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. Author manuscript; available in PMC: 2018 Oct 30.
Published in final edited form as: J Nucl Med. 2015 Oct 8;57(1):109–114. doi: 10.2967/jnumed.115.162370

PET Imaging of Copper Trafficking in a Mouse Model of Alzheimer’s Disease

Julia Baguña Torres a,#, Erica M Andreozzi a,#, Joel T Dunn a, Muhammad Siddique a, Istvan Szanda a, David R Howlett b, Kavitha Sunassee a, Philip J Blowera a,*
PMCID: PMC6207347  EMSID: EMS80166  PMID: 26449834

Abstract

Alzheimer’s disease (AD) is a fatal neurodegenerative disorder characterised by progressive neuronal loss and cognitive decline. The lack of reliable and objective diagnostic markers for AD hampers early disease detection and treatment. Growing evidence supports the existence of a dysregulation in brain copper trafficking in AD. The aim of this study was to investigate brain copper trafficking in a transgenic mouse model of AD by PET imaging with copper-64 (64Cu), to determine its potential as a diagnostic biomarker of the disorder.

Methods

Brain copper trafficking was evaluated in 6-8-month-old TASTPM transgenic mice and age-matched wild-type controls using the 64Cu bis(thiosemicarbazone) complex 64Cu-GTSM, which crosses the blood-brain barrier and releases 64Cu bioreductively into cells. Animals were intravenously injected with 64Cu-GTSM and imaged at 0-30 min and 24-25 h post-injection. The images were analysed by atlas-based quantification and texture analysis. Regional distribution of 64Cu in the brain 24 h post-injection was also evaluated via ex vivo autoradiography and compared to amyloid-β plaque deposition in TASTPM mice.

Results

PET image analysis demonstrated significantly increased (by a factor of approx. 1.3) brain concentration of 64Cu at 30 min (p<0.01) and 24 h (p<0.05) post-injection of the tracer and faster (by a factor of approx. 5) 64Cu clearance from brain (p<0.01) in TASTPM mice compared to controls. Atlas-based quantification and texture analysis revealed significant differences in regional brain uptake of 64Cu and PET image heterogeneity, respectively, between the two groups of mice. Ex vivo autoradiography showed that regional brain distribution of 64Cu at 24 h post-injection did not correlate with amyloid-β plaque distribution in TASTPM mice.

Conclusion

The trafficking of 64Cu in brain after administration of 64Cu-GTSM is significantly altered by AD-like pathology in the TASTPM mouse model, suggesting that 64Cu-GTSM PET imaging warrants clinical evaluation as a diagnostic tool for AD and possibly other neurodegenerative disorders.

Keywords: Copper trafficking, Alzheimer’s disease, positron emission tomography, copper-64

Introduction

Alzheimer’s disease (AD) is the most common form of dementia in the elderly. Its prevalence in an ageing global population is rapidly increasing, creating a major socio-economic burden. There is no satisfactory method for its early diagnosis and no effective treatment to halt or delay the cognitive decline experienced by patients. Currently, the most promising application of molecular imaging in AD is the visualisation of amyloid-β (Aβ) burden using positron emission tomography (PET) radioligands that bind specifically to Aβ plaques, such as the 11C-Pittsburgh compound B, 18F-Florbetaben, 18F-Florbetapir and 18F-Flutemetamol (1). Nevertheless, clinical data indicate that imageable Aβ plaque load alone is not an adequate predictor of disease progression (2,3); many subjects with high Aβ burden detected by PET did not subsequently develop AD, and there is a need for PET radiotracers for a wider range of molecular features of the disorder (4).

In recent years, there has been growing evidence associating changes in trafficking and distribution of trace metals, including copper, with the onset and progression of AD (5, 6). Whether this association is a cause or consequence of the pathology is unknown and the nature of the changes is poorly understood. Copper has been found to interact with key components of AD pathology (Aβ, amyloid precursor protein [APP], β-secretase 1, tau) and, as a result, is thought to contribute to amyloidosis, synaptic toxicity and oxidative stress (7–11). Aβ plaques have been described as “sinks” of iron, zinc and copper ions (12,13), which have been shown to enhance Aβ aggregation and neurotoxicity (14, 15). It has been suggested that the association of copper with Aβ aggregates and other molecular features of AD might also lead to decreased intracellular copper bioavailability and reduced activity of copper-dependent enzymes in the brain (16,17).

