Transcriptomic changes highly similar to Alzheimer’s disease are observed in a subpopulation of individuals during normal brain aging

Abstract

Aging is a major risk factor for late-onset Alzheimer’s disease (LOAD). How aging contributes to the development of LOAD remains elusive. In this study, we examine multiple large-scale human brain transcriptomic data from both normal aging and LOAD to understand the molecular interconnection between aging and LOAD. We find that shared gene expression changes between aging and LOAD are mostly seen in the hippocampus and several cortical regions. In the hippocampus, phosphoprotein, alternative splicing and cytoskeleton are the commonly dysregulated biological pathways in both aging and AD, while synapse, ion transport, and synaptic vesicle genes are commonly down-regulated. Aging-specific changes are associated with acetylation and methylation, while LOAD-specific changes are related to glycoprotein (both up- and down-regulations), inflammatory response (up-regulation), myelin sheath and lipoprotein (down-regulation). We also find that normal aging brains from relatively young donors (45-70 years old) cluster into subgroups and some subgroups show gene expression changes highly similar to those seen in LOAD brains. Using brain transcriptome data from older individuals (>70 years), we find that samples from cognitive normal older individuals cluster with the “healthy aging” subgroup while AD samples mainly cluster with the AD similar subgroups. This implies that individuals in the healthy aging subgroup will likely remain cognitive normal when they become older and vice versa. In summary, our results suggest that on the transcriptome level, aging and LOAD have strong interconnections in some brain regions in a subpopulation of cognitive normal aging individuals. This supports the theory that the initiation of LOAD occurs decades earlier than the manifestation of clinical phenotype and it may be essential to closely study the “normal brain aging” in a subgroup of individuals in their 40s-60s to identify the very early events in LOAD development.