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Synucleinopathies are neurodegenerative diseases that are associated with the misfolding and aggregation of α-synuclein, including Parkinson’s disease, dementia with Lewy bodies and multiple system atrophy 1 . Clinically, it is challenging to differentiate Parkinson’s disease and multiple system atrophy, especially at the early stages of disease 2 . Aggregates of α-synuclein in distinct synucleinopathies have been proposed to represent different conformational strains of α-synuclein that can self-propagate and spread from cell to cell 3 – 6 . Protein misfolding cyclic amplification (PMCA) is a technique that has previously been used to detect α-synuclein aggregates in samples of cerebrospinal fluid with high sensitivity and specificity 7 , 8 . Here we show that the α-synuclein-PMCA assay can discriminate between samples of cerebrospinal fluid from patients diagnosed with Parkinson’s disease and samples from patients with multiple system atrophy, with an overall sensitivity of 95.4%. We used a combination of biochemical, biophysical and biological methods to analyse the product of α-synuclein-PMCA, and found that the characteristics of the α-synuclein aggregates in the cerebrospinal fluid could be used to readily distinguish between Parkinson’s disease and multiple system atrophy. We also found that the properties of aggregates that were amplified from the cerebrospinal fluid were similar to those of aggregates that were amplified from the brain. These findings suggest that α-synuclein aggregates that are associated with Parkinson’s disease and multiple system atrophy correspond to different conformational strains of α-synuclein, which can be amplified and detected by α-synuclein-PMCA. Our results may help to improve our understanding of the mechanism of α-synuclein misfolding and the structures of the aggregates that are implicated in different synucleinopathies, and may also enable the development of a biochemical assay to discriminate between Parkinson’s disease and multiple system atrophy. Protein misfolding cyclic amplification (PMCA) technology can discriminate between patients with Parkinson’s disease and patients with multiple system atrophy on the basis of the characteristics of the α-synuclein aggregates in the cerebrospinal fluid.

BackgroundPatients presenting with parkinsonian syndromes share many clinical features, which can make diagnosis difficult. This is important as atypical parkinsonian syndromes (APSs) such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and corticobasal syndrome (CBS) carry a poor prognosis, compared with patients with Parkinson's disease (PD). In addition, there is overlap between APS and dementia diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD).ObjectiveTo use a panel of cerebrospinal fluid (CSF) biomarkers to differentiate patients with APS from PD and dementia.MethodsA prospective cohort of 160 patients and 30 control participants were recruited from a single specialist centre. Patients were clinically diagnosed according to current consensus criteria. CSF samples were obtained from patients with clinical diagnoses of PD (n=31), PSP (n=33), CBS (n=14), MSA (n=31), AD (n=26) and FTD (n=16). Healthy, elderly participants (n=30) were included as controls. Total τ (t-τ), phosphorylated τ (p-τ), β-amyloid 1–42 (Aβ42), neurofilament light chain (NFL), α-synuclein (α-syn), amyloid precursor protein soluble metabolites α and β (soluble amyloid precursor protein (sAPP)α, sAPPβ) and two neuroinflammatory markers (monocyte chemoattractant protein-1 and YKL-40) were measured in CSF. A reverse stepwise regression analysis and the false discovery rate procedure were used.ResultsCSF NFL (p<0.001), sAPPα (p<0.001) and a-syn (p=0.003) independently predicted diagnosis of PD versus APS. Together, these nine biomarkers could differentiate patients with PD from APS with an area under the curve of 0.95 and subtypes of APS from one another. There was good discriminatory power between parkinsonian groups, dementia disorders and healthy controls.ConclusionsA panel of nine CSF biomarkers was able to differentiate APS from patients with PD and dementia. This may have important clinical utility in improving diagnostic accuracy, allowing better prognostication and earlier access to potential disease-modifying therapies.

Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy are brain disorders characterised by intracellular α-synuclein deposits. We aimed to assess whether reduction of α-synuclein concentrations in CSF was a marker for α-synuclein deposition in the brain, and therefore diagnostic of synucleinopathies. We assessed potential extracellular-fluid markers of α-synuclein deposition in the brain (total α-synuclein and total tau in CSF, and total α-synuclein in serum) in three cohorts: a cross-sectional training cohort of people with Parkinson's disease, multiple system atrophy, dementia with Lewy bodies, Alzheimer's disease, or other neurological disorders; a group of patients with autopsy-confirmed dementia with Lewy bodies, Alzheimer's disease, or other neurological disorders (CSF specimens were drawn ante mortem during clinical investigations); and a validation cohort of patients who between January, 2003, and December, 2006, were referred to a specialised movement disorder hospital for routine inpatient admission under the working diagnosis of parkinsonism. CSF and serum samples were assessed by ELISA, and clinical diagnoses were made according to internationally established criteria. Mean differences in biomarkers between diagnostic groups were assessed with conventional parametric and non-parametric statistics. In our training set, people with Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies had lower CSF α-synuclein concentrations than patients with Alzheimer's disease and other neurological disorders. CSF α-synuclein and tau values separated participants with synucleinopathies well from those with other disorders (p<0·0001; area under the receiver operating characteristic curve [AUC]=0·908). In the autopsy-confirmed cases, CSF α-synuclein discriminated between dementia with Lewy bodies and Alzheimer's disease (p=0·0190; AUC=0·687); in the validation cohort, CSF α-synuclein discriminated Parkinson's disease and dementia with Lewy bodies versus progressive supranuclear palsy, normal-pressure hydrocephalus, and other neurological disorders (p<0·0001; AUC=0·711). Other predictor variables tested in this cohort included CSF tau (p=0·0798), serum α-synuclein (p=0·0502), and age (p=0·0335). CSF α-synuclein concentrations of 1·6 pg/μL or lower showed 70·72% sensitivity (95% CI 65·3–76·1%) and 52·83% specificity (39·4–66·3%) for the diagnosis of Parkinson's disease. At this cutoff, the positive predictive value for any synucleinopathy was 90·7% (95% CI 87·3–94·2%) and the negative predictive value was 20·4% (13·7–27·2%). Mean CSF α-synuclein concentrations as measured by ELISA are significantly lower in Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy than in other neurological diseases. Although specificity was low, the high positive predictive value of CSF α-synuclein concentrations in patients presenting with synucleinopathy-type parkinsonism might be useful in stratification of patients in future clinical trials. American Parkinson Disease Association, Stifterverband für die Deutsche Wissenschaft, Michael J Fox Foundation for Parkinson's Research, National Institutes of Health, Parkinson Research Consortium Ottawa, and the Government of Canada.

The pathological hallmarks of multiple system atrophy and Parkinson's disease are, respectively, misfolded-α-synuclein-laden glial cytoplasmic inclusions and Lewy bodies. CSF-soluble misfolded α-synuclein aggregates (seeds) are readily detected in people with Parkinson's disease by α-synuclein seed amplification assay (synSAA), but identification of seeds associated with multiple system atrophy for diagnostic purposes has proven elusive. We aimed to assess whether a novel synSAA could reliably distinguish seeds from Lewy bodies and glial cytoplasmic inclusions. In this multicentre cohort study, a novel synSAA that multiplies and detects seeds by fluorescence was used to analyse masked CSF and brain samples from participants with either clinically diagnosed or pathology-confirmed multiple system atrophy, Parkinson's disease, dementia with Lewy bodies, isolated rapid eye movement sleep behaviour disorder (IRBD), disorders that were not synucleinopathies, or healthy controls. Participants were from eight available cohorts from seven medical centres in four countries: New York Brain Bank, New York, USA (NYBB); University of Pennsylvania, Philadelphia, PA, USA (UPENN); Paracelsus-Elena-Klinik, Kassel, Germany (DeNoPa and KAMSA); Hospital Clinic Barcelona, Spain (BARMSA); Universität Tübingen, Tübingen, Germany (EKUT); Göteborgs Universitet, Göteborgs, Sweden (UGOT); and Karolinska Institutet, Stockholm, Sweden (KIMSA). Clinical cohorts were classified for expected diagnostic accuracy as either research (longitudinal follow-up visits) or real-life (single visit). Sensitivity and specificity were estimated according to pathological (gold standard) and clinical (reference standard) diagnoses. In 23 brain samples (from the NYBB cohort), those containing Lewy bodies were synSAA-positive and produced high fluorescence amplification patterns (defined as type 1); those containing glial cytoplasmic inclusions were synSAA-positive and produced intermediate fluorescence (defined as type 2); and those without α-synuclein pathology produced below-threshold fluorescence and were synSAA-negative. In 21 pathology-confirmed CSF samples (from the UPENN cohort), those with Lewy bodies were synSAA-positive type 1; those with glial cytoplasmic inclusions were synSAA-positive type 2; and those with four-repeat tauopathy were synSAA-negative. In the DeNoPa research cohort (which had no samples from people with multiple system atrophy), the novel synSAA had sensitivities of 95% (95% CI 88–99) for 80 participants with Parkinson's disease and 95% (76–100) for 21 participants with IRBD, and a specificity of 95% (86–99) for 60 healthy controls. Overall (combining BARMSA, EKUT, KAMSA, UGOT, and KIMSA cohorts that were enriched for cases of multiple system atrophy), the novel synSAA had 87% sensitivity for multiple system atrophy (95% CI 80–93) and specificity for type 2 seeds was 77% (67–85). For participants with multiple system atrophy just in research cohorts (BARMSA and EKUT), the novel synSAA had a sensitivity of 84% (95% CI 71–92) and a specificity for type 2 seeds of 87% (74–95), whereas cases from real-life cohorts (KAMSA, KIMSA, and UGOT) had a sensitivity of 91% (95% CI 80–97) but a decreased specificity for type 2 seeds of 68% (53–81). The novel synSAA produced amplification patterns that enabled the identification of underlying α-synuclein pathology, showing two levels of fluorescence that corresponded with different pathological hallmarks of synucleinopathy. The synSAA might be useful for early diagnosis of synucleinopathies in clinical trials, and potentially for clinical use, but additional formal validation work is needed. Michael J Fox Foundation for Parkinson's Research, Amprion.

