Oas1b-dependent Immune Transcriptional Profiles of West Nile Virus Infection in the Collaborative Cross

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Abstract

The oligoadenylate-synthetase ( Oas) gene locus provides innate immune resistance to virus infection. In mouse models, variation in the Oas1b gene influences host susceptibility to flavivirus infection. However, the impact of Oas variation on overall innate immune programming and global gene expression among tissues and in different genetic backgrounds has not been defined. We examined how Oas1b acts in spleen and brain tissue to limit West Nile virus (WNV) susceptibility and disease across a range of genetic backgrounds. The laboratory founder strains of the mouse Collaborative Cross (CC) (A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, and NZO/HlLtJ) all encode a truncated, defective Oas1b, whereas the three wild-derived inbred founder strains (CAST/EiJ, PWK/PhJ, and WSB/EiJ) encode a full-length OAS1B protein. We assessed disease profiles and transcriptional signatures of F1 hybrids derived from these founder strains. F1 hybrids included wild-type Oas1b (F/F), homozygous null Oas1b (N/N), and heterozygous offspring of both parental combinations (F/N and N/F). These mice were challenged with WNV, and brain and spleen samples were harvested for global gene expression analysis. We found that the Oas1b haplotype played a role in WNV susceptibility and disease metrics, but the presence of a functional Oas1b allele in heterozygous offspring did not absolutely predict protection against disease. Our results indicate that Oas1b status as wild-type or truncated, and overall Oas1b gene dosage, link with novel innate immune gene signatures that impact specific biological pathways for the control of flavivirus infection and immunity through both Oas1b-dependent and independent processes.

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Host genetic diversity enables Ebola hemorrhagic fever pathogenesis and resistance.

Martin Ferris, Atsushi Okumura, Heinz Feldmann … (2014)

Existing mouse models of lethal Ebola virus infection do not reproduce hallmark symptoms of Ebola hemorrhagic fever, neither delayed blood coagulation and disseminated intravascular coagulation nor death from shock, thus restricting pathogenesis studies to nonhuman primates. Here we show that mice from the Collaborative Cross panel of recombinant inbred mice exhibit distinct disease phenotypes after mouse-adapted Ebola virus infection. Phenotypes range from complete resistance to lethal disease to severe hemorrhagic fever characterized by prolonged coagulation times and 100% mortality. Inflammatory signaling was associated with vascular permeability and endothelial activation, and resistance to lethal infection arose by induction of lymphocyte differentiation and cellular adhesion, probably mediated by the susceptibility allele Tek. These data indicate that genetic background determines susceptibility to Ebola hemorrhagic fever. Copyright © 2014, American Association for the Advancement of Science.

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Modeling Host Genetic Regulation of Influenza Pathogenesis in the Collaborative Cross

Martin T. Ferris, David Aylor, Daniel Bottomly … (2013)

Introduction Influenza A virus (IAV) (orthomyxoviridae) is a negative sense RNA virus which causes severe, acute respiratory disease. Worldwide influenza infections cause several million cases annually, with severe pandemics (such as the 1918 pandemic) causing high levels of morbidity and mortality [1]. Among infected individuals there is significant variation in the clinical disease caused by IAV ranging from an asymptomatic infection to severe and acute respiratory distress syndrome [2]–[8]. Population-wide disease variation applies not only to clinical disease, but also to individual immune responses mounted in response to IAV infection [9], [10], as well as long-term complicating pathologies and co-infections [2], [11]–[13]. Despite the importance of understanding the underlying mechanisms of IAV-associated disease, the sources of the observed disease variation are unclear. Like many viruses, IAV engages in a large number of complex interactions with various host proteins [14], [15]. It is less clear how polymorphisms in these and other host genes/proteins cause variation in the disease process following infection with IAV. A study of survival data from the 1918 IAV pandemic showed that host genetic variation [16] contributes to IAV disease variation. However, in contrast with other pathogens [17]–[21], human polymorphisms have not yet been identified that contribute to variable responses to IAV infection, although there have been some suggestions of polymorphisms in HLA contributing to IAV recovery [22], [23]. As IAV disease severity is likely due to a combination of viral, host, demographic and environmental factors [7], [13], [24]–[26], this complexity has interfered with reductionist approaches to evaluating the role that host genetic variation plays in regulating different IAV-associated disease outcomes across the population. Mouse models of IAV infection have provided novel insights into the role of host genetics on IAV disease outcomes. This approach led to the discovery of the naturally polymorphic, interferon inducible Mx1 gene, which inhibits IAV replication and limits disease [27]. Subsequently, most studies of host genetic contributions have used naturally defective Mx1 mouse strains, such as C57BL/6J to study the effect of gene knock-outs on the host response to influenza. These studies have shown that many genes contribute to the host response [28]–[35] (reviewed in [36], [37]), and knock-outs often affect clinical disease primarily by altering the host inflammatory response [28], [29], [32], [35], [38]–[40]. Comparisons between inbred mouse strains [41]–[44] have confirmed that natural variation contributes to differential host responses. Given that most polymorphisms within the human population will be those that alter expression and/or function, rather than whole gene knock-outs, studies comparing naturally occurring polymorphisms are more relevant to human disease. Several recent studies [42], [45], [46] using classical recombinant inbred (RI) panels have identified a number of quantitative trait loci (QTL) contributing to host responses following IAV infection. However, traditional mouse genetics systems have limitations on their ability to accurately model the genetic structure and diversity of outbred populations, like humans [47], [48]. We developed a new model that captures host responses to IAV infection across a genetically diverse host population by using incipient lines from the Collaborative Cross (CC) octo-parental RI panel, known as the pre-CC population [49]–[51]. This population is highly genetically diverse (∼40 million single nucleotide polymorphisms (SNPs) evenly distributed across the genome), with up to eight functionally variant alleles at any given locus [52]. The pre-CC population exhibited a broad range of phenotypic outcomes, including unique combinations of disease phenotypes following infection, and we identified three novel QTL associated with multiple aspects of influenza induced disease. Furthermore, we identified a novel Mx1 allele in the CAST/EiJ mouse strain and sequenced the associated haplotype. By integrating QTL mapping with whole genome sequence information, we significantly reduced the number of candidate genes within each QTL. Our findings provide a clarification of the importance of genetic variation in the host's response to IAV infection, and a foundation of support for the hypothesis that genetically complex mouse models such as the CC will provide a robust platform for studying the role of host genetic variation in regulating the host response to infection. Results Diverse IAV-associated phenotypic and transcriptome variation We used 155 pre-CC mice, each from an independent, incipient CC line, as well as sets of mice (n = 5–11) from each of the eight CC founder strains. Mice were infected with a dose of the mouse adapted A/PR/8/34 (PR8) IAV that was known to cause severe disease in several of the CC founder strains (C57BL/6J, 129s1/SvImJ, A/J), and we assessed IAV-induced weight loss (measured as a percentage of starting weight) and clinical disease daily through four days post infection (D4), at which point the mice were euthanized and lung tissue assessed for viral replication, virus-induced inflammation and pathology, and (pre-CC mice only) transcriptional profiles by microarray analysis within the lungs (Table 1, Table S1, Dataset S1). This D4 timepoint was chosen to allow severe pathology to develop in susceptible lines, while minimizing the animals lost in this study due to humane euthanasia conditions. We examined the weight changes and clinical scores animals experienced through the course of this experiment, and found that weight loss and clinical scores of animals were highest at D4. We therefore limited our analysis of weight and clinical scores to this timepoint. Importantly, in analyzing the phenotypes of the pre-CC mice, we found no evidence for effects of age, generation of inbreeding, block effects or starting weights on gathered phenotypes, and therefore did not include these variables in our analysis. 10.1371/journal.ppat.1003196.t001 Table 1 Phenotypes measured in the pre-CC and founder strains. Phenotype Pre-CC Founders Clinical Disease D4 weight X X D4 clinical score X X Hemorrhage X Gross Edema X Viral Replication Log Titer X X IHC score X Virus-induced inflammation Airway inflammation X X Airway neutrophils X Airway monocytes X Vascular inflammation X X Vascular neutrophils X Vascular monocytes X Alveolar inflammation X X Pathology Airway damage X X Alveolar damage X X Pulmonary edema X Fibrin deposition X Transcription D4 lung expression X The infected founder strains varied significantly for all measured phenotypes, including D4 weight, log titer, virus induced inflammation and pathology, except for variation in alveolar debris (p-values ranging from 0.15 to 1.37×10−9, Figure 1, Table S1). Founder strains could be grouped into susceptible (high viral titer, inflammation and weight loss) or resistant (low viral titer, little inflammation and weight loss) groups (Figure 1, Figure S1). As with the founders, many aspects of IAV associated disease were correlated with each other across the pre-CC population (correlation coefficients ranging from −0.78 to 0.78, Figure 1, Table S2), with the exception of alveolar immune cell infiltration as well as gross edema and hemorrhage at time of harvest, which were not strongly correlated with the rest of the host response to infection. Pre-CC mice often showed unique combinations of disease-associated phenotypes (e.g. high levels of viral replication but low inflammation and weight loss, no replication but significant weight loss, Figures 1 and 2). Therefore, though the pre-CC population recapitulated the range of variation within any given phenotype (Table S1), we observed new phenotypic combinations not seen in the parental lines. 10.1371/journal.ppat.1003196.g001 Figure 1 Diverse disease-associated phenotypes across the pre-CC population. Pre-CC mice showed a wide range of variation in phenotypes including D4 weight (Y-axis histogram, A and B), Log titer (X-axis histogram, A) and Airway Inflammation (X-axis histogram, B). In addition, strong correlations existed between D4 weight and both (A) Log titer and (B) Airway Inflammation across the pre-CC population (black diamonds). Despite these correlations, individual pre-CC mice showed unique combinations of disease phenotypes (e.g. low Log titer and severe D4 weight loss) not present in the founder strains of the CC (colored circles: A/J (n = 11) = yellow, C57BL/6J (n = 6) = grey, 129S1/SvImJ (n = 5) = pink, NOD/ShiLtJ (n = 5) = dk. Blue, NZO/HILtJ (n = 12) = lt. blue, CAST/EiJ (n = 5) = green, PWK/PhJ (n = 5) = red, WSB/EiJ (n = 5) = purple). 