2. 1564 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
INTRODUCTION
Alzheimer’s disease (AD) is the main cause of
dementia, and it is predicted that there will be approx-
imately 115 million patients with AD, worldwide, by
2050 [1]. To date, no cure for AD has been devel-
oped.Severalcandidatedrugshavebeenunsuccessful
in trials, possibly as they were tested far too late in
the disease progression for meaningful therapeutic
outcomes to be obtained [2], as pathophysiological
alterations associated with AD are thought to begin
several decades before the condition may be clin-
ically diagnosed. Therefore, early diagnosis would
provide a crucial opportunity for intervention in AD
progression [3].
The levels of amyloid- (A) peptide, total tau
(t-tau) protein, and hyperphosphorylated tau (p-tau)
protein in the cerebrospinal fluid (CSF) strongly cor-
relate with AD progression [4, 5]. However, lumbar
puncture, which is used to measure A peptides
and t/p-tau proteins from the CSF, is invasive and
painful. In addition to CSF analysis, the amyloid
tracer “Pittsburgh Compound B” (PiB) is commonly
utilized in AD diagnosis for research purposes. PiB,
a radioactive analog of thioflavin T, binds to insolu-
ble fibrillary A with high affinity (0.9 (Ki, nM)) and
enters brain in amounts sufficient for imaging with
positron emission tomography (PET). Thus, PET
scans using PiB (PiB-PET) can be used as a means
of monitoring the A plaque load [6, 7]. Moreover,
a postmortem autopsy study showed that PiB bind-
ing area correlates well with histological A plaque
loads [8]. Despite the high sensitivity and specificity
of the PiB-PET imaging technique, it is expensive
for use as a normal screening method and inconve-
nient due to its short half-life of radioactivity (about
20 min) [9]. These limitations necessitate the investi-
gation of proteome profiles in order to develop an
alternative measure to effectively reflect PiB-PET
scores.
Efforts have been made to identify reliable, nonin-
vasive, and inexpensive protein biomarkers indicative
of disease states, for a number of diseases. Specif-
ically, serum protein biomarkers that effectively
represent PiB-PET scores to distinguish mild cog-
nitive impairment (MCI) (a pre-stage of AD) and
AD from normal controls may be highly useful in
the diagnosis of MCI and/or AD. Potential bene-
fits of such serum biomarkers are based on the ease
and non-invasiveness of collecting blood samples
instead of CSF, and also on the fact that blood is an
abundant source of proteomes indicative of disease
states. Moreover, blood-based diagnosis, which may
be performed routinely even in elderly people, is eco-
nomically efficient compared to PiB-PET imaging
[10].
Mass spectrometry (MS)-based proteomic
approaches have facilitated significant advances
in the identification of serum protein biomarkers.
Despite the advantages of blood-based diagnosis,
the high complexity of serum proteomes poses a
challenge for MS-based approaches. The dynamic
range of protein concentrations in serum is larger
than 10 orders of magnitude [11]. Highly abundant
proteins, such as serum albumins and immunoglobu-
lins, may constitute more than 85% of the total serum
protein content. These may hinder the detection
of less abundant proteins, which may be potential
biomarkers. This particular limitation is termed an
undersampling issue. The use of affinity depletion
columns to remove highly abundant proteins sig-
nificantly reduces undersampling issues, thereby
facilitating effective detection of serum biomarkers
[12]. Additionally, the use of the isobaric tagging for
relative and absolute quantitation (iTRAQ) labeling
method enables an effective multiplex quantitative
analysis of serum samples under different disease
conditions [13].
In our study, new serum marker candidates were
identified to reflect the pathophysiological states of
MCI and AD. In order to identify a protein profile
representative of PiB-PET scores, we first collected
serum samples (discovery cohort) from cognitively
normal controls and MCI and AD patients according
to their PiB-PET scores, and then pooled the samples
by group. Three pooled samples were then labeled
with iTRAQ and analyzed by liquid chromatography-
tandem MS (LC-MS/MS). By means of integrated
analysis of MS/MS data for serum and the genomic
and proteomic data for brain tissues, together with
network analysis, we identified biomarker candidates
representing proteolytic processes that are commonly
altered in the sera of patients with MCI and AD
and in the brain tissues of patients with AD. The
alteration of these proteins in MCI and AD was
validated in an independent set of serum samples
(validation cohort) of control, MCI, and AD by
western blotting or enzyme-linked immunosorbent
assay (ELISA) analysis. As a result, we propose
that these three proteins may serve as potential
serum biomarkers for early prediction of MCI
and AD.
3. S. Kang et al. / Serum Proteome Profiles Predict MCI and AD 1565
MATERIALS AND METHODS
Blood collection and serum separation
Serum samples were collected from patients with
AD, MCI, and nondemented elderly control subjects
with informed consent in accordance with the guide-
lines approved by the Institutional Ethical Review
Board at Samsung Medical Center (Seoul, Korea).
The detailed criteria for diagnosis of MCI due to
AD are available in the Supplementary Materials and
Methods. The procedure to collect blood samples was
slightly modified from that used in our previous pub-
lication [14]. In brief, blood samples were obtained
in BD Vacutainer® SST II Plus plastic serum tubes
(Becton, Dickinson and Company, USA). The tubes,
which were treated with clot activators, were incu-
bated at room temperature (RT) for 30 min, causing
fibrinogen in the blood to aggregate. Then, the tubes
were centrifuged at 3000 g for 10 min to separate fib-
rinogen aggregates and other cellular components.
The supernatant was collected and frozen at –80◦C
for later use.
