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. Author manuscript; available in PMC: 2017 Jan 19.
Published in final edited form as: Immunity. 2016 Jan 19;44(1):46–58. doi: 10.1016/j.immuni.2015.12.017

Limiting influenza virus, HIV and dengue virus infection by targeting viral proteostasis

Nicholas S Heaton 1,*, Natasha Moshkina 1,2,*, Romain Fenouil 3, Thomas J Gardner 1, Sebastian Aguirre 1, Priya S Shah 5, Nan Zhao 1,2, Lara Manganaro 1, Judd Hultquist 5, Justine Noel 1,2, David Sachs 3, Jennifer Hamilton 1, Paul E Leon 1, Amit Chawdury 6,7,8, Shashank Tripathy 1, Camilla Melegari 1,2, Laura Campisi 1,2, Rong Hai 1, Giorgi Metreveli 1,2, Andrea V Gamarnik 9, Adolfo García-Sastre 1,2,4, Benjamin Greenbaum 6,7,8, Viviana Simon 1,2, Ana Fernandez-Sesma 1,2, Nevan Krogan 5, Lubbertus CF Mulder 1,2, Harm van Bakel 3, Domenico Tortorella 1, Jack Taunton 5, Peter Palese 1, Ivan Marazzi 1,2
PMCID: PMC4878455  NIHMSID: NIHMS747801  PMID: 26789921

Abstract

Viruses are obligate parasites as they require the machinery of the host cell to replicate. Inhibition of host factors co-opted during active infection is a strategy to suppress viral replication and a potential pan antiviral therapy. To define the cellular proteins and processes required for a virus during infection is thus crucial to understanding the mechanisms of virally induced disease. In this report, we generated fully infectious tagged influenza viruses and used infection-based proteomics to identify pivotal arms of cellular signaling required for influenza virus growth and infectivity. Using mathematical modeling, genetic, and pharmacologic approaches, we revealed that modulation of Sec61-mediated cotranslational translocation selectively impaired glycoprotein proteostasis of influenza as well as HIV and dengue viruses, and led to inhibition of viral growth and infectivity. Thus, by studying virus-human protein-protein interactions in the context of active replication we have identified targetable host factors for broad-spectrum antiviral therapies.

Influenza A virus (IAV) is a major human pathogen and a global health threat (Shaw and Palese, 2013). Due to limited genomic space, IAV proteins have been shown to perform a multitude of functions in the host cell (Hale et al., 2008; Paterson and Fodor, 2012; Portela and Digard, 2002). As such, a molecular understanding of how each viral protein co-opts and interferes with cellular processes during infection is critical for the elucidation of mechanisms of pathogenesis and for the development of novel therapeutic strategies. To gain insight into virus-host protein interactions, studies have been performed using yeast 2-hybrid systems, complementation assays, or affinity purification of epitope tagged viral proteins transfected into cells (Bradel-Tretheway et al., 2011; de Chassey et al., 2013; Gorai et al., 2012; Guan et al., 2012; Jorba et al., 2008; Lin et al., 2012; Mayer et al., 2007; Munier et al., 2013; Ngamurulert et al., 2009; Shapira et al., 2009; Tafforeau et al., 2011). Although these approaches have indicated potential cellular factors targeted by viral proteins, they are limited by the fact that the mere expression of influenza proteins does not recapitulate key physiological aspects of the host-virus battleground. Essentially, these studies do not take into account the context of an infectious event, during which the dynamics between host and virus are co-regulated by the viral life cycle and the antiviral state that is established in the infected cell. As a result, these studies fail to capture: (i) activation of signaling networks and expression of cellular proteins in response to viral infection, (ii) large-scale modification of cellular structures/organelles (PML bodies, mitochondria, replication factories) induced by the infection, (iii) cooperation between viral proteins in performing pivotal functions of the viral life cycle (transcription / replication). In this report, we have described an approach for generating fully infectious viruses that harbor affinity purification tags and allow the study of virus-host protein interactions during active viral replication. This system was used to generate an interactome map of host and influenza virus proteins (and protein complexes) during an active infection. We identified, and validated experimentally, pivotal arms of host signaling utilized by the influenza virus to sustain its life cycle.

Guided by the infection proteomic network analysis, we focused on ER mediated processes, and specifically on Sec61-mediated regulation of cotranslational translocation and protein folding. We show that partial depletion and chemical inhibition of Sec61 specifically affect influenza virus HA (and NA) biosynthesis, and in turn, viral biogenesis. Using loss of function screening (Dengue virus) and mathematical modeling of how proteins segregate in interaction networks during infection with different pathogens (HIV-Influenza virus), we show that Sec61 partial depletion and chemical inhibition suppresses HIV and DENV virus replication with little to no effect on cellular proteostasis. We discuss the rationale of targeting essential cellular function as a strategy to develop pan-antiviral therapies.

