Familial Alzheimer's Disease-Related Mutations Differentially Alter Stability of Amyloid-Beta Aggregates

AlzDiscovery: A computational tool to identify Alzheimer's disease-causing missense mutations using protein structure information

Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD-causing variants did not consider the effect of mutations from the perspective of a protein three-dimensional environment. Here, we present a machine learning-based analysis to classify the AD-causing mutations from their benign counterparts in 21 AD-related proteins leveraging both sequence- and structure-based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD-related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state-of-the-art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user-friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD-related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.

Bifurcations in coupled amyloid-β aggregation-inflammation systems

A complex interplay between various processes underlies the neuropathology of Alzheimer's disease (AD) and its progressive course. Several lines of evidence point to the coupling between Aβ aggregation and neuroinflammation and its role in maintaining brain homeostasis during the long prodromal phase of AD. Little is however known about how this protective mechanism fails and as a result, an irreversible and progressive transition to clinical AD occurs. Here, we introduce a minimal model of a coupled system of Aβ aggregation and inflammation, numerically simulate its dynamical behavior, and analyze its bifurcation properties. The introduced model represents the following events: generation of Aβ monomers, aggregation of Aβ monomers into oligomers and fibrils, induction of inflammation by Aβ aggregates, and clearance of various Aβ species. Crucially, the rates of Aβ generation and clearance are modulated by inflammation level following a Hill-type response function. Despite its relative simplicity, the model exhibits enormously rich dynamics ranging from overdamped kinetics to sustained oscillations. We then specify the region of inflammation- and coupling-related parameters space where a transition to oscillatory dynamics occurs and demonstrate how changes in Aβ aggregation parameters could shift this oscillatory region in parameter space. Our results reveal the propensity of coupled Aβ aggregation-inflammation systems to oscillatory dynamics and propose prolonged sustained oscillations and their consequent immune system exhaustion as a potential mechanism underlying the transition to a more progressive phase of amyloid pathology in AD. The implications of our results in regard to early diagnosis of AD and anti-AD drug development are discussed.

Computational Study of Amyloidβ42 Familial Mutations and Metal Interaction: Impact on Monomers and Aggregates Dynamical Behaviors

One of the main hallmarks of Alzheimer's Disease is the formation of β-amyloid plaques, whose formation may be enhanced by metal binding or the appearance of familial mutations. In the present study, the simultaneous effect of familial mutations (E22Q, E22G, E22K, and D23N) and binding to metal ions (Cu(II) or Al(III)) is studied at the Aβ42 monomeric and fibrillar levels. With the application of GaMD and MD simulations, it is observed that the effects of metal binding and mutations differ in the monomeric and fibrillar forms. In the monomeric structures, without metal binding, all mutations reduce the amount of α-helix and increase, in some cases, the β-sheet content. In the presence of Cu(II) and Al(III) metal ions, the peptide becomes less flexible, and the β-sheet content decreases in favor of forming α-helix motifs that stabilize the system through interhelical contacts. Regarding the fibrillar structures, mutations decrease the opening of the fiber in the vertical axis, thereby stabilizing the S-shaped structure of the fiber. This effect is, in general, enhanced upon metal binding. These results may explain the different Aβ42 aggregation patterns observed in familial mutations.