An alternative STAT signaling pathway acts in antiviral immunity in Caenorhabditis elegans

Across metazoans, innate immunity is vital in defending organisms against viral infection. In mammals, antiviral innate immunity is orchestrated by interferon signaling, activating the STAT transcription factors downstream of the JAK kinases to induce expression of antiviral effector genes. In the nematode C. elegans, which lacks the interferon system, the major antiviral response so far described is RNA interference but whether additional gene expression responses are employed is not known. Here we show that, despite the absence of both interferon and JAK, the C. elegans STAT homologue STA-1 orchestrates antiviral immunity. Intriguingly, mutants lacking STA-1 show increased resistance to antiviral infection. Using gene expression analysis and chromatin immunoprecipitation we show that, in contrast to the mammalian pathway, STA-1 acts as a transcriptional repressor. Thus STA-1 might act to suppress a constitutive antiviral response in the absence of infection. Using a reverse genetic screen we identify the SID-3 as a kinase upstream of STA-1 in the response to infection. Together, our work identifies a novel STAT regulatory cascade controlling its activity in antiviral resistance, illustrating the complex evolutionary trajectory displayed by innate immune signaling pathways across metazoan organisms.


Introduction
RNA viruses, a highly diverse family known to infect organisms of almost all kingdoms of life, also represent an important burden on human health. Indeed, their highly mutagenic and adaptive nature is an ever-growing challenge for diagnostic and treatments and the underlying understanding of host-pathogen interaction. The first and critical step in mounting successful antiviral defence is the conserved innate immune response, however its complexity is yet to be fully apprehended. Several pathways have been described in various organisms that differ in their presence or relative importance. To date, the main pathways can be divided in two categories: protein based innate immunity and RNA based innate immunity. Jawed vertebrates rely mostly on the powerful interferon (IFN) system, whereas most other eukaryotes take advantage of the RNA interference (RNAi) machinery.
In mammals, the initiation of the interferon response in response to RNA viruses depends on the RNA helicase RIG-I and its paralogs. RIG-I senses double-stranded RNA (dsRNA) intermediates that are generated during the replication of single-stranded RNA (ssRNA) viruses, and initiates the production of the type 1 interferon and inflammatory cytokines via activation of the transcription factors IRF3/7 and NFKB [1,2]. Interferons can in turn activate the JAK/STAT signaling pathway to induce an antiviral state and mediate viral control. In essence, binding of interferon to the type 1 interferon receptor (IFNAR) leads to activation of the receptor associated tyrosine kinases JAK1 and TYK2, and eventually to the phosphorylation of the STAT transcription factors. Upon dimerization, STAT transcription factors can undergo translocation to the nucleus where they activate the expression of antiviral genes and inflammatory response genes [3]. On the other hand, antiviral RNAi relies on the processing by the endoribonuclease Dicer of long double stranded (ds) viral RNA molecules occurring during the viral cycle.
These dsRNA molecules appear particularly during replication of the RNA genome by the virally encoded RNA-dependant-RNA polymerase. After processing by Dicer, the resulting small interfering RNAs (siRNA) are loaded in the RNA induced silencing complex (RISC) through the binding of the siRNA to a protein of the Argonaute family. Recognition of the target viral RNA by the active RISC eventually leads to its degradation [4].
In principle, all the components required for RNAi are still present in higher vertebrates, but this might simply reflect other biological functions such as microRNA (miRNA) based gene regulation. It has also been proposed that the IFN based innate immunity and antiviral RNAi might be incompatible [5,6] or that the evolution of Dicer in mammalian somatic cells renders it inactive for long dsRNA processing [7].
However, some evidence supports potential roles RNAi based antiviral immunity in mammals [8][9][10]. Similarly, the prominence of the RNAi pathway in fighting viruses in invertebrates may obscure other mechanisms that the innate immune system may use to combat viruses in these organisms. One such example is the nematode Caenorhabditis elegans (C. elegans), where antiviral immunity has previously been shown to involve a potent RNAi response [11].
A single virus, the Orsay virus, has been so far described to infect C. elegans in the wild [11]. This small bipartite single stranded RNA virus is efficiently targeted by the nematode RNAi machinery to prevent its replication.
Surprisingly, C. elegans antiviral immunity requires DRH-1, A conserved helicase related to RIG-I, to initiate the antiviral RNAi pathway (Fig 1A) [ 12,13]. Interestingly, a previous analysis of gene expression changes upon viral infection revealed evidence for the induction of antiviral response genes upon viral infection independent of the RNAi pathway [14]. However, neither the upstream signaling pathway linking these gene expression changes to viral infection, nor the extent to which gene expression alterations directly 4 contribute to antiviral defence are known. We therefore set out to uncover new signaling pathways involved sensing and transducing viral infection.