10.1371/journal.ppat.1003196.g002 Figure 2 Diverse disease pathologies present across the pre-CC population. Histopathological examination of lung sections following IAV infection showed a diverse range of phenotypes. Each image is a single 100× magnification image of the lung section of a single pre-CC mouse (strain ID, D4 weight, and log titer (BDL = below detectable limit) are listed over each image). Disease phenotypes were scored for aspects of the damage to, and inflammatory cell infiltration around the airways (A), inflammatory cell infiltration around the vasculature (B), and damage and inflammatory cell infiltration in the alveolar spaces (C). Note that the image of OR219 shows a relatively healthy looking lung, and is useful as a baseline image. The unique combinations of disease-associated phenotypes across the pre-CC population led us to investigate the relationships between viral replication and immune cell infiltration on weight loss, a long standing question within the IAV field. Since the large number of pre-CC mice we had in this study lacked the genetic structure of the founder strains, this population was uniquely positioned to evaluate the relationships between these disease parameters. Both log titer and airway inflammation (the cellular infiltrate most clearly related to infection status) were significant predictors (p-values 2 kb) deletion that spans three coding exons (9, 10, and 11) that are highly conserved among placental mammals. As previously described [27], this deletion leads to a frame shift and early stop codon in exon 12. This haplotype results in the presence of the same non-functional Mx1 gene in A/J, C57BL/6J, 129S1/SvImJ and NOD/ShiLtJ. We confirmed that WSB/EiJ also has a non-functional allele due to a nonsense mutation in exon 10 [56]. The other three strains have full length ORFs and each has a distinct protein sequence due to the different combinations of alleles at two non-synonymous SNPs. However, the genetic variants identified in our analysis and the regional assignment of sub-specific origin [47], [58] demonstrate that the CAST/EiJ strain has a divergent haplotype of Mus musculus castaneus origin while PWK/PhJ and NZO/HILtJ both have haplotypes of M. m. musculus origin. The three functional haplotypes are characterized largely by synonymous variation and variation in untranslated regions of the gene. There was a single amino acid substitution identified in NZO/HILtJ relative to PWK/PhJ and CAST/EiJ (Gly616Arg), and a single amino acid substitution identified in CAST/EiJ relative to NZO/HILtJ and PWK/PhJ (Gly83Arg). Although we cannot preclude transcriptional differences at different time points during infection from having a role in the functional differences between the three Mx1 alleles, the nonsynonymous substitution that is unique to the CAST/EiJ haplotypes is a strong candidate to explain the intermediate phenotype of the CAST/EiJ Mx1 allele. These results demonstrate our ability to identify: 1) a known IAV resistance locus with only one mouse per line, 2) the multiple phenotypes regulated by this locus, and 3) previously unidentified allelic variants due to the multiple alleles segregating within the pre-CC population. We returned to our transcriptional data in an attempt to better understand how the CAST/EiJ Mx1 allele could contribute to protection from weight loss while showing high titers and severe inflammatory responses. Of the 11,700 transcripts that passed QA/QC and were not SNP impacted, we determined that 2156 transcripts (18.4%) had their expression levels significantly impacted by genotype at the most significant Mx1 marker (i.e. these transcripts had an expression, or eQTL at Mx1, Table S7), confirming the large role that Mx1 has on regulating the response to IAV infection. We grouped these transcripts based on their allele effects, specifically looking for those transcripts where (a) CAST/EiJ Mx1 alleles grouped with the resistant PWK/PhJ or NZO/HILtJ Mx1 alleles, or (b) where CAST/EiJ allele grouped with susceptible A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ and WSB/EiJ Mx1 alleles. A total of 307 transcripts with an eQTL at Mx1 (14.2%) had allele effects consistent with CAST/EiJ grouping with the resistant PWK/PhJ and NZO/HILtJ alleles, while 1207 transcripts with an eQTL at Mx1 (55.9%) had allele effects consistent with CAST/EiJ grouping with the susceptible A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ and WSB/EiJ alleles. Those transcripts where CAST/EiJ grouped with the susceptible alleles showed significant enrichments for a large number of GO terms (Table S8), including biosynthesis and biogenesis processes (upregulated in the lines with a PWK/PhJ or NZO/HILtJ allele) and a highly diverse array of inflammatory, apoptotic, chemotactic, cell growth and hematologic-based terms (upregulated in the lines with A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, CAST/EiJ and WSB/EiJ alleles). In contrast, those transcripts where CAST/EiJ was grouped with the resistant PWK/PhJ and NZO/HILtJ alleles showed a much more limited enrichment, with mainly cytokine and T-cell processes (downregulated in lines with CAST/EiJ, PWK/PhJ and NZO/HILtJ alleles) being enriched. Despite the large effect of Mx1 on influenza response, there was large phenotypic variation within both the functional and non-functional Mx1 allele classes. This suggested the presence of modifier alleles segregating in the pre-CC population. To find these modifiers, we conducted additional genome scans after accounting for genotype at the most significant HrI1 marker (see methods), thereby controlling for Mx1 allele. This model accounted for the large effect of HrI1 and resulted in a single significant QTL, HrI2, on chromosome 7 (1.5 LOD interval: 89130587-96764352), that explained 9.7% of the variation in D4 weight (Figure S2). This region is annotated as containing 69 genes and 10 non-coding RNAs. Analysis of the allelic effects at HrI2 suggests that animals with an A/J allele showed less weight loss than other animals, and animals with a 129S1/SvImJ allele showed more weight loss than other animals. Unique alleles contribute to disease phenotypes in a susceptible sub-population To eliminate epistatic effects of the protective Mx1 genotype, we analyzed those Mx1-/- individuals in our pre-CC population, where this group consisted of 99 mice defined as having two Mx1 alleles coming from any of the A/J, C57BL6/J, 129S1/SvImJ, NOD/ShiLtJ or WSB/EiJ strains. Although this susceptible subpopulation still showed a wide range of phenotypes (Table S9), it was skewed towards increased disease-associated phenotypes. The correlations between weight loss, viral replication, pathology and aspects of the immune cell infiltrate were weaker than those seen across the whole population (Table S10). Specifically, while aspects of pathology and immune cell infiltrate remained correlated with each other, we observed reduced correlations between titer and pathology, titer and inflammation, and clinical disease and pathology. We also reexamined the relationship between titer, airway inflammation and weight loss, to determine if our earlier observation, which linked both titer and airway inflammation as significant predictors of weight loss was independent of Mx1 status. Despite the reduced strength of relationships across the population, both titer and airway inflammation were still significant predictors of weight loss. Again, knowledge of both titer and airway inflammation was a better predictor of weight loss than either variable alone (based on both partial F tests and AIC, Table S3). RNA of high quality was recovered from 60 mice within this Mx1-/- population, and we used WCGNA to cluster the 6,000 most variable transcripts across this population (4,933 of these 6,000 transcripts were also identified in the whole population analysis). Even in the absence of a large effect resistance gene, Mx1, we were able to group transcripts into functionally relevant co-expression modules. In total, this analysis identified eleven modules labeled M-W (Table S11). Again, modules were enriched for a wide range of functional terms, and showed little overlap between categories (Table S12). Eight modules (M-O, Q, T-W) were significantly correlated with clinical disease and/or viral replication, being enriched for T-cell processes (module M), inflammatory responses (module N), and signaling processes (module O). Module Q (enriched for cell cycle processes) was exclusively associated with some virus-induced inflammation, and two modules had no clear relationships with any phenotypes (enriched for sensory and neurological processes (module U) as well as metabolic and biosynthesis (module V), Table S13). Absence of an mQTL overlapping the Mx1 region (see below) indicates that, as expected, the effect of the Mx1 locus on the coexpression network has been ameliorated in this population. This indicates, along with the continued, albeit, weaker associations between the modules and phenotypes that performing a WGCNA analysis conditioning on the Mx1 allele group provides a way to highlight additional diseases-associated genetic regulation of transcript expression. When we conducted QTL mapping in the Mx1-/- subpopulation, we identified a significant QTL, HrI3, which explained 29.73% of the variation in Pulmonary Edema on chromosome 1 (7.31 Mb, 21767867-29085401) annotated as containing 24 genes and 11 non-coding RNA (Table 2, Figure S3). Additionally, we identified a suggestive QTL, HrI4, which explained 22.77% of the variation in airway neutrophils on chromosome 15 (77427235-86625488), a 9.19 Mb region annotated as containing 206 genes and 35 non-coding RNAs (Table 2, Figure S4). In contrast to our results with the whole pre-CC population, we were not able to identify any mQTL contributing to variation in module expression within this Mx1-/- subpopulation. Genetic variation underneath HrI3 contributes to pulmonary edema In order to confirm the role of HrI3 in contributing to control of pulmonary edema, we challenged a new set of female animals from a small set of completely inbred CC lines with IAV. These animals were homozygous for various founder alleles across the entire candidate region for HrI3 and founder strain alleles were each represented by two CC lines (e.g. two lines that vary across the rest of their genome both share the WSB/EiJ allele at HrI3). We examined the severity of pulmonary edema in these animals at four days post infection. Founder strain alleles at HrI3 significantly affected pulmonary edema (F3,16 = 8.48, p = 0.0013, Figure 5), validating the role of this genome region in the host response to IAV. 10.1371/journal.ppat.1003196.g005 Figure 5 Genetic variation at HrI3 contributes to variation in pulmonary edema in an independent set of Collaborative Cross lines. Following the identification of HrI3, we infected animals from fully inbred Collaborative Cross lines, where each line was homozygous for a single founder allele at HrI3. (A) We found a significant effect of genotype at HrI3 on the extent and severity of pulmonary edema at four days post infection. Mild (B) and Severe (C) pulmonary edema can be seen at 200× magnification in animals from this experiment. Pulmonary edema was scored on the basis of evidence of transudates accumulating in the alveolar spaces (denoted by star marks in panel C). Identification of candidate genes from whole-genome sequences Having identified three novel QTL our next objective was to narrow down QTL regions to specific candidate genes/features. We used the estimated allele effects along with the whole genome sequences of the founder strains [57] to narrow the list of candidate genes within each interval (see methods). When a particular allele underlies a QTL, it is due to a causal genome feature (e.g. SNP, insertion/deletion) corresponding to that allele, contrasted with the other alleles in the cross. In the simplest case of one allele contrasted with the other seven this means that a private genome feature in the single strain is causative for the QTL. Under more complex scenarios (e.g. two strains contrasted with six), because CC mice share common ancestry due to their natural history and the unique history of laboratory mice [47], [58], we assume that causal alleles are often shared across mouse strains. That is, if two strains are segregating from the other six, it is likely due to a common feature these two strains privately share. We used the allele effects plots (Figures S2, S3, S4) to group founder strains underneath QTL peaks into two groups based on the largest difference between groups (Note that for completely inbred lines, phenotype-by-genotype plots would provide similar information. For the incompletely inbred pre-CC mice, with up to 36 allele combinations at each locus, PxG plots are difficult to interpret). For each group, we identified the regions in which all strains were identical or nearly identical (≥98%). Then we excluded regions that were not unique to the allele group (e.g. where two causative alleles had different SNP patterns). Using this approach, we narrowed the candidate regions for HrI3 (Pulmonary Edema: NZO/HILtJ and WSB/EiJ alleles reducing edema), from 7.31 Mb to 1.01 Mb, containing 10/24 genes and 1/10 annotated non-coding RNAs (Table 3). HrI4 (Airways Neutrophils: C57BL6/J, NZO/HILtJ and PWK/PhJ increasing infiltration) was similarly reduced from 9.19 Mb to 91 kb, including 12/206 genes and 2/35 non-coding RNAs (Table 3). HrI2 represented a case where a single founder allele associated with either increased resistance (A/J) or susceptibility (129S1/SvImJ) contrasted with the other six strains showing an intermediate phenotype. We were therefore looking for individual SNPs (and not regions of difference) that differentiated A/J or 129S1/SvImJ from the other strains. We identified 144 private A/J SNPs or small in/dels, and 611 private 129S1/SvImJ SNPs or small in/dels (out of a total of 106,684 SNPs or small in/dels in the region). These SNPs occur in or near 28 genes (7 genes unique to A/J, 13 unique to 129S1/SvImJ, and 8 overlapping between the two, Table 3). 