This “metal hypothesis of AD” is the basis of the development of treatments aimed at minimizing copper-induced neurotoxicity and restoring brain copper homeostasis (18). Preclinical and clinical studies demonstrated reduced Aβ burden, increased intracellular copper levels and improved cognitive function following administration of copper(II) chelators (e.g. Clioquinol, PBT2) capable of stripping copper ions from Aβ plaque and redistributing them into cells (19–21). A recent investigation showed that treatment with glyoxalbis(N4-methyl-3-thiosemicarbazonato) copper(II) (Cu-GTSM) - a copper bis(thiosemicarbazone) complex that delivers copper into cells and releases it bioreductively (22) - enhanced cognitive performance in APP/PS1 transgenic AD mice by triggering neuroprotective mechanisms that inhibited GSK3β and decreased phosphorylated tau and Aβ trimer levels (23). Hence, understanding the role of copper homeostasis in AD pathology may be critical to development of both novel diagnostics and therapeutics.

A PET radiotracer able to measure brain copper trafficking in vivo could be a valuable tool to investigate changes in copper metabolism in brain, to determine the potential of copper dyshomeostasis as an early diagnostic marker for AD and as a useful complement to other imaging biomarkers. Positron-emitting copper isotopes (60Cu, 61Cu, 62Cu, 64Cu) can in principle be used to study the trafficking of copper to, from and within brain, not only in animal models but also in humans, by PET imaging over a period of hours and repeatedly during the lifetime of the subject. Intravenous injection of 64Cu in the form of ionic copper (II) salts leads to delivery of radiotracer to the brain via native copper transport processes in quantities that are insufficient for meaningful imaging (24, 25). However, by exploiting the ability of uncharged lipophilic copper(II) complexes, such as Cu-GTSM, to penetrate the blood-brain barrier (BBB) and release their copper payload non-selectively into cells (26), the intracellular copper pool can be supplemented with tracer quantities of 64Cu sufficient to allow imaging of its subsequent trafficking. A preliminary study by Fodero-Tavoletti and co-workers showed that 60 min after 64Cu-labeled Cu-GTSM was administered intravenously, brain uptake of 64Cu as measured by ex vivo gamma-counting was significantly greater in APP/PS1 mice than in their age-matched wild-type counterparts (27). Following up on this work, in the present study we explore further the trafficking of copper in AD by PET imaging with 64Cu-GTSM in the APPswe x PS1.M146V (TASTPM) transgenic mouse model. TASTPM mice rapidly develop some of the key pathological features of AD, such as cerebral Aβ plaque deposition, neuronal loss and neuroinflammation, showing impaired cognitive function at 6 months of age (28, 29). The visualization, mapping and monitoring of 64Cu trafficking in this model of AD will provide valuable information to determine the potential of imaging brain copper trafficking as a diagnostic marker for AD.

Materials and Methods

Full experimental details for the radiosynthesis of 64Cu-GTSM (26, 30, 31), utilized animal models, solvent extraction method for determination of 64Cu-GTSM in brain tissue, nanoPET imaging (32), PET image analysis (33), texture analysis (34, 35), ex vivo biodistribution, brain autoradiography, histology, and statistical analysis are reported in the supplementary information. Animal experiments were performed in accordance with the Animals (Scientific Procedures) Act, 1986 and approved by the local ethics committee.

Results

Solvent extraction

To assess the ability of 64Cu-GTSM to release 64Cu intracellularly within the brain, we determined the amount of intact complex in brain homogenates of mice injected with 64Cu-GTSM using a previously reported octanol-extraction method (36). Almost all 64Cu partitioned in the aqueous/protein-bound in both TASTPM mice and age-matched controls; 3.81 ± 2.98 % of brain 64Cu radioactivity was associated with the octanol fraction at 30 min and 2.82 ± 0.14 % at 24 h after injection. Radio-TLC showed that even the octanol-extractable fraction contained no intact 64Cu-GTSM.

PET imaging

Whole-body copper fluxes in vivo in TASTPM mice (n = 4) were imaged with 64Cu-GTSM from 0-30 min and from 24-25 h after injection and compared to wild-type controls (n = 6). The general biodistribution of the tracer was similar for both animal groups (Fig. 1). At 30 min, 64Cu radioactivity accumulated primarily in the heart, lungs, liver, intestines, kidneys, spinal cord and brain in both TASTPM and wild-type mice. After 24 h, a notable decrease in 64Cu activity was observed in the lungs, kidneys and heart, whereas 64Cu concentration in the brain declined much less (supplemental, Fig. S1).

Fig.1.

