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J. Biol. Chem., Vol. 277, Issue 47, 44670-44676, November 22, 2002
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From the
Received for publication, May 6, 2002, and in revised form, July 16, 2002
Increased brain metal levels have been associated
with normal aging and a variety of diseases, including Alzheimer's
disease (AD). Copper and iron levels both show marked increases
with age and may adversely interact with the amyloid- Metals have been postulated to play a role in the pathogenesis of
AD1 (1, 2). Copper, zinc, and
iron are concentrated in and around amyloid plaques in AD brain (3),
and high levels of zinc (4) and iron (5) have been reported in the
amyloid plaques of the Tg2576 mouse model for AD. Numerous reports have also demonstrated transition metal imbalances in AD brain, such as
decreased copper, and increased iron, zinc, and manganese (6-11).
Studies in mice and humans show that iron and copper levels increase
with normal aging in several tissues, including brain (12-17), while
zinc levels either remain unchanged or show a slight decrease (14, 16,
18, 19). Therefore, a breakdown of metal regulation could be an
inevitable consequence of aging.
Copper and iron are redox active metals that play important catalytic
roles in many enzymes. Their levels must be strictly regulated to
prevent aberrant reactive oxygen species production resulting in
cellular toxicity. AD brain exhibits marked oxidative damage of
proteins, lipids, and nucleic acids (20, 21).
APP possesses a copper binding site in its NH2-terminal
cysteine-rich domain, which reduces Cu2+ to
Cu1+ (22). APP also has a zinc binding site, which is
believed to have a structural role (23). APP and amyloid precursor-like protein 2 (APLP2) knock-out mice show specific elevations in brain and
liver copper levels (24), which suggests that APP has a role in copper homeostasis.
A To investigate the effects of aging and APP and/or A Here we show age-related increases in copper, iron, and cobalt levels
in the brains of all mouse lines studied. These increases may
contribute to the age-dependent formation of amyloid and
oxidative damage in Tg2576 mice, and possibly also in AD brain. We also show that APP and A Mice--
All mice were housed according to standard animal care
protocols and fed standard laboratory chow and tap water ad
libitum. We maintained the Tg2576 colony (35) by crossing Tg2576
males with C57BL6/SJL F1 females and determined transgene status by PCR
of tail DNA, using primers as described previously (36). NTg
littermates were used as controls (referred to as BL6/SJL). We analyzed
mice at 2.8 months (2.81 ± 0.002 months), 3.6 months (3.59 ± 0.002 months), 11 months (11.14 ± 0.01 months), and 18 months
(18.21 ± 0.03 months). Transgenic mouse lines TgC100.V717F and
TgC100.wt were generated as described previously (34) and bred to
homozygosity in a C57BL6/DBA background. NTg controls for the C100
lines were of the same genetic background (C57BL6/DBA F2) (referred to
as BL6/DBA). We studied these mice at 2.8 months (2.76 ± 0.02 months), 8 months (7.8 ± 0.1 months), and 18 months (17.6 ± 0.1 months). All groups contained similar numbers of males and females.
Preparation of Brain Tissue--
To minimize metal contamination
of samples, we presoaked all tubes and equipment in 1% nitric acid and
rinsed them in distilled water prior to use. We sacrificed mice by
anesthetization with halothane, followed by transcardial perfusion with
phosphate-buffered saline at 100-120 mm Hg until the perfusate
ran clear. We then dissected brains to remove olfactory bulb,
cerebellum, and brain stem; weighed the remaining wet brain tissue; and
snap-froze samples and stored them at Metal Analysis--
We dissolved the freeze-dried brains
overnight in 0.6 ml of concentrated HNO3 (Aristar, BDH),
followed by heating to 80 °C for 20 min, and allowed them to cool to
room temperature. To dissolve lipid components, we added 0.6 ml of
H2O2, and once effervescing had ceased
(approximately 30 min) we heated samples to 70 °C for 15 min and
allowed them to cool. We diluted samples in triplicate by 1/51 in 1%
HNO3 (60-µl sample plus 3 ml of 1% HNO3) and
measured metal levels by inductively coupled plasma mass spectrometry
(ICP-MS) with an Ultramass 700 (Varian, Victoria, Australia) in
peak-hopping mode with spacing at 0.100 atomic mass unit, 1 point per peak, 50 scans per replicate, and 3 non-consecutive
replicates per sample. Plasma flow was 15 liter/min with an auxiliary
flow 1.5 liter/min. RF power was 1.2 kilowatts.
