Identification of a human protein network that physically interacts with 10 viral proteins
To identify host factors that may participate in the pathogenesis of influenza infection, we first sought to identify those factors that are directly manipulated through physical associations with viral proteins. We used a yeast 2-hybrid (Y2H) approach to systematically identify direct binary contacts amongst the 10 major viral proteins of the H1N1 influenza strain A/PR/8/34 (‘PR8’), as well as between each viral protein and each of ~12,000 human proteins available in the Human ORFeome v3.1 collection (Lamesch et al., 2007
). We discovered 31 intra-viral interactions (out of 55 possible interactions, including homodimers) among the ten viral proteins (, Table S1A
), and 135 pairwise interactions between the 10 viral proteins and 87 human proteins (‘H1
’ genes, , Table S1B
), 73 of which are expressed in primary human bronchial epithelial cells (HBECs). These included the previously reported association between NS1 and STAU1, interactions between NS1 and PRKRA and TARBP2 (regulators of PKR-mediated transcription; for a review of NS1-host interactions, see Hale et al., 2008
), as well as interactions between influenza and eight proteins that are targeted by other viruses (Table S1B
). Several known associations were not observed, either because the interacting protein was not among the 12,000 proteins in our assays (e.g.
TRIM25 (Gack et al., 2009
) and DDX58/RIG-I (Pichlmair et al., 2006
), or for unknown reasons (e.g.
we did not detect the PKR-NS1 interaction (Li et al., 2006
), yet we identified the kinase that phosphorylates PKR).
A map of viral-human protein interactions identifies a dense interconnected network, coupled to key cellular signaling pathways. (A) Viral-viral interactions
The connectivity pattern of the intra-viral and viral-human network revealed three important principles. First, the influenza intra-viral network is extremely interconnected (, Table S2A
), consistent with findings from other viruses (Bailer and Haas, 2009
). This may be required for forming compact virions and functional viral complexes. Second, influenza proteins are linked on average with a significantly greater number of human proteins than expected from the human interaction network (13.5 vs.
6.5 expected, P
<0.06, permutation test), even when compared to other viruses (Table S2A
), or when comparing to the full 12,000 prey human network (data not shown). This may reflect the fact that a virus has to maximize the diversity of functions per protein. Third, some of the human proteins contact a greater than expected number of influenza proteins (24 human proteins interact with at least 2 flu proteins, P
, permutation test, , Table S2B
). These may be required for the formation of viral-host multi-protein complexes.
The H1 proteins form inter-connected hubs within the cellular protein network, suggesting that the virus targets proteins that play a central role in their respective cellular pathways. The 87 H1 proteins connect with each other through 51 interactions and with other human proteins (first neighbors; ‘H2 genes’) through 2717 interactions, a higher than expected connectivity (P<10-5, permutation test). The higher density of interactions is observed even when excluding highly connected H1 proteins, or when considering only the H1 proteins associated with individual viral proteins ().
Network parameters of influenza and cellular protein interaction networks
Furthermore, we identified a core cellular sub-network that is enriched for H1
, hypergeometric test; Experimental Procedures, Table S3
). This sub-network contains six H1
proteins that bind at least three other H1
TRAF2, DVL2, FXR2) and 37 non-H1
proteins that fit the same criteria (e.g.
p53, PKR, ILF3 and PSMF1, none of which contacts any viral protein directly). The network (Figure S1
) consists of diverse proteins including RNA-binding proteins (see below for more details on functional assays), regulators of ubiquitination and sumoylation, transcription factors, mediators of apoptosis, and components of immune signaling pathways (, S1 and S2
Our observations also hold in another influenza strain, the H3N2 A/Udorn/72 influenza virus (‘Udorn’). Using the same Y2H approach, we detected 81 interactions between 10 Udorn viral proteins and 66 human proteins (U-H1
). Of these, 56 human proteins also interact with PR8 (P
; hypergeometric test, Table S1C and Figure S3A
), including most RNA binding proteins, regulators of transcription, protein transport and signaling (Figure S3B
). For example, out of 30 signaling proteins and 19 transcription factors that directly interact with PR8 (PR8-H1
) (), 28 and 16 proteins (respectively) also directly bind to Udorn proteins (Figure S3B
). Most (63%) of the H1
proteins associated with PR8 polymerase subunits (PB1, PB2, PA), NP or NS1 were also found to interact with their counterparts in the Udorn strain (Figure S3C and Table S1B
), reflecting conserved functions of viral proteins.
