Skip to main content
PMC home page
eLife logoLink to eLife
. 2024 Mar 14;12:RP87501. doi: 10.7554/eLife.87501

Plasma extracellular vesicle synaptic proteins as biomarkers of clinical progression in patients with Parkinson’s disease

Chien-Tai Hong 1,2,3, Chen-Chih Chung 1,2,3, Ruan-Ching Yu 4, Lung Chan 1,2,3,✉
Editors: Ivan Velasco5, Tony Yuen6
PMCID: PMC10939498  PMID: 38483306

Abstract

Synaptic dysfunction plays a key role in Parkinson’s disease (PD), and plasma extracellular vesicle (EV) synaptic proteins are emerging as biomarkers for neurodegenerative diseases. Assessment of plasma EV synaptic proteins for their efficacy as biomarkers in PD and their relationship with disease progression was conducted. In total, 144 participants were enrolled, including 101 people with PD (PwP) and 43 healthy controls (HCs). The changes in plasma EV synaptic protein levels between baseline and 1-year follow-up did not differ significantly in both PwP and HCs. In PwP, the changes in plasma EV synaptic protein levels were significantly associated with the changes in Unified Parkinson’s Disease Rating Scale (UPDRS)-II and III scores. Moreover, PwP with elevated levels (first quartile) of any one plasma EV synaptic proteins (synaptosome-associated protein 25, growth-associated protein 43 or synaptotagmin-1) had significantly greater disease progression in UPDRS-II score and the postural instability and gait disturbance subscore in UPDRS-III than did the other PwP after adjustment for age, sex, and disease duration. The promising potential of plasma EV synaptic proteins as clinical biomarkers of disease progression in PD was suggested. However, a longer follow-up period is warranted to confirm their role as prognostic biomarkers.

Research organism: Human

Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease (de Lau and Breteler, 2006) that is well known for its progression, which involves increased disability and burden (Bloem et al., 2021). Worsening is noted not only in motor symptoms but also in nonmotor ones, particularly cognition. The rate of disease progression varies among people with PD (PwP). In the ongoing Parkinson’s Progression Markers Initiative cohort study, approximately one-third of untreated PwP exhibited rapid progression during the first 2 years of follow-up. By contrast, the remaining PwP had a slow progressive course (Chen-Plotkin et al., 2018). Unfortunately, no disease-modifying therapy for halting disease progression and no predictor for assessing disease progression are available.

Synapses are sites of neuronal communication, and synaptic degeneration is an early functional pathogenic event in neurodegenerative diseases such as Alzheimer’s disease (AD) and PD (Picconi et al., 2012; Arendt, 2009). Postmortem studies have revealed a substantial loss of dopamine terminals in the putamen and amygdala (Iseki et al., 2001; Kordower et al., 2013). In addition, the loss of glutamatergic corticostriatal synapses has been reported (Stephens et al., 2005). Mitochondrial dysfunction contributes to some of the synaptic loss in PD, and the buildup of α-synuclein, a key synaptic protein, plays a significant role in the development of PD (Gcwensa et al., 2021). Assessment of synaptic proteins in the cerebrospinal fluid (CSF) can reflect synaptic loss in patients with neurological diseases and is a key area of research interest. Significant research has been directed toward evaluating synaptic changes in CSF to enhance early diagnosis of neurodegenerative diseases before neuron loss and track disease progression (Lleó et al., 2019; Milà-Alomà et al., 2021; Bereczki et al., 2017). Several synaptic proteins are promising biomarkers of synaptic function. Synaptosome-associated protein 25 (SNAP-25) is a presynaptic protein that plays a crucial role in neuronal survival, vesicular exocytosis, and neurite outgrowth (Antonucci et al., 2016). Increased CSF levels of SNAP-25 have been reported in PwP (Bereczki et al., 2017). In addition, growth-associated protein 43 (GAP-43) is a presynaptic protein anchored to the cytoplasmic side of the presynaptic plasma membrane (Benowitz and Routtenberg, 1997). CSF levels of GAP-43 have been reported to be significantly higher in patients with AD than in healthy controls (HCs) (Sjögren et al., 2001). Synaptotagmin-1 is a calcium sensor vesicle protein that is vital for rapid synchronous neurotransmitter release in hippocampal neurons (Courtney et al., 2019). Significantly increased CSF levels of synaptotagmin-1 have been reported in patients with AD and mild cognitive impairment (Öhrfelt et al., 2019). Regarding PD, one study reported an increase in the level of CSF SNAP-25 but not Ras-related protein 3A or neurogranin. Moreover, treated PwP exhibited higher CSF levels of SNAP-25 than did their drug-naïve counterparts (Bereczki et al., 2017).

However, collecting CSF is somewhat invasive and necessary for clinical assessments, but it may cause side effects like headaches after the procedure. Although studies have assessed blood biomarkers for PD diagnosis and progression, the results are conflicting. A lack of correlation between the peripheral blood content and the brain because of the blood–brain barrier (BBB) is a major obstacle to the identification of blood biomarkers for neurodegenerative diseases (Qin et al., 2016). Assessment of peripheral blood extracellular vesicle (EV) proteins can be an alternative approach. EVs are tiny vesicles covered with a lipid membrane. They contain proteins, lipids, and nucleic acid responsible for cell-to-cell signal transmission. The integrity of EVs can be maintained when crossing the BBB (Jan et al., 2017). Plasma EV biomarkers are being rapidly developed for PD (Leggio et al., 2021). EV-cargo α-synuclein has been the most studied target that has exhibited strong potential for distinguishing PwP from HCs and other patients with atypical parkinsonism (Chung et al., 2021b; Niu et al., 2020; Zheng et al., 2021; Si et al., 2019; Stuendl et al., 2021). EV-cargo tau, β-amyloid, neurofilament light chain, brain-derived neurotrophic factor, and insulin receptor substrate have also been assessed in PwP (Athauda et al., 2019; Chou et al., 2020; Chung et al., 2020d; Chung et al., 2020a; Chung et al., 2021a). These results indicate the potential role of EV content as biomarkers in PD.

The levels of synaptic proteins in blood exosomes, a specific type of EV, decrease in patients with AD and frontotemporal dementia (Goetzl et al., 2016). Moreover, blood exosomal SNAP-25, GAP-43, neurogranin, and synaptotagmin-1 levels are lower in patients with AD. A combination of exosomal synaptic protein biomarkers could predict cognitive impairment (Jia et al., 2021). A cross-sectional study showed that PwP have lower levels of synaptic proteins in neuron-derived exosomes in their blood compared to healthy individuals, allowing for an approximately 80% accurate distinction between PwP and HCs (Agliardi et al., 2021). Considering the plasma EVs remain stable up to 90 d (Kalra et al., 2013), this prevents the fluctuation of free-form synaptic proteins because of transient surge or degradation. Moreover, SNAP-25 is transported in the blood by EVs (Agliardi et al., 2019). However, there is no information from the cohort study of PD. Therefore, the association between plasma EV synaptic proteins and PD progression was assessed and determined whether plasma EV synaptic proteins could be used as clinical biomarkers to predict the progression of PD.

Results

The participants’ demographic data at baseline and 1-year follow-up are presented in Table 1. In total, 144 participants (101 PwP and 43 HCs, all of them are Taiwanese) were followed up. No significant difference was noted in plasma EV SNAP-25, GAP-43, and synaptotagmin-1 levels at baseline and follow-up between PwP and HCs after adjustment for age and sex (Figure 1A [representative image] and Figure 1B–D [dot plot]).

Table 1. Demographic data of study participants.

HCs (n = 43) PwP (n = 101)
Age (y) 65 (10.24) 69 (7.76)
Women 15 48
Baseline
 MMSE 27 (3.92) 26 (4.15)
 MoCA 23 (4.63) 21 (5.70)
 Disease duration (y) - 2 (2.24)
 UPDRS-II - 8 (5.58)
 UPDRS-III - 22 (9.30)
1-year follow-up
 MMSE 28 (4.12) 27 (5.61)
 MoCA 24 (5.82) 23 (6.45)
 UPDRS-II - 11 (6.41)
 UPDRS-III - 19 (9.39)
Open in a new tab

Data is presented as median (standard deviation).

HC = healthy control; PwP = people with Parkinson’s disease; MMSE = Mini-Mental State Examination; MoCA = Montreal Cognitive Assessment; UPDRS = Unified Parkinson’s Disease Rating Scale.

