Syndecan-3 as a Novel Biomarker in Alzheimer’s Disease

Early diagnosis of Alzheimer’s disease (AD) is of paramount importance in preserving the patient’s mental and physical health in a fairly manageable condition for a longer period. Reliable AD detection requires novel biomarkers indicating central nervous system (CNS) degeneration in the periphery. Members of the syndecan family of transmembrane proteoglycans are emerging new targets in inflammatory and neurodegenerative disorders. Reviewing the growing scientific evidence on the involvement of syndecans in the pathomechanism of AD, we analyzed the expression of the neuronal syndecan, syndecan-3 (SDC3), in experimental models of neurodegeneration. Initial in vitro studies showed that prolonged treatment of tumor necrosis factor-alpha (TNF-α) increases SDC3 expression in model neuronal and brain microvascular endothelial cell lines. In vivo studies revealed elevated concentrations of TNF-α in the blood and brain of APPSWE-Tau transgenic mice, along with increased SDC3 concentration in the brain and the liver. Primary brain endothelial cells and peripheral blood monocytes isolated from APPSWE-Tau mice exhibited increased SDC3 expression than wild-type controls. SDC3 expression of blood-derived monocytes showed a positive correlation with amyloid plaque load in the brain, demonstrating that SDC3 on monocytes is a good indicator of amyloid pathology in the brain. Given the well-established role of blood tests, the SDC3 expression of monocytes could serve as a novel biomarker for early AD detection.


Introduction
Alzheimer's disease (AD), a disorder characterized by the abnormal accumulation of misfolded protein aggregates and subsequent neuronal death, is the leading cause of dementia worldwide [1][2][3][4][5][6]. Due to the lack of efficient therapeutics, AD is yet untreatable [7,8]. Therefore, AD patients have to face progressive mental and physical deterioration [9]. As currently available symptomatic treatments do not decelerate or prevent the progression of the disease, early diagnosis is one of our most promising tools to tackle AD [10][11][12]. The development of early diagnostics against AD is a key to detecting the disease in the stage of reasonably mild central nervous system (CNS) degeneration [13,14]. The discovery of predictive biomarkers is thus essential for developing accurate AD diagnostics [15,16]. The discovery of such biomarkers requires a profound understanding of early pathophysiological changes leading to neurodegeneration [17][18][19]. Early pathophysiological changes could predict the onset of the disease when the patient is still in a manageable mental and physical condition, thus enabling the application of treatments that could halt the progression of AD [20][21][22].
Previously we explored the contribution of syndecans (SDCs), a family of transmembrane heparan sulfate proteoglycans (HSPGs), to the seeding and spreading of amyloid-beta (Aβ) and tau aggregates [23][24][25]. According to our studies, overexpression of SDCs, especially the neuronal SDC3, creates favorable conditions for the cellular accumulation and subsequent aggregation of misfolded proteins. Seeding and spreading of pathological

SDC3 Expression in Transgenic Mice Model of AD
APPSWE-Tau is a double mutant transgenic mice model that develops neurofibrillary tangles and progressive motor disturbance and expresses mutant beta-amyloid precursor protein (APP), thus modulating the APP-Aβ environment [70]. APPSWE-Tau mice thus successfully exhibit both hallmark pathologies in AD, and the interaction between Aβ and tau pathologies in APPSWE-Tau mice excellently mimics human AD [71]. In our studies, 12-month-old APPSWE-Tau mice, compared with wild type (WT) C57BL/6 mice, exhibited significantly increased amyloid plaque load (

SDC3 Expression in Transgenic Mice Model of AD
APPSWE-Tau is a double mutant transgenic mice model that develops neurofibrillary tangles and progressive motor disturbance and expresses mutant beta-amyloid precursor protein (APP), thus modulating the APP-Aβ environment [70]. APPSWE-Tau mice thus successfully exhibit both hallmark pathologies in AD, and the interaction between Aβ and tau pathologies in APPSWE-Tau mice excellently mimics human AD [71]. In our studies, 12-month-old APPSWE-Tau mice, compared with wild type (WT) C57BL/6 mice, exhibited significantly increased amyloid plaque load (Figure 2A-C).  Figure S1), and incubating them with TNF-α for seven days increased their SDC3 expression. SDC3 overexpression due to TNF-α was significantly increased in the two cell lines. The effect was most profound in SH-SY5Y cells and slightly less, still significant in HCMEC/D3 cells ( Figure 1A-C).