Viral proteins interact with the NF-κB, apoptosis and WNT pathways primarily through NS1 and polymerase subunits
To identify the key cellular pathways coupled to the virus, we considered the 87 H1
and 566 H2
cellular proteins from a manually curated database (IPA), and found that they are enriched for components of several signaling pathways (Table S4A
, ; see Experimental Procedures). 30 of the 87 H1
interactors couple the virus to six major pathways, including p53-, PML- and TNFR/Fas-mediated apoptosis, NF-κB and WNT/β-catenin (). The interactions with these pathways are conserved for the Udorn strain (Figure S3C
), suggesting that the discovered interactions reflect a generalized strategy of influenza to manipulate the host. A role for these pathways in viral infections has been described (e.g. p53, Turpin et al., 2005
), yet their direct physical association with influenza proteins was not previously reported.
Functional decomposition of transcriptional responses to influenza infection identifies viral-specific gene regulation
While NS1 is considered the major viral protein to modulate host signaling, we found that 26 of the 30 H1
proteins associated with these pathways interact with viral polymerase subunits and NP, but only 8 interact with NS1 (). In particular, H1 and H2
interactors of PB1, PB2, NP are highly enriched (P
, Fisher's combined probability test) for the six key pathways, but those of PA are not (; this result also holds for Udorn, Figure S3C
). This suggests that viral polymerase proteins may also act as direct modulators of host signaling pathways.
Expression profiling of the response to viral infection in primary human lung epithelial cells
We next defined the major transcriptional responses in primary human bronchial epithelial cells (HBECs) after either infection with influenza or treatment with relevant ligands. We used four different strategies, each highlighting distinct components of the response. (1) We infected cells with the wild-type PR8 influenza virus that can mount a complete replicative cycle. (2) We transfected cells with viral RNA (‘vRNA’) isolated from influenza particles. This does not result in the production of viral proteins or particles and identifies the effect of RNA-sensing pathways (e.g.
, RIG-I.). (3) We treated cells with interferon beta (IFNβ), to distinguish the portion of the response that is mediated through Type I IFNs. (4) We infected cells with a PR8 virus lacking the NS1 gene (‘ΔNS1’). The NS1 protein normally inhibits vRNA- or IFNβ-induced pathways, and its deletion can reveal an expanded response to infection. We could not assess the role of any other viral protein but NS1, since deletion strains cannot be propagated easily (Wressnigg et al., 2009
). For each of the four stimuli, we profiled the cellular transcriptional response at ten time points (.25, .5, 1, 1.5, 2, 4, 6, 8, 12, and 18 hours) in duplicate experiments.
Transcription patterns decompose the response into interferon-, RNA- and virus-responsive genes
We found twelve major temporal and functional patterns of gene expression (C1
, ), each associated with one or more stimulus, and covering 1056 genes that were all regulated in response to viral infection (VRGs, virus-regulated genes). Among these, we found 666 interferon-regulated genes (IRGs) that are affected by interferon directly (325 induced, C1
, and 341 repressed, C9
, ). All of the IRGs are similarly affected by vRNA transfection and ΔNS1 virus but with an observed time delay, likely due to the induction of IFNβ by these stimuli. PR8 infection induces IRGs to a much lower level (with few exceptions, C2
, 57 genes), and abrogates the downregulation of 49 IRGs (C9
). This is consistent with the known role of NS1 in dampening RNA sensing and downstream interferon production (Pichlmair et al., 2006
) (see Figure S4
Next, we found 721 RNA-regulated genes (RRGs) that are directly modulated by transfected vRNA (380 induced, C1-C5; 341 repressed, C9-C10, ). All of the RRGs are similarly affected by ΔNS1 virus infection, while 171 are regulated by PR8 infection. Thus, viral RNA present in the infecting virion and produced during modest viral replication (as with ΔNS1 virus) can induce a potent response. Induced RRGs (C1-5) were enriched for antigen presentation, apoptosis, NFκB and IRF signaling (P<10-4-10-17; hypergeometric test, ).