Immunodepletion and sample preparation
The 14 most abundant serum proteins were imm-
unodepleted from serum pools using the Agilent
(Santa Clara, CA, USA) Multiple Affinity Removal
System (MARS) Hu14 Column and buffer kit (Agi-
lent, Santa Clara, CA, USA) according to the
manufacturer’s instructions. The 14 proteins depleted
through this column were albumin, IgG, antit-
rypsin, IgA, transferrin, haptoglobin, fibrinogen,
alpha2-acroglobulin, alpha1-acid glycoprotein, IgM,
apolipoprotein AI, apolipoprotein AII, complement
C3, and transthyretin. The depleted fractions were
buffer-exchanged and concentrated into 20 mM Tris-
Cl (pH8.0) using Spin 5K concentrators (Agilent
Technologies, Wilmington, DE, UK). Protein con-
centration was determined by BCA assay kit (Pierce)
and proteins were stored at 4◦C before digestion.
Digestion of depleted serum proteins by trypsin
One hundred micrograms of depleted serum pro-
teins were denatured by adding urea (>6 M) and 1 M
Tris buffer (pH 8.0 and 50 mM) for 60 min at RT. Pro-
tein reduction was performed with TCEP (5 mM) for
60 min at 37◦C, and the reduced thiols were alkylated
with iodoacetamide (10 mM). After diluting 10 times
with 50 mM Tris buffer (pH 8.0), serum protein was
digested by trypsin at 37◦C overnight. Digestion was
quenched by adding trifluoroacetic acid (0.4%). The
digest was loaded and desalted on Sep-Pak C18 car-
tridge (Waters). Desalted samples were completely
dried in speed-vac and stored at 4◦C.
Isobaric tag for relative and absolute
quantitation (iTRAQ) labeling, Offgel
fractionation, and LC-MS/MS analysis
Each digested and desalted sample (100 g) was
resuspended with 20 L of 50 mM TEAB buffer (pH
8.0) and labeled with a 4-plex iTRAQ reagent (AB
Sciex). The iTRAQ reagents with reporter ion masses
of 115, 116, and 117 were labeled for control, MCI,
and AD sample, respectively. Organic solvent con-
tents were kept at >70% by adding ethanol during
the labeling process (90 min at RT), quenched at
0.4% TFA, and mixed with 1:1:1 ratios before clean-
ing. To remove the excess of iTRAQ reagents and
the TEAB buffer, the mixture solution was applied
to the MCX cartridge (Waters). The iTRAQ-labeled
peptides on cartridge was washed with 5 mL of
0.1% formic acid/H2O and then with 1 mL of 100%
methanol. The iTRAQ labeled peptides were finally
eluted by 1 mL of elution buffer (5% ammonium
hydroxide/45% H2O/50% acetonitrile, v/v/v). The
eluted peptides were immediately dried in speed-vac.
Sample fractionation, based on the isoelectric points
of the samples, was performed using the Agilent
3100 OFFGEL fractionator (Agilent Technologies)
according to the manufacturer’s protocol. A detailed
description for the Offgel fractionation, LC-MS/MS
analysis, and peptide identification is available in the
Supplementary Materials and Methods.
Identification of differentially expressed proteins
(DEPs)
The intensities of iTRAQ reporter ions for all
MS/MS scans in the triplicate experiments were nor-
malized by the quantile normalization method [15].
Using the normalized iTRAQ intensities, the inten-
sity ratios of MCI/Control (116/115) and AD/Control
(117/115) for the identified peptides were calcu-
lated in individual experiments. Protein groups were
defined for the identified peptides by bipartite graph
analysis [16]. The relative protein abundance of
the protein groups (MCI/Control or AD/Control)
in individual experiments were estimated from the
4. 1566 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
corresponding peptide intensity ratios based on the
bipartite graph, using a previously described linear-
programming method [17]. To identify the DEPs, the
one-sample t-tests were applied to the relative pro-
tein abundance values from the three replicates for
MCI/Control or AD/Control comparison. DEPs were
identified as those with at least two non-redundant
peptides, p-values <0.1 from the t-test, and |log2-
ratios| > 1SD in the fold-change distribution (0.31
for MCI/Control and 0.37 for AD/Control).
Functional enrichment analysis
To identify cellular processes significantly repre-
sented by the DEPs, functional enrichment analysis
of the DEPs was performed using the Consensus-
PathDB (CPDB) software [18]. We then selected the
gene ontology biological processes (GOBPs), cellu-
lar components (GOCCs), and molecular functions
(GOMFs) significantly represented by the DEPs as
those with p < 0.05 from the hypergeometric test
provided by the CPDB software, and also those
with more than 5 DEPs with the gene ontology
terms.
Identification of differentially expressed genes
and DEPs in brain tissues from AD patients
Gene expression data collected from hippocampal
grey matter of 22 AD patients and 8 cognitively nor-
mal controls were obtained from the Gene Expression
Omnibus (GSE28146). The microarray intensities
were normalized using the quantile normalization
method [15]. Using the normalized intensities, we
identified differentially expressed genes (DEGs)
between AD patients and controls as those with com-
bined p-values < 0.05 and absolute log2-fold-changes
> 1, as previously described [19]. Next, for proteomic
analysis of AD brain tissues, we combined the lists of
DEPs reported in 6 previous studies (Supplementary
Table 4).
Network analysis
A network model was built for the DEPs involved
in the GOBPs represented commonly by 1) the DEGs
or DEPs from AD brain tissues and 2) the DEPs in
the sera of MCI and AD. To reconstruct the network
model, we collected protein-protein interactions for
the DEPs from MetaCoreTM (ver 6.7; Thomson
Reuters, New York, NY, USA), STRING9.1 [20],
and Hitpredict [21] databases. The network model
was reconstructed using the interactions between the
DEPsandvisualizedusingCytoscape[22].Thenodes
in the network model were arranged according to
their associated GOBPs and pathways, such that the
nodes with similar functions were closely located in
the network model. Node groups that shared similar
functions were labeled with the corresponding GO
terms.
