Proteolytic shedding of the prion protein via activation of metallopeptidase ADAM10 reduces cellular binding and toxicity of amyloid-β oligomers

The cellular prion protein (PrPC) is a key neuronal receptor for amyloid-β oligomers (AβO), mediating their neurotoxicity, which contributes to the neurodegeneration in Alzheimer's disease (AD). Similarly to the amyloid precursor protein (APP), PrPC is proteolytically cleaved from the cell surface by a disintegrin and metalloprotease, ADAM10. We hypothesized that ADAM10-modulated PrPC shedding would alter the cellular binding and cytotoxicity of AβO. Here, we found that in human neuroblastoma cells, activation of ADAM10 with the muscarinic agonist carbachol promotes PrPC shedding and reduces the binding of AβO to the cell surface, which could be blocked with an ADAM10 inhibitor. Conversely, siRNA-mediated ADAM10 knockdown reduced PrPC shedding and increased AβO binding, which was blocked by the PrPC-specific antibody 6D11. The retinoic acid receptor analog acitretin, which up-regulates ADAM10, also promoted PrPC shedding and decreased AβO binding in the neuroblastoma cells and in human induced pluripotent stem cell (iPSC)-derived cortical neurons. Pretreatment with acitretin abolished activation of Fyn kinase and prevented an increase in reactive oxygen species caused by AβO binding to PrPC Besides blocking AβO binding and toxicity, acitretin also increased the non-amyloidogenic processing of APP. However, in the iPSC-derived neurons, Aβ and other amyloidogenic processing products did not exhibit a reciprocal decrease upon acitretin treatment. These results indicate that by promoting the shedding of PrPC in human neurons, ADAM10 activation prevents the binding and cytotoxicity of AβO, revealing a potential therapeutic benefit of ADAM10 activation in AD

Neurons derived from individual early Alzheimer’s disease patients reflect their clinical vulnerability

Establishing preclinical models of Alzheimer’s disease that predict clinical outcomes remains a critically important, yet to date not fully realized, goal. Models derived from human cells offer considerable advantages over non-human models, including the potential to reflect some of the inter-individual differences that are apparent in patients. Here we report an approach using induced pluripotent stem cell-derived cortical neurons from people with early symptomatic Alzheimer’s disease where we sought a match between individual disease characteristics in the cells with analogous characteristics in the people from whom they were derived. We show that the response to amyloid-β burden in life, as measured by cognitive decline and brain activity levels, varies between individuals and this vulnerability rating correlates with the individual cellular vulnerability to extrinsic amyloid-β in vitro as measured by synapse loss and function. Our findings indicate that patient-induced pluripotent stem cell-derived cortical neurons not only present key aspects of Alzheimer’s disease pathology but also reflect key aspects of the clinical phenotypes of the same patients. Cellular models that reflect an individual’s in-life clinical vulnerability thus represent a tractable method of Alzheimer’s disease modelling using clinical data in combination with cellular phenotypes

Determinants of Default in P2P Lending

This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans' data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower's debt level