| Research Article |
Open Access |
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| Metabolomic Identification in Cerebrospinal Fluid of the Effects of High Dietary Cholesterol in a Rabbit Model of Alzheimer's Disease |
| Qing Yan Liu1,2*, Erin J. Bingham3, Susan M.Twine1, Jonathan D. Geiger4 and Othman Ghribi4 |
| 1ONeurobiology Program, Institute for Biological Sciences, National Research Council of Canada, Ottawa, Ontario, K1A 0R6, Canada |
| 2Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada |
| 3Phenomenome Discoveries Inc. Saskatoon, SK, Canada S7N 4L8 |
| 4Department of Pharmacology, Physiology and Therapeutics, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, North Dakota |
| *Corresponding author: |
Qing Y. Liu
Neurobiology Program
Institute for
Biological Sciences
National Research Council of Canada, 1200 Montreal Rd
Bldg. M-54, Ottawa, Ontario, K1A 0R6, Canada
Tel: 613-990-0850
Fax: 613-941-
4475
E-mail: qing.liu@nrc.gc.ca |
|
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| Received March 06, 2012; Accepted March 27, 2012; Published March 29, 2012 |
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| Citation: Liu QY, Bingham EJ, Twine SM, Geiger JD, Ghribi O (2012) Metabolomic
Identification in Cerebrospinal Fluid of the Effects of High Dietary Cholesterol in
a Rabbit Model of Alzheimer's Disease. Metabolomics 2:109. doi:10.4172/2153-
0769.1000109 |
| |
| Copyright: © 2012 Liu QY, et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. |
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| Abstract |
| |
| Background: Alzheimer’s disease (AD) is the most common neurodegenerative disorder, manifesting clinical
symptoms of cognitive impairment and dementia. The vast majority of cases are late onset AD (LOAD), which are
genetically heterogeneous and occur sporadically. The neuropathological changes of LOAD can be reproduced by
supplementing a rabbit’s diet with 2% cholesterol for 12 weeks. |
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| Methods: In the present study, a non-targeted Fourier transform ion cyclotron resonance mass spectrometry
based metabolomics approach and multivariate statistics were used to survey the effect of cholesterol on cerebrospinal
fluid metabolites over a 12 week time-course. |
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| Results: Of the 6515 accurate masses detected in the rabbit CSF, 375 showed significant differences in intensity
(p < 0.05) between samples collected at different time points. Further analysis of top 95 (p < 0.01) revealed four clusters
of metabolites with different expression patterns throughout the course of the cholesterol treatment. The majority of
effects were observed in 12 weeks of cholesterol treated samples, while certain masses showed transient changes at 8
weeks but returned back to near the levels of the controls at 12 weeks. The masses that started to change 8 weeks into
the treatment may represent early metabolic changes linked to certain defects in the brain related to AD development.
Putative metabolite identifications revealed certain phosphorylated glycerolipids and peptide fragments decreased after
8 weeks of cholesterol treatment. |
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| Conclusion: This study showed that there are specific metabolic perturbations which occur in the CSF as a
result of high cholesterol loading. Given the changes of short peptide fragments in particular, the effects are likely the
consequence of brain degeneration caused by high cholesterol levels. Further investigations of these masses will lead
to a greater understanding of the metabolic mechanisms associated with cholesterol-related AD development. Some
of these masses may be used as candidates for the development of diagnostic, prognostic or theranostic markers. |
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| Keywords |
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| Metabolomics; Cerebrospinal Fluid; Cholesterol;
Alzheimer’s disease; Rabbit Model |
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| Introduction |
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| Alzheimer’s disease (AD) is a progressive brain-destructive
disease that manifests clinical symptoms of cognitive impairment
and dementia, and has no reliable method for early detection or
cure. Neuropathologically, AD is characterized by the deposition
of amyloid β (Aβ) leading to the development of senile plaques and
