfaffl, M. W., Tichopad, A., Prgomet, C. & Neuvians, T. P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-based tool using pair-wise correlations. Biotechnol Lett 26:509-515 (2004).