How Hepatitis C Virus Counteracts the Interferon Response: The Jury Is Still out on NS5A

AbstractInterferons (IFNs) induce an antiviral state in the cell through complex and indirect mechanisms, which culminate in a direct inhibition of viral replication and stimulation of the host adaptive responses. Viruses often counteract with elaborate strategies to interfere with the induction as well as action of IFN effector molecules. This evolutionary battle between viruses and IFN components is a subject of intense research aimed at understanding the immunopathogenesis of viruses and the molecular basis of IFN signaling and action. In the case with hepatitis C virus (HCV), this may have profound implications for the therapeutic use of recombinant IFN in treating chronic hepatitis C. Depending on the subtype of HCV, current IFN-based treatment regimens are effective for only a small subset of chronic hepatitis C patients. Thus, one of the Holy Grails in HCV research is to understand the mechanisms by which the virus may evade IFN antiviral surveillance and establish persistent infection, which may eventually provide insights into new avenues for better antiviral therapy. Despite the lack of an efficient tissue culture system and an appropriate animal model for HCV infection, several mechanisms have been proposed based on clinical studies and in vitro experiments. This minireview focuses on the HCV NS5A nonstructural protein, which is implicated in playing a role in HCV tolerance to IFN treatment, possibly in part through its ability to inhibit the cellular IFN-induced PKR protein kinase

Decoding the multifaceted HIV-1 virus-host interactome

Recently in BMC Medical Genomics, Tozeren and colleagues have uncovered virus-host interactions by searching for conserved peptide motifs in HIV and human proteins. Their computational model provides a novel perspective in the interpretation of high-throughput data on the HIV-host interactome

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information, and sequences, effectively collapsing very large datasets to less than 15% of their original size with no loss of information. Availability: Quip is freely available under the BSD license from http://cs.washington.edu/homes/dcjones/quip

A chemokine gene expression signature derived from meta-analysis predicts the pathogenicity of viral respiratory infections

<p>Abstract</p> <p>Background</p> <p>During respiratory viral infections host injury occurs due in part to inappropriate host responses. In this study we sought to uncover the host transcriptional responses underlying differences between high- and low-pathogenic infections.</p> <p>Results</p> <p>From a compendium of 12 studies that included responses to influenza A subtype H5N1, reconstructed 1918 influenza A virus, and SARS coronavirus, we used meta-analysis to derive multiple gene expression signatures. We compared these signatures by their capacity to segregate biological conditions by pathogenicity and predict pathogenicity in a test data set. The highest-performing signature was expressed as a continuum in low-, medium-, and high-pathogenicity samples, suggesting a direct, analog relationship between expression and pathogenicity. This signature comprised 57 genes including a subnetwork of chemokines, implicating dysregulated cell recruitment in injury.</p> <p>Conclusions</p> <p>Highly pathogenic viruses elicit expression of many of the same key genes as lower pathogenic viruses but to a higher degree. This increased degree of expression may result in the uncontrolled co-localization of inflammatory cell types and lead to irreversible host damage.</p

Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis

<p>Abstract</p> <p>Background</p> <p>High-throughput methods for obtaining global measurements of transcript and protein levels in biological samples has provided a large amount of data for identification of 'target' genes and proteins of interest. These targets may be mediators of functional processes involved in disease and therefore represent key points of control for viruses and bacterial pathogens. Genes and proteins that are the most highly differentially regulated are generally considered to be the most important. We present topological analysis of co-abundance networks as an alternative to differential regulation for confident identification of target proteins from two related global proteomics studies of hepatitis C virus (HCV) infection.</p> <p>Results</p> <p>We analyzed global proteomics data sets from a cell culture study of HCV infection and from a clinical study of liver biopsies from HCV-positive patients. Using lists of proteins known to be interaction partners with pathogen proteins we show that the most differentially regulated proteins in both data sets are indeed enriched in pathogen interactors. We then use these data sets to generate co-abundance networks that link proteins based on similar abundance patterns in time or across patients. Analysis of these co-abundance networks using a variety of network topology measures revealed that both degree and betweenness could be used to identify pathogen interactors with better accuracy than differential regulation alone, though betweenness provides the best discrimination. We found that though overall differential regulation was not correlated between the cell culture and liver biopsy data, network topology was conserved to an extent. Finally, we identified a set of proteins that has high betweenness topology in both networks including a protein that we have recently shown to be essential for HCV replication in cell culture.</p> <p>Conclusions</p> <p>The results presented show that the network topology of protein co-abundance networks can be used to identify proteins important for viral replication. These proteins represent targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat HCV infection.</p

Toll-Like Receptor 3 Signaling via TRIF Contributes to a Protective Innate Immune Response to Severe Acute Respiratory Syndrome Coronavirus Infection

