The effect of diabetes mellitus on aortic prostanoid synthesis and serum cholesterol levels in the rat fed a high cholesterol diet

The effect of diabetes mellitus on serum cholesterol and aortic microsomal prostanoid synthesis was studied in cholesterol fed male Lewis rats. Normal, diabetic and diabetic rats treated with pancreatic islets were divided into three diet subgroups, control diet, control +2% cholesterol for 8 weeks and control +2% cholesterol diet for 16 weeks. Serum glucose levels were elevated three-fold in the diabetic group compared to normal. Treatment with islets restored serum glucose to normal levels in diabetic rats. The 2% cholesterol diet did not significantly alter serum glucose levels in any of the groups. Body weights in the diabetic group were significantly lower than normal or diabetic rats treated with islets. Feeding 2% cholesterol for 16 weeks significantly increased weight in normal and islet treated diabetic rats but not in the diabetic group. Aortic microsomal prostanoid synthesis was similar in all experimental groups with 6-keto-PGF1 alpha (PGI2 metabolite) being the major product synthesized in all groups. Aortic microsomal prostanoid levels were not altered by the 2% cholesterol diet. Serum cholesterol levels increased 14-fold in the diabetic group which returned to the normal level in the diabetic animals treated with islets. These data show that diabetes does not alter aortic microsomal prostanoid levels in the rat. However, diabetes significantly increased serum cholesterol levels which were reversed by islet transplantation.     

Platelet activating factor (PAF) stimulates release of PGI2 from inflamed rabbit gallbladder cell cultures

This study examines the hypothesis that PAF stimulates release of PGI₂ from inflamed rabbit gallbladder explant cell cultures. New Zealand white rabbits underwent bile duct ligation for 72 h (72 h BDL), or sham operation, Sham and 72 h BDL gallbladder explants were placed in culture, and the cells grown to 75% confluence. The cells were exposed to increasing concentrations of PAF for 60 min. The media analyzed for eicosanoid release by EIA and the cells analyzed for cyclooxygenase and prostacyclin synthase content by immunoblot analysis. PAF increased release of 6-keto-PGF_1α from the 72 h BDL gallbladder cell cultures in a dose-related manner which was inhibited by indomethacin preincubation by 90%. The increased 72 h BDL cell release of 6-keto-PGF_1α was not associated with changes in the content of cyclooxygenase or prostacyclin synthase. PAF did not alter eicosanoid release from sham control cell cultures. These data suggest that PAF can only up-regulate endogenous 6-keto-PGF_1α release from the 72 h BDL cells that had been previously stimulated by inflammation. PAF may thus contribute to gallbladder distention and injury by chronic stimulation of inflamed gallbladder PGI₂ release.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.