The IE0–IE1 interaction contributed to the expression of other IE genes in a distinct manner.

<p>(A) IE mRNA abundance in <i>ie0</i>, <i>ie1</i> single, and <i>ie0</i>/<i>ie1</i> double knockout viruses 6 hours post-transfection. Error bars are standard errors of the estimated means. Asterisks indicate significant regulatory functions: **, <i>q</i>-value < 0.05. The <i>q</i>-value is the <i>p</i>-value adjusted using the False Discovery Rate. (B) Values used for model fitting to estimate the contribution of IE0 alone, IE1 alone, the IE0–IE1 interaction to the expression of other IE genes, and the rest. Top, real-time qPCR data for the expression of <i>ie2</i>, as an example. The mean expression levels of <i>ie2</i> were estimated using a linear model, with the viral genotypes indicated below. Letters C, D, and E in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g005" target="_blank">Fig. 5C, D, and E</a> refer to the letters C (control), D (Δie0), and E (Δie0/ie1) above the columns in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g005" target="_blank">Fig. 5B</a>. Bottom, the values assigned to the variables in the model for the single gene and interaction effects. The values (0 or 1) indicate presence or absence of the genes or interaction (indicated by the row label) in the genotype (indicated by the column below the matrix). The values were used to fit the linear model shown in F. (C, D, F) Schematic representations of the decomposition of the contribution of single genes and the IE0–IE1 interaction to IE gene expression. Red and blue bars indicate the positive and negative contribution of each gene or interaction to IE gene expression, respectively. (C) The wild-type virus had a complete set of genes. (D) The <i>ie0</i> knockout virus lacked the contribution of IE0 to IE gene expression, and naturally lacked the IE0–IE1 interaction. (E) <i>ie0</i>/<i>ie1</i> knockout virus lacks IE0, IE1, and the IE0–IE1 interaction. (F) The linear model used to estimate the contribution of single gene and interaction effects. The values (0 or 1) shown in B were assigned to the gene and interaction variables (IE0, IE1, IE0:IE1, ME53, PE38, and REST). (G) Estimated contribution of IE0, IE1, and the IE0–IE1 interaction to the other IE genes. Note that the height of the bars indicates the absolute values of the estimated contributions. Red and blue bars indicate the positive and negative contribution of each gene or interaction to IE gene expression, respectively. Error bars are standard errors of the estimated means. (H) Transient expression analysis to confirm the effects of the IE0–IE1 interaction on the expression of <i>ie2</i> and <i>pe38</i>. The <i>ie2</i> or <i>pe38</i> promoter plasmids were co-transfected with the plasmid(s) expressing the IE gene(s), indicated as “Driver IE gene” in the column. The estimated mean values of the IE gene cassettes were adjusted by the value of the corresponding pGEM-transfected samples. The reported estimated mean values are the sums of the estimated effect size of the basal promoter activity and the IE gene on the promoter. Estimation of the values was performed using the mixed linear model. Error bars are standard errors of the estimated means for the IE gene effects. Two sets of experiments to estimate the random variation in a biological replicate were done (These replicates were defined as “set” and “replicate” in Materials and Methods, respectively). Luciferase activity was measured in transfected cells at 48 hours post-transfection. Error bars are standard errors of the estimated means.</p

Tightly Regulated Expression of <i>Autographa californica</i> Multicapsid Nucleopolyhedrovirus Immediate Early Genes Emerges from Their Interactions and Possible Collective Behaviors

