Abstract
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.
Highlights
Parkinson’s disease (PD) and multiple system atrophy (MSA) are synucleinopathies, which develop by the transcellular propagation of α-synuclein in neurons and glial cells, causing inclusion bodies such as Lewy bodies and Papp–Lantos bodies [1,2].Owing to the many clinical similarities between the diseases, differential diagnosis of PD from MSA is difficult
Due to the small size of metabolites, there was no significant difference in the functional analysis of the metabolomic network of cerebrospinal fluid (CSF) between PD and MSA using in-silico prediction (Figures 1B and 2B)
Protein O-linked mannose beta-1,2-N-acetylglucosaminyltransferase 1 (POMGNT1), extracellular matrix protein 1 (ECM1), cadherin-2, secretogranin-2, SLIT and NTRK-like protein 1 (SLITRK1), contactin-1, and interleukin 7 (IL7) were upregulated in PD but downregulated in MSA compared to healthy controls
Summary
Parkinson’s disease (PD) and multiple system atrophy (MSA) are synucleinopathies, which develop by the transcellular propagation of α-synuclein in neurons and glial cells, causing inclusion bodies such as Lewy bodies (leading to Lewy body disease such as PD) and Papp–Lantos bodies (leading to Papp–Lantos body diseases such as MSA) [1,2]. Metabolomics is a powerful approach to understand the phenotypes of PD and MSA to identify clinically relevant biomarkers. CSF has been the target biological fluid as a biomarker for metabolites, microRNAs, and proteins in PD and MSA [8,13–16]. The metabolomics approach is useful, the integrated omics approach provides precise and sensitive information by integrating delicate biological information. Integrated omics with metabolomics and proteomics and/or microRNA analysis may contribute to more precise evaluations based on symptoms between PD and MSA in the future. We discuss the following: (i) characteristic differences between PD and MSA, (ii) metabolomics approaches for PD and MSA, and (iii) the use of proteomics and microRNA data for discovering specific biomarkers for PD and MSA
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