Identification of a STAT transcription factor as a modulator of the infection response
In order to identify key factors regulating the immune state of C. elegans after infection by the Orsay virus (OrV), we set out to identify conserved regulatory motifs for the genes that might be involved in a transcriptional response to infection. Previously we identified a set of such putative antiviral response genes that were upregulated upon infection with the Orsay Virus in the laboratory reference strain N2 [14]. Interestingly, these genes were not upregulated in another domesticated strain of C. elegans, JU1580, which is hypersensitive to the Orsay Virus [12,14]. The JU1580 strain lacks the C. elegans RIG-I homologue DRH-1 ( Fig 1A). This suggested that DRH-1 and/or signaling might act upstream of a transcriptional response to infection.
However, as JU1580 and N2 differ at many loci besides drh-1, including some infection response genes [11,12], we decided to test this further by performing microarray analysis to compare infection induced genes in N2, a drh-1 knockout in the N2 strain, JU1580, and JU1580 carrying a transgene containing the N2 drh-1 locus (JU1580 drh-1 rescue). Hierarchical clustering of differentially regulated genes recapitulated our earlier findings allowing us to identify a set of genes upregulated upon viral infection that are likely dependent on DRH-1 activity (S1 Fig). Thus, we conclude that there is a specific transcriptional response to viral infection downstream of viral RNA recognition.
To get at the nature of the signaling downstream of viral recognition, DRH-1dependent or other, we extracted the promoters from the set of genes upregulated in N2 after infection and searched for associated motifs using the motif-prediction software MEME [15]. We then compared these motifs to known transcription factor DNA binding motifs in the JASPAR core database using Tomtom [16,17]. Remarkably, we identified an enriched motif with similarity to the one of STAT transcription factors (Fig 1B), well known to have a conserved role in antiviral defence in mammals. There are two STAT homologs, STA-1 and STA-2 in C. elegans (Fig 1C). STA-1 has all classical functional STAT domains: a coiled-coil domain for protein-protein interaction, a DNA binding domain, a SH2 domain and a putative tyrosine phosphorylation motif [18]. STA-2 is similar but lacks the coiled-coil domain as well as the tyrosine phosphorylation motif [19]. Consistent with this, a phylogenetic tree with C. elegans STA-1 and STA-2 and representative STATs from other organisms suggests that STA-1 is closely related to mammalian STATs but that STA-2 is more divergent (Fig 1D). Interestingly, STA-2 has previously been implicated in an antifungal response [19], whereas STA-1 has been linked to developmental signaling in C. elegans [20]. We therefore speculated that STA-1 and/or STA-2 might have a previously unappreciated role in antiviral defence.
To evaluate a potential role for STAT signaling in antiviral defence in C. elegans, we used RT-qPCR to quantify the viral loads of sta-1 and sta-2 mutants following infection by the Orsay virus. As controls we used wild-type N2 animals and rde-1 mutants, which are hypersensitive to infection due to the lack of the Argonaute protein RDE-1, which is essential for the initiation of antiviral RNAi (Fig 1A) [11,21]. Surprisingly, sta-1 mutants were 100-fold more resistant to infection than wild-type animals, whereas sta-2 mutants showed normal sensitivity (Fig 2A). Double mutants lacking both STAT homologs were no more sensitive than sta-1 single mutants. We conclude that STA-1 but not STA-2 acts in regulating antiviral defense. Consistent with these observations of viral load, induction of a viral response reporter gene, comprising gfp driven by the promoter of the viral response gene sdz-6 [14], was reduced in sta-1 mutants compared to N2 and rde-1 mutants (S2 Fig). Next we generated a single-copy, intrachromosomal sur-5::gfp::sta-1 transgene, which drives expression of a GFP-STA-1 fusion protein in the intestine and other somatic tissues ( Fig 2B). GFP-STA-1 accumulated on chromatin in the nuclei of intestinal cells (Fig 2B). Importantly, the sur-5::gfp::sta-1 transgene restored normal sensitivity to the Orsay virus in a strain lacking endogenous sta-1 ( Fig   2C). Finally, we asked if STA-1 acts independently of the antiviral RNAi pathway using epistasis analysis (Fig 2D). In rde-1 mutants lacking the antiviral RNAi response ( Fig 1A) the Orsay virus accumulates to much higher levels than in the wild-type N2 strain. However, sta-1; rde-1 double loss-offunction mutants show significantly reduced viral loads as compared to rde-1 single mutants (Fig 2D) suggesting that STA-1 acts, at least in part, independently of the RNAi pathway in viral infection ( Fig 2D). Together, these results identify the C. elegans STAT transcription factor STA-1 as acting in parallel to RNAi in the immune response to viral infection in C. elegans.

