Brain extracellular matrix changes during normal aging and in Alzheimer disease

University Of South Florida, Tampa FL

Investigators

Abstract

Project Summary/Abstract The long-term objective of this application is to identify novel biomarkers for early diagnosis and/or prognosis prediction of Alzheimer’s disease (AD), a neurodegenerative disorder that affects 5.5 million people in the US. This is consistent with the mission of NIA. This proposal aims to determine brain extracellular matrix (ECM) composition changes during normal aging and in AD in both rodents and humans using an innovative decellularization-based proteomic approach. In Aim 1, Mouse ECM composition in different brain regions and at various ages will be determined using an innovative decellularization-based proteomic approach optimized in our laboratory. In Aim 2, brain ECM composition changes in both 5xFAD and PS19 mouse models of AD will be investigated similarly. Different brain regions and various ages (representing distinct stages of AD) will be analyzed to determine any region-specific and age-dependent changes. In Aim 3, ECM composition alterations in human AD brains will be explored using postmortem prefrontal cortex samples from AD patients and age/gender-matched controls. In addition, the contributions of CAA (a vascular pathology frequently found in AD brains) and ApoE4 (a major genetic risk factor for AD) in ECM composition will be determined by including samples with and without CAA and ApoE4 in each condition. Successful completion of this study will establish ECM composition in mouse brains in a region-specific manner, elucidate ECM changes during normal aging, characterize the temporary and spatial changes of the ECM in two mouse models of AD, and determine ECM alterations in AD patients. These findings will provide a comprehensive picture on how each ECM protein changes during normal aging and in AD in any brain region at any time, which will pave the way for future research and substantially move the field forward. This proposal may lead to the identification of novel biomarkers in early AD diagnosis and/or prognosis prediction.