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Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Its classic motor symptoms may be preceded by non-motor symptoms (NMS). Population studies have identified GBA variants as risk factors for idiopathic PD. The increased risk of PD has also been suggested in other Lysosomal Storage Disorders (LSDs). Objective: To assess the evolution of the prevalence of NMS compatible with PD in a cohort of South Brazilian adult patients with Gaucher Disease (GD) type 1, already evaluated 3 years ago (2018). Cerebrospinal Fluid (CSF) was collected to assess the levels of LSD enzymes (beta-hexosaminidases, beta-glucuronidase) and biomarker of macrophage activation (chitotriosidase, ChT), compared to controls (metachromatic leukodystrophy, MLD). Cognition was evaluated by the Montreal Cognitive Assessment (MoCA) questionnaire, daytime sleepiness by the Epworth Sleepiness Scale (ESS), depression by Beck´s Inventory, constipation by the Unified Multiple System Atrophy Rating Scale (UMSARS) scale, and REM sleep behavior disorder by the single-question screen. Hyposmia was assessed with Sniffin' Sticks (SST). Results: Nineteen patients completed the follow-up (mean age of the sample was 44 years; range, 26–71). The patient with the highest number of NMS at the baseline (4 including the lowest SST score) was diagnosed with PD four years later. Apart from an improvement in the ESS score, no other statistical significance was found between the number of NMS between the first and second evaluation, nor between patients with one L444P variant (n = 5) and the rest of the cohort. CSF was collected in five patients (mean age of the sample was 40 years, range 30–53. A significant difference was found in the mean CSF activity levels of beta-hexosaminidases and beta-glucuronidase between GD1 and MLD patients. Mean ChT (CSF) was 62 nmol/h/mL in GD patients and 142 in MLD (n = 6) patients. Conclusions: The patient with the highest number of NMS in our 2018 cohort was the one that developed PD, corroborating with the importance of this longitudinal follow-up. CSF and plasma analysis might allow a better understanding of the neurodegenerative processes connecting PD and the lysosomal environment. Further analysis is needed to understand this relationship. [ABSTRACT FROM AUTHOR]