Data analysis also showed that 1569 genes are located in the neighborhood of the 283 binding sites and 191 genes in the neighborhood of the 42 binding sites validated by dsDNA microarray. We consulted 38 c-Jun target genes in previous studies and 16 among these 38 genes were also detected in this study. The identification of c-Jun binding sites and

potential target genes in the genome scale may improve our fundamental understanding in the molecular mechanisms underlying the transcription regulation related to c-Jun.”
“An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF). Conventional approaches to prediction of TF binding sites involve the definition of consensus selleck chemical sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational

chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are SNX-5422 trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis

and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, Selleckchem DZNeP and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similar to SiteSleuth in that it involves a learning algorithm. The main advantage of SiteSleuth is a lower false positive rate.”
“Background: Ischemia/reperfusion injury after liver transplantation (LT) may be associated with primary graft dysfunction (PDF) or non-function. Prostaglandins were demonstrated to be beneficial in reducing ischemic injury by improving microcirculation and protecting endothelial cells. The aim of this study was to analyze the effect of the continuously administered prostaglandin I-2 analog iloprost on allograft function after LT.

Methods: Eighty patients were prospectively randomized and assigned to two groups. Patients in the treatment group received iloprost for seven d after transplantation, and those in the control group did not. The primary end point was graft dysfunction.

Results: The incidence of PDF was 20% (n = 8) in the control group and 5% (n = 2) in the treatment group, respectively (p = 0.087).