Each sample was introduced using a glass nebulizer at a flow of 0.88 liter/min. We calibrated the instrument using a 1% HNO3
mixed calibration standard (Merck Pty. Ltd.) containing 10, 50, and 100 ppb of all metals measured in 1% HNO3.
Statistical Analysis--
We diluted and measured each brain
sample in triplicate and used the average value for analysis. The metal
values presented are the mean ng or µg/g wet weight of the original
prefrozen brain sample. Error bars represent S.E. of the mean of each
group of mouse brains analyzed. We performed statistical analyses using StatisticaTM for the Macintosh (StatSoftTM). To
determine whether metal levels changed significantly with age within
each mouse line, and whether this differed for males and females, we
performed two-way analysis of variance (ANOVA) on data from all age
groups, with age group and sex as independent variables. If a
significant age-sex interaction was found, males and females were
re-analyzed separately. To determine whether metal levels in each age
group were significantly different from the youngest age group (2.8 months), we performed post-hoc Scheffe tests. To determine whether
brain metal levels in each line of transgenic mouse differed from their
corresponding NTg controls, and whether this differed between males and
females, we performed two-way ANOVA on metal level data from the two
mouse lines in all age groups, including sex and age group as
independent variables. If a significant mouse type-sex interaction was
found, males and females were analyzed separately. Significant
differences between transgenic and NTg mice in each age group
individually were determined by applying simple contrasts to the data.
Average percentage changes in metal levels due to transgene expression
represent the average of the percentage differences in each age
group ± S.E., hence giving equal weighting to data in each age group.
Normal Age-related Changes in Brain Metal Levels--
We first
examined how brain metal levels are affected by age in both strains of
non-transgenic mouse. Two-way ANOVA of metal levels in both BL6/SJL and
BL6/DBA mouse lines, with age group and sex as independent variables,
revealed marked and significant age dependent increases
(p < 0.001) in copper, iron, and cobalt levels (Fig.
1). No significant age-sex interaction
was found for any of these metal level increases, indicating that the
effects of age on brain copper, iron, and cobalt levels is similar for both males and females. In comparison with the youngest (2.8 mo) group,
copper, iron, and cobalt levels in the BL6/SJL line had not changed
significantly by 3.4 months, but by 11 and 18 months, significant
increases were observed in each metal (p < 0.001) (Fig. 1A). Copper levels increased by 46% from 5.1 ± 0.1 µg/g (wet weight) at 2.8 months to 7.5 ± 0.2 µg/g at 18 months; iron levels increased 51% from 17.3 ± 0.3 µg/g at 2.8 months to 26.1 ± 0.7 µg/g at 18 months; and copper levels
increased 66% from 11.6 ± 0.3 ng/g at 2.8 months to 19.3 ± 0.7 ng/g at 18 months. Zinc levels showed no significant overall change
with age, but a significant age sex interaction was found
(p < 0.01). When male and female zinc levels were
analyzed separately, we found that males displayed a relatively small
increase (10%, p < 0.01) in brain zinc levels across
the age groups from 20.5 µg/g at 2.8 months to 22.5 µg/g at 18 months. Females zinc levels, however, remained between 20.1 and 21.1 µg/g in all age groups measured. Manganese levels showed no
significant overall change with age, and no significant age-sex
interaction, with levels remaining between 0.44 and 0.46 µg/g in all
age groups measured.
