Substrate docking to γ-secretase allows access of γ-secretase modulators to an allosteric site

γ-Secretase generates the peptides of Alzheimer's disease, Aβ40 and Aβ42, by cleaving the amyloid precursor protein within its transmembrane domain. γ-Secretase also cleaves numerous other substrates, raising concerns about γ-secretase inhibitor off-target effects. Another important class of drugs, γ-secretase modulators, alter the cleavage site of γ-secretase on amyloid precursor protein, changing the Aβ42/Aβ40 ratio, and are thus a promising therapeutic approach for Alzheimer's disease. However, the target for γ-secretase modulators is uncertain, with some data suggesting that they function on γ-secretase, whereas others support their binding to the amyloid precursor. In this paper we address this controversy by using a fluorescence resonance energy transfer-based assay to examine whether γ-secretase modulators alter Presenilin-1/γ-secretase conformation in intact cells in the absence of its natural substrates such as amyloid precursor protein and Notch. We report that the γ-secretase allosteric site is located within the γ-secretase complex, but substrate docking is needed for γ-secretase modulators to access this site

Selective amyloid-β lowering agents

The amyloid-β peptide (Aβ), implicated in the pathogenesis of Alzheimer's disease (AD), is produced through sequential proteolysis of the Aβ precursor protein (APP) by β- and γ-secretases. Thus, blocking either of these two proteases, directly or indirectly, is potentially worthwhile toward developing AD therapeutics. β-Secretase is a membrane-tethered pepsin-like aspartyl protease suitable for structure-based design, whereas γ-secretase is an unusual, heterotetrameric membrane-embedded aspartyl protease. While γ-secretase inhibitors entered clinical trials first due to their superior pharmacological properties (for example, brain penetration) over β-secretase inhibitors, it has since become clear that γ-secretase inhibitors can cause mechanism-based toxicities owing to interference with the proteolysis of another γ-secretase substrate, the Notch receptor. Strategies for targeting Aβ production at the γ-secretase level without blocking Notch signalling will be discussed. Other strategies utilizing cell-based screening have led to the identification of novel Aβ lowering agents that likewise leave Notch proteolysis intact. The mechanism by which these agents lower Aβ is unknown, but these compounds may ultimately reveal new targets for AD therapeutics

Presenilin Is the Molecular Target of Acidic γ-Secretase Modulators in Living Cells

The intramembrane-cleaving protease γ-secretase catalyzes the last step in the generation of toxic amyloid-β (Aβ) peptides and is a principal therapeutic target in Alzheimer's disease. Both preclinical and clinical studies have demonstrated that inhibition of γ-secretase is associated with prohibitive side effects due to suppression of Notch processing and signaling. Potentially safer are γ-secretase modulators (GSMs), which are small molecules that selectively lower generation of the highly amyloidogenic Aβ42 peptides but spare Notch processing. GSMs with nanomolar potency and favorable pharmacological properties have been described, but the molecular mechanism of GSMs remains uncertain and both the substrate amyloid precursor protein (APP) and subunits of the γ-secretase complex have been proposed as the molecular target of GSMs. We have generated a potent photo-probe based on an acidic GSM that lowers Aβ42 generation with an IC50 of 290 nM in cellular assays. By combining in vivo photo-crosslinking with affinity purification, we demonstrated that this probe binds the N-terminal fragment of presenilin (PSEN), the catalytic subunit of the γ-secretase complex, in living cells. Labeling was not observed for APP or any of the other γ-secretase subunits. Binding was readily competed by structurally divergent acidic and non-acidic GSMs suggesting a shared mode of action. These findings indicate that potent acidic GSMs target presenilin to modulate the enzymatic activity of the γ-secretase complex

Transductive Learning for Spatial Data Classification

Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the nave Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented

Anti-Aβ Drug Screening Platform Using Human iPS Cell-Derived Neurons for the Treatment of Alzheimer's Disease

Background:Alzheimer's disease (AD) is a neurodegenerative disorder that causes progressive memory and cognitive decline during middle to late adult life. The AD brain is characterized by deposition of amyloid β peptide (Aβ), which is produced from amyloid precursor protein by β- and γ-secretase (presenilin complex)-mediated sequential cleavage. Induced pluripotent stem (iPS) cells potentially provide an opportunity to generate a human cell-based model of AD that would be crucial for drug discovery as well as for investigating mechanisms of the disease. Methodology/Principal Findings:We differentiated human iPS (hiPS) cells into neuronal cells expressing the forebrain marker, Foxg1, and the neocortical markers, Cux1, Satb2, Ctip2, and Tbr1. The iPS cell-derived neuronal cells also expressed amyloid precursor protein, β-secretase, and γ-secretase components, and were capable of secreting Aβ into the conditioned media. Aβ production was inhibited by β-secretase inhibitor, γ-secretase inhibitor (GSI), and an NSAID; however, there were different susceptibilities to all three drugs between early and late differentiation stages. At the early differentiation stage, GSI treatment caused a fast increase at lower dose (Aβ surge) and drastic decline of Aβ production. Conclusions/Significance:These results indicate that the hiPS cell-derived neuronal cells express functional β- and γ-secretases involved in Aβ production; however, anti-Aβ drug screening using these hiPS cell-derived neuronal cells requires sufficient neuronal differentiation

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties

Tar DNA Binding Protein-43 (TDP-43) Associates with Stress Granules: Analysis of Cultured Cells and Pathological Brain Tissue

Tar DNA Binding Protein-43 (TDP-43) is a principle component of inclusions in many cases of frontotemporal lobar degeneration (FTLD-U) and amyotrophic lateral sclerosis (ALS). TDP-43 resides predominantly in the nucleus, but in affected areas of ALS and FTLD-U central nervous system, TDP-43 is aberrantly processed and forms cytoplasmic inclusions. The mechanisms governing TDP-43 inclusion formation are poorly understood. Increasing evidence indicates that TDP-43 regulates mRNA metabolism by interacting with mRNA binding proteins that are known to associate with RNA granules. Here we show that TDP-43 can be induced to form inclusions in cell culture and that most TDP-43 inclusions co-localize with SGs. SGs are cytoplasmic RNA granules that consist of mixed protein - RNA complexes. Under stressful conditions SGs are generated by the reversible aggregation of prion-like proteins, such as TIA-1, to regulate mRNA metabolism and protein translation. We also show that disease-linked mutations in TDP-43 increased TDP-43 inclusion formation in response to stressful stimuli. Biochemical studies demonstrated that the increased TDP-43 inclusion formation is associated with accumulation of TDP-43 detergent insoluble complexes. TDP-43 associates with SG by interacting with SG proteins, such as TIA-1, via direct protein-protein interactions, as well as RNA-dependent interactions. The signaling pathway that regulates SGs formation also modulates TDP-43 inclusion formation. We observed that inclusion formation mediated by WT or mutant TDP-43 can be suppressed by treatment with translational inhibitors that suppress or reverse SG formation. Finally, using Sudan black to quench endogenous autofluorescence, we also demonstrate that TDP-43 positive-inclusions in pathological CNS tissue co-localize with multiple protein markers of stress granules, including TIA-1 and eIF3. These data provide support for accumulating evidence that TDP-43 participates in the SG pathway