221 research outputs found

    ApoCIII-Enriched LDL in Type 2 Diabetes Displays Altered Lipid Composition, Increased Susceptibility for Sphingomyelinase, and Increased Binding to Biglycan

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    Objective- Apolipoprotein CIII (apoCIII) is an independent risk factor for cardiovascular disease, but the molecular mechanisms involved are poorly understood. Here, we investigated potential proatherogenic properties of apoCIII-containing LDL from hypertriglyceridemic patients with type 2 diabetes. Research design and methods - LDL was isolated from controls and subjects with type 2 diabetes, and from apoB transgenic mice. LDL-biglycan binding was analyzed with a solid-phase assay using immunoplates coated with biglycan. Lipid composition was analyzed with mass spectrometry. Hydrolysis of LDL by sphingomyelinase was analyzed after labeling plasma LDL with [(3)H]sphingomyelin. ApoCIII isoforms were quantified after isoelectric focusing. Human aortic endothelial cells were incubated with desialylated apoCIII or with LDL enriched with specific apoCIII isoforms. Results- We showed that enriching LDL with apoCIII only induced a small increase in LDL-proteoglycan binding, and this effect was dependent on a functional Site A in apoB100. Our findings indicated that intrinsic characteristics of the diabetic LDL other than apoCIII per se are responsible for further increased proteoglycan binding of diabetic LDL with high endogenous apoCIII, and we showed alterations in the lipid composition of diabetic LDL with high apoCIII. We also demonstrated that high apoCIII increased susceptibility of LDL to hydrolysis and aggregation by SMase. In addition, we demonstrated that sialylation of apoCIII increased with increasing apoCIII content, and that sialylation of apoCIII was essential for its proinflammatory properties. Conclusions- We have demonstrated a number of features of apoCIII-containing LDL from hypertriglyceridemic patients with type 2 diabetes that could explain the proatherogenic role of apoCIII

    A National Collaboratory to Advance the Science of High Temperature Plasma Physics for Magnetic Fusion

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    This report summarizes the work of the National Fusion Collaboratory (NFC) Project to develop a persistent infrastructure to enable scientific collaboration for magnetic fusion research. The original objective of the NFC project was to develop and deploy a national FES Grid (FusionGrid) that would be a system for secure sharing of computation, visualization, and data resources over the Internet. The goal of FusionGrid was to allow scientists at remote sites to participate as fully in experiments and computational activities as if they were working on site thereby creating a unified virtual organization of the geographically dispersed U.S. fusion community. The vision for FusionGrid was that experimental and simulation data, computer codes, analysis routines, visualization tools, and remote collaboration tools are to be thought of as network services. In this model, an application service provider (ASP provides and maintains software resources as well as the necessary hardware resources. The project would create a robust, user-friendly collaborative software environment and make it available to the US FES community. This Grid's resources would be protected by a shared security infrastructure including strong authentication to identify users and authorization to allow stakeholders to control their own resources. In this environment, access to services is stressed rather than data or software portability

    Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

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    Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases

    Oxidised LDL-lipids increase beta amyloid production by SH-SY5Y cells through glutathione depletion and lipid raft formation

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    Elevated total cholesterol in midlife has been associated with increased risk of dementia in later life. We have previously shown that low-density lipoprotein (LDL) is more oxidized in the plasma of dementia patients, although total cholesterol levels are not different from those of age-matched controls. β-Amyloid (Aβ) peptide, which accumulates in Alzheimer disease (AD), arises from the initial cleavage of amyloid precursor protein by β-secretase-1 (BACE1). BACE1 activity is regulated by membrane lipids and raft formation. Given the evidence for altered lipid metabolism in AD, we have investigated a mechanism for enhanced Aβ production by SH-SY5Y neuronal-like cells exposed to oxidized LDL (oxLDL). The viability of SH-SY5Y cells exposed to 4 μg oxLDL and 25 μM 27-hydroxycholesterol (27OH-C) was decreased significantly. Lipids, but not proteins, extracted from oxLDL were more cytotoxic than oxLDL. In parallel, the ratio of reduced glutathione (GSH) to oxidized glutathione was decreased at sublethal concentrations of lipids extracted from native and oxLDL. GSH loss was associated with an increase in acid sphingomyelinase (ASMase) activity and lipid raft formation, which could be inhibited by the ASMase inhibitor desipramine. 27OH-C and total lipids from LDL and oxLDL independently increased Aβ production by SH-SY5Y cells, and Aβ accumulation could be inhibited by desipramine and by N-acetylcysteine. These data suggest a mechanism whereby oxLDL lipids and 27OH-C can drive Aβ production by GSH depletion, ASMase-driven membrane remodeling, and BACE1 activation in neuronal cells. © 2014 The Authors

    2022 Review of Data-Driven Plasma Science

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    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required
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