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Combined deletions of amyloid precursor protein and amyloid precursor-like protein 2 reveal different effects on mouse brain metal homeostasis† B. Elise Needham,a Giuseppe D. Ciccotostoab and Roberto Cappai*ab Alterations to the expression of the Amyloid Precursor Protein (APP) and its paralogue Amyloid Precursor-Like Protein 2 (APLP2) affect metal homeostasis in vitro and in vivo. Analysis of the in vivo effects of the APP and APLP2 knockouts on metal homeostasis has been restricted to APP and APLP2 single knockout mice, and up to12 month old animals. To define the redundancy and inter-relationship
Received 3rd December 2013, Accepted 7th January 2014
between the APP and APLP2 genes as regulators of metal homeostasis, and how this is influenced by
DOI: 10.1039/c3mt00358b
as well as homozygous:hemizygous knockout mice at 3, 12 and 18 plus months of age. These studies identified age and genotype dependent changes in metal levels, and established differences in the
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relative roles played by APP and APLP2 in modulating metal homeostasis.
aging, we investigated copper, iron, zinc and manganese levels in APP and APLP2 single knockout mice
Introduction The Amyloid Precursor Protein (APP) is a type 1 transmembrane glycoprotein that together with its paralogues, Amyloid-PrecursorLike Protein 1 and 2 (APLP1 and APLP2), constitute the APP-family.1 Amongst the numerous functions attributed to APP, its role as a metalloprotein is supported by large body biochemical, structural and cellular studies. APP can bind metals via at least three different sites in the APP ectodomain: the cysteine-rich N-terminal domain, the heparin binding C-terminal domain (E2/D6a/CAPPD) and the Ab peptide.2–9 APP and APLP2 expression modulates copper, zinc and iron homeostasis.10–14 In vivo, adult APP and APLP2 knockout mice have significantly elevated copper levels in the cerebral cortex and liver.10 Older, 12 month, APP knockout mice have elevated iron levels in the brain, liver and kidneys.14 Conversely, APP overexpressing transgenic mice had significantly reduced copper levels in the brain,11,15,16 and these were altered in a gender and genetic background dependent manner.13 Analysis of the in vivo effects of the APP family knockouts on metal homeostasis has been restricted to single knockout APP and APLP2 mice and up to 12 month old animals.10 We sought to define the redundancy in function between the APP and
a
Department of Pathology, The University of Melbourne, VIC 3010, Australia. E-mail: [email protected] b Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, VIC 3010, Australia † Electronic supplementary information (ESI) available. See DOI: 10.1039/ c3mt00358b
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APLP2 genes as regulators of metal homeostasis and the effects of age. Therefore, we investigated APP and APLP2 knockout mice lacking various combinations of both genes at 3, 12 and 18 plus (18+) months.
Methods APP and APLP2 knockout mice Generation of APP / ,17 APLP2 / ,18 APP / APLP2+/ (ref. 19) and APP+/ APLP2 / (ref. 19) mice has been described. The wildtype mice (APP+/+APLP2+/+), APP / , APLP2 / , APP / APLP2+/ and APP+/ APLP2 / knockout mice were of the same background strain (C57BL6J 129/Sv). Genotypes were determined by PCR using specific primer sets.18 Animals were housed with a 12 h light/dark cycle and had ad libitum access to standard rodent chow and tap water. Inductively-coupled plasma mass spectrometry analysis of metal levels The cortical region was collected, freeze-dried and then dissolved overnight in 0.6 mL of analytical grade concentrated HNO3. The samples were then heated at 80 1C for 20 minutes and allowed to cool to ambient temperature. To dissolve the lipid components, 0.6 mL of H2O2 was added and once effervescing had ceased, the samples were heated to 70 1C for 15 minutes and then allowed to cool. Triplicate samples were diluted 1/51 in 1% HNO3 (60 mL sample plus 3 mL of 1% HNO3). Tubes used as background blanks and those containing the bovine liver standard metal
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Metallomics
reagent (Merck, VIC, Australia) were treated the same way as the samples. Metal levels were measured by inductively-coupled plasma mass spectrometry (ICP-MS) using an Ultramass 700 (Varian, VIC, Australia) in peak-hopping mode with spacing of 0.100 atomic mass units, I point per peak, 50 scans per replicate and 3 non-consecutive replicates per sample. Plasma flow was 15 L min 1 with an auxiliary flow of 1.5 L min 1. Rf power was 1.2 kW. Each sample was introduced using a glass nebuliser at a flow of 0.88 L min 1. The instrument was calibrated using a 1% HNO3 mixed calibration standard (Merck, VIC, Australia) containing 10, 50 and 100 ppb of all metals measured in 1% NO3. The average values of the triplicate measurements were used for analysis. Metal values are expressed as the mean mg g 1 wet weight of the original pre-frozen brain sample. Statistical analysis Where multiple variables existed (such as the age and genotype), two-way analysis of variance (ANOVA) was performed, with each variable classed as an independent variable. A post-hoc Tukey HSD test was performed with each ANOVA. The statistical procedures were performed using PRISM software. All data are expressed as the mean SEM. p o 0.05 was considered significant.
Results Since the large majority of APP / APLP2 / double knockout mice die shortly after birth19–21 we investigated knockout mice that were also hemizygous knockout for the other paralogues: i.e. APP / APLP2+/ and APP+/ APLP2 / . Therefore, the five lines investigated were wildtype (WT), APP / , APLP2 / , APP+/ APLP2 / and APP / APLP2+/ at 3, 12 and 18+ (ages ranged 18–24 months) months. We chose the cortical region since our previous published work showed that other regions, such as the cerebellum, were un-affected.10 The cortical region would contain, in addition to neurons, non-neuronal cells such as astrocytes and glia. Analysis of metal levels within a genotype The analysis of metal levels, by ICP-MS, within a genotype at the three ages is shown in Fig. 1. Cu displayed a robust, age-dependent increase across all five genotypes, with a significant increase when compared to the previous age group at all three time points. Fe showed similar age-dependent increases that were significant for all genotypes across the three time points, except for APP / APLP2+/ . Fe levels in APP / APLP2+/ mice were not significantly different between the 12 and 18+ month groups. Zn levels were significantly increased with age, however the changes were more modest when compared to the Cu and Fe changes and only occurred in certain genotypes. Zn levels in WT and APLP2 / were significantly increased across all three age groups. In contrast APP / and APP+/ APLP2 / only showed a significant increase at 18+ months, compared to the 3 and 12 month age groups. In APP / APLP2+/ , Zn levels were only significantly increased at 18+ months. Mn levels behaved differently to the other metals and displayed both age dependent decreases and
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Fig. 1 Changes in metal levels within a genotype over time: Cu, Fe, Zn and Mn levels in 3, 12 and 18+ month old mice plotted within a genotype. Changes in metal levels between time points were statistically significant unless denoted with an ‘‘ns’’ (not significant). The ns label covers the two time points being compared, except for the comparison of Mn levels between 3 and 18+ months in WT mice which is denoted by a –ns–. The individual p values are shown in Table 1. Results are expressed as mean SEM. N = 9. 28 mice per group. The individual p values when comparing changes in metal levels between 3 and 12 month, 3 and 18+ month and 12 and 18+ month old mice within a genotype are shown in the ESI,† Table S1.
