Loss of neuronal lysosomal acid lipase drives amyloid pathology in Alzheimer's disease.

Underlying drivers of late-onset Alzheimer's disease (LOAD) pathology remain unknown. However, multiple biologically diverse risk factors share a common pathological progression. To identify convergent molecular abnormalities that drive LOAD pathogenesis we compared two common midlife risk factors for LOAD, heavy alcohol use and obesity. This revealed that disrupted lipophagy is an underlying cause of LOAD pathogenesis. Both exposures reduced lysosomal flux, with a loss of neuronal lysosomal acid lipase (LAL). This resulted in neuronal lysosomal lipid (NLL) accumulation, which opposed Aß localization to lysosomes. Neuronal LAL loss both preceded (with aging) and promoted (targeted knockdown) Aß pathology and cognitive deficits in AD mice. The addition of recombinant LAL ex vivo and neuronal LAL overexpression in vivo prevented amyloid increases and improved cognition. In WT mice, neuronal LAL declined with aging and correlated negatively with entorhinal Aß. In healthy human brain, LAL also declined with age, suggesting this contributes to the age-related vulnerability for AD. In human LOAD LAL was further reduced, correlated negatively with Aß1-42, and occurred with polymerase pausing at the LAL gene. Together, this finds that the loss of neuronal LAL promotes NLL accumulation to impede degradation of Aß in neuronal lysosomes to drive AD amyloid pathology.

Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life.

The decline of CD8+ T cell functions contributes to deteriorating health with aging, but the mechanisms that underlie this phenomenon are not well understood. We use single-cell RNA sequencing with both cross-sectional and longitudinal samples to assess how human CD8+ T cell heterogeneity and transcriptomes change over nine decades of life. Eleven subpopulations of CD8+ T cells and their dynamic changes with age are identified. Age-related changes in gene expression result from changes in the percentage of cells expressing a given transcript, quantitative changes in the transcript level, or a combination of these two. We develop a machine learning model capable of predicting the age of individual cells based on their transcriptomic features, which are closely associated with their differentiation and mutation burden. Finally, we validate this model in two separate contexts of CD8+ T cell aging: HIV infection and CAR T cell expansion in vivo.