Most of the induced RRGs are also induced by interferon treatment (C1-C4, 325 of 380), but a few IFNβ-independent RRGs (C5) were induced only by vRNA and ΔNS1 virus. These include important antiviral genes (e.g. IFNB1, IL7R, ING3, IRF2, PELI1) and are enriched for TLR pathway components, cytokines, chemokines, and cell cycle and apoptosis (P<10-3, ). An IFNβ-independent mechanism (e.g. IRF3 based on promoter sequence analysis, data not shown) likely mediates the transcription of these genes.
Finally, we identified virus-specific response genes (VSRGs) that are transcriptionally regulated following PR8 or ΔNS1 virus infection, but not following vRNA transfection or IFNβ treatment (C6-8 and C11-12). 68 VSRGs are induced only by ΔNS1 (C6), and are enriched for regulators of apoptosis and NFκB (e.g. BCL10, TRAF6, NFKB1 and NFKBIE). NS1 may block their induction and dampen the NFκB pathway by an unknown, RNA- and IFN-independent mechanism. 60 VSRGs are induced by both PR8 and ΔNS1 (C7) and are enriched for regulators of apoptosis, cell cycle and transcription factors (P<10-3, ). Finally, 31 VSRGs (C8) are induced only by PR8, possibly directly by NS1 or as the result of the higher burden of a replicating virus.
NF-κB, MAPK and apoptosis pathways are regulated through both transcriptional and physical interactions
Some of the host systems affected at the transcriptional level by viral infection may be linked to the virus through physical interactions. Indeed, we found that the cellular network of direct interactors (H1) and first neighbors (H2) is enriched for genes that are transcriptionally regulated upon viral infection (70 of 1056 VRGs, P<4×10-4, hypergeometric test). For example, the NS1 neighborhood is enriched in C6 (P<0.01), a VSRG cluster induced only in response to infection with ΔNS1 virus (e.g. NFKB1, BCL10). Similarly, the neighborhood of the polymerase subunit PB2 and NP is also enriched in C6 (P<7×10-4), further supporting the potential role of the viral polymerase in modulating host pathways in concert with NS1.
While some cellular pathways are uniquely associated with either the physical network (e.g. WNT, Ras/Rho) or transcriptional responses (e.g. Type I IFN and antigen presentation), many are enriched for both (P<3.5×10-7, hypergeometric test, ). These include p53-mediated apoptosis, PML, NFκB, MAPK and p38 signaling. These pathways are mostly associated with rapidly and highly-induced IRGs (C2) or VSRGs that are inhibited by NS1 (C6). Thus, the virus physically engages critical pathways while inducing transcriptional changes in their components.
Functional interrogation of viral interactors and transcriptionally responsive genes
The physical interactions, transcriptional responses and associated pathways together identified 1745 candidate genes that could impact influenza infection. These included 1056 genes that were transcriptionally regulated, 259 direct interactors and their first neighbors (H1/H2) (67 of them are also transcriptionally regulated), and 504 further candidates predicted from our analyses (e.g. pathway members) that are expressed in HBECs.
To test the functional contribution of these genes to viral replication and type I IFN production, we measured the effect of perturbing each gene using targeted siRNA pools in three functional assays. In the viral replication assay, we infected siRNA-transfected primary HBECs with PR8 virus and measured virus production after 48 hours using a cellular reporter system that is analogous to conventional plaque assays (Experimental Procedures). In two independent assays, we used a reporter cell line to measure levels of IFNβ in siRNA-transfected HBECs in response to ΔNS1 virus infection or vRNA transfection.