Figure 1. Baseline and follow-up synaptic protein levels in plasma extracellular vesicles (EVs) between patients with Parkinson’s disease (PwP) and healthy controls (HCs).

Figure 1.

Open in a new tab

(A) Representative protein blot images of different synaptic proteins, including SNAP-25, GAP-43, and synaptotagmin-1. Heat shock protein 70 (HSP-70) was the protein loading control. (B–D) Comparison of plasma SNAP-25, GAP-43, and synaptotagmin-1 levels between PwP and HCs at baseline and follow-up. Data are presented using a dot plot displaying the median and first and third quartile values. n.s., nonsignificant.

Figure 1—source data 1. The original blot image of the representative GAP-43 (A), synaptotagmin-1 (B), SNAP-25 (C), and HSP-70 (D).

The association between changes in plasma EV synaptic protein levels and clinical parameters in PwP was assessed through a generalized linear model (Table 2). The changes in the total score of Unified Parkinson’s Disease Rating Scale (UPDRS)-II was positively associated with the change in plasma EV synaptic proteins (SNAP-25, GAP-43, and synaptotagmin-1). The changes in total score of UPDRS-III and akinetic rigidity (AR) subscore were significantly associated with the changes in plasma EV GAP-43 and synaptotagmin-1. The changes in Mini-Mental Status Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores were nonsignificantly associated with the changes in plasma EV synaptic protein levels.

Table 2. The association between the change in plasma EV synaptic protein abundance (between baseline and follow-up) with the change in clinical severity in motor and cognitive domains (between baseline and follow-up) in people with Parkinson’s disease.

A generalized linear model was employed, and the data is presented as coefficient (p-value).

UPDRS-II UPDRS-III Tremor AR PIGD MMSE MoCA
SNAP-25 * follow-up 0.218 (0.049) 0.312 (0.076) 0.004 (0.432) 0.016 (0.066) 0.009 (0.440) –0.007 (0.932) 0.048 (0.647)
GAP-43 * follow-up 0.984 (0.031) 1.711 (0.018) 0.001 (0.972) 0.089 (0.011) 0.073 (0.115) –0.099 (0.767) –0.054 (0.901)
Synaptomagtin-1 * follow-up 1.543 (0.012) 2.205 (0.024) 0.007 (0.815) 0.107 (0.023) 0.109 (0.080) –0.361 (0.421) –0.260 (0.661)
Open in a new tab

UPDRS = Unified Parkinson's Disease Rating Scale; AR = akinetic rigidity; PIGD = postural instability and gait disturbance; MMSE = Mini-Mental Status Examination; MoCA = Montreal Cognitive Assessment.

The association between the severity of clinical parameters of PD at follow-up and baseline plasma EV synaptic protein levels was further evaluated. After adjustment for age, sex, and disease duration, the plasma EV SNAP-25, GAP-43, and synaptotagmin-1 levels were nonsignificantly associated with the UPDRS-II, UPDRS-III, MMSE, and MoCA scores at follow-up (Figure 2; for details, refer to Supplementary file 1). However, when UPDRS-III scores were divided into tremor, AR, and postural instability and gait disturbance (PIGD) subscores, the initial levels of plasma EV SNAP-25 and GAP-43 showed a significant positive correlation with PIGD subscores at follow-up. Additionally, a similar trend was observed with the plasma EV synaptotagmin-1 level.

Figure 2. Heatmap of the association between baseline plasma extracellular vesicle (EV) synaptic protein levels and clinical assessment parameters at follow-up in patients with Parkinson’s disease (PwP).

Figure 2.

Open in a new tab

The logistic regression model was used to assess the baseline plasma EV SNAP-25, GAP-43, and synaptotagmin-1 levels. The motor symptoms were assessed based on the Unified Parkinson’s Disease Rating Scale (UPDRS)-II and UPDRS-III scores and tremor, akinetic rigidity (AR), and postural instability and gait disturbance (PIGD) subscores, and cognitive function was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. The association is presented using standardized β values. Detailed results of the regression model are provided in Supplementary file 1. *p<0.05; **p<0.01.

Participants with PD were grouped based on their baseline plasma EV synaptic protein levels using the first quartile as a cutoff. Overall, PwP with elevated baseline levels of plasma EV synaptic proteins had poor total scores of UPDRS-II and UPDRS-III and poor PIGD subscores of UPDRS-III (Table 3). In contrast, a significant greater improvement in the tremor score was noted in the PwP with elevated baseline levels of plasma EV synaptic proteins. Moreover, PwP with elevated baseline levels of plasma EV synaptic proteins exhibited significantly greater deterioration, as assessed using the UPDRS-II scores and PIGD subscores in UPDRS-III. After adjusting for age, sex, and disease duration, repeated-measures analysis of covariance showed that PwP who had elevated levels of any one plasma EV synaptic protein had significantly higher UPDRS-II scores and PIGD subscores at follow-up, but not at baseline, compared to those without elevated levels. This indicates a notably faster deterioration in the patient group with elevated plasma EV synaptic protein levels (Figure 3).

Table 3. The clinical severity in people with Parkinson’s disease with and without elevated (first quartile) baseline plasma extracellular vesicle (EV) synaptosome-associated protein 25 (SNAP-25), growth-associated protein 43 (GAP-43), and synaptotagmin-1.

p-Value indicates the inter-group comparisons for the changes.

Plasma EV SNAP-25 GAP-43 Synaptotagmin-1
L (n = 74) H (n = 28) p L (n = 74) H (n = 28) p L (n = 74) H (n = 28) p
UPDRS-
II		 Baseline 8.26 ± 5.80 9.04 ± 4.90 <0.001 8.53 ± 5.67 8.32 ± 5.33 <0.001 8.46 ± 5.70 8.50 ± 5.24 <0.001
Follow-up 10.38 ± 6.11 13.36 ± 6.72 10.65 ± 6.30 12.64 ± 6.53 10.57 ± 6.25 12.86 ± 6.58
UPDRS-III Baseline 22.31 ± 9.54 23.89 ± 8.65 0.259 22.22 ± 9.66 24.14 ± 8.25 0.244 21.97 ± 9.37 24.79 ± 8.93 0.145
Follow-up 20.01 ± 9.33 24.21 ± 8.87 19.96 ± 9.25 24.36 ± 8.99 20.04 ± 9.39 24.14 ± 8.70
Tremor Baseline 0.34 ± 0.46 0.46 ± 0.38 0.037 0.34 ± 0.34 0.46 ± 0.37 0.034 0.33 ± 0.34 0.47 ± 0.38 0.014
Follow-up 0.27 ± 0.26 0.35 ± 0.32 0.27 ± 0.26 0.35 ± 0.32 0.28 ± 0.29 0.32 ± 0.22
AR Baseline 1.04 ± 0.46 1.09 ± 0.44 0.300 1.03 ± 0.47 1.11 ± 0.42 0.325 1.03 ± 0.46 1.13 ± 0.45 0.195
Follow-up 0.96 ± 0.46 1.10 ± 0.42 0.95 ± 0.45 1.12 ± 0.43 0.96 ± 0.46 1.10 ± 0.42
PIGD Baseline 0.76 ± 0.61 0.81 ± 0.44 0.023 0.77 ± 0.60 0.77 ± 0.45 0.046 0.74 ± 0.56 0.86 ± 0.58 0.027
Follow-up 0.74 ± 0.59 1.07 ± 0.75 0.78 ± 0.61 0.98 ± 0.74 0.73 ± 0.56 1.11 ± 0.80
MMSE Baseline 25.32 ± 4.45 25.29 ± 3.29 0.483 25.58 ± 4.16 24.61 ± 4.11 0.470 25.62 ± 3.91 24.50 ± 4.69 0.342
Follow-up 24.78 ± 5.93 25.14 ± 4.64 25.05 ± 5.79 24.43 ± 5.10 25.23 ± 5.61 23.96 ± 5.50
MoCA Baseline 20.68 ± 6.00 21.21 ± 5.00 0.834 21.14 ± 5.77 20.04 ± 5.62 0.899 21.12 ± 5.33 20.07 ± 6.69 0.926
Follow-up 20.60 ± 6.85 21.46 ± 5.37 21.10 ± 6.55 20.18 ± 6.30 21.19 ± 6.33 19.93 ± 6.80
Open in a new tab

L = second to fourth quartile at baseline; H = first quartile at baseline; UPDRS = Unified Parkinson’s Disease Rating Scale; AR = akinetic rigidity; PIGD = postural instability and gait disturbance; MMSE = Mini-Mental Status Examination; MoCA = Montreal Cognitive Assessment.