10.1371/journal.ppat.1003196.t003 Table 3 Candidate genes within QTL regions. HrI2 HrI3 HrI4 9930013L23Rik (A) Vmn2r73 (A) 4931308C20Rik Atxn10 AC139576.1 (A) 1700026D08Rik (C) Bai3 AW121686 AC156557.1 AC099601.2 (C) Col19a1 Cacng2 AdamTsl3 (A) AC111022.1 (C) Fam135a Card10 Ctsc (A) AC161439.1 (C) Gm5697 Cbx7 Folh1 Arnt2 (C) Gm9884 Celsr1 Grm5 Eftud1 (C) Gm11161 Enthd1 Il16 Fam108c (C) Kcnq5 Grap2 Mesdc2 Fam154b (C) Rims1 Lgals2 Nox4 Mex3b (C) Smap1 Mirlet7c-2 Sh3gl3 Olfr301 (C) SNORA17 Pdgfb Tmc3 Tmem135 (C) Sstr3 Tyr (A) Vmn2r72.ps (C) Syngr1 Vmn2r66 (A) zfand6 (C) For HrI2, (A) refers to genes with a private A/J SNP, (C) refers to genes with a private 129S1/SvImJ SNP. Unmarked genes had both A/J and 129S1/SvImJ SNPs. In all three of these cases, the high priority candidate genes we identified covered a range of biological functions, including a large number with no annotated functions. While no obvious candidates jump out with HrI3, HrI4 includes Grap2, involved in leucocyte specific signaling [59]. Similarly, HrI2 includes the chemoattractant/T-cell modulator Il16 [60] as well as Nox4, which is potentially involved in production of reactive oxygen species and interacts with the TLR4 pathway [61]. Discussion The host response to infection represents a complex set of interacting phenotypes, where variation in these phenotypes is likely influenced by interactions between multiple polymorphic genes as well as other factors (specific virus-host interactions, environment, exposure, age). While reverse genetics approaches have afforded insight into the role of viral genes in infection [62]–[64], well defined models do not exist for understanding how polymorphic host genes interact to regulate host response to infection. A number of mouse models including gene specific knockouts and transgenic lines [28]–[30], [32], [34], [35], [39], [65], panels of genetically distinct mouse lines [44], [66], and classical RI panels [42], [45], [46], have been used to provide key insights into the role of specific genes in pathogenesis, however, these systems do not accurately reflect the situation in outbred populations. While these systems either interrogate the role of specific genes in the context of a single genetic background (e.g. knockouts) or analyze the impact of two variant alleles (e.g. classic RI panels) on disease pathogenesis, in genetically complex populations, such as humans, disease outcomes are likely determined by interactions between multiple polymorphic genes, with multiple polymorphic alleles at these loci. Therefore, we chose to use the pre-CC population to assess how genetic polymorphisms impact the host response to influenza infection in a population of animals that more closely represents the genetic diversity found in outbred populations. Our results show that even within the constraints of this pre-CC study (i.e. one animal/incipient line, single time point), we were able to uncover underlying relationships between host responses to infection, identify new disease phenotype combinations not present within the founder strains, and identify novel QTL impacting aspects of the host response to infection, suggesting that the CC panel represents a powerful system for studying pathogen interactions within genetically complex populations. Host genetic control of infectious disease responses Host genetic polymorphisms have been shown to contribute to differential disease outcomes, and evidence exists for influenza [16], [22], [23], [67] that suggests host genetic variants are important regulators of influenza pathogenesis. The identification of a QTL of major effect sitting over the anti-influenza gene Mx1 was not surprising. Mx1 is known to strongly inhibit influenza virus replication, limiting the resultant IAV-induced disease symptoms in mice [68]. As was to be expected, within the pre-CC population, functional Mx1 alleles reduced viral titers, weight loss and clinical disease, inflammation and pathology. Mx1 also acted to influence the expression levels of a large number of transcripts. While it is unlikely a direct transcriptional regulator, Mx1's potent ability to inhibit IAV replication likely alters the signaling environments and host response pathways triggered in response to infection. Within human populations, it is possible for multiple alleles to exist at any given locus. Similarly, at any locus within the pre-CC population, up to eight distinct alleles exist. In addition to increasing the probability of having functionally variant alleles segregating within the population, multiple alleles at a locus can give rise to distinct phenotypic outcomes across the population. The effects of this allelic variation can clearly be seen when considering Mx1. A total of 5 distinct Mx1 haplotypes exist in the pre-CC population, and they can be grouped into three functionally distinct alleles based on their effects during influenza infection. Of particular interest is the CAST/EiJ allele, which disassociates the effects of Mx1 on control of viral replication from its' ability to protect from a clinical disease aspects. We utilized the large number of transcripts that had an eQTL at Mx1 to better understand potential ways in which the CAST/EiJ allele might provide clinical protection while being unable to control IAV replication. We identified a set of transcripts that were significantly upregulated in those individuals with defective Mx1s, but were downregulated in individuals with CAST/EiJ, PWK/PhJ and NZO/HILtJ Mx1 allele). These transcripts included sets of inflammatory and immune related transcripts, such as SOCS3, Irf1, and Interferon-gamma. GTPases, such as Mx1, are important in a number of signaling and protein production activities [69], [70], and it is possible that the signaling activities of Mx1 are responsible for regulating specific transcripts, such as those mentioned above, in limiting clinical disease independent of Mx1's previously described anti-IAV activities. However, additional studies are needed to better define whether the differential effect of the CAST/EiJ Mx1 allele are due to Mx1-associated signaling or more subtle effects on viral replication which subsequently affect inflammation and disease. In a more general sense, these results illustrate the advantages provided by using a system such as the CC compared to using classical inbred strains such as the founder strains. Because of the inherent genome structure of the founder strains [47], it would be difficult to differentiate between the disassociated effects of the CAST/EiJ Mx1 allele we found in the pre-CC population and the alternate hypothesis that CAST/EiJ's Mx1 was completely non-functional, and that there was another polymorphic gene within CAST/EiJ that provided some protection from clinical disease. It is only through the recombination present within the CC, and the use of large populations of unique lines that can be evaluated within the CC that such hypothesis can be differentiated. This lack of structure across the genomes also allowed us to gain new insight into the relationships between coexpressed transcripts and disease outcomes, as well as the relationships between specific disease processes (see population-wide pattern section below). Though the identification of Mx1 served to validate our mapping study, the presence of a large effect allele within genetically variable populations can mask those with smaller effect sizes. Indeed, this genetic architecture is common to pathogen resistance, as several other large effect genes have been identified for viral (e.g. flaviviruses [71], mouse CMV [72], norovirus [20], HIV [18]), bacterial (NRAMP [73]) and parasitic (malaria [74]) diseases. Due to these genes of large effect, many studies of these pathogens have been conducted within susceptible models (e.g. mouse models of influenza infection are almost universally Mx1-/- models). It is likely that there are specific alleles influencing disease processes that only act in the context of the presence or absence of major resistance alleles (e.g. an allele that affected the degree of tissue repair would only act when there had been significant tissue damage, which a functional Mx1 allele would prevent). To address this issue, we conducted further genome mapping while accounting for Mx1 status using two complementary approaches: asking whether there are loci that act in addition to Mx1 in regulating disease-associated phenotypes (HrI2), and also asking if there are loci that act only in the highly susceptible Mx1 negative population (HrI3, HrI4). By doing so, we were able to identify three additional loci influencing weight loss, pulmonary edema and neutrophil infiltration into the airways, further validating the role of genetic variation at HrI3 in contributing to pulmonary edema differences in a separate set of fully inbred CC animals. The development of pulmonary edema [6], [75], as well as neutrophilic infiltration [76] have been shown to contribute to disease severity and long-term lung disease in the human population. Our results show that host genetic variation not only contributes to direct responses to infection, but also to other aspects of the host response that can lead to long-term complications following infection. In addition to allowing us to identify novel QTL that impacted the host response to IAV, our mapping using only Mx1-/- animals also allowed us to compare our study to other studies using QTL mapping within Mx1-/- panels [42], [45], [46]. While these studies all identified QTL contributing to IAV responses, there was no overlap between these QTL and the ones we discovered. This result is unsurprising given the differences in virus strains, phenotypes measured, and polymorphisms within the two panels. Nevertheless, the aggregate of these studies further strengthens the idea that virus-host interactions are highly complex, and that polymorphic host genes are critical for numerous responses. Our results further emphasize the unique genetic interactions that occur in specific sub-populations of infected individuals that regulate disease processes. While we limited our analysis to the Mx1-/- animals in the pre-CC population, due to sample size, it is likely that similar QTL can be identified in future work using animals with functional Mx1. Ultimately, the goal of QTL mapping studies is to identify the causal polymorphic genes or genome features responsible for variation in disease processes. A variety of studies have used transcriptional data [42], [49] to narrow QTL regions into candidate genes. However, transcriptional analysis can be confounded by the dynamic nature of transcriptional responses, and can also be confounded by SNPs residing underneath the expression probes that can impact binding [77], [78]. An alternate approach, taking advantage of the allelic complexity of the CC is the use of the sequence of the founder strains [57] to interpret the mapping results and to prioritize candidate genes within QTL. This approach is quite powerful; as causative polymorphisms must be contained within QTL regions, and has been used effectively in other pre-CC studies [49], [55]. However, Mx1 provides a cautionary note for regions of the genome in that there is a large in/del differentiating the eight CC founders. In fact, C57BL/6J, and thus the assembly, has the deletion. In such regions, functional genomic features may be misannotated and more importantly the genetic variants present in founders without the deletion is currently not annotated. This lack of annotation makes it difficult to conclusively dissect the polymorphisms within these regions that might cause phenotypic variation across the population, and currently requires more intensive sequencing efforts on a case-by-case basis. The Mx1 result illustrates the importance of improving the annotation of genetic variants in the mouse genome. It also suggests that in addition of the processed lists of SNPs and in/dels available at the Mouse Genome Projects from the Sanger Institute, the analysis of the allele effect in QTL intervals should be analyzed by searching for signatures of structural variation that might be present in the raw reads. As further QTL analyses are undertaken within the CC system, approaches to narrow down onto candidate genes and polymorphisms will need to be further developed, likely integrating both transcriptional and refined analysis of sequence data to account for other potential causative genome features. These approaches will be facilitated by the interrogation of transcriptional activity at multiple time points in completely inbred CC lines. Population-wide patterns of host response to influenza infection The pre-CC experiment also allowed us to identify specific CC lines that might be useful models for specific disease phenotypes (e.g. animals with high titers but little/no clinical disease as super-spreaders), as well as other interesting relationships between disease components across the pre-CC population. For example, while lung hemorrhage is a clinically important influenza-associated