<div><p>To infect their hosts, DNA viruses must successfully initiate the expression of viral genes that control subsequent viral gene expression and manipulate the host environment. Viral genes that are immediately expressed upon infection play critical roles in the early infection process. In this study, we investigated the expression and regulation of five canonical regulatory immediate-early (IE) genes of <i>Autographa californica</i> multicapsid nucleopolyhedrovirus: <i>ie0</i>, <i>ie1</i>, <i>ie2</i>, <i>me53</i>, and <i>pe38</i>. A systematic transient gene-expression analysis revealed that these IE genes are generally transactivators, suggesting the existence of a highly interactive regulatory network. A genetic analysis using gene knockout viruses demonstrated that the expression of these IE genes was tolerant to the single deletions of activator IE genes in the early stage of infection. A network graph analysis on the regulatory relationships observed in the transient expression analysis suggested that the robustness of IE gene expression is due to the organization of the IE gene regulatory network and how each IE gene is activated. However, some regulatory relationships detected by the genetic analysis were contradictory to those observed in the transient expression analysis, especially for IE0-mediated regulation. Statistical modeling, combined with genetic analysis using knockout alleles for <i>ie0</i> and <i>ie1</i>, showed that the repressor function of <i>ie0</i> was due to the interaction between <i>ie0</i> and <i>ie1</i>, not <i>ie0</i> itself. Taken together, these systematic approaches provided insight into the topology and nature of the IE gene regulatory network.</p></div

IE gene regulatory functions observed in the transient expression analysis.

<p>Luciferase activity was measured in cells 48 hours post-transfection in all pairwise combinations of the reporter and IE gene expression plasmids. (A) Comparison of the responsiveness of IE gene promoters to each co-expressed IE gene. The IE gene cassettes and the reporter plasmid used in each measurement are indicated in the columns below the panel and in the title of the panel, respectively. The estimated mean expression values of the IE gene cassettes are reported, after accounting for the value of the corresponding pGEM-transfected sample. The reported estimated mean values are the sums of the estimated effect of basal promoter activity and the effect of the tested gene on the promoter. Estimation of the values was performed using the mixed linear model. Error bars are the standard errors of the estimated means for the IE gene effects. Two sets of experiments, each containing a biological replicate, to estimate the random variation were done (These replicates were defined as “set” and “replicate” in Materials and Methods, respectively). (B) Comparison of the regulatory activities of the IE genes on each promoter. The reported values are the estimated gene effects. The IE gene cassette and the reporter plasmid used in each measurement are indicated in the title and in the columns of the panels, respectively. Asterisks indicate significant regulatory functions: *, <i>q</i>-value < 0.1; **, <i>q</i>-value < 0.05. The <i>q</i>-value is the <i>p</i>-value adjusted using the False Discovery Rate.</p

Network analysis of IE gene regulatory functions.

<p>(A) A network graph of the IE gene regulatory functions observed in the transient expression analysis. Orange and blue links indicate positive and negative regulatory functions, respectively. The width of the links corresponds proportionally to the absolute values of the ratio values shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g002" target="_blank">Fig. 2B</a>, representing the strength of the regulatory function. (B) The number of links to each IE gene in our network model. “In” and “out” represent incoming links to and outgoing links from the IE genes as indicated below. (C) Normalized betweenness of IE genes in our IE gene network model. (D) Possible path redundancy in the IE gene regulatory network. The simulation of genetic perturbations was performed by calculating the shortest path length following removal of one IE gene from the model. Crossed cells indicate combinations of input and output IE genes in which the shortest path length could not be computed. Shaded cells indicate the isolation of the output IE gene due to the removal of IE genes connected with it.</p

Study and construct designs.

<p>(A) Study design. <i>ie0</i>, <i>ie1</i>, <i>ie2</i>, <i>me53</i>, and <i>pe38</i> were selected because they are validated or inferred regulators of IE gene expression. 1, Plasmids for transient expression analysis were constructed from the AcMNPV-C6 genome. Transient expression analyses to measure the IE gene promoter activities were performed in all combinations of the five IE gene expression cassettes and six reporter plasmids (five containing IE gene promoters and one empty vector). The results obtained from this were used in a network analysis (2). 3, Reverse genetic analyses were performed using bacmids that each lacked one of the 5 IE genes. IE gene transcription in the knockout viruses was quantified using quantitative real-time PCR. 4, The results obtained in the transient expression analysis and the genetic analysis were compared and used to build and test hypotheses. (B and C) The designs of constructs used for transient expression analysis. (B) The reporter plasmids. Approximately 500 bp of the sequence upstream of the IE ORFs were used as the IE gene promoters in this study. (C) IE gene expression plasmids. The IE gene ORFs and the upstream sequences used in the reporter plasmids were inserted into a pGEM-Teasy vector carrying a fibroin poly(A) signal. (D) Schematic representation of the generation of IE gene knockout viruses. The entire IE gene ORFs, from the initiation codons to the termination codons, were replaced with an ampicillin resistance gene. (E) The structure of immediate early ie0 transcript and the genomic region used to knock out <i>ie0</i>. Immediate early ie0 transcript consists of two exons. Δ<i>ie0</i> bacmid was generated by replacing a part of <i>Orf141</i> ORF with an ampicillin resistance gene.</p

