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      Affinity Purification of the Hepatitis C Virus Replicase Identifies Valosin-Containing Protein, a Member of the ATPases Associated with Diverse Cellular Activities Family, as an Active Virus Replication Modulator

      , , , , , , , ,
      Journal of Virology
      American Society for Microbiology

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          ABSTRACT

          Like almost all of the positive-strand RNA viruses, hepatitis C virus (HCV) induces host intracellular membrane modification to form the membrane-bound viral replication complex (RC), within which viral replicases amplify the viral RNA genome. Despite accumulated information about how HCV co-opts host factors for viral replication, our knowledge of the molecular mechanisms by which viral proteins hijack host factors for replicase assembly has only begun to emerge. Purification of the viral replicase and identification of the replicase-associated host factors to dissect their roles in RC biogenesis will shed light on the molecular mechanisms of RC assembly. To purify the viral replicase in the context of genuine viral replication, we developed an HCV subgenomic replicon system in which two different affinity tags were simultaneously inserted in frame into HCV NS5A and NS5B. After solubilizing the replicon cells, we purified the viral replicase by two-step affinity purification and identified the associated host factors by mass spectrometry. We identified valosin-containing protein (VCP), a member of the ATPases associated with diverse cellular activities (AAA+ATPase) family, as an active viral replication modulator whose ATPase activity is required for viral replication. A transient replication assay indicated that VCP is involved mainly in viral genome amplification. VCP associated with viral replicase and colocalized with a viral RC marker. Further, in an HCV replicase formation surrogate system, abolishing VCP function resulted in aberrant distribution of HCV NS5A. We propose that HCV may co-opt a host AAA+ATPase for its replicase assembly.

          IMPORTANCEAlmost all of the positive-strand RNA viruses share a replication strategy in which viral proteins modify host membranes to form the membrane-associated viral replicase. Viruses hijack host factors to facilitate this energy-unfavorable process. Understanding of this fundamental process is hampered by the challenges of purifying the replicase because of the technical difficulties involved. In this study, we developed an HCV subgenomic replicon system in which two different affinity tags were simultaneously inserted in frame into two replicase components. Using this dual-affinity-tagged replicon system, we purified the viral replicase and identified valosin-containing protein (VCP) AAA+ATPase as a pivotal viral replicase-associated host factor that is required for viral genome replication. Abolishing VCP function resulted in aberrant viral protein distribution. We propose that HCV hijacks a host AAA+ATPase for its replicase assembly. Understanding the molecular mechanism of VCP regulates viral replicase assembly may lead to novel antiviral strategies targeting the most conserved viral replication step.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search

            We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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              A statistical model for identifying proteins by tandem mass spectrometry.

              A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.
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                Author and article information

                Journal
                Journal of Virology
                J. Virol.
                American Society for Microbiology
                0022-538X
                1098-5514
                October 14 2016
                November 01 2016
                November 01 2016
                August 24 2016
                : 90
                : 21
                : 9953-9966
                Article
                10.1128/JVI.01140-16
                5068543
                27558430
                8577a1d5-b3be-4350-8796-64781ab72c35
                © 2016
                History

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