hyperphosphorylated tau protein aggregates within the cortical neurons
that form neurofibrillary tangles (NFTs) [1]. Our current understanding
of early-onset (familial) AD is derived primarily from studies on genes
or gene products identified in a genetically-determined fashion. Three
genes have been definitively implicated in the etiology of early-onset
AD; mutations of the amyloid beta precursor protein (AβPP) gene and
the presenilin 1 and 2 genes (PSEN1, PSEN2) cause rare Mendelian
forms of the disease [2]. Although these discoveries have been helpful
in elucidating the basic molecular pathogenesis of familial AD, they
only represent a relatively small fraction of the AD population. The
large majority of cases are late onset AD (LOAD), which are genetically
heterogeneous and occur sporadically [3]. Only apolipoprotein E4
(APOE4) has been established unequivocally as a susceptibility gene for
LOAD [4]. Two recent large-scale Genome-Wide Association Studies
[5] in large patient cohorts have identified clusterin, also known as
apolipoprotein J, as being independently associated with LOAD. Both
ApoE and ApoJ are involved in lipid metabolism/homeostasis [6] as
components of HDL [6,7]. Epidemiologically, altered cholesterol metabolism has been linked to increased Aβ production and AD
pathogenesis [8-11]. Hypercholesterolemia is an early risk factor
for AD, while decreased prevalence of AD is associated with the use
of cholesterol-lowering drugs (statins) that inhibit 3-hydroxy-3-
methylglutaryl coenzyme A reductase [9-11]. |
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| There is evidence that the pathological changes in dementiacausing
AD begin decades before the appearance of the first clinical
manifestation [12]. Disease-modifying treatments might be most
effective when initiated early in the course of AD, before amyloid
plaques and neurodegeneration become widespread. Therefore,
identifying predictive/diagnostic markers that can detect preclinical
changes at the earliest stages is the key for finding effective treatments.
Although mild cognitive impairment (MCI) is often used as a transition state between normal aging and dementia, a substantial
number of MCI patients are reclassified as normal aging on follow
up [13]. The description of MCI has been derived from clinical and
neuropathological settings and its definition is continually being
revised. Much of our understanding of disease progression of AD
has been driven by animal models, which enable controlled studies of
physiopathology and biomarker identification as well as allow for the
differentiation between causes and consequences. While the usefulness
of transgenic (Tg) mice is undisputed in pre-clinical studies, Tg mice
fundamentally represent the familial subtype of AD. In contrast, there
is a paucity of models for LOAD and it has been suggested that rabbits
fed a cholesterol-enriched diet models this disorder [14,15]. The rabbit
Aβ peptide sequence is identical to human. When rabbits are fed a diet
supplemented with 2% cholesterol alone, or 1% cholesterol plus trace
amounts of copper in drinking water, they develop AD pathology. This
includes cortical amyloid deposits, and up to twelve other pathological
markers also seen in human AD brains [14-18]. Aβ immumoreactive
neurons are observed in the hippocampus and adjacent cortex after
4 weeks of the cholesterol enriched diet, and in the frontal cortex
after 6 weeks. ApoE immunoreactivity and fully activated microglial
cells are observed in rabbits fed the cholesterol diet for 8 weeks [14].
After 10 weeks, soluble Aβ becomes detectable (50-100 pg/mg) by
ELISA in the hippocampus, reaching 100-150 pg/mg by 12 weeks,
when insoluble Aβ appears (Ghribi unpublished results) along with
hyperphosphorylated tau. Significant neuronal loss is observed in the
frontal cortex, hippocampus and cerebellum [14-18]. High cholesterol
content in neurons is accompanied by an increase in BACE1 activity
and a shift of AβPP processing in favour of Aβ production [17]. Despite
their potential utility in LOAD research, this model remains more
costly to maintain and is less well characterized compared to ‘gold
standard’ transgenic rodent models. |
| |
| In the present study, we have used Fourier transform ion cyclotron
resonance mass spectrometry (FTICR-MS)-based metabolomics to
obtain an unbiased survey of the metabolomic effects of cholesterol
in the etiology of AD-like progression using cerebrospinal fluid (CSF)
collected from a time-course during the cholesterol diet treatment.
