Proteomics Analysis of Blood Serums from Alzheimer’s Disease Patients Using iTRAQ Labeling Technology

Abstract

Alzheimer’ disease (AD) is the most common form of dementia affecting up to 6% of the population over the age of 65. In order to discover differentially expressed proteins that might serve as potential biomarkers, the serums from AD patients and healthy controls were compared and analyzed using the proteomics approach of isobaric tagging for relative and absolute quantitation (iTRAQ). For the first time, AD biomarkers in serums are investigated in the Han Chinese population using iTRAQ labeled proteomics strategy. Twenty-two differentially expressed proteins were identified and out of which nine proteins were further validated with more sample test. Another three proteins that have been reported in the literature to be potentially associated with AD were also investigated for alteration in expression level. Functions of those proteins were mainly related to the following processes: amyloid-β (Aβ) metabolism, cholesterol transport, complement and coagulation cascades, immune response, inflammation, hemostasis, hyaluronan metabolism, and oxidative stress. These results support current views on the molecular mechanism of AD. For the first time, differential expression of zinc-alpha-2-glycoprotein (AZGP1), fibulin-1 (FBLN1), platelet basic protein (PPBP), thrombospondin-1 (THBS1), S100 calcium-binding protein A8 (S100A8), and S100 calcium-binding protein A9 (S100A9) were detected in the serums of AD patients compared with healthy controls. These proteins might play a role in AD pathophysiology and serve as potential biomarkers for AD diagnosis. Specifically, our results strengthened the crucial role of Aβ metabolism and blood coagulation in AD pathogenesis and proteins related to these two processes may be used as peripheral blood biomarkers for AD.

Keywords

Alzheimer’ disease biomarker iTRAQ proteomics serum

INTRODUCTION

Alzheimer’ disease (AD) is the most common form of dementia in the elderly characterized by progressive cognitive decline such as memory loss and executive dysfunction, as well as psychiatric symptoms and behavioral disturbances. Senile plaques and neurofibrillary tangles are hallmark lesions in the brains of AD patients in addition to neuronal loss [1]. According to recent reports, around 44 million people in the world are suffering from dementia including AD, which is considered as one of the biggest global public health challenges and a substantial burden to patients, caregivers, and the healthcare system [2].

However, over a hundred years after its discovery, AD medical treatment remains unsuccessful. Clinical diagnosis is difficult, in particular for non-specialists as it is still primarily based on detailed history and neuropsychological examinations of patients and confirmed by postmortem test of brain pathology. There is an ever-increasing necessity to find new biomarkers of AD in peripheral fluid to make diagnosis easier, earlier, and more accurate and to predict the progression of disease [3]. Current approaches to diagnose mild cognitive impairment (MCI) and AD patients include genetic testing, neuroimaging techniques, and neurochemical assays of body fluids (such as cerebrospinal fluid (CSF), plasma, serum, blood cells, urine, and saliva) [4]. A variety of imaging techniques have been found to be able to support clinical diagnosis of MCI and AD. The levels of Aβ_1 - 40, Aβ_1 - 42, total tau protein (t-tau), and phosphorylated tau protein (p-tau) in CSF have also been confirmed valuable in AD diagnosis. Together with neuroimaging results, those CSF markers have recently been added to the newly revised criteria for AD diagnosis [5]. However, CSF collection is invasive and thus its sampling is difficult and clinical application is limited. Imaging techniques are costly which prohibit their application either as routine screening tools or for repeat tests to monitor drug treatment or pathological progress. For example, positron emission tomography (PET) coupled with amyloid-targeted ligands have been deployed in various studies and considered as a sensitive and specific modality for detecting amyloid accumulation. However, PET is clinically impractical as it requires special equipment and operating staff; meanwhile its use is limited to local production and regional distribution of isotope tracer [6]. Therefore, discovery and application of blood-based biomarkers are urgent and attractive for diagnosis of AD.

