Diacylglycerol triggers Rim101 pathway dependent necrosis in yeast: a model for lipotoxicity

The loss of lipid homeostasis can lead to lipid overload and is associated with a variety of disease states. However, little is known as to how the disruption of lipid regulation or lipid overload affects cell survival. In this study we investigated how excess diacylglycerol (DG), a cardinal metabolite suspected to mediate lipotoxicity, compromises the survival of yeast cells. We reveal that increased DG achieved by either genetic manipulation or pharmacological administration of 1,2-dioctanoyl-sn-glycerol (DOG) triggers necrotic cell death. The toxic effects of DG are linked to glucose metabolism and require a functional Rim101 signaling cascade involving the Rim21 dependent sensing complex and activation of a calpain-like protease. The Rim101 cascade is an established pathway that triggers a transcriptional response to alkaline or lipid stress. We propose that the Rim101 pathway senses DG-induced lipid perturbation and conducts a signaling response that either facilitates cellular adaptation or triggers lipotoxic cell death. Using established models of lipotoxicity i.e. high fat diet in Drosophila and palmitic acid administration in cultured human endothelial cells, we present evidence that the core mechanism underlying this calpain-dependent lipotoxic cell death pathway is phylogenetically conserved

Sources of Bias in Specimens for Research About Molecular Markers for Cancer

Claims about the diagnostic or prognostic accuracy of markers often prove disappointing when “discrimination” found between cancers versus normals is due to bias, a systematic difference between compared groups. This article describes a framework to help simplify and organize current problems in marker research by focusing on the role of specimens as a source of bias in observational research and using that focus to address problems and improve reliability. The central idea is that the “fundamental comparison” in research about markers (ie, the comparison done to assess whether a marker discriminates) involves two distinct processes that are “connected” by specimens. If subject selection (first process) creates baseline inequality between groups being compared, then laboratory analysis of specimens (second process) may erroneously find positive results. Although both processes are important, subject selection more fundamentally influences the quality of marker research, because it can hardwire bias into all comparisons in a way that cannot be corrected by any refinement in laboratory analysis. An appreciation of the separateness of these two processes—and placing investigators with appropriate expertise in charge of each—may increase the reliability of research about cancer biomarkers