phenotype [79], we found that lung hemorrhage was only correlated with alveolar inflammation and not with other metrics of viral spread or disease severity. This result bears further study, but it suggests that while hemorrhage indicates a severe response to influenza infection, hemorrhage is governed by processes that are largely disassociated from those processes contributing to overall severity of infection at least through day 4. Results such as this further highlight the complexity of the host response to infection, and the need to consider genetically diverse populations when attempting to understand disease processes. While the individual pre-CC mice used within this study were insufficient to develop these new models of disease processes, as CC lines become increasingly available [52], utilization of specific CC lines with unique responses to infectious diseases might well become a critical resource for uncovering avenues of viral pathogenesis in specific subpopulations. In addition, we identified transcriptional modules that correlated with overall disease severity, or with specific aspects of the host response to infection (e.g. inflammatory components, clinical disease). Recent efforts from a number of groups [10], [80] have focused on identifying markers associated with different disease states (e.g. protective vaccine responses, asymptomatic individuals) across human cohorts. While the nature of the pre-CC study (restricted to a single time-point) makes it difficult to draw broad conclusions about these results, it does suggest that there are unique transcriptional signatures relating to different aspects of the host response to infection. Future studies leveraging the full power of the CC (identical animals at different time points, compared to baseline transcriptional levels) will provide the opportunity for identification of molecular signatures of different disease-associated phenotypes, informing us both of the mechanisms through which these processes are occurring, as well as providing non-invasive diagnostic markers of various disease-related phenotypes. The findings of this study also provide new insights into the relative contribution of viral replication versus virus-induced inflammation in the pathogenesis of influenza infection. There is conflicting evidence from a variety of in vivo studies as to the importance of virus-induced inflammation [28], [39], [64], [81], [82] and control of viral load [30], [39], [66] on disease severity. However, these studies have all used different mouse strains, influenza strains and experimental conditions, making direct comparisons difficult. The novel allele combinations in the pre-CC population allowed new insight by dissociating phenotypes that were correlated in the founder strains. This allowed us to assess the relative contribution of inflammation and viral replication on disease outcome. Consistent with the complex nature of virus-induced disease, we found that both viral replication and levels of inflammation were predictive of disease outcome independently of one another. Although the overall correlations between viral titer, weight loss and inflammation were reduced within the Mx1-/- subpopulation of pre-CC animals, we again found that both viral replication and levels of inflammation together were better predictors of disease outcome than either was alone. Consistent with this analysis, we identified CC lines that showed high levels of replication and weight loss, but little inflammation as well as lines with excessive inflammation and weight loss, but low viral titers suggesting that multiple pathways can lead to similar clinical disease outcomes across a genetically diverse population. Future studies utilizing this model with fully inbred CC lines, will allow us to more fully evaluate how the kinetics, magnitude, and duration of viral replication and inflammation contribute to disease outcome over the temporal course of the infection. These results illustrate the potential and the power of using genetically diverse mice to study the relative contribution of specific aspects of the pathogen and host response which together drive disease outcome. Summary There is an increased appreciation for the role that host polymorphisms play in the host response to infectious diseases [83]. However, for a number of reasons, Genome Wide Association Studies (GWAS) of responses to acute infectious diseases within the human population have been difficult to conduct [84]. While future studies using the CC panel will allow for the evaluation of multiple animals/line and allow for integration of information across multiple timepoints, for this study we had access to only a single animal/line within the pre-CC population making it similar in design to GWAS and raising concerns about our ability to identify host response QTL within this study. However, the identification of several disease associated QTL, including a QTL containing the known IAV associated resistance gene Mx1, even when using a single mouse/time point, suggests that the CC lines represent a robust system for identifying polymorphic genes that regulate host responses to infectious diseases. As the CC can be recreated and manipulated, it will increasingly become a useful tool to a) identify candidate genes and pathways for more targeted association studies within human populations, and b) allow us to increase our understanding of how critical demographic and environmental factors, as well as specific genetic subpopulations impact some of the variability in GWAS studies of acute infectious diseases in humans. Host genetic variation clearly plays an important role in regulating differential response phenotypes to infectious disease progression. Herein, we provide proof of concept and a framework for identifying the role of polymorphic genes on microbial pathogenesis using a genetically diverse population: underlying relationships between different disease phenotypes, genetic control of phenotypes following infection (both those of large effect, as well as those that modulate the host response), and transcriptional profiles that related to specific disease-associated phenotypes. In summary, this study shows that a genetically complex in vivo model represents a useful system for modeling pathogen interactions within genetically diverse populations and identifying novel genetic loci controlling multiple aspects of disease pathogenesis. Though this study had clear limitations, the pre-CC population provided the appropriate framework to develop the methodological approaches that resulted in the identification and prioritization of genes within novel disease loci. These results strongly support the hypothesis that studies using the fully inbred CC lines, with the use of replicate animals and evaluation of phenotypic variation during influenza infection over time, will be even more successful in identifying polymorphic genes that regulate multiple disease associated phenotypes including those phenotypes associated with adaptive immune responses and disease recovery. Furthermore, through careful selection of CC lines, studies can be designed to specifically investigate how interactions between allelic variants in two or more genes interact to influence complex phenotypic outcomes during infection. Methods Ethics statement Mouse studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All mouse studies were performed at the University of North Carolina (Animal Welfare Assurance # A3410-01) using protocols approved by the UNC Institutional Animal Care and Use Committee (IACUC). All studies were performed in a manner designed to minimize pain and suffering in infected animals, and any animals that exhibited severe disease signs was euthanized immediately in accordance with IACUC approved endpoints. Animals 8–16 week old female animals from the 8 founder strains (A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ) were derived from the Jackson labs (jax.org), and bred at UNC Chapel Hill under specific pathogen free conditions. 8–12 week old female Pre-CC mice were bred at Oak Ridge National Laboratories under specific pathogen free conditions, and transferred directly into a BSL-3 containment laboratory at UNC Chapel Hill. Inbred CC mice were bred at UNC Chapel Hill under specific pathogen free conditions. All experiments were approved by the UNC Chapel Hill Institutional Animal Care and Use Committee. Virus and cell lines The mouse adapted influenza A strain A/PR/8/34 (H1N1) was used for all infection studies. A/PR/8/34 stocks were made by infection of 10-day old embryonated chicken eggs. MDCK cells grown in high glucose Dulbecco's modified Eagle's medium (10% FBS, 1% Penicillin-Streptomycin) were used for titering virus. Infections Animals were lightly anesthetized via inhalation with Isoflurane (Piramal, Bethlehem, Pa). Following anesthesia, animals were infected intranasally with 5×10∧2 pfu of PR8 in 50 µL of phosphate buffered saline (PBS), while mock infected animals received only 50 µL of PBS. Animals were assayed daily for morbidity (determined as % weight loss), mortality and clinical disease scores. At 4 days post infection, animals were euthanized via Isoflurane overdose and cardiac puncture, animals were assessed for gross pathology (lung hemorrhage and edema) and tissues were taken for various assays. TCID50 assay MDCK cells were seeded into 96 well plates at a density of 1.5×10∧5 cells/well in DMEM (10% FBS, 1% Pen-strep) and incubated at 37 degrees overnight. Cells were washed 2 times with PBS, before addition of 100 µL of DMEM to each well. Media was removed from all wells in the 1st column of the plate, and 146 uL of lung homogenate in DMEM was added to these wells (each biological sample was added to 4 wells). Serial dilutions of 46 µL (0.5 log dilutions) were carried out across the plate. Plates were incubated at 37 degrees C for 1 hour, inoculum was removed and 150 µL of serum free DMEM with 1 µg/mL of trypsin was added to each well. Plates were then incubated at 37oC for 3 days. Media was then removed, and wells were stained with a 1% Crystal Violet solution. The stain was washed off with water. Titer is determined as follows: (1) Where Xp is the last dilution where all of the replicates of a given sample are positive, D is the serial dilution log and Sp is the sum of the proportion of replicates at all dilutions where positives are seen (starting with the Xp dilution). Histopathological analysis The right lung was removed and submerged in 10% buffered formalin (Fischer) without inflation for 1 week before being submitted to the UNC Linberger Comprehensive Cancer Center histopathology core for processing. Two 5 micron thick Hematoxylin and Eosin stained lung sections (step-separated by 100 microns) were blind-scored by microscopic evaluation performed by two independent scorers for a variety of metrics relating to the extent and severity of immune cell infiltration and pathological damage on a 0–3 (none, mild, moderate, severe) scale. Immunohistochemical analysis of viral replication For detection of influenza virus antigen, we used serial sections from formalin-fixed, paraffin-embedded lung samples. After deparaffinization and rehydration, antigen retrieval was performed using 0.1% protease (10 min at 37°C). Endogenous peroxidase was blocked with 3% hydrogen peroxide and slides were briefly washed with phosphate-buffered saline (PBS)/0.05% Tween 20. Mouse anti- influenza virus nucleoprotein (clone Hb65, ATCC) and horseradish peroxidase labeled goat anti-mouse IgG2a were used for 1 h at room temperature. Peroxidase activity was revealed by incubating slides in 3-amino-9-ethylcarbazole (AEC, Sigma) for 10 minutes, resulting in a bright red precipitate, followed by counterstaining with hematoxylin. Tissue sections from non-infected BALB/c mice and mouse IgG2a isotype antibody (R&D) were used as negative controls. The extent of influenza viral antigen spread across these slides was then scored in a blinded fashion on a 0–3 scale. RNA preparation and oligonucleotide microarray processing At 4 days after infection, mice were killed and lung tissue harvested and placed in RNAlater (Applied Biosystems/Ambion, Austin, TX) and stored at −80°. The tissues were subsequently homogenized, and RNA extracted as previously described [85]. RNA samples were spectroscopically verified for purity, and the quality of the intact RNA was assessed using an Agilent 2100 Bioanalyzer. cRNA probes were generated from each sample by the use of an Agilent one-color Low Input Quick Amp Labeling Kit (Agilent Technologies, Santa Clara, CA). Individual cRNA samples were hybridized to Agilent mouse whole-genome oligonucleotide 4×44 microarrays according to manufacturer instructions. Samples from individual mice were evaluated to enable examination of animal-to-animal variation as part of the data analysis. Slides were scanned with an Agilent DNA microarray scanner, and the resulting images were analyzed using Agilent Feature Extractor version 8.1.1.1. The Agilent Feature Extractor software was used to perform