Alzheimer’s Disease: Exploring the origins of nucleation
An approach called deep mutational scanning is improving our understanding of amyloid beta aggregation.
- VIB-KU Leuven Center for Brain & Disease Research, Belgium
- Department of Neurosciences, Leuven Research Institute for Neuroscience and Disease (LIND), Belgium
Alzheimer’s disease is a progressive neurodegenerative disease that initially compromises memory and ultimately stops people from being able to perform everyday tasks. The exact causes of the disease are still unknown, but genetic, lifestyle and environmental factors could all play a role, with age being the strongest risk factor for developing the condition. The lack of treatment to halt or slow down the progression Alzheimer’s disease makes it one of the greatest challenges of our times.
Alzheimer’s disease begins decades before the appearance of clinical symptoms. It is thought that changes in the metabolism of a peptide called amyloid beta (Aβ) lead to its misfolding. Although the details are not fully understood, scientists propose that the accumulation of these misfolded molecules in the brain triggers a series of molecular events which, in turn, lead to neuroinflammation, the aggregation of tau proteins in neurons, and eventually, neuronal death (Selkoe and Hardy, 2016).
Preventing the build-up of Aβ has been explored as a potential therapeutic approach. However, designing strategies that specifically target the formation of neurotoxic Aβ, rather than the aggregation of Aβ in general, requires a detailed understanding (in terms of both mechanisms and kinetics) of how the protein misfolds and how this is connected to the disease (Yang et al., 2017; Brinkmalm et al., 2019). Now, in eLife, Benedetta Bolognesi (IBEC in Barcelona), Ben Lehner (the Center for Genomic Regulation, also in Barcelona) and colleagues – including Mireia Seuma as first author, Andre Faure and Marta Badia – report new insights into how mutations affect the initial nucleation of Aβ aggregates (Seuma et al., 2021).
The researchers used an approach called deep mutational scanning (Gray et al., 2019) to generate a library of 468 single and 14,015 double mutant Aβ variants to see how the mutations affect the ability of Aβ to form new aggregates. They focused on the first step of this process, known as nucleation, and then determined the relationship between amino acid sequence and the nucleation rate of Aβ42 (a form of Aβ that contains 42 amino acids) (Figure 1).
Figure 1
Amyloid beta peptide and Alzheimer’s disease.
It is thought that Alzheimer’s disease is caused by amyloid beta (Aβ) (yellow circles; top) first forming dimers and then oligomers in a process called nucleation, with the oligomers then going on to build protofibrils and fibrils. Aggregated Aβ then deposits in amyloid plaques, which are a pathological hallmark of Alzheimer’s disease. Familial Alzheimer’s disease is hereditary and is marked by an unusually early onset of symptoms. Several genes have been linked to this form of the disease, including the gene for the amyloid precursor protein (APP). Certain amino acid residues in Aβ are called 'gatekeepers’ of nucleation (magenta stars; bottom) because they prevent nucleation. The mutations indicated (blue arrows), affecting amino acid residues highlighted in blue, cause familial Alzheimer’s disease or protect (A2T) against it. Pathogenic mutations in APP are shown in text (the numbering corresponds to the positions with Aβ). The sequence of amino acids shown here is from the N-terminal of Aβ42. Seuma et al. found that dominant pathogenic mutations within the Aβ42 peptide (indicated in bold text; bottom) all display increased rate of nucleation.
Seuma et al. found that mutations that reduce nucleation are clustered in the hydrophobic part of Aβ42 (the C-terminal), while those that increase this process are located in the N-terminal part, which is hydrophilic. This suggests that Aβ is organized in a modular manner, with the N- and C-terminal parts having different roles in nucleation and aggregation. Intriguingly, such a modularity is also reflected in the distribution of pathogenic mutations in the Aβ precursor called APP (Figure 1). These mutations have been linked to familial Alzheimer’s disease, a rare genetic form presenting a much earlier onset (people usually develop symptoms in their thirties, forties and fifties).