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Table 1 Percentage change in Cu, Fe, Zn or Mn levels at 18+ months when compared to 3 month old mice. Up arrow indicates (%) increase. Down arrow indicates (%) decrease
Cu (%)
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WT APLP2 / APP / APP+/ APLP2 / APP / APLP2+/
65.1 76.8 78.6 61.1 57.2
m m m m m
Fe (%) 61.7 80.4 103.5 76.2 71.1
m m m m m
Zn (%) 13.0 16.1 13.7 10.8 12.4
m m m m m
Mn (%) 4.0 37.2 15.8 15.1 22.3
k k k k k
increases across different genotypes. All genotypes, except APP / APLP2+/ , had a significant decrease in Mn levels from 3 to 12 months. At 18+ months, Mn levels in APLP2 / , APP / and APP+/ APLP2 / were still significantly reduced compared to 3 months, but were no different to 12 month levels. Mn levels in WT mice increased from 12–18+ months and were no longer different to 3 month levels. APP / APLP2+/ mice differed to the other genotypes and only displayed a significant reduction in Mn levels at 18+ months.
Analysis of metal levels between the genotypes The analysis of metal levels between the genotypes at a specific age is shown in Fig. 2. Copper. Cu levels in APLP2 / were significantly higher than WT only at 12 months. In contrast, Cu levels in APP / were significantly higher than WT at all ages. Cu levels in APP+/ APLP2 / were significantly higher than WT at all ages, indicating that removing one APP allele (on an APLP2 / background) was sufficient to significantly alter Cu levels. APP+/ APLP2 / Cu levels were also significantly higher than APLP2 / at 3 and 12 months. Cu levels in APP / APLP2+/ were significantly higher than WT at all ages. Cu levels in APP / APLP2+/ were significantly higher than APP / at 3 months, but not at 12 or 18+ months. Therefore, removing a single APLP2 allele (on an APP / background) was sufficient to alter Cu levels, but only in younger mice. Iron. Fe levels in APLP2 / were significantly different to WT mice only at 3 months, where they were reduced. In contrast, APP / had elevated Fe levels compared to WT at 12 and 18+ months. At 3 months, Fe levels between APP / and APLP2 / were significantly different. APP+/ APLP2 / had similar Fe levels to WT and APLP2 / , indicating removing one APP allele was insufficient to alter the APLP2 / phenotype, or to create an APP / phenotype at 12 or 18+ months. APP / APLP2+/ had similar Fe levels to WT and APP / at 3 months, indicating removing one APLP2 allele in conjunction with no APP alleles was insufficient to induce a phenotype in younger mice. However, at 12 months the APP / APLP2+/ had significantly higher Fe levels than WT and APP / indicating a role for APLP2 in older mice on an APP / background. But as noted above, this effect did not alter with age, as there was no further change at 18+ months (Fig. 1). Zinc. Zn levels were similar between the genotypes at all three ages, except for APP / mice at 3 months that showed a significant increase compared to WT and APLP2 / .
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Fig. 2 Changes in metal levels between genotypes: Cu, Fe, Zn or Mn levels were compared between the different genotypes at 3, 12 or 18+ months. Only statistically significant changes, when compared to either wt, APLP2 / or APP / are labeled. Symbols correspond to the following comparisons: (a) vs. wt; (b) vs. APLP2 / ; (c) vs. APP / . Results are expressed as mean SEM. N = 9. 28 mice per group.
Manganese. Mn levels in APLP2 / and APP+/ APLP2 / were significantly increased at 3 months compared to WT. APP / showed no differences in Mn levels compared to WT. The APP / APLP2+/ line had a significant increase, compared to WT and APP / , at 12 months, indicating that the removal of one APLP2 allele on an APP / background increased Mn levels.
Discussion This current study has expanded our understanding of the inter-relationship between APP and APLP2 as regulators of metal homeostasis. Altering APP and APLP2 expression modulates Cu, Zn and Fe homeostasis.10–14 We found that Cu, Fe and Zn levels increased with age across all five genotypes studied. This is consistent with WT mice showing age dependent increases
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in metals.11,13,22,23 The changes in Zn levels were more modest when compared to Cu and Fe, suggesting that Zn homeostasis remains relatively stable with aging and only increased by approximately 11–14%. Both APP and APLP2 possess Zn binding sites in the APP ectodomain located juxtaposed to the N-terminal cysteine rich E1 domain24 and the C-terminal E2/D6a/CAPPD region.8 These Zn sites are presumed to have a structural role that modulates APP function, possibly its heparin binding activity as the E1 and E2/D6a/CAPPD regions bind heparin and Zn affects the APP:heparin interaction.2,24–27 This would be consistent with APP and APLP2 interacting with the heparin sulfated proteoglycan glypican-1 and regulating its autodegradation, via a process that requires Zn and Cu.28 Mn homeostasis differs to the other metals studied as it decreased with age across all genotypes, except WT mice, with Mn levels in the knockout mice being reduced by 15–28% over the 3 to 18+ month period. However, in WT mice, while Mn levels decreased in the 3 to 12 month period, by 18+ months they had risen back to 3 month levels. In contrast, the absence of APP or APLP2 expression prevented this rebound in Mn levels from 12 to 18+ months, and 18+ month Mn levels remained significantly lower compared to 3 month levels. The overexpression of either APP or Ab increased Mn levels.11 Our current study shows that endogenous levels of APP or APLP2 are sufficient to affect Mn homeostasis. Our data do not allow us to distinguish which form or region of the APP molecule (i.e. full-length, ectodomain, intracellular domain and/or Ab) is responsible for modulating Mn levels. A Mn binding site on APP has not been described. The regulation of Mn in the brain is not resolved and a number of potential regulators have been proposed with divalent metal transporter (DMT1) as the strongest candidate (reviewed by Farina et al.29). Interestingly, there is a clear interaction between APP and DMT1, since RNAi silencing of DMT1 expression decreased APP RNA and protein expression and attenuated Cu and Fe mediated modulation of APP processing.30 However, the effect of Mn on APP processing and the role of DMT1 was not studied. It would be interesting to investigate this and DMT1 expression levels in the APP / and APLP2 / mice in light of our findings. The ongoing reduction in Mn levels in 18+ month APP and APLP2 knockout lines suggests APP/APLP2 may be affecting DMT1 function. Cu and Fe displayed the largest increases over time, with approximately 60–80% higher levels over the 3 to 18+ month time span. APP / mice show the greatest increase of 103% in Fe levels. While APP / mice are known to have raised Fe levels at 12 months,14 Fe levels in young and old APP / mice have not been reported. We established that APP expression had no effect on Fe homeostasis in 3 month mice, but with aging the APP / mice displayed the largest increase (two-fold) in the 3 and 18+ month period. In contrast, APLP2 / had a decrease in Fe levels at 3 months, which returned to WT levels by 12 months. Interestingly, the removal of an APLP2 allele in 12 month old APP / APLP2+/ mice exacerbated the APP / Fe phenotype. But this effect was no longer present at 18+ months. The effect of APP on Fe homeostasis required both APP alleles to be deleted since the APP+/ APLP2 / mice were no different to APLP2 / .