This ‘non-targeted’ approach has the advantage of detecting novel
compounds and is therefore ideally suited for biomarker-driven
discovery applications. The identification of a metabolomic signature
in this model may be subsequently applied to human AD research and
may facilitate pathway, network analysis and identification of possible
candidate biomarkers for downstream targeted analyses. |
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| Materials and Methods |
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| Experimental animals and laboratory procedures |
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| New Zealand white male rabbits (1.5 year old, weighing 3–4 kg)
were used in this study. Animals were randomly assigned to two
groups as follows: group 1 was fed normal chow (n = 3), and group
2 was fed chow supplemented with 2% cholesterol (n = 9), (Harlan
Teklad Global Diets, Madison, WI). Diets were kept frozen at −10°C
to reduce the risk of oxidation. The animals were allowed water filtered
through activated carbon filters. Animals were euthanized with 1 ml
intravenous injections of euthasol. Cholesterol-treated rabbits were
euthanized (three each time point) at 4, 8 and 12 weeks and control
rabbits were euthanized (one each time point) at 4, 8 and 12 weeks.
Since the average life span of indoor New Zealand white rabbits is
10-12 years, the control rabbits sacrificed along with treated rabbits
at different time points were considered similar in age. Cerebrospinal
fluid was collected by puncturing a 25 G needle into the cisterna magna. Unfortunately, we had to discard CSF samples from the 4
week control rabbit and one of the 8 week cholesterol-fed rabbit due
to visible blood contamination, resulting in a final list of 2 control and
8 treated CSF samples. The samples were centrifuged at 4500 × g at
5°C for 5 min to obtain cell free supernatants. The supernatants were
divided in 500 μL aliquots, and frozen immediately in liquid nitrogen
and stored at -80°C until taken for analysis. At necropsy, animals were
perfused with Dulbecco’s phosphate-buffered saline at 37 °C and the
brains were promptly removed. All animal procedures were carried
out in accordance with the U.S. Public Health Service Policy on the
Humane Care and Use of Laboratory Animals and were approved by
the Institutional Animal Care and Use Committee at the University of
North Dakota. |
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| Quantification of Aβ levels by ELISA |
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| The control and 12 week cholesterol treated rabbits described
above plus 4 addition control and 3 12-week cholesterol treated rabbits
from a previous study were used for this experiment [19]. Aβ40 and
Aβ42 levels were quantified in the cortex of all animals using an
ELISA kit from Biosource (Carlasbad, CA) as per the manufacturer’s
protocol and as we have described previously [19]. The values of Aβ
levels obtained by ELISA were normalized to the amount of protein in
the samples. The values were expressed as means ± standard deviation.
The changes in the levels of Aβ were considered significant at p < 0.05.
Levels of Aβ40 and Aβ42 were expressed as pg/mg of protein. |
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| Thioflavin staining |
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| Coronal frozen sections (30 μm), cut at the level of the hippocampus
from controls and cholesterol-treated rabbits (12 weeks) and kept in
PBS, were incubated in 0.25% potassium permanganate for 20 min,
rinsed in 2 X 2 min dH2O and then incubated in bleaching solution (1
g potassium metabisulfite, 1 g oxalic acid, in 100 ml dH2O) until they
appeared white. Section were washed in dH2O, allowed to float in 0.25%
acetic acid, washed again in dH2O, mounted on slides and allowed to
dry. Section were again washed in dH2O, stained for 5 minutes with a
solution of 0.015% Thioflavin-S in 50% ethanol, and briefly rinsed in 2
changes of 50% ethanol followed by 2 brief changes of dH2O, and then
mounted with glycerin gelly and visualized under a Leica fluorescence
microscope. |
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| Sample extraction |
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| After the addition of tracking standards, CSF samples were
prepared for FTICR-MS analysis by sequentially extracting three-time
200 μL of CSF with equal volumes of 1% ammonium hydroxide and
ethyl acetate (EtOAc). All extractions were performed on ice. Samples
were centrifuged between extractions at 4°C for 10 min at 3500 rpm
and the organic layer was removed and transferred to a new tube
(extract A). A 1:5 ratio of EtOAc (extract A) to butanol (BuOH) was
then evaporated under nitrogen to the original BuOH starting volume
(extract B). Aqueous molecules were isolated from extract A using
0.33% formic acid (extract C). All extracts were stored at -80°C until
FTICR-MS analysis. |
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| FTICR-MS analysis |
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| The prepared extracts were analyzed as previously described
[20] using electrospray ionization (ESI) (positive and negative) and
atmospheric pressure chemical ionization (APCI) (positive and
negative) each with organic and aqueous phases of the extraction.
