Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease

• Main Authors: Schmidt, Vanessa, Baum, Katharina, Lao, Angelyn R., Rateitschak, Katja, Schmitz, Yvonne, Teichmann, Anke, Wiesner, Burkhard, Petersen, Claus Munck, Nykjaer, Anders, Wolf, Jana, Wolkenhauer, Olaf, Willnow, Thomas E.
• Format: text
• Published: Animo Repository 2011
• Subjects: Amyloid beta-protein precursor—Mathematical models; Alzheimer's disease; Mathematics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/7442
• Institution: De La Salle University

The extent of proteolytic processing of the amyloid precursor protein (APP) into neurotoxic amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD). Accordingly, modifiers that increase Aβ production rates are risk factors in the sporadic form of AD. In a novel systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish a kinetic model of amyloidogenic processing, and to evaluate the influence by SORLA/SORL1, an inhibitor of APP processing and important genetic risk factor. Contrary to previous hypotheses, our studies demonstrate that secretases represent allosteric enzymes that require cooperativity by APP oligomerization for efficient processing. Cooperativity enables swift adaptive changes in secretase activity with even small alterations in APP concentration. We also show that SORLA prevents APP oligomerization both in cultured cells and in the brain in vivo, eliminating the preferred form of the substrate and causing secretases to switch to a less efficient non-allosteric mode of action. These data represent the first mathematical description of the contribution of genetic risk factors to AD substantiating the relevance of subtle changes in SORLA levels for amyloidogenic processing as proposed for patients carrying SORL1 risk alleles.