Figure 3. Changes in estimated marginal means of Unified Parkinson’s Disease Rating Scale (UPDRS)-II total scores.

Figure 3.

Open in a new tab

(A) and postural instability and gait disturbance (PIGD) subscores (B) after adjustment for age, sex, and disease duration in patients with Parkinson’s disease (PwP) with (n=36) and without (n=66) elevated levels of any one plasma extracellular vesicle synaptic protein (first quartile) at baseline and follow-up. Data are presented as means with 95% confidence intervals. n.s., nonsignificant.

Discussion

Although no significant difference in plasma EV synaptic protein levels was observed between PwP and HCs in the research, alterations in plasma EV synaptic protein levels in PwP were linked to motor decline. In addition, baseline plasma EV synaptic protein levels were associated with clinical outcomes, as assessed using PIGD subscores at follow-up. PwP with elevated baseline levels of any one plasma EV synaptic protein (SNAP-25, GAP-43, or synaptotagmin-1) exhibited significant deterioration in the activities of daily living (as assessed by UPDRS-II scores and PIGD subscores in UPDRS-III) between baseline and follow-up. The promising potential of plasma EV synaptic proteins as biomarkers for PD, particularly its progression, was indicated.

Significant worsening of UPDRS-II scores and PIGD subscores in UPDRS-III was noted in PwP with higher baseline levels of plasma EV synaptic proteins (first quartile). UPDRS-II is a crucial but underestimated parameter for assessing PwP; it can be used to assess the daily functional capability related to motor symptoms in PwP without temporary drug interruptions, as in the case of UPDRS-III (Rodriguez-Blazquez et al., 2013; Sampaio, 2009). PIGD subtype is linked to a higher Lewy bodies burden, a rapid decline, and greater risks of cognitive impairment, falls, and mortality compared to the tremor-dominant (TD) subtype. The TD subtype, in contrast, tends to have a relatively benign course (Johnson et al., 2016; Burn et al., 2006; Ren et al., 2020; Nutt, 2016). PwP may convert from the TD subtype to the PIGD subtype during disease progression (Lee et al., 2019). Moreover, PIGD-related motor symptoms respond to dopaminergic medications to a lesser extent than do tremor-, akinesia-, and rigidity-related symptoms (Kempster et al., 2007), which may more directly reflect the progression of the disease. PwP with elevated levels of any one plasma EV synaptic protein exhibited greater deterioration, as assessed using UPDRS-II scores and PIGD subscores, as observed in the research. High EV synaptic protein levels conventionally indicate increased synaptogenesis; this contrasts the synaptic loss pattern noted in PD. Decreased blood exosomal synaptic protein levels have been reported in patients with AD and frontotemporal dementia (Goetzl et al., 2016). However, in PwP who are at the early disease stage, compensatory synaptic sprouting and increased synaptic plasticity are noted in the striatum. This compensation, also known as motor reserve, may temporarily decrease the clinical disease burden. However, the patient would be more vulnerable to deterioration if the compensation is overshadowed by degeneration (Chung et al., 2020e). For instance, in one study, PwP who engaged in higher premorbid exercises exhibited milder motor symptoms despite having a similar gradient of striatal dopaminergic reduction at baseline; however, they exhibited more rapid deterioration (Sunwoo et al., 2017). The inclusion of primarily early-stage PwP (mean disease duration of less than 3 y) with mild disease burden (baseline UPDRS-II score = 8.49) in the research suggests that elevated plasma EV synaptic protein levels might indicate activation of a compensatory process. Such PwP are at an increased risk of rapid deterioration and disease progression and should be candidates for disease-modifying interventions, including pharmacological and nonpharmacological treatments.

A notable pioneering role is the evaluation of changes in plasma EV synaptic protein levels and elucidating the relationship between these changes and cognitive decline in PwP. Considering the well-established role of synaptic degeneration and plasticity in PD, the theoretical background of the use of these synaptic proteins as plasma EV biomarkers for PD is confirmed. Moreover, most synaptic proteins are neuron derived, thereby preventing contamination from non-neuronal tissues. The significant link between changes in plasma EV synaptic protein levels and changes in UPDRS-II, UPDRS-III, and AR subscore indicates the effectiveness of these proteins in detecting motor decline in PwP. This suggests that these proteins could serve as an objective parameter for future disease-modification clinical trials. Elevated baseline plasma EV synaptic protein levels can also predict rapid deterioration in PwP, highlighting the importance of motor reserve and synaptic plasticity in the progression of PD for future research. Although no single synaptic protein can precisely predict the progression of PD, these protein targets may be considered for inclusion in a biomarker panel. They can be analyzed using an artificial intelligence-assisted artificial neural network, a method commonly used to predict outcomes in various neurological diseases (Chung et al., 2020b; Chung et al., 2020c; James et al., 2021; Chou et al., 2022).

There were several limitations to the study. Technically, semiquantitative assessment of plasma EV synaptic protein (SNAP-25, GAP-43, and synaptotagmin-1) levels was performed using western blot analysis. The lack of absolute values, that is, from the results of enzyme-linked immunosorbent assay, limits further clinical application. In addition, because numerous synaptic proteins are involved in the pathogenesis of PD, the three selected proteins may not reflect all features of the synaptic condition. The selection of HSP-70 as the control for the synaptic proteins quantification of plasma EV is not undisputable. Two types of EV proteins are used for this role: membrane proteins and intravesicular proteins. Membrane proteins include CD9, CD63, and CD81; intravesicular proteins include TSG101, annexins, and chaperone proteins, such as HSP-70. Since the expression of HSP-70 is usually steady, the HSP-70 was used as the internal control, but it is a limitation of the present study. Furthermore, the evaluation of the overall plasma EVs rather than specifically focusing on neuron-derived exosomes potentially introduced a bias toward somatic-origin EVs. Nonetheless, it is worth noting that synaptic proteins primarily originate from neurons. Even when considering neuron-derived exosomes, it is important to recognize that they are not exclusively derived from the brain, which can lead to contamination from the peripheral nervous system. The final technical issue in the present study was the relatively small size of the isolated EVs. Despite the primary focus on isolating exosomes, which are the smallest type of EVs, it is important to consider that the presence of small-sized EVs could potentially be attributed to EV fragmentation that occurs during the freezing and thawing processes. Regarding the limitation from the cohort, it is also worth mentioning that the 1-year follow-up period to assess the progression of PD was relatively short and may have been insufficient to detect significant disease progression. On the other hand, the evaluation of motor symptoms took place in a hospital setting. In this context, patients were not asked to stop taking their anti-PD medications. This decision was made due to safety concerns, such as the risk of falls. As a result, specific motor symptoms, particularly tremor and AR, which are more sensitive to medication compared to PIGD, may have been effectively managed by the anti-PD medications. This could potentially explain the improvement in tremor observed between the baseline and 1-year follow-up, especially among PwP with elevated baseline plasma EV synaptic proteins. Additionally, synaptic dysfunction is a frequently observed phenomenon in several neurological diseases, and it is not exclusive to PD. Consequently, the HC group may have included individuals with coexisting neurological conditions in the research, potentially explaining the lack of a significant difference between the PD group and the HCs. However, this approach also illuminates the significance of synaptic dysfunction in the advancement of PD. This insight can be invaluable for monitoring disease progression, particularly in the context of clinical trials focused on disease modification. Choosing the first quartile as a cutoff value of plasma EV synaptic proteins is also one of the limitations of the study. While developing new biomarkers, there was no clear cutoff value as reference for the continuous variable, and percentile is considered for predicting the prognosis (Duarte, 2021). Further studies are required to validate this application. Lastly, the results form a mono-centric, small-scale and short-period PD cohort required further validation.

In conclusion, the changes in the levels of plasma EV synaptic proteins, namely SNAP-25, GAP-43, and synaptotagmin-1, are associated with motor decline in PwP. Elevated baseline plasma EV synaptic protein levels can predict increased deterioration of motor function, particularly PIGD symptoms, in PwP. Plasma EV synaptic proteins have the potential to be used as biomarkers of PD progression and detection. A longer longitudinal follow-up is warranted to clearly assess the prognostic efficacy of plasma EV synaptic proteins in PwP.

