4,343 research outputs found
A Novel ELISA to Detect Methionine Sulfoxide−Containing Apolipoprotein A−I
Atherosclerosis manifests a state of increased oxidative stress characterized by comparable lipid and protein oxidation in the affected arterial wall. While oxidative modification of low density lipoprotein (LDL) has been extensively studied, increasing attention has been focused recently on oxidation of high-density lipoproteins (HDL) and its functional consequences in relation to atherosclerosis. Oxidative modification is thought to generate “dysfunctional” HDL that has lost anti-atherosclerotic activities, including the ability to remove cholesterol from lipid-laden cells. Therefore, there has been much interest in the detection of oxidized HDL. Unfortunately, available methods to detect oxidized HDL are limited at present, in part because oxidative modification of HDL is a complex process and ‘oxidized HDL’ is not a chemically defined entity. What is known however is that conversion of methionine (Met) residues of apolipoprotein (apo) A-I to methionine sulfoxide (MetO) is a process that occurs commonly as HDL undergoes oxidative modification. For example, human apoA-I+16 (containing MetO86 or MetO112) and apoA-I+32 (MetO86 plus MetO112) are generated when apoA-I reacts with lipid hydroperoxides formed as a consequence of the lipoprotein being exposed to 1e−oxidants. The formation of MetO in apoA−I induced by 2e−oxidants (i.e., hydrogen peroxide, hypochlorous acid or myeloperoxidase/hydrogen peroxide/chloride system) is associated with an impaired ability of the apolipoprotein to facilitate reactions relevant to reverse cholesterol transport. In addition, a previous study has suggested the plasma content of apoA-I+32 to be increased in certain subjects that have an increased risk to develop cardiovascular disease (CVD). Moreover, the MetO content in circulating, HDL−associated apoA−I is elevated in type 1 diabetes, a disorder commonly associated with increased oxidative stress and a risk factor for atherosclerosis. Therefore, in the present study, an existing HPLC method was applied to HDL samples from the Fletcher−Challenge study, a nested case control study, to test the potential usefulness of MetO-containing apoA-I as a marker of oxidative stress and/or CVD in a general population. Plasma samples whose HDL contained detectable apoA-I+16 and/or apoA-I+32 had significantly elevated levels of F2-isoprostanes, a marker of in vivo lipid oxidation, consistent with MetO-containing apoA-I being a useful marker of in vivo protein oxidation. Despite this however, there was no significant difference between controls and cases in their concentrations of HDL apoA-I+16 and apoA-I+32 or F2-isoprostanes, suggesting that markers of protein and lipid oxidation are not associated with the risk of coronary heart disease (CHD) in this general population. A limitation of the Fletcher−Challenge study was that only 22% of the 534 HDL samples analyzed contained apoA-I+16 and/or apoA-I+32. In addition, the HPLC−based method used is expensive and time−consuming and may lack the sensitivity needed for apolipoproteins to clinical studies. Thus, a mouse monoclonal anti-human apoA-I+32 antibody (MOA−1) was raised using HPLC−purified apoA-I+32 as immunogen. A sensitive ELISA was then developed using a commercial anti-human apoA-I monoclonal antibody as capture and biotinylated MOA−1 as detection antibody, respectively. The assay detected lipid−free HPLC−purified human apoA-I+32 in a concentration-dependent manner and with a significantly lower limit of detection (i.e., 3 ng/mL) than the HPLC method (1 μg/mL). The ELISA also detected lipid-free apoA-I modified by 2e-oxidants (hydrogen peroxide, hypochlorous acid, peroxynitrite), and HDL oxidized by 1e- or 2e-oxidants and present in buffer or human plasma. Moreover, the extent of recognition of MetO by MOA−1 increased with increasing numbers of MetO in apoA−I, as assessed by the experiments with H2O2−oxidized forms of apoA−I mutants, in which one, two or three Met residues were replaced with Leu. Their detection was concentration-dependent, reproducible, and exhibited a linear response over a physiologically plausible range of concentrations of oxidized HDL. In contrast, MOA-I failed to recognize native apoA-I, native apoA-II, apoA-I modified by hydroxyl radicals or metal ions, or LDL modified by 2e-oxidants. Furthermore, MOA−1 did not detect other Met−containing proteins oxidized by either hypochlorous acid or hydrogen peroxide. Taken together, the results showed that recognition of oxidized proteins by MOA−1 is limited to MetO contained in apoA−I. Finally, in a pilot study, plasma samples obtained from subjects with coronary artery disease (CAD) proven by angiography, and samples from CAD patients undergoing percutaneous coronary intervention (PCI) were analyzed by the ELISA. The preliminary data obtained showed elevated levels of MetO-containing apoA-I in plasma samples of CAD patients compared to those of corresponding control subjects. Unexpectedly, levels of MetOcontaining apoA-I decreased PCI compared to before PCI. A possible explanation for these results is that HDL−associated apoA−I become displaced by acute phase proteins, such as serum amyloid A, in response to PCI. In summary, the ELISA developed here specifically detects apoA-I containing MetO in HDL and human plasma. As such it may provide a useful tool for investigating the relationship between oxidized HDL and CAD
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines
to quickly grasp the informative and distinctive information of an entity.
Entities in most knowledge graphs (KGs) still lack such descriptions, thus
calling for automatic methods to supplement such information. However, existing
generative methods either overlook the grammatical structure or make factual
mistakes in generated texts. To solve these problems, we propose a
head-modifier template-based method to ensure the readability and data fidelity
of generated type descriptions. We also propose a new dataset and two automatic
metrics for this task. Experiments show that our method improves substantially
compared with baselines and achieves state-of-the-art performance on both
datasets.Comment: ACL 201
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