Samples were directly injected using ESI and APCI at a flow rate of
600 μL per hour. Collectively, six separate injections were performed on each sample (Table 1). All analyses were performed on a Bruker
Daltonics APEX III FTICR-MS equipped with a 7.0 T actively shielded
superconducting magnet (Bruker Daltonics, MA, USA). Ion transfer/
detection parameters were optimized as described previously [20].
Calibration standards were used to internally calibrate each sample
over the m/z range 100 - 1000 amu as detailed in earlier work [20].
FTICR data were analyzed using a linear least square regression line
such that the internal standard peak had a mass error of < 1 ppm when
compared to the theoretical mass. A peaklist containing the accurate
mass and absolute intensity of each ion was created for the mass
spectra from each analysis using XMASS software (Bruker Daltonics,
MA, USA) as described previously [20]. A two dimensional array of
mass versus ion intensity was created using DISCOVAmetrics software
(Phenomenome Discoveries Inc, Saskatoon, Canada) and data were
integrated from multiple files to determine unique masses. Processing
and peak picking was carried out as described by Ritchie et al. [20] and
produced a single data file per sample that was then merged and aligned
to create a two dimensional metabolite array where rows represented
metabolites and columns represented metabolite intensity values. The
files generated contained the neutral mass of all of the 12C and higher
intensity 13C metabolites. The intensities were expressed as a signalto-
noise (S/N) ratio. It also includes calibration statistics for validation
purposes. Metabolite array tables were then used for statistical analyses. |
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Table 1: Extraction and ionization mode and detected masses per analytical mode. |
|
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| Statistical analysis |
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| FTICR-MS accurate mass array alignments were performed using
DISCOVAmetrics™ version 4.0, including principal component
analysis (PCA), hierarchical clustering analysis (HCA) and unique PCA
loading visualizations (Phenomenome Discoveries Inc, Saskatoon,
Canada). Statistical analysis and graphs of FTICR-MS data were carried
out using Microsoft Office Excel 2007. F-tests and Paired Student’s
T-tests were used to assess significance with P-values of less than 0.05
considered significant. For every sample analyzed, there were a total
of six analytical modes which were monitored for suppression effects
and reproducibility through internal standards [20]. The coefficient of
variation (CV) should average less than 20% per mode. In most cases
the CV average was between 10 and 15%. Analytical reproducibility
was monitored by analyzing a pooled CSF extract six times within each
run. Each of the replicate profiles were then compared to each other
by plotting the intensities of all combinations of the six replicates.
R-squared values are typically > 0.95 (using non-log transformed data
results in R-squared values > 0.999). |
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| Results |
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| Pathological characterization of the rabbit model used in this
study |
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| To ensure that the rabbits fed with diet supplemented with 2%
cholesterol develop AD characteristics, we performed ELISA analysis
of the cortex from control and 12 week cholesterol treated rabbits
(Figure 1A). This analysis confirmed that the treated rabbits had significantly higher amyloid load (Aβ 40 and Aβ 42) in their brains.
Next, we performed immunohistochemical staining for Aβ plaques in
the hippocampus from controls and cholesterol treated rabbits using
Thioflavin S staining. Only sections from 12 week cholesterol treated
rabbits showed Aβ like-plaques (Figure 1B, arrows). |
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|
Figure 1: Cholesterol-enriched diet induced the accumulation of A β-like
plaques in rabbit brain.