image analysis, including significance of signal and spatial detrending and to apply a universal error model. For these hybridizations, the most conservative error model was applied. Raw data were then loaded into a custom-designed laboratory information management system (LIMS). Data were warehoused in a Labkey system (Labkey, Inc., Seattle, WA). Raw array data are available from GEO with accession GSE30506. The Agilent arrays were background corrected by applying the Normal-Exponential convolution model [86] and normalized using quantile normalization [87] with the Agi4×44PreProcess Bioconductor package (www.bioconductor.org). The probes were filtered requiring that all probes meet specific QC requirements (probe intensity had to be found, well above background, not saturated, and not be nonuniformity or population outliers as defined by the standard parameters in Agi4×44PreProcess package) for all samples. Differential expression analysis was performed using the LIMMA Bioconductor package [88], and the false discovery rate was calculated using the qvalue Bioconductor package [89]. Probes were mapped to the mm9 genome using BLAT [90] requiring at least 98% identity. Probes that did not map, mapped to multiple locations equally well, or contained a high confidence single nucleotide polymorphism (SNP) from one of the eight progenitor strains from the Sanger Institute/Wellcome Trust mouse sequencing project [57] in the probe sequence were excluded from analysis. There were 11,700 probes passing QC and not potentially impacted by a SNP. The Gene Ontology (GO) analysis was performed using the standard hypergeometric test from the Gostats Bioconductor package [91] with a universe consisting of the unique genes from the probes entered into the DE analysis. Only the Biological Process subset of the Gene Ontology was used for testing. The Benjamini and Yekutieli false discovery rate (FDR) [92] was computed for the P-value distribution for this analysis to address dependencies inherent from the hierarchical/nested structure of the GO categories. De-novo network (module) analysis For both the full analysis and the Mx1-/- analysis, six thousand probes were chosen to be entered into the analysis based on both high variability across samples as well as a measure of how connected they were [93]. Arrays were preprocessed separately for both analyses. These probes were used for the formation of coexpression modules through the weighted gene coexpression network analysis (WGCNA) procedure [93], [94]. Module formation was signed [95] and was carried out using the dynamicTreeCut R package [96] with pruning carried out based only on the dendrogram. All modules were checked for statistical significance through a permutation procedure whereby the mean topological overlap of those probes within a module was compared to the mean topological overlap of 10,000 random modules of the same size chosen from the initial set of 6,000 probes. Using the WGCNA package the module eigengene (first principle component of the expression matrix) for each module was computed [97]. The module eigengene can be viewed as the representative profile that summarizes the module expression profile. The module eigengene was first correlated with the clinical traits using Pearson's correlation with P-values provided as Student's asymptotic P-value. The module QTL scan was carried out similar to below but using the eigengene for each module as a phenotype. Specifically, each eigengene was regressed on the expected haplotype contribution from each of the eight founding inbred strains. Significance was assessed using the –log10 P-values (using a Bonferroni type correction (α = 0.05)) from the model and support intervals were computed using the 1.5 LOD drop method [54]. This method of defining an mQTL is essentially the same as a previous study using F2 intercrosses [53]. A related approach looking at overrepresentation of eQTLs in a module [98] could potentially be sensitive to significance cutoffs and module size and necessitates a full eQTL scan. Genotyping and haplotype reconstruction Genotyping and haplotype reconstruction were done as described in [49]. Briefly, each pre-CC animal was genotyped using Mouse Diversity [47] test A-array at 181,752 well performing SNPs which were polymorphic across the founder strains. Once genotypes were determined (Dataset S2), founder strain haplotype probabilities were computed for all genotyped loci using the HAPPY algorithm [99]. Genetic map positions were based on the integrated mouse genetic map using mouse genome build 37 [100]. Genome scans Genome scans were run as described in [49]. Briefly, QTL mapping was conducted using the BAGPIPE package [101] to regress each phenotype on the computed haplotypes in the interval between adjacent genotype markers, producing a LOD score in each interval to evaluate significance. Genome-wide significance was determined by permutation test, with 250 permutations conducted per scan. A more complex model was also used to control for Mx1 status, whereby the null model included the haplotype information from the most significant marker at the Mx1 locus (JAX00072951). LOD scores are then computed for each haplotype interval based on the increase in fit of genotype to phenotype when Mx1 haplotype is already taken into account. Identifying candidate regions For the likely regions of identified QTL peaks, SNP data for the eight founder strains from the Sanger mouse genomes project was downloaded, and filtered to include only homozygous calls. In the case where a single founder strain allele drives a QTL peak, all private SNPs for that strain are candidates for the observed phenotype. In the case where multiple founder strains drive a QTL peak, the most likely hypothesis is that the causative polymorphism exists in a region of shared ancestry between these founder strains. SNPs were categorized into 3 classes: Consistent with a shared ancestry (SNPs where the driver strains share a private SNP), Inconsistent with a shared ancestry (SNPs where the driver strains share different alleles with other strains), and Uninformative with regards to ancestry (SNPs private to a single strain and SNPs shared by driver strains as well as others). Candidate regions were defined as regions containing at least one consistent SNP, and were bounded by the 1st nucleotide after the last inconsistent SNP until the last nucleotide before the next inconsistent SNP, and also had to exceed 100 base pairs in length. We identified all annotated genes and non-coding RNAs that were within 500 bases of, or in consistent regions and classify these as our likely candidates. Mx1 gene structure To characterize the genetic variation at the Mx1 locus we combined data from the mouse genome assembly, the Sanger Institute's Mouse Genomes sequencing project and a mouse full-length Mx1 cDNA clone (CT010406). By aligning the cDNA to the Mx1 genomic locus we identified three missing exons (exons 9, 10 and 11). We used this information to design primers to amplify and sequence every exon and the span the deletion boundaries in each strain (Table S14). The deletion occurs between positions 97674078 and 97674079 of the reference on chromosome 10. The deletion is 921 bp upstream from exon 12 and 302 bp downstream of exon 8. Chromosome walking was used to partially sequence the introns missing in the assembly. All sequence variants have been submitted to NCBI (GenBank Accession numbers: JQ860141-JQ860220). Real-time PCR analysis Whole lung RNA from 8 week old female C57BL/6J, CAST/EiJ, and PWK/PhJ that had been either mock or flu infected were isolated using Trizol (Invitrogen, Carlsbad CA), and following their protocol. One microgram of total isolated RNA from each sample was reverse transcribed using MMLV-RT (Promega, Madison WI), and following their protocol. We ran TaqMan real time PCR with two primer-probe pairs (Applied Biosystems Foster City, CA): Mm01217999_m1 to amplify the 5′ gene region of Mx1 transcripts, and Hs03928985_g1 to amplify 18s mRNA. Fold-induction was calculated as the difference in expression levels for infected animals as compared to their strain-matched mock animals. Supporting Information Dataset S1 Phenotypes of Pre-CC animals. (CSV) Click here for additional data file. Dataset S2 Genotypes of Pre-CC animals. (ZIP) Click here for additional data file. Figure S1 Variation in weight loss curves across the eight founder strains of the Collaborative Cross. Following infection with IAV, n = 5–12 animals from each of the eight founder strains had their weights recorded through four days post infection. Shown are weight loss curves (with standard deviations) for these eight strains (A/J (n = 11) = yellow, C57BL/6J (n = 6) = grey, 129S1/SvImJ (n = 5) = pink, NOD/ShiLtJ (n = 5) = dk. Blue, NZO/HILtJ (n = 12) = lt. blue, CAST/EiJ (n = 5) = green, PWK/PhJ (n = 5) = red, WSB/EiJ (n = 5) = purple).). The high variation present within NOD/ShiLtJ and CAST/EiJ was due to bimodal weight loss responses in each strain, and repeated in multiple experiments, (TIF) Click here for additional data file. Figure S2 QTL underlying weight loss following influenza infection. (A) QTL scans showing LOD score (Y-axis) and genome position (X-axis) for weight loss. HrI1 on chromosome 16 (dark peak) was identified and influenced many disease phenotypes We remapped weight loss after accounting for the large effect of HrI1 in our QTL model, and identified another QTL, HrI2 on chromosome 7 (light grey peak). Allele effects plot for (B) HrI1 and (C) HrI2, showing the estimated contributions of each founder strain allele (y-axis) across the likely region for these loci (position in locus, x-axis) on weight loss. A phenotype by haplotype plot (D) showing the weight loss phenotypes for those animals that were homozygous for founder strain alleles at HrI2 (A/J = yellow, C57BL/6J = grey, 129S1/SvImJ = pink, NOD/ShiLtJ = dk. blue, NZO/HILtJ = lt. blue, CAST/EiJ = green, PWK/PhJ = red, WSB/EiJ = purple). (TIF) Click here for additional data file. Figure S3 QTL underlying pulmonary edema following influenza infection. (A) QTL scans within the Mx1 -/- subpopulation (light grey line) identified a QTL, HrI3 on chromosome 1, influencing pulmonary edema that was not detectable within the whole pre-CC population (black line). (B) Allele effects plot for HrI3, showing the estimated contributions of each founder strain allele across this locus on pulmonary edema. A phenotype by haplotype plot (C) showing the pulmonary edema score for those animals that were homozygous for founder strain alleles at HrI3 (A/J = yellow, C57BL/6J = grey, 129S1/SvImJ = pink, NOD/ShiLtJ = dk. blue, NZO/HILtJ = lt. blue, CAST/EiJ = green, PWK/PhJ = red, WSB/EiJ = purple). (TIF) Click here for additional data file. Figure S4 QTL underlying neutrophil infiltration following influenza infection. (A) QTL scans within the Mx1 -/- subpopulation (light grey line) identified a QTL, HrI4 on chromosome 15, influencing neutrophil infiltration that was not detectable within the whole pre-CC population (black line). (B) Allele effects plot for HrI4, showing the estimated contributions of each founder strain allele across this locus on neutrophil infiltration. A phenotype by haplotype plot (C) showing the neutrophil infiltrate score for those animals that were homozygous for founder strain alleles at HrI4 (A/J = yellow, C57BL/6J = grey, 129S1/SvImJ = pink, NOD/ShiLtJ = dk. blue, NZO/HILtJ = lt. blue, CAST/EiJ = green, PWK/PhJ = red, WSB/EiJ = purple). (TIF) Click here for additional data file. Table S1 Mean (range) of IAV associated phenotypes comparing founder strains and the Pre-CC. (DOCX) Click here for additional data file. Table S2 Phenotypic correlations across the pre-CC population. (DOCX) Click here for additional data file. Table S3 Significance of predictors of D4 weight. (DOCX) Click here for additional data file. Table S4 Transcripts within expression modules. (DOCX) Click here for additional data file. Table S5 GO-term enrichment by module. (DOCX) Click here for additional data file. Table S6 Mx1 sequence variants. (DOCX) Click here for additional data file. Table S7 Transcripts with an eQTL at Mx1 . (DOCX) Click here for additional data file. Table S8 GO-Term enrichment of eQTL that differentiate the CAST/EiJ Mx1 allele's effects. (DOCX) Click here for additional data file. Table S9 Phenotypic mean (ranges) between entire pre-CC population and the Mx1 -/- subpopulation. (DOCX) Click here for additional data file. Table S10 Phenotypic correlations across the Mx1 -/- pre-CC subpopulation. (DOCX) Click here for additional data file. Table S11 Transcripts within modules in the Mx1 -/- subpopulation. (DOCX) Click here for additional data file. Table S12 GO-term enrichment by module in Mx1 -/- subpopulation. (DOCX) Click here for additional data file. Table S13 Transcriptional module correlates with influenza phenotypes in the Mx1 -/- subpopulation. (DOCX) Click here for additional data file. Table S14 Primers used in sequencing Mx1 . (DOCX) Click here for additional data file.

