The genetic landscape for amyloid beta nucleation accurately discriminates familial Alzheimer’s disease mutations
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA634890 | ENA |
REPOSITORIES: ENA
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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.
2021-02-02 | GSE151147 | GEO
Project description: Not available
| S-EPMC7943193 | biostudies-literature
Project description:Different forms of dementia are caused by stop-loss mutations in the ITM2B gene, also known as Bri2, which result in the expression of 34 amino acid long peptides that accumulate as amyloids in human brains. In order to gather mechanistic insights into the formation of amyloids by two of these peptides, ADan and ABri - hallmarks of Danish and British dementia respectively - we employed saturation mutagenesis combined to a massively parallel selection assay that reports on amyloid nucleation. Our results reveal that ADan aggregates into amyloids remarkably faster than both the unextended peptide Bri2 and the extended ABri sequence. The complete mutational landscape of ADan reveals asparagines and charged residues as key players in the nucleation process in addition to aliphatic residues within positions 20-25. What is more, we show that extending Bri2 with just two specific residues is enough to generate a novel amyloid core which we suggest builds the structured core of ADan fibrils. On the other hand, only a handful of mutations can boost the ability of ABri to nucleate amyloids, including a SNV replacing the Bri2 stop codon by a Cys codon. Overall, the remarkably different aggregation profiles and mutational landscapes for the two peptides suggest that different disease mechanisms underlie disease in Danish and British dementia and highlight the importance of accurately measuring the impact of stop extension mutations for these and other sequences across the genome.
2023-10-09 | GSE244612 | GEO
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.
2022-10-27 | GSE193837 | GEO
Project description:Nucleation and spreading rejuvenate polycomb domains every cell cycle
2023-12-01 | GSE209818 | GEO
Project description:Alzheimer’s Disease (AD) is a disorder characterized by cognitive decline, neurodegeneration, and accumulation of amyloid plaques and tau neurofibrillary tangles in the brain. Dysregulation of epigenetic histone modifications may lead to expression of transcriptional programs that play a role either in protecting against disease genesis or in worsening of disease pathology. One such histone modification, acetylation of histone H3 lysine residue 27 (H3K27ac), is primarily localized to genomic enhancer regions and promotes active gene transcription. We previously discovered H3K27ac to be more abundant in AD patient brain tissue compared to the brains of age-matched non-demented controls. In this study, we use iPSC-neurons derived from familial AD patients with an amyloid precursor protein (APP) duplication (APPDup neurons) as a model to study the functional effect of lowering CBP/P300 enzymes that catalyze H3K27ac. We found that homeostatic amyloid-reducing genes were upregulated in the APPDup neurons compared to non-demented controls. We lowered CBP/P300 to reduce H3K27ac, which led to decreased expression of numerous of these homeostatic amyloid-reducing genes, along with increased extracellular secretion of a toxic amyloid-β species, Aβ(1-42). Our findings suggest that epigenomic histone acetylation, including H3K27ac, drives expression of compensatory genetic programs in response to AD-associated insults, specifically those resulting from APP duplication, and thus may play a role in mitigating AD pathology in neurons.
2023-12-22 | GSE250567 | GEO
Project description:We have previously demonstrated that Sirt3 gene deletion, a model for metabolic syndrome, leads to brain mitochondrial dysfunction and neuroinflammation. We also reported that silencing of Sirt3 gene in APP/PS1 mice results in exacerbation of insulin resistance, neuroinflammation and β amyloid plaque deposition. To further understand how metabolic syndrome and amyloid pathology interact, we performed RNA-seq analysis of the brain samples from wild type, Sirt3-/- , APP/PS1 and APP/PS1/Sirt3-/- mice.
2022-10-24 | GSE216081 | GEO
Project description:Crespo2012 - Kinetics of Amyloid Fibril
Formation
This model is described in the article:
A generic
crystallization-like model that describes the kinetics of
amyloid fibril formation.
Crespo R, Rocha FA, Damas AM,
Martins PM.