Results

Fully infectious Flag-tagged influenza viruses generated by mutagenesis

In order to produce a global model of the virus-host relationship in the context of an active viral infection, we developed a system to generate fully infectious reporter viruses. In brief, we mutagenized IAV segments (PB1, PB2, PA, HA, NA, NS1, NEP, M2, M1, NP) to encode a Flag epitope in transposition-prone regions of the viral segments (Figure 1A) (Heaton et al., 2013b). The IAV reverse genetics system (Garcia-Sastre and Palese, 1993) utilized to rescue mutant viruses led to successful recovery of tagged and replicating virus for eight of the ten major viral proteins of the H1N1 PR8 influenza strain, A/Puerto Rico/8/1934 (Figure S1A). Viruses harboring tagged versions of the highly conserved matrix protein M1 or the nucleoprotein NP were not recovered, likely due to the high number of constraints on the structures of these proteins (Heiny et al., 2007; Noton et al., 2007; Portela and Digard, 2002). We tested the replication of the tagged viruses by performing multi-cycle growth curves on Madin-Darby Canine Kidney Epithelial Cells (MDCK) cells (Figure 1B). Although a slight delay in the kinetics of growth was observed with the PB2-Flag, M2-Flag, and NEP-Flag viruses relative to wild type (WT) or the other tagged viruses, the plaque forming units at 48 hour post-infection indicated that the tagged viruses displayed wild-type (PB1-Flag, PA-Flag, NS1-Flag, HA-Flag, NA-Flag) or near wild-type (PB2-Flag, M2-Flag, and NEP-Flag) titers (Figure 1C). To test whether our compendium of Flag viruses retained the tagged segments, we sequenced viruses after several rounds (2-4) of viral stock amplification and found that the tags were retained, indicating that the insertion sites were stable (Figure S1B). To assess whether the tagged segments were expressed and if viral protein complexes were formed during Flag-tagged viral infection, we set up an affinity purification of Flag-PB2 after infecting A549 human lung epithelial cells. This led to the recovery of PB2, PA, PB1, and NP (Figure S1C), indicative of the isolation of biologically relevant viral ribonucleoprotein complexes (vRNPs). Finally, we tested viral fitness in an animal model of infection. BALB/c mice were infected with doses ranging from 10-105 PFUs and analysis of morbidity and mortality indicated that all the viruses had comparable median lethal doses (Figure 1D), even in cases where the kinetics of weight loss were slightly reduced (Figure 1E). Taken together, the stability and replication properties of the Flag-tagged viruses provided us with useful tools for characterizing the molecular mechanisms of viral infection.

Figure 1. Development of fully infectious Flag-tagged influenza viruses.

Figure 1

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(A) Schematic representation of the strategy used to generate the compendium of the tagged influenza Flag-viruses. (B) MDCK cells were infected at an MOI=0.001. Multi-cycle viral growth was quantified by hemagglutination assay at the indicated time points. (C) Viral titer (PFU) of flagged-viruses and control at 48hr post-infection. Means and s.d. are shown. (D) Percentage survival in BALB/c mice (n=5) after infection with Flag tagged viruses. (E) Morbidity in BALB/c mice (n=5) after infection with Flag tagged viruses. 6-8 week old female BALB/c mice were challenged with the indicated doses of virus and body weight was monitored over a 14-day time course. Means and s.d. are shown. See also Figure S1.

Influenza virus-host protein interactions during infection

We then aimed to generate a map of host-influenza protein interactions during an active infection. We infected the human lung epithelial cell line A549 with each of the eight Flag-tagged viruses (Figure S2A, schematic of the experimental strategy) and used both untagged WT virus as well as Flag-green fluorescent protein (GFP) as controls. Flag-affinity purified preparations of each viral bait, along with the co-immunoprecipitated host and viral factors, were then submitted for mass spectrometry protein identification, using previously published protocols optimized for the identification of viral-host interactions (Jager et al., 2011). Key to such analysis was the use of i) biologically replicated data sets to control for reproducibility and specificity, ii) interaction data sets originating from wild-type (non-tagged) virus and Flag-GFP expression, which both removed all the resident and infection-induced proteins that bound non-specifically to the Flag antibody or resin, iii) stringent statistical criteria for data analysis [(Mist score ≥ 0.9; see Tables S1 to S4 with raw data and processed data; Supplemental Experimental Procedures for statistical analysis and Figure S2B-D for visual representation of stringency of the cutoff in relationship with known false-positive abundant contaminants and published data set (Watanabe et al., 2014)]. We thus identified a set of high-confidence interactors and generated an influenza-host interactome during active infection (Figure 2A; Figures S2E and S3; Tables S1-S3 and Table S4 for additional biased filtering based on biological rationales).

Figure 2. Infectious-proteomics of influenza virus proteins and validation of targeted pathways.

Figure 2

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(A) The interacting virus proteins (green) and KEGG pathway membership (red) are indicated for each host protein. KEGG pathway enrichment scores (-10log10 P-value) for interactors with each viral protein are indicated in blue (see color scale). Boxes highlight clusters of interactions. (B) A549 cells were treated with the inhibitors Castanospermine, Oligomycin A, UK5099, Bortezomib and Spliceostatin in a range of concentrations where no cytotoxicity was detected and viral growth was estimated by Renilla Luciferase Assay at 12, 24, 36 and 48 hours after infection. Data represent means and s.d. of three biological replicates. Note that high concentration of UK5099 has also been suggested to inhibit mitochondrial respiration (Divakaruni and Murphy, 2012; Halestrap, 2012). Luciferase read outs are normalized to DMSO. Statistical analysis between datasets was performed using a two-tailed student's t-test. For all panels, *p≤0.05, **p≤0.005, ***p≤0.0005, ns=not significant (indicated in the order: lower concentration first, followed by higher concentration of inhibitors). See also Figures S2-S3.

