| Amyloid-beta (Aβ) | |
|---|---|
| Gene | APP |
| UniProt | P05067 |
| PDB | 1IYT, 1BA4, 2BEG, 5OQV |
| Mol. Weight | 4 kDa (Aβ40/42) |
| Localization | Extracellular, membrane-associated |
| Family | Amyloid precursor protein family |
| Diseases | Alzheimer's Disease, Cerebral Amyloid Angiopathy |
Amyloid Beta (Aβ) plays an important role in the study of neurodegenerative diseases. This page provides comprehensive information about this topic, including its mechanisms, significance in disease processes, and therapeutic implications.
Amyloid-beta (Aβ) is a 39-43 amino acid peptide derived from the amyloid precursor protein (APP) through sequential proteolytic cleavage by β-secretase (BACE1) and γ-secretase. Aβ is the principal component of senile plaques in Alzheimer's disease brains and represents the central therapeutic target in the amyloid hypothesis of AD pathogenesis. This page provides comprehensive coverage of Aβ's biochemistry, aggregation mechanisms, toxicity pathways, and therapeutic strategies.
Key takeaway: Aβ40 and Aβ42 are the primary amyloid-beta isoforms; Aβ42 is more aggregation-prone and is the major component of plaques. The Aβ42/Aβ40 ratio is critical in determining disease severity.
Aβ is produced in multiple isoforms differing in length:
| Isoform | Amino Acids | Abundance | Aggregation Propensity |
|---|---|---|---|
| Aβ1-40 (Aβ40) | 40 residues | ~80-90% of total Aβ | Lower |
| Aβ1-42 (Aβ42) | 42 residues | ~5-10% of total Aβ | Higher |
| Aβ1-43 | 43 residues | Trace | Highest |
| Aβ1-38 | 38 residues | Trace (CAA) | Lowest |
Aβ40: DAEFRHDSEFEVHHQKLVFFAEDVGSNKGAIIGLMVGGVV
Aβ42: DAEFRHDSEFEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA
Key structural features:
Aβ aggregation follows a nucleation-dependent polymerization model:
Despite its pathological role, Aβ has normal physiological functions:
Aβ is central to the amyloid cascade hypothesis:
Key toxic mechanisms:
Monoclonal antibodies targeting Aβ:
Mechanisms:
The study of Amyloid Beta (Aβ) has evolved significantly over the past decades. Research in this area has revealed important insights into the underlying mechanisms of neurodegeneration and continues to drive therapeutic development.
Historical context and key discoveries in this field have shaped our current understanding and will continue to guide future research directions.
Amyloid Beta (Aβ) plays an important role in the study of neurodegenerative diseases. This page provides comprehensive information about this topic, including its mechanisms, significance in disease processes, and therapeutic implications.
✓ Established
Fluorescence excitation/emission spectra of amyloid-binding dyes, when analyzed with machine learning (PCA, UMAP, neural networks), can distinguish distinct conformational amyloid strains in vitro and in situ
Successfully identified 6 distinct conformational strains in vitro with 98% discrimination; validated on transgenic mouse models and human brain samples showing distinct clustering for different disease types
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
Different neurodegenerative diseases (sAD, fAD PSEN1, fAD APP, Down syndrome) have distinct conformational strains of Aβ plaques that can be differentiated spectroscopically
UMAP plots show 99% discrimination between sAD and fAD; fAD PSEN1 and fAD APP clusters overlap; DS forms separate cluster with patient-specific subclusters
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
Tau tangles from different neurodegenerative diseases (sAD, fAD, DS, Pick's disease) exist as distinct conformational strains
PCA and UMAP show 5 distinct clusters for tau tangles across diseases; fAD PSEN1 and fAD APP share cluster suggesting same tau strain induced by different Aβ backgrounds; PiD tau clearly separated from AD tau
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
In neurodegenerative disorders, the equilibrium between accumulation and clearance of toxic misfolded proteins is broken, leading to protein aggregation in plaques that propagate between brain regions along functional or structural networks, causing cellular dysfunction and neuronal loss
Based on literature citations [1-7] describing protein misfolding, aggregation, and propagation in ND. The paper states this as a common hypothesis in the field.