Fig.1

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Exemplar sagittal and coronal PET/CT images of wild-type controls and TASTPM mice at 30 min and 24 h post-administration of 64Cu-GTSM. Organs are indicated by arrows: B = brain; Sp = spinal cord; H = heart; Lu = lungs; L = liver; K = kidneys; I = intestines.

64Cu biokinetics within the central nervous system (CNS: brain and spinal cord) were compared in AD mice and healthy controls using PET ROI analysis (see Fig. 2 and Fig. S2). PET quantification revealed that global average brain concentration of 64Cu was significantly higher in TASTPM transgenic mice than in wild-type controls at 30 min (7.83 ± 1.08 %ID/g vs. 5.57 ± 0.70 %ID/g, p<0.01) and 24 h (7.04 ± 0.94 %ID/g vs. 5.46 ± 0.70 %ID/g, p<0.05). After 24 h, a significant decrease in brain 64Cu concentration was found in TASTPM mice (-10.10 ± 2.23 %, p<0.01) but not in age-matched controls (-2.04 ± 3.57 %, p>0.05). TASTPM mice also exhibited significantly higher 64Cu concentration in the spinal cord than controls at both 30 min (5.20 ± 0.67 %ID/g vs. 3.73 ± 0.54 %ID/g, p<0.01) and 24 h (4.65 ± 0.46 %ID/g vs. 3.48 ± 0.46 %ID/g, p<0.05).

Fig.2.

Fig.2

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In vivo brain concentration of 64Cu (%ID/g) and corresponding time-activity curves, in TASTPM mice and wild-type controls measured by PET ROI analysis. Significant differences are indicated by * (p<0.05) and ** (p<0.01). Data are mean (n = 4 for TASTPM mice; n = 6 for controls) ± SD. Similar results were obtained for spinal cord (see supplementary data, Fig. S2).

Atlas-based image analysis of regional brain concentration of 64Cu

As depicted in Fig.3 (see also supplementary information, Fig. S3), 64Cu-GTSM distribution in the brains of both wild-type and TASTPM mice was heterogeneous both at 30 min and 24 h. To quantify heterogeneity in a functional/anatomical context, brain PET images were analyzed using a 3D mouse brain atlas (Fig. 4). TASTPM mice showed higher 64Cu concentration than controls in all defined regions, reaching statistical significance in the medulla (p<0.05), cerebellum (p<0.01), midbrain (p<0.001), hippocampus (p<0.05), thalamus (p<0.01), hypothalamus (p<0.05), pallidum (p<0.05), olfactory bulb (p<0.001) and corpus callosum (p<0.05) at 30 min, and in the medulla (p<0.001), cerebellum (p<0.01), midbrain (p<0.05), hypothalamus (p<0.01) and olfactory bulb (p<0.05) and pons (p<0.05), at 24 h (Supplemental Table S1).

Fig.3.

Fig.3

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Representative PET/CT MIP images illustrating heterogeneous distribution of 64Cu in brains of wild-type and TASTPM mice at 30 min (top) and 24 h (bottom) post-injection of 64Cu-GTSM. Images of all TASTPM (n = 4) and wild-type (n = 6) mice are included in the supplementary data (Fig. S3).

Fig.4.

Fig.4

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Regional brain distribution of 64Cu in TASTPM mice and controls at 30 min (top) and 24 h (bottom). Statistically significant differences are indicated by * (p<0.05), ** (p<0.01) and *** (p<0.001). Data are mean (n = 4 for TASTPM mice; n = 6 for controls) ± SD.

Texture analysis

Statistical data emerging from texture analysis are listed in supplementary data (Table S2). The TASTPM group showed increased standard deviation (p<0.05) and coefficient of variation (p<0.01) compared to controls, demonstrating a higher spread and non-uniformity in voxel intensity values. TASTPM brain images had also higher entropy (p<0.01) than those of wild-type mice, revealing an increased level of disorder in voxel intensity distribution. The energy of TASTPM brain images was lower (p<0.05) compared to that of control datasets, suggesting a broader histogram and spread of voxel intensity values. The TASTPM group also showed a lower kurtosis (p<0.01) than controls, which is indicative of a voxel intensity distribution less clustered around the mean and more spread, compared to the control group. Skewness was more negative (p<0.01) for controls than for TASTPM mice, indicating a greater spread for voxel intensity values lower than the median than for those higher than the median in wild-type datasets. All measures of central tendency of the datasets (mean, median, maximum, minimum, range and percentiles) were higher for the TASTPM group than for wild-type mice, which is indicative of increased tracer uptake in the brains of TASTPM mice compared to controls.