A. ELISA assay showed that the 2% cholesterol-enriched diet significantly
increased both Aβ42 and Aβ40 levels. Values from control (n = 6) and 12 week
cholesterol-fed (n = 6) rabbits were expressed as mean ± standard deviation,
*p<0.05, ***p<0.01. B. While Thioflavin staining revealed no immunoreactivity
to Aβ42 in control rabbits, Aβ42-like plaques was present in hippocampus
of 12 week cholesterol-fed rabbits (arrows) as observed under fluorescence
microscope. Scale bar = 50 μm. |
|
|
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| Global statistical overview of the metabolomic dataset |
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| Non-targeted metabolomic profiles of CSF from rabbits fed a
high cholesterol diet over a twelve week time-course and control
animals were generated. CSF metabolites (100-1500 Da) were captured
through liquid extraction and infused directly into an FTICR mass
spectrometer, using either electrospray ionization or atmospheric
pressure ionization. Alignment of the mass spectrometry profiles of
each sample allowed generation of two-dimensional metabolite arrays
that were used for global statistical analyses. In total, the non-targeted
metabolomic analysis of these CSF samples resulted in the detection of
6515 accurate masses. Table 1 summarizes the metabolomic dataset in
terms of the total number of mass detected across each of the six analysis
modes. A total of 3987 masses were detected from the two aqueous
extractions and 2528 masses from four organic metabolite extractions.
A principal components analysis (PCA) based on 375 masses with an
F-test p-value less than 0.05, revealed a clear separation of the samples
collected at the 4 time points (Figure 2 left panel). The PCA created
using a more stringent cut-off with a p-value less than 0.01, 95 masses,
revealed three clusters of samples (Figure 2 right panel). Samples
collected at 12 weeks were clearly separated from the other time points
along PC1, indicating that the greatest metabolic variance occurred
between the early collections (controls, weeks 4 and 8) and the later
collections (weeks 12). Although samples from the same time points
are well clustered together, the week 4 and week 8 samples appeared
metabolically very similar. |
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|
Figure 2: Principal component analysis plots (PCA) of all statistically
significant masses.
This analysis was based on the data from an F-test comparison of all samples
with a p-value < 0.05 (left panel, 375 masses, log2 scaled) and a p-value <
0.01 (right panel, 95 masses, log2 scaled). Groups are indicated in the legend
between the PCA plots. Separation was observed between early (control to 8
weeks) and late time points (12 weeks) along PC1. Controls and week 4, 8
samples were separated along PC2. |
|
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| Identification of masses correlating with cholesterol
treatment |
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| To begin identifying masses specifically associated with the diet,
pair-wise t-tests were completed between each of the time points (Table
2). The number of significant masses increased with the length of time
the rabbits were fed the cholesterol diet, with 382 masses with p < 0.05
present at week 4 versus control and 595 masses (p < 0.05) between 8
and 12 week samples. |
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|
Table 2: The number of masses deemed statistically significant from t-test
comparisons between each pair of samples. |
|
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| A hierarchically clustered two-dimensional heatmap (HCA) of
the metabolite intensities (p < 0.01, log2 scaled) is shown in Figure 3.
Relative loadings contributions of each mass for principal components
one through three are shown as red and green bar graphs on the left
hand side of the HCA plot. Samples were clustered using a Euclidean
distance metric and masses were clustered using a Pearson correlation
coefficient distance metric, which produces clusters of masses based
on similarity of expression from sample to sample. Similar to the
PCA, complete separation was achieved between the four different
collection time points, with the 12 week samples clustering separately
from the other samples. The masses clustered into four distinct groups,
as identified on the right side of the figure. The masses in the first 3
clusters all appeared to be detected at lower intensities in the samples
collected at 12 weeks, and the fourth cluster contains masses that were
elevated at 12 weeks. |
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|
Figure 3: A hierarchically clustered two-dimensional heatmap (HCA) of
the metabolite intensities.