Source: Garbarino, Sara et al., Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain (2021) DOI:10.1016/j.neuroimage.2021.117980
◉ Supported
Protein propagation in neurodegeneration occurs along anatomical/structural brain networks rather than functional correlations
The authors argue that anatomical connectivity networks estimate physical connections between brain regions, providing a natural substrate for propagation models.
Source: Garbarino, Sara et al., Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain (2021) DOI:10.1016/j.neuroimage.2021.117980
✓ Established
Fluorescence excitation/emission spectra of amyloid-binding dyes, when analyzed with machine learning (PCA, UMAP, neural networks), can distinguish distinct conformational amyloid strains in vitro and in situ
Successfully identified 6 distinct conformational strains in vitro with 98% discrimination; validated on transgenic mouse models and human brain samples showing distinct clustering for different disease types
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
Different neurodegenerative diseases (sAD, fAD PSEN1, fAD APP, Down syndrome) have distinct conformational strains of Aβ plaques that can be differentiated spectroscopically
UMAP plots show 99% discrimination between sAD and fAD; fAD PSEN1 and fAD APP clusters overlap; DS forms separate cluster with patient-specific subclusters
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
Tau tangles from different neurodegenerative diseases (sAD, fAD, DS, Pick's disease) exist as distinct conformational strains
PCA and UMAP show 5 distinct clusters for tau tangles across diseases; fAD PSEN1 and fAD APP share cluster suggesting same tau strain induced by different Aβ backgrounds; PiD tau clearly separated from AD tau
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
✓ Established
Fluorescence excitation/emission spectra of amyloid-binding dyes, when analyzed with machine learning (PCA, UMAP, neural networks), can distinguish distinct conformational amyloid strains in vitro and in situ
Successfully identified 6 distinct conformational strains in vitro with 98% discrimination; validated on transgenic mouse models and human brain samples showing distinct clustering for different disease types
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
Different neurodegenerative diseases (sAD, fAD PSEN1, fAD APP, Down syndrome) have distinct conformational strains of Aβ plaques that can be differentiated spectroscopically
UMAP plots show 99% discrimination between sAD and fAD; fAD PSEN1 and fAD APP clusters overlap; DS forms separate cluster with patient-specific subclusters
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
◉ Supported
Tau tangles from different neurodegenerative diseases (sAD, fAD, DS, Pick's disease) exist as distinct conformational strains
PCA and UMAP show 5 distinct clusters for tau tangles across diseases; fAD PSEN1 and fAD APP share cluster suggesting same tau strain induced by different Aβ backgrounds; PiD tau clearly separated from AD tau
Source: Yang, Hyunjun et al., EMBER multidimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains (2023) DOI:10.1073/pnas.2300769120
✓ Established
Current tau PET tracers (18F-flortaucipir, 18F-MK6240, 18F-RO948, 18F-PI2620) bind tau aggregates formed in AD in the more advanced Braak stages (IV)
Strong evidence from end-of-life study showing 87.5% accuracy for detecting tau load in Braak stages V-VI, and strong correlations (R2 0.66-0.76) between tau PET levels and quantitative neuropathologic tau burden
Source: Groot, Colin et al., Tau PET Imaging in Neurodegenerative Disorders (2022) DOI:10.2967/jnumed.121.263196
◉ Supported
Tracer binding in most non-AD tauopathies is weaker and overlaps with off-target binding regions, limiting quantification and visualization of non-AD tau pathology in vivo
Lower affinity for 3R and 4R isoforms; autopsy studies show limited correlation between PET signal and 4R tau pathology; off-target binding in regions overlapping with signal
Source: Groot, Colin et al., Tau PET Imaging in Neurodegenerative Disorders (2022) DOI:10.2967/jnumed.121.263196
◉ Supported
Elevated tau PET signal predicts cognitive decline over time in cognitively unimpaired individuals (preclinical AD)
PREVENT-AD study showing T-positive participants demonstrated cognitive decline; both increased amyloid and tau PET associated with decline, but relationship predominantly driven by tau
Source: Groot, Colin et al., Tau PET Imaging in Neurodegenerative Disorders (2022) DOI:10.2967/jnumed.121.263196
Page auto-generated from NeuroWiki protein database. Last updated: 2026-02-26.