Quantitative thermophoretic study of disease-related protein aggregates

Amyloid fibrils are a hallmark of a range of neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. A detailed understanding of the physico-chemical properties of the different aggregated forms of proteins, and of their interactions with other compounds of diagnostic or therapeutic interest, is crucial for devising effective strategies against such diseases. Protein aggregates are situated at the boundary between soluble and insoluble structures, and are challenging to study because classical biophysical techniques, such as scattering, spectroscopic and calorimetric methods, are not well adapted for their study. Here we present a detailed characterization of the thermophoretic behavior of different forms of the protein a-synuclein, whose aggregation is associated with Parkinson's disease. Thermophoresis is the directed net diffusional flux of molecules and colloidal particles in a temperature gradient. Because of their low volume requirements and rapidity, analytical methods based on this effect have considerable potential for high throughput screening for drug discovery. In this paper we rationalize and describe in quantitative terms the thermophoretic behavior of monomeric, oligomeric and fibrillar forms of a-synuclein. Furthermore, we demonstrate that microscale thermophoresis (MST) is a valuable method for screening for ligands and binding partners of even such highly challenging samples as supramolecular protein aggregates

Simultaneous measurement of a range of particle sizes during Ab1–42 fibrillogenesis quantified using fluorescence correlation spectroscopy

Low molecular weight oligomers of amyloid beta (Aβ) are important drivers of Alzheimer's disease. A decrease in Aβ monomer levels in human cerebrospinal fluid (CSF) is observed in Alzheimers' patients and is a robust biomarker of the disease. It has been suggested that the decrease in monomer levels in CSF is due to the formation of Aβ oligomers. A robust technique capable of identifying Aβ oligomers in CSF is therefore desirable. We have used fluorescence correlation spectroscopy and a five Gaussian distribution model (5GDM) to monitor the aggregation of Aβ1-42 in sodium phosphate buffer and in artificial cerebrospinal fluid (ACSF). In buffer, several different sized components (monomer, oligomers, protofibrils and fibrils) can be identified simultaneously using 5GDM. In ACSF, the faster kinetics of fibrillogenesis leads to the formation of fibrils on very short timescales. This analysis method can also be used to monitor the aggregation of other proteins, nanoparticles or colloids, even in complex biological fluids

Understanding the Kinetics of Protein–Nanoparticle Corona Formation

When a pristine nanoparticle (NP) encounters a biological fluid, biomolecules spontaneously form adsorption layers around the NP, called “protein corona”. The corona composition depends on the time-dependent environmental conditions and determines the NP’s fate within living organisms. Understanding how the corona evolves is fundamental in nanotoxicology as well as medical applications. However, the process of corona formation is challenging due to the large number of molecules involved and to the large span of relevant time scales ranging from 100 μs, hard to probe in experiments, to hours, out of reach of all-atoms simulations. Here we combine experiments, simulations, and theory to study (i) the corona kinetics (over 10^–3–10³ s) and (ii) its final composition for silica NPs in a model plasma made of three blood proteins (human serum albumin, transferrin, and fibrinogen). When computer simulations are calibrated by experimental protein–NP binding affinities measured in single-protein solutions, the theoretical model correctly reproduces competitive protein replacement as proven by independent experiments. When we change the order of administration of the three proteins, we observe a <i>memory</i> effect in the final corona composition that we can explain within our model. Our combined experimental and computational approach is a step toward the development of systematic prediction and control of protein–NP corona composition based on a hierarchy of equilibrium protein binding constants