Methods

Study participants

A total of 101 PwP and 43 HCs were enrolled. PD was diagnosed in accordance with the criteria used in another study (Hughes et al., 1992). Patients diagnosed as having early-to-mid-stage PD (Hoehn and Yahr stages I–III) were invited to participate in this study. HCs were excluded if they had comorbidities, such as neurodegenerative, psychiatric, or major systemic diseases (malignant neoplasm or chronic kidney disease). HCs were mainly recruited from neurological outpatient clinics; they had minor chronic health conditions, such as hypertension, diabetes, or hyperlipidemia. The research protocol was approved by the Joint Institutional Review Board of Taipei Medical University (approval nos. N201609017 and N201801043).

Clinical assessments

The participants’ background data were obtained through a personal interview. Their cognitive function was assessed by trained nurses using the Taiwanese versions of the MMSE and MoCA. The severity of PD was assessed using parts I–III of the UPDRS during clinic visits. PwP were assumed to be in their ‘on’ time. Tremor, AR, and PIGD subscores were calculated from the subitems in UPDRS-III as described previously (Lewis et al., 2005), with some modifications.

Plasma EV isolation and characterization

Venous blood was collected by from PwP and HCs after their outpatient clinic (non-fasting) by 21-gauge needle, and the plasma was isolated through centrifugation at 13,000 × g for 20 min immediately. Plasma was stored in the −80°C freezer before EV isolation. Plasma EVs were isolated from 1 mL of plasma using exoEasy Maxi Kit (QIAGEN, Valencia, CA) in accordance with the manufacturer’s instructions and stored in the −80°C freezer. It was a membrane-based affinity binding step to isolate exosomes and other EVs without relying on a particular epitope. The isolated plasma EVs were then eluted and stored. Usually, 400 μL of eluate is obtained per mL of plasma. The isolated plasma EVs underwent validation in accordance with the guidelines of the International Society of Extracellular Vesicles. This validation process encompassed several steps. First, the presence of markers was confirmed, including CD63 (ab59479, Abcam, Cambridge, UK), CD9 (ab92726, Abcam), and tumor susceptibility gene 101 protein (GTX118736, GeneTex, CA), along with the absence of cytochrome c (ab110325; Abcam). Second, physical characterization was performed using nanoparticle tracking analysis. This analysis revealed that the majority of EVs were primarily within the 50–100 nm size range. Third, the morphology of the EVs was examined through electron microscopy analysis. The validation had been described previously (Chung et al., 2020d; Chung et al., 2020a; Chung et al., 2021a).

Quantification of plasma EV synaptic proteins

The isolated plasma EVs were directly lysed using protein sample buffer (RIPA Lysis Buffer, Millipore) and analyzed using protein sodium dodecyl sulfate–polyacrylamide gel electrophoresis. Antibodies against SNAP-25 (GeneTex, GTX113839, 1:1000), GAP-43 (GeneTex, GTX114124, 1:5000), and synaptotagmin-1 (GeneTex, GTX127934, 1:1000) were used for the analysis. The antibodies were prepared in Tris-buffered saline containing 0.1% Tween 20 and 5% bovine serum albumin. Secondary antibodies, including antimouse immunoglobulin G (IgG)-conjugated horseradish peroxidase (HRP; 115-035-003) and antirabbit IgG-conjugated HRP (111-035-003), were purchased from Jackson ImmunoResearch. Protein blot intensities were quantified using ImageJ software. The expression levels of plasma EV synaptic proteins were normalized to that of heat shock protein 70 (Proteintech, Cat# 10995-1-AP, 1:2000). For each participant, equal volume of EV suspension (5 μL) was applied to the protein quantification. To ensure that the data could be compared between different gels, all the data were normalized to the average of the control group in the same gel.

Statistical analyses

All statistical analyses were performed using SPSS for Windows 10 (version 26; SPSS Inc, Chicago, IL). A linear mixed model was used to assess whether the changes in plasma EV synaptic protein levels differed between PwP and HCs after adjustment for age and sex. A generalized linear model was used to determine the association between the changes in plasma EV synaptic protein levels and the changes in clinical parameters in PwP after adjustment for age, sex, and disease duration. Multivariate logistic regression was performed to assess the association between plasma EV synaptic proteins and clinical parameters at follow-up in PwP after adjustment for age, sex, and disease duration. Repeated-measures analysis of covariance with estimated marginal means was employed to compare the changes in clinical parameters between baseline and follow-up in PwP with elevated baseline levels (first quartile) of any one plasma EV synaptic protein. Finally, p-values<0.05 were considered statistically significant.

Acknowledgements

The Ministry of Science and Technology, Taiwan, funded this study (MOST 110-2314-B-038-096 and NSC 111-2314-B-038-136).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Lung Chan, Email: cjustinmd@tmu.edu.tw.

Ivan Velasco, Universidad Nacional Autónoma de México, Mexico.

Tony Yuen, Icahn School of Medicine at Mount Sinai, United States.

Funding Information

This paper was supported by the following grants:

  • Minister of Science and Technology MOST 110-2314-B-038-096 to Chien-Tai Hong.

  • National Science Council 111-2314-B-038-136 to Chien-Tai Hong.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Resources, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration.

Resources, Data curation, Software, Funding acquisition, Investigation, Methodology, Writing – review and editing.

Data curation, Formal analysis, Methodology.

Conceptualization, Resources, Data curation, Software, Validation, Investigation, Writing – review and editing.

Ethics

Human subjects: This study was approved by the Joint Institutional Review Board of Taipei Medical University (TMU-JIRB approval no. N201609017 and N201801043). Written informed consent was obtained from all participants for participation in the study.

Additional files

Supplementary file 1. Association between the baseline plasma EV synaptic proteins with the clinical severity in people with Parkinson’s disease at follow-up with the adjustment of age, sex, disease duration, and the baseline severity of corresponding item, presented as standardized β and p-values.

UPDRS, Unified Parkinson’s Disease Rating Scale; AR, akinetic rigidity; PIGD, postural instability and gait disturbance; MMSE, Mini-Mental Status Examination; MoCA, Montreal Cognitive Assessment.

elife-87501-supp1.docx (16.7KB, docx)
MDAR checklist

Data availability

The dataset is deposited in Dryad: https://doi.org/10.5061/dryad.4qrfj6qh9.

The following dataset was generated:

Hong CT, Chung CC, Yu RC, Chan L. 2024. Plasma extracellular vesicle synaptic proteins as biomarkers of clinical progression in patients with Parkinson's disease. Dryad Digital Repository.