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Metallomics
These data show APP and APLP2 molecules have different effects on Fe homeostasis and this occurs in an age dependent manner. APP and APLP2’s interaction with Cu is the most thoroughly studied. The large body of cellular, biochemical and structural studies supports APP and APLP2 being intimately linked with Cu. APP modulates Cu-mediated toxicity since APP / neurons are less susceptible to Cu-mediated toxicity.31 Cu can alter APP metabolism towards non-amyloidogenic processing,32 while changes to the E1 Cu-binding site affect APP stability and metabolism.33 We previously demonstrated, using cultured primary cortical neurons and embryonic fibroblasts, that reducing the number of APP and APLP2 alleles correlated with increased accumulation of cellular Cu.12 Our current study shows this was maintained in vivo with Cu levels being increased as the number of APP and APLP2 alleles was reduced. The structure and Cu-binding properties of the E1 Cu-binding site are consistent with APP’s role in modulating Cu homeostasis.6,7,9 As was the case for Fe, APP had a dominant and distinct role in modulating Cu homeostasis, compared to APLP2. We can conclude that the APP and APLP2 paralogues possess common but also distinct roles in modulating metal homeostasis. This is consistent with APP-family knockout studies showing that APP and APLP2 have non-redundant functions within the APP-family in vivo.1,19,21,34,35 From a clinical perspective, these studies indicate we need to consider the possible effects of APP metal regulated pathways when developing therapeutic agents to target APP metabolism. Moreover, since APLP2 is processed by the same proteases that cleave APP this could exacerbate metal perturbations.36
Acknowledgements This work was supported by funds from Australian National Health and Medical Research Council. R.C. is an NHMRC Senior Research Fellow. We thank the Biomedical Sciences animal facility staff for assistance with mouse breeding. We thank Irene Volitakis (Florey Institute of Neuroscience and Mental Health, The University of Melbourne) for performing the ICP-MS.
References 1 U. C. Muller and H. Zheng, Physiological functions of APP family proteins, Cold Spring Harbor Perspect. Med., 2012, 2, a006288. 2 A. I. Bush, G. Multhaup, R. D. Moir, T. G. Williamson, D. H. Small, B. Rumble, P. Pollwein, K. Beyreuther and C. L. Masters, A novel zinc(II) binding site modulates the function of the beta A4 amyloid protein precursor of Alzheimer’s disease, J. Biol. Chem., 1993, 268, 16109–16112. 3 L. Hesse, D. Beher, C. L. Masters and G. Multhaup, The beta A4 amyloid precursor protein binding to copper, FEBS Lett., 1994, 349, 109–116. 4 G. Multhaup, A. Schlicksupp, L. Hesse, D. Beher, T. Ruppert, C. L. Masters and K. Beyreuther, The amyloid precursor protein of Alzheimer’s disease in the reduction of copper(II) to copper(I), Science, 1996, 271, 1406–1409.
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5 A. I. Bush, W. H. Pettingell, G. Multhaup, M. d. Paradis, J. P. Vonsattel, J. F. Gusella, K. Beyreuther, C. L. Masters and R. E. Tanzi, Rapid induction of Alzheimer A beta amyloid formation by zinc, Science, 1994, 265, 1464–1467. 6 K. J. Barnham, W. J. McKinstry, G. Multhaup, D. Galatis, C. J. Morton, C. C. Curtain, N. A. Williamson, A. R. White, M. G. Hinds, R. S. Norton, K. Beyreuther, C. L. Masters, M. W. Parker and R. Cappai, Structure of the Alzheimer’s disease amyloid precursor protein copper binding domain. A regulator of neuronal copper homeostasis, J. Biol. Chem., 2003, 278, 17401–17407. 7 G. K. Kong, J. J. Adams, H. H. Harris, J. F. Boas, C. C. Curtain, D. Galatis, C. L. Masters, K. J. Barnham, W. J. McKinstry, R. Cappai and M. W. Parker, Structural studies of the Alzheimer’s amyloid precursor protein copper-binding domain reveal how it binds copper ions, J. Mol. Biol., 2007, 367, 148–161. 8 S. O. Dahms, I. Konnig, D. Roeser, K. H. Guhrs, M. C. Mayer, D. Kaden, G. Multhaup and M. E. Than, Metal binding dictates conformation and function of the amyloid precursor protein (APP) E2 domain, J. Mol. Biol., 2012, 416, 438–452. 9 S. L. Leong, T. R. Young, K. J. Barnham, A. G. Wedd, M. G. Hinds, Z. Xiao and R. Cappai, Quantification of copper binding to amyloid precursor protein domain 2 and its Caenorhabditis elegans ortholog. Implications for biological function, Metallomics, 2013, 6, 105–116. 10 A. R. White, R. Reyes, J. F. Mercer, J. Camakaris, H. Zheng, A. I. Bush, G. Multhaup, K. Beyreuther, C. L. Masters and R. Cappai, Copper levels are increased in the cerebral cortex and liver of APP and APLP2 knockout mice, Brain Res., 1999, 842, 439–444. 11 C. J. Maynard, R. Cappai, I. Volitakis, R. A. Cherny, A. R. White, K. Beyreuther, C. L. Masters, A. I. Bush and Q. X. Li, Overexpression of Alzheimer’s disease amyloid-beta opposes the age-dependent elevations of brain copper and iron, J. Biol. Chem., 2002, 277, 44670–44676. 12 S. A. Bellingham, G. D. Ciccotosto, B. E. Needham, L. R. Fodero, A. R. White, C. L. Masters, R. Cappai and J. Camakaris, Gene knockout of amyloid precursor protein and amyloid precursor-like protein-2 increases cellular copper levels in primary mouse cortical neurons and embryonic fibroblasts, J. Neurochem., 2004, 91, 423–428. 13 C. J. Maynard, R. Cappai, I. Volitakis, R. A. Cherny, C. L. Masters, Q. X. Li and A. I. Bush, Gender and genetic background effects on brain metal levels in APP transgenic and normal mice: implications for Alzheimer beta-amyloid pathology, J. Inorg. Biochem., 2006, 100, 952–962. 14 J. A. Duce, A. Tsatsanis, M. A. Cater, S. A. James, E. Robb, K. Wikhe, S. L. Leong, K. Perez, T. Johanssen, M. A. Greenough, H. H. Cho, D. Galatis, R. D. Moir, C. L. Masters, C. McLean, R. E. Tanzi, R. Cappai, K. J. Barnham, G. D. Ciccotosto, J. T. Rogers and A. I. Bush, Iron-export ferroxidase activity of beta-amyloid precursor protein is inhibited by zinc in Alzheimer’s disease, Cell, 2010, 142, 857–867. 15 A. L. Phinney, B. Drisaldi, S. D. Schmidt, S. Lugowski, V. Coronado, Y. Liang, P. Horne, J. Yang, J. Sekoulidis,
