Inflammation in Alzheimer's disease (AD) patients is characterized by increased cytokines and activated microglia. Epidemiological studies suggest reduced AD risk associates with long-term use of nonsteroidal anti-inflammatory drugs (NSAIDs). Whereas chronic ibuprofen suppressed inflammation and plaque-related pathology in an Alzheimer transgenic APPSw mouse model (Tg2576), excessive use of NSAIDs targeting cyclooxygenase I can cause gastrointestinal, liver, and renal toxicity. One alternative NSAID is curcumin, derived from the curry spice turmeric. Curcumin has an extensive history as a food additive and herbal medicine in India and is also a potent polyphenolic antioxidant. To evaluate whether it could affect Alzheimer-like pathology in the APPSw mice, we tested a low (160 ppm) and a high dose of dietary curcumin (5000 ppm) on inflammation, oxidative damage, and plaque pathology. Low and high doses of curcumin significantly lowered oxidized proteins and interleukin-1beta, a proinflammatory cytokine elevated in the brains of these mice. With low-dose but not high-dose curcumin treatment, the astrocytic marker GFAP was reduced, and insoluble beta-amyloid (Abeta), soluble Abeta, and plaque burden were significantly decreased by 43-50%. However, levels of amyloid precursor (APP) in the membrane fraction were not reduced. Microgliosis was also suppressed in neuronal layers but not adjacent to plaques. In view of its efficacy and apparent low toxicity, this Indian spice component shows promise for the prevention of Alzheimer's disease.
The brain in Alzheimer's disease (AD) shows a chronic inflammatory response characterized by activated glial cells and increased expression of cytokines and complement factors surrounding amyloid deposits. Several epidemiological studies have demonstrated a reduced risk for AD in patients using nonsteroidal anti-inflammatory drugs (NSAIDs), prompting further inquiries about how NSAIDs might influence the development of AD pathology and inflammation in the CNS. We tested the impact of chronic orally administered ibuprofen, the most commonly used NSAID, in a transgenic model of AD displaying widespread microglial activation, age-related amyloid deposits, and dystrophic neurites. These mice were created by overexpressing a variant of the amyloid precursor protein found in familial AD. Transgene-positive (Tg+) and negative (Tg-) mice began receiving chow containing 375 ppm ibuprofen at 10 months of age, when amyloid plaques first appear, and were fed continuously for 6 months. This treatment produced significant reductions in final interleukin-1beta and glial fibrillary acidic protein levels, as well as a significant diminution in the ultimate number and total area of beta-amyloid deposits. Reductions in amyloid deposition were supported by ELISA measurements showing significantly decreased SDS-insoluble Abeta. Ibuprofen also decreased the numbers of ubiquitin-labeled dystrophic neurites and the percentage area per plaque of anti-phosphotyrosine-labeled microglia. Thus, the anti-inflammatory drug ibuprofen, which has been associated with reduced AD risk in human epidemiological studies, can significantly delay some forms of AD pathology, including amyloid deposition, when administered early in the disease course of a transgenic mouse model of AD.
Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.
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