Western blot analysis
Thirty micrograms of crude serum was mixed with
1× sample buffer (60 mM Tris-HCl (pH 6.8), 2%
SDS, 5% 2-mercaptoethanol, 10% glycerol, 0.01%
bromophenol blue) and heated at 95◦C for 5 min.
After separation by 4∼15% gradient or 7.5% SDS
polyacrylamide gel electrophoresis (SDS-PAGE),
proteins on the gels were electrically transferred
onto PVDF membranes (Bio-Rad, Hercules, CA).
Membranes were incubated with rabbit polyclonal
anti-PCSK9 antibody (1:200, abcam) or mouse mon-
oclonal anti-Factor XIII antibody (1:1000, abcam)
in blocking buffer (5% non-fat milk in washing
buffer) overnight at 4◦C. The membranes were then
incubated in HRP-conjugated goat anti-rabbit IgG
(1:5000, Santa Cruz) or goat anti-mouse IgG (1:2000,
Santa Cruz) for 1 h at RT. Membranes were visualized
by ECL (Thermo Scientific Pierce, Waltham, MA).
The intensity of western blot bands was quantified
using the GelQuantNET program (BiochemLabSo-
lution, San Francisco, CA) and normalized to the
albumin band by ponceau S stain as previously
described [23, 24].
Enzyme-linked immunosorbent assay (ELISA)
Human serum DCD levels were measured using
a human proteolysis inducing factor (PIF)/dermcidin
(DCD) detection ELISA kit (Cusabio Life Science,
HP, China) according to the manufacturer’s guide-
lines. In brief, human serum was diluted 1:500
with sample diluents, loaded onto plates coated
with human DCD-specific biotin-conjugated anti-
body, and incubated with horseradish peroxidase
(HRP)–conjugated avidin-specific antibody for 1 h at
37◦C. After 3 washes, stabilized TMB substrate solu-
tion was added for 15–30 min at 37◦C, after which
stop solution was added. Absorbance at 450 nm was
measured on a plate reader (POWER-XS, BIO-TEK,
VT, USA).
5. S. Kang et al. / Serum Proteome Profiles Predict MCI and AD 1567
Statistical analysis
Western blot and ELISA data were analyzed using
Matlab software (version 7.12.0 (R2011a), Math-
Works Inc., Natick, MA, USA). Data were analyzed
by one-way analysis of variance (ANOVA) with
Tukey-Kramer method as a post-hoc test.
RESULTS
Comprehensive global profiling of PiB-PET
imaging-based serum proteomes in MCI and AD
In this study, we used serum samples collected
from 9 subjects (discovery cohort) who were ana-
lyzed by PiB-PET imaging for serum proteome
profiling (Fig. 1). Their PiB-PET scores, MMSE
scores, ages, and APOE genotype information are
listed in Supplementary Table 1A. Serum samples
for cognitively normal controls were collected from
three subjects with negative PiB-PET scores. In
contrast, serum samples for MCI and AD patients
were collected from three patients with positive PiB-
PET scores (PiB-PET uptake ratio ≥1.5 for MCI
and AD) and criteria for diagnosis of MCI due to
AD is included in the Supplementary Materials and
Methods (Fig. 1).
The overall procedures for sample preparation and
LC-MS/MS analysis are depicted in Fig. 1. Three
serum samples of each group were first pooled,
and the top 14 abundant serum proteins were then
immunodepleted from the pooled sample using the
Agilent Hu14 depletion column (Methods). After
the depletion of the abundant proteins, the flow-
through containing relatively less abundant proteins
was digested with trypsin. The resulting peptides
from controls, MCI, and AD serum samples were
then labeled with iTRAQ reagents: iTRAQ 115–117
for control, MCI and AD, respectively. The iTRAQ-
labeled peptides were fractionated into 12 fractions
usingtheAgilentOffgelfractionator.The12fractions
were then analyzed 3 times by LC-MS/MS.
For the triplicate MS/MS data, peptides were iden-
tified using MS-GF+ and SEQUSET search engines,
followed by protein identification and quantification
(Supplementary Materials and Methods; Supplemen-
tary Figure 1). A total of 12,750 non-redundant
peptides (10,248, 10,397, and 10,326 peptides from
the triplicates) were detected from the sera of con-
trols and patients with MCI and AD in the discovery
cohort(SupplementaryFigure2A).Ofthesepeptides,
8,157 (64.0%) were identified in all the triplicates,
and 10,064 (79.0%) were identified in more than 2
of the triplicates. Furthermore, Pearson correlation
coefficients of iTRAQ intensities of the 8,157 pep-
tides between the triplicates were larger than 0.9
(Supplementary Figure 2B-D). These data indicate
that both peptide identification and quantitation by
LC-MS/MS analysis were highly reproducible. The
12,750 peptides belonged to 1,580 protein groups
based on bipartite graph analysis [16]. Among these,
we focused on 827 protein groups of high confidence
with more than 2 non-redundant peptides, which were
mapped to 809 protein coding genes.
Characteristics of the proteomes detected
from AD and MCI serum samples
In order to assess the comprehensiveness of the
serum proteome analysis, we compared the 809 gene
products detected in our study with AD- and MCI-
related plasma proteomes reported in two previous
studies (Fig. 2A). Song et al. [25] profiled the pro-
teomes of control, AD, and MCI plasma samples
obtained from the Sydney Memorial and Ageing
Study using iTRAQ experiments, and identified 146
proteins in all the plasma samples used. These were
mapped to 102 protein coding genes (Fig. 2A).