IE gene regulatory functions observed in the reverse genetic analysis.

<p>The abundance of IE mRNA in cells transfected with each IE gene knockout virus 6 hours post-transfection was quantified using real-time PCR. (A) Comparison of IE gene expression level in each knockout virus. The estimated mean copy numbers of each transcript (indicated by the label on the panel) with each genotype (indicated by the columns below the panel) represented as heights of the bars (the vertical axis). The values are the sums of the estimated steady-state expression level in the control virus and the estimated genotype effect of the knockout virus. Error bars are standard errors of the estimated copy numbers. These values were estimated by fitting a mixed linear model. Asterisks indicate significant regulatory functions: *, <i>q</i>-value < 0.1; **, <i>q</i>-value < 0.05. The <i>q</i>-value is the <i>p</i>-value adjusted using the False Discovery Rate. The reported values are calculated from three technical replicates and three biological replicates. (B) Comparison of IE gene regulatory activity on IE gene transcription. The estimated genotype effect on the copy number of each transcript (indicated by the columns below the panel) used in (A) multiplied by -1, represented as the estimated regulatory activity of the wild-type viral gene. The vertical axis indicates quantity of regulatory activity and direction of the regulation. (C) Comparison of IE gene regulatory functions in the genetic analysis using knockout viruses to those in the transient expression analysis using the reporter assay. The values and statistics are shown in Figs. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g002" target="_blank">2B</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119580#pone.0119580.g004" target="_blank">4B</a> for the “Reporter assay” and “Genetics”, respectively. The logarithmic bases of the values for the transient expression analysis and the genetic analysis are 10 and 2, respectively. Shaded cells indicate transcripts not measured because the genes were removed from the genome. Bold boundaries indicate significant regulatory relationships (<i>q</i>-value < 0.1). Asterisks indicate a genotype–phenotype paradox, in which a perturbation to a gene with an activator function or no regulatory function in the transient expression analysis resulted in enhanced target gene expression in the genetic analysis.</p

Binding of HSV-1 Glycoprotein K (gK) to Signal Peptide Peptidase (SPP) Is Required for Virus Infectivity

<div><p>Glycoprotein K (gK) is a virion envelope protein of herpes simplex virus types 1 (HSV-1) and 2 (HSV-2), which plays important roles in virion entry, morphogenesis and egress. Two-hybrid and pull-down assays were utilized to demonstrate that gK and no other HSV-1 genes specifically binds to signal peptide peptidase (SPP), also known as minor histocompatibility antigen H13. SPP dominant negative mutants, shRNA against SPP significantly reduced HSV-1 replication <i>in vitro</i>. SPP also affected lysosomes and ER responses to HSV-1 infection. Thus, in this study we have shown for the first time that gK, despite its role in fusion and egress, is also involved in binding the cytoplasmic protein SPP. These results also suggest that SPP plays an important role in viral replication and possibly virus pathogenesis. This makes SPP unique in that its function appears to be required by the virus as no other protein can compensate its loss in terms of viral replication.</p></div

Blocking HSV-1 replication <i>in vitro</i> by SPP shRNA.