Ex vivo biodistribution

Following PET imaging, the ex vivo biodistribution of 64Cu at 24 h post-injection of 64Cu-GTSM was evaluated by gamma-counting of explanted organs (Fig. 5). Only lung tracer uptake was found to differ significantly between groups and was higher in TASTPM than controls (21.30 ± 5.39 %ID/g vs. 17.54 ± 1.22 %ID/g, p<0.05). Global brain uptake was consistently raised in TASTPM mice compared to age-matched controls, but by the tests employed the difference was not significant (p>0.05). The brain %ID/g values determined by ex vivo organ counting showed a moderately strong correlation (r2 = 0.61) with whole-brain uptake measured by PET (see supplementary data, Fig. S4).

Fig.5.

Fig.5

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Tissue biodistribution (ex vivo) of 64Cu in 6-8-month-old TASTPM mice and age-matched controls at 24 h post-injection of 64Cu-GTSM. Statistically significant differences (p < 0.05) from controls are indicated by *. Data are mean (n = 4 for TASTPM mice; n = 6 for controls) ± SD.

Ex vivo brain autoradiography

To evaluate further the heterogeneity in the brain distribution of 64Cu, sagittal brain slices of TASTPM (n = 4) and control mice (n = 6) sacrificed 24 h after 64Cu-GTSM injection were imaged using phosphor imaging autoradiography. Ex vivo autoradiographs (Fig. 6) confirmed heterogeneous 64Cu uptake within brain tissue and the localization of radioactive hotspots in the medulla, cerebellum, pons, midbrain, caudate putamen and olfactory bulb seen by PET imaging. Visual inspection of representative digital autoradiographs showed increased tracer uptake and greater heterogeneity in 64Cu brain distribution in TASTPM mice compared to controls.

Fig.6.

Fig.6

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Surface plots of brain autoradiographs from a 6-8-month-old TASTPM (left) and control (right) mouse sacrificed 24 h after administration of 64Cu-GTSM.

Histology

To determine whether the regional brain distribution of 64Cu was related to Aβ plaque deposition, TASTPM brain slices were autoradiographed and subsequently stained with Congo red (Fig. 7). Although 64Cu was taken up by the entire brain, the areas with the most prominent radioactive hotspots (olfactory bulb, medulla, cerebellum, midbrain, pons and caudate putamen) did not correspond to the regions with highest Aβ plaque load (hippocampus, thalamus and cerebral cortex).

Fig.7.

Fig.7

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Photomicrograph of sagittal brain section from a TASTPM mouse (top) and corresponding brain autoradiograph (bottom) 24 h post-injection of 64Cu-GTSM. ME = medulla; CE = cerebellum; M = midbrain; P = pons; C1 = cerebral cortex; H = hippocampus; TH = thalamus; CP = caudate putamen; LV = lateral ventricle; C2 = cerebral cortex; ON = anterior olfactory nucleus; OB = olfactory bulb.

Discussion

In this study, we aimed to explore brain copper trafficking in TASTPM mice and age-matched controls at the earliest stage at which cognitive impairment can be detected in this transgenic model (i.e. 6 months old) (28), by PET imaging using 64Cu-GTSM. The chosen route of administration of 64Cu-GTSM was intravenous injection, which does not mimic the gastrointestinal uptake and biodistribution of endogenous copper but ensures rapid delivery of the radiotracer into the brain. To confirm the ability of the tracer to release radiocopper intracellularly within the brain, we used solvent extraction to measure the amount of intact (octanol-soluble) 64Cu-GTSM present in brain homogenates, at different time-points after injection. The low 64Cu present in the octanol-extractable fraction, and the lack of 64Cu-GTSM complex detectable by radio-TLC of that fraction, indicated that 64Cu-GTSM was almost completely dissociated by 30 min. Combined with the PET imaging studies which showed remarkably high and rapid 64Cu-GTSM uptake in the brain, this is consistent with a process in which the lipophilicity of the 64Cu-GTSM complex facilitates its passage across the BBB and into cells where it dissociates, releasing 64Cu intracellularly, as previously suggested for this family of compounds (22, 26, 37, 38). The speciation of the dissociated 64Cu in the intracellular copper pool remains unknown. 64Cu-GTSM PET imaging also indicated that clearance of radiocopper from the brain is considerably slower than from most other tissues (e.g. heart, kidneys, lungs), for reasons not yet clear; it may be that brain tissue has greater active sequestration of copper ions driven by increased metabolic needs associated with regular brain function, or less active efflux mechanisms.