References

  1. Agliardi C, Guerini FR, Zanzottera M, Bianchi A, Nemni R, Clerici M. SNAP-25 in serum is carried by exosomes of neuronal origin and is a potential biomarker of Alzheimer’s Disease. Molecular Neurobiology. 2019;56:5792–5798. doi: 10.1007/s12035-019-1501-x. [DOI] [PubMed] [Google Scholar]
  2. Agliardi C, Meloni M, Guerini FR, Zanzottera M, Bolognesi E, Baglio F, Clerici M. Oligomeric α-Syn and SNARE complex proteins in peripheral extracellular vesicles of neural origin are biomarkers for Parkinson’s disease. Neurobiology of Disease. 2021;148:105185. doi: 10.1016/j.nbd.2020.105185. [DOI] [PubMed] [Google Scholar]
  3. Antonucci F, Corradini I, Fossati G, Tomasoni R, Menna E, Matteoli M. SNAP-25, a known presynaptic protein with emerging postsynaptic functions. Frontiers in Synaptic Neuroscience. 2016;8:7. doi: 10.3389/fnsyn.2016.00007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Arendt T. Synaptic degeneration in Alzheimer’s disease. Acta Neuropathologica. 2009;118:167–179. doi: 10.1007/s00401-009-0536-x. [DOI] [PubMed] [Google Scholar]
  5. Athauda D, Gulyani S, Karnati HK, Li Y, Tweedie D, Mustapic M, Chawla S, Chowdhury K, Skene SS, Greig NH, Kapogiannis D, Foltynie T. Utility of Neuronal-Derived Exosomes to Examine Molecular Mechanisms That Affect Motor Function in Patients With Parkinson Disease: A Secondary Analysis of the Exenatide-PD Trial. JAMA Neurology. 2019;76:420–429. doi: 10.1001/jamaneurol.2018.4304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Benowitz LI, Routtenberg A. GAP-43: an intrinsic determinant of neuronal development and plasticity. Trends in Neurosciences. 1997;20:84–91. doi: 10.1016/s0166-2236(96)10072-2. [DOI] [PubMed] [Google Scholar]
  7. Bereczki E, Bogstedt A, Höglund K, Tsitsi P, Brodin L, Ballard C, Svenningsson P, Aarsland D. Synaptic proteins in CSF relate to Parkinson’s disease stage markers. NPJ Parkinson’s Disease. 2017;3:7. doi: 10.1038/s41531-017-0008-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bloem BR, Okun MS, Klein C. Parkinson’s disease. The Lancet. 2021;397:2284–2303. doi: 10.1016/S0140-6736(21)00218-X. [DOI] [PubMed] [Google Scholar]
  9. Burn DJ, Rowan EN, Allan LM, Molloy S, O’Brien JT, McKeith IG. Motor subtype and cognitive decline in Parkinson’s disease, Parkinson’s disease with dementia, and dementia with Lewy bodies. Journal of Neurology, Neurosurgery, and Psychiatry. 2006;77:585–589. doi: 10.1136/jnnp.2005.081711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chen-Plotkin AS, Albin R, Alcalay R, Babcock D, Bajaj V, Bowman D, Buko A, Cedarbaum J, Chelsky D, Cookson MR, Dawson TM, Dewey R, Foroud T, Frasier M, German D, Gwinn K, Huang X, Kopil C, Kremer T, Lasch S, Marek K, Marto JA, Merchant K, Mollenhauer B, Naito A, Potashkin J, Reimer A, Rosenthal LS, Saunders-Pullman R, Scherzer CR, Sherer T, Singleton A, Sutherland M, Thiele I, van der Brug M, Van Keuren-Jensen K, Vaillancourt D, Walt D, West A, Zhang J. Finding useful biomarkers for Parkinson’s disease. Science Translational Medicine. 2018;10:eaam6003. doi: 10.1126/scitranslmed.aam6003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chou SY, Chan L, Chung CC, Chiu JY, Hsieh YC, Hong CT. Altered Insulin Receptor Substrate 1 Phosphorylation in Blood Neuron-Derived Extracellular Vesicles From Patients With Parkinson’s Disease. Frontiers in Cell and Developmental Biology. 2020;8:564641. doi: 10.3389/fcell.2020.564641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chou SY, Bamodu OA, Chiu WT, Hong CT, Chan L, Chung CC. Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management. Scientific Reports. 2022;12:7254. doi: 10.1038/s41598-022-11201-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chung CC, Chan L, Chen JH, Bamodu OA, Hong CT. Neurofilament light chain level in plasma extracellular vesicles and Parkinson’s disease. Therapeutic Advances in Neurological Disorders. 2020a;13:1756286420975917. doi: 10.1177/1756286420975917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chung CC, Hong CT, Huang YH, Su ECY, Chan L, Hu CJ, Chiu HW. Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks. Journal of the Neurological Sciences. 2020b;410:116667. doi: 10.1016/j.jns.2020.116667. [DOI] [PubMed] [Google Scholar]
  15. Chung CC, Chan L, Bamodu OA, Hong CT, Chiu HW. Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death. Scientific Reports. 2020c;10:20501. doi: 10.1038/s41598-020-77546-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chung CC, Huang PH, Chan L, Chen JH, Chien LN, Hong CT. Plasma Exosomal Brain-Derived Neurotrophic Factor Correlated with the Postural Instability and Gait Disturbance–Related Motor Symptoms in Patients with Parkinson’s Disease. Diagnostics. 2020d;10:684. doi: 10.3390/diagnostics10090684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chung SJ, Lee JJ, Lee PH, Sohn YH. Emerging Concepts of Motor Reserve in Parkinson’s Disease. Journal of Movement Disorders. 2020e;13:171–184. doi: 10.14802/jmd.20029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chung CC, Chan L, Chen JH, Bamodu OA, Chiu HW, Hong CT. Plasma extracellular vesicles tau and β-amyloid as biomarkers of cognitive dysfunction of Parkinson’s disease. FASEB Journal. 2021a;35:e21895. doi: 10.1096/fj.202100787R. [DOI] [PubMed] [Google Scholar]
  19. Chung CC, Chan L, Chen JH, Hung YC, Hong CT. Plasma extracellular vesicle α-synuclein level in patients with Parkinson’s Disease. Biomolecules. 2021b;11:744. doi: 10.3390/biom11050744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Courtney NA, Bao H, Briguglio JS, Chapman ER. Synaptotagmin 1 clamps synaptic vesicle fusion in mammalian neurons independent of complexin. Nature Communications. 2019;10:4076. doi: 10.1038/s41467-019-12015-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. de Lau LML, Breteler MMB. Epidemiology of Parkinson’s disease. The Lancet Neurology. 2006;5:525–535. doi: 10.1016/S1474-4422(06)70471-9. [DOI] [PubMed] [Google Scholar]
  22. Duarte PS. Cut points identification of continuous biomarkers: A challenge that goes beyond statistical aspects. Journal of Nuclear Medicine. 2021;62:1833. doi: 10.2967/jnumed.121.262905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gcwensa NZ, Russell DL, Cowell RM, Volpicelli-Daley LA. Molecular mechanisms underlying synaptic and axon degeneration in Parkinson’s Disease. Frontiers in Cellular Neuroscience. 2021;15:626128. doi: 10.3389/fncel.2021.626128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Goetzl EJ, Kapogiannis D, Schwartz JB, Lobach IV, Goetzl L, Abner EL, Jicha GA, Karydas AM, Boxer A, Miller BL. Decreased synaptic proteins in neuronal exosomes of frontotemporal dementia and Alzheimer’s disease. FASEB Journal. 2016;30:4141–4148. doi: 10.1096/fj.201600816R. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. Journal of Neurology, Neurosurgery, and Psychiatry. 1992;55:181–184. doi: 10.1136/jnnp.55.3.181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Iseki E, Kato M, Marui W, Uéda K, Kosaka K. A neuropathological study of the disturbance of the nigro-amygdaloid connections in brains from patients with dementia with Lewy bodies. Journal of the Neurological Sciences. 2001;185:129–134. doi: 10.1016/s0022-510x(01)00481-6. [DOI] [PubMed] [Google Scholar]