The proteome interaction network revealed several findings. First, we detected common sets of host proteins that were immunoprecipitated by distinct viral proteins (Figure 2A; Figure S2E). This reflected the physiological formation of vRNPs during infection, as suggested by the relationship between viral proteins and known cellular functions (Figure 2A GO category, blue boxes) and complexes (Figure S2E). This indicated the main ‘nodes’ of interaction of influenza with the cellular proteome.

Second, we detected potentially novel interactions, such as those between viral Polymerase complex (vPol) and the CTCF complex, which controls the 3D structure of the genome, and suggests that chromatin insulators could play a role in controlling viral replication and/or antiviral gene expression. We also detected the targeting of important cellular complexes such as the SPT complex, which regulates the synthesis of sphingolipids and it is co-opted by other viruses (Schneider-Schaulies and Schneider-Schaulies, 2015) and the CCT complex, which regulate protein folding and it is targeted by a different influenza strain (Fislova et al., 2010)] (Figure S2E). These results suggest that inhibition of these cellular complexes may represent strategies for interspecies- and pan-viral inhibition. Third, our analysis confirmed previous findings, including the targeting of RNAPII transcriptional complexes by the influenza replicative machinery (PA, PB1 and PB2), that underscored the importance of cellular transcription for viral ribogenesis (Amorim et al., 2007; Engelhardt et al., 2005) as well as the interaction of M2 and NA viral proteins with components of the cellular respiration complex (Alsuwaidi et al., 2013; Ritter et al., 2010) (Figure S3). Fourth, our data allowed for a comparison between previously generated global proteomic interaction datasets performed in the absence of infection (Watanabe et al., 2014) (Figure S2, B and D). To validate the role of host pathways during infection, we pharmacologically inhibited proteins controlling specific targeted complexes identified by hierarchical functional clustering of host pathways co-opted by viral proteins (Figure 2A GO category, blue boxes and Table S2). We measured viral replication of IAV in A549 cells upon treatment with inhibitors controlling the top most statistically significant host pathways identified in Figure 2A (underlined GO category). We used: Castanospermine (alpha-glucosidase inhibitor) for endoplasmic reticulum (ER)-processes, UK5099 (MCP pyruvate carrier inhibitor) and Olygomycin A (inhibitor of the Fo subunit of the ATP synthase) for events linked to oxidative phosphorylation; Bortezomil (inhibitor of the 26S proteasome); Spliceostatin A (SF3B-inhibitor) for spliceosome inhibition. Our results evidenced that targeting of nuclear (SF3B) and cytosolic (alpha-glucosidases) factors can suppress viral replication (Figure 2B) and indicated that cotranscriptional events and host factors controlling protein glycosylation and apical trafficking could be targetable interfaces for antiviral activity (see further).

Sec61A1 is a required host factor for influenza virus replication

We then decided to characterize in detail the most highly enriched biological pathway of protein processing in the ER (Figure 2A, KEGG pathway analysis; p=10−9) and specifically the heterotrimeric Sec61 complex, which is essential for the biogenesis of most secretory and membrane proteins (Gogala et al., 2014).

Although Sec61 had not been previously studied in the context of influenza virus infection, it scored in our proteomic analysis as a high-confidence interactor with the viral proteins HA and NA, but not M2 (Figure 3A, Table S3). All three vial membrane proteins likely require transient association with Sec61 for cotranslational insertion into the ER, but only HA and NA have large glycosylated extracellular domains with complex folding requirements (Braakman et al., 1991; Copeland et al., 1986; Copeland et al., 1988; Daniels et al., 2003; Hebert et al., 2014; Hebert et al., 1997; Saito et al., 1995; Wang et al., 2008). In agreement with this, we also noted that HA and NA, but not M2, bound Calnexin (CANX), a molecular chaperone that physically associates with the Sec61 translocon and assists glycoproteins folding and maturation (Figure 3A) (Caramelo and Parodi, 2008; Lakkaraju et al., 2012; Lynes et al., 2013). These shared attributes, along with i) unique regulatory mechanisms of protein maturation occurring during infection (Molinari et al., 2004; Pieren et al., 2005), ii) the possibility of prolonged substrate-specific interaction with the translocon required for protein folding and oligomeric assembly (Conti et al., 2015; Fayadat and Kopito, 2003) and iii) the fact that viruses must biosynthesize large amount of proteins in a short time frame of infection, led us to hypothesize that, a) Sec61A1 might be an important host factor controlling influenza biogenesis and, b) that Sec61 transient inhibition could be well tolerated by the cell but not by the virus.

Figure 3. Sec61A1 controls HA surface expression levels and influenza viral replication.

