Profiling Inflammatory Extracellular Vesicles in Plasma and Cerebrospinal Fluid: An Optimized Diagnostic Model for Parkinson’s Disease

Extracellular vesicles (EVs) play a central role in intercellular communication, which is relevant for inflammatory and immune processes implicated in neurodegenerative disorders, such as Parkinson’s Disease (PD). We characterized and compared distinctive cerebrospinal fluid (CSF)-derived EVs in PD and atypical parkinsonisms (AP), aiming to integrate a diagnostic model based on immune profiling of plasma-derived EVs via artificial intelligence. Plasma- and CSF-derived EVs were isolated from patients with PD, multiple system atrophy (MSA), AP with tauopathies (AP-Tau), and healthy controls. Expression levels of 37 EV surface markers were measured by a flow cytometric bead-based platform and a diagnostic model based on expression of EV surface markers was built by supervised learning algorithms. The PD group showed higher amount of CSF-derived EVs than other groups. Among the 17 EV surface markers differentially expressed in plasma, eight were expressed also in CSF of a subgroup of PD, 10 in MSA, and 6 in AP-Tau. A two-level random forest model was built using EV markers co-expressed in plasma and CSF. The model discriminated PD from non-PD patients with high sensitivity (96.6%) and accuracy (92.6%). EV surface marker characterization bolsters the relevance of inflammation in PD and it underscores the role of EVs as pathways/biomarkers for protein aggregation-related neurodegenerative diseases.

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Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity

British Journal of Radiology ◽

10.1259/bjr.20180886 ◽

2019 ◽

Vol 92 (1101) ◽

pp. 20180886 ◽

Cited By ~ 6

Author(s):

Christian Rubbert ◽

Christian Mathys ◽

Christiane Jockwitz ◽

Christian J Hartmann ◽

Simon B Eickhoff ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Resting State ◽

Good Accuracy ◽

Network Connectivity ◽

High Sensitivity ◽

Data Driven ◽

Healthy Controls ◽

Whole Brain ◽

Model Based

Objective: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson’s disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). Methods: Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. Results: Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 – 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 – 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 – 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. Conclusion: A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. Advances in knowledge: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson’s disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

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Faculty Opinions recommendation of Diagnostic biomarkers for Parkinson's disease: focus on α-synuclein in cerebrospinal fluid.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734486941.793553768 ◽

2018 ◽

Author(s):

Joseph Jankovic ◽

Arjun Tarakad

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cerebrospinal Fluid ◽

Diagnostic Biomarkers ◽

Disease Focus

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Comparison of Cytocidal Activities of L-DOPA and Dopamine in S. cerevisiae and C. glabrata

Current Bioactive Compounds ◽

10.2174/1573407214666180108144216 ◽

2020 ◽

Vol 16 (1) ◽

pp. 90-93

Author(s):

Carmen E. Iriarte ◽

Ian G. Macreadie

Keyword(s):

Saccharomyces Cerevisiae ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dopaminergic Neurons ◽

Candida Glabrata ◽

High Sensitivity ◽

Time Dependent ◽

Colony Forming Units ◽

Dependent Cell ◽

The Brain

Background: Parkinson's Disease results from a loss of dopaminergic neurons, and reduced levels of the neurotransmitter dopamine. Parkinson's Disease treatments involve increasing dopamine levels through administration of L-DOPA, which can cross the blood brain barrier and be converted to dopamine in the brain. The toxicity of dopamine has previously studied but there has been little study of L-DOPA toxicity. Methods: We have compared the toxicity of dopamine and L-DOPA in the yeasts, Saccharomyces cerevisiae and Candida glabrata by cell viability assays, measuring colony forming units. Results: L-DOPA and dopamine caused time-dependent cell killing in Candida glabrata while only dopamine caused such effects in Saccharomyces cerevisiae. The toxicity of L-DOPA is much lower than dopamine. Conclusion: Candida glabrata exhibits high sensitivity to L-DOPA and may have advantages for studying the cytotoxicity of L-DOPA.

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Protein Binding Partners of Dysregulated miRNAs in Parkinson’s Disease Serum

Cells ◽

10.3390/cells10040791 ◽

2021 ◽

Vol 10 (4) ◽

pp. 791

Author(s):

Wolfgang P. Ruf ◽

Axel Freischmidt ◽

Veselin Grozdanov ◽

Valerie Roth ◽

Sarah J. Brockmann ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Protein Binding ◽

Neurodegenerative Diseases ◽

Extracellular Vesicles ◽

Abundance Pattern ◽

Binding Partners ◽

Direct Binding ◽

Unsupervised Approach ◽

High Degree

Accumulating evidence suggests that microRNAs (miRNAs) are a contributing factor to neurodegenerative diseases. Although altered miRNA profiles in serum or plasma have been reported for several neurodegenerative diseases, little is known about the interaction between dysregulated miRNAs and their protein binding partners. We found significant alterations of the miRNA abundance pattern in serum and in isolated serum-derived extracellular vesicles of Parkinson’s disease (PD) patients. The differential expression of miRNA in PD patients was more robust in serum than in isolated extracellular vesicles and could separate PD patients from healthy controls in an unsupervised approach to a high degree. We identified a novel protein interaction partner for the strongly dysregulated hsa-mir-4745-5p. Our study provides further evidence for the involvement of miRNAs and HNF4a in PD. The demonstration that miRNA-protein binding might mediate the pathologic effects of HNF4a both by direct binding to it and by binding to proteins regulated by it suggests a complex role for miRNAs in pathology beyond the dysregulation of transcription.

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