602 | Metallomics, 2014, 6, 598--603
Paper
16
17
18
19
20
21
22
23
24
25
J. Coomaraswamy, M. A. Chishti, D. W. Cox, P. M. Mathews, R. A. Nixon, G. A. Carlson, P. St George-Hyslop and D. Westaway, In vivo reduction of amyloid-beta by a mutant copper transporter, Proc. Natl. Acad. Sci. U. S. A., 2003, 100, 14193–14198. T. A. Bayer, S. Schafer, A. Simons, A. Kemmling, T. Kamer, R. Tepest, A. Eckert, K. Schussel, O. Eikenberg, C. SturchlerPierrat, D. Abramowski, M. Staufenbiel and G. Multhaup, Dietary Cu stabilizes brain superoxide dismutase 1 activity and reduces amyloid Abeta production in APP23 transgenic mice, Proc. Natl. Acad. Sci. U. S. A., 2003, 100, 14187–14192. H. Zheng, M. Jiang, M. E. Trumbauer, D. J. Sirinathsinghji, R. Hopkins, D. W. Smith, R. P. Heavens, G. R. Dawson, S. Boyce, M. W. Conner, K. A. Stevens, H. H. Slunt, S. S. Sisoda, H. Y. Chen and L. H. Van der Ploeg, beta-Amyloid precursor protein-deficient mice show reactive gliosis and decreased locomotor activity, Cell, 1995, 81, 525–531. C. S. von Koch, H. Zheng, H. Chen, M. Trumbauer, G. Thinakaran, L. H. van der Ploeg, D. L. Price and S. S. Sisodia, Generation of APLP2 KO mice and early postnatal lethality in APLP2/APP double KO mice, Neurobiol. Aging, 1997, 18, 661–669. B. E. Needham, M. E. Wlodek, G. D. Ciccotosto, B. C. Fam, C. L. Masters, J. Proietto, S. Andrikopoulos and R. Cappai, Identification of the Alzheimer’s disease amyloid precursor protein (APP) and its homologue APLP2 as essential modulators of glucose and insulin homeostasis and growth, J. Pathol., 2008, 215, 155–163. C. S. von Koch, D. K. Lahiri, A. L. Mammen, N. G. Copeland, D. J. Gilbert, N. A. Jenkins and S. Sisodia, The mouse APLP2 gene. Chromosomal localization and promoter characterization, J. Biol. Chem., 1995, 270, 25475–25480. S. Heber, J. Herms, V. Gajic, J. Hainfellner, A. Aguzzi, T. Rulicke, H. von Kretzschmar, C. von Koch, S. Sisodia, P. Tremml, H. P. Lipp, D. P. Wolfer and U. Muller, Mice with combined gene knock-outs reveal essential and partially redundant functions of amyloid precursor protein family members, J. Neurosci., 2000, 20, 7951–7963. L. M. Wang, J. S. Becker, Q. Wu, M. F. Oliveira, F. A. Bozza, A. L. Schwager, J. M. Hoffman and K. A. Morton, Bioimaging of copper alterations in the aging mouse brain by autoradiography, laser ablation inductively coupled plasma mass spectrometry and immunohistochemistry, Metallomics, 2010, 2, 348–353. H. Wang, M. Wang, B. Wang, M. Li, H. Chen, X. Yu, Y. Zhao, W. Feng and Z. Chai, The distribution profile and oxidation states of biometals in APP transgenic mouse brain: dyshomeostasis with age and as a function of the development of Alzheimer’s disease, Metallomics, 2012, 4, 289–296. A. I. Bush, W. H. Pettingell, Jr., M. de Paradis, R. E. Tanzi and W. Wasco, The amyloid beta-protein precursor and its mammalian homologues. Evidence for a zinc-modulated heparin-binding superfamily, J. Biol. Chem., 1994, 269, 26618–26621. G. Multhaup, Identification and regulation of the high affinity binding site of the Alzheimer’s disease amyloid
This journal is © The Royal Society of Chemistry 2014
View Article Online
Paper
26
Published on 08 January 2014. Downloaded by Northeastern University on 31/10/2014 07:13:44.
27
28
29
30
31
protein precursor (APP) to glycosaminoglycans, Biochimie, 1994, 76, 304–311. G. Multhaup, A. I. Bush, P. Pollwein and C. L. Masters, Interaction between the zinc(II) and the heparin binding site of the Alzheimer’s disease beta A4 amyloid precursor protein (APP), FEBS Lett., 1994, 355, 151–154. G. Multhaup, H. Mechler and C. L. Masters, Characterization of the high affinity heparin binding site of the Alzheimer’s disease beta A4 amyloid precursor protein (APP) and its enhancement by zinc(II), J. Mol. Recognit., 1995, 8, 247–257. R. Cappai, F. Cheng, G. D. Ciccotosto, B. E. Needham, C. L. Masters, G. Multhaup, L. A. Fransson and K. Mani, The amyloid precursor protein (APP) of Alzheimer disease and its paralog, APLP2, modulate the Cu/Zn-Nitric Oxidecatalyzed degradation of glypican-1 heparan sulfate in vivo, J. Biol. Chem., 2005, 280, 13913–13920. M. Farina, D. S. Avila, J. B. da Rocha and M. Aschner, Metals, oxidative stress and neurodegeneration: a focus on iron, manganese and mercury, Neurochem. Int., 2013, 62, 575–594. W. Zheng, N. Xin, Z. H. Chi, B. L. Zhao, J. Zhang, J. Y. Li and Z. Y. Wang, Divalent metal transporter 1 is involved in amyloid precursor protein processing and Abeta generation, FASEB J., 2009, 23, 4207–4217. A. R. White, G. Multhaup, F. Maher, S. Bellingham, J. Camakaris, H. Zheng, A. I. Bush, K. Beyreuther,
This journal is © The Royal Society of Chemistry 2014
Metallomics
32
33
34
35
36
C. L. Masters and R. Cappai, The Alzheimer’s disease amyloid precursor protein modulates copper-induced toxicity and oxidative stress in primary neuronal cultures, J. Neurosci., 1999, 19, 9170–9179. T. Borchardt, J. Camakaris, R. Cappai, C. L. Masters, K. Beyreuther and G. Multhaup, Copper inhibits betaamyloid production and stimulates the non-amyloidogenic pathway of amyloid-precursor-protein secretion, Biochem. J., 1999, 344(Pt 2), 461–467. L. Spoerri, L. J. Vella, C. L. Pham, K. J. Barnham and R. Cappai, The amyloid precursor protein copper binding domain histidine residues 149 and 151 mediate APP stability and metabolism, J. Biol. Chem., 2012, 287, 26840–26853. Q. Guo, Z. Wang, H. Li, M. Wiese and H. Zheng, APP physiological and pathophysiological functions: insights from animal models, Cell Res., 2012, 22, 78–89. B. Midthune, S. H. Tyan, J. J. Walsh, F. Sarsoza, S. Eggert, P. R. Hof, D. L. Dickstein and E. H. Koo, Deletion of the amyloid precursor-like protein 2 (APLP2) does not affect hippocampal neuron morphology or function, Mol. Cell. Neurosci., 2012, 49, 448–455. S. Hogl, P. H. Kuhn, A. Colombo and S. F. Lichtenthaler, Determination of the proteolytic cleavage sites of the amyloid precursor-like protein 2 by the proteases ADAM10, BACE1 and gamma-secretase, PLoS One, 2011, 6, e21337.