STA-1 is a repressor of infection response genes
In order to test whether the hyper-resistance of sta-1 mutants to viral infection reflects a role for STA-1 in the regulation of antiviral response genes we performed RNAseq analysis of wild-type N2 and sta-1 mutant animals with or without OrV infection ( Fig 3A). We then selected transcripts altered significantly after OrV infection (DESeq, q <0.1). We observed a robust response to infection in N2 animals, whereas fewer transcripts were significantly altered in sta-1 mutants upon infection ( Fig 3B). However, sta-1 mutants displayed a constitutive de-regulation of gene expression as compared to N2 animals in the absence of OrV infection ( Fig 3B).

Furthermore, genes that changed expression significantly upon infection in N2
showed a strong trend to be constitutively upregulated in sta-1 mutants, while this was not the case when considering all genes ( Fig 3C). These data suggest that STA-1 largely acts as a transcriptional repressor of an antiviral gene expression program. However, this antiviral gene expression program includes genes that are up-and downregulated upon infection, either directly or indirectly (Fig 3B). We therefore propose that the increased resistance of sta-1 mutants to viral infection is caused by a constitutive antiviral defence genes expression program.
Next we asked whether STA-1, similarly to STATs in mammals, might bind to DNA in a sequence specific manner to regulate antiviral response genes. We therefore performed ChIPseq analysis of GFP::STA-1 to determine its genomic binding pattern in animals expressing the sur-5::gfp::sta-1 transgene but lacking endogenous sta-1. Through GFP::STA-1 ChIPseq, we identified 7 transcription start sites (TSS) of about 20% of genes with an enriched binding peak ~200bp upstream of the TSS (Fig 3D). These data are in agreement with STA-1 acting as a specific DNA-binding transcription factor to regulate gene expression. To test further the association of STA-1 binding with the antiviral gene expression response we used the sequences surrounding the STA-1 peaks in order to search for enriched motifs. Importantly, we recovered an enriched motif that was nearly identical to the motif predicted from our gene expression analysis ( Fig 1B) and matching the mammalian IRF and STAT motifs in the JASPAR core database and the consensus core interferon sensitive response element (ISRE) TTCNNTTT (Fig 3E, 3F) [22]. Intriguingly, we additionally identified a separate highly enriched motif with strong similarity to the consensus sequence for GATA-like transcription factors, suggesting that a GATA protein may be a cofactor for STA-1 at some of its binding sites (S4 Fig). The predicted GATA and STAT motifs did not intersect suggesting that GATA-like transcription factors may be able to recruit STA-1 to DNA independently of STA-1 DNA binding, consistent with previous studies in mammalian cells [23].
We next tested the association between STA-1 DNA binding and gene expression. STA-1 binding was strongly enriched at genes with increased expression in sta-1 mutant animals (p<1e-25, Fig 3G, S3 Fig), as compared to all genes. This confirmed our earlier observation that STA-1 largely acts as a constitutive repressor of gene expression. The intersection of STA-1 binding with all N2 infection response genes including those not upregulated in sta-1 mutants was not significant; however, STA-1 binding was enriched strongly at N2 response genes that were also upregulated in sta-1 mutants, although these were few in number ( Fig 3G).

