Spectrum of γ-Secretase dysfunction as a unifying predictor of ADAD age at onset across PSEN1, PSEN2 and APP causal
Sara Gutiérrez Fernández1,2, Cristina Gan Oria1,2, Dieter Petit1,2
1VIB-KU Leuven Center for Brain & Disease Research, Herestraat 49 Box 602, Louvain, 3000, Belgium.
Background:
Autosomal Dominant Alzheimer's Disease (ADAD), caused by mutations in Presenilins (PSEN1/2) and Amyloid Precursor Protein (APP) genes, typically manifests with early onset (< 65 years). 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 understanding disease mechanisms, developing predictive models and tailored interventions in ADAD, with potential implications for sporadic AD.
Methods:
We performed biochemical assessment of γ-secretase dysfunction on 28 PSEN2 and 19 APP mutations, including disease-associated, unclear and benign variants. This analysis has been valuable in the assessment of PSEN1 variant pathogenicity, disease onset and progression.
Results:
Our analysis reveals linear correlations between the molecular composition of Aβ profiles and AAO for both PSEN2 (R2 = 0.52) and APP (R2 = 0.69) mutations. The integration of PSEN1, PSEN2 and APP correlation data shows parallel but shifted lines, suggesting a common pathogenic mechanism with gene-specific shifts in onset. We found overall "delays" in AAOs of 27 years for PSEN2 and 8 years for APP variants, compared to PSEN1. Notably, extremely inactivating PSEN1 variants delayed onset, suggesting that reduced contribution to brain APP processing underlies the later onset of PSEN2 variants.
Conclusion:
This study supports a unified model of ADAD pathogenesis wherein γ-secretase dysfunction and the resulting shifts in Aβ profiles are central to disease onset across all causal genes. While similar shifts in Aβ occur across causal genes, their impact on AAO varies in the function of their contribution to APP processing in the brain. This biochemical analysis establishes quantitative relationships that enable predictive AAO modelling with implications for clinical practice and genetic research. Our findings also support the development of therapeutic strategies modulating γ-secretase across different genetic ADAD forms and potentially more broadly in AD.