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Combined deletions of amyloid precursor protein and amyloid precursor-like protein 2 reveal different effects on mouse brain metal homeostasis† B. Elise Needham,a Giuseppe D. Ciccotostoab and Roberto Cappai*ab Alterations to the expression of the Amyloid Precursor Protein (APP) and its paralogue Amyloid Precursor-Like Protein 2 (APLP2) affect metal homeostasis in vitro and in vivo. Analysis of the in vivo effects of the APP and APLP2 knockouts on metal homeostasis has been restricted to APP and APLP2 single knockout mice, and up to12 month old animals. To define the redundancy and inter-relationship
Received 3rd December 2013, Accepted 7th January 2014
between the APP and APLP2 genes as regulators of metal homeostasis, and how this is influenced by
DOI: 10.1039/c3mt00358b
as well as homozygous:hemizygous knockout mice at 3, 12 and 18 plus months of age. These studies identified age and genotype dependent changes in metal levels, and established differences in the
www.rsc.org/metallomics
relative roles played by APP and APLP2 in modulating metal homeostasis.
aging, we investigated copper, iron, zinc and manganese levels in APP and APLP2 single knockout mice
Introduction The Amyloid Precursor Protein (APP) is a type 1 transmembrane glycoprotein that together with its paralogues, Amyloid-PrecursorLike Protein 1 and 2 (APLP1 and APLP2), constitute the APP-family.1 Amongst the numerous functions attributed to APP, its role as a metalloprotein is supported by large body biochemical, structural and cellular studies. APP can bind metals via at least three different sites in the APP ectodomain: the cysteine-rich N-terminal domain, the heparin binding C-terminal domain (E2/D6a/CAPPD) and the Ab peptide.2–9 APP and APLP2 expression modulates copper, zinc and iron homeostasis.10–14 In vivo, adult APP and APLP2 knockout mice have significantly elevated copper levels in the cerebral cortex and liver.10 Older, 12 month, APP knockout mice have elevated iron levels in the brain, liver and kidneys.14 Conversely, APP overexpressing transgenic mice had significantly reduced copper levels in the brain,11,15,16 and these were altered in a gender and genetic background dependent manner.13 Analysis of the in vivo effects of the APP family knockouts on metal homeostasis has been restricted to single knockout APP and APLP2 mice and up to 12 month old animals.10 We sought to define the redundancy in function between the APP and
a
Department of Pathology, The University of Melbourne, VIC 3010, Australia. E-mail: [email protected] b Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, VIC 3010, Australia † Electronic supplementary information (ESI) available. See DOI: 10.1039/ c3mt00358b
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APLP2 genes as regulators of metal homeostasis and the effects of age. Therefore, we investigated APP and APLP2 knockout mice lacking various combinations of both genes at 3, 12 and 18 plus (18+) months.
Methods APP and APLP2 knockout mice Generation of APP / ,17 APLP2 / ,18 APP / APLP2+/ (ref. 19) and APP+/ APLP2 / (ref. 19) mice has been described. The wildtype mice (APP+/+APLP2+/+), APP / , APLP2 / , APP / APLP2+/ and APP+/ APLP2 / knockout mice were of the same background strain (C57BL6J 129/Sv). Genotypes were determined by PCR using specific primer sets.18 Animals were housed with a 12 h light/dark cycle and had ad libitum access to standard rodent chow and tap water. Inductively-coupled plasma mass spectrometry analysis of metal levels The cortical region was collected, freeze-dried and then dissolved overnight in 0.6 mL of analytical grade concentrated HNO3. The samples were then heated at 80 1C for 20 minutes and allowed to cool to ambient temperature. To dissolve the lipid components, 0.6 mL of H2O2 was added and once effervescing had ceased, the samples were heated to 70 1C for 15 minutes and then allowed to cool. Triplicate samples were diluted 1/51 in 1% HNO3 (60 mL sample plus 3 mL of 1% HNO3). Tubes used as background blanks and those containing the bovine liver standard metal
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Metallomics
reagent (Merck, VIC, Australia) were treated the same way as the samples. Metal levels were measured by inductively-coupled plasma mass spectrometry (ICP-MS) using an Ultramass 700 (Varian, VIC, Australia) in peak-hopping mode with spacing of 0.100 atomic mass units, I point per peak, 50 scans per replicate and 3 non-consecutive replicates per sample. Plasma flow was 15 L min 1 with an auxiliary flow of 1.5 L min 1. Rf power was 1.2 kW. Each sample was introduced using a glass nebuliser at a flow of 0.88 L min 1. The instrument was calibrated using a 1% HNO3 mixed calibration standard (Merck, VIC, Australia) containing 10, 50 and 100 ppb of all metals measured in 1% NO3. The average values of the triplicate measurements were used for analysis. Metal values are expressed as the mean mg g 1 wet weight of the original pre-frozen brain sample. Statistical analysis Where multiple variables existed (such as the age and genotype), two-way analysis of variance (ANOVA) was performed, with each variable classed as an independent variable. A post-hoc Tukey HSD test was performed with each ANOVA. The statistical procedures were performed using PRISM software. All data are expressed as the mean SEM. p o 0.05 was considered significant.
Results Since the large majority of APP / APLP2 / double knockout mice die shortly after birth19–21 we investigated knockout mice that were also hemizygous knockout for the other paralogues: i.e. APP / APLP2+/ and APP+/ APLP2 / . Therefore, the five lines investigated were wildtype (WT), APP / , APLP2 / , APP+/ APLP2 / and APP / APLP2+/ at 3, 12 and 18+ (ages ranged 18–24 months) months. We chose the cortical region since our previous published work showed that other regions, such as the cerebellum, were un-affected.10 The cortical region would contain, in addition to neurons, non-neuronal cells such as astrocytes and glia. Analysis of metal levels within a genotype The analysis of metal levels, by ICP-MS, within a genotype at the three ages is shown in Fig. 1. Cu displayed a robust, age-dependent increase across all five genotypes, with a significant increase when compared to the previous age group at all three time points. Fe showed similar age-dependent increases that were significant for all genotypes across the three time points, except for APP / APLP2+/ . Fe levels in APP / APLP2+/ mice were not significantly different between the 12 and 18+ month groups. Zn levels were significantly increased with age, however the changes were more modest when compared to the Cu and Fe changes and only occurred in certain genotypes. Zn levels in WT and APLP2 / were significantly increased across all three age groups. In contrast APP / and APP+/ APLP2 / only showed a significant increase at 18+ months, compared to the 3 and 12 month age groups. In APP / APLP2+/ , Zn levels were only significantly increased at 18+ months. Mn levels behaved differently to the other metals and displayed both age dependent decreases and
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Fig. 1 Changes in metal levels within a genotype over time: Cu, Fe, Zn and Mn levels in 3, 12 and 18+ month old mice plotted within a genotype. Changes in metal levels between time points were statistically significant unless denoted with an ‘‘ns’’ (not significant). The ns label covers the two time points being compared, except for the comparison of Mn levels between 3 and 18+ months in WT mice which is denoted by a –ns–. The individual p values are shown in Table 1. Results are expressed as mean SEM. N = 9. 28 mice per group. The individual p values when comparing changes in metal levels between 3 and 12 month, 3 and 18+ month and 12 and 18+ month old mice within a genotype are shown in the ESI,† Table S1.