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IRF-3, IRF-5, and IRF-7 Coordinately Regulate the Type I IFN Response in Myeloid Dendritic Cells Downstream of MAVS Signaling

Helen M. Lazear, Alissa Lancaster, Courtney Wilkins … (2013)

Introduction The type I interferon (IFN) signaling network is an essential component of the innate immune response against viral infections, and also functions to shape adaptive immunity [1]–[4]. Infected cells initiate an antiviral response upon recognition of non-self pathogen-associated molecular patterns (PAMPs), which are detected by host pattern recognition receptors (PRRs) [2], . PRRs that recognize RNA viruses include members of the Toll-like receptor (TLR3 and TLR7) and the RIG-I-like receptor (RLR; RIG-I and MDA5) families. TLRs and RLRs recognize distinct PAMPs in different locations (extracellular/endosomes and cytoplasm, respectively) and activate signaling cascades to initiate antiviral and inflammatory responses. TLR3 binds to double-stranded RNA and recruits the adaptor molecule TRIF to activate the kinases TRAF and IKK-ε, which in turn activates the latent transcription factors IRF-3, IRF-7, and NF-κB. Single-stranded RNA is recognized by TLR7, which uses the adaptor molecule MyD88 to activate TRAF and IKK-ε, and subsequently NF-κB- and IRF-7-dependent transcription. RLRs interact with the mitochondria-associated adapter molecule MAVS (also called IPS-1, VISA, or CARDIF), which signals through the kinases TBK1 and IKK-ε to activate IRF-3, IRF-7, and NF-κB and initiate type I IFN production. A canonical model for type I IFN production after RNA virus infection is a two-step positive feedback loop that is regulated by IRF-3 and IRF-7 [9], [10]. In the first phase, viral sensing by TLRs or RLRs induces nuclear localization of IRF-3, which in concert with NF-κB and ATF-2/c-Jun stimulates transcription, synthesis, and secretion of IFN-β and IFN-α4 by infected cells. In the second phase, extracellular IFN-β and IFN-α4 bind to the type I IFN receptor (IFNAR), which triggers activation of the JAK-STAT signaling pathway and induction of IFN-stimulated genes (ISGs) [11]. ISGs act by a variety of mechanisms to render cells resistant to viral replication [12], [13]. Although type I IFN signaling is required to activate the full antiviral response, a subset of ISGs is induced directly by IRF-3 [14], [15]. While IRF-3 is constitutively expressed in many tissues, IRF-7 is an ISG required for the expression of most IFN-α subtypes, and thus a key mediator of the type I IFN amplification loop [2], [9], [10]. Certain cells, including plasmacytoid dendritic cells and macrophages, express IRF-7 constitutively, which makes them poised for rapid IFN-α production [16]–[20]. West Nile virus (WNV) is a mosquito-transmitted, enveloped, positive-sense RNA virus and member of the Flaviviridae family. Studies in mice with targeted gene deletions have provided insight into mechanisms of innate immune restriction of WNV infection. The type I IFN response is essential to the control of WNV infection, as mice that are defective at producing or responding to IFN cannot control virus replication and succumb rapidly to infection [17], [21]–[25]. The host antiviral response in vivo is dependent upon both TLR and RLR signaling, as deficiencies in TLRs, RLRs, or their downstream adaptor molecules (including MyD88 and MAVS) result in enhanced viral replication and lethality [8], [22], [26]–[30]. Recent studies with WNV have suggested that some cell types use non-canonical signaling pathways to induce type I IFN responses. The combined absence of IRF-3 and IRF-7 resulted in uncontrolled WNV replication and more rapid death in Irf3−/−×Irf7−/− double knockout (DKO) mice compared to the individual single gene knockout mice [17], [21], [22], [31]. However, even without IRF-3 or IRF-7, type I IFN was produced by DKO mice infected with WNV or murine cytomegalovirus, albeit at reduced levels compared to wild type mice [22], [32]. Consistent with the sustained production of type I IFN, lethality in DKO mice infected with WNV or chikungunya virus was not as rapid or complete as in Ifnar−/− mice [22], [31], [33], [34]. Ex vivo experiments with primary myeloid dendritic cells (mDC) and macrophages revealed that the IFN-β response after WNV infection was sustained in DKO cells but abrogated in the absence of MAVS [22], [27]. In contrast, the IFN-β response in neurons and fibroblasts was abolished in the absence of either IRF-3 and IRF-7 or MAVS [22], [27]. These studies suggested cell type-specific requirements for the transcription factors that induce IFN-β expression in response to WNV infection. To define the transcription factor(s) responsible for the IRF-3 and IRF-7-independent production of IFN-β in myeloid cells, we considered another member of the IRF family, IRF-5. Although IRF-5 was originally identified as an inducer of inflammatory cytokines (IL-6 and TNF-α) downstream of TLR-7 and MyD88 signaling, subsequent studies suggested that it could contribute to type I IFN production after viral infection [35]–[37]. In response to Newcastle disease virus (NDV) infection, IRF-5 induced overlapping and distinct sets of genes compared to IRF-7, including stronger induction of IFN-β and the antiviral gene Rsad2 (Viperin) [38]. We generated Irf3−/− ×Irf5−/− ×Irf7−/− triple knockout (TKO) mice and found that these mice were highly vulnerable to infection with WNV. The combined loss of IRF-3, IRF-5, and IRF-7 largely abrogated type I IFN and ISG expression in mDC, and microarray analysis of WNV-infected mDC revealed a set of genes induced in DKO but not in TKO cells. Because the limited set of genes induced in WNV-infected TKO mDCs was absent in Mavs−/− mDCs, we conclude that the RLR-MAVS signaling pathway dominantly regulates innate immune gene induction in mDCs during WNV infection, and that IRF-3, IRF-5, and IRF-7 coordinately mediate this response. Our results establish a new linkage between the IRF-5 and the RLR signaling pathways in induction of the antiviral IFN response. Results TKO mice are highly vulnerable to viral infections We hypothesized that IRF-5 might be responsible for the residual IFN-β production in DKO mice, because IRF-5 contributes to Ifnb mRNA expression downstream of the PRR TLR7 and adaptor molecule MyD88, both of which limit WNV pathogenesis in vivo [28], [30], [39]. To test this, we generated Irf3−/−×Irf5−/−×Irf7−/− TKO mice ( Figure S1 ) and defined their response to viral infection. TKO mice were viable, fertile, and produced progeny according to normal Mendelian frequencies (data not shown). We infected WT, DKO, and TKO mice with a virulent WNV strain (New York 2000, WNV-NY) and found that TKO mice succumbed to infection earlier than DKO mice (mean time to death (MTD): 4.0 days versus 5.7, P 0.05), and no statistical difference in MTD was observed (9.0 days for TKO versus 8.2 days for Ifnar−/− mice, P>0.05). Similar results were observed upon infection with murine norovirus (MNV), an unrelated non-enveloped positive-sense RNA virus. TKO mice were more vulnerable to MNV infection than DKO mice, with only 1 of 11 TKO mice surviving, compared to 100% survival for DKO mice (P 0.05) in all tissues examined, except for the spleen after WNV-MAD infection where titers in TKO mice were greater than in Ifnar−/− mice (25-fold, P 0.05). While the serum levels of type I IFN in TKO and DKO mice were diminished compared to WT mice after WNV-NY infection and equivalent to WT after WNV-MAD infection, substantially higher levels of type I IFN were detected in the serum from Ifnar−/− mice (29-fold after WNV-NY infection, P 0.05), suggesting that IRF-3, IRF-5, and IRF-7 regulate innate immune defense to control WNV replication in mDC. In comparison, TKO macrophages showed little increase in WNV-NY replication compared to DKO cells, and reached lower (11-fold, P 0.05). Nonetheless, for all genotypes tested, only a fraction (up to 15%) of cells stained positive for WNV antigen at 24 hours after infection, suggesting that uninfected cells contributed substantially to the gene induction profile observed in this experiment. 10.1371/journal.ppat.1003118.g005 Figure 5 Microarray analysis of WNV infected mDC. mDC from WT, Irf3−/−×Irf7−/− DKO, Irf3−/−×Irf5−/−×Irf7−/− TKO, Mavs−/− and Ifnar−/− mice were infected with WNV-NY at an MOI of 25 and total RNA was harvested 24 hours later. A. WNV infection of mDC from the indicated genotypes as assessed by anti-WNV MAb staining at 24 hours after infection. B. Heatmap showing the 50 genes with the greatest fold change in expression in WNV-infected mDC compared to mock-infected cells, according to the indicated color scale. The gray portion of the color scale, labeled “ns” for non-significant, represents genes that failed to meet the cutoff criteria for induction. Gene expression was assessed by microarray analysis on Illumina chips. Each column represents the mean of three independent samples per genotype. C. Quantitative RT-PCR was performed on the same RNA samples analyzed by microarray to detect expression of the indicated target genes. Gene expression was normalized to Gapdh and is displayed as the fold increase compared to mock-infected cells on a log2 scale. Data represent the average of three independent samples and are expressed as the mean ± SEM. The dotted line indicates a 1.5-fold increase in expression. Gene induction was measured by comparing WNV-infected samples to mock-infected cells of the same genotype, to control for differential basal expression of some genes. We considered genes to be expressed differentially in response to WNV infection if they exhibited a fold change of ≥1.5 and a P-value 0.05). The dotted line represents the limit of detection of the assay. The absence of gene induction in TKO mDC compared to DKO cells could reflect a direct role for IRF-5 in ISG induction or an indirect effect of the loss of IFN-β production in TKO mDC. To test this, we inhibited type I IFN signaling in DKO cells using an IFNAR-blocking monoclonal antibody (MAR1-5A3, [50]) and used qRT-PCR to measure gene induction in response to WNV-NY infection ( Figure 6D ). As expected, the IFNAR-blocking antibody prevented induction of Oas1a, a known IFN-dependent ISG [15], but did not impair induction of Ifnb. Ccl5 and Tnf were induced too weakly to observe differences between the IFNAR-blocking and control MAbs. However, the IFNAR-blocking antibody abolished induction of Cxcl10, Rsad2, Ifit1, and Ifit2, even though these genes are considered to be