PET imaging of copper trafficking with 64Cu-GTSM revealed significantly increased brain and spinal cord uptake of 64Cu in TASTPM mice at 30 min and 24 h, compared to healthy controls. Despite differences in the animal model, age range and methods used, these results agree with those reported by Fodero-Tavoletti et al. (27), who reported enhanced 64Cu brain concentration in the APP/PS1 mouse model of AD. Unlike PET, the increase in 64Cu brain uptake in TASTPM mice compared to controls measured by ex vivo gamma-counting at 24 h post-injection of the tracer did not reach statistical significance; ex vivo organ counting is subject to greater opportunities for non-systematic error (e.g. cross contamination between tissues during dissection, weighing errors, incomplete explantation) that may account for this discrepancy. This may also explain why gamma-counting measurements did not show a stronger linear correlation with PET quantitative data (r2 = 0.61).

While 64Cu clearance from brain was slower than that from other tissues in both TASTPM and wild-type mice, washout between 30 min and 24 h was significantly faster, by a factor of 5, in TASTPM mice than in healthy controls (-10 % vs. -2 %). Nevertheless, TASTPM mice still had significantly higher 64Cu concentration in brain than controls both at 30 min and 24 h. These differences in brain copper trafficking between the two groups may be due to more active copper influx and efflux mechanisms in the TASTPM model, or different patterns of copper bioavailability to these processes. The molecular mechanisms underlying the increased uptake in TASTPM brains have yet to be elucidated; it may be driven by the regulation of total brain copper levels in an effort to maintain brain copper homeostasis and/or enhance copper-dependent antioxidant enzyme activity as a response to oxidative stress. On the other hand, faster brain clearance of 64Cu in TASTPM transgenic mice may be caused by the overexpression of APP, for which a role in normal copper metabolism as an efflux transporter has been suggested (11).

Both PET and ex vivo autoradiography demonstrated that accumulation of 64Cu within the brain at 30 min and 24 h was heterogeneous. Seeking to rationalize 64Cu this heterogeneity, we measured 64Cu brain regional uptake using a 3D mouse brain atlas and found it to be higher in all brain regions in TASTPM mice compared to controls, with differences between the two groups reaching statistical significance in specific areas at 30 min and 24 h post-injection of the tracer. It should be noted that many of these brain regions are very small compared to the limited spatial resolution of the PET/CT scanner. The heterogeneity in 64Cu concentration will therefore inevitably have been underestimated by atlas-based quantification due to the partial volume effect; combined with the lack of flexibility of segmentation boundaries and the potential for imperfect matching of atlas regions onto real brain images, these results must be interpreted cautiously.

To avoid subjectivity in interpreting regional variations in brain uptake of 64Cu because of these limitations, PET brain signal heterogeneity was alternatively measured by texture analysis, which interrogates global and regional patterns of image heterogeneity without an underlying biological or structural model. Statistical analysis of first-order texture parameters revealed a higher spread, asymmetry and non-uniformity of the voxel intensity distribution in TASTPM brain images, indicating a greater degree of image heterogeneity in the disease model compared to controls. While this does not offer a biological interpretation of differences in brain uptake of 64Cu between the two animal groups, it does offer a basis for parameterisation of imaging for clinical diagnostic purposes.

The sequestration of copper by extracellular Aβ plaque deposits is thought to contribute to brain copper dyshomeostasis in AD by reducing the intracellular bioavailability of copper ions (6, 12, 13, 39, 40). To determine the extent of correlation between the regional distribution of 64Cu in the brain and Aβ plaque deposition in TASTPM mice, brain sections were imaged by autoradiography and subsequently stained with the Aβ-binding dye Congo red. The brain regions with the highest density of Congo red-positive amyloid deposits (hippocampus, thalamus and cerebral cortex) showed the lowest accumulation of radioactivity among the brain regions observed in the autoradiographs of the same sections at 24 h post-injection. This lack of correlation between the main radioactive hotspots and the regions of highest Aβ plaque load does not support the hypothesis that amyloid plaques serve as a sink for copper ions (12,13), at least acutely, although it does not preclude the possibility that copper may accumulate in amyloid plaques chronically over the lifetime of the animal. It also suggests that metabolic/pathological factors other than Aβ plaque deposition may influence acute brain distribution of 64Cu, consistent with the view that amyloidosis may be a localised indicator of global disease.

