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Molecular NeurodegenerationNeurosciencePeer-reviewed journal article

Spectrum of gamma-Secretase dysfunction as a unifying predictor of ADAD age at onset across PSEN1, PSEN2 and APP causal genes

Sara Gutiérrez FernándeziD, Cristina Gan Oria, Dieter PetitiD, Wim G. AnnaertiD, John Matthew RingmaniD, Nick C. FoxiD, Natalie Sarah RyaniD, Lucía Chávez‐GutiérreziD

PDFDOIPreprint

Abstract

Autosomal Dominant Alzheimers Disease (ADAD), caused by mutations in Presenilins (PSEN1/2) and Amyloid Precursor Protein (APP) genes, typically manifests before age 65. Age at symptom onset (AAO) is relatively consistent among carriers of the same PSEN1 mutation, but more variable for PSEN2 and APP variants, with these mutations associated with later AAOs than PSEN1. Understanding this clinical variability is crucial for developing predictive models and tailored interventions in ADAD. Biochemical in vitro assessment of {gamma}-secretase function is valuable in evaluating PSEN1 variant pathogenicity, disease onset and progression. Here, we examined A{beta} profiles relationships to AAO across causal genes. Our analysis showed linear correlations between mutation-induced shifts in A{beta} profiles and AAO for PSEN2 and APP mutations. Integration with PSEN1 data revealed parallel but shifted correlations, indicating a common pathogenic mechanism with gene-specific onset timing shifts. Our data support a unified model of ADAD pathogenesis wherein {gamma}-secretase dysfunction and shifts in A{beta} profiles define disease onset. This biochemical analysis of ADAD causality and established quantitative relationships deepen our understanding of ADAD pathogenesis, offering potential for predictive AAO modelling with implications for clinical practice, genetic research and development of therapeutic strategies modulating {gamma}-secretase across ADAD forms and potentially more broadly in AD. SummaryWe examined the relationships between (full) A{beta} peptide profiles and age at symptom onset (AAO) across all Alzheimers disease causal genes. Our findings establish a quantitative framework for mutations pathogenicity assessment and AAO prediction; with implications for clinical practice, genetic counselling, fundamental and translational research.

Citations
11
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top 2%

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