ABSTRACTToll-like receptors (TLRs) are sensors that recognize molecular patterns from viruses, bacteria, and fungi to initiate innate immune responses to invading pathogens. The emergence of highly pathogenic coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) is a concern for global public health, as there is a lack of efficacious vaccine platforms and antiviral therapeutic strategies. Previously, it was shown that MyD88, an adaptor protein necessary for signaling by multiple TLRs, is a required component of the innate immune response to mouse-adapted SARS-CoV infection in vivo. Here, we demonstrate that TLR3−/−, TLR4−/−, and TRAM−/− mice are more susceptible to SARS-CoV than wild-type mice but experience only transient weight loss with no mortality in response to infection. In contrast, mice deficient in the TLR3/TLR4 adaptor TRIF are highly susceptible to SARS-CoV infection, showing increased weight loss, mortality, reduced lung function, increased lung pathology, and higher viral titers. Distinct alterations in inflammation were present in TRIF−/− mice infected with SARS-CoV, including excess infiltration of neutrophils and inflammatory cell types that correlate with increased pathology of other known causes of acute respiratory distress syndrome (ARDS), including influenza virus infections. Aberrant proinflammatory cytokine, chemokine, and interferon-stimulated gene (ISG) signaling programs were also noted following infection of TRIF−/− mice that were similar to those seen in human patients with poor disease outcome following SARS-CoV or MERS-CoV infection. These findings highlight the importance of TLR adaptor signaling in generating a balanced protective innate immune response to highly pathogenic coronavirus infections.IMPORTANCEToll-like receptors are a family of sensor proteins that enable the immune system to differentiate between “self” and “non-self.” Agonists and antagonists of TLRs have been proposed to have utility as vaccine adjuvants or antiviral compounds. In the last 15 years, the emergence of highly pathogenic coronaviruses SARS-CoV and MERS-CoV has caused significant disease accompanied by high mortality rates in human populations, but no approved therapeutic treatments or vaccines currently exist. Here, we demonstrate that TLR signaling through the TRIF adaptor protein protects mice from lethal SARS-CoV disease. Our findings indicate that a balanced immune response operating through both TRIF-driven and MyD88-driven pathways likely provides the most effective host cell intrinsic antiviral defense responses to severe SARS-CoV disease, while removal of either branch of TLR signaling causes lethal SARS-CoV disease in our mouse model. These data should inform the design and use of TLR agonists and antagonists in coronavirus-specific vaccine and antiviral strategies

Functional gene analysis of individual response to challenge of SIVmac239 in M. mulatta PBMC culture

AbstractIt has previously been shown in macaques that individual animals exhibit varying responses to challenge with the same strain of SIV. We attempted to elucidate these differences using functional genomics and correlate them to biological response. Unfractionated PBMC from three rhesus macaques were isolated, activated, and infected with SIVmac239. Interestingly, one of the three animals used for these experiments exhibited a completely unique response to infection relative to the other two. After repeated attempts to infect the PBMC from this animal, little or no infectivity was seen across the time points considered, and corresponding to this apparent lack of infection, few genes were seen to be differentially expressed when compared to mock-infected cells. For the remaining two animals, gene expression analysis showed that while they exhibited responses for the same groups of pathways, these responses included differences specific to the individual animal at the gene level. In instances where the patterns of differential gene expression differed between these animals, the genes being differentially expressed were associated with the same categories of biological process, mainly immune response and cell signaling. At the pathway level, these animals again exhibited similar responses that could be predicted based on the experimental conditions. Even in these expected results, the degree of response and the specific genes being regulated differed greatly from animal to animal. The differences in gene expression on an individual level have the potential to be used as markers in identification of animals suitable for lentiviral infection experiments. Our results highlight the importance of individual variation in response to viral challenge

Application of functional genomics to the chimeric mouse model of HCV infection: optimization of microarray protocols and genomics analysis

BACKGROUND: Many model systems of human viral disease involve human-mouse chimeric tissue. One such system is the recently developed SCID-beige/Alb-uPA mouse model of hepatitis C virus (HCV) infection which involves a human-mouse chimeric liver. The use of functional genomics to study HCV infection in these chimeric tissues is complicated by the potential cross-hybridization of mouse mRNA on human oligonucleotide microarrays. To identify genes affected by mouse liver mRNA hybridization, mRNA from identical human liver samples labeled with either Cy3 or Cy5 was compared in the presence and absence of known amounts of mouse liver mRNA labeled in only one dye. RESULTS: The results indicate that hybridization of mouse mRNA to the corresponding human gene probe on Agilent Human 22 K oligonucleotide microarray does occur. The number of genes affected by such cross-hybridization was subsequently reduced to approximately 300 genes both by increasing the hybridization temperature and using liver samples which contain at least 80% human tissue. In addition, Real Time quantitative RT-PCR using human specific probes was shown to be a valid method to verify the expression level in human cells of known cross-hybridizing genes. CONCLUSION: The identification of genes affected by cross-hybridization of mouse liver RNA on human oligonucleotide microarrays makes it feasible to use functional genomics approaches to study the chimeric SCID-beige/Alb-uPA mouse model of HCV infection. This approach used to study cross-species hybridization on oligonucleotide microarrays can be adapted to other chimeric systems of viral disease to facilitate selective analysis of human gene expression

Distinct cellular responses differentiating alcohol- and hepatitis C virus-induced liver cirrhosis

BACKGROUND: Little is known at the molecular level concerning the differences and/or similarities between alcohol and hepatitis C virus induced liver disease. Global transcriptional profiling using oligonucleotide microarrays was therefore performed on liver biopsies from patients with cirrhosis caused by either chronic alcohol consumption or chronic hepatitis C virus (HCV). RESULTS: Global gene expression patterns varied significantly depending upon etiology of liver disease, with a greater number of differentially regulated genes seen in HCV-infected patients. Many of the gene expression changes specifically observed in HCV-infected cirrhotic livers were expectedly associated with activation of the innate antiviral immune response. We also compared severity (CTP class) of cirrhosis for each etiology and identified gene expression patterns that differentiated ethanol-induced cirrhosis by class. CTP class A ethanol-cirrhotic livers showed unique expression patterns for genes implicated in the inflammatory response, including those related to macrophage activation and migration, as well as lipid metabolism and oxidative stress genes. CONCLUSION: Stages of liver cirrhosis could be differentiated based on gene expression patterns in ethanol-induced, but not HCV-induced, disease. In addition to genes specifically regulating the innate antiviral immune response, mechanisms responsible for differentiating chronic liver damage due to HCV or ethanol may be closely related to regulation of lipid metabolism and to effects of macrophage activation on deposition of extracellular matrix components