Conclusion

64Cu-GTSM can effect efficient delivery and release of 64Cu into the brain, and has potential for PET imaging to study changes in copper ion trafficking associated with AD and possibly other neurological disorders. PET imaging with 64Cu-GTSM showed statistically significant differences in 64Cu global brain uptake and clearance, as well as in brain regional 64Cu concentration and PET image heterogeneity, between TASTPM transgenic mice and age-matched wild-type controls. These results support clinical evaluation of 64Cu-GTSM PET imaging as a tool to investigate alterations in copper trafficking that might have added value in the diagnosis of AD.

Supplementary Material

Table; Figures

Acknowledgements

This research was supported by the Alzheimer’s Society, Alzheimer’s Research UK, the King’s Health Partners Research Challenge Fund, the Whitaker International Program, the Centre of Excellence in Medical Engineering funded by the Wellcome Trust and the EPSRC under grant number WT088641/Z/09/Z, the King’s College London and University College London Comprehensive Cancer Imaging Centre funded by CRUK and EPSRC in association with the MRC and DoH (England), and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. PET and SPECT scanning equipment at KCL was funded by an equipment grant from the Wellcome Trust under grant number WT 084052/Z/07/Z. We thank GlaxoSmithKline (GSK) for providing the TASTPM mouse model, Dr. Karen Shaw and Dr. Zilin Yu for the production of 64CuCl2, Mr. Stephen Clark for assistance with in vivo imaging experiments, and Ms. Ciara Finucane for advice on the inviCRO brain atlas NM/CT module. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Financial Support: AS, ARUK, EPSRC, NIHR, Wellcome Trust, CRUK

Abbreviations

AD

Alzheimer’s disease

Aβ

amyloid-β

PET

positron emission tomography

APP

amyloid precursor protein

64Cu-GTSM

glyoxalbis(N4-methyl-3-thiosemicarbazonato) copper (II)