In the BL6/DBA line (Fig. 1B), copper levels increased by
34% from 4.9 ± 0.1 µg/g at 2.8 months to 6.5 ± 0.1 µg/g at 18 months; iron levels increased 34% from 16.5 ± 0.2 µg/g at 2.8 months to 21.9 ± 0.6 µg/g at 18 months; and
copper levels increased 41% from 12.1 ± 0.2 ng/g at 2.8 months
to 17.6 ± 0.7 ng/g at 18 months. Manganese levels, in contrast to
the BL6/SJL strain, showed a significant overall decrease with age
(p < 0.01), dropping by 14% from 0.51 ± 0.02 µg/g at 2.8 months to 0.44 ± 0.01 µg/g at 18 months
(p < 0.001). No significant age-sex interaction was found for any of these metal level changes. Zinc levels showed no
significant overall change with age, but a significant age-sex interaction was found (p < 0.05). Separate analysis of
male and female zinc levels revealed no more than a 5% fluctuation in
zinc levels across the three age groups in both males and females. These changes reached significance only in females (p < 0.05), where a 5% drop in zinc levels was observed from 2.8 to 8 months (p < 0.05), whereas 18-month zinc levels were
unchanged from 2.8-month levels. Zinc levels remained between 19.0 ± 0.2 and 20.1 ± 0.6 µg/g in males and females in all age
groups analyzed. The three Tg mouse lines (Tg2576, TgC100.wt, and
TgC100.V717F) displayed the same direction of age-related changes in
copper, iron, cobalt, and manganese levels as their corresponding NTg
background controls, but exhibited transgene-related adjustments in
metal levels.
The Effect of APP Overexpression on Metal Levels in Tg2576 Mouse
Brain--
Tg2576 mice express relatively stable human APP695.swe
levels across the lifespan, but human A The Effect of A
We found significant overall reductions in copper and iron levels in
the TgC100.V717F line compared with BL6/DBA controls (p < 0.001) that showed no dependence on sex. Significantly reduced copper and iron levels were found in each age group (p < 0.001), and only in the case of iron was a significant effect of age
observed (p < 0.05), whereby the decrease was greatest
in the older age groups. A small but significant (3.5 ± 0.3%,
p < 0.05) overall increase in zinc levels that bore no
effect of sex or age was found in the TgC100.V717F line compared with
BL6/DBA controls. Cobalt levels, however, showed a significant
(p < 0.01) increase that was dependent on age.
TgC100.V717F mice at 18 months had 19% higher cobalt levels than
age-matched BL6/DBA controls (p < 0.001), while in the
younger age groups, there were no significant differences. Manganese
levels were significantly increased in TgC100.V717F brain compared with
BL6/DBA controls (11 ± 3%, p < 0.001). This
increase showed no dependence on sex or age, and significant increases
were observed in each age group (p < 0.01).
The TgC100.wt line, similarly to the TgC100.V717F line, showed
significantly reduced copper and iron levels (p < 0.001) in conjunction with significantly increased manganese levels
(p < 0.001) when compared with the BL6/DBA controls.
However, a significant effect of sex was observed for each of these
metals. On separate analysis of males and females, we found that
although copper levels were significantly reduced in both sexes, the
decrease was greater in females (13 ± 3%, p < 0.001) than males (7 ± 4%, p < 0.01). In
addition, iron levels were significantly decreased in female TgC100.wt
mice (9 ± 1%, p < 0.001) but were unchanged in
males. Conversely, manganese levels were significantly increased in
males (20 ± 1%, p < 0.001) but unchanged in
females. No significant effect of age was found on any of these metal
level changes. We found no significant difference in zinc levels in the
TgC100.wt compared with the BL6/DBA line and no effect of sex. Cobalt
levels showed the same alterations as in the TgC100.V717F line, with a
significant increase (p < 0.01) that was dependent on
age. The oldest (18 months) age group displayed 19% higher cobalt
levels than BL6/DBA controls (p < 0.001), while levels
in the younger age groups were unchanged.