We determined the relative effect of each of the 1745 siRNA pools in each assay using a statistical scoring approach (Experimental Procedures) that identifies significant changes in phenotypes relative to the background of all tested genes. Since we selected a focused set of candidates for functional testing, this scoring approach is highly conservative. Furthermore, because cell number impacts production of IFN, we used AlamarBlue to determine cellular viability following siRNA knockdown and to effectively normalize IFN values to the number of cells in each well. We used a 2-fold threshold (see Experimental Procedures) to identify genes whose perturbation significantly impacted the phenotypes evaluated in each of the three assays, distinguishing positive and negative regulators of each phenotype.
616 of the 1745 candidate genes affected at least one of the phenotypes significantly. The number of genes with two or more significant phenotypes is substantially higher than expected by chance (P<10-4
, permutation test, Figure S5
). These included all the major sources of candidate genes, including 361 transcriptionally responsive genes, 88 direct interactors (H1
) and first neighbors (H2
), and 174 additional members of identified pathways. This suggests that many of the transcriptional and physical target pathways play an important role in infection.
Distinct functional signatures for regulators of IFN production and viral replication
We divided the 616 validated genes into 20 ‘phenoclusters’ based on the combinatorial behavior of each gene across the three functional assays (). vRNA-dependent regulators of IFNβ production are members of phenoclusters in which IFNβ levels changed in response to vRNA (211 positive regulators, P1-6
; 145 negative regulators, P7
). These genes correctly include many well-known regulators of Type I IFN, both activators (e.g.
, VISA, IRF3, RELA, IκBKγ, IκBKε, IκBKβ, and IRF9) and repressors (e.g.
, PTPN6, IRF2). Some of these genes did not affect PR8 replication (P1,2,7,8. e.g.
, IFNβ in P1
), likely because the NS1 protein already ensures low levels of IFNβ post-infection. Others (P3,4,9,10, e.g.
IRF3, IRF2) had opposing effects on IFN levels and viral replication, including genes (P4,10
) that were essential for IFNβ-dependent anti-viral effects even in NS1-inhibited cells. Genes that were not previously known to affect IFN production included a potential ubiquitin ligase complex (CUL1, FBXO34), regulators of vesicle trafficking (e.g.
CHMP6, ARL4A), peroxisomal components (PEX14), WNT pathway genes (below), and genes known to be involved in the life cycle of other viruses (e.g.
, TMF1 binding to the HIV TATA element (Table S5
; Garcia et al., 1992
Functional interrogation and classification of candidate genes identified through integrative analysis of influenza-human interactions
Virus-dependent, vRNA independent, regulators of IFNβ production (137 genes, P13-14
) affect ΔNS1-induced, but not vRNA-induced, IFN production. These are subdivided into genes that do not affect (P13,14
) or concordantly affect PR8 replication (P17,18
; we cannot rule out the possibility that these genes affect IFN production as a consequence of their effect on replication). These genes include PRKRA, a known regulator of PKR (in P20
) and TRAF6 (in P13
); known essential regulators of PR8 replication such as NXF1 (P17
) and PGD (P17
) (Hao et al., 2008
; Satterly et al., 2007
); and inflammasome-associated components (NOD2 and NLRP9, P18
), consistent with recent findings (Sabbah et al., 2009
). We also observe candidates such as TTC12 (P18
) that associates physically with PB1 (). PKR, a known repressor of viral replication, is also a member of this group (P14
) and shows no effect on PR8 replication in our assay. This likely reflects masking of PKR activity by the NS1 protein (Li et al., 2006
), suggesting that other regulators of viral replication masked by NS1 are members of this class.
IFN-independent regulators of viral replication (107 genes, P15
) are genes that affected PR8 replication, but did not affect IFN production. These include PML, an ‘H2
’ gene and a well-established negative regulator of viral replication (Everett and Chelbi-Alix, 2007
), and an unappreciated negative regulator, USHPB1 that interacts physically with PB1 and PB2 ( and Table S1B
includes several candidate positive regulators of replication, including RIOK3 that is induced only by PR8 virus (C8
) and ZMAT4 which interacts directly with M1/PB1/NS1 ().
