Proteomic analysis of hepatitis C virus (HCV) core protein transfection and host regulator PA28γ knockout in HCV pathogenesis: a network-based study

Data Warehousing with TargetMine for Omics Data Analysis

Most biological processes including diseases are multifactorial and determined by a complex interplay of various genetic and environmental factors. This chapter aims to provide a user guide to data querying, analysis, and visualization with TargetMine and the associated auxiliary toolkit. We have also discussed some of the commonly used data queries for the researchers who are interested in gene set analysis within a data warehouse framework. Overall, TargetMine provides a convenient web browser-based interface that enables the discovery of new hypotheses interactively, by performing analysis of omics data using complicated searches without any scripting and programming efforts on the part of the user and also by providing the results in an easy-to-comprehend output format.

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

Dissecting the regulation rules of cancer-related miRNAs based on network analysis

miRNAs (microRNAs) are a set of endogenous and small non-coding RNAs which specifically induce degradation of target mRNAs or inhibit protein translation to control gene expression. Obviously, aberrant miRNA expression in human cells will lead to a serious of changes in protein-protein interaction network (PPIN), thus to activate or inactivate some pathways related to various diseases, especially carcinogenesis. In this study, we systematically constructed the miRNA-regulated co-expressed protein-protein interaction network (CePPIN) for 17 cancers firstly. We investigated the topological parameters and functional annotation for the proteins in CePPIN, especially for those miRNA targets. We found that targets regulated by more miRNAs tend to play a more important role in the forming process of cancers. We further elucidated the miRNA regulation rules in PPIN from a more systematical perspective. By GO and KEGG pathway analysis, miRNA targets are involved in various cellular processes mostly related to cell cycle, such as cell proliferation, growth, differentiation, etc. Through the Pfam classification, we found that miRNAs belonging to the same family tend to have targets from the same family which displays the synergistic function of these miRNAs. Finally, the case study on miR-519d and miR-21-regulated sub-network was performed to support our findings.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format.

Database URL:http://targetmine.mizuguchilab.org

Proteomic approaches to analyzing hepatitis C virus biology

Hepatitis C virus (HCV) is a major cause of liver disease worldwide. Acute infection often progresses to chronicity resulting frequently in fibrosis, cirrhosis, and in rare cases, in the development of hepatocellular carcinoma. Although HCV has proven to be an arduous object of research and has raised important technical challenges, several experimental models have been developed all over the last two decades in order to improve our understanding of the virus life cycle, pathogenesis and virus-host interactions. The recent development of direct acting-agents, leading to considerable progress in treatment of patients, represents the direct outcomes of these achievements. Proteomic approaches have been of critical help to shed light on several aspect of the HCV biology such as virion composition, viral replication, and virus assembly and to unveil diagnostic or prognostic markers of HCV-induced liver disease. Here, we review how proteomic approaches have led to improve our understanding of HCV life cycle and liver disease, thus highlighting the relevance of these approaches for studying the complex interactions between other challenging human viral pathogens and their host.

Proteasome- and ethanol-dependent regulation of HCV-infection pathogenesis

This paper reviews the role of the catabolism of HCV and signaling proteins in HCV protection and the involvement of ethanol in HCV-proteasome interactions. HCV specifically infects hepatocytes, and intracellularly expressed HCV proteins generate oxidative stress, which is further exacerbated by heavy drinking. The proteasome is the principal proteolytic system in cells, and its activity is sensitive to the level of cellular oxidative stress. Not only host proteins, but some HCV proteins are degraded by the proteasome, which, in turn, controls HCV propagation and is crucial for the elimination of the virus. Ubiquitylation of HCV proteins usually leads to the prevention of HCV propagation, while accumulation of undegraded viral proteins in the nuclear compartment exacerbates infection pathogenesis. Proteasome activity also regulates both innate and adaptive immunity in HCV-infected cells. In addition, the proteasome/immunoproteasome is activated by interferons, which also induce "early" and "late" interferon-sensitive genes (ISGs) with anti-viral properties. Cleaving viral proteins to peptides in professional immune antigen presenting cells and infected ("target") hepatocytes that express the MHC class I-antigenic peptide complex, the proteasome regulates the clearance of infected hepatocytes by the immune system. Alcohol exposure prevents peptide cleavage by generating metabolites that impair proteasome activity, thereby providing escape mechanisms that interfere with efficient viral clearance to promote the persistence of HCV-infection.