STA-1 is required for normal lifespan
The hyper-resistance of sta-1 mutants to infection raises the question of whether there are negative fitness consequences associated with STA-1 deficiency, that might act as trade-offs between resistance to infection and optimal growth. No obvious defects on development or fecundity were observed in sta-1 mutants, consistent with published data [18]. However, we observed a significant decrease in the median lifespan of sta-1 mutants (Fig   4). This may be due to the constitutive activation of pathogen response genes in sta-1 mutants imposing a cost on animal development and/or physiology as has been shown in other systems, e.g. insects [10].

The SID-3 kinase acts upstream of STA-1 in regulation of the accumulation of the Orsay virus
Having demonstrated that STA-1 acts as a constitutive repressor of antiviral response genes, we wondered how STA-1 activity was inhibited in response to viral infection. In mammals STAT proteins are regulated by JAK tyrosine kinase phosphorylation (Fig 5A). There is no conserved homolog of the JAK kinases in C. elegans, however the canonical tyrosine phosphorylation site on STA-1 is conserved. Thus other kinases may regulate STA-1 activity [18].
There is also a growing body of evidence of JAK-independent phosphorylation of STAT transcription factors, including serine phosphorylation [24,25].
Additionally, tyrosine and serine/threonine kinases play an essential role in antibacterial and antifungal defence in C. elegans [26,27]. To identify potential kinases upstream of STA-1, we performed an RNAi screen, testing genes with the serine/threonine/tyrosine-protein kinase catalytic domain IPR001245 [28].
The C. elegans genome encodes 176 proteins with this domain, 116 of which were available as clones as part of C. elegans genome-wide RNAi libraries [29,30]. Following RNAi by feeding, we infected animals with the Orsay virus and then quantified viral load for each of the 116 candidates and additional controls (Fig 5B, 5C). Using a stringent cut-off (|Z-score| > 3) we only identified a single regulator of viral load, sid-3. RNAi of sid-3 resulted in ~100fold reduction of viral RNA accumulation compared to control (Fig 5C). We confirmed that sid-3 is required for sensitivity to viral infection by testing two independent deletion mutants of sid-3 (Fig 6A, 6B). SID-3 is a tyrosine kinase, implicated in systemic RNAi, and is presumed to assist in the import of dsRNA into the cell during experimental RNAi [31]. However, this role in RNAi is not likely to be linked to its role in antiviral defence, because other genes required for systemic RNAi such as sid-1, sid-2 and sid-5 do not show a significant difference in antiviral sensitivity from wild-type N2 ( [32], Fig 6C).
We therefore wondered whether SID-3 might act in the same pathway as STA-1. We quantified gene expression in sid-3 mutants using RNAseq.
Similar to what we observed for sta-1 mutants, genes that changed expression significantly upon infection in N2 showed a strong trend to be constitutively upregulated in sid-3 mutants, while this was not the case when considering all genes ( Fig 6D). Furthermore, the genes upregulated in sid-3 mutant animals showed a striking overlap with the genes upregulated in sta-1 mutant animals (p=1.2e-15). Additionally, sid-3 upregulated genes were enriched for antiviral response genes (p=5.55e-10) (Fig 6E and S3 Fig).
Moreover, genes upregulated in sid-3 mutants, including those shared with sta-1, were enriched for STA-1 binding by ChIPseq, suggesting that sid-3 acts upstream of sta-1 in the antiviral gene expression response (Fig 6F). We conclude that SID-3 and STA-1 act in the same pathway to regulate an innate antiviral immunity program.
Finally, we addressed the relationship between SID-3 and the antiviral RNAi pathway using epistasis analysis. Interestingly, sid-3; rde-1 double loss-offunction mutants were as resistant to OrV infection as sid-3 mutants (Fig 6G). This is in contrast to what we observed in sta-1;rde-1 mutants (Fig 3D). This suggests that SID-3 might act at an extremely early stage of viral infection.
We conclude that SID-3 acts both upstream of antiviral RNAi and upstream of a STA-1-dependent antiviral gene expression program to regulate the response to OrV infection. Together, these data are in support of a model (Fig   7) whereby in uninfected animals, SID-3 signals to STA-1, potentially by phosphorylation, to maintain repression of antiviral response genes. Upon infection, this signaling is curtailed, leading to loss of repression and upregulation of antiviral response genes.