We next determined the relative contribution of transcriptionally regulated genes to each of the phenoclusters. Virus-specific regulated genes (VSRGs) that are induced by both PR8 and ΔNS1 infection (C7) included 30 genes with effects in our functional assays. This class of genes was enriched in phenotypes (P<0.007) with (mostly positive) regulators of IFN production in ΔNS1. The observation that a wild type virus and a virus lacking NS1 could induce anti-viral (i.e. pro-IFN) genes suggests that influenza virus may possess NS1-independent mechanisms to bypass this anti-viral response.
A subnetwork of RNA binding proteins affects IFNβ production during infection
The functional assays revealed the importance of a number of densely connected areas of the influenza-cellular interaction network (Figure S1
). One of these areas was enriched for RNA binding proteins (P
, Figure S1
), including the known regulator PKR, and RBPMS, ILF3, FMR1, DHX9, ZNF346 and HNRPC. ILF3 is phosphorylated by PKR and associates with XBP-1 (both are key mediators of the stress response; Patel et al., 1999
), and ILF3 in turn interacts with DHX9 and HNRPC (Reichman et al., 2003
). Since influenza is an RNA virus, this enrichment may reflect direct regulation of the influenza life cycle. Indeed, we found that shRNA-mediated depletion of four of these genes significantly affected interferon production following ΔNS1 infection (). Two are negative regulators (PKR and ILF3) and two as positive regulators (DHX9 and HNRPC).
Functional roles of an RNA-binding protein subnetwork, the WNT pathway and the viral polymerase
WNT pathway components modulate cellular responses to infection
Another highly enriched sub-network involved components of the WNT signaling pathway. There is a significant number of interactions between influenza proteins and members of the WNT/β-catenin pathway, and deletion of WNT pathway components significantly impacts influenza replication and interferon production (Figure S6
). Consistently, recent studies have implicated the WNT pathway in the modulation of immune function (Staal et al., 2008
), and in regulating cell survival and proliferation in EBV infected B-cells (Hayward et al., 2006
). To test the involvement of the WNT pathway in influenza pathogenesis, we measured the effect of WNT3α treatment on interferon production following influenza infection or vRNA transfection. We found that direct treatment of cells with WNT3α increased IFN production in both assays (). The mechanism of action is yet to be defined.
The viral polymerase may mediate a non-NS1 effect on IFNβ production
The non-NS1 physical interactors and their direct neighbors (non-NS1 H1/H2) have a higher number of positive regulators of interferon production in the ΔNS1 assay than in the vRNA transfection assay (P<0.001, KS test, , right). This distinction is in marked contrast to the overall similarity in phenotypic effects of all the remaining genes (i.e. 616 non-H1/H2 genes) on IFNβ production in both assays (, left). This suggested that non-NS1 viral proteins participate in the manipulation of the IFN production in response to viral RNA.
To identify candidate non-NS1 mechanisms that mediate the effect on IFN production, we ranked each of the viral proteins based on their neighborhood enrichment for cellular pathways. The most prominently enriched neighborhoods of non-NS1 proteins were for two of the three viral polymerase subunits (PB1, PB2) and NP ( ). These neighborhoods are also conserved in the Udorn strain (P<10-10, hypergeometric test), and are enriched for VSRGs (C6) whose expression is induced only in ΔNS1 infection. We thus hypothesized that the viral polymerase may play a previously unappreciated, NS1-independent, role in modulating interferon production.
To test this hypothesis, we measured the effect of over-expressing viral-polymerase subunits and NP on cellular production of IFN. Indeed, we found that over-expression of PB1, PB2 and NP, individually and in combination, was sufficient to inhibit cellular interferon responses to either vRNA transfection or ΔNS1 infection (). This disruptive effect is more prominent for PB1, PB2 and NP than for PA. This is consistent with the lower enrichment of the PA neighborhoods across immune functions and pathways and with the lower connectivity of PA in the PR8 and Udorn interaction networks (, ). Taken together, our results illustrate the functional relevance of the Y2H interactions, and implicate non-NS1 viral proteins in cooperating with NS1 to modulate host responses.