This study demonstrates age-dependent increases in
copper, iron, and cobalt levels in bulk brain tissue from two normal
mouse strains and three strains of APP- or A Synaptic zinc released by the glutamatergic synapses (42) is critical
for A AD is more prevalent in women, and A Overexpression of APP and C100 resulted in altered metal homeostasis in
transgenic mouse brain (Table I). The
TgC100 mouse models utilize the ubiquitous Brain copper levels in TgC100 and Tg2576 mice were lowered to similar
degrees (Table I); and since A The decrease in copper and increase in manganese in the brains of APP
and C100 Tg mice mirrors changes in the AD brain, which has also been
reported to have decreased levels of copper relative to age-matched
controls (7, 8, 10, 11), and increased levels of manganese (11). Brain
coppper concentration is related to plasma copper concentration (51),
and both plasma (52, 53) and CSF (54) copper levels are
significantly elevated in AD. Taken together, these findings imply that
there is a pooling of extracellular copper, and a deficiency of
intracellular copper, in the AD brain. Due to the catalytic nature of
reactive oxygen species generation by redox active metals such as
copper and iron, small changes in the levels or distribution of these
metals could cause severe oxidative stress. Therefore, elaboration of
the compartments of metal ions altered in AD or Tg mouse brain, in
contrast to the compartments of metal ions (particularly copper and
iron) that are increased due to the aging process, warrants further investigation.
In vitro studies have demonstrated no significant
interaction between A The consistent decrease in iron levels in both TgC100.wt and
TgC100.V717F lines suggests a role of A Copper and zinc level decreases induced by transgene expression showed
little enhancement with age in the Tg2576 brain, despite the fact that
A Taken together, our findings demonstrate that overexpression of human
A
Overexpression of Alzheimer's Disease Amyloid-
Opposes
the Age-dependent Elevations of Brain Copper and
Iron*
§,
§,
§,
§,
,
§,
§**
, and
§
Department of Pathology, The University of
Melbourne, Victoria 3010, Australia, § The Mental Health
Research Institute of Victoria, Parkville, Victoria 3052, Australia,
the ¶ Zentrum für Molekulare Biologie Heidelberg (ZMBH),
Universität Heidelberg, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany, and the ** Laboratory for Oxidation
Biology, Genetics and Aging Research Unit, and Department of
Psychiatry, Harvard Medical School, Massachusetts General Hospital,
Charlestown, Massachusetts 02129
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ABSTRACT
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EXPERIMENTAL PROCEDURES
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DISCUSSION
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(A
) peptide
causing its aggregation and the production of neurotoxic hydrogen
peroxide (H2O2), contributing to the
pathogenesis of AD. Amyloid precursor protein (APP) possesses
copper/zinc binding sites in its amino-terminal domain and in the A
domain. Here we demonstrate that overexpression of the
carboxyl-terminal fragment of APP, containing A
, results in
significantly reduced copper and iron levels in transgenic mouse brain,
while overexpression of the APP in Tg2576 transgenic mice results in
significantly reduced copper, but not iron, levels prior to the
appearance of amyloid neuropathology and throughout the lifespan of the
mouse. Concomitant increases in brain manganese levels were observed
with both transgenic strains. These findings, complemented by our
previous findings of elevated copper levels in APP knock-out mice,
support roles for APP and A
in physiological metal regulation.
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DISCUSSION
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, a product of APP proteolytic processing, accumulates in the
neocortex in AD. This peptide also possesses selective high and low
affinity Cu2+ and Zn2+ binding sites. A
reduces Cu2+ to Cu1+ and Fe3+ to
Fe2+, catalyzing the O2-dependent
production of H2O2 (25). This interaction of
A
with copper mediates toxicity (26), while zinc inhibits
A
-mediated H2O2 production and toxicity
(27). Interaction with copper, zinc, or iron mediates the aggregation of A
(28-30). Chelation of metal ions reverses the aggregation of
synthetic A
peptide and dissolves amyloid in post-mortem human brain
specimens (29, 31, 32). Treatment of the Tg2576 transgenic mouse model
for AD with clioquinol, an orally bioavailable metal chelator induced a
marked inhibition of cortical amyloid accumulation (33).
overexpression
on metal levels, we measured copper, zinc, iron, copper, and manganese
levels in the brains of normal and transgenic (Tg) mice across the
majority of their lifespan (2.8-18 months (mo)). We utilized
transgenic mice overexpressing human APP695.K670N-M671L (Swedish
mutation) (Tg2576) as well as two lines of mice expressing the
carboxyl-terminal 100 residues of APP (C100), with and without the
familial AD mutation V717F (TgC100.V717F and TgC100.wt, respectively). TgC100 mice express human A
at lower levels than the Tg2576 line, and display no A
accumulation nor amyloid plaques up to 20 months (34). These mice provide a model to study the effect of increased human
A
production without holoAPP overexpression. The TgC100.V717F line
produces relatively more A
x-42, which is of interest, since A
1-42 binds copper with much greater affinity than A
1-40 (29, 30), and is more readily precipitated (29), more redox active, and more
toxic (26) than A
1-40 when bound to copper.