This HCA analysis was based on 95 masses showing significant differences
with p value < 0.01. Cells are colored according to the signal to noise (S/N)
intensity (log2 scaled). Masses clustered using a Pearson correlation,
samples clustered using Euclidean correlation. Light blue - low intensity, dark
blue/purple/red - medium intensity, orange/yellow/green - highest intensity. |
|
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| In order to identify the specific trend in each cluster, the data was
normalized to the control samples and the average fold change across
all of the masses was calculated and plotted (Figure 4). The masses
in the first cluster were significantly (p < 0.05) lower in the samples
collected from animals fed the high cholesterol diet (weeks 4, 8 and 12)
compared to levels detected in the control animals. The masses in the
second cluster were detected at lower levels in the week 12 samples in
comparison to all other samples. The masses in the third cluster were
detected at elevated levels in the 8 week samples (in comparison to the
control and week 4 samples), and decreased in the week 12 samples
(in comparison to the control and week 8 samples). The fourth cluster
contained all masses that have been detected at significantly elevated
levels in the week 12 samples in comparison to all other samples. |
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|
Figure 4: Graphs showing patterns of metabolic changes during the
course of cholesterol treatment.
Graphs represent the average fold change (log2 scale) from controls for all
masses contained within each of the four clusters identified in the HCA. Error
bars represent standard deviations. a = p < 0.05 from control, b = p < 0.05
from week 4, c = p < 0.05 from week 8. |
|
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| Putative Metabolite Identifications |
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| Masses within each cluster were further investigated by attempting
to assign putative identifications using computational molecular
formulas assignments and database searching in combination with
contextual data such as ionization mode, extraction method, statistical
clustering and biological relevance. |
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| A number of phosphorylated glycerolipids have been identified
in the second cluster of the HCA (Table 3). These masses are
significantly decreased in the samples collected after 8 weeks of
treatment. Two masses, 706.5527 and 674.5236, have been putatively
identified as plasmanic acids, which are precursors for ether lipids
such as plasmalogens. These lipids are likely all precursors, or
breakdown products, of larger membrane phospholipids. The second and third clusters of the HCA contain a number of masses that have
been putatively identified as peptide fragments, including posttranslationally
modified and multiply charged peptide fragments. The
putative molecular formula assignments and metabolite identifications
of these peptide fragments are summarized in Table 4. They are
likely peptide fragments ranging in length from 4-8 amino acids. It is
important to note that the identifications in the table represent putative
amino acid assignments; however, it is not possible using this analytical
method to determine their order. |
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Table 3: Putative molecular formula assignments and metabolite identifications for a group of fatty alcohol phosphate decreased after 8 weeks of treatment. |
|
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Table 4: Putative molecular formula assignments and metabolite identifications for peptide fragments changed due to cholesterol treatment. |
|
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| Many of the masses detected in this study, cluster 1 and 4 in
particular, do not correspond to known metabolites in publicly
accessible databases, thus representing novel masses that have been
uniquely detected in the CSF of this rabbit model. Since these masses
have not been detected before, further analysis by MS-MS or NMR will
be required in order to correctly determine their formula and identity. |
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| Discussion |
| |
| Cholesterol fed rabbits has been adopted as a model for sporadic
late-onset Alzheimer’s disease. Although pathological aspects of this
model have been characterized [14-18], systematic studies on the
biochemical changes including transcriptomics, proteomics and
metabolomics have yet to be reported. We report here a study of the
effect of dietary cholesterol on the metabolomic profile of rabbit CSF
over a course of 12 weeks using high resolution FTICR-MS coupled
with flow injection technology. Our data indicates that dietary
cholesterol has a profound impact on the composition of metabolites
in the CSF. Metabolomic differences were detected as early as four
weeks after the high cholesterol diet was initiated with more dramatic
metabolic changes occurring after 8 weeks of cholesterol treatment.