Proteomics is likely to play an increasing role in identifying biomarkers. Proteomics analysis of brain tissue, peripheral tissues, and body fluids is now a promising tool to better understand the complexity of neuropsychiatric disorders and drug effects, as well as to explore corresponding disease markers [7]. Several studies have emerged using different proteomic approaches to identify blood-based biomarkers of MCI and AD [8 –10]. However, the association between proteins and MCI/AD was often reported inconsistently in different studies; some of them were even contradictory. Only a few proteins have been reported to be repeatable in independent studies [11]. Therefore, these findings need to be replicated in longitudinal studies and more extensive proteomic studies need to be conducted. In recent years, developments occur fast in the techniques and methods of proteomic experiments. Apart from the gel-based techniques (e.g., 2D-PAGE and 2D-DIGE), gel-free techniques (e.g., iTRAQ) have been dominating the field of biomarker discovery [12]. The method of iTRAQ with multidimensional liquid chromatography and tandem mass spectrometry has also been extensively used to identify potential blood biomarkers of different diseases and disorders including neuro-psychiatric disorders, which has revealed many candidate proteins as potential biomarkers of AD or MCI pathologies [9 , 14].

This study is specifically designed to obtain new insights into AD biomarker discovery, focusing on serum protein biomarkers. Serum samples from AD patients and healthy controls were analyzed using the iTRAQ technique. Differentially expressed proteins identified by proteomics analysis were further validated by western blotting and enzyme-linked immunosorbent assay (ELISA). In addition, three proteins have been identified as potential biomarkers or AD-associated proteins, including clusterin (CLU) [15, 16], S100-A8, and S100-A9 [17], which were also validated via ELISA in the present study.

MATERIALS AND METHODS

Serum samples

In this study, two independent iTRAQ experiments were carried out. The research protocol was approved by the Human Research Ethics Committee of Shenzhen University. A total of 30 subjects were enrolled in each experiment, including 15 AD patients and their age- and sex-matched healthy controls. All healthy controls were enrolled from the Shenzhen People’s Hospital, with no history of chronic disease. In experiment 1 (E1), 8 AD patients were enrolled from the Qingdao Municipal Hospital and 7 were from the Shenzhen People’s Hospital. In experiment 2 (E2), 5 AD patients were enrolled from the Qingdao Municipal Hospital, Shenzhen People’s Hospital and China Japan Union Hospital of Jilin University, respectively. All AD patients were diagnosed by the neuropsychiatrists in the hospitals based on the criteria as defined in the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV). The age of the subjects ranged from 65–85 years. The male to female ratio was 3:2. All subjects are ethnic Han Chinese.

Blood samples (5 ml) were collected in glass tubes without additive and allowed to clot at room temperature for 30 min. Serum was separated by centrifugation at 3000 g for 10 min. Aliquots of serum were taken and stored at –80°C until use.

Sample preparation

An overview of the work flow used for quantitative proteomics analysis in this study is shown in Fig. 1. In E1, serum samples were analyzed in two groups, each group pooled separately with equal amount of serum from AD patients (n = 5) or aged/sex-matched healthy controls (n = 5). The pooled serum samples were pretreated with ProteoExtract Albumin/IgG Removal Kit (Calbiochem, Darmstadt, Germany) according to the manufacturer’s instruction, which selectively removes albumin and immunoglobulin (IgG) from the serum samples. After high-abundance proteins depletion, the samples were centrifuged at 12000 g in YM-3 centrifugal filter (Millipore, Billerica, MA, USA) and buffer-exchanged with the sample buffer (consisting of 7 M urea, 2 M thiourea, 4% CHAPS, 65 mM DTT, and 30 mM Tris). In E2, serum samples were analyzed in three groups (n = 5 in each group). The AD patients were enrolled from three hospitals as described above. The pooled serum samples were processed using Multiple Affinity Removal Column (Hu6; MARC; 4.6×50 mm, Agilent, Palo Alto, CA, USA) according to the manufacturer’s instructions and were performed using an HP1090 HPLC system (Agilent, Sydney, Australia). The flow-through fraction containing low abundance proteins were collected and concentrated. Six of the high abundance proteins in serum were depleted and these proteins include albumin, IgG, antitrypsin, IgA, transferrin, and haptoglobin. After the pre-treatment, protein concentrations were determined using the Bradford assay.

