Increased susceptibility of Huh7 cells to HCV replication does not require mutations in RIG-I

<p>Abstract</p> <p>Background</p> <p>The cytosolic retinoic acid-inducible gene I (RIG-I) is a pattern recognition receptor that senses HCV double-stranded RNA and triggers type I interferon pathways. The clone Huh7.5 of human hepatoma Huh7 cells contains a mutation in RIG-I that is believed to be responsible for the improved replication of HCV in these cells relative to the parental strain. We hypothesized that, in addition to RIG-I, other determinant(s) outside the RIG-I coding sequence are involved in limiting HCV replication in cell culture. To test our hypothesis, we analyzed Huh7 cell clones that support the efficient replication of HCV and analyzed the RIG-I gene.</p> <p>Results</p> <p>One clone, termed Huh7D, was more permissive for HCV replication and more efficient for HCV-neomycin and HCV-hygromycin based replicon colony formation than parental Huh7 cells. Nucleotide sequence analysis of the RIG-I mRNA coding region from Huh7D cells showed no mutations relative to Huh7 parental cells.</p> <p>Conclusions</p> <p>We derived a new Huh7 cell line, Huh7D, which is more permissive for HCV replication than parental Huh7 cells. The higher permissiveness of Huh7D cells is not due to mutations in the RIG-I protein, indicating that cellular determinants other than the RIG-I amino-acid sequence are responsible for controlling HCV replication. In addition, we have selected Huh7 cells resistant to hygromycin via newly generated HCV-replicons carrying the hygromycin resistant gene. Further studies on Huh7D cells will allow the identification of cellular factors that increased the susceptibility to HCV infection, which could be targeted for anti-HCV therapies.</p

How many people have had a myocardial infarction? Prevalence estimated using historical hospital data

<p>Abstract</p> <p>Background</p> <p>Health administrative data are increasingly used to examine disease occurrence. However, health administrative data are typically available for a limited number of years – posing challenges for estimating disease prevalence and incidence. The objective of this study is to estimate the prevalence of people previously hospitalized with an acute myocardial infarction (AMI) using 17 years of hospital data and to create a registry of people with myocardial infarction.</p> <p>Methods</p> <p>Myocardial infarction prevalence in Ontario 2004 was estimated using four methods: 1) observed hospital admissions from 1988 to 2004; 2) observed (1988 to 2004) and extrapolated unobserved events (prior to 1988) using a "back tracing" method using Poisson models; 3) DisMod incidence-prevalence-mortality model; 4) self-reported heart disease from the population-based Canadian Community Health Survey (CCHS) in 2000/2001. Individual respondents of the CCHS were individually linked to hospital discharge records to examine the agreement between self-report and hospital AMI admission.</p> <p>Results</p> <p>170,061 Ontario residents who were alive on March 31, 2004, and over age 20 years survived an AMI hospital admission between 1988 to 2004 (cumulative incidence 1.8%). This estimate increased to 2.03% (95% CI 2.01 to 2.05) after adding extrapolated cases that likely occurred before 1988. The estimated prevalence appeared stable with 5 to 10 years of historic hospital data. All 17 years of data were needed to create a reasonably complete registry (90% of estimated prevalent cases). The estimated prevalence using both DisMod and self-reported "heart attack" was higher (2.5% and 2.7% respectively). There was poor agreement between self-reported "heart attack" and the likelihood of having an observed AMI admission (sensitivity = 63.5%, positive predictive value = 54.3%).</p> <p>Conclusion</p> <p>Estimating myocardial infarction prevalence using a limited number of years of hospital data is feasible, and validity increases when unobserved events are added to observed events. The "back tracing" method is simple, reliable, and produces a myocardial infarction registry with high estimated "completeness" for jurisdictions with linked hospital data.</p

GSVD Comparison of Patient-Matched Normal and Tumor aCGH Profiles Reveals Global Copy-Number Alterations Predicting Glioblastoma Multiforme Survival

Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in 3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern

Elucidating the aetiology of human Campylobacter coli infections

Peer reviewedPublisher PD

A Novel Unsupervised Method to Identify Genes Important in the Anti-viral Response: Application to Interferon/Ribavirin in Hepatitis C Patients

