An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE193837 | GEO | 2022/10/27
REPOSITORIES: GEO
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Project description:Alzheimer’s Disease (AD) is the most common form of dementia and a leading global cause of human mortality and morbidity. Amyloid plaques of the amyloid beta (Aß) peptide are a universal pathological hallmark of AD and rare mutations in Aß can cause familial forms of AD (fAD). However, hundreds of additional mutations in Aß of uncertain significance are likely to exist in the human population, making clinical interpretation of genetic variation a difficult challenge even in this short peptide. Here, we use deep mutagenesis to quantify the effects of all possible single nucleotide variants and thousands of double mutations on the ability of Aß to nucleate amyloid fibrils in vivo. This data provides the first comprehensive view of how mutations alter the nucleation of an amyloid, reveals a modular organisation of mutational effects in Aß, and demonstrates the importance of charge in preventing nucleation. Moreover, the in vivo nucleation data, unlike computational predictors, accurately discriminates the known dominant fAD mutations. These results illustrate how deep mutagenesis can be used to genetically validate assays as discovery platforms. They also further prioritize nucleation as the critical biochemical event to target in order to prevent and treat AD.
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Project description:Changes in the amino acid sequences of proteins cause thousands of human genetic diseases. However, only a subset of variants in any protein is typically pathogenic, with variants having a diversity of molecular consequences. Determining which of the thousands of possible variants in any protein have similar molecular effects is very challenging, but crucial for identifying pathogenic variants, determining disease mechanisms, understanding clinical phenotypic variation, and developing targeted therapeutics. Here we present a general method to classify variants by their molecular effects that we term intramolecular genetic interaction profiling. The approach relies on the principle that variants with similar molecular consequences have similar genetic interactions with other variants in the same protein. These intramolecular genetic interactions are straightforward to quantify for any protein with a selectable function. We apply intramolecular genetic interaction profiling to amyloid beta, the protein that aggregates in Alzheimer’s disease (AD) and is mutated in familial AD (fAD). Genetic interactions identify two classes of gain-of-function variants, with all known familial Alzheimer’s disease variants having very similar genetic interaction profiles, consistent with a common gain-of-function mechanism leading to pathology. We believe that intramolecular genetic interaction profiling is a powerful approach for classifying variants in disease genes that will empower rare variant association studies and the discovery of disease mechanisms.
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Project description:Proctor2012 - Amyloid-beta aggregation
This model supports the current thinking that levels of dimers are important in initiating the aggregation process.
This model is described in the article:
Aggregation, impaired degradation and immunization targeting of amyloid-beta dimers in Alzheimer's disease: a stochastic modelling approach.
Proctor CJ, Pienaar IS, Elson JL, Kirkwood TB
Molecular Neurodegeneration. 2012; 7:32
Abstract:
BACKGROUND:
Alzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (Aß), particularly Aß₄₂, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting Aß₄₂ immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine Aß₄₂ turnover and its aggregation in detail and to test the effect of immunization against Aß dimers.
RESULTS:
Our model demonstrates for the first time that even a slight decrease in the clearance rate of Aß₄₂ monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of Aβ are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.
CONCLUSION:
Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies.
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