The number of effected masses continued to increase with the length of
time the rabbits were fed the enriched cholesterol diet. These findings
are in accordance with the pathological properties of this model; rabbits
have developed AD-like pathology by 10 weeks characterized in part by
the detection of Aβ peptide (10 weeks) and hyperphosphorylated tau
protein (12 weeks) in the brain [14-18]. There are a large number of
masses detected in this model that have not been reported to be detected
in blood and do not match any known metabolites in the databases that were interrogated [20]. This suggests that these novel masses may be
unique to CSF and brain tissue and represent interesting targets for
analyzing AD pathology. Identification of these currently unknown
metabolites from accurate mass values alone is hampered by the lack
of knowledge of CSF metabolites for humans or other animals. Only
a limited number of studies have reported metabolomic profiling of
‘normal’ metabolite concentration ranges in human CSF [21]. |
| |
| Cholesterol, as either free cholesterol or cholesterol esters, is
present in two pools in the body, which are separated by the blood
brain barrier. Cholesterol is synthesized de novo in the brain, is present
as free cholesterol in myelin, and is an integral component of plasma
membranes [22-24]. By contrast, free cholesterol in the circulation
is transported by lipoproteins (for example LDL, HDL, VLDL).
Cholesterol does not have the ability to cross the blood brain barrier
in healthy animals due to the impermeability of the blood brain barrier
to proteins that carry cholesterol in the circulation. Therefore a direct
link between dietary cholesterol, CSF cholesterol metabolites, and
LOAD is not immediately apparent. This is especially true because
brain cholesterol levels remain unchanged in rabbits that are fed a
cholesterol rich diet [18]. However, an over accumulation of cholesterol
in hippocampal neurons has been observed due to focal disruption of
the BBB and redistribution of cholesterol within brain cells [16,17].
Cholesterol breakdown metabolites, such as 27-hydroxycholesterol,
and (24S)-hydroxycholesterol do have the ability to cross the blood
brain barrier. We have shown that it is 27-hydroxycholesterol that
enhances the production of Aβ by up-regulating APP42 in SH-SY5Y
cells [25]. It was therefore proposed that 27-hydroxycholesterol is
likely the link between circulating cholesterol and AD like brain
pathology [18]. Interestingly, none of the changed masses identified as
significant in the rabbit CSF matches the cholesterol metabolites such
as 27-hydroxycholesterol, or (24S)-hydroxycholesterol, suggesting that
these oxysterols might have been further metabolized or converted into
others molecules secreted into the CSF. |
| |
| In the present study, the significantly affected masses could be
separated into four clusters based on how they varied throughout the
time-course of the experiment. The first cluster represented a small
number of metabolites with decreased levels observed at 4 weeks on
the cholesterol diet which remained significantly decreased for the
remainder of the 12 week treatment. These masses are likely directly
associated with high cholesterol content in the diet. Masses which
are present in the second cluster remain unchanged throughout the
first 8 weeks of treatment but undergo a significant decrease at the
12 week time-point. Identification of these masses suggested that
the majority belong to a common family including phosphorylated
fatty alcohols, akylacyl or dialkyl-glycerophosphates (Table 3). All of
these metabolites are potential precursors or degradation products
of phospholipids including phosphatidylcholines and plasmalogens.