Discussion
Here, we uncover a hitherto unappreciated role for the C. elegans homologue of the mammalian STAT family of transcription factors. We demonstrate that the tyrosine kinase SID-3 and the STAT transcription factor STA-1 are tightly linked functionally. Additionally, we uncover a novel potential signaling pathway linking STAT activity to viral RNA recognition. Our results have intriguing implications both for innate immunity in C. elegans and for the evolution of eukaryotic signaling pathways.

Regulation of antiviral gene expression in C. elegans
Previously there has been some debate over the extent to which gene expression responses to infection in C. elegans represent specific pathogen response pathways or more general responses to stress. Our finding that a STAT-family transcription factor is responsible for constitutively repressing antiviral response genes, and that this is relieved upon infection, suggests that at least some part of the antiviral gene response is a specific response. in viral infection comes from its proposed role in endocytosis [31]. Nonenveloped viruses require the endosomal pathway for entry into cells ; it is therefore plausible viral entry into endosomes may lead to titration of SID-3 away from its role in signaling to STA-1, leading to relief of repression. Such an early role for sid-3 in infection is consistent with the ability of sid-3 mutation to suppress the sensitivity of RNAi pathway mutants. Indeed, in a cosubmitted paper, Dave Wang and colleagues demonstrate that sid-3 is required for viral entry. Thus, we hypothesise that the dependence of the virus on sid-3 for its entry has been exploited by C. elegans as a mechanism to regulate antiviral gene expression. Further work, potentially testing the role of other kinases we identified in our screen, will be required to complete this signaling pathway.

Evolution of STAT signaling
It is remarkable that the STAT-family of transcription factors has a role in antiviral gene regulation in both mammals and C. elegans, despite the fact that both upstream and downstream genes in the pathway are not conserved between the two. Even more remarkably, the mechanism whereby STAT

Datasets
High-throughput sequencing datasets are accessible through the GEO repository.

Nematode culture and strains
We grew C. elegans under standard conditions at 20°C. The wild type strain was var. Bristol N2. The food source used was E. coli strain HB101 (Caenorhabditis Genetics Center, University of Minnesota, Twin Cities, MN, USA). Detailed information about all strains generated and used in this study are in the S1 Table.

Viral filtrate
Stably infected populations of sensitive animals (JU1580) were transferred to 2 L liquid culture with HB101 food and grown for 7 days. The supernatant of the culture was harvested on ice and filtered with a 0.22 µm filter. The resulting viral filtrate was aliquoted and stored at -80°c.

Viral infection
Two WT or three mutants L4 animals were added on seeded 50 mm plates. 16 hours later, 20 µl of Orsay virus filtrate was added to the edge of the bacterial lawn. Animals were collected 3 days after infection. The animals were washed off the plates with M9 buffer. The animals pellets were washed another three times by pelleting the animals either by gravity on ice or by centrifugation at 800 g for 2 minutes in a swinging bucket centrifuge, snapfreeze in liquid nitrogen and stored at -80°C.

