PROJECT ABSTRACT There is an urgent need for novel therapies for Alzheimer’s disease (AD) and related dementias (ADRD). The over-production of longer, aggregation-prone β-amyloid (Aβ) fragments (including Aβ42 and 43) relative to shorter, non-aggregating fragments (including Aβ37 and 38) are likely critical drivers of disease in both late- onset, sporadic AD (LOAD) and autosomal dominant AD (ADAD). The balance between production of aggregating and non-aggregating forms of Aβ is a direct result of the kinetics by which the γ-secretase complex sequentially cleaves amyloid precursor protein. Alterations in γ-secretase function can have profound neurodegenerative and cognitive consequences, while modulation of γ-secretase has therapeutic potential in LOAD and ADAD. Pathogenic variants in Presenilin-1 (PSEN1), the key catalytic subunit in the γ-secretase complex, are the most common cause of ADAD. There is substantial heterogeneity in Age of Symptom Onset (a range of >30 years) and biomarker progression across PSEN1 variants. The proposed K01 project will characterize differences in γ-secretase function across ~250 unique ADAD-causing PSEN1 variants in the presence and absence of two structurally distinct γ-secretase modulators (GSMs), measured in a cell-based model system (Aim 1). In Aim 2, variant-specific characteristics will be combined with cross-sectional cognitive and biomarker measures from carriers of corresponding PSEN1 variants participating in the Dominantly Inherited Alzheimer’s Network Observational Study (DIAN-Obs), a large international study in which over 80 unique PSEN1 pathogenic variants are represented. This translational dataset will be used to examine whether differences in γ-secretase function between PSEN1 mutations can account for the heterogeneity in Age of Symptom Onset, cognition, and AD biomarkers. Aim 3 will utilize longitudinal data from DIAN-Obs to examine whether mutation-specific characteristics 1) predict change in biomarkers and cognition and 2) increase power to detect estimated treatment effects for relevant trial outcomes. Leveraging available data from DIAN Trials Unit, exploratory analyses will assess whether inclusion of variant-dependent characteristics can improve stratification approaches for future clinical trials. This K01 will elucidate mechanisms underlying AD, facilitate ongoing ADAD clinical trials, and support the development of GSMs as possible therapeutics. To help Dr. Stephanie Schultz achieve these aims, a multidisciplinary mentorship team has been assembled from the Harvard Medical School community and DIAN Leadership team to complement coursework in protein characterization, translational research, advanced statistical analysis, and clinical trials. Dr. Jasmeer Chhatwal will be the primary mentor overseeing research and career progress. Dr. Schultz will receive mentoring from Dr. Dennis Selkoe on the mechanisms underlying AD, Drs. Reisa Sperling and Eric McDade on translational and clinical tr...