Phospholipids and their precursors are largely synthesized in the liver
and then excreted from cells into lipoproteins (predominantly HDL and
LDL) for transport throughout the body where they are incorporated
into the cellular membrane [26]. Transport of phospholipids into
the CSF has been shown to occur by receptor-mediated transcytosis
of LDL transporters [27,28]. Hepatic damage is also known to occur
after long term cholesterol loading [29]. Phosphatidylcholine and
phosphatidylethanolamine levels were shown to be decreased in the
liver after cholesterol-loading suggesting that liver damage may affect
its ability to produce phospholipids. A decrease in the concentrations
of circulating LDL-phopholipid was also reported [30]. These previous
results suggest that the observed decrease of fatty alcohols, alkylacyl
and dialkyl-glycerophosphates in the CSF could result from either altered transport from the liver to the CSF or decreased liver synthesis,
or a combination of both processes. These findings are in agreement
with a previous report showing a correlation between decreased liver
phospholipid metabolism and Alzheimer’s disease [31]. |
| |
| The observed decrease in these possible precursors of phospholipid
synthesis at week 12 could represent a change in the membrane
composition of cells within the brain. Increased membrane cholesterol
causes a shift in the composition of the cellular membrane to include
more cholesterol-rich lipid rafts. A clear link exists between membrane
cholesterol levels, increased dietary cholesterol and the production of
Aβ and AD pathogenesis [8-11,32,33]. Alpha-secretase is located in
phospholipid-rich regions of the cell membrane and performs nonpathological
APP processing, while β-secretase is located in lipid rafts
and is involved in the processing of APP into Aβ. Increased membrane
cholesterol has been shown to decrease α-secretase activity [34] and
increase β-secretase activity which explains the increased production
of Aβ [35]. |
| |
| Only after 12-weeks of cholesterol treatment did animals show Aβ
like-plaques (Figure 1B, arrows). This is in agreement with the decrease
in phospholipid precursors only at this time point, suggesting a shift
in membrane composition towards increased lipid rafts, which would
lead to increased β-secretase activity and Aβ productions resulting in
the observed Aβ like-plaques. Further MS-MS analysis of these masses
could confirm their identities and may allow them to be used as CSF
markers of AD pathology. One successful example of such a metabolite
is the discovery of the negative correlation of serum plasmalogen with
age and its link with altered cholesterol processing and AD [36,37].
Plasmalogens are currently being explored as AD diagnostic markers and its synthetic precursor compounds are being tested as potential
therapeutics for AD [37]. |
| |
| Putative identifications of masses revealed a number of peptide
fragments decreased after 8 weeks of cholesterol treatment, some of
them are transiently increased at the 8 week time-points and then
undergo a significant decline at week 12 to levels lower than control.
These results suggest that cholesterol treatment causes significant
protein expression changes in the rabbit brain. The proteins from
which these peptide fragments are derived are currently unknown. In
order to determine which proteins are potentially being affected by
the cholesterol treatment, analysis using MALDI-TOF MS-MS would
need to be performed to determine the precise sequence of the amino
acids, which could provide insight into the link between cholesterol
treatment and AD pathology. Cluster 4 was the only group that showed
a significant increase in levels at the 12 week time-point but was
unaltered at any of the earlier points. Majority of masses in this group
are novel metabolites. Follow up MS-MS study is required to determine
their identities. |
| |
| CSF may be a useful fluid for the study of neurodegenerative
diseases because it is close to the site of pathology and more directly
reflects the metabolic state of the brain due to the free exchange of
molecules between the brain and the CSF. However, animals must be
euthanized before collection and scarified after collection in order to
collect sufficient volume of clean CSF samples. One technical difficulty
encountered during this study was blood contamination into the
CSF of two rabbits, resulting in n=2 for two variables. However, the
percentage of CVs between the replicates on average was very good, which provided good confidence even in the time points with only two
replicates. |
| |
| Conclusions |
| |
| We have performed a non-targeted metabolomic analysis of the
CSF from a rabbit LOAD model generated by feeding a cholesterolenriched
diet. The objectives were to further characterize this model
at the molecular level and identify biomarkers for LOAD. This study
revealed a number of metabolites that were either responsive to
cholesterol or change as a consequence of brain degeneration caused
by high cholesterol levels. We are in the process of further identifying
these masses and the molecular pathways in which they participate.
It is hoped that follow up investigations of these masses will help us
understand the molecular mechanisms of cholesterol-related AD
development. These metabolites, or their derivatives, may be potential
LOAD diagnostic, prognostic or theranostic markers. |
| |
| Acknowledgements |
| |
| This work was supported in part by grants to JDG and OG from the NCRR
(P20RR017699), a component of the NIH. The authors would like to thank Ms.
Joy Lei for her assistance on sample handing, Ms. Dushmanthi Jayasunghe for
her assistance on some of the mass identifications and to Drs Tara M. Smith and
Shawn Ritchie for providing valuable advice in the preparation of this manuscript. |
| |
|
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