| Literature DB >> 31702142 |
Kristine Steen Jensen1, Sara Linse2, Mathias Nilsson3, Mikael Akke1, Anders Malmendal2,4.
Abstract
Amyloid fibril formation is a hallmark of neurodegenerative disease caused by protein aggregation. Oligomeric protein states that arise during the process of fibril formation often coexist with mature fibrils and are known to cause cell death in disease model systems. Progress in this field depends critically on development of analytical methods that can provide information about the mechanisms and species involved in oligomerization and fibril formation. Here, we demonstrate how the powerful combination of diffusion NMR and multilinear data analysis can efficiently disentangle the number of involved species, their kinetic rates of formation or disappearance, spectral contributions, and diffusion coefficients, even without prior knowledge of the time evolution of the process or chemical shift assignments of the various species. Using this method we identify oligomeric species that form transiently during aggregation of human superoxide dismutase 1 (SOD1), which is known to form misfolded aggregates in patients with amyotrophic lateral sclerosis. Specifically, over a time course of 42 days, during which SOD1 fibrils form, we detect the disappearance of the native monomeric species, formation of a partially unfolded intermediate in the dimer to tetramer size range, subsequent formation of a distinct similarly sized species that dominates the final spectrum detected by solution NMR, and concomitant appearance of small peptide fragments.Entities:
Year: 2019 PMID: 31702142 PMCID: PMC7188332 DOI: 10.1021/jacs.9b07952
Source DB: PubMed Journal: J Am Chem Soc ISSN: 0002-7863 Impact factor: 15.419
Figure 1PARAFAC model of pwtSOD1ΔC15N-HSQC-DOSY experiments. (A–D) Factor contributions to the NMR spectrum as a function of reaction time. (E–H) Intensity profiles versus gradient strength for each factor. (I–L) Factor contributions to the signal intensity of each peak number (1–223); the contributions were scaled individually for each factor. The contributions to each factor cannot be directly interpreted as the relative population of each species, because differences in relaxation between residues and states affect the signal intensities, but they do show how each factor varies with time and signal number. Factor 1 (A, E, I) describes a disappearing monomeric state (I), factor 2 (B, F, J) an intermediate state (II), and factor 3 (C, G, K) a state (III) that builds up during the process. Factor 4 (D, H, L) describes a state (IV) that involves only three peaks. The dashed line in panel A–D indicates the time point at which the population of state I is reduced to 50% of its starting value. The signals in (I–L) are ordered such that the contribution from factor 1 deceases with increasing signal number, and the contribution from factor 3 increases. The colored areas indicate signals dominated by factor 1 in gray, factor 2 in pink, factors 2 + 3 in green, factor 3 in blue, and factor 4 in red. The individual curves in panels A–H represent 10 models created using 50% randomly chosen signals.
Figure 2Intensity variation with time (A), average diffusion rates (B), and peak positions (C–F) of signals dominated by factor 1 (gray; n = 60), factor 2 (magenta; n = 11), factor 2 and 3 (green; n = 40), factor 3 (blue; n = 10), and factor 4 (red; n = 3). The signals are marked in panels I–L of Figure . Intensities in (A) are scaled to unit variance for each residue in the same way as for the PARAFAC analysis. In panel (B), the left-hand vertical axis reports the diffusion rate for factors 1–3, while the right-hand axis reports on factor 4. Error bars correspond to one standard deviation.