Genetic variation in selenoprotein S influences inflammatory response

Chronic inflammation has a pathological role in many common diseases and is influenced by both genetic and environmental factors. Here we assess the role of genetic variation in selenoprotein S (SEPS1, also called SELS or SELENOS), a gene involved in stress response in the endoplasmic reticulum and inflammation control. After resequencing SEPS1, we genotyped 13 SNPs in 522 individuals from 92 families. As inflammation biomarkers, we measured plasma levels of IL-6, IL-1beta and TNF-alpha. Bayesian quantitative trait nucleotide analysis identified associations between SEPS1 polymorphisms and all three proinflammatory cytokines. One promoter variant, -105G --> A, showed strong evidence for an association with each cytokine (multivariate P = 0.0000002). Functional analysis of this polymorphism showed that the A variant significantly impaired SEPS1 expression after exposure to endoplasmic reticulum stress agents (P = 0.00006). Furthermore, suppression of SEPS1 by short interfering RNA in macrophage cells increased the release of IL-6 and TNF-alpha. To investigate further the significance of the observed associations, we genotyped -105G --> A in 419 Mexican American individuals from 23 families for replication. This analysis confirmed a significant association with both TNF-alpha (P = 0.0049) and IL-1beta (P = 0.0101). These results provide a direct mechanistic link between SEPS1 and the production of inflammatory cytokines and suggest that SEPS1 has a role in mediating inflammation.     

Filtered Backprojection Reconstruction with Depth-Dependent Filtering

A direct filtered-backprojection(FBP) reconstruction algorithm is presented for circular cone-beam computed tomography(CB-CT) that allows the filter operation to be applied efficiently with shift-variant band-pass characteristics on the kernel function.Our algorithm is derived from the ramp-filter based FBP method of Feldkamp et al.and obtained by decomposing the ramp filtering into a convolution involving the Hilbert kernel(global operation) and a subsequent differentiation operation(local operation).The d...     

A comprehensive linkage analysis for myocardial infarction and its related risk factors

Coronary artery disease and myocardial infarction (MI) are leading causes of death in the western world. Numerous studies have shown that risk factors such as diabetes mellitus, arterial hypertension and hypercholesterolemia contribute to the development of the disease. Although each risk factor by itself is partly under genetic control, a positive family history is an independent predictor, which suggests that there are additional susceptibility genes. We have scanned the whole genome in 513 families to identify chromosomal regions linked to myocardial infarction and related risk factors that are known to be under genetic control. Here we show, by using variance component analysis and incorporating risk factors, that risk of myocardial infarction maps to a single region on chromosome 14 with a significant lod score of 3.9 (pointwise P=0.00015, genome-wide P<0.05), providing evidence of a principal MI locus. To characterize this locus we analyzed each risk factor by itself. Serum concentrations of lipoprotein (a) show linkage to both the apolipoprotein (a) locus (lod score 26.99) and a new locus on chromosome 1 (lod score 3.8). There is suggestive linkage for diabetes mellitus on chromosome 6 (lod score 2.96), for hypertension on chromosomes 1 and 6, for high-density and low-density lipoprotein cholesterol on chromosomes 1 and 17, and for triglyceride concentrations on chromosome 9. Although some of these risk factors overlap with previously identified loci, none overlaps with the newly identified susceptibility locus for myocardial infarction and coronary artery disease.