Background: Treating hepatitis C with interferon/ribavirin results in a varied response in terms of decrease in viral titer and ultimate outcome. Marked responders have a sharp decline in viral titer within a few days of treatment initiation, whereas in other patients there is no effect on the virus (poor responders). Previous studies have shown that combination therapy modifies expression of hundreds of genes in vitro and in vivo. However, identifying which, if any, of these genes have a role in viral clearance remains challenging. Aims: The goal of this paper is to link viral levels with gene expression and thereby identify genes that may be responsible for early decrease in viral titer. Methods: Microarrays were performed on RNA isolated from PBMC of patients undergoing interferon/ribavirin therapy. Samples were collected at pre-treatment (day 0), and 1, 2, 7, 14 and 28 days after initiating treatment. A novel method was applied to identify genes that are linked to a decrease in viral titer during interferon/ribavirin treatment. The method uses the relationship between inter-patient gene expression based proximities and inter-patient viral titer based proximities to define the association between microarray gene expression measurements of each gene and viral-titer measurements. Results: We detected 36 unique genes whose expressions provide a clustering of patients that resembles viral titer based clustering of patients. These genes include IRF7, MX1, OASL and OAS2, viperin and many ISG's of unknown function. Conclusion: The genes identified by this method appear to play a major role in the reduction of hepatitis C virus during the early phase of treatment. The method has broad utility and can be used to analyze response to any group of factors influencing biological outcome such as antiviral drugs or anti-cancer agents where microarray data are available. © 2007 Brodsky et al

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Introduction: Rethinking the Impact of the Inter-American Human Rights System

This chapter introduces the central themes of the book and argues that the Inter-American Human Rights System (IAHRS) is activated by political actors and institutions in ways that transcend traditional compliance perspectives and that have the potential to meaningfully alter politics and provoke positive domestic human rights change. The chapter identifies key gaps in existing human rights scholarship, particularly in relation to the IAHRS, and outlines three core perspectives on the System’s impact on human rights. It offers a synthesis of the key findings of the volume, and provides reflections on the future prospects of the System by locating it in its broader global context

Missing value imputation for microarray gene expression data using histone acetylation information

<p>Abstract</p> <p>Background</p> <p>It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.</p> <p>Results</p> <p>The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(<it>k</it>-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.</p> <p>Conclusion</p> <p>We demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.</p

Modulations of cell cycle checkpoints during HCV associated disease

Background Impaired proliferation of hepatocytes has been reported in chronic Hepatitis C virus infection. Considering the fundamental role played by cell cycle proteins in controlling cell proliferation, altered regulation of these proteins could significantly contribute to HCV disease progression and subsequent hepatocellular carcinoma (HCC). This study aimed to identify the alterations in cell cycle genes expression with respect to early and advanced disease of chronic HCV infection. Methods Using freshly frozen liver biopsies, mRNA levels of 84 cell cycle genes in pooled RNA samples from patients with early or advanced fibrosis of chronic HCV infection were studied. To associate mRNA levels with respective protein levels, four genes (p27, p15, KNTC1 and MAD2L1) with significant changes in mRNA levels (\u3e 2-fold, p-value \u3c 0.05) were selected, and their protein expressions were examined in the liver biopsies of 38 chronic hepatitis C patients. Results In the early fibrosis group, increased mRNA levels of cell proliferation genes as well as cell cycle inhibitor genes were observed. In the advanced fibrosis group, DNA damage response genes were up-regulated while those associated with chromosomal stability were down-regulated. Increased expression of CDK inhibitor protein p27 was consistent with its mRNA level detected in early group while the same was found to be negatively associated with liver fibrosis. CDK inhibitor protein p15 was highly expressed in both early and advanced group, but showed no correlation with fibrosis. Among the mitotic checkpoint regulators, expression of KNTC1 was significantly reduced in advanced group while MAD2L1 showed a non-significant decrease. Conclusion Collectively these results are suggestive of a disrupted cell cycle regulation in HCV-infected liver. The information presented here highlights the potential of identified proteins as predictive factors to identify patients with high risk of cell transformation and HCC development

Hepatitis C Virus Infection in Phenotypically Distinct Huh7 Cell Lines

In 2005, the first robust hepatitis C virus (HCV) infectious cell culture system was developed based on the HCV genotype 2a JFH-1 molecular clone and the human-derived hepatoma cell line Huh7. Although much effort has been made to dissect and expand the repertoire of JFH-1-derived clones, less attention has been given to the host cell despite the intriguing facts that thus far only Huh7 cells have been found to be highly permissive for HCV infection and furthermore only a limited number of Huh7 cell lines/stocks appear to be fully permissive. As such, we compiled a panel of Huh7 lines from disparate sources and evaluated their permissiveness for HCV infection. We found that although Huh7 lines from different laboratories do vary in morphology and cell growth, the majority (8 out of 9) were highly permissive for infection, as demonstrated by robust HCV RNA and de novo infectious virion production following infection. While HCV RNA levels achieved in the 8 permissive cell lines were relatively equivalent, three Huh7 lines demonstrated higher infectious virion production suggesting these cell lines more efficiently support post-replication event(s) in the viral life cycle. Consistent with previous studies, the single Huh7 line found to be relatively resistant to infection demonstrated a block in HCV entry. These studies not only suggest that the majority of Huh7 cell lines in different laboratories are in fact highly permissive for HCV infection, but also identify phenotypically distinct Huh7 lines, which may facilitate studies investigating the cellular determinants of HCV infection