Endothelial expression of human amyloid precursor protein leads to amyloid β in the blood and induces cerebral amyloid angiopathy in knock-in mice

The deposition of amyloid β (Aβ) in blood vessels of the brain, known as cerebral amyloid angiopathy (CAA), is observed in most patients with Alzheimer’s disease (AD). Compared with the pathology of CAA in humans, the pathology in most mouse models of AD is not as evident, making it difficult to examine the contribution of CAA to the pathogenesis of AD. On the basis of biochemical analyses that showed blood levels of soluble amyloid precursor protein (APP) in rats and mice were markedly lower than those measured in human samples, we hypothesized that endothelial APP expression would be markedly lower in rodents and subsequently generated mice that specifically express human WT APP (APP770) in endothelial cells (ECs). The resulting EC-APP770⁺ mice exhibited increased levels of serum Aβ and soluble APP, indicating that endothelial APP makes a critical contribution to blood Aβ levels. Even though aged EC-APP770⁺ mice did not exhibit Aβ deposition in the cortical blood vessels, crossing these animals with APP knock-in mice (App^NL-F/NL-F) led to an expanded CAA pathology, as evidenced by increased amounts of amyloid accumulated in the cortical blood vessels. These results highlight an overlooked interplay between neuronal and endothelial APP in brain vascular Aβ deposition. We propose that these EC-APP770⁺:App^NL-F/NL-F mice may be useful to study the basic molecular mechanisms behind the possible breakdown of the blood–brain barrier upon administration of anti-Aβ antibodies.     

Predicting protein complex geometries with a neural network

A major challenge of the protein docking problem is to define scoring functions that can distinguish near‐native protein complex geometries from a large number of non‐native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom‐pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near‐native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge‐based energy functions for scoring. We show that a distance‐dependent atom pair potential performs much better than a simple atom‐pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge‐based scoring functions such as ZDOCK 3.0, ZRANK, ITScore‐PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network‐based scoring function achieves a reasonable performance in rigid‐body unbound docking of proteins. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Atomistic mechanisms of huntingtin N‐terminal fragment insertion on a phospholipid bilayer revealed by molecular dynamics simulations

The huntingtin protein is characterized by a segment of consecutive glutamines (Q_N) that is responsible for its fibrillation. As with other amyloid proteins, misfolding of huntingtin is related to Huntington's disease through pathways that can involve interactions with phospholipid membranes. Experimental results suggest that the N‐terminal 17‐amino‐acid sequence (htt^NT) positioned just before the Q_N region is important for the binding of huntingtin to membranes. Through all‐atom explicit solvent molecular dynamics simulations, we unveil the structure and dynamics of the htt^NTQ_N fragment on a phospholipid membrane at the atomic level. We observe that the insertion dynamics of this peptide can be described by four main steps—approach, reorganization, anchoring, and insertion—that are very diverse at the atomic level. On the membrane, the htt^NT peptide forms a stable α‐helix essentially parallel to the membrane with its nonpolar side‐chains—mainly Leu‐4, Leu‐7, Phe‐11 and Leu‐14—positioned in the hydrophobic core of the membrane. Salt‐bridges involving Glu‐5, Glu‐12, Lys‐6, and Lys‐15, as well as hydrogen bonds involving Thr‐3 and Ser‐13 with the phospholipids also stabilize the structure and orientation of the htt^NT peptide. These observations do not significantly change upon adding the Q_N region whose role is rather to provide, through its hydrogen bonds with the phospholipids' head group, a stable scaffold facilitating the partitioning of the htt^NT region in the membrane. Moreover, by staying accessible to the solvent, the amyloidogenic Q_N region could also play a key role for the oligomerization of htt^NTQ_N on phospholipid membranes. Proteins 2014; 82:1409–1427. © 2014 Wiley Periodicals, Inc.     

Mutations in amyloid precursor protein and presenilin-1 genes increase the basal oxidative stress in murine neuronal cells and lead to increased sensitivity to oxidative stress mediated by amyloid beta-peptide (1-42), HO and kainic acid: implications for Alzheimer's disease

Oxidative stress is observed in Alzheimer's disease (AD) brain, including protein oxidation and lipid peroxidation. One of the major pathological hallmarks of AD is the brain deposition of amyloid beta-peptide (Abeta). This 42-mer peptide is derived from the beta-amyloid precursor protein (APP) and is associated with oxidative stress in vitro and in vivo. Mutations in the PS-1 and APP genes, which increase production of the highly amyloidogenic amyloid beta-peptide (Abeta42), are the major causes of early onset familial AD. Several lines of evidence suggest that enhanced oxidative stress, inflammation, and apoptosis play important roles in the pathogenesis of AD. In the present study, primary neuronal cultures from knock-in mice expressing mutant human PS-1 and APP were compared with those from wild-type mice, in the presence or absence of various oxidizing agents, viz, Abeta(1-42), H2O2 and kainic acid (KA). APP/PS-1 double mutant neurons displayed a significant basal increase in oxidative stress as measured by protein oxidation, lipid peroxidation, and 3-nitrotyrosine when compared with the wild-type neurons (p < 0.0005). Elevated levels of human APP, PS-1 and Abeta(1-42) were found in APP/PS-1 cultures compared with wild-type neurons. APP/PS-1 double mutant neuron cultures exhibited increased vulnerability to oxidative stress, mitochondrial dysfunction and apoptosis induced by Abeta(1-42), H2O2 and KA compared with wild-type neuronal cultures. The results are consonant with the hypothesis that Abeta(1-42)-associated oxidative stress and increased vulnerability to oxidative stress may contribute significantly to neuronal apoptosis and death in familial early onset AD.     

Computational prediction and analysis of the DR6-NAPP interaction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that involves a devastating clinical course and that lacks an effective treatment. A biochemical model for neuronal development, recently proposed by Nikolaev et al., that may also have implications for AD, hinges on a novel protein–protein interaction between the death cell receptor 6 (DR6) ectodomain and an Nterminal fragment of amyloid precursor protein (NAPP), specifically, the growth factor-like domain of NAPP (GFD NAPP). Given all of this, we used a pure computational work-flow to dock a binding competent homology model of the DR6 ectodomain to a binding competent crystal structure of GFD NAPP. The DR6 homology model was built according to a template supplied by the neurotrophin p75 receptor. The best docked model was selected according to an empirical estimate of the binding affinity and represents a high quality model of probable structural accuracy, especially with respect to the residue-level contribution of GFD NAPP. The final model was tested and verified against a variety of biophysical and theoretical data sets. Particularly, worth noting is the excellent observed agreement between the theoretically calculated DR6–GFD NAPP binding free energy and the experimental quantity. The model is used to provide a satisfying structural and energetic interpretation of DR6–GFD NAPP binding and to suggest the possibility of and a mechanism for spontaneous apoptosis. The evidence suggests that the DR6–NAPP model proposed here is of probable accuracy and that it will prove useful in future studies, modeling work, and structure-based AD drug design.