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Table 1 Percentage change in Cu, Fe, Zn or Mn levels at 18+ months when compared to 3 month old mice. Up arrow indicates (%) increase. Down arrow indicates (%) decrease
Cu (%)
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WT APLP2 / APP / APP+/ APLP2 / APP / APLP2+/
65.1 76.8 78.6 61.1 57.2
m m m m m
Fe (%) 61.7 80.4 103.5 76.2 71.1
m m m m m
Zn (%) 13.0 16.1 13.7 10.8 12.4
m m m m m
Mn (%) 4.0 37.2 15.8 15.1 22.3
k k k k k
increases across different genotypes. All genotypes, except APP / APLP2+/ , had a significant decrease in Mn levels from 3 to 12 months. At 18+ months, Mn levels in APLP2 / , APP / and APP+/ APLP2 / were still significantly reduced compared to 3 months, but were no different to 12 month levels. Mn levels in WT mice increased from 12–18+ months and were no longer different to 3 month levels. APP / APLP2+/ mice differed to the other genotypes and only displayed a significant reduction in Mn levels at 18+ months.
Analysis of metal levels between the genotypes The analysis of metal levels between the genotypes at a specific age is shown in Fig. 2. Copper. Cu levels in APLP2 / were significantly higher than WT only at 12 months. In contrast, Cu levels in APP / were significantly higher than WT at all ages. Cu levels in APP+/ APLP2 / were significantly higher than WT at all ages, indicating that removing one APP allele (on an APLP2 / background) was sufficient to significantly alter Cu levels. APP+/ APLP2 / Cu levels were also significantly higher than APLP2 / at 3 and 12 months. Cu levels in APP / APLP2+/ were significantly higher than WT at all ages. Cu levels in APP / APLP2+/ were significantly higher than APP / at 3 months, but not at 12 or 18+ months. Therefore, removing a single APLP2 allele (on an APP / background) was sufficient to alter Cu levels, but only in younger mice. Iron. Fe levels in APLP2 / were significantly different to WT mice only at 3 months, where they were reduced. In contrast, APP / had elevated Fe levels compared to WT at 12 and 18+ months. At 3 months, Fe levels between APP / and APLP2 / were significantly different. APP+/ APLP2 / had similar Fe levels to WT and APLP2 / , indicating removing one APP allele was insufficient to alter the APLP2 / phenotype, or to create an APP / phenotype at 12 or 18+ months. APP / APLP2+/ had similar Fe levels to WT and APP / at 3 months, indicating removing one APLP2 allele in conjunction with no APP alleles was insufficient to induce a phenotype in younger mice. However, at 12 months the APP / APLP2+/ had significantly higher Fe levels than WT and APP / indicating a role for APLP2 in older mice on an APP / background. But as noted above, this effect did not alter with age, as there was no further change at 18+ months (Fig. 1). Zinc. Zn levels were similar between the genotypes at all three ages, except for APP / mice at 3 months that showed a significant increase compared to WT and APLP2 / .
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Fig. 2 Changes in metal levels between genotypes: Cu, Fe, Zn or Mn levels were compared between the different genotypes at 3, 12 or 18+ months. Only statistically significant changes, when compared to either wt, APLP2 / or APP / are labeled. Symbols correspond to the following comparisons: (a) vs. wt; (b) vs. APLP2 / ; (c) vs. APP / . Results are expressed as mean SEM. N = 9. 28 mice per group.
Manganese. Mn levels in APLP2 / and APP+/ APLP2 / were significantly increased at 3 months compared to WT. APP / showed no differences in Mn levels compared to WT. The APP / APLP2+/ line had a significant increase, compared to WT and APP / , at 12 months, indicating that the removal of one APLP2 allele on an APP / background increased Mn levels.
Discussion This current study has expanded our understanding of the inter-relationship between APP and APLP2 as regulators of metal homeostasis. Altering APP and APLP2 expression modulates Cu, Zn and Fe homeostasis.10–14 We found that Cu, Fe and Zn levels increased with age across all five genotypes studied. This is consistent with WT mice showing age dependent increases
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in metals.11,13,22,23 The changes in Zn levels were more modest when compared to Cu and Fe, suggesting that Zn homeostasis remains relatively stable with aging and only increased by approximately 11–14%. Both APP and APLP2 possess Zn binding sites in the APP ectodomain located juxtaposed to the N-terminal cysteine rich E1 domain24 and the C-terminal E2/D6a/CAPPD region.8 These Zn sites are presumed to have a structural role that modulates APP function, possibly its heparin binding activity as the E1 and E2/D6a/CAPPD regions bind heparin and Zn affects the APP:heparin interaction.2,24–27 This would be consistent with APP and APLP2 interacting with the heparin sulfated proteoglycan glypican-1 and regulating its autodegradation, via a process that requires Zn and Cu.28 Mn homeostasis differs to the other metals studied as it decreased with age across all genotypes, except WT mice, with Mn levels in the knockout mice being reduced by 15–28% over the 3 to 18+ month period. However, in WT mice, while Mn levels decreased in the 3 to 12 month period, by 18+ months they had risen back to 3 month levels. In contrast, the absence of APP or APLP2 expression prevented this rebound in Mn levels from 12 to 18+ months, and 18+ month Mn levels remained significantly lower compared to 3 month levels. The overexpression of either APP or Ab increased Mn levels.11 Our current study shows that endogenous levels of APP or APLP2 are sufficient to affect Mn homeostasis. Our data do not allow us to distinguish which form or region of the APP molecule (i.e. full-length, ectodomain, intracellular domain and/or Ab) is responsible for modulating Mn levels. A Mn binding site on APP has not been described. The regulation of Mn in the brain is not resolved and a number of potential regulators have been proposed with divalent metal transporter (DMT1) as the strongest candidate (reviewed by Farina et al.29). Interestingly, there is a clear interaction between APP and DMT1, since RNAi silencing of DMT1 expression decreased APP RNA and protein expression and attenuated Cu and Fe mediated modulation of APP processing.30 However, the effect of Mn on APP processing and the role of DMT1 was not studied. It would be interesting to investigate this and DMT1 expression levels in the APP / and APLP2 / mice in light of our findings. The ongoing reduction in Mn levels in 18+ month APP and APLP2 knockout lines suggests APP/APLP2 may be affecting DMT1 function. Cu and Fe displayed the largest increases over time, with approximately 60–80% higher levels over the 3 to 18+ month time span. APP / mice show the greatest increase of 103% in Fe levels. While APP / mice are known to have raised Fe levels at 12 months,14 Fe levels in young and old APP / mice have not been reported. We established that APP expression had no effect on Fe homeostasis in 3 month mice, but with aging the APP / mice displayed the largest increase (two-fold) in the 3 and 18+ month period. In contrast, APLP2 / had a decrease in Fe levels at 3 months, which returned to WT levels by 12 months. Interestingly, the removal of an APLP2 allele in 12 month old APP / APLP2+/ mice exacerbated the APP / Fe phenotype. But this effect was no longer present at 18+ months. The effect of APP on Fe homeostasis required both APP alleles to be deleted since the APP+/ APLP2 / mice were no different to APLP2 / .