Muenchhoff et al. [26] also profiled the proteomes of
control, MCI, and AD plasma samples, which were
obtained from two independent cohorts, the Sydney
Memorial and Ageing Study and the Hunter Com-
munity Study, and identified 135 proteins that were
mapped to 134 protein coding genes (Fig. 2A). The
comparison showed that more than 95% of the two
previous proteomes [97 (95.10% of 102) and 128
(95.52% of 134) gene products] were detected in our
serum proteome (Fig. 2A), supporting the validity of
the serum proteome profile. Moreover, the present
serum proteome further revealed 680 (84.1% of 809)
additional gene products not detected in the two
studies. Furthermore, unlike the previously reported
proteomes, the proteome in the present study pro-
vided information relevant to PiB-PET scores. These
data indicate that the proteome in the present study
mayrepresentthecomprehensiveserumproteomesof
MCI and AD patients associated with their PiB-PET
scores.
To obtain a further understanding of AD and MCI,
as enabled by the serum proteome described in this
study, we examined subcellular proteomes and cellu-
lar processes uniquely represented by the 680 newly
identified gene products (not the 129 shared ones).
To this end, we performed enrichment analysis of
6. 1568 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
Fig. 1. The overall workflow of serum proteome profiling using iTRAQ-based LC-MS/MS analysis. Three groups of serum samples of
discovery cohort (cognitively normal controls, MCI, and AD) were collected (n = 3 in each group) and pooled. Top 14 abundant proteins
were depleted using the Hu14 depletion column. The depleted samples were then subjected to tryptic digestion. Next, the resulting peptides
were iTRAQ-labeled and fractionated into 12 fractions. Each fraction was analyzed using LC-MS/MS. Finally, peptide identification was
performed using both MS-GF+ and SEQUEST, and peptide quantification was carried out using the intensities of the 115–117 iTRAQ
reporter ions.
gene ontology cellular components (GOCCs) and
gene ontology biological processes (GOBPs) for
the two sets of gene products (680 and 129 pro-
teins) using CPDB software [18]. Comparison of the
GOCCs showed that both sets of 680 and 129 proteins
commonly represented the secreted proteomes, such
as the ‘extracellular proteome’ (extracellular vesic-
ular exosome and extracellular space), the ‘blood
7. S. Kang et al. / Serum Proteome Profiles Predict MCI and AD 1569
Fig. 2. Comprehensive serum proteome profiles of MCI and AD patients. A) Comparison of the serum proteome in the present study with two
previous plasma proteomes of MCI and AD patients. The Venn diagram shows the relationships between the detected proteins in individual
studies. The number in parenthesis represents the total number of proteins detected in the corresponding study. The table shows 1) sample
types (serum or plasma), 2) sample groups used (AD, MCI, and Control), 3) whether PiB-PET imaging was used for diagnosis of the samples,
4) the numbers of proteins detected, and 5) the number of genes encoding the detected proteins. GOCCs (B) and GOBPs (C) represented by
the 680 newly identified proteins in our study (blue) and the 129 shared proteins in this study and previous studies (gray). The lengths of the
bars indicate the numbers of proteins with the corresponding GOCC (B) or GOBP (C) in the two sets of proteins (680 and 129 proteins). The
numbers in each bar represent the total number of proteins with the GOCC or GOBP and the shared proteins / newly detected proteins in
parenthesis. The asterisks indicate that the corresponding GOCCs or GOBPs are significantly enriched by the 680 newly detected proteins
but not by the 129 shared proteins.
particle proteome’ (blood microparticle and plasma
lipoprotein particles), and ‘organelle lumen pro-
teomes’ (endoplasmic reticulum and Golgi lumens)
(Fig. 2B), supporting the validity of our serum
proteome. Additionally, the 680 proteins uniquely
represented proteomes related to the ‘neurologi-
cal system’ (neuron projection, axon, neuronal cell
body, and myelin sheath). Moreover, the comparison
of the GOBPs further showed that the 680 pro-
teins uniquely represented the GOBPs related to the
‘neurological system’ (nervous system development,
neuron projection development, synapse assembly,
and transmission of nerve impulse) (Fig. 2C). These
data indicate that our serum proteome provides addi-
tional information that can reflect aberrations in
neurological systems in MCI and AD, enabling the
monitoring of these alterations in the serum.
Serum proteomes altered in MCI and AD
To identify serum proteins whose levels were
altered in MCI and/or AD, we first estimated
the relative abundance of serum proteins in MCI
(MCI/Control) and AD (AD/Control), compared to
controls, separately in individual experiments (trip-
licates) using iTRAQ intensities, using a previously
described linear-programming method [17] (Meth-
ods). By applying the t-test to the resulting data,
we identified 121 differentially expressed proteins
(DEPs) in MCI (79 DEPs) and AD (72 DEPs)
with positive PiB-PET scores, compared to controls
with negative PiB-PET scores (Methods; Supple-
mentary Table 2). The comparison showed that 30
DEPs were common to MCI and AD, suggesting
the possibility that both MCI and AD with positive
8. 1570 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
PiB-PET scores can be predicted using the same
protein profiles (Fig. 3A). Moreover, 49 and 42
DEPs were uniquely identified in MCI and AD,
respectively, indicating that MCI and AD can be dis-
tinguished using these unique DEPs, which reflect
the difference in the PiB-PET scores between MCI
and AD.
