The accumulation of alpha-synuclein, ubiquitin and other proteins in Lewy bodies in degenerating dopaminergic neurones in substantia nigra in idiopathic Parkinson's disease (PD) suggest that inhibition of normal/abnormal protein degradation may contribute to neuronal death. We now show for the first time that the chymotrypsin- (39%), trypsin- (42%) and postacidic-like (33%) hydrolysing activities of 20/26S proteasome are impaired in substantia nigra in PD. Proteasome inhibition does not appear to result from drug treatment since high concentrations of L-3,4-dihydroxyphenylalanine had no effect on enzymatic activity in vitro. These observations provide the first direct evidence that inhibition of the ubiquitin-proteasome pathway leading to altered protein handling and Lewy body formation may be responsible for degeneration of the nigrostriatal pathway in idiopathic PD.
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http://dx.doi.org/10.1016/s0304-3940(00)01701-8 | DOI Listing |
NMR Biomed
February 2025
Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Asymmetry is a natural characteristic of Parkinson's disease (PD), which can be used to distinguish PD from atypical parkinsonism. Chemical exchange saturation transfer (CEST) has demonstrated value in reflecting the subtle changes related to neuron loss and abnormal protein accumulation in PD but has not been used to investigate asymmetry in PD. This study aimed to examine asymmetrical changes in the mesencephalic nucleus of PD patients with motor asymmetry using four-pool CEST analysis and to explore the relationship between imaging asymmetry and motor asymmetry.
View Article and Find Full Text PDFTransl Neurodegener
December 2024
Department of Anatomy and Medical Imaging, University of Auckland, 85 Park Road, Grafton, , Auckland, 1142, New Zealand.
Background: Parkinson's disease (PD) and multiple system atrophy (MSA) are classified as α-synucleinopathies and are primarily differentiated by their clinical phenotypes. Delineating these diseases based on their specific α-synuclein (α-Syn) proteoform pathologies is crucial for accurate antemortem biomarker diagnosis. Newly identified α-Syn pathologies in PD raise questions about whether MSA exhibits a similar diversity.
View Article and Find Full Text PDFNan Fang Yi Ke Da Xue Xue Bao
December 2024
Anhui Provincial Center for Neural Regeneration Technology and New Medical Materials Engineering Research, Bengbu Medical University, Bengbu 233000, China.
Objectives: To investigate the role of mitochondrial autophagy disorder caused by deletion of E3 ubiquitin ligase Parkin in neuroinflammation in a mouse model of MPTP-induced Parkinson's disease (PD).
Methods: Wild-type (WT) male C57BL/6 mice and Parkin mice were given intraperitoneal injections with MPTP or PBS for 5 consecutive days, and the changes in motor behaviors of the mice were observed using open field test. The effects of Parkin deletion on PD development and neuroinflammation were evaluated using immunofluorescence and Western blotting.
Int Immunopharmacol
December 2024
Laboratory of Neurodegenerative Diseases and Neuroinjury Diseases, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu 214122, China; MOE Medical Basic Research Innovation Center for Gut Microbiota and Chronic Diseases, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu 214122, China. Electronic address:
Astrocyte-mediated neuroinflammation plays a key role in Parkinson's disease (PD) progression. The proinflammatory protein S100A9 is linked to various neurodegenerative diseases, but its involvement in astrocyte activation in PD remains unclear. Here, we investigate the role of S100A9 in astrocyte-mediated neuroinflammation in PD.
View Article and Find Full Text PDFNeurol Sci
December 2024
Department of Radiology, The First People's Hospital of Foshan, #81 North Lingnan Avenue, Foshan, Guangdong, China.
Background: Identifying Parkinson's disease (PD) during its initial phases presents considerable hurdles for clinicians.
Purpose: To examine the feasibility and efficacy of a machine learning model based on quantitative multiparametric magnetic resonance imaging (MRI) features in identifying early-stage PD.
Methods: We recruited 33 participants, including 19 with early-stage PD, 14 with advanced-stage PD and 20 healthy control subjects.
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