Figure 3

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(A) Schematic of influenza proteins interaction with Sec61 and CANX based on KEGG enrichment overrepresented in ER pathway. (B) Post-nuclear lysate of A549 cells infected with HA-Flag virus was fractionated over an OptiPrep gradient. Fractions were analysed via western blot for the indicated proteins. (C) A549 cells were treated with mock or infected with influenza virus HA-Flag. The HA protein was immunoprecipitated with anti-Flag or control beads. Co-immunoprecipitated proteins were detected by western blot. Representative of 2 experiments is shown. (D) A549 cells were treated with the Sec61 inhibitor CT8 at the indicated concentrations for 2 hours. Effects on viral titer (left) and cellular viability (right) are shown at time point 18 hours. (E) HEK293 cells that are Ct8 sensitive (WT) or insensitive (Mut) were infected with Flag-HA at an MOI=3 and treated with indicated concentrations of CT8. Total cell lysates were split in three parts, one was used to isolate total surface membrane proteins and detect via western blotting the levels HA and MHC-I. The other two thirds were subjected to immunoprecipitation with the HA specific antibodies that recognize monomers (PY102) or trimers (6F12). Total and trimeric HA were detected by western blot (WB) and their amounts were analyzed by densitometry quantification done by single area measurements of the bands. HA and MHC-I amounts at different CT8 concentrations are quantified as percent of samples treated with DMSO. The same film was used for both HA detection and quantification. A representative of two experiments is shown. (F-H) A549 cells were treated with control or Sec61A1 targeting siRNAs. 48 hours after knockdown, cells were infected with wild-type PR8 for 8 hours. Cells were fixed and stained to detect surface expression of HA (F), M2 (G), or MHC-I (H). For F-H, the percent of cells positive for expression and the mean fluorescent intensity of positive cells are shown. For all panels, *p≤0.05, **p≤0.001, ns=not significant. See also Figure S4.

We first validated the IAV and HA-Sec61 translocon interaction by density gradient fractionation of post-nuclear lysates derived from IAV-infected A549 cells (Figure 3B) and by co-immunoprecipitation (Figure 3C). These results indicated that the interaction of HA with the Sec61 translocon was occurring during, and possibly after, the obligate step of co-translational translocation into the ER.

We then assessed the effect of Sec61 perturbation on viral replication by transient and partial depletion of Sec61 via small interfering (si)RNA or by chemical inhibition. The latter was achieved by using a small-molecule inhibitor (CT8) that can specifically affect Sec61-dependent co-translational translocation depending on the physicochemical properties of a given substrate (Besemer et al., 2005; Garrison et al., 2005; Kalies and Romisch, 2015; Mackinnon et al., 2014).

As shown in Figure 3D (left panel), increasing concentrations of the inhibitor CT8 led to a dose-dependent decrease in the release of IAV infectious viral particles with minimal effects on cellular viability (Figure 3D, right panel). Similarly, siRNA knockdown of Sec61A1 led to a reduction in the levels of Sec61 (Figure S4, A and B) and a concomitant reduction in the release of infectious IAV particles (Figure S4C) with no major effects on cellular viability (Figure S4D) or viral entry (Figure S4E).

To determine whether the reduced infectivity in the presence of CT8 was caused by reduced HA expression, we performed orthogonal biotinylation and subsequent purification of cell surface HA (Figure 3E, upper panel). We performed this experiment in both wild-type (WT) as well as Sec61A1 mutant cells which lacked CT8 sensitivity (Mackinnon et al., 2014). Our results showed that, upon infection, CT8 treatment decreased HA membrane protein in WT cells but not in mutant cells (Figure 3E, upper panel). These data genetically linked Sec61A1 with the specificity of HA inhibition by CT8.

Similarly, flow cytometry analysis revealed that transport of HA to the plasma membrane was significantly reduced after treatment with siRNAs targeting Sec61A1 (Figure 3F). As controls, we looked at surface levels of M2, another virally encoded transmembrane protein that is transported from the ER to the plasma membrane and the host MHC-I protein, a type I membrane protein that utilizes the Sec61 translocon for membrane insertion (Schnell and Hebert, 2003). Strikingly, M2 and MHC-I displayed no reduction in surface expression under Sec61A1 depletion (Figure 3, G and H). These results suggest that some viral glycoproteins, possibly the ones that have prolonged interaction with Sec61 like HA and NA (Figure 3A), are more sensitive to translocon inhibition than other membrane proteins. These results were further supported by metabolic chase analysis monitoring the initial step of HA synthesis and folding (Braakman et al., 1991) in which we observed a delay in glycans maturation in CT8-treated cells (Figure S4, F and G) and in Sec61A1 depleted cells compared to controls (Figure S4H).

Finally, since the physiologically active form of HA is trimeric, we assessed HA oligomerization with respect to Sec61 protein levels. Control and CT8 treated WT and Sec61A1-mutant cells were infected with IAV HA-Flag viruses, followed by immunoprecipitation of monomeric and homo-trimeric HA (Figure 3E, middle and lower panel). The results revealed that the diminished protein levels of surface HA caused by CT8 is primarily the result of reduced trimer formation (Figure 3E, middle panel), which occurs to a greater extent than reduced monomer formation (Figure 3E, lower panel). Overall, these data indicate that diminished cellular level or chemical perturbation of Sec61 results in the temporal delay of the physiological processing and maturation of HA resulting in diminished functional HA surface expression and viral infectivity. This interpretation is supported by the fact that the rate of folding of HA is dependent on cellular factors (Braakman et al., 1991). Delayed folding will then result in a decrease of HA which is more evident at the trimeric level due to the fact that oligomerization assembly approximates second order reactions (Grasberger et al., 1986).