SDC3 Expression in Transgenic Mice Model of AD
APPSWE-Tau is a double mutant transgenic mice model that develops neurofibrillary tangles and progressive motor disturbance and expresses mutant beta-amyloid precursor protein (APP), thus modulating the APP-Aβ environment [70]. APPSWE-Tau mice thus successfully exhibit both hallmark pathologies in AD, and the interaction between Aβ and tau pathologies in APPSWE-Tau mice excellently mimics human AD [71]. In our studies, 12-month-old APPSWE-Tau mice, compared with wild type (WT) C57BL/6 mice, exhibited significantly increased amyloid plaque load (Figure 2A   (C) The amyloid plaque load was quantified on Aβ1-42 antibody-stained frontal brain slices from 12-month-old APPSWE-Tau and WT mice. Each group contained 8 animals; the plaque load was measured in two slices of each animal. Data are expressed as mean + SEM. *** p < 0.001. Increased Aβ plaque load was also associated with elevated TNF-α concentrations in APPSWE-Tau mice's brain samples, as shown by ELISA measurements ( Figure 3A). TNF-α blood concentrations of APPSWE-Tau were also significantly higher than WT controls ( Figure 3B). TNF-α concentration in the blood and the brain exhibited a strong correlation with a covariance of 0.80 ( Figure 3C). Increased Aβ plaque load was also associated with elevated TNF-α concentrations in APPSWE-Tau mice's brain samples, as shown by ELISA measurements ( Figure 3A). TNFα blood concentrations of APPSWE-Tau were also significantly higher than WT controls ( Figure 3B). TNF-α concentration in the blood and the brain exhibited a strong correlation with a covariance of 0.80 ( Figure 3C). ELISA measurements also revealed increased SDC3 concentrations in the brain (Figure 4A) and the periphery, namely the liver ( Figure 4B). As shown in Figure 4C,D, increased SDC3 concentrations in the brain and the liver correlated well with blood TNF-α concentrations, suggesting a cause and effect relationship between TNF-α and SDC3 expression, as shown in previous in vitro studies.  ELISA measurements also revealed increased SDC3 concentrations in the brain ( Figure 4A) and the periphery, namely the liver ( Figure 4B). As shown in Figure 4C,D, increased SDC3 concentrations in the brain and the liver correlated well with blood TNF-α concentrations, suggesting a cause and effect relationship between TNF-α and SDC3 expression, as shown in previous in vitro studies. Increased Aβ plaque load was also associated with elevated TNF-α concentrations in APPSWE-Tau mice's brain samples, as shown by ELISA measurements ( Figure 3A). TNFα blood concentrations of APPSWE-Tau were also significantly higher than WT controls ( Figure 3B). TNF-α concentration in the blood and the brain exhibited a strong correlation with a covariance of 0.80 ( Figure 3C). ELISA measurements also revealed increased SDC3 concentrations in the brain (Figure 4A) and the periphery, namely the liver ( Figure 4B). As shown in Figure 4C,D, increased SDC3 concentrations in the brain and the liver correlated well with blood TNF-α concentrations, suggesting a cause and effect relationship between TNF-α and SDC3 expression, as shown in previous in vitro studies.  Considering the emerging role of endothelial cells' SDC3 in the inflammatory response, along with our initial in vitro data on TNF-α-induced SDC3 expression in human bloodbrain barrier (BBB) endothelial cells, we also analyzed the SDC3 expression of primary BBB endothelial cells isolated from mice. Primary brain endothelial cells (PBECs) were isolated with the method of Assmann et al., and SDC3 expression was analyzed with imaging flow cytometry using fluorescent SDC3 antibodies [72]. A fluorescently labeled PECAM-1 (platelet endothelial cell adhesion molecule-1) antibody was used as an endothelial marker to identify PBECs during the flow cytometry analyses [73]. As Figure 5A-C shows that the SDC3 expression of PBECs isolated from APPSWE-Tau was significantly increased compared with those isolated from WT mice. Considering the emerging role of endothelial cells' SDC3 in the inflammatory response, along with our initial in vitro data on TNF-α-induced SDC3 expression in human blood-brain barrier (BBB) endothelial cells, we also analyzed the SDC3 expression of primary BBB endothelial cells isolated from mice. Primary brain endothelial cells (PBECs) were isolated with the method of Assmann et al., and SDC3 expression was analyzed with imaging flow cytometry using fluorescent SDC3 antibodies [72]. A fluorescently labeled PECAM-1 (platelet endothelial cell adhesion molecule-1) antibody was used as an endothelial marker to identify PBECs during the flow cytometry analyses [73]. As Figure 5A-C shows that the SDC3 expression of PBECs isolated from APPSWE-Tau was significantly increased compared with those isolated from WT mice. The roles of SDC3 on leukocytes are emerging in inflammatory responses [37]. TNF-α induces the SDC3 expression of cultured monocytes. Furthermore, SDC3 is also massively induced on inflammatory monocytes in vivo in sections of inflamed synovia from the joints of patients with rheumatoid arthritis [74]. Considering the role of monocytes in the progression of AD, we also analyzed SDC3 expression of monocytes isolated from the blood of APPSWE-Tau and WT mice. Thus blood-derived monocytes were isolated with EasySep™ Mouse Monocyte Isolation Kit, stained with CD11b monocyte marker, and the SDC3 expression of CD11b positive cells was measured with imaging flow cytometry [75]. Monocytes isolated from APPSWE-Tau mice exhibited increased SDC3 expression than those from WT mice ( Figure 6A-C). SDC3 expression of blood-derived monocytes showed a positive correlation (r = 0.81) with Aβ plaque deposition in the brain, showing that SDC3 on monocytes is a good indicator of amyloid pathology in the brain ( Figure 6C). The roles of SDC3 on leukocytes are emerging in inflammatory responses [37]. TNFα induces the SDC3 expression of cultured monocytes. Furthermore, SDC3 is also massively induced on inflammatory monocytes in vivo in sections of inflamed synovia from the joints of patients with rheumatoid arthritis [74]. Considering the role of monocytes in the progression of AD, we also analyzed SDC3 expression of monocytes isolated from the blood of APPSWE-Tau and WT mice. Thus blood-derived monocytes were isolated with EasySep™ Mouse Monocyte Isolation Kit, stained with CD11b monocyte marker, and the SDC3 expression of CD11b positive cells was measured with imaging flow cytometry [75]. Monocytes isolated from APPSWE-Tau mice exhibited increased SDC3 expression than those from WT mice ( Figure 6A-C). SDC3 expression of blood-derived monocytes showed a positive correlation (r = 0.81) with Aβ plaque deposition in the brain, showing that SDC3 on monocytes is a good indicator of amyloid pathology in the brain ( Figure 6C).