iTRAQ labeling and high-pH RPLC fractionation

Serum proteins (100 μg) were reduced by 10 mM dithiothreitol (DTT, Sigma-Aldrich Co., St. Louis, USA) at 37°C for 1 h, and alkylated with 50 mM iodoacetamide (IAA, Sigma-Aldrich) in the dark at room temperature for 10 min to block the cysteine residues. The samples were then desalted and buffer-changed three times with 100 μL 0.5 M triethylammonium bicarbonate (TEAB, AB Sciex, Foster City, CA, USA) by using ultracentrifugal filter devices (10 kDa cutoff; Millipore). Subsequently, the proteins were digested with trypsin (Promega, Madison, WI, USA) at a ratio of 1:30 for 37°C overnight. The resulting peptides were collected by centrifugation and further eluted by 50 μL TEAB, and then were labeled with the iTRAQ reagents (AB Sciex). The required iTRAQ reagents were thawed at room temperature and 150 μL isopropyl alcohol was added to solubilize the iTRAQ reagent. In E1, the two samples from the healthy controls were labeled with iTRAQ tags 113 and 114 and the AD samples were labeled with tags 115 and 116, respectively. In E2, three pooled serum samples from three healthy control groups were labeled with 118, 119, and 121, respectively, and three pooled serum samples from three AD groups were labeled with 113, 114, and 115, respectively. They were incubated at room temperature for 2 h, and then were mixed and lyophilized. The dried samples were reconstituted in 100 μL deionized water and injected into an Agilent HPLC with a high pH reverse phase column (Durashell, C18, 250 mm×4.6 mm, 5 μm). The mobile phases B used were composed of 10 mM ammonium formate (pH 10) in 90% acetonitrile (ACN). Peptides were eluted at a flow rate of 0.8 mL/min with the following gradient: 5% buffer B (0–5 min), 6–38% buffer B (5–45 min), 38–90% buffer B (45–54.5 min), and 5% buffer B (55–65 min). Fractions were collected at an interval of 1 min. A total of 48 fractions were collected, merged into 10 pooled fractions and lyophilized. Prior to LC-MS/MS analysis, the 10 pooled fractions were reconstituted in 30 μL of 2% ACN and 0.1% formic acid, then centrifuged at 12,000 g for 10 min, with 8 μL of each fraction used per NanoLC-MS/MS analysis.

NanoLC-MS/MS analysis

An Ultra 2D Plus nanoflow HPLC (Eksigent Inc., Dublin, CA, USA) coupled with Triple TOF 5600 system (AB Sciex) was used for analysis [18]. Microfluidic traps and nanofluidic columns packed with ChromXP C18 (3 μm, 2.1×100 mm Eksigent) were utilized for online trapping, and desalting, while nanofluidic columns packed with ChromXP C18 (3 μm×150 cm, Eksigent) were employed in analytical separation. The RP mobile phase A was composed of water/acetonitrile/formic acid (98:2:0.1, v/v/v), while RP mobile phase B was composed of water/acetonitrile/formic acid (2:98:0.1, v/v/v). Samples of labeled peptides were loaded on the column with trapping and desalting carried out at 2 μL/min for 15 min using mobile phase A. Analytical separation was carried out at a flow rate of 300 nL/min. The mass spectrometer data were acquired in the positive ion mode with a selected mass range of 350–1500 m/z. Peptides with +2 to +5 charge states were selected for MS/MS. MS/MS spectra were acquired in the m/z range of 100–1500. Smart information-dependent acquisition was activated with automatic collision energy and automatic MS/MS accumulation.

Database search and iTRAQ quantification

The raw peptides, protein identification and quantification were performed using ProteinPilot v4.5 (AB Sciex) with the Paragon Algorithm against the UniProt ‘complete proteome’ human proteins database. To reduce false positive identification results, a minimum unused score of 1.3 (equivalent to 95% confidence) and false discovery rate (FDR) less than 1% were required for all reported proteins. At least one unique peptide per protein group was required for identification of proteins, while two quantified peptides were required for quantification of proteins. Based on a 95% confidence level, cutoff values of 1.5-fold for upregulated proteins and of 0.67-fold for downregulated proteins were used to identify differentially expressed proteins between the patients and healthycontrols [19].