To systematically investigate the characteristics of
the DEPs, we categorized them into 7 groups (Groups
1–7) based on their alteration patterns in MCI and
AD (Fig. 3B). Groups 2 and 6 showed the same
alteration patterns in MCI and AD, while Groups 1,
3, 5, and 7 showed alterations that were predomi-
nant in either MCI or AD. In order to understand
the cellular processes represented by Groups 1–7,
we performed enrichment analysis of GOBPs for the
DEPs in Groups 1–7 using CPDB software (Sup-
plementary Table 3). This analysis revealed that the
proteins upregulated in both MCI and AD (Group 2)
represented ‘protein metabolic processes’, and the
downregulated proteins in both MCI and AD
(Group 6) represented ‘vesicle-mediated transport’
and ‘response to stress’ (Fig. 3C, left panel). Inter-
estingly, the proteins that were downregulated only
in MCI (Group 5) represented GOBPs related to
the ‘neurological system’ (regulation of cell com-
munication and hydrolase activity, neuron projection
development, and cytoskeleton organization), indi-
cating the degeneration of the neurological system in
MCI. Moreover, the proteins that were upregulated
in either MCI or AD (Groups 1 and 3) represented
the same GOBPs (vesicle-mediated transport and
response to stress) as Group 6, including the proteins
downregulated in both MCI and AD, suggesting that
different parts of the networks for these GOBPs were
upregulated or downregulated in MCI and AD. These
data provide a list of GOBPs associated with posi-
tive PiB-PET scores, which can be monitored in the
serum.
A serum proteome profile representing
the pathophysiology of MCI and AD
Serum proteomes measured under pathological
conditions have shown that serum can often serve
as a window of disease-perturbed cellular networks
in the tissues [27–30]. To understand this aspect, we
performed integrated analysis of the present serum
proteome with both the genomic and proteomic data
previously generated for the AD brain tissues (see
Supplementary Table 4) for detailed information
of these datasets). To this end, we first identified
3,465 differentially expressed genes (DEGs) and 611
DEPs in AD brain tissues, compared to controls,
from the genomic and proteomic datasets, respec-
tively (Methods). We then compared the cellular
processes (GOBPs) represented by the DEGs and
DEPs with those represented by the DEPs in Groups
1–7 (Fig. 3C). The comparison revealed that the 9
GOBPs represented by the serum DEPs identified
in the present study (Fig. 3C, left panel) were also
represented by the tissue DEGs or DEPs (Fig. 3C,
right panel). This suggests that alterations of these
processes in AD brains can be monitored by these
serum DEPs (88 of 121 DEPs), which are involved
in the 9 GOBPs and also associated with the positive
PiB-PET scores.
In order to understand how the 88 serum DEPs
collectively define alterations of these processes at
the molecular level, we constructed a network model
describing the interactions between the 88 DEPs
using protein-protein interactions from the inter-
actome database (Methods). The network model
showed dense connections between upregulated and
downregulated processes representing the 9 GOBPs,
suggesting close functional associations between
these processes and demonstrating that the present
serum DEPs provide a view of the closely associated
processes in the brains of patients with AD.
The DEPs common to both MCI and AD can
serve as early diagnostic markers that can repre-
sent altered processes commonly in both MCI and
AD. Therefore, in order to select biomarker candi-
dates, among the processes in the network model,
we focused on the processes (protein metabolic pro-
cess, vesicle-mediated transport, and response to
stress) represented by the shared DEPs (Groups 2
and 6 in Fig. 3B; Supplementary Figure 3). More-
over, upregulated proteins are easy to monitor the
disease progression [31–33]. Therefore, among the 3
processes represented by the shared DEPs, we fur-
ther focused on the protein metabolic process which
is represented by 7 upregulated shared DEPs (Supple-
mentary Figure 3). Finally, of the 7 DEPs, we selected
the following 6 proteins as biomarker candidates:
PSMA1, PCSK9, DCD, WARS, F13A1, and MMP9.
During the selection, SERPINB1 was removed as it
was found to be affected by the depletion of the top
14 abundant serum proteins. Many previous studies
have demonstrated that proteolysis is closely linked
to A accumulation in AD brains [34, 35], support-
ing the validity of the 6 biomarkers in the selected
process and also its association with the PiB-PET
scores.
9. S. Kang et al. / Serum Proteome Profiles Predict MCI and AD 1571
Fig. 3. Identification of marker candidates related to AD pathogenesis. A) Venn diagram showing the relationships between the DEPs in
MCI (79 DEPs) and AD (72 DEPs). B) Seven groups (G1-7) of the DEPs, defined by their up- or downregulation patterns in triplicate iTRAQ
experiments. Colors in the heat map indicate upregulation (red) and downregulation (green) in MCI (MCI/Control) and AD (AD/Control),
compared to the controls. The color bar represents the gradient of log2-protein-ratios. Numbers in parentheses indicate the number of
proteins in individual groups. C) GOBPs represented by the groups of DEPs. The color bar represents –log10(P) where P is the enrichment
p-value obtained from the CPDB pathway enrichment tool. Also, GOBPs represented by the DEGs (DEG AD/Control) and the DEPs
(DEP AD/Control) identified from AD brain tissues were denoted in blue and orange, respectively. D) A network model showing the
interactions between 88 DEPs involved in the 9 GOBPs commonly represented by the serum DEPs and the tissue DEGs and DEPs (GOBPs
labeled in color). Node colors represent upregulation (red) and downregulation (green) of the proteins in MCI/Control (center) and AD/Control
(boundary). The color bar represents the gradient of log2-protein-ratios. Edges represent protein-protein interactions obtained from Metacore,
STRING, and Hitpredict interactome databases.
10. 1572 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
Fig. 4. Validation of the selected marker candidates in the serum of control, MCI and AD patients. Results of western blotting of PCSK9
(A) and F13A1 (B). Serum levels of the proteins were measured in independent samples of validation cohort (for PCSK9, 10 controls,
MCI, and AD samples; and for F13A1, 12 controls, MCI, and AD samples). Data are normalized to the albumin band by ponceau S stain
as previously described [23,24]. C) Results of ELISA of DCD. Serum levels of DCD were measured in independent samples of validation
cohort (15 control, 10 MCI, and 15 AD samples). ∗p < 0.1, ∗∗p < 0.05, and ∗∗∗p < 0.01 from one-way analysis of variance (ANOVA) with
Tukey-Kramer method as a post-hoc test.