  27. James C, Ranson JM, Everson R, Llewellyn DJ. Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients. JAMA Network Open. 2021;4:e2136553. doi: 10.1001/jamanetworkopen.2021.36553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jan AT, Malik MA, Rahman S, Yeo HR, Lee EJ, Abdullah TS, Choi I. Perspective Insights of Exosomes in Neurodegenerative Diseases: A Critical Appraisal. Frontiers in Aging Neuroscience. 2017;9:317. doi: 10.3389/fnagi.2017.00317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jia L, Zhu M, Kong C, Pang Y, Zhang H, Qiu Q, Wei C, Tang Y, Wang Q, Li Y, Li T, Li F, Wang Q, Li Y, Wei Y, Jia J. Blood neuro-exosomal synaptic proteins predict Alzheimer’s disease at the asymptomatic stage. Alzheimer’s & Dementia. 2021;17:49–60. doi: 10.1002/alz.12166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Johnson AR, Bucks RS, Kane RT, Thomas MG, Gasson N, Loftus AM. Motor subtype as a predictor of future working memory performance in idiopathic Parkinson’s Disease. PLOS ONE. 2016;11:e0152534. doi: 10.1371/journal.pone.0152534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kalra H, Adda CG, Liem M, Ang CS, Mechler A, Simpson RJ, Hulett MD, Mathivanan S. Comparative proteomics evaluation of plasma exosome isolation techniques and assessment of the stability of exosomes in normal human blood plasma. Proteomics. 2013;13:3354–3364. doi: 10.1002/pmic.201300282. [DOI] [PubMed] [Google Scholar]
  32. Kempster PA, Williams DR, Selikhova M, Holton J, Revesz T, Lees AJ. Patterns of levodopa response in Parkinson’s disease: a clinico-pathological study. Brain. 2007;130:2123–2128. doi: 10.1093/brain/awm142. [DOI] [PubMed] [Google Scholar]
  33. Kordower JH, Olanow CW, Dodiya HB, Chu Y, Beach TG, Adler CH, Halliday GM, Bartus RT. Disease duration and the integrity of the nigrostriatal system in Parkinson’s disease. Brain. 2013;136:2419–2431. doi: 10.1093/brain/awt192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lee JW, Song YS, Kim H, Ku BD, Lee WW. Alteration of tremor dominant and postural instability gait difficulty subtypes during the progression of Parkinson’s Disease: Analysis of the PPMI Cohort. Frontiers in Neurology. 2019;10:471. doi: 10.3389/fneur.2019.00471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Leggio L, Paternò G, Vivarelli S, Falzone GG, Giachino C, Marchetti B, Iraci N. Extracellular Vesicles as Novel Diagnostic and Prognostic Biomarkers for Parkinson’s Disease. Aging and Disease. 2021;12:1494–1515. doi: 10.14336/AD.2021.0527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lewis SJG, Foltynie T, Blackwell AD, Robbins TW, Owen AM, Barker RA. Heterogeneity of Parkinson’s disease in the early clinical stages using a data driven approach. Journal of Neurology, Neurosurgery, and Psychiatry. 2005;76:343–348. doi: 10.1136/jnnp.2003.033530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lleó A, Núñez-Llaves R, Alcolea D, Chiva C, Balateu-Paños D, Colom-Cadena M, Gomez-Giro G, Muñoz L, Querol-Vilaseca M, Pegueroles J, Rami L, Lladó A, Molinuevo JL, Tainta M, Clarimón J, Spires-Jones T, Blesa R, Fortea J, Martínez-Lage P, Sánchez-Valle R, Sabidó E, Bayés À, Belbin O. Changes in Synaptic Proteins Precede Neurodegeneration Markers in Preclinical Alzheimer’s Disease Cerebrospinal Fluid. Molecular & Cellular Proteomics. 2019;18:546–560. doi: 10.1074/mcp.RA118.001290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Milà-Alomà M, Brinkmalm A, Ashton NJ, Kvartsberg H, Shekari M, Operto G, Salvadó G, Falcon C, Gispert JD, Vilor-Tejedor N, Arenaza-Urquijo EM, Grau-Rivera O, Sala-Vila A, Sanchez-Benavides G, González-de-Echávarri JM, Minguillon C, Fauria K, Niñerola-Baizán A, Perissinotti A, Kollmorgen G, Suridjan I, Zetterberg H, Molinuevo JL, Blennow K, Suárez-Calvet M, Beteta A, Cacciaglia R, Cañas A, Deulofeu C, Cumplido I, Dominguez R, Emilio M, Fuentes S, Hernandez L, Huesa G, Huguet J, Marne P, Mench´on T, Polo A, Pradas S, Soteras A, Vilanova M, on behalf of the ALFA Study CSF Synaptic Biomarkers in the Preclinical Stage of Alzheimer Disease and Their Association With MRI and PET: A Cross-sectional Study. Neurology. 2021;97:e2065–e2078. doi: 10.1212/WNL.0000000000012853. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Niu M, Li Y, Li G, Zhou L, Luo N, Yao M, Kang W, Liu J. A longitudinal study on α-synuclein in plasma neuronal exosomes as A biomarker for Parkinson’s disease development and progression. European Journal of Neurology. 2020;27:967–974. doi: 10.1111/ene.14208. [DOI] [PubMed] [Google Scholar]
  40. Nutt JG. Motor subtype in Parkinson’s disease: Different disorders or different stages of disease? Movement Disorders. 2016;31:957–961. doi: 10.1002/mds.26657. [DOI] [PubMed] [Google Scholar]
  41. Öhrfelt A, Brinkmalm A, Dumurgier J, Zetterberg H, Bouaziz-Amar E, Hugon J, Paquet C, Blennow K. A Novel ELISA for the Measurement of Cerebrospinal Fluid SNAP-25 in Patients with Alzheimer’s Disease. Neuroscience. 2019;420:136–144. doi: 10.1016/j.neuroscience.2018.11.038. [DOI] [PubMed] [Google Scholar]
  42. Picconi B, Piccoli G, Calabresi P. Synaptic dysfunction in Parkinson’s disease. Advances in Experimental Medicine and Biology. 2012;970:553–572. doi: 10.1007/978-3-7091-0932-8_24. [DOI] [PubMed] [Google Scholar]
  43. Qin XY, Zhang SP, Cao C, Loh YP, Cheng Y. Aberrations in Peripheral Inflammatory Cytokine Levels in Parkinson Disease: A Systematic Review and Meta-analysis. JAMA Neurology. 2016;73:1316–1324. doi: 10.1001/jamaneurol.2016.2742. [DOI] [PubMed] [Google Scholar]
  44. Ren J, Hua P, Li Y, Pan C, Yan L, Yu C, Zhang L, Xu P, Zhang M, Liu W. Comparison of Three Motor Subtype Classifications in de novo Parkinson’s Disease Patients. Frontiers in Neurology. 2020;11:601225. doi: 10.3389/fneur.2020.601225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Rodriguez-Blazquez C, Rojo-Abuin JM, Alvarez-Sanchez M, Arakaki T, Bergareche-Yarza A, Chade A, Garretto N, Gershanik O, Kurtis MM, Martinez-Castrillo JC, Mendoza-Rodriguez A, Moore HP, Rodriguez-Violante M, Singer C, Tilley BC, Huang J, Stebbins GT, Goetz CG, Martinez-Martin P. The MDS-UPDRS Part II (motor experiences of daily living) resulted useful for assessment of disability in Parkinson’s disease. Parkinsonism & Related Disorders. 2013;19:889–893. doi: 10.1016/j.parkreldis.2013.05.017. [DOI] [PubMed] [Google Scholar]
  46. Sampaio C. Can focusing on UPDRS Part II make assessments of Parkinson disease progression more efficient? Nature Clinical Practice. Neurology. 2009;5:130–131. doi: 10.1038/ncpneuro1049. [DOI] [PubMed] [Google Scholar]
  47. Si X, Tian J, Chen Y, Yan Y, Pu J, Zhang B. Central Nervous System-Derived Exosomal Alpha-Synuclein in Serum May Be a Biomarker in Parkinson’s Disease. Neuroscience. 2019;413:308–316. doi: 10.1016/j.neuroscience.2019.05.015. [DOI] [PubMed] [Google Scholar]
  48. Sjögren M, Davidsson P, Gottfries J, Vanderstichele H, Edman A, Vanmechelen E, Wallin A, Blennow K. The cerebrospinal fluid levels of tau, growth-associated protein-43 and soluble amyloid precursor protein correlate in Alzheimer’s disease, reflecting a common pathophysiological process. Dementia and Geriatric Cognitive Disorders. 2001;12:257–264. doi: 10.1159/000051268. [DOI] [PubMed] [Google Scholar]
  49. Stephens B, Mueller AJ, Shering AF, Hood SH, Taggart P, Arbuthnott GW, Bell JE, Kilford L, Kingsbury AE, Daniel SE, Ingham CA. Evidence of a breakdown of corticostriatal connections in Parkinson’s disease. Neuroscience. 2005;132:741–754. doi: 10.1016/j.neuroscience.2005.01.007. [DOI] [PubMed] [Google Scholar]
  50. Stuendl A, Kraus T, Chatterjee M, Zapke B, Sadowski B, Moebius W, Hobert MA, Deuschle C, Brockmann K, Maetzler W, Mollenhauer B, Schneider A. α-Synuclein in plasma-derived extracellular vesicles is a potential biomarker of Parkinson’s Disease. Movement Disorders. 2021;36:2508–2518. doi: 10.1002/mds.28639. [DOI] [PubMed] [Google Scholar]
  51. Sunwoo MK, Lee JE, Hong JY, Ye BS, Lee HS, Oh JS, Kim JS, Lee PH, Sohn YH. Premorbid exercise engagement and motor reserve in Parkinson’s disease. Parkinsonism & Related Disorders. 2017;34:49–53. doi: 10.1016/j.parkreldis.2016.10.023. [DOI] [PubMed] [Google Scholar]
  52. Zheng H, Xie Z, Zhang X, Mao J, Wang M, Wei S, Fu Y, Zheng H, He Y, Chen H, Xu Y. Investigation of α-synuclein species in plasma exosomes and the oligomeric and phosphorylated α-synuclein as potential peripheral biomarker of parkinson’s disease. Neuroscience. 2021;469:79–90. doi: 10.1016/j.neuroscience.2021.06.033. [DOI] [PubMed] [Google Scholar]