Western blot analysis

To validate the differentially expressed proteins obtained in MS/MS, western blot analysis was performed on 6 serum samples from AD patients and controls by specific antibodies. Undepleted serum proteins (20 μg) were separated by SDS-PAGE on 12% polyacrylamide gels. The proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Millipore), and the blots were blocked with 5% non-fat-dried milk in phosphate-buffered saline (PBS: 137 mM NaCl, 10 mM phosphate buffer, and 2.7 mM KCl, pH 7.4) for 1 h and then washed with PBS containing 0.2% (v/v) tween 20 (PBST) and further incubated with overnight at 4°C using primary antibodies against alpha-1-antichymotrypsin (SERPINA3), ceruloplasmin (CP), and inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1) at optimized dilutions with PBST. All primary antibodies were purchased from Boster Biological Technology, Ltd (Boster, Wuhan, China). After washing (5 min×3) with PBST, the blots were then incubated in 1:5000-diluted horseradish peroxidase (HRP)-conjugated secondary antibody (Abmart Inc, Shanghai, China) for 2 h at room temperature. The membranes were washed three times each for 5 min in PBST and developed with enhanced chemiluminescence (ECL) kit (Pierce ECL detection kit, Thermo Fisher Scientific Inc, Rockford, USA). Immunoreactive signals were detected with a Kodak Image Station 4000MM imaging system (Carestream Health Inc., Rochester, NY, USA). Quantitative analysis of protein bands was conducted using Quantity One analysis software (Bio-Rad, USA). Equal amount proteins were separated by SDS-PAGE and stained with Coomassie blue, which was used as the loading control.

ELISA analysis

The levels of alpha-1-antitrypsin (SERPINA1), apolipoprotein A-I (APOA1), haptoglobin (HP), FBLN1, plasma protease C1 inhibitor (SERPING1), and THBS1 were further determined by ELISA using serum from individual AD patients and healthy subjects. Although the proteins CLU, S100-A8, and S100A9 have not been identified in iTRAQ analysis, CLU was proposed as a candidate biomarker for AD in previous studies [15, 16]. S100A8/A9 proteins are pro-inflammatory proteins and predominantly expressed in phagocytes, which have high intrinsic amyloid-forming capacities and may lead to their amyloid depositions and contribute to AD pathogenesis [17]. The peptides associated with S100A8 and S100A9 were observed with a significant increase in the urine of AD patients compared to healthy controls (our unpublished data). Thus, CLU, S100-A8, and S100-A9 were selected for verification by ELISA. For ELISA analysis, commercial ELISA kits were obtained from Uscn Life Science Inc. (Wuhan, China) and the protein concentrations were measured according to the manufacturer’s instructions. For each treatment, the ELISA results were normalized with total protein concentration. The protein concentrations were determined by BCA protein assay kit (Beyotime, Jiangsu, China).

Bioinformatics analysis

Differentially expressed proteins in AD from the present study were entered to the DAVID (Database for Annotation, Visualization and Integrated Discovery) database (http://david.abcc.ncifcrf.gov) for functional classifications and GO enrichment analysis. Pathways were elucidated according to the KEGG pathway, PANTHER pathway, and REACTOME pathway. The related disease was analyzed based on the genetic association database (GAD) disease and GAD disease class. The protein-protein interaction (PPI) networks associated with these proteins were generated through a multi-omics data analysis tool OmicsBean (http://www.omicsbean.com:88/), which including protein-protein interaction, KEGG pathway enrichment, and biological process enrichment. The web-based tool LENS (Enrichment and Network Studies of human proteins, http://severus.dbmi.pitt.edu/LENS/) was further used to determine the interaction of these proteins between them and connects it.

Statistical analysis

These data are presented as mean±standard error of the mean (SEM) and statistical analyses were performed by the two-tailed Student’s t-test. p < 0.05 was considered significantly different between the AD and healthy controls.

RESULTS

Identification of differentially expressed serum proteins between AD and healthy persons by iTRAQ analysis