J. Biol. Chem. 2012 Aug; 287(36):
30585-30594
Abstract:
Associated with neurodegenerative disorders such as
Alzheimer, Parkinson, or prion diseases, the conversion of
soluble proteins into amyloid fibrils remains poorly
understood. Extensive "in vitro" measurements of protein
aggregation kinetics have been reported, but no consensus
mechanism has emerged until now. This contribution aims at
overcoming this gap by proposing a theoretically consistent
crystallization-like model (CLM) that is able to describe the
classic types of amyloid fibrillization kinetics identified in
our literature survey. Amyloid conversion represented as a
function of time is shown to follow different curve shapes,
ranging from sigmoidal to hyperbolic, according to the relative
importance of the nucleation and growth steps. Using the CLM,
apparently unrelated data are deconvoluted into generic
mechanistic information integrating the combined influence of
seeding, nucleation, growth, and fibril breakage events. It is
notable that this complex assembly of interdependent events is
ultimately reduced to a mathematically simple model, whose two
parameters can be determined by little more than visual
inspection. The good fitting results obtained for all cases
confirm the CLM as a good approximation to the generalized
underlying principle governing amyloid fibrillization. A
perspective is presented on possible applications of the CLM
during the development of new targets for amyloid disease
therapeutics.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000531.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2014-07-17 | BIOMD0000000531 | BioModels
Project description:Deposition of amyloid beta (Aβ) and hyperphosphorylated tau along with glial cell-mediated neuroinflammation are prominent pathogenic hallmarks of Alzheimer’s disease (AD). In recent years, impairment of autophagy has been found to be another important feature contributing to AD progression. Therefore, the potential of the autophagy activator spermidine, a small body-endogenous polyamine often used as dietary supplement, was assessed on Aβ pathology and glial cell-mediated neuroinflammation. Oral treatment of the amyloid prone AD-like APPPS1 mice with spermidine reduced neurotoxic soluble Aβ and decreased AD-associated neuroinflammation during disease progression. Mechanistically, single nuclei sequencing revealed AD-associated microglia to be the main target of spermidine. This microglia population was characterized by increased AXL levels and expression of genes implicated in cell migration and phagocytosis. Our data highlight that the autophagy activator spermidine holds the potential to enhance Aβ degradation and to counteract glia-mediated neuroinflammation in AD pathology.
2022-07-04 | GSE206202 | GEO
Project description:Vazquez2014 - Chemical inhibition from
amyloid protein aggregation kinetics
This model is described in the article:
Modeling of chemical
inhibition from amyloid protein aggregation kinetics.
Vázquez JA.
BMC Pharmacol Toxicol 2014; 15(1):
9
Abstract:
BACKGROUNDS: The process of amyloid proteins aggregation
causes several human neuropathologies. In some cases, e.g.
fibrillar deposits of insulin, the problems are generated in
the processes of production and purification of protein and in
the pump devices or injectable preparations for diabetics.
Experimental kinetics and adequate modelling of chemical
inhibition from amyloid aggregation are of practical importance
in order to study the viable processing, formulation and
storage as well as to predict and optimize the best conditions
to reduce the effect of protein nucleation. RESULTS: In this
manuscript, experimental data of insulin, A?42 amyloid protein
and apomyoglobin fibrillation from recent bibliography were
selected to evaluate the capability of a bivariate sigmoid
equation to model them. The mathematical functions (logistic
combined with Weibull equation) were used in reparameterized
form and the effect of inhibitor concentrations on kinetic
parameters from logistic equation were perfectly defined and
explained. The surfaces of data were accurately described by
proposed model and the presented analysis characterized the
inhibitory influence on the protein aggregation by several
chemicals. Discrimination between true and apparent inhibitors
was also confirmed by the bivariate equation. EGCG for insulin
(working at pH?=?7.4/T?=?37°C) and taiwaniaflavone for
A?42 were the compounds studied that shown the greatest
inhibition capacity. CONCLUSIONS: An accurate, simple and
effective model to investigate the inhibition of chemicals on
amyloid protein aggregation has been developed. The equation
could be useful for the clear quantification of inhibitor
potential of chemicals and rigorous comparison among them.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000532.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2014-07-30 | BIOMD0000000532 | BioModels