Validation of the selected protein profile
To test the validity of the 6 selected candidates,
we collected sera from independent sets (validation
cohort) of controls, and MCI and AD patients based
on the criteria (Methods) employed for the collection
of the discovery cohort samples used for LC-MS/MS
analysis. The sex, MMSE scores, ages, and APOE
genotype information of the patients in the valida-
tion cohort are listed in Supplementary Table 1B. In
these new samples, we then tested upregulation of the
5 selected proteins measured by LC-MS/MS analy-
sis (PSMA1, PCSK9, WARS, F13A1, and MMP9)
using western blotting and also upregulation of DCD
using ELISA (Methods). Of the 6 selected proteins,
we finally selected the 3 proteins (PCSK9, F13A1,
and DCD) that showed the upregulation in the inde-
pendent validation cohort of MCI and AD samples,
consistent to those shown in the discovery cohort
by LC-MS/MS analysis (Fig. 4A-C; Supplementary
Figure 4). Interestingly, F13A1 showed the gradual
elevation in its abundance from controls to MCI and
AD, which reflects the PiB-PET scores. As a result,
these data demonstrate that the 3 proteins can be used
as a protein profile predictive of MCI and AD with
the positive PiB-PET scores.
DISCUSSION
Serum biomarkers have several advantages, such
as non-invasiveness, ease of use, and cost-effective-
ness, in clinical applications. Moreover, blood is an
abundant source of proteomes that can reflect AD
states [10, 36]; for instance, due to the disruption of
theblood-brainbarrier,whichisawell-knownpathol-
ogy of AD, resulting in the exchange of materials
between the CSF and blood in both directions [37].
In this study, using iTRAQ-based LC-MS/MS anal-
yses of controls with negative PiB-PET scores and
MCI and AD with positive PiB-PET scores, we iden-
tified 6 biomarker candidates as upregulated proteins
in both MCI and AD, compared to controls, which
reflected the positivity of the PiB-PET scores. Out of
these 6 candidates, we selected 3 proteins (PCSK9,
F13A1, and DCD) whose upregulation in both MCI
and AD was confirmed in the validation cohort by
western blotting or ELISA.
A number of previous studies have demonstrated
that these 3 proteins are associated with AD, as
follows: 1) PCSK9 is known to regulate the levels
of BACE1, which is involved in the generation of
A [38]; 2) PCSK9 promotes neuronal apoptosis,
which is crucial for A-dependent neurodegener-
ation [39]; 3) F13A1 was immunohistochemically
detected in reactive microglia during gliosis, a hall-
mark feature of AD pathogenesis [40]; 4) a common
polymorphism of F13A1 is associated with spo-
radic AD [40]; and 5) the amino-terminal sequence
of DCD binds to HSPA4 and affects its ability to
trigger the aggregation of toxic A proteins [41].
However, to our knowledge, the levels of these pro-
teins have not been previously reported to be altered
in the serum of patients with MCI or AD. More-
over, the alteration in proteolysis in which the 3
selected proteins are involved is a common feature
of neurodegenerative diseases. Thus, we examined
11. S. Kang et al. / Serum Proteome Profiles Predict MCI and AD 1573
associations of PCSK9, F13A1, and DCD with other
neurodegenerative diseases (e.g., Parkinson’s disease
or Huntington’s disease) than AD. However, to our
knowledge, there have been no previous studies that
reported associations of PCSK9, F13A1, and DCD
with other neurodegenerative diseases than AD.
Several studies have elucidated the serum pro-
teomes of AD patients, as illustrated in Fig. 2A, and
also the DEPs in AD, compared to controls. Song
et al. [25] reported the analysis of 146 plasma proteins
in the serum of 411 controls and 19 AD and 261 MCI
patients, and also identified a total of 77 DEPs for
AD (49 DEPs) and MCI patients (70 DEPs). Second,
Muenchhoff et al. [26] also reported the analysis of
135 plasma proteins from 564 controls and 24 AD and
441 MCI patients, and further identified 44 DEPs for
AD (41 DEPs) or MCI patients (19 DEPs). Although
they could have served as useful resources for discov-
ery of AD-related biomarkers, none of these proteins
were found to be correlated with positive PiB-PET
scores. Additionally, several studies [42–44] have
performed comparative proteomic analyses of CSF
samples from AD patients and controls, revealing
proteomes and identifying DEPs in these CSF sam-
ples. Of the three biomarker candidates, DCD has
been detected in CSF [43]. However, none of the
three candidates have been previously reported to be
altered in terms of their protein levels in human CSF
or serum.
The three biomarker candidates were selected as
DEPs in MCI and AD with positive PiB-PET scores,
compared to controls with negative PiB-PET scores.
Thus, their levels in the serum are expected to show
the correlation with PiB-PET scores or other clinic
features, such as MMSE scores. For the valida-
tion cohort, we measured the serum levels of the
three candidates using western blotting and ELISA.
For the validation cohort, however, PiB-PET scores
were not available, while MMSE scores were avail-
able. Thus, we analyzed whether the serum levels
for the 3 candidates are correlated with the MMSE
score. Correlation analysis showed that PCSK9 and
F13A1 showed significant negative correlations with
MMSE scores (p < 0.01 from t-test for correlation in
MedCalc software, version 15.11.0) (Supplementary
Figure 5), suggesting that the serum levels of these
biomarker candidates can reflect quantitatively the
clinical features of AD patients. Next, we also exam-
ined whether each of the three candidates can be used
as a biomarker alone or needed to be combined as
a panel using ROC analysis in MedCalc software.