eLife assessment

Ivan Velasco 1

This useful study presents data regarding the presence of synaptic proteins in the extracellular vesicle pool present in the blood of Parkinson's patients and non-Parkinson neurological outpatients, trying to correlate changes in such levels with the progression of Parkinson's symptoms. The results are semi-quantitative and preliminary, suggesting that these biomarkers could be used in the follow-up of a specific group of Parkinson’s patients. The evidence is incomplete at this point, and more quantitative approaches are required to propose this correlation. The isolation of extracellular vesicles was appropriate as revealed by their sizes, but they are not exclusively from neuronal origin. The presented approach is not ready to be used in the clinical setting.

Reviewer #1 (Public Review):

Anonymous

The study isolated extracellular vesicles (EV) from healthy controls (HCs) and Parkinson patients (PwP), using plasma from the venous blood of non-fasting people. Such EVs were characterized and validated by the presence of markers, their size, and their morphology. The main aim of the manuscript is to correlate the presence of synaptic proteins, namely SNAP-25, GAP-43, and SYNAPTOTAGMIN-1, normalized with HSP70, with the clinical progression of PwP. Changes in synaptic proteins have been documented in the CSF of Alzheimer's and Parkinson's patients. The demographics of participants are adequately presented. One important limiting, as well as puzzling aspect, is the fact that authors did not find differences between groups at the beginning of the study nor after one year, after age and sex adjustment.

Reviewer #2 (Public Review):

Anonymous

Hong and collaborators investigated variations in the amount of synaptic proteins in plasma extracellular vesicles (EV) in Parkinson's Disease (PD) patients on one-year follow-up. Their findings suggest that plasma EV synaptic proteins may be used as clinical biomarkers of PD progression.

It is a preliminary study using semi-quantitative analysis of synaptic proteins.

The authors have a cohort of PD patients with clinical examination and a know-how on EV purification. Regarding this latter part, they may improve their description of EV purification. EV may be broken into smaller size EV after freezing. Does it explain the relatively small size in their EV preparation? Do the authors refer to the MISEV guidelines for EV purity? Regarding synaptic protein quantification, the choice of western blotting may not be the best one. ELISA and other multiplex arrays are available. How the authors do justify their choice? Do the authors try to sort plasma EV by membrane-associated neuronal EV markers using either vesicle sorting or immunoprecipitation?

Many technical aspects may be improved. Such technical questions weakened the authors' conclusions.

eLife. 2024 Mar 14;12:RP87501. doi: 10.7554/eLife.87501.3.sa3

Author Response

Chien-Tai Hong 1, Chen-Chih Chung 2, Ruan-Ching Yu 3, Lung Chan 4

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

The study isolated extracellular vesicles (EV) from healthy controls (HCs) and Parkinson patients (PwP), using plasma from the venous blood of non-fasting people. Such EVs were characterized and validated by the presence of markers, their size, and their morphology. The main aim of the manuscript is to correlate the presence of synaptic proteins, namely SNAP-25, GAP-43, and SYNAPTOTAGMIN-1, normalized with HSP70, with the clinical progression of PwP. Changes in synaptic proteins have been documented in the CSF of Alzheimer's and Parkinson's patients. The demographics of participants are adequately presented.

  • One important limiting, as well as puzzling aspect, is the fact that authors did not find differences between groups at the beginning of the study nor after one year, after age and sex adjustment.

Response: Thanks for your comments. We acknowledge your observation that the absence of a discernible difference in plasma EV synaptic protein levels between the PD and control subjects constitutes a significant limitation of our study. This outcome could be attributed to the fact that the controls were recruited from the neurology outpatient clinic, representing a group that could be considered "sub-healthy." Moreover, these individuals are not exempt from aging-related neurodegenerative processes. Considering that our PD subjects are in the early stages of the disease (with a mean disease duration of less than 3 years) and that synaptic dysfunction is a broader indicator rather than specific to PD, these factors could collectively contribute to the lack of distinction between the PD and control groups.

However, our primary intention was also to explore the potential of plasma EV synaptic proteins as predictive markers for disease progression in PD. In this regard, we have identified their applicability within the current PD cohort. We are committed to conducting further follow-up with these study subjects over an extended duration to delve deeper into these findings.

We revised the following statement in the discussion part to address this issue as following “Additionally, synaptic dysfunction is a frequently observed phenomenon in several neurological diseases, and it is not exclusive to PD. Consequently, the HC group in our current study may have included individuals with coexisting neurological conditions, potentially explaining the lack of a significant difference between the PD group and the HCs. However, this approach also illuminates the significance of synaptic dysfunction in the advancement of PD. This insight can be invaluable for monitoring disease progression, particularly in the context of clinical trials focused on disease modification.”

  • Tables in general are hard to follow. Specifically, Table 2 does not convey a clear message nor in the text of the Table itself, and the per 100% of change needs to be explained in the corresponding legend.

Response: Thanks for your comment. In Table 2, our aim was to demonstrate the association between the change of plasma EV synaptic proteins with the change of clinical severity, and presented as coefficient (p value). We apologize for any prior ambiguity in the main text's description of these results and have since made revisions to enhance clarity.

Regarding the "per 100% change," this is due to the quantification of plasma EV synaptic proteins being based on a semi-quantitative Western blot method. Each measurement was normalized by the average baseline plasma synaptic protein levels of healthy controls (HCs). The term "per 100% change" denotes the increase or decrease in plasma EV synaptic protein abundance relative to the average baseline levels observed in healthy controls. We apologize for any confusion caused and removed this term. In addition, we rephrased the statement to ensure better understanding and readability in the Table legend of revised manuscript as following “The association between the change of plasma EV synaptic proteins abundance (between baseline and follow-up) with the change of clinical severity in motor and cognitive domains (between baseline and follow-up) in people with Parkinson’s disease. A generalized linear model was employed and the data was presented as coefficient (p value).”

  • It is only when PwP were classified as a first quartile that a significantly greater deterioration was found. However, in the case of tremor, the top 25% had values going from 0.46-0.47 to 0.32-0.35, whereas the lower three quarters went from 0.33-0.34 to 0.27-0.28 depending on the protein analyzed. This needs to be clarified in the text.

Response: Thanks for your comments. As per the unified Parkinson's disease rating score (UPDRS), a higher score indicates greater severity of symptoms. Regarding tremor, we observed a general trend of improvement in both groups. PwP with elevated baseline plasma EV proteins had a trendy of worse tremor score at baseline, and the improvement was significantly better than the rest of PwP. This improvement seems to contradict the progressive nature of PD, and one possible explanation could be the alleviation of symptoms due to medication usage. The assessment of motor symptoms took place within the hospital setting, where we refrained from requesting patients to withhold their anti-PD medications due to concerns about safety issues such as falls. Consequently, certain motor symptoms might have been effectively controlled by the anti-PD medication. Traditionally, symptoms like tremor and rigidity (as reflected by the akinetic rigidity score) respond well to medications, while postural instability and gait disturbance (PIGD) are less responsive. In our cohort, we noted an improvement in tremor scores and stability in akinetic rigidity (AR) scores. Conversely, PD patients with higher baseline plasma EV synaptic protein levels exhibited notable progression in PIGD scores. These findings have been documented in the results section and discussed comprehensively within the revised manuscript as following “On the other hand, the evaluation of motor symptoms occurred in a hospital setting where we did not ask patients to stop taking their anti- PD medications due to safety concerns like the risk of falls. As a result, specific motor symptoms, particularly tremor and AR, which are more sensitive to medication compared to PIGD, may have been effectively managed by the anti-PD medications. This could potentially explain the improvement in tremor observed between the baseline and one-year follow-up, especially among PwP with elevated baseline plasma EV synaptic proteins.”

  • Table 3 is hard to read and some of the values seem repetitive, especially for tremor, AR, and PIGD. It looks as if Figure 2 represents the same information as Table 3.

Response: Thanks for your information. We have ensured the accuracy of the results presented in Table 2. While some of the entries may appear similar, they do indeed possess distinct differences.

To enhance readability, we streamlined the information in Table 3 by removing the p-values from the intra-group comparisons between baseline and the 1-year follow-up within each domain. We retained the original p-values for trend related to the inter-group comparisons for changes. Detailed information has been relocated to the supplementary section of the revised manuscript. In Figure 2, we illustrated the relationship between baseline plasma extracellular vesicle (EV) synaptic protein levels and the clinical assessment parameters during follow-up in patients with Parkinson's disease (PwP). This portrayal is distinct from the information depicted in Table 3.