expression modulates metal levels, particularly copper, in transgenic mouse brain. These data suggest that the corrupted metabolism of A
in AD may cause severe perturbances of
essential metal homeostasis. These imbalances may contribute to the
neurodegenerative phenotype.
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80 °C until use.
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RESULTS
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Fig. 1.
Age-related changes in brain metal
levels. Metal levels were measured in BL6/SJL and BL6/DBA mouse
lines at several ages from 2.8 to 18 months. Percentage change with
respect to 2.8-month age groups is presented for each metal.
Significance of the effect of age on metal levels was determined by
two-way ANOVA across all age groups. ***, p < 0.001; **, p < 0.01; *,
p < 0.05. Error bars represent S.E. of the
mean. A significant age group-sex interaction was found in zinc levels.
Therefore zinc data are presented separately for males and
females.
levels in their brains
increase exponentially with age, resulting in amyloid plaques by around 10-12 months (35, 37). To determine the effect of APP695.swe overexpression and A
accumulation on brain metal homeostasis, we
compared brain metal levels between Tg2576 and BL6/SJL NTg littermates
at 2.8, 3.6, 11, and 18 months. Two-way ANOVA of metal levels in Tg2576
compared with BL6/SJL brain, with age group and sex as independent
variables, revealed a significant overall reduction in copper levels by
14 ± 1% in Tg2576 compared with BL6/SJL brain (p < 0.001) (Fig. 2). This reduction showed
no dependence on sex, but a significant age group effect was observed
(p < 0.05). Significantly reduced (p < 0.001) copper levels were observed in each age group, and this
effect increased with increasing age (11% at 2.8 months, 14% at 3.6 months, 14% at 11 months, and 16% at 18 months). We also found a
small but significant reduction in zinc levels (4 ± 1%,
p < 0.001) in Tg2576 compared with BL6/SJL brains.
While the percentage reduction in zinc levels was relatively small
compared with the percentage reduction in copper levels, the average
absolute decreases in zinc and copper levels were identical (both 13 nmol/g). In contrast, manganese levels were significantly increased in Tg2576 compared with BL6/SJL brain, by 5 ± 3% (p < 0.001). Neither of these metal level changes showed any significant
dependence on age or sex. Iron levels showed no significant overall
difference between Tg2576 and BL6/SJL groups when males and females
were analyzed together; however, significant mouse type-sex
(p < 0.01) and mouse type-age (p < 0.05) interactions were found. Separate analysis of males and females
revealed a significant change in iron levels in males that was
dependent on age (p < 0.001), a significant reduction
(13%, p < 0.001) was observed in the 11-month age
group, but no other age groups. Females, in contrast, showed no
significant overall change in iron levels due to APP695.swe expression;
however, a significant effect of age group was observed (p < 0.05), whereby iron levels were significantly
increased (14%, p < 0.01) only in the oldest (18 month) age group compared with age-matched BL6/SJL controls. Cobalt
levels showed no significant overall difference between the Tg2576 and
BL6/SJL groups and no significant effect of sex or age.

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Fig. 2.
Effect of APP overexpression on brain metal
levels. Metal levels were measured in Tg2576 and NTg brain at 2.8 months (n = 22 and 25 respectively), 3.6 months
(n = 21 and 24, respectively), 11 months
(n = 12 and 19, respectively), and 18 months
(n = 12 and 12, respectively) by ICP-MS. Each group
contained similar numbers of males and females. Metal levels are given
as µg/g or ng/g wet brain weight. ANOVA results represent significant
differences between Tg2576 and BL6/SJL by two-way ANOVA of data in all
groups, including age group and sex as independent variables. Where
significant differences were found a contrast analysis was performed on
each age group. Asterisks represent significant differences
between Tg2576 and BL6/SJL within each age group. a represents
a significant sex-mouse type interaction. In this case, male and female
data were re-analyzed separately.