Discussion
AD is the leading cause of senile dementia [76]. AD patients are usually diagnosed in a stage of cognitive deficits with underlying CNS dementia [9,77]. Due to the irreversible nature of neurodegeneration, patients diagnosed with symptomatic AD progress into gradual mental and physical deterioration. As curative AD therapeutics are lacking, early AD diagnosis has paramount importance in preserving a patient's level of function for a more extended period [11][12][13]. The emergence of AD-specific biomarkers improved diagnostic specificity [15,78]. Newly discovered biomarkers allow AD diagnosis before the onset of dementia, namely in the stage of mild cognitive impairment (MCI), which has variously been termed prodromal AD in the presence of amyloid biomarkers [15,79]. The medical practice still requires more AD biomarkers to validate their usefulness in the clinics [15,80,81]. Given the ongoing worldwide AD epidemics and the inequality of available high-tech laboratory instrumentation, one of the most critical requirements for AD biomarkers is easy detection in low-cost diagnostic settings [80,81]. The widespread use of blood testing makes blood-based biomarkers ideal for AD diagnosis [80]. Blood-based biomarkers can improve detection and low-cost diagnosis of AD by the ease of testing. Blood-based tests enable the detection of a wide range of exploratory and candidate pathophysiological biomarkers, reflecting the full spectrum of AD-driving molecular mechanisms beyond the conventional amyloid-and tau-based tests [80]. Blood-based biomarkers could facilitate a more profound understanding of AD molecular pathophysiology and accelerate the development of disease-modifying therapies [80,81].
Considering the evidence on the contribution of SDC3 to inflammation and neurodegeneration, we decided to analyze the changes of SDC3 expression due to proinflammatory and neurodegenerative stimuli. In vitro, TNF-α significantly increased SDC3 expression in the neuronal-like SH-SY5Y and BBB-derived hCMEC/D3 cells. In the transgenic mice model of AD, APPSWE-Tau, increased Aβ plaque load was associated with elevated blood and brain TNF-α concentrations (those correlated with each other, too), a clear indicator of the inflammatory aspects of the ongoing neurodegeneration. In correlation with the increased blood TNF-α, SDC3 concentrations also increased in the CNS (i.e., brain) and the periphery (i.e., liver), suggesting that neurodegeneration-related inflammation also extends to the periphery. PBECs isolated from APPSWE-Tau mice also exhibited higher SDC3 expression than WT mice, highlighting the molecular changes occurring in the BBB during the progress of AD pathology. Monocytes isolated from peripheral blood of APPSWE-Tau mice also showed increased SDC3 expression that correlated with CNS plaque load. As SDC3 monocytes isolated from the systemic circulation reflect amyloid pathology, SDC3 expression of monocytes could serve as a blood-based biomarker diagnosing AD.
In summary, our data confirm the expression changes of SDC3, a proteoglycan with an established role in protein aggregation and inflammation, in preclinical models of AD. The detected increase in SDC3 expression in both the brain and the periphery correlates with the increased TNF-α concentrations, an established indicator of inflammation. Inflammation associated with neurodegeneration also affects BBB, as reflected by the increased SDC3 expression of BBB-derived primary endothelial cells. The selective expression of endothelial SDC3 was already explored in the chronically inflamed synovium, where SDC3 plays a part in arthritis pathophysiology by binding cytokines and modulating the migration and retention of leukocytes [38,39]. In the BBB, SDC3 modulates the transendothelial migration of monocytes [40]. An increase in endothelial SDC3 could thus demonstrate a novel link between BBB vascular changes and neuroinflammation during AD pathogenesis, thus facilitating peripheral monocytes migrating into the brain to phagocytose Aβ plaques [41][42][43][44]. A recent study revealed distinct phenotypic and functional changes in monocyte and macrophage populations as AD progress [83]. As detected in our studies, the increased SDC3 expression of blood monocytes confirms monocyte activation due to systemic inflammation associated with AD. The correlation of SDC3 expression changes of peripheral monocytes with Aβ pathology highlights the relevance of monocytes' SDC3 as a predictive biomarker of AD progression. Further clinical studies should confirm our findings obtained in preclinical models. However, considering the involvement of SDC3 in inflammatory conditions in general, other established AD biomarkers should also supplement the utilization of SDC3 as a peripheral biomarker of AD pathology.
SDC3 expression of primary mouse brain endothelial cells and monocytes was analyzed with imaging flow cytometry using mouse SDC3 antibody and specific monocyte or endothelial markers (CD11b or PECAM-1). After isolation, the isolated primary cells were incubated with primary SDC3 antibody (cat. no. sc-398194, Santa Cruz Biotechnology, Inc., Dallas, TX, USA) and fluorescently labeled secondary antibody (Alexa Fluor 633-labeled goat anti-mouse IgM, cat. no. A-21046, Invitrogen, Waltham, MA, USA). Respective cellular markers (PBECs: mouse PECAM-1 Alexa Fluor 488-conjugated Antibody, cat. no. FAB6874G, RnD Systems and monocytes: Alexa Fluor 488-labeled CD11b Monoclonal Antibody, Invitrogen, cat. no. 53-0112-82) were used to identify PBECs and monocytes. A minimum of 5000 events per sample was analyzed. Appropriate gating was utilized to exclude cellular debris and aggregates. Fluorescence analysis was carried out with the Amnis IDEAS analysis software.