Depleted serum protein samples were analyzed by iTRAQ analysis. A total of 553 serum proteins were successfully identified, out of which 279 and 386 serum proteins were identified in E1 and E2, respectively. Comparing the proteins, 112 proteins were found to be common among the two experiments (Fig. 1B), suggesting that the two approaches with depletion of high-abundant proteins give complementary results. As expected, more proteins were detected by Multiple Affinity Removal Column. Additionally, in E1, quantitative analyses revealed that 19 proteins in group 1 and 18 proteins in group 2 were altered in AD patients’ serum compared with that in healthy controls. Among them, 11 proteins were identified to be in common within the two groups and shown in Table 1, meanwhile 10 proteins were found downregulated dramatically and 1 protein upregulated in AD patients. The downregulated proteins identified are SERPINA3, APOA1, apolipoprotein B-100 (APOB), AZGP1, complement factor B (CFB), CP, ITIH1, inter-alpha-trypsin inhibitor heavy chain H2 (ITIH2), HP, and SERPINA1. The upregulated protein was gelsolin (GSN). In E2, 11, 11 and 13 proteins were identified as differentially expressed proteins between AD patients and healthy subjects in the group 1, group 2, and group 3, respectively, and 12 proteins were common among the three groups (Table 2). Among these common proteins, 5 of them were significantly upregulated and 7 were noticeably downregulated. The upregulated proteins were identified as AFM, FBLN1, fibrinogen alpha chain (FGA), fibrinogen beta chain (FGB), and fibrinogen gamma chain (FGG). The downregulated proteins were CP, inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), fibronectin (FN1), platelet basic protein (PPBP), plasma protease C1 inhibitor (SERPING1), thrombospondin-1 (THBS1), and von willebrand factor (VWF). CP was found to be common in both E1 and E2.

Validation of differential expression proteins by western blot and ELISA analyses

Based on comparison with the previous studies and bioinformatics analysis, we selected 10 proteins involved in different pathological processes for validation. By western blot analysis, the expression levels of CP, ITIH1, and SERPINA3 were detected in the serum samples from 6 individuals with AD and 6 age- and sex-matched healthy controls. As shown in Fig. 2, densitometry analysis of western blots revealed downregulation of CP, ITIH1, and SERPINA3 in AD patients (p < 0.05), which confirmed the results obtained in iTRAQ experiment.

Six differentially expressed serum proteins were selected for ELISA analysis. Consistent with the iTRAQ results, significant decrease in the serum levels of APOA1, HP, SERPINA1, and SERPING1 in the AD patients were detected (Fig. 3). Two proteins include FBLN1 and THBS1, which were reported for the first time to be decreased in AD serum, were also validated to be consistent with the results of iTRAQ analysis as shown in Fig. 3C and F.

Detection of three proteins might be associated with AD by ELISA

As shown in Fig. 3, the decrease levels of CLU, S100A8, and S100A9 were detected in the serum of AD patients compared with that of healthy controls. The result of CLU is inconsistent with some literature [15, 16], but it is also consistent with other studies [20]. Recent studies showed that S100A9 levels were not significantly different in plasma [21] but significantly lower in CSF [22] between AD and the healthy controls. Another member of the S100 family, S100B, was decreased in AD serum [23]. Here, the levels of S100A8 and S100A9 were reported for the first time to decrease in the serums of AD patients.

Functional classification of differentially expressed proteins

By bioinformatics analysis and referring to the literature, the biological function associated with the differentially expressed proteins and the studies associated with these proteins are presented in Tables 1 and 2, respectively. The top 20 biological processes associated with those proteins are shown in Fig. 4A. The diseases and signal transduction pathways related to these proteins are shown in Fig. 4B and Supplementary Table 1. The results indicated that most of these proteins were associated with blood coagulation, complement activation, immune response, inflammatory response, hyaluronan metabolic process, hemostasis, cholesterol metabolism, and ion homeostasis. The protein-protein interaction networks are shown in Fig. 5A using OmicsBean database analysis. The proteins were found to be tightly networked and several important signal transduction pathways were enriched. The pathways enriched in the network are agreed well with the results of DAVID analysis (Fig. 4B, Supplementary Table 1), suggesting that these processes may play important roles in the pathophysiology of AD. In addition, with online tool LENS, network generated with the differentially expressed proteins as candidates and AD related genes (Alzheimer’s disease KEGG disease) listed in LENS as target genes, including amyloid beta (A4) precursor protein (APP), apolipoprotein E (APOE), presenilin 1 (PSEN1), and presenilin 2 (PSEN2). As shown in Fig. 5B, except for AFM, all of the proteins were enriched in the interaction networks. Interestingly, the APP gene was enriched in the center of networks, and seven proteins (APOA1, CLU, FBLN1, GSN, S100A8, SEPINA3, and SERPING1) were directly connected with it, supporting the opinion that APP plays a critical role in AD pathogenesis.