Overexpression on Metal Levels in TgC100 Mouse
Brain--
The alterations to metal levels in the Tg2576 brain could
be attributed either to the metal binding sites in the APP
NH2-terminal domain or to the metal binding sites on A
.
To test whether A
expression alone could modify metal levels, we
also measured metal levels in the brains of the TgC100.V717F (Fig.
3) and TgC100.wt (Fig.
4) mouse lines at 2.8, 8, and 18 months
and compared them with BL6/DBA NTg controls.

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Fig. 3.
Effect of C100.V717F overexpression on brain
metal levels. Metal levels were measured in TgC100.V717F and
BL6/DBA brain at 2.8 months (n = 16 and 17, respectively), 8 months (n = 15 and 23, respectively),
and 18 months (n = 16 and 14, respectively) by ICP-MS.
Each group contained similar numbers of males and females. Metal levels
are given as µg/g or ng/g wet brain weight. ANOVA results represent
significant differences between Tg-C100.V717F and BL6/DBA brain metal
levels by two-way ANOVA of data in all age groups, including age group
and sex as independent variables. Significant differences in metal
levels at each age group are indicated by asterisks. These
represent contrast analyses of TgC100.V717F compared with BL6/DBA brain
within each age group.

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Fig. 4.
Effect of C100.wt overexpression on brain
metal levels. Metal levels were measured in TgC100.wt and BL6/DBA
brain at 2.8 months (n = 20 and 17, respectively), 8 months (n = 28 and 23, respectively), and 18 months
(n = 20 and 14, respectively) by ICP-MS. Each group
contained similar numbers of males and females. Metal levels are given
as µg/g or ng/g wet brain weight. ANOVA results represent significant
differences between TgC100.wt and BL6/DBA brain metal levels by two-way
ANOVA of data in all age groups, including age group and sex as
independent variables. Significant differences in metal levels at each
age group are indicated by asterisks. These represent
contrast analysis of TgC100.wt compared with BL6/DBA brain within each
age group. a represents a significant sex-mouse type
interaction. In these cases, male and female data were re-analyzed
separately.
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DISCUSSION
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ABSTRACT
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EXPERIMENTAL PROCEDURES
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DISCUSSION
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-overexpressing Tg mice. We hypothesize that the marked elevations in copper and iron as a
product of age could explain the age-dependent onset of
amyloid neuropathology in the Tg2576 model (35, 37). Studies in humans suggest that the aging human brain follows a similar pattern of age-related changes, at least for iron (13, 15, 17, 38, 39) and cobalt
(40). In the following model, if the changes in metals we observed in
mice are also reflected in the aging human brain, then a senescent rise
in brain copper and iron could be the neurochemical basis for age being
the major risk factor for AD neuropathology (41).
plaque formation (43), although we find that zinc
concentrations averaged through the whole brain remain relatively constant with age (Fig. 1). However, A
plaque indeed contains a
mixture of supraphysiological concentrations of copper (
0.4 mM), iron (
1 mM), and zinc (
1
mM) (3). We hypothesize that excess binding of copper and
iron to A
could alter the metabolism of A
leading to its
precipitation by the constitutively high ambient zinc concentrations in
the synaptic (and corticovascular) milieu. Copper and iron binding to
A
engenders H2O2 production by A
(25),
which may inhibit LRP-mediated clearance mechanisms (44),
leading to A
accumulation. An alternative possibility is that A
is oxidatively modified by reaction with excess copper or iron (2) and
that these modified forms of A
are more vulnerable to zinc- (or
other metal) induced precipitation. Such oxidative modification
inhibits catabolic degradation of polypeptides (45, 46), which may also
contribute to plaque accumulation. Of the biometals that have been
observed to precipitate A
in vitro (28, 29, 47), zinc,
copper, and iron are the only ones with sufficient abundance and
availability in the neocortex to affect A
aggregation and plaque
formation in vivo. Cobalt, which is also elevated with age
in the mice, has an effect on A
precipitation similar to that of
zinc (29), but the concentration of cobalt in the brain is 1000-fold
lower than copper, iron, and zinc and is mostly found in a non-ionic
form within vitamin B12 (cyanocobalamin). The increasing levels of cobalt with age are therefore unlikely to interact
significantly with A
.
