Network Analysis Identifies Disease-Specific Pathways for Parkinson’s Disease

Abstract

Neurodegenerative diseases are characterized by the progressive loss of specific neurons in selected regions of the central nervous system. The main clinical manifestation (movement disorders, cognitive impairment, and/or psychiatric disturbances) depends on the neuron population being primarily affected. Parkinson’s disease is a common movement disorder, whose etiology remains mostly unknown. Progressive loss of dopaminergic neurons in the substantia nigra causes an impairment of the motor control. Some of the pathogenetic mechanisms causing the progressive deterioration of these neurons are not specific for Parkinson’s disease but are shared by other neurodegenerative diseases, like Alzheimer’s disease and amyotrophic lateral sclerosis. Here, we performed a meta-analysis of the literature of all the quantitative proteomic investigations of neuronal alterations in different models of Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis to distinguish between general and Parkinson’s disease-specific pattern of neurodegeneration. Then, we merged proteomics data with genetics information from the DisGeNET database. The comparison of gene and protein information allowed us to identify 25 proteins involved uniquely in Parkinson’s disease and we verified the alteration of one of them, i.e., transaldolase 1 (TALDO1), in the substantia nigra of 5 patients. By using open-source bioinformatics tools, we identified the biological processes specifically affected in Parkinson’s disease, i.e., proteolysis, mitochondrion organization, and mitophagy. Eventually, we highlighted four cellular component complexes mostly involved in the pathogenesis: the proteasome complex, the protein phosphatase 2A, the chaperonins CCT complex, and the complex III of the respiratory chain

Integrated Metabolomics and Proteomics Analysis Reveals Plasma Lipid Metabolic Disturbance in Patients With Parkinson’s Disease

“…A lot of proteomics research has been conducted in PD patients, mainly from brain tissues (substantia nigra, thalamus, locus coeruleus, olfactory bulb, and cerebral cortex), biofluids (cerebrospinal fluid, tears, and blood) and subcellular structures (mitochondria) (Dixit et al, 2019). Most of this research referred to pathways involved in "mitochondrial dysfunction, oxidative stress, protein aggregation or degradation, autophagy, and inflammation" (Ren et al, 2015;Monti et al, 2018). Here, forty proteins were differentially expressed in PD patients from the plasma proteomics analysis and the levels of all the seven apolipoproteins (apolipoprotein C-I, apolipoprotein C-III, protein APOC4-APOC2, apolipoprotein C-IV, apolipoprotein B variant, apolipoprotein B, and apolipoprotein M) were significantly decreased.…”

MENDA: a comprehensive curated resource of metabolic characterization in depression

“…Compared with traditional methods that only focus on a small number of metabolites, biological function analysis that integrates complex information from heterogeneous datasets allows for analysis of all available candidate molecules within a systematic framework, to elucidate the biological mechanism in complex diseases [ 39 , 40 ]. Pathway and network analyses, which examine the interactions between metabolites, genes and proteins within biological pathways or networks [ 41 , 42 ], are the most common methods for big-data driven research [ 43 , 44 ]. In addition to MetaboAnalyst or IPA mentioned above, users can choose other potential tools for bioinformatics analysis.…”

Common X-chromosome variants are associated with Parkinson’s disease risk

“…More generally, in a gene and protein network analysis of PD, Alzheimer’s disease and amyotrophic lateral sclerosis, Monti et al . emphasized that 6 out of 25 PD-specific proteins were ribosomal 51 . Additionally, ribosomal proteins interact with LRRK2 and elevate kinase activity 52 .…”

“…In conclusion, our work provides compelling evidence for two novel loci on the X chromosome that increase risk for PD and that can be used to refine polygenic risk scores of PD. Most critically, these results focus attention on a new-to-PD gene, RPL10 , that should further increase enthusiasm for the role of ribosomal proteins in PD pathogenesis 51,52 .…”