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- W2980668875 abstract "In recent years, maturation of molecular profiling technologies and analytic methods have enabled the generation of multiple layers of high-dimensional data from large numbers of neuropathologically well-characterized brain samples, offering an unprecedented perspective on molecular events associated with neuropathologic features. We analyzed genomic, transcriptomic, epigenomic, and proteomic data from cortical samples and peripheral monocytes of up to 1200 individuals using the Speakeasy algorithm to reduce the data to the level of modules of co-expressed genes/proteins and a Bayesian network structure learning approach to infer the architecture of molecular networks within the tissue. Neuropathologic measures were systematically collected following a structured protocol in all subjects. Over 3000 genes are altered in expression in the dorsolateral prefrontal cortex in relation to aging-related cognitive decline. Together, these changes account for ∼12% of the variance in cognitive decline that is not explained by AD-related pathologies. The contribution of neurovascular pathologies to these cortical transcriptomic changes is minor. However, peripheral monocyte transcriptomes are associated with intracranial atherosclerosis, and new single nucleus data from the anterior watershed white matter is uncovering changes in cell type proportion in relation to vascular phenotypes. Most of the data generated have come from the frontal cortex and have only a modest association to intracranial vascular disease; however, the approaches that have successfully identified “omic” changes related to AD pathology in the frontal cortex are now being applied to other brain regions and peripheral monocytes in which the relationship to neurovascular disease may be more pronounced." @default.
- W2980668875 created "2019-10-25" @default.
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- W2980668875 date "2019-07-01" @default.
- W2980668875 modified "2023-10-02" @default.
- W2980668875 title "F2-04-04: LEVERAGING NEUROPATHOLOGY TO INTEGRATE WITH RNA AND OTHER OMICS" @default.
- W2980668875 doi "https://doi.org/10.1016/j.jalz.2019.06.4431" @default.
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