BBB

blood-brain barrier

ROI

region of interest

%ID/g

percentage of injected dose per gram

Footnotes

Disclosures

None

References

  • 1.Rowe CC, Villemagne VL. Brain amyloid imaging. J Nucl Med. 2011;52:1733–1740. doi: 10.2967/jnumed.110.076315. [DOI] [PubMed] [Google Scholar]
  • 2.Aizenstein HJ, Nebes RD, Saxton JA, et al. Frequent amyloid deposition without significant cognitive impairment among the elderly. JAMA Neurol. 2008;65:1509–1517. doi: 10.1001/archneur.65.11.1509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Josephs KA, Whitwell JL, Ahmed Z, et al. Beta-amyloid burden is not associated with rates of brain atrophy. Ann Neurol. 2008;63:204–212. doi: 10.1002/ana.21223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hickey JL, Donnelly PS. Diagnostic imaging of Alzheimer's disease with copper and technetium complexes. Coord Chem Rev. 2012;256:2367–2380. [Google Scholar]
  • 5.Hung YH, Bush AI, Cherny RA. Copper in the brain and Alzheimer's disease. J Biol Inorg Chem. 2010;15:61–76. doi: 10.1007/s00775-009-0600-y. [DOI] [PubMed] [Google Scholar]
  • 6.Kozlowski H, Luczkowski M, Remelli M, Valensin D. Copper, zinc and iron in neurodegenerative diseases (Alzheimer's, Parkinson's and prion diseases) Coord Chem Rev. 2012;256:2129–2141. [Google Scholar]
  • 7.Eskici G, Axelsen PH. Copper and oxidative stress in the pathogenesis of Alzheimer's disease. Biochem. 2012;51:6289–6311. doi: 10.1021/bi3006169. [DOI] [PubMed] [Google Scholar]
  • 8.Sayre LM, Perry G, Harris PLR, Liu Y, Schubert KA, Smith MA. In situ oxidative catalysis by neurofibrillary tangles and senile plaques in Alzheimer’s disease. J Neurochem. 2000;74:270–279. doi: 10.1046/j.1471-4159.2000.0740270.x. [DOI] [PubMed] [Google Scholar]
  • 9.Smith DP, Smith DG, Curtain CC, et al. Copper-mediated amyloid-beta toxicity is associated with an intermolecular histidine bridge. J Biol Chem. 2006;281:15145–15154. doi: 10.1074/jbc.M600417200. [DOI] [PubMed] [Google Scholar]
  • 10.Angeletti B, Waldron KJ, Freeman KB, et al. BACE1 cytoplasmic domain interacts with the copper chaperone for superoxide dismutase-1 and binds copper. J Biol Chem. 2005;280:17930–17937. doi: 10.1074/jbc.M412034200. [DOI] [PubMed] [Google Scholar]
  • 11.Bayer TA, Multhaup G. Involvement of amyloid beta precursor protein (A beta PP) modulated copper homeostasis in Alzheimer's disease. J Alzheimers Dis. 2005;8:201–206. doi: 10.3233/jad-2005-8212. [DOI] [PubMed] [Google Scholar]
  • 12.Lovell MA, Robertson JD, Teesdale WJ, Campbell JL, Markesbery WR. Copper, iron and zinc in Alzheimer's disease senile plaques. J Neurol Sci. 1998;158:47–52. doi: 10.1016/s0022-510x(98)00092-6. [DOI] [PubMed] [Google Scholar]
  • 13.Miller Y, Ma BY, Nussinov R. Metal binding sites in amyloid oligomers: Complexes and mechanisms. Coord Chem Rev. 2012;256:2245–2252. [Google Scholar]
  • 14.Faller P, Hureau C. Bioinorganic chemistry of copper and zinc ions coordinated to amyloid-beta peptide. Dalton Trans. 2009;21:1080–1094. doi: 10.1039/b813398k. [DOI] [PubMed] [Google Scholar]
  • 15.Atwood CS, Moir RD, Huang X, et al. Dramatic aggregation of Alzheimer abeta by Cu(II) is induced by conditions representing physiological acidosis. J Biol Chem. 1998;273:12817–12826. doi: 10.1074/jbc.273.21.12817. [DOI] [PubMed] [Google Scholar]
  • 16.Cross AJ, Crow TJ, Perry EK, Perry RH, Blessed G, Tomlinson BE. Reduced dopamine-beta-hydroxylase activity in Alzheimer's disease. Br Med J (Clin Res Ed) 1981;282:93–94. doi: 10.1136/bmj.282.6258.93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Esposito L, Raber J, Kekonius L, et al. Reduction in mitochondrial superoxide dismutase modulates Alzheimer's disease-like pathology and accelerates the onset of behavioral changes in human amyloid precursor protein transgenic mice. J Neurosci. 2006;26:5167–5179. doi: 10.1523/JNEUROSCI.0482-06.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Crouch PJ, Barnham KJ. Therapeutic redistribution of metal ions to treat Alzheimer's disease. Acc Chem Res. 2012;45:1604–1611. doi: 10.1021/ar300074t. [DOI] [PubMed] [Google Scholar]
  • 19.Faux NG, Ritchie CW, Gunn A, et al. PBT2 rapidly improves cognition in Alzheimer's disease: additional phase II analyses. J Alzheimers Dis. 2010;20:509–516. doi: 10.3233/JAD-2010-1390. [DOI] [PubMed] [Google Scholar]
  • 20.Ritchie CW, Bush AI, Mackinnon A, et al. Metal-protein attenuation with iodochlorhydroxyquin (clioquinol) targeting Abeta amyloid deposition and toxicity in Alzheimer’s disease: a pilot phase 2 clinical trial. Arch Neurol. 2003;60:1685–1691. doi: 10.1001/archneur.60.12.1685. [DOI] [PubMed] [Google Scholar]
  • 21.White AR, Du T, Laughton KM, et al. Degradation of the Alzheimer’s disease amyloid beta-peptide by metal-dependent up-regulation of metalloprotease activity. J Biol Chem. 2006;281:17670–17680. doi: 10.1074/jbc.M602487200. [DOI] [PubMed] [Google Scholar]
