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The genetic landscape for amyloid beta nucleation accurately discriminates familial Alzheimer’s disease mutations

ABSTRACT: The genetic landscape for amyloid beta nucleation accurately discriminates familial Alzheimer’s disease mutations

PROVIDER: PRJNA634890 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11841946_1.fastq.gz	Fastqsanger.gz
	SRR11841946_2.fastq.gz	Fastqsanger.gz
	SRR11841947_1.fastq.gz	Fastqsanger.gz
	SRR11841947_2.fastq.gz	Fastqsanger.gz
	SRR11841948_1.fastq.gz	Fastqsanger.gz

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The genetic landscape for amyloid beta nucleation accurately discriminates familial Alzheimer’s disease mutations

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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The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer's disease mutations.

Project description:Plaques of the amyloid beta (Aß) peptide are a pathological hallmark of Alzheimer's disease (AD), the most common form of dementia. Mutations in Aß also cause familial forms of AD (fAD). Here, we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of Aß. The resulting genetic landscape reveals mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the empirical nucleation scores accurately identify all known dominant fAD mutations in Aß, genetically validating that the mechanism of nucleation in a cell-based assay is likely to be very similar to the mechanism that causes the human disease. These results provide the first comprehensive atlas of how mutations alter the formation of any amyloid fibril and a resource for the interpretation of genetic variation in Aß.

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An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation

Project description:Multiplexed assays of variant effects (MAVEs) guide clinical variant interpretation and reveal disease mechanisms. To date, MAVEs have focussed on a single mutation type - amino acid (AA) substitutions - despite the diversity of coding variants that cause disease. Here we use Deep Indel Mutagenesis (DIM) to generate the first comprehensive atlas of diverse variant effects for a disease protein, amyloid beta (Aß) that aggregates in Alzheimer’s disease (AD) and is mutated in familial AD (fAD). The atlas identifies known fAD variants and many mutations beyond substitutions that accelerate Aß aggregation. Truncations, substitutions, insertions, single- and multi-AA deletions differ in their propensity to enhance or impair aggregation, but likely pathogenic variants from all classes are strongly enriched in the polar N-terminus of Aß. This first comparative atlas for any disease gene highlights the importance of including diverse mutation types in MAVEs and provides important mechanistic insights into amyloid nucleation.

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Project description:More than 50 human diseases are characterized by the deposition of specific protein aggregates in the form of insoluble amyloid fibrils. However, only a very small number of proteins are known to form amyloids with high propensity, limiting our ability to understand, predict and engineer amyloid aggregation from sequence. Here we use a massively parallel assay to quantify the amyloid nucleation propensity of >100,000 random 20 amino acid sequences. Approximately 5% of assayed random sequences nucleate the formation of aggregates, generating a very large and diverse training dataset from which to train models to predict amyloid nucleation. We use this dataset to train CANYA, a convolution-attention hybrid neural network that predicts the propensity of any primary sequence to form amyloids. CANYA outperforms previous predictors of protein aggregation on additional random sequences and out-of-sample datasets including human disease-causing amyloids, with very stable performance across diverse prediction tasks. We adapt and extend recent advances in interpretability of genomic neural networks to elucidate CANYA’s decision-making process and learned grammar and to provide mechanistic insights into amyloid formation. Our results demonstrate the power of massive experimental random sequence-space exploration and provide an interpretable and robust neural network model for understanding, predicting and designing amyloid-forming proteins.

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