Mathematical modeling of influenza and HIV interactions

As different viruses often target common host proteins, we compared our proteomic data sets for influenza to those previously published for HIV (Jager et al., 2012). We mathematically inferred (Menche et al., 2015) the distance relatedness of host interaction partners targeted by the two viruses (Figure 4A). Enrichment analysis of the most closely related partners indicated that “protein localization to ER” (Figure 4A, red dots indicating paired interactions) is the most significant process both viruses utilize during infection. Analysis of influenza HA-HIV protein pairwise interactions (see Supplementary methods) prompted the investigation of the HIV glycoprotein (gp)120 as a potential target of Sec61 inhibition.

Figure 4. Sec61A1 controls HIV-1 Env surface expression levels and viral replication.

Figure 4

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(A) Scatterplot containing the gene ontology categories that applied to the top five most closely related interaction partners. The y-axis is the number of paired interaction partners that generated the category (SAB<0), and the x-axis is the negative log of the category's average p-value across all its generating partners. Red highlights ontology category related to ER-mediated events. (B) Full-length HIV-1 genomic constructs expressing GFP were transfected into 293T cells treated with control or Sec61A1 siRNAs. Surface gp120 data are indicated as the fluorescent index of transfected cells (GFP) expressing HIV-1 Env (see Methods). Expression in cells treated with control siRNA (SiCont) was set to 100%. HIV-1 Env was probed with the monoclonal antibodies 2G12 (monomeric form) and PG9 (trimeric form). Experiment done 3 times in triplicates. (C) Infectivity of viruses was determined using 3× serial dilutions of viral supernatants of control and Sec61A1 siRNAs treated cells transfected with full length HIV-1 and measured using the TZM-bl luciferase reporter cell line (one representative silencing experiment of 3 is shown). (D) Three HIV-1 strains were used to infect 3×105 A3R5 T-cell line (per well) in the presence of different concentrations the inhibitor CT8. Viral replication growth curve, measured by the infectivity of a fixed volume of viral supernatant harvested at the indicated days, is shown. Experiment done in triplicate for each viral strain. (E) Cellular viability from (C) measured the end point of the experiment (day 7) (F) HEK293T cells transfected with full-length HIV-1 genomic constructs (R7/3-33A GFP and LAI GFP) were treated with increasing amounts of CT8. Monomeric and trimeric gp120 expression was determined by staining with 2G12 and PG9 antibodies, respectively, followed by flow cytometry analysis. Results are expressed as the ratio between the fluorescent index of cells expressing trimeric gp120 (probed with PG9), and the fluorescent index of cells expressing momomeric gp120 (probed with 2G12). Three independent experiments for each CT8 concentration and for each viral strain. All error bars represent s.d. and *p <0.05, **<0.01. See also Figure S5.

Sec61 controls HIV biogenesis and can be targeted to suppress HIV infectivity

We first analyzed gp120 trafficking in the context of Sec61A1 knockdown. siRNA treated cells were transfected with plasmids encoding full-length HIV-1 clone R7.3 33A expressing GFP in the Nef position (Chakrabarti et al., 2002; Lue et al., 2002), and gp120 surface expression was assessed by flow cytometry with conformation specific antibodies that recognize gp120 in monomeric (2G12) and trimeric (PG9) forms (Figure S5, A and B). Our data indicated that, at similar transfection efficiency (Figure S5A), reduced amount of Sec61 affected the surface detection of gp120 (Figure 4B). The effect was more prominent when cells were probed with the PG9 antibody which was consistent with gp120 trimer formation being particularly sensitive to low Sec61 protein levels in the cell (Figure 4B). Accordingly, impaired surface expression of gp120 also leads to a reduction in HIV-1 infectivity (Figure 4C). These data show that physiological amount of Sec61A1 protein are important for HIV-1 biogenesis.

We then used chemical inhibition of Sec61 to validate the specific requirement for the Sec61 complex in HIV-1 replication. We performed multi-cycle viral growth experiments by infecting the A3R5.7 T-cell line with 3 different HIV strains in the presence of increasing concentrations of CT8. Growth of all three HIV-1 strains was significantly inhibited (Figure 4D), with no major reduction in cellular viability at seven days post-treatment (Figure 4E). Finally, we quantified trimeric versus monomeric gp120 surface expression in the presence of increasing concentrations of CT8. The results of this analysis performed on 2 viral strains show that the detection of gp120 trimer was inhibited in a dose-dependent manner by CT8 (Figure 4F) with CT8 not having an impact on cell viability (Figure S5C).

