Polymorphism in disease-related apolipoprotein C-II amyloid fibrils: a structural model for rod-like fibrils

DMSO-Quenched H/D-Exchange 2D NMR Spectroscopy and Its Applications in Protein Science

Hydrogen/deuterium (H/D) exchange combined with two-dimensional (2D) NMR spectroscopy has been widely used for studying the structure, stability, and dynamics of proteins. When we apply the H/D-exchange method to investigate non-native states of proteins such as equilibrium and kinetic folding intermediates, H/D-exchange quenching techniques are indispensable, because the exchange reaction is usually too fast to follow by 2D NMR. In this article, we will describe the dimethylsulfoxide (DMSO)-quenched H/D-exchange method and its applications in protein science. In this method, the H/D-exchange buffer is replaced by an aprotic DMSO solution, which quenches the exchange reaction. We have improved the DMSO-quenched method by using spin desalting columns, which are used for medium exchange from the H/D-exchange buffer to the DMSO solution. This improvement has allowed us to monitor the H/D exchange of proteins at a high concentration of salts or denaturants. We describe methodological details of the improved DMSO-quenched method and present a case study using the improved method on the H/D-exchange behavior of unfolded human ubiquitin in 6 M guanidinium chloride.

Association of the lipoprotein lipase and Apolipoprotein C-II gene polymorphisms with risk of dyslipidemia in smokers and non-smokers male

Objective

Dyslipidaemia is considered a metabolic abnormality andan important risk factor that leads to atherogenic cardiovascular diseases. Cigarette smoking is associated with dyslipidaemia. This study aimed to demonstrate whether lipoprotein lipase enzyme (LPL) and Apolipoprotein CII (APOCII) gene polymorphisms can be considered as independent genetic risk factors for dyslipidaemia among smokers with various smoking durations.

Methods

A total of 185 males (90 smokers and 95 non-smokers)were included in this study, Lipid profiles were measured and DNA was isolated. The LPL-Hind III and APO CII-Ava II polymorphisms were determined using the polymerase reaction-restriction fragment length polymorphisms (RFLP) technique.

Results

For the LPL-Hind IIIpolymorphism H+H+ genotype group, the triglycerides TG and very-low-density lipoprotein cholesterol VLDL-C concentrations were significantly higher and the high-density lipoprotein cholesterol HDL-C concentration was significantly lower than those of the H–H- genotype. ForAPO CII-Ava II polymorphisms, compared with those of the A2A2 genotype group, the total cholesterol TC, TG, low-density lipoprotein cholesterol LDL-C and VLDL-C concentrations were significantly increased in the A1A2 genotype group, while the HDL-C concentration was significantly decreased.

Conclusions

The study revealed that the H+H+ or H + H-genotype of the LPL-Hind III polymorphism and the A1A1or A1A2 genotype of the APOCII-Ava II polymorphism were at higher risk of developing dyslipidaemia compared to the H–H- genotype of the LPL-Hind III polymorphism and A2A2 genotype of the APOCII-Ava II polymorphism.

Atomic-level differences between brain parenchymal- and cerebrovascular-seeded Aβ fibrils

Alzheimer's disease is characterized by neuritic plaques, the main protein components of which are β-amyloid (Aβ) peptides deposited as β-sheet-rich amyloid fibrils. Cerebral Amyloid Angiopathy (CAA) consists of cerebrovascular deposits of Aβ peptides; it usually accompanies Alzheimer's disease, though it sometimes occurs in the absence of neuritic plaques, as AD also occurs without accompanying CAA. Although neuritic plaques and vascular deposits have similar protein compositions, one of the characteristic features of amyloids is polymorphism, i.e., the ability of a single pure peptide to adopt multiple conformations in fibrils, depending on fibrillization conditions. For this reason, we asked whether the Aβ fibrils in neuritic plaques differed structurally from those in cerebral blood vessels. To address this question, we used seeding techniques, starting with amyloid-enriched material from either brain parenchyma or cerebral blood vessels (using meninges as the source). These amyloid-enriched preparations were then added to fresh, disaggregated solutions of Aβ to make replicate fibrils, as described elsewhere. Such fibrils were then studied by solid-state NMR, fiber X-ray diffraction, and other biophysical techniques. We observed chemical shift differences between parenchymal vs. vascular-seeded replicate fibrils in select sites (in particular, Ala2, Phe4, Val12, and Gln15 side chains) in two-dimensional 13C-13C correlation solid-state NMR spectra, strongly indicating structural differences at these sites. X-ray diffraction studies also indicated that vascular-seeded fibrils displayed greater order than parenchyma-seeded fibrils in the "side-chain dimension" (~ 10 Å reflection), though the "hydrogen-bond dimensions" (~ 5 Å reflection) were alike. These results indicate that the different nucleation conditions at two sites in the brain, parenchyma and blood vessels, affect the fibril products that get formed at each site, possibly leading to distinct pathophysiological outcomes.

The Enigma of Amyloid Forming Proteins: Insights From Molecular Simulations

Molecular level insight into the interplay between protein sequence, structure, and conformational dynamics is crucial for the comprehensive understanding of protein folding, misfolding, and aggregation phenomena that are pertinent to the formation of amyloid fibrils implicated in several degenerative diseases. Computational modelling provides insight into protein behaviour at spatial and temporal resolution still largely outside the reach of experiments. Herein we present an account of our theoretical modelling research conducted in collaboration with several experimental groups where we explored the effects of local environment on the structure and aggregation propensity of several types of amyloidogenic peptides and proteins, including apolipoprotein C-II, insulin, amylin, and amyloid-β using a variety of computational approaches.