The ROC analysis showed that PCSK9 and F13A1
reached AUC > 0.8 (p < 0.01 from Z-test for AUC
in MedCalc software) only for AD versus Control,
among the three comparisons including MCI versus
Control and AD versus MCI, while DCD reached
AUC > 0.8 (p < 0.01) only for MCI versus Con-
trol (Supplementary Figure 6). Moreover, with two
biomarker candidates as a panel, PCSK9 + F13A1
reached AUC > 0.8 (p < 0.01) for AD versus Control
and AD versus MCI, but not for MCI versus Control.
Also, F13A1 + DCD reached AUC > 0.8 (p < 0.01)
for AD versus Control and MCI versus Control and
AUC close to 0.8 (0.78) with p-value close to 0.01
(0.018) for AD versus MCI. These data indicate that
the biomarkers should be used as a panel to provide
reliable prediction of MCI or AD.
Our data demonstrate, for the first time, the upreg-
ulation of three proteins, PCSK9, F13A1, and DCD,
in the sera of patients with MCI or AD, thereby
supporting their potential use as indicators of AD
pathogenesis. The clinical implications of these three
proteins should be further tested using a larger num-
ber of AD serum samples. In addition, longitudinal
studies may be designed to further demonstrate the
nature of dynamic changes of the proposed protein
profile during the course of AD pathogenesis. More-
over, considering pathological similarities of AD
to other neurodegenerative diseases, such as alpha-
synucleinopathies and Huntington’s disease, these
three proteins can be associated with such neurode-
generative diseases. Thus, the specificity of the three
proteins in AD can be evaluated by testing their
validity in such neurodegenerative diseases. Further-
more, novel subtypes of MCI and AD might be
further characterized based on the correlation of the
selected protein profile with the PiB-PET scores in
AD patients. The correlation between the selected
protein profile and the PiB-PET imaging data sug-
gests that the three proteins are associated with
pathophysiological processes related to A accu-
mulation. However, more detailed functional studies
should be carried out to elucidate the mechanisms
underlying the correlation between the selected pro-
tein profile and the PiB-PET scores.
In this study, of the three processes (protein
metabolic process, vesicle-mediated transport, and
response to stress) represented by the shared DEPs
(Groups 2 and 6 in Fig. 3B), we finally focused on
“protein metabolic process” represented by 7 upregu-
lated shared DEPs. However, the other two processes
(vesicle-mediated transport and response to stress)
represented by Group 6 in Fig. 3B could be also asso-
ciated with AD pathogenesis. First, vesicle-mediated
12. 1574 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
transport was previously reported to be associated
with AD pathogenesis [45]. For example, RAB11
in Group 6 regulates A trafficking via recycling
vesicles and its alteration induces A accumulation
in mouse neuroblastoma cells [46]. Second, stress
response is also known to occur early at the onset
of AD progression [47, 48]. For example, SAA1 in
Group 6 is involved in A deposition induced by
systemic acute-phase response using a mouse model
of amyloidosis [49]. Also, S100A12 in Group 6 has
a role in AD pathogenesis caused by inflammation
and protein complex formation in human brain tis-
sues [50]. These data suggest potential associations
of the two processes with AD pathogenesis. Nonethe-
less, our data showed that the two processes were
represented by both up- and downregulated genes
in Groups 1, 3, 5, and 6, suggesting that they were
altered in MCI or AD in a complex manner. Thus,
we focused on the protein metabolic process because
it was represented uniquely by the upregulated pro-
teins that are easy to monitor the disease progression
[31–33].
In this study, we depleted the following 14 highly
abundant serum proteins that can hamper detec-
tion of low abundant biomarker candidates in serum
using the MARS Hu14 column: albumin, IgG, antit-
rypsin, IgA, transferrin, haptoglobin, fibrinogen,
alpha2-acroglobulin, alpha1-acid glycoprotein, IgM,
apolipoprotein AI, apolipoprotein AII, complement
C3, and transthyretin. Several of these depleted pro-
teins were previously reported to be associated with
AD. For example, alpha-2-macroglobulin was upreg-
ulated in serum samples from AD patients, compared
to those from healthy subjects [51, 52], whereas
transthyretin (TTR) [14] and complement component
3 (C3) [25] were downregulated in serum from AD
patients. Due to partial depletion rates of 10∼50%
for these proteins [53], despite the depletion, they
are often detected from LC-MS/MS analysis and can
be even identified as DEPs. However, in this study,
we removed these proteins from biomarker candi-
dates (i.e., DEPs) because their abundances could be
affected by the depletion, thereby resulting in inaccu-
rate quantification. Thus, our approach is limited to
identify the 14 highly abundant proteins mentioned
above as biomarker candidates though their abun-
dances are truly altered in serum of AD patients.
In addition to the three proteins investigated in this
study, our approach provided a comprehensive list
of DEPs in MCI and AD, thus extensively extend-
ing the current list of candidate biomarkers identified
by conventional small-scale approaches. Our data
also confirmed a previously reported list of DEPs in
AD serum: 1) alpha-2-macroglobulin (upregulated in
AD) [51, 52], 2) TTR (downregulated in AD) [14],
and 3) C3 and APOC2 (downregulated in AD) [25].