Immunohistochemistry
For immunohistochemistry, mouse brain samples (n = 8 mice per group) were fixed for 18 h in 4% paraformaldehyde (cat. no. P6148, Sigma-Aldrich), then dehydrated in an ethanol series, cleared with xylene (cat. no. 00699464, Avantor Inc., Radnor, PA, USA), and embedded in paraffin (cat. no. 26154.291, Avantor Inc.). Ten µm thick sections were 9 of 13 finally cut with a microtome (Leica Biosystems Inc., Buffalo Grove, IL, USA), and sections were collected on SuperFrost Plus ® slides (Thermo Fisher Scientific Inc., Waltham, MA, USA). Antigen detection was carried out with heat-induced antigen recovery. Slides were first immersed in citrate buffer heated to 95-100 degrees for 10 min, then cooled to room temperature for about 20 min. Next, the slides were placed in blocking solution (5% goat or donkey serum diluted in 0.1% PBST) at room temperature for 30 min, and then the blocking solution was removed without rinsing. The slides were then incubated with 100 µL of primary antibody (Aβ specific antibody, MOAB-2, cat. no. NBP2-13075, NOVUS Biologicals, Littleton, CO, USA) diluted in blocking solution (1% BSA or goat serum in 0.1% PBST) at room temperature for 1 h or at 4 • C overnight. Slides were then rinsed with PBST 3x for 10 min each at room temperature, followed by staining with 100 µL of Alexa Fluor 488-labeled secondary antibody (cat. no. A-21141, Thermo Fisher Scientific) diluted in blocking solution for 1h at room temperature. The slides were then rinsed 3 times with PBS at room temperature for 10 min and, using mounting media, were covered with cover plates.