Seuma et al. further identified five negatively charged residues in Aβ42, which acted as 'gatekeepers’, preventing the peptides from sticking to each other (Rousseau et al., 2006). Mutations at these sites frequently resulted in increased nucleation. This raises several questions: could the gatekeepers of nucleation prevent neurodegeneration; and do mutations that promote nucleation prime the system for the onset of Alzheimer’s disease?
To investigate this further, Seuma et al. studied the nucleation rates for 12 mutations in Aβ42, which have been linked to familial Alzheimer’s disease. The mutations all resulted in increased nucleation rates, although the rates did not correlate with the severity of the disease (as reflected by the age at disease onset). These results suggest that Aβ nucleation plays a key role in the formation of neurotoxic aggregates, but other factors are also involved.
It is worth noting that the reservoir of Aβ in the brain contains a heterogeneous mixture of peptides of varying lengths, generated by the sequential cleavage of APP. Pathogenic mutations in both APP and the enzymes responsible for its cleavage favor the production of longer Aβ peptides (Szaruga et al., 2017). Importantly a large number of mutations linked to familial Alzheimer’s disease do not change the amino acid sequence of Aβ42, but affect the composition of Aβ profiles by shifting them towards longer peptides (Szaruga et al., 2015). Whether these pathogenic changes in peptide length (and hydrophobicity) lead to an increase in nucleation, and how this is related to the clinical phenotypes of Alzheimer’s disease, warrant further investigation.
Disentangling the complex pathogenesis of Alzheimer’s disease will likely require the interrogation of various pathogenic variants under controlled conditions and the plotting of biochemical and cellular data against clinical severity of the condition. The study by Seuma et al. certainly shows that the nucleation assay is a valuable tool for such investigations.
References
-
Elucidating the molecular determinants of aβ aggregation with deep mutational scanningG3: Genes, Genomes, Genetics 9:3683–3689.https://doi.org/10.1534/g3.119.400535
-
How evolutionary pressure against protein aggregation shaped chaperone specificityJournal of Molecular Biology 355:1037–1047.https://doi.org/10.1016/j.jmb.2005.11.035
-
The amyloid hypothesis of Alzheimer's disease at 25 yearsEMBO Molecular Medicine 8:595–608.https://doi.org/10.15252/emmm.201606210
-
Qualitative changes in human γ-secretase underlie familial Alzheimer’s diseaseJournal of Experimental Medicine 212:2003–2013.https://doi.org/10.1084/jem.20150892
Article and author information
Author details
Katarzyna Marta Zoltowska
Lucía Chávez-Gutiérrez
Publication history
- Version of Record published: March 10, 2021 (version 1)
Copyright
© 2021, Zoltowska and Chávez-Gutiérrez
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,206
- Page views
-
- 119
- Downloads
-
- 0
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Alzheimer’s Disease: Exploring the origins of nucleation
eLife 10:e67269.
https://doi.org/10.7554/eLife.67269
Further reading
-
- Biochemistry and Chemical Biology
- Computational and Systems Biology
Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicates the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.
-
- Computational and Systems Biology
- Physics of Living Systems
Diabetes is caused by the inability of electrically coupled, functionally heterogeneous -cells within the pancreatic islet to provide adequate insulin secretion. Functional networks have been used to represent synchronized oscillatory [Ca2+] dynamics and to study -cell subpopulations, which play an important role in driving islet function. The mechanism by which highly synchronized -cell subpopulations drive islet function is unclear. We used experimental and computational techniques to investigate the relationship between functional networks, structural (gap-junction) networks, and intrinsic -cell dynamics in slow and fast oscillating islets. Highly synchronized subpopulations in the functional network were differentiated by intrinsic dynamics, including metabolic activity and KATP channel conductance, more than structural coupling. Consistent with this, intrinsic dynamics were more predictive of high synchronization in the islet functional network as compared to high levels of structural coupling. Finally, dysfunction of gap junctions, which can occur in diabetes, caused decreases in the efficiency and clustering of the functional network. These results indicate that intrinsic dynamics rather than structure drive connections in the functional network and highly synchronized subpopulations, but gap junctions are still essential for overall network efficiency. These findings deepen our interpretation of functional networks and the formation of functional sub-populations in dynamic tissues such as the islet.
