5 research outputs found

    Somatic SNCA Copy Number Variants in Multiple System Atrophy Are Related to Pathology and Inclusions

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    BACKGROUND: Somatic α-synuclein (SNCA) copy number variants (CNVs, specifically gains) occur in multiple system atrophy (MSA) and Parkinson's disease brains. OBJECTIVE: The aim was to compare somatic SNCA CNVs in MSA subtypes (striatonigral degeneration [SND] and olivopontocerebellar atrophy [OPCA]) and correlate with inclusions. METHODS: We combined fluorescent in situ hybridization with immunofluorescence for α-synuclein and in some cases oligodendrocyte marker tubulin polymerization promoting protein (TPPP). RESULTS: We analyzed one to three brain regions from 24 MSA cases (13 SND, 11 OPCA). In a region preferentially affected in one subtype (putamen in SND, cerebellum in OPCA), mosaicism was higher in that subtype, and cells with CNVs were 4.2 times more likely to have inclusions. In the substantia nigra, nonpigmented cells with CNVs and TPPP were about six times more likely to have inclusions. CONCLUSIONS: The correlation between SNCA CNVs and pathology (at a regional level) and inclusions (at a single-cell level) suggests a role for somatic SNCA CNVs in MSA pathogenesis. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

    Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers.

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    Funder: Alzheimer's Society; Id: http://dx.doi.org/10.13039/501100000320Funder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100007155Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations
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