IFN-independent [14], [15] and were induced in Ifnar−/− mDC ( Figure 5C ). Collectively, these results suggest that IRF-5 contributes to the induction of IFN-β expression after WNV infection in mDC, but does not induce ISG expression directly. To further define the contribution of IRF-5 to IFN and ISG induction in mDC, we infected WT, Irf5−/− , and DKO mDC with WNV ( Figure 6E ) and WT, Irf5−/− , DKO, and TKO cells with Sendai virus (SeV), a negative sense RNA paramyxovirus ( Figure 6F ) and measured gene expression by qRT-PCR. We found no change in the induction of Ifnb, Oas1a, Rsad2, or Cxcl10 in Irf5−/− mDC compared to WT cells (P>0.05), indicating that loss of IRF-5 alone in mDCs is not sufficient to impact the antiviral response, analogous to results seen with IRF-3 [21]. Consistent with this observation, we observed no significant difference in WNV-NY replication between Irf5−/− and WT mDC (P>0.05) ( Figure 6G ). Although DKO mDC retained intact IFN and ISG responses after WNV infection, this pattern surprisingly was not observed following SeV infection: the induced expression of several ISGs (Oas1a, Rsad2, and Cxcl10) was lost in both DKO and TKO mDC. While our results with DKO and TKO cells after WNV infection establish that IRF-5 contributes to the type I IFN response in mDCs, the critical nature of the IFN induction pathways in these key sentinel cells may have resulted in the maintenance of redundant signaling pathways to sustain antiviral gene programs. Indeed, the distinct ISG induction phenotypes after WNV and SeV infection in DKO and TKO mDCs suggest that activation of these parallel pathways may differ among diverse viruses. The similar gene induction profiles observed between TKO and Mavs−/− mDC by microarray and qRT-PCR suggested a functional interaction between IRF-5 and MAVS. To test this hypothesis, we transfected WT, DKO, and TKO immortalized mouse embryonic fibroblasts (MEFs) with plasmids encoding myc-tagged forms of a constitutively active RIG-I (N-RIG) and/or IRF-5. Ectopic expression of N-RIG and IRF-5 was detected in MEFs 24 hours after transfection by western blotting ( Figure 7A ) and qRT-PCR (data not shown). As expected, we observed increased expression of ISGs (e.g., Rsad2, Ifit1, and Oas1a) in WT MEFs transfected with N-RIG compared to untransfected cells ( Figure 7B–D ). Transfection of N-RIG alone in DKO cells failed to induce these ISGs, suggesting that endogenous IRF-5 in MEFs is not adequately expressed or activated to induce ISGs after a MAVS-dependent signal; these results agree with prior studies showing that the combined loss of IRF-3 and IRF-7 in MEFs abolished the ISG response after WNV infection [22], [27]. In comparison, co-transfection of N-RIG and IRF-5 together but not IRF-5 alone enhanced ISG induction in DKO and TKO MEFs. Thus, MAVS-dependent induction of ISGs can occur through an IRF-5-dependent yet IRF-3 and IRF-7-independent pathway. 10.1371/journal.ppat.1003118.g007 Figure 7 WT, Irf3−/−×Irf7−/− DKO, and Irf3−/−×Irf5−/−×Irf7−/− TKO immortalized MEFs were transfected with plasmids expressing myc-tagged IRF-5 or residues 1–229 of RIG-I (N-RIG) and analyzed at 24 hours after transfection by western blot (A) or qRT-PCR (B–D). A. Transfected cell lysates were separated by SDS-PAGE and N-RIG or IRF-5 were detected with an anti-myc-tag antibody. Un: no transfection. Expression of N-RIG and IRF-5 was decreased slightly upon co-transfection, likely secondary to promoter competition. B–D. Expression of the indicated ISGs was measured from total RNA by qRT-PCR. Gene expression was normalized to Gapdh and is displayed as the fold increase compared to untransfected cells on a log2 scale. Data represent the average of four samples from two independent experiments and are expressed as the mean ± SEM. The co-transfection group was compared to transfection with the individual plasmids by two-way ANOVA for DKO and TKO groups; asterisks indicate differences that are statistically significant (****, P<0.0001; ***, P<0.001; **, P<0.01; *, P<0.05). Discussion In the present study, we generated Irf3−/− ×Irf5−/− ×Irf7−/− TKO mice to establish that these three IRF family transcription factors coordinately regulate IFN-β production and ISG expression in mDC. We found that antiviral gene induction was ablated almost entirely in mDC from TKO or Mavs−/− mice, suggesting a dominant role for MAVS in initiating the antiviral response and pointing to a novel signaling interaction between IRF-5 and the RLR signaling pathway. As TKO mice succumbed to WNV infection with similar kinetics compared to Ifnar−/− mice, we expected they would be completely defective at producing type I IFN. Nonetheless, we detected type I IFN activity in the serum of infected TKO mice, suggesting that some cells must produce type I IFN by a pathway that is independent of IRF-3, IRF-5, and IRF-7. Macrophages or related cells (e.g., inflammatory monocytes) may be one source of this residual type I IFN in vivo, as TKO macrophages cultured ex vivo expressed Ifnb as well as a subset of ISGs in response to WNV infection. Type I IFN induction in TKO macrophages could be mediated in part by IRF-1, which regulates expression of antiviral genes independently of type I IFN in the context of several other viral infections [13], [51], [52]. Consistent with this, Irf1−/− macrophages supported enhanced WNV replication compared to WT controls [40], and viral replication in TKO macrophages did not phenocopy Ifnar− /− cells. Nonetheless, IRF-1 was not sufficient to induce the full complement of ISGs in macrophages, as Ifnb and ISG expression in TKO macrophages was diminished and delayed compared to WT cells. Furthermore, IFIT3 was not expressed in TKO macrophages, although it was sustained in DKO cells [22]. It remains unclear whether the genes upregulated in TKO macrophages were induced by IRF-1 directly, by another transcription factor, or downstream of IFN-β production by these cells. We measured ISG induction in infected mDC to determine whether a lack of antiviral effector gene expression explained the failure of TKO mice and mDC to control WNV replication. In our experiments, fewer than 15% of mDC were infected at 24 hours, even when a high MOI of 25 was used. Increasing the MOI to 100 achieved only marginally higher rates of infection (data not shown) and was not practical for the scale of the microarray experiments. Sorting infected cells by flow cytometry prior to transcriptional profiling analysis was not feasible as infected cells must be permeabilized to detect intracellular WNV antigens and recombinant WNV expressing green fluorescent protein are attenuated and/or unstable [53]–[55]. In our microarray studies, uninfected cells likely contributed substantially to the ISG expression signatures observed. Indeed, few genes were induced in WNV-infected TKO or Mavs−/− mDC, even though these cells would be expected to upregulate genes associated with cell stress, survival, and metabolism in response to replication by a cytopathic virus. Some components of the unfolded protein response, including Ddit3 and Gadd45a, were upregulated in infected TKO mDC; additional genes likely were induced in infected cells but may have been below the statistical cutoffs used in our analysis due to dilution of the transcripts in a large pool of mRNA from uninfected cells. Viral infection induces the expression of ISGs both directly (by IRF-3 after PAMP detection and PRR signaling) and indirectly (by IFN-β production and IFNAR signaling), the latter occurring in both infected and uninfected cells. Given the large proportion of uninfected cells, we would expect genes induced by IFNAR signaling to predominate. Indeed, only a small subset of genes was induced after WNV infection of Ifnar−/− mDC (22 genes, compared to 445 in WT mDC). This may reflect the relatively low infection rates, an inherent inefficiency of IFNAR-independent gene induction pathways, or viral countermeasures that antagonize the type I IFN response in highly infected cells [56]. Of the 22 genes induced in WNV-infected Ifnar−/− mDC, several (Ifnb, Cxcl10, Rsad2, Ifit1, and Ifit2) have direct or indirect antiviral activity against WNV [13], [24], [41], [42], [57]–[59] and are induced directly by IRF-3 [14], [15]. Other genes induced in WNV-infected Ifnar−/− mDC included components of the unfolded protein response, such as Ddit3 and Ppp1r15a. Ddit3 (CHOP) has been shown to promote expression of Ppp1r15a (Gadd34) and Trib3 [60]–[62], two IFN-independent induced genes detected in our microarray analysis. While induction of these genes may represent a response to the cellular stress caused by viral infection, the unfolded protein response also constitutes a cellular defense that limits replication of diverse viruses, including WNV [60], [63], [64]. DDIT3 inhibits WNV replication, and WNV may induce expression of Ppp1r15a to reverse DDIT3-mediated translational inhibition [60]. In contrast, PPP1R15A is required for IFN-β production and contributes to controlling replication of chikungunya virus [65]. Although global gene induction in response to WNV infection has been reported previously [46]–[49], [66], [67], our results represent the first such analysis in DCs, which are a sentinel cell type coordinating the innate and adaptive antiviral immune responses, as well as among the first cells infected following a mosquito bite [8], [68]. Some of the genes we identified in mDCs also were detected in microarray analyses of WNV-infected MEFs [46], human kidney epithelial cells [48], or human retinal pigmented epithelium [47]. Induction of these genes (e.g., Rsad2, Ifit2, Isg15, Isg20, and Stat1) thus does not depend on cell type-specific transcription factors. Other WNV-induced genes, however, may be specific to DCs or restricted cell types. As an example, the chemokine Cxcl10 was one of the most highly induced genes in our analysis, yet it was induced at much lower levels or not at all in fibroblasts and epithelial cells [46]–[48]. CXCL10 contributes to clearance of WNV infection from the CNS by recruiting effector T cells, and is the dominant chemokine secreted by neurons after WNV infection [57]. Only one of the 22 genes differentially expressed in Ifnar−/− mDC, Ddit3, was induced in Mavs−/− mDC, suggesting that the IFN-independent induction signal is conveyed almost entirely by MAVS. Since Mavs−/− mDC failed to produce IFN-β, we surmise that both type I IFN-dependent and -independent pathways of ISG induction are abrogated in these cells. This conclusion agrees with