  • 22.Donnelly PS, Caragounis A, Du T, et al. Selective intracellular release of copper and zinc ions from bis(thiosemicarbazonato) complexes reduces levels of Alzheimer’s disease amyloid-beta peptide. J Biol Chem. 2008;283:4568–4577. doi: 10.1074/jbc.M705957200. [DOI] [PubMed] [Google Scholar]
  • 23.Crouch PJ, Hung LW, Adlard PA, et al. Increasing Cu bioavailability inhibits Abeta oligomers and tau phosphorylation. Proc Natl Acad Sci USA. 2009;106:381–386. doi: 10.1073/pnas.0809057106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Peng F, Lutsenko S, Sun X, Muzik O. Positron emission tomography of copper metabolism in the Atp7b-/- knock-out mouse model of Wilson's disease. Mol Imaging Biol. 2012;14:70–78. doi: 10.1007/s11307-011-0476-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Nomura S, Nozaki S, Hamazaki T, et al. PET imaging analysis with 64Cu in disulfiram treatment for aberrant copper biodistribution in Menkes disease mouse model. J Nucl Med. 2014;55:845–851. doi: 10.2967/jnumed.113.131797. [DOI] [PubMed] [Google Scholar]
  • 26.Dearling JL, Lewis JS, Mullen GE, Welch MJ, Blower PJ. Copper bis(thiosemicarbazone) complexes as hypoxia imaging agents: structure-activity relationships. J Biol Inorg Chem. 2002;7:249–259. doi: 10.1007/s007750100291. [DOI] [PubMed] [Google Scholar]
  • 27.Fodero-Tavoletti MT, Villemagne VL, Paterson BM, et al. Bis(thiosemicarbazonato) Cu-64 complexes for positron emission tomography imaging of Alzheimer's disease. J Alzheimers Dis. 2010;20:49–55. doi: 10.3233/JAD-2010-1359. [DOI] [PubMed] [Google Scholar]
  • 28.Howlett DR, Richardson JC, Austin A, et al. Cognitive correlates of Abeta deposition in male and female mice bearing amyloid precursor protein and presenilin-1 mutant transgenes. Brain Res. 2004;1017:130–136. doi: 10.1016/j.brainres.2004.05.029. [DOI] [PubMed] [Google Scholar]
  • 29.Howlett DR, Bowler K, Soden PE, et al. Abeta deposition and related pathology in an APP x PS1 transgenic mouse model of Alzheimer's disease. Histol Histopathol. 2008;23:67–76. doi: 10.14670/HH-23.67. [DOI] [PubMed] [Google Scholar]
  • 30.Gingras BA, Suprunchuk T, Bayley CH. The preparation of some thiosemicarbazones and their copper complexes: part III. Can J Chem. 1962;40:1053–1059. [Google Scholar]
  • 31.McCarthy DW, Shefer RE, Klinkowstein RE, et al. Efficient production of high specific activity 64Cu using a biomedical cyclotron. Nucl Med Biol. 1997;24:35–43. doi: 10.1016/s0969-8051(96)00157-6. [DOI] [PubMed] [Google Scholar]
  • 32.Szanda I, Mackewn J, Patay G, et al. National Electrical Manufacturers Association NU-4 performance evaluation of the PET component of the NanoPET/CT preclinical PET/CT scanner. J Nucl Med. 2011;52:1741–1747. doi: 10.2967/jnumed.111.088260. [DOI] [PubMed] [Google Scholar]
  • 33.Magdics M, Szirmay-Kalos L, Toth B, et al. Performance evaluation of scatter modeling of the GPU-based “Tera-Tomo” 3D PET reconstruction. NSS/MIC. 2011 [Google Scholar]
  • 34.Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–140. doi: 10.1007/s00259-012-2247-0. [DOI] [PubMed] [Google Scholar]
  • 35.Cook GJ, Yip C, Siddique M, et al. Are pretreatment 18F-FDG PET tumor textural features in non−small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26. doi: 10.2967/jnumed.112.107375. [DOI] [PubMed] [Google Scholar]
  • 36.Mathias CJ, Bergmann SR, Green MA. Development and validation of a solvent extraction technique for determination of Cu-PTSM in blood. Nucl Med Biol. 1993;20:343–349. doi: 10.1016/0969-8051(93)90057-2. [DOI] [PubMed] [Google Scholar]
  • 37.Xiao Z, Donnelly PS, Zimmermann M, Wedd AG. Transfer of copper between bis(thiosemicarbazone) ligands and intracellular copper-binding proteins. insights into mechanisms of copper uptake and hypoxia selectivity. Inorg Chem. 2008;47:4338–4347. doi: 10.1021/ic702440e. [DOI] [PubMed] [Google Scholar]
  • 38.Price KA, Crouch PJ, Volitakis I, et al. Mechanisms controlling the cellular accumulation of copper bis(thiosemicarbazonato) complexes. Inorg Chem. 2011;50:9594–9605. doi: 10.1021/ic201334q. [DOI] [PubMed] [Google Scholar]
  • 39.Syme CD, Nadal RC, Rigby SE, Viles JH. Copper binding to the amyloid-beta (Abeta) peptide associated with Alzheimer's disease: folding, coordination geometry, pH dependence, stoichiometry, and affinity of Abeta-(1-28): insights from a range of complementary spectroscopic techniques. J Biol Chem. 2004;279:18169–18177. doi: 10.1074/jbc.M313572200. [DOI] [PubMed] [Google Scholar]
  • 40.Jiang D, Zhang L, Grant GP, et al. The elevated copper binding strength of amyloidbeta aggregates allows the sequestration of copper from albumin: a pathway to accumulation of copper in senile plaques. Biochem. 2013;52:547–556. doi: 10.1021/bi301053h. [DOI] [PMC free article] [PubMed] [Google Scholar]

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