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Open Access Research article

Multi-compartmental modeling of SORLA’s influence on amyloidogenic processing in Alzheimer’s disease

Angelyn Lao1, Vanessa Schmidt2, Yvonne Schmitz1, Thomas E Willnow2* and Olaf Wolkenhauer13*

Author affiliations

1 Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Ulmenstrasse 69, Rostock, 18057, Germany

2 Max-Delbrück-Center for Molecular Medicine, Robert-Roessle-Str. 10, Berlin, D-13125, Germany

3 Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, 7600, South Africa

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Citation and License

BMC Systems Biology 2012, 6:74  doi:10.1186/1752-0509-6-74

Published: 22 June 2012

Abstract

Background

Proteolytic breakdown of the amyloid precursor protein (APP) by secretases is a complex cellular process that results in formation of neurotoxic Aβ peptides, causative of neurodegeneration in Alzheimer’s disease (AD). Processing involves monomeric and dimeric forms of APP that traffic through distinct cellular compartments where the various secretases reside. Amyloidogenic processing is also influenced by modifiers such as sorting receptor-related protein (SORLA), an inhibitor of APP breakdown and major AD risk factor.

Results

In this study, we developed a multi-compartment model to simulate the complexity of APP processing in neurons and to accurately describe the effects of SORLA on these processes. Based on dose–response data, our study concludes that SORLA specifically impairs processing of APP dimers, the preferred secretase substrate. In addition, SORLA alters the dynamic behavior of β-secretase, the enzyme responsible for the initial step in the amyloidogenic processing cascade.

Conclusions

Our multi-compartment model represents a major conceptual advance over single-compartment models previously used to simulate APP processing; and it identified APP dimers and β-secretase as the two distinct targets of the inhibitory action of SORLA in Alzheimer’s disease.

Keywords:
Amyloidogenic processing; Compartmental modeling; LR11; Secretases; SORL1; VPS10P domain receptors