WebPARE: web-computing for inferring genetic or transcriptional interactions

Summary: Inferring genetic or transcriptional interactions, when done successfully, may provide insights into biological processes or biochemical pathways of interest. Unfortunately, most computational algorithms require a certain level of programming expertise. To provide a simple web interface for users to infer interactions from time course gene expression data, we present WebPARE, which is based on the pattern recognition algorithm (PARE). For expression data, in which each type of interaction (e.g. activator target) and the corresponding paired gene expression pattern are significantly associated, PARE uses a non-linear score to classify gene pairs of interest into a few subclasses of various time lags. In each subclass, PARE learns the parameters in the decision score using known interactions from biological experiments or published literature. Subsequently, the trained algorithm predicts interactions of a similar nature. Previously, PARE was shown to infer two sets of interactions in yeast successfully. Moreover, several predicted genetic interactions coincided with existing pathways; this indicates the potential of PARE in predicting partial pathway components. Given a list of gene pairs or genes of interest and expression data, WebPARE invokes PARE and outputs predicted interactions and their networks in directed graphs

Inferring genetic interactions via a nonlinear model and an optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.</p> <p>Results</p> <p>An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in <it>S. cerevisiae</it>, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.</p> <p>Conclusions</p> <p>GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.</p

Endotoxin and CD14 in the progression of biliary atresia

<p>Abstract</p> <p>Background</p> <p>Biliary atresia (BA) is a typical cholestatic neonatal disease, characterized by obliteration of intra- and/or extra-hepatic bile ducts. However, the mechanisms contributing to the pathogenesis of BA remain uncertain. Because of decreased bile flow, infectious complications and damaging endotoxemia occur frequently in patients with BA. The aim of this study was to investigate endotoxin levels in patients with BA and the relation of these levels with the expression of the endotoxin receptor, CD14.</p> <p>Methods</p> <p>The plasma levels of endotoxin and soluble CD14 were measured with a pyrochrome Limulus amebocyte lysate assay and enzyme-linked immunosorbent assay in patients with early-stage BA when they received the Kasai procedure (KP), in patients who were jaundice-free post-KP and followed-up at the outpatient department, in patients with late-stage BA when they received liver transplantation, and in patients with choledochal cysts. The correlation of CD14 expression with endotoxin levels in rats following common bile duct ligation was investigated.</p> <p>Results</p> <p>The results demonstrated a significantly higher hepatic CD14 mRNA and soluble CD14 plasma levels in patients with early-stage BA relative to those with late-stage BA. However, plasma endotoxin levels were significantly higher in both the early and late stages of BA relative to controls. In rat model, the results demonstrated that both endotoxin and CD14 levels were significantly increased in liver tissues of rats following bile duct ligation.</p> <p>Conclusions</p> <p>The significant increase in plasma endotoxin and soluble CD14 levels during BA implies a possible involvement of endotoxin stimulated CD14 production by hepatocytes in the early stage of BA for removal of endotoxin; whereas, endotoxin signaling likely induced liver injury and impaired soluble CD14 synthesis in the late stages of BA.</p

APASL HCV guidelines of virus-eradicated patients by DAA on how to monitor HCC occurrence and HBV reactivation

In the direct-acting antiviral (DAA) era for hepatitis C virus (HCV) infection, sustained virological response (SVR) is very high, but close attention must be paid to the possible occurrence of hepatocellular carcinoma (HCC) and reactivation of hepatitis B virus (HBV) in patients with co-infection who achieved SVR in short term. HCC occurrence was more often observed in patients with previous HCC history. We found occurrence of HCC in 178 (29.6%) of 602 patients with previous HCC history (15.4 months mean follow-up post-DAA initiation) but, in contrast, in only 604 (1.3%) of 45,870 patients without previous HCC history (18.2 months mean follow-up). Thus, in these guidelines, we recommend the following: in patients with previous HCC history, surveillance at 4-month intervals for HCC by ultrasonography (US) and tumor markers should be performed. In patients without previous HCC history, surveillance at 6- to 12-month intervals for HCC including US is recommended until the long-term DAA treatment effects, especially for the resolution of liver fibrosis, are confirmed. This guideline also includes recommendations on how to follow-up patients who have been infected with both HCV and HBV. When HCV was eradicated in these HBsAg-positive patients or patients with previous HBV infection (anti-HBc and/or anti-HBs-positive), it was shown that HBV reactivation or HBV DNA reappearance was observed in 67 (41.4%) of 162 or 12 (0.9%) of 1317, respectively. For these co-infected patients, careful attention should be paid to HBV reactivation for 24 weeks post-treatment