Lipid oxidation controls peptide self-assembly near membranes through a surface attraction mechanism

Abstract

The self-assembly of peptides into supramolecular structures has been linked to neurodegenerative diseases but has also been observed in functional roles. Peptides are physiologically exposed to crowded environments of biomacromolecules, and particularly cellular membrane lipids. Previous research has shown that membranes can both accelerate and inhibit peptide self-assembly. Here, we studied the impact of membrane models that mimic cellular oxidative stress and compared this to mammalian and bacterial membranes. Using molecular dynamics simulations and experiments, we propose a model that explains how changes in peptide-membrane binding, electrostatics, and peptide secondary structure stabilization determine the nature of peptide self-assembly. We explored the influence of zwitterionic (POPC), anionic (POPG) and oxidized (PazePC) phospholipids, as well as cholesterol, and mixtures thereof, on the self-assembly kinetics of the amyloid β (1-40) peptide (Aβ₄₀), linked to Alzheimer's disease, and the amyloid-forming antimicrobial peptide uperin 3.5 (U3.5). We show that the presence of an oxidized lipid had similar effects on peptide self-assembly as the bacterial mimetic membrane. While Aβ₄₀ fibril formation was accelerated, U3.5 aggregation was inhibited by the same lipids at the same peptide-to-lipid ratio. We attribute these findings and peptide-specific effects to differences in peptide-membrane adsorption with U3.5 being more strongly bound to the membrane surface and stabilized in an α-helical conformation compared to Aβ₄₀. Different peptide-to-lipid ratios resulted in different effects. We found that electrostatic interactions are a primary driving force for peptide-membrane interaction, enabling us to propose a model for predicting how cellular changes might impact peptide self-assembly in vivo.

Fig. 1. Overview of peptide self-assembly into fibrils and studied peptides and lipids. (a) Typical nucleation–elongation kinetics of fibril formation with the slow formation of critical nuclei and subsequent rapid fibril growth. The presence of lipids accelerates or inhibits peptide fibril formation, resulting in shorter or longer times for the nucleation phase, respectively. (b) Peptide sequences of Aβ₄₀ and U3.5 in one-letter code (acidic groups: red and bold italics, basic groups: blue and bold) and α-helical and random coil secondary structures. (c) Phospholipids with hydrophilic head groups and hydrophobic tails typically spontaneously self-assemble into micelles, liposomes and lipid bilayers. (d) The chemical structure of the studied phospholipids POPC, POPG and PazePC as well as of cholesterol is shown. The carboxyl group of PazePC may be (partially) deprotonated under experimental conditions.

Fig. 2. ThT fluorescence assays were performed to follow the kinetics of fibril formation. (a and b) The peptides Aβ₄₀ (100 μM) and U3.5 (50 μM) were studied in PBS buffer at pH 7.4 at 37 °C. (c–h) Peptides were studied without and with different amounts of lipids present (peptide-to-lipid molar ratio: 1 : 1, 1 : 9). The largest impact of the lipids on peptide aggregation was observed when lipid was added in excess (1 : 9). When peptide and lipid had the same concentration in the sample (1 : 1), smaller effects were observed. Data for pure POPC and lipid mixtures consisting of POPC-POPG (4 : 1) and POPC-PazePC (7 : 3) are shown. Additional data can be found in ESI Fig. S3. The data for U3.5 (b) without lipid present was previously reported and is included as a reference to all other lipids and Aβ₄₀. The lines refer to the mean and the shadow areas to the SEM (standard error of the mean) of the replicates. Data were normalized to a maximum fluorescence of 1 (except in cases with inhibition of peptide aggregation).

Fig. 3. CD spectra of (a) Aβ₄₀ and (b) U3.5 without and with excess of lipid (peptide-to-lipid molar ratio 1 : 9) in PBS buffer at pH 7.4 at 37 °C. Aβ₄₀ aggregation was studied at 100 μM with 900 μM of lipid present (for CD, it was diluted to 20 μM peptide and 180 μM lipid) and U3.5 peptide was studied at 50 μM with 450 μM lipid present. Samples were measured after 2 days or 15 hours, respectively. Note that the data for U3.5 (b) without lipid present and with cholesterol were previously reported and are included here as reference to all other lipids and Aβ₄₀. Note that the symbols are used to distinguish the data sets and data were recorded every 0.5 nm.

Fig. 4. QCM changes in frequency of (a and c) Aβ₄₀ and (b, d) U3.5 peptide (25 μM) interacting with (a and b) POPC and (c and d) POPC-POPG (4 : 1) lipid bilayers in PBS buffer at pH 7.4 at 22 °C. The larger the negative change in frequency, the stronger the peptide mass binding to the membrane. A lipid bilayer is first deposited on the sensor surface before the peptide is introduced (0–15 min), kept incubating (15–60 min), and finally rinsed with buffer (60–70 min). The solid lines refer to the mean and the shadow areas to the SEM (standard error of the mean) of the replicates.

Fig. 5. (I) Representative structures of the peptide-membrane simulations with five peptide monomers at 303.15 K and with 0.15 mM NaCl in water. The central structure of the largest structural cluster during the last 10 ns simulation time of all replicates for each peptide is shown when studied near POPC, POPC-POPG (4 : 1), and POPC-PazePC (protonated) (7 : 3) membranes. (II) The average secondary structure content of the peptides during the last 10 ns simulation time of all replicates is shown. MD simulation snapshots were visualized in VMD 1.93. Note that we studied membranes containing both protonated as well as deprotonated PazePC due to the potential presence of both under experimental conditions (pH 7.4).

Fig. 6. (I) Average distances between the phosphate head groups of POPC in the outer membrane leaflet and the peptide C_α atoms of (a) Aβ₄₀ and (b) U3.5 with random starting structures (perpendicular to the membrane along z-axis) during the last 10 ns simulation time of all replicates. (II) Average minimum distances between the peptides (c) Aβ₄₀ and (d) U3.5 with random starting structures and the lipid bilayer components POPC, cholesterol, POPG, and PazePC during the last 10 ns simulation time of all replicates. The vertical lines at residues 5 (arginine), 16 (lysine) and 28 (lysine) for Aβ₄₀ and at residues 7 (arginine), 8 (arginine) and 14 (lysine) for U3.5 indicate the positively charged residues in both peptides to guide identifying the closest peptide-membrane interactions. Note that the symbols are used to distinguish the data sets and each residue has a data point.

Fig. 7. Model illustrating the differential impact of (oxidized) membranes on peptide self-assembly into amyloid fibrils. While both Aβ₄₀ and U3.5 form β-sheet rich structures in solution without membranes, oxidized membranes accelerated Aβ₄₀ aggregation, while U3.5 aggregation was inhibited when lipids were present in excess. This was driven by stronger peptide-membrane attraction and α-helical stabilization, whereas peptide–peptide interactions drive self-assembly into fibrils. A balance between peptide aggregation propensity and surface attraction determines the fate of peptide self-assembly; thus small changes in lipid composition can alter membrane impact.