neuropathology is more
prevalent and abundant in female Tg2576 mice (48). This has recently
been linked to an age-dependent relative increase in hippocampal synaptic zinc concentrations in female Tg2576 mice (43). In
contrast, our current findings demonstrate no significant change in
zinc in bulk brain tissue in female BL6/SJL mice, while males show a
small relative increase. This suggests an age-dependent change in the distribution of zinc might occur in females, with an
enrichment in neocortical synaptic zinc levels, which facilitate amyloid formation, accompanied by a relative depletion of non-cortical zinc levels. Further studies of dissected subregions of the brains of
these mouse strains are needed to confirm possible region-specific alterations in metal levels.
-actin promotor (34),
while the Tg2576 model uses a relatively brain-specific prion protein
promotor (36, 49). Moreover, the two models have different genetic backgrounds and differences in transgene expression levels. Despite these differences, a consistent finding in all three transgenic mouse
lines was a decrease in copper and an increase in manganese levels
across their lifespan.
Summary of changes in metal levels due to transgene expression
is the only known copper binding
domain in the C100 construct, our findings are compatible with an
independent role for unprecipitated A
in lowering brain copper
levels. The dose-dependent influence of the expression of
APP or its derivatives in lowering brain copper levels is also evidenced by an increase in cortical copper levels in mice that have
had genetic ablation of APP (APP
/
) or APLP2
(APLP2
/
) (24). However, indirect interactions of
APP/C100 with other proteins that affect metal homeostasis, such as the
neuronal adaptor protein X11
(50), is an alternative explanation for
our findings that cannot yet be excluded. Our current data are limited
to analysis of the bulk effects of the transgene upon metal levels
using post-mortem tissue. These data are useful in illuminating the
most conspicuous effects of APP expression upon metal homeostasis, but
dynamic studies that could dissect associations between the
concentration of APP derivatives and their effects on metal transport
would help elucidate the mechanism of these changes. Such studies of the transport of metal ions in cell cultures transfected with appropriate constructs are currently being pursued.
or APP and manganese (23, 28, 29, 47).
Therefore the increased manganese levels we observed in transgenic mice may be a result of secondary effects of altered metal homeostasis or an
up-regulation of manganese-binding proteins such as mitochondrial manganese-superoxide dismutase, in response to increased intracellular oxidative stress. Alternatively, in the brain microenvironment, A
may interact with manganese in a manner not yet observed by in
vitro studies.
in iron homeostasis. This
may be occurring via direct interaction with A
, but may alternatively reflect a homeostatic adjustment to the reduction in
copper levels. The Tg2576 line, in contrast, did not show this decrease. Human AD brain exhibits substantially increased iron levels
(10, 11, 55). Although a portion of this increase may be due to the
iron content of accumulated A
, a more generalized increase in brain
tissue may be a consequence of other AD-associated pathogenic changes
affecting iron homeostasis such as elevated ferritin levels (6). The
exaggerated retention of iron in A
deposits or in increased ferritin
deposits in the Tg2576 mouse brain may oppose the tendency of human
A
expression to decrease brain iron levels in the C100 mouse models,
explaining why there is no net decrease in brain iron in Tg2576 mice.