Sec61 controls DENV biogenesis and can be targeted to suppress DENV infectivity

We then focused our attention on dengue virus (DENV), an emerging pathogen and a global threat that relies heavily on the ER to coordinate viral assembly and life cycle (Diamond and Pierson, 2015; Lindenbach and Rice, 1999). As global proteomic mapping for DENV is not available, we knocked down Sec61 and other 12 factors controlling ER events (GO category 0005789) and then monitored viral replication (Figure 5A). Our analysis revealed that only Sec61 depletion resulted in suppression of DENV replication (Figure 5A). Prompted by this, we infected monocyte-derived dendritic cells (MDDCs) from 3 independent human donors and assessed DENV mRNA dynamics and DENV replication in the presence and absence of CT8. Our analysis showed that CT8 inhibited DENV mRNA expression and replication in a dose dependent manner (Figure 5, B and C), with no effect on cell viability (Figure 5D).

Figure 5. Sec61A inhibition suppresses DENV replication.

Figure 5

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(A) Heatmap of DENV-luc reporter replication in cells knockdown for the indicated host factors (left). Degree of knockdown of each respective factor is shown (right). (B) Human dendritic cells from three individual donors were infected with MOCK, or DENV-2 (16681) using a MOI=0.5, and total RNA was extracted at 24, 48 and 72 hpi. relative expression of viral RNA was measured by qRT-PCR at specific time points normalized against rsp11. (C) Accumulation of infectious DENV particles in the supernatant was quantified by plaque assay at 12, 24, 48 and 72 hpi for the three individual donors. Error bars indicate s.d of the mean from duplicate samples. (D) Macrophage-derived dendritic cells (MDDC) were incubated with MOCK, CT8 100nM, or CT8 500nM using the same final concentration of DMSO for the three conditions. Treatment was stopped at 24h, 48h, and 72h and cytotoxicity was measured using CytoTox 96® Non-Radioactive Cytotoxicity Assay (Promega). DCs lysate was used as a positive control for the assay (see Supplemental Experimental Procedures). Error bars indicate standard deviations of the mean from duplicate samples.

Discussion

We reported the interaction network of influenza during an active infection event. An unbiased hierarchical clustering of the host pathways affected by each viral protein led to several functional groupings of viral proteins. This approach, analyzing protein-protein interaction datasets through an integrated view of cellular and viral complexes can serve to identify critical cellular “nodes” which, upon disruption, could have effects on multiple viral proteins at the same time. As a validation of this method, we characterized the contribution (with respect to viral replication) of inhibiting novel nuclear and cytosolic host factors that control co-opted host pathways.

One of the major strengths of our approach, the fact that viral protein complexes like RNPs are formed during infection, also complicates the interpretation of the data. Independent purification of different viral proteins can lead to the recovery of the same viral complexes, and consequently, we observed that different samples were enriched for some of the same host factors. It is therefore difficult in certain cases to determine a unique binding partner of a particular factor, for which quantitative analysis of binary interactions might be useful (Jager et al., 2012).

Finally, among the strengths of our strategy is that i) it is physiologically relevant ii) it can be applied to in vivo infection models, and iii) it can reveal important cognate viral protein interactions (Kuo and Krug, 2009; Zhao et al., 2010) the molecular understanding of which could shed light on the viral genomic segment incompatibility often seen in viral re-assortment studies. Elucidating the basis of this mechanism could help predicting the fixation of genomic segments between different species that are at the basis of increased viral pathogenicity and pandemic potential.

The host factor that we focused on in this study, Sec61A1, represents an essential host factor whose inhibition affects the proteostasis of influenza virus, HIV, DENV and potentially other viruses (Iwasa et al., 2011; Panda et al., 2013). We show that HA is highly sensitive to Sec61A1 levels, much more than M2 or MHC-I and that Sec61A1 inhibition leads to reduced HA trimer formation and subsequent surface expression. Interestingly, previous works have both hypothesized (Fayadat and Kopito, 2003) and shown (Pitonzo and Skach, 2006; Pitonzo et al., 2009; Skach, 2007), that Sec61-mediated translocation can directly play a role in protein folding and processing. Other works have suggested that nascent chain-Sec61 interaction dictates cotranslational events (Conti et al., 2015). In line with these, our data show that when Sec61A1 is inhibited or its levels are reduced, a larger percentage of HA is not glycosylated and possibly terminally misfolded. Alternatively, partially misfolded HA might still engage in oligomerization generating aberrant trimers not detected with trimer specific antibodies. In this case, a single misfolded monomer could ‘poison’ two correctly folded HA and generate HA trimers with defect in membrane insertion, budding, and trypsin sensitivity. Interestingly such defective HA conformers are formed constitutively in considerable amount during infection, previously estimated to be 10% of total HA (Copeland et al., 1986). This suggests the existence of regulators or cellular antagonists to proper HA trimerization. Strikingly, defective HA trimers level increases in infected cells when both Calnexin and Calreticulin activity is compromised (Molinari et al., 2004).

As such, alteration of glycosylation-deglycosylation kinetics and quality control, as revealed in both our current and previous studies, results in the inhibition of viral growth and underlines unique folding requirement for viral glycoproteins (Pieren et al., 2005). The recent finding that Calnexin is in physical contact with the translocon (Lakkaraju et al., 2012) and can be regulated following short-term ER stress (Lynes et al., 2013) further supports this interpretation and indicates that signaling can regulate viral glycoproteostasis.

