Ganglioside-Enriched Phospholipid Vesicles Induce Cooperative Aβ Oligomerization and Membrane Disruption

A major hallmark of Alzheimer’s disease (AD) is the accumulation of extracellular aggregates of amyloid-β (Aβ). Structural polymorphism observed among Aβ fibrils in AD brains seem to correlate with the clinical subtypes suggesting a link between fibril polymorphism and pathology. Since fibrils emerge from a templated growth of low-molecular-weight oligomers, understanding the factors affecting oligomer generation is important. Membrane lipids are key factors to influence early stages of Aβ aggregation and oligomer generation, which cause membrane disruption. We have previously demonstrated that conformationally discrete Aβ oligomers can be generated by modulating the charge, composition, and chain length of lipids and surfactants. Here, we extend our studies into liposomal models by investigating Aβ oligomerization on large unilamellar vesicles (LUVs) of total brain extracts (TBE), reconstituted lipid rafts (LRs), or 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). Varying the vesicle composition by specifically increasing the amount of GM1 gangliosides as a constituent, we found that only GM1-enriched liposomes induce the formation of toxic, low-molecular-weight oligomers. Furthermore, we found that the aggregation on liposome surface and membrane disruption are highly cooperative and sensitive to membrane surface characteristics. Numerical simulations confirm such a cooperativity and reveal that GM1-enriched liposomes form twice as many pores as those formed in the absence GM1. Overall, this study uncovers mechanisms of cooperativity between oligomerization and membrane disruption under controlled lipid compositional bias, and refocuses the significance of the early stages of Aβ aggregation in polymorphism, propagation, and toxicity in AD

A simple model of mergers and innovation

We analyze the impact of a merger on firms’ incentives to innovate. We show that the merging parties always decrease their innovation efforts post-merger while the outsiders to the merger respond by increasing their effort. A merger tends to reduce overall innovation. Consumers are always worse off after a merger. Our model calls into question the applicability of the “inverted-U” relationship between innovation and competition to a merger setting

Additional file 4: of In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

List of 181 essential proteins. The amino acid sequence of hubs proteins was compared against bacterial proteins sequence from Database of Essential Genes (DEG). (TXT 62 kb

Additional file 5: Figure S2. of In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Homology distribution of Cp essential proteins aligned against hosts. Dark green: proteins homologous to host; Yellow: Proteins with low identity against hosts (identity < 30 %). Dark red: non-host homologous proteins, proteins with low identity and low coverage alignment against hosts (identity x coverage < = 10 %). Dark blue: non-host homologous proteins, proteins with no alignment hits against O. aires and C. hircus. Light blue: non-host homologous proteins, proteins with no alignment hits against the five hosts. The alignment summary is depicted in Additional file 6. (JPG 318 kb

Prières à l'usage de la communauté des Filles de la Charité

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Compounds/Libraries name, MolDock scores and predicted hydrogen bonds for the selected molecules against NP_939445.1 (DIP1084, Putative iron transport membrane protein, FecCD-family).

<p>Compounds/Libraries name, MolDock scores and predicted hydrogen bonds for the selected molecules against NP_939445.1 (DIP1084, Putative iron transport membrane protein, FecCD-family).</p

Compounds/Libraries name, MolDock scores and predicted hydrogen bonds for the selected molecules against NP_938502.1 (bioB, Biotin synthase).

<p>Compounds/Libraries name, MolDock scores and predicted hydrogen bonds for the selected molecules against NP_938502.1 (bioB, Biotin synthase).</p

An integrative <i>in-silico approach</i> for therapeutic target identification in the human pathogen <i>Corynebacterium diphtheriae</i> - Fig 11

<p><b>A-I</b> 3D cartoon representation of the docking analyses for the most druggable protein cavity of <b>NP_939445.1</b> (<b>DIP1084,</b> Putative iron transport membrane protein, FecCD-family) with Jacarandic Acid (CID 73645). <b>A-II:</b> 3D surface representation of the docking analyses for the structure of Jacarandic Acid with <b>DIP1084,</b> Putative iron transport membrane protein. Figs <b>B-I, II, C-I, II</b> & <b>D-1, II D</b> represent same information for compounds 16-hydrazonisosteviol <b>ZINC13142972</b> and <b>ZINC70454922</b> respectively, for the same protein cavity.</p

Superposition of co-crystallized and Docked ligand; Dark Khaki represents the co crystallized ligand and Dark Cyan the re-docked conformation of the ligand.

<p>Superposition of co-crystallized and Docked ligand; Dark Khaki represents the co crystallized ligand and Dark Cyan the re-docked conformation of the ligand.</p

Strains of <i>C</i>. <i>diphtheriae</i> employed in the pan-modelome study with information on genomes statistics, disease prevalence and location of isolation.

<p>Strains of <i>C</i>. <i>diphtheriae</i> employed in the pan-modelome study with information on genomes statistics, disease prevalence and location of isolation.</p