Lifespan assay
100 animals of each strain were picked onto ten 50mm NGM plates. Adults animals were transferred every 2 days onto fresh plates for the duration of egg laying. Their survival was measured by movement of the head. Animals showing no head movement were gently touched twice with a worm pick and observed for 30 seconds following each touch. Animals not moving were considered dead and removed from the plate. The survival curves were plotted and analysed using OASIS (Online Application for the Survival Analysis) [33].
The experiment was reproduced 3 times with one representative example illustrated.

RT-qPCR
The worm pellets lysis was performed with 5 µl of worm pellet and 45 µl of the lysis solution from the Power SYBR Green Cells-to-Ct kit (Ambion). Ten freeze-thaw cycles and 30 minutes shaking at room temperature were performed before the lysis incubation step. The RT-qPCR was performed according to the manufacturer's instructions. The qPCR was run on a Step One Plus Real Time PCR system (Applied Biosystems). The analysis was done using the ∆∆Ct method. The Ct values of 4 to 6 biological replicates per experiments (as indicated) were pooled to generate the average ∆Ct for each strain and an associated standard error (sd). The ∆∆Ct value was calculated with N2∆Ct as the calibrator. The 2^(-∆∆Ct) was plotted as the fold change and the interval of confidence is represented by error bars as 2^(-(∆∆Ct+sd(∆Ct))) and 2^(-(∆∆Ct-sd(∆Ct))). The significance of the differences observed were tested by a two-tailed Mann-Whitney U test at a significance level of p<0.01.

DNA constructs
The viral sensor construct and the gfp::sta-1 construct were generated by Gateway cloning using Multisite Gateway Three-Fragment vector construction kit (Life Technologies). Gateway entry clones containing each of the following were generated by standard techniques: sur-5 promoter, sdz-6 promoter, sta-1 coding sequence, eGFP(F64L/S65T), tbb-2 3'UTR. Details on cloning and plasmid sequences are available upon request. The single-copy transgene was generated by transposase-mediated integration (MosSCI), as described [34,35], at insertion site ttTi5605 on chromosome II. Injection mixes contained: 20 ng/µl of vector, 20 ng/µl of Mos1 transposase (only for MosSCI), and 5 ng/µl of a pharynx marker. Integration of the extrachromosomal array was performed by EMS treatment (50 mM EMS for 4 hours).

Sensor scoring
Scoring of the GFP was made under a Leica MZ16F fluorescence stereomicroscope.

Imaging
Adults animals were harvested from a plate, washed off in M9. After 3 additional washes in M9 buffer, the pelleted animals were fixed in 0.5 ml of pre-cooled methanol at -20°C for 10 minutes. The pellet was washed 3 times in TBS / 0.1% Tween20 (TBS-T) and then incubated for 10 minutes in a DAPI solution (0.5 mg/ml in TBS-T). The pellet of animals was washed 3 times in PBS-T. 5µL of the pelleted animals were pipetted directly onto a Cel-line diagnostic microscope slide (Thermo Scientific) and imaged using a Leica SP8 upright microscope.

Microarray
Microarray data were processed using rma and annotations were obtained through the Bioconductor pipeline in the R programming environment.
Differentially regulated genes in any condition identified using t-test, p<0.01 and a 2-fold difference upon infection. Data are available in the S1 File.
Hierarchical clustering on the drh-1 array was carried out on the set of differentially regulated genes using the hclust function in R and Ward's method.

Motif analysis
To specifically identify potential viral response genes we normalized the array by cul-6 as this removed variation in the final level of infection. Qualitatively this identified similar trends to those previously published [14], such as putative antibacterial response genes upregulated specifically in JU1580 and drh-1 upon infection. We identified genes that were >40% induced relative to cul-6 and used BiomaRt to download the upstream sequences (S2 File).
These were used as an input to Meme attempting to find 0 or 1 site per gene.
We screened these manually to avoid spurious hits and then compared potential motifs identified to the JASPAR core database. To test enrichment of STAT motifs in STA-1 ChIP-seq peaks we used FIMO to search for the STAT-1 core motif within the 500bp sequences upstream of genes containing a STA-1 binding site, applying a false-discovery rate cutoff of 0.2, which were expressed at increased level (DESeq q<0.1) in sta-1 mutants relative to N2.
We compared this to a random set of genes chosen from the RNAseq data (see below).

