Including Aortic Valve Morphology in Computational Fluid Dynamics Simulations: Initial Findings and Application to Aortic Coarctation

Computational fluid dynamics (CFD) simulations quantifying thoracic aortic flow patterns have not included disturbances from the aortic valve (AoV). 80% of patients with aortic coarctation (CoA) have a bicuspid aortic valve (BAV) which may cause adverse flow patterns contributing to morbidity. Our objectives were to develop a method to account for the AoV in CFD simulations, and quantify its impact on local hemodynamics. The method developed facilitates segmentation of the AoV, spatiotemporal interpolation of segments, and anatomic positioning of segments at the CFD model inlet. The AoV was included in CFD model examples of a normal (tricuspid AoV) and a post-surgical CoA patient (BAV). Velocity, turbulent kinetic energy (TKE), time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) results were compared to equivalent simulations using a plug inlet profile. The plug inlet greatly underestimated TKE for both examples. TAWSS differences extended throughout the thoracic aorta for the CoA BAV, but were limited to the arch for the normal example. OSI differences existed mainly in the ascending aorta for both cases. The impact of AoV can now be included with CFD simulations to identify regions of deleterious hemodynamics thereby advancing simulations of the thoracic aorta one step closer to reality

The Role of Presenilin and its Interacting Proteins in the Biogenesis of Alzheimer’s Beta Amyloid

The biogenesis and accumulation of the beta amyloid protein (Aβ) is a key event in the cascade of oxidative and inflammatory processes that characterises Alzheimer’s disease. The presenilins and its interacting proteins play a pivotal role in the generation of Aβ from the amyloid precursor protein (APP). In particular, three proteins (nicastrin, aph-1 and pen-2) interact with presenilins to form a large multi-subunit enzymatic complex (γ-secretase) that cleaves APP to generate Aβ. Reconstitution studies in yeast and insect cells have provided strong evidence that these four proteins are the major components of the γ-secretase enzyme. Current research is directed at elucidating the roles that each of these protein play in the function of this enzyme. In addition, a number of presenilin interacting proteins that are not components of γ-secretase play important roles in modulating Aβ production. This review will discuss the components of the γ-secretase complex and the role of presenilin interacting proteins on γ-secretase activity

The structure and function of Alzheimer's gamma secretase enzyme complex

The production and accumulation of the beta amyloid protein (Aβ) is a key event in the cascade of oxidative and inflammatory processes that characterizes Alzheimer’s disease (AD). A multi-subunit enzyme complex, referred to as gamma (γ) secretase, plays a pivotal role in the generation of Aβ from its parent molecule, the amyloid precursor protein (APP). Four core components (presenilin, nicastrin, aph-1, and pen-2) interact in a high-molecular-weight complex to perform intramembrane proteolysis on a number of membrane-bound proteins, including APP and Notch. Inhibitors and modulators of this enzyme have been assessed for their therapeutic benefit in AD. However, although these agents reduce Aβ levels, the majority have been shown to have severe side effects in pre-clinical animal studies, most likely due to the enzymes role in processing other proteins involved in normal cellular function. Current research is directed at understanding this enzyme and, in particular, at elucidating the roles that each of the core proteins plays in its function. In addition, a number of interacting proteins that are not components of γ-secretase also appear to play important roles in modulating enzyme activity. This review will discuss the structural and functional complexity of the γ-secretase enzyme and the effects of inhibiting its activity

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field