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These data show APP and APLP2 molecules have different effects on Fe homeostasis and this occurs in an age dependent manner. APP and APLP2’s interaction with Cu is the most thoroughly studied. The large body of cellular, biochemical and structural studies supports APP and APLP2 being intimately linked with Cu. APP modulates Cu-mediated toxicity since APP / neurons are less susceptible to Cu-mediated toxicity.31 Cu can alter APP metabolism towards non-amyloidogenic processing,32 while changes to the E1 Cu-binding site affect APP stability and metabolism.33 We previously demonstrated, using cultured primary cortical neurons and embryonic fibroblasts, that reducing the number of APP and APLP2 alleles correlated with increased accumulation of cellular Cu.12 Our current study shows this was maintained in vivo with Cu levels being increased as the number of APP and APLP2 alleles was reduced. The structure and Cu-binding properties of the E1 Cu-binding site are consistent with APP’s role in modulating Cu homeostasis.6,7,9 As was the case for Fe, APP had a dominant and distinct role in modulating Cu homeostasis, compared to APLP2. We can conclude that the APP and APLP2 paralogues possess common but also distinct roles in modulating metal homeostasis. This is consistent with APP-family knockout studies showing that APP and APLP2 have non-redundant functions within the APP-family in vivo.1,19,21,34,35 From a clinical perspective, these studies indicate we need to consider the possible effects of APP metal regulated pathways when developing therapeutic agents to target APP metabolism. Moreover, since APLP2 is processed by the same proteases that cleave APP this could exacerbate metal perturbations.36
Acknowledgements This work was supported by funds from Australian National Health and Medical Research Council. R.C. is an NHMRC Senior Research Fellow. We thank the Biomedical Sciences animal facility staff for assistance with mouse breeding. We thank Irene Volitakis (Florey Institute of Neuroscience and Mental Health, The University of Melbourne) for performing the ICP-MS.
References 1 U. C. Muller and H. Zheng, Physiological functions of APP family proteins, Cold Spring Harbor Perspect. Med., 2012, 2, a006288. 2 A. I. Bush, G. Multhaup, R. D. Moir, T. G. Williamson, D. H. Small, B. Rumble, P. Pollwein, K. Beyreuther and C. L. Masters, A novel zinc(II) binding site modulates the function of the beta A4 amyloid protein precursor of Alzheimer’s disease, J. Biol. Chem., 1993, 268, 16109–16112. 3 L. Hesse, D. Beher, C. L. Masters and G. Multhaup, The beta A4 amyloid precursor protein binding to copper, FEBS Lett., 1994, 349, 109–116. 4 G. Multhaup, A. Schlicksupp, L. Hesse, D. Beher, T. Ruppert, C. L. Masters and K. Beyreuther, The amyloid precursor protein of Alzheimer’s disease in the reduction of copper(II) to copper(I), Science, 1996, 271, 1406–1409.
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Published on 08 January 2014. Downloaded by Northeastern University on 31/10/2014 07:13:44.
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5 A. I. Bush, W. H. Pettingell, G. Multhaup, M. d. Paradis, J. P. Vonsattel, J. F. Gusella, K. Beyreuther, C. L. Masters and R. E. Tanzi, Rapid induction of Alzheimer A beta amyloid formation by zinc, Science, 1994, 265, 1464–1467. 6 K. J. Barnham, W. J. McKinstry, G. Multhaup, D. Galatis, C. J. Morton, C. C. Curtain, N. A. Williamson, A. R. White, M. G. Hinds, R. S. Norton, K. Beyreuther, C. L. Masters, M. W. Parker and R. Cappai, Structure of the Alzheimer’s disease amyloid precursor protein copper binding domain. A regulator of neuronal copper homeostasis, J. Biol. Chem., 2003, 278, 17401–17407. 7 G. K. Kong, J. J. Adams, H. H. Harris, J. F. Boas, C. C. Curtain, D. Galatis, C. L. Masters, K. J. Barnham, W. J. McKinstry, R. Cappai and M. W. Parker, Structural studies of the Alzheimer’s amyloid precursor protein copper-binding domain reveal how it binds copper ions, J. Mol. Biol., 2007, 367, 148–161. 8 S. O. Dahms, I. Konnig, D. Roeser, K. H. Guhrs, M. C. Mayer, D. Kaden, G. Multhaup and M. E. Than, Metal binding dictates conformation and function of the amyloid precursor protein (APP) E2 domain, J. Mol. Biol., 2012, 416, 438–452. 9 S. L. Leong, T. R. Young, K. J. Barnham, A. G. Wedd, M. G. Hinds, Z. Xiao and R. Cappai, Quantification of copper binding to amyloid precursor protein domain 2 and its Caenorhabditis elegans ortholog. Implications for biological function, Metallomics, 2013, 6, 105–116. 10 A. R. White, R. Reyes, J. F. Mercer, J. Camakaris, H. Zheng, A. I. Bush, G. Multhaup, K. Beyreuther, C. L. Masters and R. Cappai, Copper levels are increased in the cerebral cortex and liver of APP and APLP2 knockout mice, Brain Res., 1999, 842, 439–444. 11 C. J. Maynard, R. Cappai, I. Volitakis, R. A. Cherny, A. R. White, K. Beyreuther, C. L. Masters, A. I. Bush and Q. X. Li, Overexpression of Alzheimer’s disease amyloid-beta opposes the age-dependent elevations of brain copper and iron, J. Biol. Chem., 2002, 277, 44670–44676. 12 S. A. Bellingham, G. D. Ciccotosto, B. E. Needham, L. R. Fodero, A. R. White, C. L. Masters, R. Cappai and J. Camakaris, Gene knockout of amyloid precursor protein and amyloid precursor-like protein-2 increases cellular copper levels in primary mouse cortical neurons and embryonic fibroblasts, J. Neurochem., 2004, 91, 423–428. 13 C. J. Maynard, R. Cappai, I. Volitakis, R. A. Cherny, C. L. Masters, Q. X. Li and A. I. Bush, Gender and genetic background effects on brain metal levels in APP transgenic and normal mice: implications for Alzheimer beta-amyloid pathology, J. Inorg. Biochem., 2006, 100, 952–962. 14 J. A. Duce, A. Tsatsanis, M. A. Cater, S. A. James, E. Robb, K. Wikhe, S. L. Leong, K. Perez, T. Johanssen, M. A. Greenough, H. H. Cho, D. Galatis, R. D. Moir, C. L. Masters, C. McLean, R. E. Tanzi, R. Cappai, K. J. Barnham, G. D. Ciccotosto, J. T. Rogers and A. I. Bush, Iron-export ferroxidase activity of beta-amyloid precursor protein is inhibited by zinc in Alzheimer’s disease, Cell, 2010, 142, 857–867. 15 A. L. Phinney, B. Drisaldi, S. D. Schmidt, S. Lugowski, V. Coronado, Y. Liang, P. Horne, J. Yang, J. Sekoulidis,