RNA seq
Animals were grown on 50mm NGM plates, infected or not and harvested as RNA-seq analysis. RNAseq data was aligned to the C. elegans transcriptome WS190 using bowtie2. Counts were obtained from resulting bam files using bedtools [36] and these were used to generate normalized data tables using DESeq [37] (S2, S3, S6 Tables). Significance between intersecting datasets was calculated by a Fisher's exact Test (S3 Fig and S5   Fig). A cutoff of mean 25 normalised reads (normalised according to DESeq's negative binomial distribution) for at least one condition was used and significantly altered genes were selected (DESeq Benjamini-Hochberg multiple test correction q<0.1).

ChIP sequencing
Animals were grown on NGM plates seeded with thick HB101 food and

ChIP-seq analysis.
Alignment to reference genome. Chip-seq and RNA-seq libraries were sequenced using Illumina HiSeq. Reads were aligned to the WS220/ce10 assembly of the C. elegans genome using BWA v. 0.7.7 [38] with default settings (BWA-backtrack algorithm). The SAMtools v. 0.1.19 'view' utility was used to convert the alignments to BAM format. Normalized ChIP-seq coverage tracks were generated using the R implementation of BEADS algorithm [39,40]. Summed ChIP-seq input We generated summed input BAM files by combining good quality ChIP-seq input experiments from different extracts (8 experiments). The same summed inputs were used for BEADS normalisation and peak calls. Peak calls Initial ChIP-seq peaks were called using MACS v. 2.1.1 [41] with permissive 0.7 q-value cutoff and fragment size of 150 bp against summed ChIP-seq input. To generate combined peak calls, we used the modified IDR procedure (https://www.encodeproject.org/software/idr/) with an IDR threshold of 0.05 to combine replicates (S4 Table). The pipeline for generating IDR peaks is avilable here: https://github.com/Przemol/biokludge/blob/master/macs2_idr/macs2_idr.ipy.

Mean signal distribution plots and heatmaps
The summarized signal profile and heatmap for GFP::STA-1 were generated using SeqPlots exploratory analyses and plotting software [42].

RNAi screen
RNAi clones from the Ahringer library [29,30] were isolated on agar plates containing carbenicillin (50 µg/ml). Single colonies were picked in 2 ml LB + ampicillin (50 µg/ml) and grown 9 hours with shaking at 37°C. Bacteria were then seeded onto 50 mm NGM plates containing IPTG (1 mM), carbenicillin (25 µg/ml) and Fungizone (2.5 µg/ml). Two days after seeding, two L4 were added and then grown at 20°C. After 16 hours, plates were inoculated with 20 µl Orsay virus filtrate. Animals were collected after 3 days and viral relative genome copy number was measured by RT-qPCR. For each round of the screen, we used the following internal controls: N2 strain grown on empty vector (L4440 E. coli strain) was considered as normalization control; drh-1 mutant fed on GFP RNAi as positive control for infection; and N2 fed on drh-1 RNAi clone as positive control for RNAi. The full list of the RNAi clones and number of replicates used in this study is available in the S5 Table. Each replicate of the screen was performed with 3 biological replicates per RNAi clone treatment and the screen was repeated at least twice. The plates with accidental fungal contamination as well as the plates where the RNAi treatment led to embryonic lethality were removed from the analysis. The resulting exact number of biological replicates is indicated in the S5 Table. Empty vector treatment was included on each qPCR plate analysed and used as the calibrator. The ∆∆Ct values were transformed as z-score, calculated as follow: zscore i = (∆∆Cti -µ∆∆Ct emptyvector, n=161 )/stdev(∆∆Ct emptyvector ). Boxplots of the Z-score for each treatment are represented in Fig 5. Individual values used for the analysis are available in the S5 Table.