<p><u>A) Viral Titer is reduced by SPP knockdown</u>. RS cells were transfected for 24 hr with either SPP shRNA or scramble shRNA and infected with 0.1 PFU/cell of HSV-1 strain McKrae. Titers were measured by standard plaque assays at 2.5, 5, 7.5, 10, 20 and 40 hr PI. Each point represents the mean ± SEM from 3 independent experiments per time point; B<u>) HSV-1 gene expression is reduced by SPP knockdown</u>. RS cells were transfected and infected as above. Transfected and infected cells were harvested 2, 4, 6, 8 and 20 hr PI, RNA extracted and cDNA synthesized. Expression of tk, gB and gK were measured using qRT-PCR and each point represents the mean ± SEM from 3 independent experiments; and <u>C) HSV-1 protein expression is reduced by SPP knockdown.</u> RS cells were transfected and infected as in A for 24 hr PI. Cells were stained with anti-HSV-1-gC-FITC (green) and costained with DAPI (blue). Photomicrographs are shown at 10X magnification.</p

Signal Peptidase Complex Subunit 1 Participates in the Assembly of Hepatitis C Virus through an Interaction with E2 and NS2

<div><p>Hepatitis C virus (HCV) nonstructural protein 2 (NS2) is a hydrophobic, transmembrane protein that is required not only for NS2-NS3 cleavage, but also for infectious virus production. To identify cellular factors that interact with NS2 and are important for HCV propagation, we screened a human liver cDNA library by split-ubiquitin membrane yeast two-hybrid assay using full-length NS2 as a bait, and identified signal peptidase complex subunit 1 (SPCS1), which is a component of the microsomal signal peptidase complex. Silencing of endogenous SPCS1 resulted in markedly reduced production of infectious HCV, whereas neither processing of structural proteins, cell entry, RNA replication, nor release of virus from the cells was impaired. Propagation of Japanese encephalitis virus was not affected by knockdown of SPCS1, suggesting that SPCS1 does not widely modulate the viral lifecycles of the <i>Flaviviridae</i> family. SPCS1 was found to interact with both NS2 and E2. A complex of NS2, E2, and SPCS1 was formed in cells as demonstrated by co-immunoprecipitation assays. Knockdown of SPCS1 impaired interaction of NS2 with E2. Our findings suggest that SPCS1 plays a key role in the formation of the membrane-associated NS2-E2 complex via its interaction with NS2 and E2, which leads to a coordinating interaction between the structural and non-structural proteins and facilitates the early step of assembly of infectious particles.</p></div

Reverse Genetics for Fusogenic Bat-Borne Orthoreovirus Associated with Acute Respiratory Tract Infections in Humans: Role of Outer Capsid Protein σC in Viral Replication and Pathogenesis

<div><p>Nelson Bay orthoreoviruses (NBVs) are members of the fusogenic orthoreoviruses and possess 10-segmented double-stranded RNA genomes. NBV was first isolated from a fruit bat in Australia more than 40 years ago, but it was not associated with any disease. However, several NBV strains have been recently identified as causative agents for respiratory tract infections in humans. Isolation of these pathogenic bat reoviruses from patients suggests that NBVs have evolved to propagate in humans in the form of zoonosis. To date, no strategy has been developed to rescue infectious viruses from cloned cDNA for any member of the fusogenic orthoreoviruses. In this study, we report the development of a plasmid-based reverse genetics system free of helper viruses and independent of any selection for NBV isolated from humans with acute respiratory infection. cDNAs corresponding to each of the 10 full-length RNA gene segments of NBV were cotransfected into culture cells expressing T7 RNA polymerase, and viable NBV was isolated using a plaque assay. The growth kinetics and cell-to-cell fusion activity of recombinant strains, rescued using the reverse genetics system, were indistinguishable from those of native strains. We used the reverse genetics system to generate viruses deficient in the cell attachment protein σC to define the biological function of this protein in the viral life cycle. Our results with σC-deficient viruses demonstrated that σC is dispensable for cell attachment in several cell lines, including murine fibroblast L929 cells but not in human lung epithelial A549 cells, and plays a critical role in viral pathogenesis. We also used the system to rescue a virus that expresses a yellow fluorescent protein. The reverse genetics system developed in this study can be applied to study the propagation and pathogenesis of pathogenic NBVs and in the generation of recombinant NBVs for future vaccines and therapeutics.</p></div