Integrated pathway clusters with coherent biological themes for target prioritisation

Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

Unfolded protein response in hepatitis C virus infection

Hepatitis C virus (HCV) is a single-stranded, positive-sense RNA virus of clinical importance. The virus establishes a chronic infection and can progress from chronic hepatitis, steatosis to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The mechanisms of viral persistence and pathogenesis are poorly understood. Recently the unfolded protein response (UPR), a cellular homeostatic response to endoplasmic reticulum (ER) stress, has emerged to be a major contributing factor in many human diseases. It is also evident that viruses interact with the host UPR in many different ways and the outcome could be pro-viral, anti-viral or pathogenic, depending on the particular type of infection. Here we present evidence for the elicitation of chronic ER stress in HCV infection. We analyze the UPR signaling pathways involved in HCV infection, the various levels of UPR regulation by different viral proteins and finally, we propose several mechanisms by which the virus provokes the UPR.

Cytokeratin 8 is increased in hepatitis C virus cells and its ectopic expression induces apoptosis of SMMC7721 cells

AIM: To investigate cytokeratin 8 (CK8) overexpression during hepatitis C virus (HCV) infection and its pathogenesis, and the effect of ectopic CK8 expression on hepatoma cell lines.

METHODS: We successfully established an in vitro HCV cell culture system (HCVcc) to investigate the different expression profiles of CK8 in Huh-7-HCV and Huh-7.5-HCV cells. The expression of CK8 at the mRNA level was determined by real-time polymerase chain reaction (RT-PCR). The expression of CK8 at the protein level was evaluated by Western blotting. We then constructed a eukaryotic expression combination vector containing the coding sequence of human full length CK8 gene. CK8 cDNA was amplified by reverse transcription-PCR and inserted into pEGFP-C1 and the positive clone pEGFP-CK8 was obtained. After confirming the sequence, the recombinant plasmid was transfected into SMMC7721 cells with lipofectamine2000 and CK8 expression was detected using inverted fluorescence microscopy, RT-PCR and Western blotting. Besides, we identified biological function of CK8 on SMMC7721 cells, including cell proliferation, cell cycle and apoptosis detection.

RESULTS: RT-PCR showed that the expression level of CK8 in Huh-7-HCV and Huh-7.5-HCV cells was 2.88 and 2.95 times higher than in control cells. Western blot showed that CK8 expression in Huh-7-HCV and Huh-7.5-HCV cells was 2.53 and 3.26 times higher than that in control cells, respectively. We found that CK8 at mRNA and protein levels were both significantly increased in HCVcc. CK8 was up-regulated in SMMC7721 cells. CK8 expression at the mRNA level was significantly upregulated in SMMC7721/pEGFP-CK8 cells. CK8 expression in SMMC7721/ pEGFP-CK8 cells was 2.69 times higher than in SMMC7721 cells, and was 2.64 times higher than in SMMC7721/pEGFP-C1 cells. CK8 expression at the protein level in SMMC7721/pEGFP-CK8 cells was 2.46 times higher than in SMMC7721 cells, and was 2.29 times higher than in SMMC7721/pEGFP-C1 cells. Further analysis demonstrated that forced expression of CK8 slowed cell growth and induced apoptosis of SMMC7721 cells.

CONCLUSION: CK8 up-regulation might have a functional role in HCV infection and pathogenesis, and could be a promising target for the treatment of HCV infection.

A combined proteomics and computational approach provides a better understanding of HCV-induced liver disease

HCV is a major cause of chronic liver disease worldwide and is a formidable therapeutic challenge. Recently, Diamond et al. analyzed the proteomic profiles of liver samples from HCV-positive liver transplant recipients, supplemented with an independent metabolite analysis. They used a computational approach, which highlighted the enriched functional themes and topological attributes associated with the protein association network based on their clinical data and suggested a crucial role of oxidative stress in fibrosis progression in HCV infection. Their findings provide new insights into the mechanisms that regulate the progression of HCV-associated liver fibrosis, which may be useful for identification of suitable biomarkers to evaluate the onset and severity of hepatic fibrosis and the development of new therapeutic and anti-HCV strategies.