DISCUSSION

In this study, we successfully identified multiple differentially expressed proteins between AD patients and healthy subjects. Most of them have already been reported as candidate biomarkers for AD in previous studies being strongly associated with the pathophysiology of AD [24, 25]. Interestingly, 16 differentially expressed proteins reported in this study are consistent with the recently identified potential biomarkers among the MCI, AD, and healthy subjects [9, 10], using the same technique (i.e., iTRAQ analysis). These proteins include APOA1, APOB, AZGP1, CFB, CP, FGA, FGB, FGG, FN1, GSN, ITIH1, ITIH2, ITIH4, SERPING1, and SERPINA3. To our knowledge, this is the first report of AZGP1, PPBP, S100A8, S100A9, and THBS1 downregulation while FBLN1 upregulation in the serums of AD patients. It is worth noting that the peptides associated with 11 proteins, including APOA1, CP, FGB, HP, ITIH1, ITIH2, ITIH4, SERPINA1, SERPINA3, S100A8, and S100A9, were observed to be significantly increased in urine of AD patients compared to the controls in our other study (unpublished data), implicating that the “degradation” of these proteins may be increased in AD patients. It is important to further investigate the potential relation between the protein levels decreased in serums and peptide numbers increased in urine.

Substantial evidence supports that inflammation plays a critical role in the pathogenesis of AD [17]. Here, 10 proteins were found involved in the inflammatory process. These proteins include CFB, CLU, FN1, ITIH4, S100A8, S100A9, SERPINA1, SERPINA3, SERPING1, and THBS1. Among them, SERPINA1 and SERPINA3 have been found to be associated with AD and localized in neurofibrillary tangles and senile plaques [26]. The levels of SERPINA1 were significantly increased in AD blood as reported previously [20 , 27–33], but no significant change was also reported in other studies [34 –39]. In the present study, its level was lower in AD patients. Thus, more studies are required to validate the level change of SERPINA1 in AD. SERPINA3 is a protease inhibitor and an acute phase reactant. It is increasingly produced by reactive astrocytes in response to large amounts of the proinflammatory cytokine interleukin (IL)-1 secreted by reactive microglia in the AD brain [40]. This protein has been indicated to influence phosphorylation of tau protein and apoptosis in neuronal cells [41]. Several reports showed increased concentrations of SERPINA3 in the CSF and blood of AD patients [35–38 , 42–49], while other reports showed no significant change [20 , 50–52]. Our results indicated that SERPINA3 levels were decreased in AD serums. Gene polymorphism may contribute to the different levels of SERPINA3 in different reports [53].

ITIH4 is an IL6–dependent positive acute-phase protein that can act as an anti-inflammatory protein [54]. Additionally, eight proteins including CP, FGA, FGB, FGG, FN1, Hp, ITIH1, and ITIH2 are also acute-phase proteins. ITIH1, ITIH2, and ITIH4 are the heavy chains of a serine protease inhibitor, which may act as carrier of hyaluronan in serum or as binding protein between hyaluronan and other matrix protein, including those on cell surfaces to regulate the localization, synthesis, and degradation of hyaluronan. Previous studies showed that ITIH1 [27], ITIH2 [27], and ITIH4 [10 , 55] levels were lowered in AD blood. Consistent with the literature, those proteins were also significantly downregulated in AD serum in the present paper. However, their roles in AD have not been well studied and need to be further investigated.

The most important physiological role of Hp is its binding activity to hemoglobin, leading to the formation of a Hp-Hb complex, which functions as a way to prevent Hb-mediated production of reactive oxygen species [56]. The levels of Hp are usually upregulated in response to both endogenous and exogenous stress, mostly during inflammation. However, it was found to be downregulated in AD subjects in this paper as observed in previous studies [57, 58], suggesting that the acute-phase proteins decreased in AD may be disease specific and represent a discerning factor between AD and other diseases [57]. Likewise, HP was found to be either extremely downregulated or oxidized in AD and MCI compared with controls, indicating that this protein may be used as a biomarker with high specificity in AD [57]. Hp was found to be associated with Aβ plaques in AD patients [59], and inhibiting Aβ aggregation in vitro and oxidation of Hp β chain suppressed this kind of ability [57]. It is reasonable to suggest that lower levels of a more oxidized HP are not efficient in preventing or delaying Aβ aggregation, thus contributing to the pathogenesis of AD [57]. Interestingly, CFB [57], FGG, and SERPINA1 [60] have also been identified as specifically carbonylated proteins in plasma of AD patients using redox proteomic analysis.

