Mass Spectrometry–Based Proteomics Analysis of Human Substantia Nigra From Parkinson's Disease Patients Identifies Multiple Pathways Potentially Involved in the Disease

Jang, Yura; Pletniková, Olga; Troncoso, Juan C.; Pantelyat, Alexander; Dawson, Ted M.; Rosenthal, Liana S.; Na, Chan Hyun

doi:10.1016/j.mcpro.2022.100452

Cited by 18 publications

(17 citation statements)

References 88 publications

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“…This relationship exhibits a level of complexity that not only varies across different disease stages but also among specific regions of the brain [65]. Adding to our study, the down-regulation of RPS28 in PD had been observed in a similar study using Mass Spectrometry-Based Proteomics Analysis of Human Substantia Nigra [66]. Taken together, our analyses indicate that HLA-F, IRF1, and RPS28 could potentially serve as valuable markers for identifying Parkinson's disease patients at a stage when therapeutic interventions could be most effective, as well as for monitoring the severity of the disease.…”

Section: Discussionsupporting

confidence: 77%

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Bhattacharjee,

Jamal,

Ahammad

et al. 2023

Preprint

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Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the central nervous system, often diagnosed in its advanced stages due to the absence of sensitive biomarkers. With this objective in mind, our study conducted a comprehensive analysis of differentially expressed genes (DEGs) sourced from blood-based microarray datasets to uncover potential biomarkers and developed a machine learning based classifier to conduct two step validations. By analyzing gene expression of three projects, we identified 678 DEGs, consisting of 337 genes showing upregulation and 341 genes presenting downregulation. Additionally, insights from functional enrichment and the protein-protein network analysis indicate that HLA-F, IRF-1, and RPS28 have the potential to serve as biomarkers for diagnosing PD. Simultaneously, we employed feature selection techniques such as Least Absolute Shrinkage and Selection Operator with Cross Validation (LassoCV) followed by Recursive Feature Elimination with Cross Validation (REFCV) to filter our initial dataset of 13,249 genes down to 43 genes, which were subsequently used to train the machine learning-based classifier models. These 43 genes formed the basis for training and testing various machine learning models, including logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, and deep learning based artificial neural networks. Our models demonstrated robust performance, with Support Vector Machine outperforming others by 0.65 accuracy (95%CI: 0.58-0.66), 0.70 AUC-ROC (95%CI: 0.70-0.71) and 0.35 MCC (95%CI: 0.34-0.39). The model was implemented to develop the PitArray tool for non-invasive detection of PD from blood. PitArray is available at: https://github.com/Arittra95/PitArray.

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Section: Discussionsupporting

confidence: 77%

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Bhattacharjee,

Jamal,

Ahammad

et al. 2023

Preprint

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“…Database searches were conducted as described in prior publications with minor modifications. 16,17 The acquired MS/MS spectra were searched against a human UniProt database (released in May 2018, containing protein entries of common contaminants) using SEQUEST search algorithm in the Thermo Proteome Discoverer platform (version 2.2.0.388, Thermo Scientific). The database search parameters used were as follows.…”

Section: Methodsmentioning

confidence: 99%

“…The LC-MS/MS analysis was conducted as described in previous publications with minor modifications. 16,17 The peptide samples were analyzed on an Orbitrap Fusion Lumos Tribrid mass spectrometer interfaced with an Ultimate 3000 RSLCnano nanoflow liquid chromatography system (Thermo Scientific). The fractionated peptides were reconstituted in 0.5% formic acid (FA) and loaded onto a trap column (Acclaim™ PepMap™ 100, LC C18, 5 μm, 100 μm × 2 cm, nanoViper, Thermo Scientific) at a flow rate of 8 μL/min.…”

Section: Lc-ms/ms Analysismentioning

confidence: 99%

Biomarker discovery in progressive supranuclear palsy from human cerebrospinal fluid using mass spectrometry-based proteomics

Jang,

Oh,

Hall

et al. 2023

Preprint

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Progressive supranuclear palsy (PSP) is a neurodegenerative disorder that is often misdiagnosed as Parkinson’s Disease (PD) because of shared symptoms. PSP is characterized by the accumulation of tau protein in specific brain regions, which results in loss of balance, gaze impairment, and dementia. Diagnosing PSP is often challenging, and there’s a significant demand for reliable biomarkers. However, existing biomarkers, including tau protein and neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF), show inconsistencies in distinguishing PSP from other neurodegenerative disorders. To overcome these limitations, we conducted a comprehensive proteome analysis for CSF samples from 40 PSP, 40 PD, and healthy controls (HC) using the tandem mass tag-based quantification method, identifying 3,653 unique proteins. Our statistical analysis identified 190, 152, and 247 differentially expressed proteins when comparing PSP vs. HC, PSP vs. PD, and PSP against both PD and HC, respectively. Gene set enrichment analysis and interactome analysis conducted with the differentially expressed proteins in PSP CSF indicated that most of them were implicated in cell adhesion, cholesterol metabolism, and glycan biosynthesis. Cell-type enrichment analysis revealed that neuronally-derived proteins were predominant among the differentially expressed proteins. Potential biomarker classification performance showed that ATP6AP2 (reduced in PSP) had the highest AUC (0.922), followed by NEFM, EFEMP2, LAMP2, CHST12, FAT2, B4GALT1, LCAT, CBLN3, FSTL5, ATP6AP1, and GGH. This is the first large-scale mass spectrometry-based proteome analysis to discover CSF PSP biomarkers differentiating from both controls and PD, thereby laying a foundation for further development and validation.

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“…Activated microglia use the complement system [ 28 ], also acquiring the capacity of delivering the antigen for T cells to perform their phagocytic function [ 29 ]. In the context of CNS inflammation, activating products of the complement system have been detected, suggesting that the complement system may be triggered by substances besides antibodies [ 30 ].…”

Section: Neuroinflammationmentioning

confidence: 99%

Infectious Microorganisms Seen as Etiologic Agents in Parkinson’s Disease

Stroe

Jurja

Mehedinți

et al. 2023

Life

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Infections represent a possible risk factor for parkinsonism and Parkinson’s disease (PD) based on information from epidemiology and fundamental science. The risk is unclear for the majority of agents. Moreover, the latency between infection and PD seems to be very varied and often lengthy. In this review, the evidence supporting the potential involvement of infectious microorganisms in the development of Parkinson’s disease is examined. Consequently, it is crucial to determine the cause and give additional treatment accordingly. Infection is an intriguing suggestion regarding the cause of Parkinson’s disease. These findings demonstrate that persistent infection with viral and bacterial microorganisms might be a cause of Parkinson’s disease. As an initiating factor, infection may generate a spectrum of gut microbiota dysbiosis, engagement of glial tissues, neuroinflammation, and alpha-synuclein accumulation, all of which may trigger and worsen the onset in Parkinson’s disease also contribute to its progression. Still uncertain is the primary etiology of PD with infection. The possible pathophysiology of PD infection remains a matter of debate. Furthermore, additional study is required to determine if PD patients develop the disease due to infectious microorganisms or solely since they are more sensitive to infectious causes.

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Mass Spectrometry–Based Proteomics Analysis of Human Substantia Nigra From Parkinson's Disease Patients Identifies Multiple Pathways Potentially Involved in the Disease

Cited by 18 publications

References 88 publications

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Biomarker discovery in progressive supranuclear palsy from human cerebrospinal fluid using mass spectrometry-based proteomics

Infectious Microorganisms Seen as Etiologic Agents in Parkinson’s Disease

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