Another important aspect of our study is revealed by our metabolic-chase experiments in the presence of CT8. Although the more characterized consequence of Sec61 inhibition by CT8 is proteasome-dependent destruction of non-translocated substrates (Besemer et al., 2005; Garrison et al., 2005), recent evidences indicate that a delay in the initiation of translocation (as opposed to a complete block) can differentially interfere with the expression of cellular and prion proteins (Conti et al., 2015). Indeed, our results indicate that altering Sec61A1 levels results in delayed HA translocation and trimer formation.

Viral proteins whose correct folding and ER exit require oligomerization, e.g. HA and NA, (Ceriotti and Colman, 1990; Copeland et al., 1988; Hogue and Nayak, 1992; Saito et al., 1995; Wang et al., 2008), may be highly sensitive to the net flux into the ER and therefore to Sec61 activity. When the flux of protein translocation is reduced, HA monomers may fall into a kinetic trap, entering a terminally misfolded state. In support of this hypothesis, previous reports show that HA oligomeric assembly is dependent on the expression level of HA while the rate of folding is independent from it (Braakman et al., 1991; Ceriotti and Colman, 1990). This observation would also explain why, during infection, highly expressed viral oligomeric proteins are preferentially affected by Sec61 level. In agreement with this, HIV-1 gp160/gp120 is also known to oligomerize before ER exit (Earl et al., 1990; Earl et al., 1991; Land et al., 2003) and shows the same sensitivity to the reduction in Sec61A1 levels or CT8 treatment. In line with this model, M2 and MHC-I are not known to have an ER oligomerization requirement.

Why inhibition of an essential cell function can be a valid antiviral therapy? Overall, our data indicate that the viral biogenesis of DENV, HIV and IAV is regulated by Sec61 levels and, as such, Sec61 chemical inhibition can suppress viral growth with little to no effect on cellular homeostasis. The basis of this is likely the result of both the unique requirement for viral glycoprotein folding and maturation (Pieren et al., 2005) and the fact that, unlike the majority of cellular proteins, viral proteins rely on high synthetic rates and high expression levels during the short time frame of acute infection (Braakman et al., 1991; Ceriotti and Colman, 1990). The advantage of using, for the purpose of containing an infection, inhibitors such as the one described here (CT8) and others that lead to derailment of N-linked glycoprotein maturation [i.e. glucosyl-inhibitors, see reviews (Dalziel et al., 2014; Hebert et al., 2014)], stands on the intrinsic requirements of viral life cycles. Based on this, transient (and partial) inhibition of an essential cell function becomes particularly detrimental for the synthesis of viral proteins and viral biogenesis, while it is well tolerated by the host cell. In our case, there seems to be a CT8-substrate-specific effect on HA, likely dependent on the substrate primary sequence. In these respect, inhibitors of protein translocation across the ER membrane have been developed and display partial substrate specificities and allosteric effect on many targets (Kalies and Romisch, 2015). Since virus can mutagenize their proteins easily, retaining broad inhibitory capacity of chemical features (i.e. hydrophobicity of signal peptide) could be advantageous with respect to specific inhibitors with one substrate.

In essence, the infectious-based strategy delineated here is potentially applicable to any virus to guide i) functional studies ii) the analysis of disease-disease relationship and the identification of common targets for inhibition of multiple viruses, and iii) the search for novel antiviral therapies.

In conclusion, we provide a path and a biologically relevant approach to study the interplay between viruses and their hosts.

Experimental Procedures

Cells and virus growth

293T, A549 and MDCK cells (ATCC) were primarily used with respect to influenza virus studies. For HIV-1 experiments, the cell line A3R5.7 and TZM-bl, were obtained from the NIH AIDS Reagent Program. We used human DC in the studies of DENV replication (see Supplementary methods). All cells were maintained in DMEM supplemented with 10% fetal calf serum, L-glutamine, and Pen/Strep. Cells are routinely checked for contamination using MycoAlert (Lonza). For infection, virus was diluted in PBS supplemented with 3% BSA and used to infect cells for one hour at 37C followed by replacement of culture media. For multi-cycle growth curves, MDCK cells were infected at an MOI=0.001. For determination of titer, virus was plaqued on MDCK cells as previously described (Heaton et al., 2013a).

Cloning and rescue of Flag-viruses

Flag epitopes were introduced into the viral coding regions in the ambisense pDZ vector. Flag-epitope insertion was achieved by inserting the Flag sequence into overlapping primers and InfusionHD cloning (Clontech). Insertion sites were cloned in after the following nucleotides, utilizing existing amino acids wherever possible: HA-Flag; 437, NA-Flag; 206, NS1-Flag; 515, NEP-Flag; 531, M2-Flag; 800, PA-Flag, PB1-Flag, PB2-Flag express the Flag epitope on the C-terminus with duplicated RNA packaging signals of 184, 160, 169 nucleotides respectively. In cases where a packaging signal was duplicated, the original packaging signal was eliminated via the introduction of silent mutations. The insertion sites are based on the sequences of the viral segments corresponded to GenBank accession numbers: AF389115, AF389116, AF389117, AF389118, AF389119, AF389120, AF389121, and AF389122). Clones were sequence verified and rescued via 293T transfection and amplification in embryonated chicken eggs. In all cases, viruses were plaque or dilution purified. RT-PCR and sequencing of viral stocks was used to confirm insertion of Flag-epitope in rescued viruses.