602 | Metallomics, 2014, 6, 598--603
Paper
16
17
18
19
20
21
22
23
24
25
J. Coomaraswamy, M. A. Chishti, D. W. Cox, P. M. Mathews, R. A. Nixon, G. A. Carlson, P. St George-Hyslop and D. Westaway, In vivo reduction of amyloid-beta by a mutant copper transporter, Proc. Natl. Acad. Sci. U. S. A., 2003, 100, 14193–14198. T. A. Bayer, S. Schafer, A. Simons, A. Kemmling, T. Kamer, R. Tepest, A. Eckert, K. Schussel, O. Eikenberg, C. SturchlerPierrat, D. Abramowski, M. Staufenbiel and G. Multhaup, Dietary Cu stabilizes brain superoxide dismutase 1 activity and reduces amyloid Abeta production in APP23 transgenic mice, Proc. Natl. Acad. Sci. U. S. A., 2003, 100, 14187–14192. H. Zheng, M. Jiang, M. E. Trumbauer, D. J. Sirinathsinghji, R. Hopkins, D. W. Smith, R. P. Heavens, G. R. Dawson, S. Boyce, M. W. Conner, K. A. Stevens, H. H. Slunt, S. S. Sisoda, H. Y. Chen and L. H. Van der Ploeg, beta-Amyloid precursor protein-deficient mice show reactive gliosis and decreased locomotor activity, Cell, 1995, 81, 525–531. C. S. von Koch, H. Zheng, H. Chen, M. Trumbauer, G. Thinakaran, L. H. van der Ploeg, D. L. Price and S. S. Sisodia, Generation of APLP2 KO mice and early postnatal lethality in APLP2/APP double KO mice, Neurobiol. Aging, 1997, 18, 661–669. B. E. Needham, M. E. Wlodek, G. D. Ciccotosto, B. C. Fam, C. L. Masters, J. Proietto, S. Andrikopoulos and R. Cappai, Identification of the Alzheimer’s disease amyloid precursor protein (APP) and its homologue APLP2 as essential modulators of glucose and insulin homeostasis and growth, J. Pathol., 2008, 215, 155–163. C. S. von Koch, D. K. Lahiri, A. L. Mammen, N. G. Copeland, D. J. Gilbert, N. A. Jenkins and S. Sisodia, The mouse APLP2 gene. Chromosomal localization and promoter characterization, J. Biol. Chem., 1995, 270, 25475–25480. S. Heber, J. Herms, V. Gajic, J. Hainfellner, A. Aguzzi, T. Rulicke, H. von Kretzschmar, C. von Koch, S. Sisodia, P. Tremml, H. P. Lipp, D. P. Wolfer and U. Muller, Mice with combined gene knock-outs reveal essential and partially redundant functions of amyloid precursor protein family members, J. Neurosci., 2000, 20, 7951–7963. L. M. Wang, J. S. Becker, Q. Wu, M. F. Oliveira, F. A. Bozza, A. L. Schwager, J. M. Hoffman and K. A. Morton, Bioimaging of copper alterations in the aging mouse brain by autoradiography, laser ablation inductively coupled plasma mass spectrometry and immunohistochemistry, Metallomics, 2010, 2, 348–353. H. Wang, M. Wang, B. Wang, M. Li, H. Chen, X. Yu, Y. Zhao, W. Feng and Z. Chai, The distribution profile and oxidation states of biometals in APP transgenic mouse brain: dyshomeostasis with age and as a function of the development of Alzheimer’s disease, Metallomics, 2012, 4, 289–296. A. I. Bush, W. H. Pettingell, Jr., M. de Paradis, R. E. Tanzi and W. Wasco, The amyloid beta-protein precursor and its mammalian homologues. Evidence for a zinc-modulated heparin-binding superfamily, J. Biol. Chem., 1994, 269, 26618–26621. G. Multhaup, Identification and regulation of the high affinity binding site of the Alzheimer’s disease amyloid
This journal is © The Royal Society of Chemistry 2014
View Article Online
Paper
26
Published on 08 January 2014. Downloaded by Northeastern University on 31/10/2014 07:13:44.
27
28
29
30
31
protein precursor (APP) to glycosaminoglycans, Biochimie, 1994, 76, 304–311. G. Multhaup, A. I. Bush, P. Pollwein and C. L. Masters, Interaction between the zinc(II) and the heparin binding site of the Alzheimer’s disease beta A4 amyloid precursor protein (APP), FEBS Lett., 1994, 355, 151–154. G. Multhaup, H. Mechler and C. L. Masters, Characterization of the high affinity heparin binding site of the Alzheimer’s disease beta A4 amyloid precursor protein (APP) and its enhancement by zinc(II), J. Mol. Recognit., 1995, 8, 247–257. R. Cappai, F. Cheng, G. D. Ciccotosto, B. E. Needham, C. L. Masters, G. Multhaup, L. A. Fransson and K. Mani, The amyloid precursor protein (APP) of Alzheimer disease and its paralog, APLP2, modulate the Cu/Zn-Nitric Oxidecatalyzed degradation of glypican-1 heparan sulfate in vivo, J. Biol. Chem., 2005, 280, 13913–13920. M. Farina, D. S. Avila, J. B. da Rocha and M. Aschner, Metals, oxidative stress and neurodegeneration: a focus on iron, manganese and mercury, Neurochem. Int., 2013, 62, 575–594. W. Zheng, N. Xin, Z. H. Chi, B. L. Zhao, J. Zhang, J. Y. Li and Z. Y. Wang, Divalent metal transporter 1 is involved in amyloid precursor protein processing and Abeta generation, FASEB J., 2009, 23, 4207–4217. A. R. White, G. Multhaup, F. Maher, S. Bellingham, J. Camakaris, H. Zheng, A. I. Bush, K. Beyreuther,
This journal is © The Royal Society of Chemistry 2014
Metallomics
32
33
34
35
36
C. L. Masters and R. Cappai, The Alzheimer’s disease amyloid precursor protein modulates copper-induced toxicity and oxidative stress in primary neuronal cultures, J. Neurosci., 1999, 19, 9170–9179. T. Borchardt, J. Camakaris, R. Cappai, C. L. Masters, K. Beyreuther and G. Multhaup, Copper inhibits betaamyloid production and stimulates the non-amyloidogenic pathway of amyloid-precursor-protein secretion, Biochem. J., 1999, 344(Pt 2), 461–467. L. Spoerri, L. J. Vella, C. L. Pham, K. J. Barnham and R. Cappai, The amyloid precursor protein copper binding domain histidine residues 149 and 151 mediate APP stability and metabolism, J. Biol. Chem., 2012, 287, 26840–26853. Q. Guo, Z. Wang, H. Li, M. Wiese and H. Zheng, APP physiological and pathophysiological functions: insights from animal models, Cell Res., 2012, 22, 78–89. B. Midthune, S. H. Tyan, J. J. Walsh, F. Sarsoza, S. Eggert, P. R. Hof, D. L. Dickstein and E. H. Koo, Deletion of the amyloid precursor-like protein 2 (APLP2) does not affect hippocampal neuron morphology or function, Mol. Cell. Neurosci., 2012, 49, 448–455. S. Hogl, P. H. Kuhn, A. Colombo and S. F. Lichtenthaler, Determination of the proteolytic cleavage sites of the amyloid precursor-like protein 2 by the proteases ADAM10, BACE1 and gamma-secretase, PLoS One, 2011, 6, e21337.
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