Three proteins were involved in the complement system, including CLU, CFB, and SERPING1. Complement activation, a pivotal process of neuroinflammation, has been reported in AD [50]. A recent study showed that CFB was specifically more oxidized and downregulated in AD than in MCI persons [57]. Similarly, our results revealed that this protein was downregulated in AD compare to the controls. CLU was considered as a promising AD biomarkers in the previous studies [15, 16]. A genome-wide association studies identified CLU gene as a risk loci for AD [61]. Plasma CLU concentration is associated with longitudinal brain atrophy in MCI [15], and reported to be associated with brain atrophy, baseline disease severity, and rapid clinical progression in AD [16]. However, it was not found to be increased in presymptomatic AD plasma in an independent small case-control study [62]. In addition, a recent study demonstrated that compared to wild type mice, the levels of CLU in serums from 3×Tg-AD mice were decreased, and CoQ10 reversed the changes [58]. Our results showed that serum CLU levels were significantly downregulated in AD patients. Clearly, the inconsistencies in results suggest that larger scale of subjects and more extensive investigation are required to elucidate the role of CLU in the diagnosis of AD. In addition, the alteration of the immune system has been implicated in AD progress [63]. In this paper, six differentially expressed proteins including AZGP1, CFB, CLU, PPBP, SERPING1, and VWF were found to take part in immune response toward AD pathological change. Consistent with previous studies [9 , 64], decrease levels of SERPING1 were observed in the present study. AZGP1 is a soluble glycoprotein and present in a variety of body fluids, and its biological functions are largely unknown [65]. By 2-D gel based proteomic analysis, it was found to be increased in the CSF of AD [66], even at early stages [65].

CP, one copper-transporting protein, is a major antioxidant protein that is synthesized in several tissues, including brain [67]. It plays an important role in protecting the central nervous system from iron-mediated free radical [68], acts as ferroxidase and superoxide dismutase and protects polyunsaturated fatty acids in red blood cell membranes from active oxygen radicals [69]. Consistent with the literature [9, 70], here, the serum levels of CP were significantly lower in AD patients. However, it was also significantly upregulated in the AD blood [32 , 72] and no significant change in several previous studies [34 , 73–76] and need for further confirmation. Of note, CP is a unique protein consistently changing in E1 and E2 in this study, which suggests its important role in AD pathogenesis. Additionally, our data implicated that downregulation of antioxidant property of HP and CP may contribute to the increase of oxidative stress responses in AD.

Vascular factors are believed to play an important role in the pathogenesis of AD. A number of studies suggest that AD patients may have an enhanced potential for thrombosis in the circulation, and increased platelet activation has been found in AD patients [77]. Interestingly, through analysis over 7,700 brain images, a very recent study suggested that intra-brain vascular dysregulation is an early pathological event during disease development, which highlights the early role of vascular dysregulation on late-onset [78]. We found the levels of six proteins, including FGA, FGB, FGG, SERPING1, SERPINA1, and VWF that participate in blood coagulation and hemostasis, were significantly altered in AD serums. Among which FGA, FGB, FGG, and VMF were involved in platelet activation. Consistent with our findings, VWF was abnormally high in patients with AD in a previous study [23]. Elevated fibrinogen levels have been reported to be associated with increased risk for AD and dementia [26, 29]. An in vitro study suggested that the interaction between Aβ and fibrinogen modifies fibrinogen’s structure which may lead to abnormal fibrin clot formation and vascular abnormalities in AD [79]. Reducing fibrinogen levels pharmacologically or genetically led to a decrease in the neurovascular pathology and inflammatory response in mice [79]. Consistently, our results showed that three different chains (α, β, and γ) of fibrinogen (i.e., FGA [9], FGB [9, 27], FGG [57]) were significantly increased in the AD serum. The results support that the opinion that vascular factors may contribute to the pathogenesis of AD. Moreover, hemostasis abnormalities were more frequent and marked in vascular dementia. Besides the six proteins as mentioned above, APOA1, APOB, CLU, FN1, PPBP, and THBS1 were also involved in hemostasis.

