• 2018-07
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  • 2019-05
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  • br Outcome of COX overexpression Although a


    Outcome of COX-2 overexpression Although a causal role for COX-2 has been proposed, mechanisms by which COX-2 function contributes to the pathogenesis of hyperplastic disease are not well defined. To examine if there is any correlation between COX-2 and p53 protein levels, Kumagai et al. [15] recently established the COX-2-overexpressing derivatives of RL34 Mianserin HCl by stable transfection with COX-2 cDNA. They investigated the COX-2-mediated change in gene expression by microarray analysis and observed significant up-regulation of acetylcholinesterase-associated collagen, isopentenyl diphosphate-dimethylallyl diphosphate isomerase, and p38 MAPK genes. In addition, the expression of genes involved in the phase II detoxification response, such as glutathione S-transferase Yb and Yc subunits, was also significantly up-regulated. They also observed significant down-regulation of proteasome subunits RC1 and RN3, transforming growth factor β−3, heat shock protein 27, apolipoprotein E, and prostacyclin synthase. Most notably, the proteasome RC1 subunit was dramatically down-regulated by ~26-fold in the COX-2 overexpressed cells. Consistent with the COX-2-mediated down-regulation of proteasome, a moderate reduction of the proteasome activities was observed. This proteasome dysfunction mediated by the COX-2 overproduction was associated with the enhanced accumulation of p53 and ubiquitinated proteins, leading to the enhanced sensitivity toward HNE. These results suggest the existence of a causal link between COX-2 and p53, which may represent a toxic mechanism of electrophilic lipid peroxidation products (Fig. 3).
    A link between lipoprotein modification and inflammatory response A unique finding related to the HNE-induced COX-2 gene expression is that the modified LDLs might be involved in the COX-2 induction. Kanayama et al. [18] found that HNE could induce COX-2 only in the presence of serum. They also identified the modified LDL, including oxLDLs, as a bona fide active component essential for the induction of COX-2 by HNE. In addition, they characterized cellular events and established that the combination of HNE and oxLDLs cooperatively induced COX-2 gene expression through a novel mechanism, by which HNE up-regulates gene expression of the scavenger receptor CD36 and promotes the CD36-mediated COX-2 induction by the modified LDLs (Fig. 4). These findings represent a demonstration of a link between the oxidative modification of LDLs and the activation of the inflammatory potential of macrophages. However, an association of the CD36/oxidized LDLs pathway with the MAPK pathways and/or the transcription factors (p53 and Sp1) in the HNE-mediated induction of COX-2 expression still remain unclear.
    Concluding remarks
    Introduction Cox processes, and in particular log-Gaussian Cox processes (LGCP), have been used extensively as flexible models of spatial point pattern data Møller et al., 1998, Møller and Waagepetersen, 2007, Illian et al., 2012, Diggle, 2014. These are hierarchical point process models where the point locations are assumed to be independent given a random intensity function where is a, possibly multivariate, function of covariates and is a Gaussian random field, which is typically assumed to be stationary. The random field captures spatial structure in the point pattern that the given covariates cannot capture. In this work, we relax the assumption that a single stationary Gaussian field can account for those remaining spatial structures and develop a mixture model based on level set inversion. To motivate the relevance of the approach we consider a point pattern data set formed by the locations of the tree species Beilschmiedia Pendula, one of the species in the tropical rainforest plot on Barro Colorado Island Condit, 1998a, Condit et al., 2000, Burslem et al., 2001, Hubbell et al., 2005. The point pattern comprises point locations in a rectangular observation window (500 m 1000 m), see Fig. 1, Fig. 1(a). This pattern has been analyzed repeatedly in the literature and is one of the example patterns in the R (The R project for statistical computing, 2017) package spatstat (Baddeley et al., 2015a). Previous analyses have fitted a log-Gaussian Cox process (Møller and Waagepetersen, 2007) to this and related data sets to draw conclusions on the association of habitat preferences based on a number of spatial covariates reflecting local soil chemistry and topography Møller and Waagepetersen, 2007, Illian et al., 2012.