Parkinson’s disease (PD) and multiple system atrophy (MSA) are both part of the spectrum of neurodegenerative movement disorders and α-synucleinopathies with overlap of symptoms especially at early stages of the disease but with distinct disease progression and responses to dopaminergic treatment. Therefore, having biomarkers that specifically classify patients, which could discriminate PD from MSA, would be very useful. MicroRNAs (miRNAs) regulate protein translation and are observed in biological fluids, including cerebrospinal fluid (CSF), and may therefore have potential as biomarkers of disease. The aim of our study was to determine if miRNAs in CSF could be used as biomarkers for either PD or MSA. Using quantitative PCR (qPCR), we evaluated expression levels of 10 miRNAs in CSF patient samples from PD ( n  = 28), MSA ( n  = 17), and non-neurological controls ( n  = 28). We identified two miRNAs (miR-24 and miR-205) that distinguished PD from controls and four miRNAs that differentiated MSA from controls (miR-19a, miR-19b, miR-24, and miR-34c). Combinations of miRNAs accurately discriminated either PD (area under the curve (AUC) = 0.96) or MSA (AUC = 0.86) from controls. In MSA, we also observed that miR-24 and miR-148b correlated with cerebellar ataxia symptoms, suggesting that these miRNAs are involved in cerebellar degeneration in MSA. Our findings support the potential of miRNA panels as biomarkers for movement disorders and may provide more insights into the pathological mechanisms related to these disorders.

Background Multiple system atrophy (MSA), an atypical parkinsonian syndrome, is a rapidly progressive neurodegenerative disease with currently no established fluid biomarkers available. MSA is characterized by an oligodendroglial α-synucleinopathy, progressive neuronal cell loss and concomitant astrocytosis. Here, we investigate glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) as fluid biomarkers for differential diagnosis, assessment of clinical disease severity and prediction of disease progression in MSA. Methods GFAP and NfL levels were analyzed in plasma and CSF samples of 47 MSA patients as well as 24 Parkinson’s disease (PD) and 25 healthy controls (HC) as reference cohorts. In MSA, biomarker levels were correlated to baseline and longitudinal clinical disease severity (UMSARS scores). Results In MSA, GFAP levels in CSF and plasma predicted baseline clinical disease severity as indicated by UMSARS scores, while NfL levels predicted clinical disease progression as indicated by longitudinal changes in UMSARS scores. Cross-sectionally, NfL levels in CSF and plasma were significantly elevated in MSA compared to both PD and HC. Receiver operating curves (ROC) indicated high diagnostic accuracy of NfL for distinguishing MSA from PD (CSF: AUC = 0.97, 95% CI 0.90–1.00; plasma: AUC = 0.90, 95% CI 0.81–1.00). Discussion In MSA, GFAP shows promise as novel biomarker for assessing current clinical disease severity, while NfL might serve as biomarker for prediction of disease progression and differential diagnosis of MSA against PD.