Two extracellular matrix (ECM) proteins, FBLN1 and FN1, were identified as differentially expressed proteins in the serums of patients with AD. FBLN1 may play a significant role in modulating the neurotrophic activity of APP, particularly soluble APP [80, 81]. FBLN1 was found to be increased in AD serum in this study, which is reported for the first time. Owing to its ability to bind with fibrinogen and to be incorporated into clots, FBLN1 may play a role in hemostasis and thrombosis [81]. It can also be incorporated into fibronectin-containing matrix fibers and play a role in cell adhesion and migration along protein fibers within the ECM [81]. FN1 has been reported capable of binding Aβ and downregulated in the plasma of AD subjects compared with the controls [27, 82], which is consistent with our results. However, increased level of FN1 in AD was also observed in another study [9], suggesting further study on this protein in AD progression.

APOA1 and APOB, two members of apolipoprotein family, were found to be significantly downregulated in AD patients in the present study. ApoA1 is a major component of the high density lipoproteins. It has been found in association with various types of amyloid deposits and suggested to modulate the toxic effect of amyloid at several levels, including a capacity to prevent β-sheet organization and physically inhibit amyloid aggregation [83]. A triple transgenic mouse model showed that overexpression of ApoA1 prevented the development of age-related learning and memory deficits despite continued Aβ deposition [84]. Consistently, decreased ApoA1 has been reported in the blood [85 –87] of AD patients, and the plasma of MCI [88]. Similarly, the expression of APOB was reported to decrease in AD serum [9], which is consistent with the results in thispaper.

The other differentially expressed proteins identified by comparative proteomic analysis are GSN and AFM. GSN, a cytoskeletal protein, is present both intracellularly and extracellularly [89]. Previous studies have identified the anti-amyloidogenic role of GSN in AD and showed that both plasma and cytosolic GSNs bound to Aβ to inhibit the fibrillization of Aβ and to solubilize preformed fibrils of Aβ [89]. Another study showed that plasma GSN levels were decreased in AD subjects [27], but brain GSN levels did not change significantly between AD and control subjects [34, 39]. In this study, GSN level was significantly increased in the serum of AD patients compared to the controls. We hypothesis that the increased expression of GSN reflecting a neuroprotective response might be initiated through the compensatory system in AD subjects. AFM is a vitamin E binding protein and plays a role in vitamin E transport. Vitamin E is the major lipid-soluble antioxidant protecting lipids against peroxidative damage, thus AFM may have potent neuroprotective property. It has been reported that AMF was downregulated in MCI and AD plasma in a recent study [9, 10]. On the contrary, here, the higher levels of AMF were detected in AD serum. Thus, the role of AMF in AD pathogenesis needs to be studiedfurther.

Indeed, the majority of the proteins identified in this study belong to multifunctional proteins, and some of them may contribute greatly to AD pathophysiology. This suggests that the single-protein markers of AD-related diseases in blood may have limited disease-specificity. Given the complexity of AD pathogenesis, a single protein is unlikely to be a definitive biomarker of AD applicable to the clinical diagnosis. Thus, it has been speculated that a panel of proteins rather than a single protein may have high sensitivity and specificity as a biomarker and may collectively describe and characterize the disease and its pathology [90, 91]. In addition, as senile plaque formation plays a critical role in AD progression, based on interaction networks analysis and literatures, those proteins identified in this paper including ApoA1, CLU, HP, GSN, FBLN1, S100A8, S100A9, SERPINA1, and SERPINA3 are linked to Aβ metabolism, alterations in those amyloid-related proteins should be biologically relevant to AD and their peripheral blood signatures may be used as AD biomarkers.

Conclusion

In this study, 25 differentially expressed proteins were found in the serum samples from AD patients using an iTRAQ-based proteomic approach, among them three proteins have been reported in previous studies associated with AD and six proteins were firstly reported to be significant different in the serums of AD. Most of these proteins are related with inflammatory reaction, complement and coagulation cascades, hemostasis, immune response, lipid metabolism, oxidative stress, and Aβ metabolism. These processes have previously been reported to be linked to the pathophysiology of AD. Thus, our results support the existing theories of AD pathophysiology and the proteins identified here are potential biomarkers for AD diagnosis. Obviously, further studies are required to validate these findings in a large scale of investigation.

Footnotes

ACKNOWLEDGMENTS

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 21271131, 31470804, 81372984) and the Shenzhen Bureau of Science, Technology and Information (Nos. JCYJ20150529164656093; JSGG20140703163838793; JCYJ20140418095735542).

Authors’ disclosures available online ().

Appendix

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