levels accumulate several hundredfold from 2.8 to 18 months, with
plaque formation becoming conspicuous from 10 months in these mice
(37). The decrease in copper and zinc levels that we observed (Fig. 2)
are therefore not a consequence of insoluble A
aggregates or plaque
pathology. This decrease must either be due to secreted APP and/or A
promoting the efflux of the metal ions or APP/A
preventing their
uptake. Supporting this latter possibility is evidence that A
scavenges extracellular Cu2+, possibly to prevent oxidation
(56). Furthermore, treatment of 21-month-old Tg2576 mice with
clioquinol, an antibiotic with copper/zinc chelation properties, both
inhibited plaque formation and paradoxically elevated soluble brain
copper and zinc levels. Iron, cobalt, and manganese levels were
unaltered (33). In NTg mice, clioquinol treatment decreases copper,
iron, and cobalt levels (57). In light of our current findings, this
paradoxical increase in copper and zinc in clioquinol-treated Tg2576
mice may be explained by clioquinol preventing Cu2+ and
Zn2+ from complexing with extracellular A
, so securing
metal for uptake into metal-deficient brain tissue instead of being
sequestered into amyloid. The consequent lowering of extracellular
metal concentrations inhibited the formation, or possibly facilitated
the dissolution, of amyloid deposits.
in TgC100 mice replicates the lowering of copper and raising of
manganese levels that is observed due to APP overexpression in Tg2576
brain (Table I). Different effects on iron, zinc, and cobalt in these
Tg mice suggest discrete and perhaps opposing roles for A
and APP
ectodomain metal binding sites. Assuming the effects of C100 expression
on metal levels are due to A
, and that the APP expression involves
the combined actions of both A
and APP ectodomain metal binding
sites, we can estimate the effect of the APP ectodomain on brain metal
metabolism (Table II). Our study reveals
that A
is involved in the reduction of copper levels. A compounding
role of both A
and the APP ectodomain in reducing copper levels is
supported by the observation that APLP2
/
mice, like
APP
/
mice, have increased brain copper levels (24).
APLP2 does not produce A
, so its influence on copper levels is
probably mediated by its ectodomain metal binding sequence, which is
homologous with that of APP (23, 58). The role of APP ectodomain in
regulation of brain manganese is unknown, but A
is able to increase
manganese levels. The A
and the APP ectodomain appear to have
opposing effects with respect to iron, zinc, and cobalt levels. This
differential activity could be due to amyloid deposition either
sequestering zinc and cobalt or indirectly altering metal homeostasis
in the Tg2576 mice. Alternatively, it could be due to the export of
zinc and cobalt by the overexpressed APP ectodomain.
Model for the differential effects of APP and A
upon metal
regulation
overexpression in the TgC100 lines
against the effect of APP.swe overexpression in the Tg2576 line.
represents decrease,
represents increase, and n.c. represents no
significant change. The parentheses represent variable effects relating
to age and sex.
Correlation of these findings to human aging and AD is limited by
differences between humans and mice in their metal regulatory machinery. In addition, the Tg2576 model does not possess the full
spectrum of AD pathology, in particular neurofibrillary tangles. With
these caveats, our findings suggest that amyloid pathology in AD may
represent the corruption of a compensatory system for preventing the
entry of excess copper, which rises as a consequence of aging, into
brain tissue. Treatment with certain metal chelators, such as
clioquinol, which has been recently shown benefit to AD patients in a
phase two clinical trial (59), may exert their therapeutic effects not
just by clearing amyloid deposition, but also by restoring brain metal homeostasis.
| |
ACKNOWLEDGEMENTS |
|---|
We thank Karen Hsiao-Ashe for the Tg2576 mice, Rachel Borg for assistance with animal breeding, and Andrew McKinnon for assistance with statistical analysis.
| |
FOOTNOTES |
|---|
* This work was supported in part by grants from the National Health and Medical Research Council of Australia, NIA Grant RO1AG12656, and Alzheimer Association (to A. I. B.) and by Prana Biotechnology Ltd.The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Supported by the Deutsche Forschungsgemeinschaft and the
Bundesministerium für Forschung und Technologie.

To whom correspondence may be addressed. E-mail:
qiao@unimelb.edu.au (for Q.-X. L.) or E-mail:
BUSH@helix.mgh.harvard.edu (for A. I. B.).
Published, JBC Papers in Press, September 4, 2002, DOI 10.1074/jbc.M204379200
| |
ABBREVIATIONS |
|---|
The abbreviations used are:
AD, Alzheimer's disease;
APP, Amyloid precursor protein;
APLP, amyloid
precursor-like protein;
A
, amyloid-
;
Tg, transgenic;
ICP-MS, inductively coupled plasma mass spectrometry;
ANOVA, analysis of
variance.
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