Amyloid-β protein oligomerization and the importance of tetramers and dodecamers in the aetiology of Alzheimer's disease.
ABSTRACT In recent years, small protein oligomers have been implicated in the aetiology of a number of important amyloid diseases, such as type 2 diabetes, Parkinson's disease and Alzheimer's disease. As a consequence, research efforts are being directed away from traditional targets, such as amyloid plaques, and towards characterization of early oligomer states. Here we present a new analysis method, ion mobility coupled with mass spectrometry, for this challenging problem, which allows determination of in vitro oligomer distributions and the qualitative structure of each of the aggregates. We applied these methods to a number of the amyloid-β protein isoforms of Aβ40 and Aβ42 and showed that their oligomer-size distributions are very different. Our results are consistent with previous observations that Aβ40 and Aβ42 self-assemble via different pathways and provide a candidate in the Aβ42 dodecamer for the primary toxic species in Alzheimer's disease.
SourceAvailable from: Charlotte A Scarff[Show abstract] [Hide abstract]
ABSTRACT: Expansion of polyglutamine stretches leads to the formation of polyglutamine-containing neuronal aggregates and neuronal death in nine diseases for which there are no treatments or cures currently. This is largely due to a lack in understanding of the mechanisms by which expanded polyglutamine regions contribute to aggregation and disease. To complicate matters further, several of the polyglutamine-disease related proteins, including ataxin-3, have a multi-stage aggregation mechanism in which flanking domain self-assembly precedes polyglutamine aggregation yet is influenced by polyglutamine expansion. How polyglutamine expansion influences flanking domain aggregation is poorly understood. Here we use a combination of mass spectrometry and biophysical approaches to investigate this issue for ataxin-3. We show that the conformational dynamics of the flanking Josephin domain in ataxin-3 with an expanded polyglutamine tract are altered in comparison to those exhibited by its non-expanded counterpart, specifically within the aggregation prone region of the Josephin domain (amino acid residues 73-96). Expansion thus exposes this region more frequently in ataxin-3 containing an expanded polyglutamine tract, providing a molecular explanation of why aggregation is accelerated upon polyglutamine expansion. Here, harnessing the power of ion mobility spectrometry-mass spectrometry, oligomeric species formed during aggregation are characterised and a model for oligomer growth proposed. The results suggest that a conformational change occurs at the dimer level that initiates self-assembly. New insights into ataxin-3 fibril architecture are also described, revealing the region of the Josephin domain involved in protofibril formation and demonstrating that polyglutamine aggregation proceeds as a distinct second step after protofibril formation without requiring structural rearrangement of the protofibril core. Overall, the results enable the effect of polyglutamine expansion on every stage of ataxin-3 self-assembly, from monomer through to fibril, to be described and a rationale for expedited aggregation upon polyglutamine expansion to be provided. Copyright © 2015, The American Society for Biochemistry and Molecular Biology.Molecular & Cellular Proteomics 02/2015; DOI:10.1074/mcp.M114.044610 · 7.25 Impact Factor
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ABSTRACT: β-amyloid aggregation and formation of senile plaques is one of the hallmarks of Alzheimer's disease (AD). It leads to degeneration of neurons and decline of cognitive functions. The most aggregative and toxic form of β-amyloid is Aβ1-42 but in experiments, the shorter forms able to form aggregates are also used. The early stages of amyloid formation are of special interest due to the influence of this peptide on progression of AD. Here, we employed nine helices of undecapeptide Aβ13-23 and studied progress of amyloid formation using 500ns molecular dynamics simulation and implicit membrane environment. The small β-sheets emerged very early during simulation as separated two-strand structures and a presence of the membrane facilitated this process. Later, the larger β-sheets were formed. However, the ninth helix which did not form paired structure stayed unchanged till the end of MD simulation. Paired helix-helix interactions seemed to be a driving force of β-sheet formation at early stages of amyloid formation. Contrary, the specific interactions between α-helix and β-sheet can be very stable and be stabilized by the membrane. Copyright © 2015 Elsevier Ltd. All rights reserved.Computational Biology and Chemistry 02/2015; 56C. DOI:10.1016/j.compbiolchem.2015.02.014 · 1.60 Impact Factor
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ABSTRACT: Ion mobility spectrometry experiments allow the mass spectrometrist to determine an ion's rotationally averaged collision cross section ΩEXP. Molecular modelling is used to visualize what ion three-dimensional structure(s) is(are) compatible with the experiment. The collision cross sections of candidate molecular models have to be calculated, and the resulting ΩCALC are compared with the experimental data. Researchers who want to apply this strategy to a new type of molecule face many questions: (1) What experimental error is associated with ΩEXP determination, and how to estimate it (in particular when using a calibration for traveling wave ion guides)? (2) How to generate plausible 3D models in the gas phase? (3) Different collision cross section calculation models exist, which have been developed for other analytes than mine. Which one(s) can I apply to my systems? To apply ion mobility spectrometry to nucleic acid structural characterization, we explored each of these questions using a rigid structure which we know is preserved in the gas phase: the tetramolecular G-quadruplex [dTGGGGT]4, and we will present these detailed investigation in this tutorial. © 2015 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd.Journal of Mass Spectrometry 05/2015; 50(5). DOI:10.1002/jms.3590 · 2.71 Impact Factor