Immunoprecipitation and Mass Spectrometry

We employed an Ap-ms strategy similar to (Miller et al., 2015). Briefly, for each Flag-influenza protein purification, 4×108 were infected for 10 hours at an MOI=3, collected and lyzed in 20 ml of cold lysis buffer (0.2% NP40, 50 mM Tris–HCl pH 7.5; 150 mM NaCl, 1 mM EDTA, protease and phosphatase inhibitors). Douncing and mild sonication (5 cycle 30′ON/OFF with Diagenode Bioruptor) were followed by Centrifugation for 30′ at 13K. The supernatant was precleared with dynabeads (No Ab) for 2h at 4C. To the precleared extract, 300 μl of Flag-dynabeads were added and incubated for 2 h at 4 °C to allow immunoprecipitation of Flag-bound complexes. After 6 washes in Wash Buffer (0.2% NP40, 50 mM Tris–HCl pH 7.5, 200 mM NaCl, 1 mM EDTA), 2 extra washes were conducted in no-detergent wash buffer. Immunocomplexes were then eluted with 3×Flag-peptide with 100 μg/ml of 3×Flag peptide (3 elution of 15′ each). Eluted material was then precipitated overnight with acetone. A brief spin (13K for 15 seconds) was used to pellet the precipitated proteins that were then resolubilized, trypsin-digested and subjected to ms-ms.

Mass Spectrometry Analysis

Samples were analyzed by the Proteomics Resource Center at The Rockefeller University. Proteins were denatured, reduced, alkylated, and proteolytically digested with endoproteinase LysC (Wako Chemicals), and subsequently with trypsin (Promega). Peptides were desalted and analyzed by reversed phase nano-HPLC-MS/MS (Ultimate 3000 coupled to QExactive from Thermo Scientific). Data were extracted and searched against Human Uniprot database using Proteome Discoverer 1.4 (Thermo Scientific) and Mascot 2.4 (Matrix Science). Identified peptides were filtered using 1% false discovery rate and Percolator (Kall et al., 2007). In the table, proteins are sorted out according to their relative abundance, which is represented by the peak area. The protein area was calculated by the software as an average of the areas of the three most abundant peptides for that protein.

Supplementary Material

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2
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4
5

Acknowledgments

We would like to thank Ryan Langlois and Megan Shaw, Andres Finzi for their expertise and helpful discussions, Daria Brinzevich and Raymond Alvarez for their technical assistance, and Hector Delgado for his artwork in graphical abstract. We would also like to thank the flow cytometry shared resource facilitiy as well as the high performance scientific computing facility at the Icahn School of Medicine at Mount Sinai and the proteomics facility at Rockefeller University. N.S.H. is a Merck fellow of the Life Sciences Research Foundation. V.S. is partially supported by the NIH-NIAID grants R01 AI089246 and P01 AI090935. D.T. is partially supported by NIH Grant AI101820. P.P., A.G., G.M., H.vB. and I.M. are partially supported by HHSN272201400008C - Center for Research on Influenza Pathogenesis (CRIP) a NIAID-funded Center of Excellence for Influenza Research and Surveillance (CEIRS). L.C.F.M is supported by a NIH-NIGMS grant R01 GM113886. N.M. is supported in part by a Public Health Service Institutional Research Training Award (AI07647). I.M. is supported in part by The Department of Defense W911NF-14-1-0353 (to I.M.) and by NIH grants U19AI106754 (to I.M. and A.G-S.), 1R01AN3663134 (to I.M and H.vB) and 1R56AI114770-01A1 (to I. M.)

Footnotes

See also Supplemental Experimental Procedures for detailed protocols

Author contributions: N.M., N.Z., C.M., L.C., and I.M. performed experiments to identify virus:host interactions during infection. R.F. and H.vB. performed the IAV-host interactome bioinformatics analysis. A.C. and B.G. did the HIV-influenza virus disease-disease relationship analysis. D.S. and N.K. helped with bioinformatics analysis. J.H., P.E.L., A.V.G. and N.K. performed dengue siRNA screen and analysis. S.A. and A.F-S performed DENV analysis in primary cells. N.S.H., J.H., P.E.L., S.T., R.H., G.M., and A.G-S. and P.P. performed mutagenesis, viral rescue and Sec61 loss of function studies during IAV infection. T.J.G., N.Z., and D.T. designed and performed pulse-chase and fractionation experiments. J.T. provided CT-8, and CT8-sensitive and insensitive cells and helped with experimental design. L.M., V.S. and L.C.F.M. designed and executed experiments with HIV. J.N. and C.M. carried out experiments with inhibitors for IAV. S.T., G.M., and A.G-S. helped with IAV rescue, passaging, sequence of the tagged viruses and experimental design. I.M. coordinated the study and wrote the paper with suggestions from all the authors.

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Supplementary Materials

1
NIHMS747801-supplement-1.pdf (1.9MB, pdf)
2
NIHMS747801-supplement-2.xls (212KB, xls)
3
NIHMS747801-supplement-3.xls (51.5KB, xls)
4
NIHMS747801-supplement-4.pdf (13.5MB, pdf)
5
NIHMS747801-supplement-5.xlsx (20.7KB, xlsx)

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