Molecular Insights into the Pathogenesis of Alzheimer's Disease and Its Relationship to Normal Aging

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition

Blood-gene expression reveals reduced circadian rhythmicity in individuals resistant to sleep deprivation.

To access publisher's full text version of this article click on the hyperlink at the bottom of the pageTo address whether changes in gene expression in blood cells with sleep loss are different in individuals resistant and sensitive to sleep deprivation.Blood draws every 4 h during a 3-day study: 24-h normal baseline, 38 h of continuous wakefulness and subsequent recovery sleep, for a total of 19 time-points per subject, with every 2-h psychomotor vigilance task (PVT) assessment when awake.Sleep laboratory.Fourteen subjects who were previously identified as behaviorally resistant (n = 7) or sensitive (n = 7) to sleep deprivation by PVT.Thirty-eight hours of continuous wakefulness.We found 4,481 unique genes with a significant 24-h diurnal rhythm during a normal sleep-wake cycle in blood (false discovery rate [FDR] < 5%). Biological pathways were enriched for biosynthetic processes during sleep. After accounting for circadian effects, two genes (SREBF1 and CPT1A, both involved in lipid metabolism) exhibited small, but significant, linear changes in expression with the duration of sleep deprivation (FDR < 5%). The main change with sleep deprivation was a reduction in the amplitude of the diurnal rhythm of expression of normally cycling probe sets. This reduction was noticeably higher in behaviorally resistant subjects than sensitive subjects, at any given P value. Furthermore, blood cell type enrichment analysis showed that the expression pattern difference between sensitive and resistant subjects is mainly found in cells of myeloid origin, such as monocytes.Individual differences in behavioral effects of sleep deprivation are associated with differences in diurnal amplitude of gene expression for genes that show circadian rhythmicity.NIH/ HL94307 Eimskip Fund of the University of Iceland Merck Research Laboratories (Merck Co., Inc.) National Center for Research Resources UL1RR024134 Intramural Research Program of the NIH, National Institute of Environmental Health Sciences ZIA ES103166-01 ResMed foundatio

Human Alzheimer’s disease gene expression signatures and immune profile in APP mouse models: a discrete transcriptomic view of Aβ plaque pathology

Abstract Background Alzheimer’s disease (AD) is a chronic neurodegenerative disease with pathological hallmarks including the formation of extracellular aggregates of amyloid-beta (Aβ) known as plaques and intracellular tau tangles. Coincident with the formation of Aβ plaques is recruitment and activation of glial cells to the plaque forming a plaque niche. In addition to histological data showing the formation of the niche, AD genetic studies have added to the growing appreciation of how dysfunctional glia pathways drive neuropathology, with emphasis on microglia pathways. Genomic approaches enable comparisons of human disease profiles between different mouse models informing on their utility to evaluate secondary changes to triggers such as Aβ deposition. Methods In this study, we utilized two animal models of AD to examine and characterize the AD-associated pathology: the Tg2576 Swedish APP (KM670/671NL) and TgCRND8 Swedish plus Indiana APP (KM670/671NL + V717F) lines. We used laser capture microscopy (LCM) to isolate samples surrounding Thio-S positive plaques from distal non-plaque tissue. These samples were then analyzed using RNA sequencing. Results We determined age-associated transcriptomic differences between two similar yet distinct APP transgenic mouse models, known to differ in proportional amyloidogenic species and plaque deposition rates. In Tg2576, human AD gene signatures were not observed despite profiling mice out to 15 months of age. TgCRND8 mice however showed progressive and robust induction of lysomal, neuroimmune, and ITIM/ITAM-associated gene signatures overlapping with prior human AD brain transcriptomic studies. Notably, RNAseq analyses highlighted the vast majority of transcriptional changes observed in aging TgCRND8 cortical brain homogenates were in fact specifically enriched within the plaque niche samples. Data uncovered plaque-associated enrichment of microglia-related genes such as ITIM/ITAM-associated genes and pathway markers of phagocytosis. Conclusion This work may help guide improved translational value of APP mouse models of AD, particularly for strategies aimed at targeting neuroimmune and neurodegenerative pathways, by demonstrating that TgCRND8 more closely recapitulates specific human AD-associated transcriptional responses