The list of proteins provided by our study is expected
to serve as a comprehensive resource for the study
of MCI and AD. Furthermore, the network model
should provide a basis for understanding cellular pro-
cesses altered in AD that can be monitored in serum
(Fig. 3D). The network showed that the monitoring of
neurological processes (neuron projection develop-
ment, regulation of cell communication, cytoskeleton
organization, and regulation of hydrolase activity)
in AD serum may be useful for the prediction of
AD pathogenesis. In summary, our approach success-
fully identified a protein profile which correlated well
with positive PiB-PET scores, and the protein profile
is expected to provide high predictive value for the
pathogenesis of MCI and AD.
ACKNOWLEDGMENTS
This work was supported by grants from NRF
(2015R1A2A1A05001794, 2014M3C7A1046047,
2015M3C7A1028790, MRC (2011-0030738)) to
I.M-J., from “Cooperative Research Program for
Agriculture Science & Technology Development
(PJ009103)” Rural Development Administration,
Republic of Korea to Y.K. and from Institute for
Basic Science (IBS-R013-G1-2015-a00) funded to
D.H. by Korean ministry of science, ICT, and future
planning.
Authors’ disclosures available online (http://j-alz.
com/manuscript-disclosures/16-0025r1).
SUPPLEMENTARY MATERIAL
The supplementary material is available in the
electronic version of this article: http://dx.doi.org/
10.3233/JAD-160025.
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![1564 S. Kang et al. / Serum Proteome Profiles Predict MCI and AD
INTRODUCTION
Alzheimer’s disease (AD) is the main cause of
dementia, and it is predicted that there will be approx-
imately 115 million patients with AD, worldwide, by
2050 [1]. To date, no cure for AD has been devel-
oped.Severalcandidatedrugshavebeenunsuccessful
in trials, possibly as they were tested far too late in
the disease progression for meaningful therapeutic
outcomes to be obtained [2], as pathophysiological
alterations associated with AD are thought to begin
several decades before the condition may be clin-
ically diagnosed. Therefore, early diagnosis would
provide a crucial opportunity for intervention in AD
progression [3].
The levels of amyloid- (A) peptide, total tau
(t-tau) protein, and hyperphosphorylated tau (p-tau)
protein in the cerebrospinal fluid (CSF) strongly cor-
relate with AD progression [4, 5]. However, lumbar
puncture, which is used to measure A peptides
and t/p-tau proteins from the CSF, is invasive and
painful. In addition to CSF analysis, the amyloid
tracer “Pittsburgh Compound B” (PiB) is commonly
utilized in AD diagnosis for research purposes. PiB,
a radioactive analog of thioflavin T, binds to insolu-
ble fibrillary A with high affinity (0.9 (Ki, nM)) and
enters brain in amounts sufficient for imaging with
positron emission tomography (PET). Thus, PET
scans using PiB (PiB-PET) can be used as a means
of monitoring the A plaque load [6, 7]. Moreover,
a postmortem autopsy study showed that PiB bind-
ing area correlates well with histological A plaque
loads [8]. Despite the high sensitivity and specificity
of the PiB-PET imaging technique, it is expensive
for use as a normal screening method and inconve-
nient due to its short half-life of radioactivity (about
20 min) [9]. These limitations necessitate the investi-
gation of proteome profiles in order to develop an
alternative measure to effectively reflect PiB-PET
scores.
Efforts have been made to identify reliable, nonin-
vasive, and inexpensive protein biomarkers indicative
of disease states, for a number of diseases. Specif-
ically, serum protein biomarkers that effectively
represent PiB-PET scores to distinguish mild cog-
nitive impairment (MCI) (a pre-stage of AD) and
AD from normal controls may be highly useful in
the diagnosis of MCI and/or AD. Potential bene-
fits of such serum biomarkers are based on the ease
and non-invasiveness of collecting blood samples
instead of CSF, and also on the fact that blood is an
abundant source of proteomes indicative of disease
states. Moreover, blood-based diagnosis, which may
be performed routinely even in elderly people, is eco-
nomically efficient compared to PiB-PET imaging
[10].
Mass spectrometry (MS)-based proteomic
approaches have facilitated significant advances
in the identification of serum protein biomarkers.
Despite the advantages of blood-based diagnosis,
the high complexity of serum proteomes poses a
challenge for MS-based approaches. The dynamic
range of protein concentrations in serum is larger
than 10 orders of magnitude [11]. Highly abundant
proteins, such as serum albumins and immunoglobu-
lins, may constitute more than 85% of the total serum
protein content. These may hinder the detection
of less abundant proteins, which may be potential
biomarkers. This particular limitation is termed an
undersampling issue. The use of affinity depletion
columns to remove highly abundant proteins sig-
nificantly reduces undersampling issues, thereby
facilitating effective detection of serum biomarkers
[12]. Additionally, the use of the isobaric tagging for
relative and absolute quantitation (iTRAQ) labeling
method enables an effective multiplex quantitative
analysis of serum samples under different disease
conditions [13].
In our study, new serum marker candidates were
identified to reflect the pathophysiological states of
MCI and AD. In order to identify a protein profile
representative of PiB-PET scores, we first collected
serum samples (discovery cohort) from cognitively
normal controls and MCI and AD patients according
to their PiB-PET scores, and then pooled the samples
by group. Three pooled samples were then labeled
with iTRAQ and analyzed by liquid chromatography-
tandem MS (LC-MS/MS). By means of integrated
analysis of MS/MS data for serum and the genomic
and proteomic data for brain tissues, together with
network analysis, we identified biomarker candidates
representing proteolytic processes that are commonly
altered in the sera of patients with MCI and AD
and in the brain tissues of patients with AD. The
alteration of these proteins in MCI and AD was
validated in an independent set of serum samples
(validation cohort) of control, MCI, and AD by
western blotting or enzyme-linked immunosorbent
assay (ELISA) analysis. As a result, we propose
that these three proteins may serve as potential
serum biomarkers for early prediction of MCI
and AD.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)