If you had concerns about the resemblance between Table 3 and Figure 3, please note that the values in Table 3 represent raw scores, while the values in Figure 3, namely the estimated marginal means, are the "adjusted" scores for UPDRS-II and PIGD at baseline and follow-up. These adjustments encompass age, sex, and disease duration. We sincerely apologize for any lack of clarity in our previous description and have since revised it accordingly.

  • The text and figure legends are not helpful in guiding the reader to understand the presented information.

Response: Thanks for your comments and we apologized for the unclear statement. We revised the figure legend and the main text for better understanding of the readers.

Reviewer #2 (Public Review):

Hong and collaborators investigated variations in the amount of synaptic proteins in plasma extracellular vesicles (EV) in Parkinson's Disease (PD) patients on one-year follow-up. Their findings suggest that plasma EV synaptic proteins may be used as clinical biomarkers of PD progression.

  • It is a preliminary study using semi-quantitative analysis of synaptic proteins.

Response: Thanks for your comments. The present study represents the initial phase of our investigation into the role of plasma EV synaptic proteins within our PD cohort. Our findings have revealed the potential predictive significance of these synaptic proteins in relation to PD progression. We are committed to conducting further follow-up with these study subjects over an extended period.

Furthermore, it's important to acknowledge that the semi-quantitative approach employed to assess protein abundance was a limitation of this study. This limitation stems from the low concentration of plasma EV synaptic proteins, which restricts the feasibility of utilizing techniques such as ELISA or other quantitative methods for protein assessment. We have duly acknowledged this limitation within the scope of the present study as following “Semiquantitative assessment of plasma EV synaptic protein (SNAP-25, GAP-43, and synaptotagmin-1) levels was performed using western blot analysis. The lack of absolute values limits further clinical application.”

Moving forward, we intend to adopt alternative EV isolation methods that enable the extraction of a larger abundance of plasma EV proteins, facilitating more accurate quantitative assessments. In addition, a longer longitudinal follow-up is warranted to clearly assess the prognostic efficacy of plasma EV synaptic proteins in PwP, which we had mentioned in the manuscript.

  • The authors have a cohort of PD patients with clinical examination and a know-how on EV purification. Regarding this latter part, they may improve their description of EV purification. EV may be broken into smaller size EV after freezing. Does it explain the relatively small size in their EV preparation? Do the authors refer to the MISEV guidelines for EV purity?

Response: Thanks for your comments. In the previous manuscript, we provided a relatively detailed account of the procedures related to EV isolation and validation (https://doi.org/10.1096/fj.202100787R). In the revised manuscript, we added some information about the principle of the EV isolation kit, and the validation antibody as following “Plasma EVs were isolated from 1 mL of plasma by exoEasy Maxi Kit (Qiagen, Valencia, CA, USA), a membrane-based affinity binding step to isolate exosomes and other EVs without relying on a particular epitope, in accordance with the manufacturer’s instructions and storaged in the −80。C freezer. The isolated plasma EVs were then eluted and stored. Usually, 400 μL of eluate is obtained per mL of plasma. The isolated plasma EVs were validated according to the International Society of Extracellular Vesicles guidelines, which include1.markers, including the presence of CD63 (ab59479, Abcam, Cambridge, UK), CD9(ab92726, Abcam, Cambridge, UK), tumor susceptibility gene 101 protein (GTX118736, GeneTex, CA, USA) and negative of cytochrome c (ab110325; Abcam, Cambridge, UK) 2. Physical characterization through the nanoparticle tracking analysis, which demonstrated the majority of the size of EV are mainly within 50-100nm 3. The morphology from the electron microscopy analysis. The validation had been described previously [29-31]. “

It's important to note that our primary focus was on exosomes, the smallest subtype of EVs. Through nanoparticle tracking analysis, we observed that the majority of isolated EVs fell within the diameter range of 50-150nm, exhibiting significant surface marker (i.e. CD63 and CD9) expression. Moreover, electron microscopy confirmed their vesicular morphology. These meticulously validated EVs were promptly analysed post-isolation.

However, we acknowledge that the plasma obtained from study participants might have undergone freezing prior to EV isolation. This freezing process has the potential to diminish the yield rate of EVs and result in some degree of fragmentation. We have duly included this issue as a limitation in our revised manuscript as following “The final technical issue in the present study was the relatively small size of the isolated EVs. Despite the primary focus on isolating exosomes, which are the smallest type of EVs, it's important to consider that the presence of small-sized EVs could potentially be attributed to EV fragmentation that occurs during the freezing and thawing processes.”

  • Regarding synaptic protein quantification, the choice of western blotting may not be the best one. ELISA and other multiplex arrays are available. How the authors do justify their choice?

Response: Thanks for your comments. We appreciate your input regarding the semi-quantitative western blot analysis not being the most optimal approach. Owing to the limited quantity of isolated plasma EVs and the significant protein abundance of synaptic proteins within these EVs, we did explore the use of an ELISA assay. However, it's worth noting that for a specific subset of the samples, the readout obtained was lower than the lower limit of detection of the ELISA kit. In response, we have incorporated this point as limitation within the discussion section of the revised manuscript as following “Semiquantitative assessment of plasma EV synaptic protein (SNAP-25, GAP-43, and synaptotagmin-1) levels was performed using western blot analysis. The lack of absolute values, i.e. from the results of enzyme-linked immunosorbent assay, limits further clinical application.”

  • Do the authors try to sort plasma EV by membrane-associated neuronal EV markers using either vesicle sorting or immunoprecipitation?

Response: Thanks for your comments. The current study did not specifically isolate neuron-derived extracellular vesicles (EVs), potentially introducing some bias to the results. However, it's important to note that synaptic proteins, such as SNAP-25, exhibit a high degree of neuron-specific expression, with a predominant presence in the brain (as indicated by https://www.proteinatlas.org/ENSG00000132639-SNAP25/tissue). Given this context, the limitation of not analyzing neuron-derived EVs could be mitigated to some extent. In response, we have incorporated this point as limitation within the discussion section of the revised manuscript as following “Furthermore, this study evaluated the overall plasma EVs rather than specifically focusing on neuron-derived exosomes, potentially introducing a bias towards somatic-origin EVs. Nonetheless, it is worth noting that synaptic proteins primarily originate from neurons. Even when considering neuron-derived exosomes, it's important to recognize that they are not exclusively derived from the brain, which can lead to contamination from the peripheral nervous system.”

  • Many technical aspects may be improved. Such technical questions weakened the authors' conclusions.

Response: Thanks for your comments. We recognize that the aforementioned issues represent limitations of our current study. In response, we have incorporated these points as limitations, including the semi-quantitative assessments, the isolation of total but not neuron-derived exosomes in the plasma, and the short follow-up time within the discussion section of the revised manuscript.

  • The discussion is pretty long to justify the data. It may be shortened by adding some information in the introduction.

Response: Thanks for your comments. We have repositioned a statement from the second paragraph of the discussion to the introduction. This adjustment serves to enrich the background understanding of the link between synaptic dysfunction and neurodegenerative diseases.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Hong CT, Chung CC, Yu RC, Chan L. 2024. Plasma extracellular vesicle synaptic proteins as biomarkers of clinical progression in patients with Parkinson's disease. Dryad Digital Repository. [DOI] [PMC free article] [PubMed]

    Supplementary Materials

    Figure 1—source data 1. The original blot image of the representative GAP-43 (A), synaptotagmin-1 (B), SNAP-25 (C), and HSP-70 (D).
    elife-87501-fig1-data1.pdf (326.9KB, pdf)
    Supplementary file 1. Association between the baseline plasma EV synaptic proteins with the clinical severity in people with Parkinson’s disease at follow-up with the adjustment of age, sex, disease duration, and the baseline severity of corresponding item, presented as standardized β and p-values.

    UPDRS, Unified Parkinson’s Disease Rating Scale; AR, akinetic rigidity; PIGD, postural instability and gait disturbance; MMSE, Mini-Mental Status Examination; MoCA, Montreal Cognitive Assessment.

    elife-87501-supp1.docx (16.7KB, docx)
    MDAR checklist
    elife-87501-mdarchecklist1.docx (100.3KB, docx)

    Data Availability Statement

    The dataset is deposited in Dryad: https://doi.org/10.5061/dryad.4qrfj6qh9.

    The following dataset was generated:

    Hong CT, Chung CC, Yu RC, Chan L. 2024. Plasma extracellular vesicle synaptic proteins as biomarkers of clinical progression in patients with Parkinson's disease. Dryad Digital Repository.

    Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

    RESOURCES