Plaque Load Assessment
Morphometry for Aβ load determination was performed using ImageJ image processing and analysis software by interactive measurement of plaque areas in the total area of interest. The plaque load was calculated as the percentage of the area of interest covered by amyloid plaques stained with the Aβ specific antibody [84,85]. Plaque load of two samples from each animal were calculated.

Measuring TNF-α Tissue Concentrations
Brain samples were homogenized in lysis buffer (cat. no. 79216, QIAGEN, Düsseldorf, Germany) in 1% NP-40/PBS in cOmplete, Mini, EDTA-free protease inhibitor cocktail (cat. no. 11836170001, Roche, Basel, Switzerland), and tissue lysates were analyzed with mouse TNF-alpha Quantikine ELISA Kit (cat. no. MTA00B, RnD Systems, Minneapolis, MN, USA) according to the manufacturer's instructions. The TNF-α concentration of whole blood isolated from mice was also measured with the same ELISA kit.

Measuring SDC3 Tissue Concentration
Brain and liver samples were homogenized in lysis buffer (QIAGEN) in 1% NP-40/PBS in Complete Mini EDTA-free protease inhibitor cocktail (Roche). Tissue lysates were analyzed with mouse SDC3 ELISA Kit PicoKine ® (cat. no. EK1556, BOSTER Biological Technology, Pleasanton, CA, USA) according to the manufacturer's instructions.

Isolation of Mouse Monocytes and Brain Endothelial Cells
Monocytes were isolated from the collected blood samples with the EasySep™ Mouse Monocyte Isolation Kit (cat. no. 19861, Stemcell Technologies Inc, Vancouver, BC, Canada) according to the manufacturer's instructions. Mouse brain endothelial cells were isolated using the method of Assmann et al. [72]. SDC3 expression of isolated monocytes and primary brain endothelial cells (PBECs) was analyzed with imaging flow cytometry using mouse SDC3 antibody and specific monocyte or endothelial markers (CD11b or PECAM-1). After isolation, the isolated primary cells were incubated with primary SDC3 antibody (cat. no. sc-398194, Santa Cruz Biotechnology, Inc., Dallas, TX, USA) and fluorescently labeled secondary antibody (Alexa Fluor 633-labeled goat anti-mouse IgM, cat. no. A-21046, Invitrogen, Waltham, MA, USA). Respective cellular markers (PBECs: mouse PECAM-1 Alexa Fluor 488-conjugated Antibody, cat. no. FAB6874G, RnD Systems and monocytes: Alexa Fluor 488-labeled CD11b Monoclonal Antibody, Invitrogen, cat. no. 53-0112-82) were used to identify PBECs and monocytes.

Statistical Analysis
Results are expressed as means + standard error of the mean (SEM). Differences between experimental groups were evaluated using a one-way analysis of variance (ANOVA). Values of p < 0.05 were accepted as significant. Pearson's correlation coefficient was used to measure the strength of a linear association between two variables.