Background It is well known that myelin disruption and neuroinflammation are early and distinct pathological hallmarks in multiple system atrophy (MSA) as well as in idiopathic Parkinson’s disease and in other atypical Parkinsonian syndromes. The objective of this study was to assess the value of non-neuronal biomarker candidates that reflect myelin disruption and neuroinflammation. Methods Myelin basic protein (MBP) and the soluble form of TREM2 were quantified in a comprehensive movement disorder cohort from two different neurological centers, comprising a total of 171 CSF samples. Commercially available ELISA systems were employed for quantification. Results The results of the MBP analysis revealed a significant increase in cerebrospinal fluid (CSF) MBP levels in all atypical Parkinsonian conditions compared to PD. This differentiation was more pronounced in the MSA-c subtype compared to MSA-p. Receiver operating characteristic (ROC) analysis revealed a significant discrimination between PD and MSA ( p  = 0.032, AUC = 0.70), PD and DLB ( p  = 0.006, AUC = 0.79) and PD and tauopathies ( p  = 0.006, AUC = 0.74). The results of the TREM2 analysis demonstrated no significant differences between the PD and atypical Parkinsonian groups if not adjusted for confounders. After adjusting for age, sex, and disease duration, the PD group exhibited significantly higher TREM2 levels compared to the DLB group ( p  = 0.002). Conclusions In conclusion, MBP, but not TREM2, is elevated in the CSF of not only MSA but in all atypical Parkinsonian conditions compared to idiopathic Parkinson’s disease. This highlights the value of the evaluation of myelin/oligodendrocyte-associated markers in neurodegenerative movement disorders.

Seeding amplification assays, specifically the Real-Time Quaking-Induced Conversion method (RT-QuIC), have shown great diagnostic potential for α-synucleinopathies. Numerous research groups have demonstrated the method’s high sensitivity and specificity using cerebrospinal fluid (CSF) samples and various RT-QuIC workflows. However, establishing a uniform and stably performing RT-QuIC protocol remains challenging. To address this, we established an RT-QuIC protocol with a Universal Control Fluid (UCF), which is simple to adopt, performs stably, and allows uniform preparation of both sample and control reactions. Firstly, we adapted and established a published 48-hour RT-QuIC protocol, including the in-house production of recombinant α-synuclein (rec α-syn), and evaluated its sensitivity and specificity through a blinded screening of an 81 CSF sample cohort consisting of Parkinson’s disease (PD), dementia with Lewy bodies (DLB), Alzheimer’s disease, motor neuron disease, multiple system atrophy, unidentified neurodegenerative diseases, and healthy controls. Additionally, we tested all CSF samples in three volumes to determine which volume provides the best diagnostic accuracy. The established RT-QuIC performs nearly equally well with 7 µL and 15 µL added CSF, resulting in 94% and 94.5% diagnostic accuracy, respectively. Secondly, we developed a UCF solution and tested its performance with the established RT-QuIC protocol. Results indicate that UCF, used in defined volume and concentration, standardizes the preparation of both sample and control reactions without compromising the assay’s diagnostic accuracy and provides a stabilizing environment for the reactions, ensuring higher reproducibility. The established RT-QuIC protocol for pathologic α-synuclein detection in PD and DLB CSF samples is highly sensitive (92–96%) and specific (93–96%). Therefore, it is important that its adoption in clinical laboratories is uncomplicated and uniform. RT-QuIC with UCF simplifies, standardizes, and stabilizes the assay’s performance and, thus, could be recommended as a standard protocol for accurate detection of α-synucleinopathies.

Introduction Neuroinflammation has been established to be part of the neuropathological changes in Parkinson’s disease (PD) and atypical parkinsonism (APD). Activated microglia play a key role in neuroinflammation by release of cytokines. Evidence of the disparity, if any, in the neuroinflammatory response between PD and APD is sparse. In this study, we investigated CSF cytokine profiles in patients with PD, multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Methods On a sensitive electrochemiluminescence-based platform (Quickplex, Meso Scale Discovery®), we examined a panel of C-reactive protein (CRP) and eight selected cytokines, IFN-γ, IL-10, IL-18, IL-1β, IL-4, IL-6, TGF-β1, and TNF-α, among patients with PD ( n  = 46), MSA ( n  = 35), and PSP ( n  = 39) or controls ( n  = 31). Additionally, CSF total tau protein levels were measured as a marker of nonspecific neurodegeneration for correlation estimates. Results CRP and the pro-inflammatory cytokines TNF-α, IL-1β, and Il-6 were statistically significantly elevated in MSA and PSP patients compared to PD patients but not compared to control patients. No analytes differed statistically significantly between MSA and PSP patients. The best diagnostic discrimination, evaluated by ROC curve (AUC 0.77, p  = 007, 95% CI 0.660–0.867), between PD and MSA patients was seen for a subset of analytes: CRP, TNF-α, IL-1β, and IFN-γ. Conclusion Among the investigated cytokines and CRP, we found a statistically significant increase of microglia-derived cytokines in MSA and PSP patients compared to PD patients.

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.