earlier studies on induction of selected sets of genes in Mavs−/− mDC infected with WNV or rabies virus [27], [69]. Although Mavs−/− cells should retain TLR-mediated antiviral gene induction pathways (which signal through TRIF and MyD88), we observed almost no ISG induction in Mavs−/− mDC after WNV infection. Thus, RLRs likely are the dominant PRRs that sense WNV infection in mDC; these results are consistent with the essentially intact antiviral responses reported in WNV-infected Tlr3−/− and Myd88−/− mDC [26], [28]. Although our microarray and qRT-PCR analyses identified 16 genes that were differentially expressed in WNV-infected Ifnar−/− and DKO but not TKO mDC, when gene expression was analyzed from WNV-infected DKO cells that were treated with an antibody blocking type I IFN signaling, only Ifnb gene induction was sustained. These data suggest that in the absence of IRF-3 and IRF-7, IRF-5 is sufficient to induce IFN-β production in response to WNV infection, but unlike IRF-3 [14], [15], does not induce ISGs directly ( Figure 8 ). Although IRF-5 has been suggested to promote IFN-independent expression of some ISGs including Pkr and Isg20 in NDV-infected cells [38], IRF-3 may have contributed to these responses. The observed anti-WNV response in DKO mDC likely results from IRF-5-dependent IFN-β production, and the uncontrolled viral replication in TKO mDC is secondary to a lack of IFN-β and resultant absence of ISG induction. This model suggests that cell types having ancillary pathways for IFN-β induction (such as IRF-1 in macrophages) can mount antiviral responses even in the absence of IRF-3, IRF-5, and IRF-7. 10.1371/journal.ppat.1003118.g008 Figure 8 Model of Type I IFN and ISG induction in mDC. WNV infection is sensed by PRR from the RLR family (RIG-I and MDA5, green) or TLR family (TLR3 and TLR7, yellow and orange). PRR signal through their respective adaptor molecules (MAVS, TRIF, MyD88), which activates cellular kinases (TBK1, IKKε, TRAF6, IRAK1). Phosphorylation of IRF-3, IRF-5, and IRF-7 (blue) induces nuclear localization, and in concert with other transcription factors (e.g., NF-κB), results in induced expression of Ifnb and ISGs. IRF-3, IRF-5, and IRF-7 are each sufficient to induce expression of IFN-β (red), which can signal through IFNAR to activate expression of hundreds of ISGs (pink). Some ISGs, including Ifna, Oas1a, and Pkr, are dependent strictly upon IFN signaling for their induction. Others, including Ifit1, Ifit2, Rsad2, and Cxcl10, can be induced directly by IRF-3, although IRF-5 apparently is not sufficient to induce these genes independently of IFN signaling. In addition to being activated by TLR7 signaling through MyD88, IRF-5 is activated by MAVS through an uncharacterized pathway. We did not anticipate that the Mavs− /− and TKO mDC would phenocopy one other with respect to ISG induction, since IRF-5 has not been previously implicated in the RLR signaling pathway [35]–[37]. IRF-5 originally was described as an inducer of pro-inflammatory cytokines (e.g., IL-6 and TNF-α) but subsequently was suggested to contribute to the type I IFN antiviral response. Irf5−/− mice have increased susceptibility to viral infections, slightly reduced levels of type I IFN in serum, and more significantly reduced levels of pro-inflammatory cytokines [35], [37]. IRF-5 expression and antiviral activity, however, appears restricted to a limited set of cell types, including monocytes and DCs [35], [39], [70]. Thus, a relative absence of IRF-5 expression in fibroblasts and neurons may explain the observation that type I IFN induction after WNV infection in these cell types is abolished by the combined deletion of IRF-3 and IRF-7 [22]. However, the ability of alternate IRFs to compensate for IRF-3 and IRF-7 in fibroblasts also may depend on the particular viral stimulus, as type I IFN production was essentially absent in DKO fibroblasts infected with WNV, herpes simplex virus, vesicular stomatitis virus, or encephalomyocarditis virus [19], [22], but low-level production of Ifnb and Ifna2 mRNA was sustained in DKO fibroblasts infected with chikungunya virus [33]. IRF-5 preferentially stimulates the IFN-β and IFN-α4 promoters, rather than other IFN-α subtypes, which also suggests that it contributes to the primary type I IFN response, prior to amplification via autocrine and paracrine signaling [35]. The IFN-α subtypes induced in IRF-5-expressing cells vary from those induced in IRF-7-expressing cells, suggesting that the IRF expression patterns within a cell modulate the breadth of the type I IFN response [70]. Although MAVS previously was known to induce IFN-β production via IRF-3 and IRF-7, our experiments suggest that RLR signaling also activates IRF-5 to induce IFN-β production in mDC; the subcellular location where this occurs (e.g., mitochondrion) and through what signaling intermediates remains unknown. A recent study suggested that activation of RLR signaling acts to inhibit induction of inflammatory cytokines by IRF-5 [71]; although the net result was different, this study is consistent with our observation of a functional interaction between IRF-5 and MAVS and with a prior proteomic study demonstrating a physical interaction between these two proteins [72]. Future studies will be required to delineate the mechanistic and functional intermediates that link and regulate the IRF-5 and RLR signaling pathways. Materials and Methods Viruses The WNV-NY strain (3000.0259) was isolated in New York in 2000 and passaged once in C6/36 Aedes albopictus cells to generate a virus stock that was used in all experiments except for the microarray analysis [73], [74]. For the microarray studies, mDCs were infected in the Früh laboratory with the WNV New York 1999 strain that was propagated in C6/36 cells [75]. The attenuated strain WNV-MAD was amplified in Vero cells and has been previously described [23]. MNV strain MNV1.CW3 [76] was propagated in RAW 264.7 cells (ATCC) and a concentrated stock was prepared as previously described [77]. The SeV virus strain Fushimi was propagated in chicken embryos and provided by D. Lenschow and M. Holtzman (Washington University, St Louis, MO). Ethics statement This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Institutional Animal Care and Use Committee at the Washington University School of Medicine (Assurance Number: A3381-01). Dissections and footpad injections were performed under anesthesia that was induced and maintained with ketamine hydrochloride and xylazine, and all efforts were made to minimize suffering. Mouse experiments All mice used were on an inbred C57BL/6 background. WT mice were commercially obtained (Jackson Laboratories). Irf3−/−×Irf7−/− DKO, Irf5−/− , and Ifnar−/− mice have been reported previously [22], [31], [36]. Irf3−/−×Irf5−/−×Irf7−/− TKO mice were generated by crossing DKO and Irf5−/− mice. Irf5−/− and TKO mice were genotyped for a mutation in the Dock2 gene, which can arise spontaneously in some Irf5− /− mice [78]; none of the TKO mice had homozygous mutations in Dock2. Mavs−/− mice were generated directly from C57BL/6 embryonic stem cells [34]. All deficient mice were bred in the animal facilities of the Washington University School of Medicine and genotyped prior to experimentation. For WNV infections, 102 PFU was diluted in Hank's Balanced Salt Solution supplemented with 1% heat-inactivated fetal bovine serum and 8 to 12 week-old mice were inoculated by footpad injection in a volume of 50 µl. For MNV infections, 7 to 8 week-old mice were inoculated orally with 3×107 PFU in 25 µl of PBS and monitored for survival for 21 days. Measurement of viral burden To monitor viral spread in vivo, mice were infected with 102 PFU of virus and sacrificed at 2 days after infection (WNV-NY) or 6 days after infection (WNV-MAD). After extensive perfusion with PBS, organs were harvested, weighed, homogenized and virus was titered by plaque assay on BHK21-15 cells [74]. Viral burden in serum and inguinal lymph node was measured using fluorogenic qRT-PCR using primers and probes to WNV-NY or WNV-MAD envelope gene sequences ( Table S4 ). Viral RNA in the lymph node was normalized to Gapdh levels in tissue samples. Viral RNA from serum was isolated using a Viral RNA Mini Kit (Qiagen). Total RNA from lymph nodes was extracted using the E.Z.N.A. total RNA kit (Omega Bio-tek) and DNase-treated to remove genomic DNA. Quantitative RT-PCR was performed using One-Step RT-PCR Master Mix and a 7500 Fast Real-Time PCR System (Applied Biosystems). Quantification of type I IFN activity Levels of biologically active type I IFN in serum were determined using an encephalomyocarditis virus L929 cytopathic effect bioassay as described [79]. The amount of type I IFN per ml of serum was calculated from a standard curve using IFN-β (PBL InterferonSource) and adjusted for the background inhibitory activity of naïve serum (approximately 0.1 IU/ml). The inhibitory activity of naïve serum was type I IFN-independent because it was acid labile but resistant to treatment with heat (56°C) or the IFNAR-blocking antibody MAR1-5A3 [17], [50]. Primary cell infections Macrophage and mDC cultures were generated as described previously [79]. Briefly, bone marrow was isolated from WT, DKO, TKO, Irf5−/− , or Ifnar−/− mice and cultured for seven days in the presence of 40 ng/ml M-CSF (PeproTech) to generate macrophages or with 20 ng/ml GM-CSF and 20 ng/ml IL-4 (PeproTech) to produce mDC. Multi-step virus growth analysis was performed after infection at a MOI of 0.01 for macrophages or 0.001 for mDCs. Supernatants were titered by focus-forming assay on Vero cells using humanized E16 anti-WNV MAb as the detection antibody [80], horseradish peroxidase conjugated anti-human IgG (Sigma), and True Blue Peroxidase Substrate (KPL). For western blotting, cells were infected at an MOI of 1. For measurement of ISG induction by qRT-PCR, cells were infected at an MOI of 0.1. To block signaling by type I IFN, DKO cells were treated with 25 µg/ml of the IFNAR-blocking MAb MAR1-5A3 for one hour prior to infection. A non-binding MAb against human IFN-γ receptor (GiR-208) was used as an isotype control [50]. Microarray analysis of mDCs Bone marrow cells were cultured in RPMI supplemented with 10% fetal bovine serum, penicillin/streptomycin, L-glutamine, non-essential amino acids, 55 µM β-mercaptoethanol and 20 ng/ml recombinant mouse GM-CSF (eBioscience) for six days in non-tissue culture treated plates. GM-CSF was replenished after two days and non-adherent cells were sub-cultured after 4 days. Sub-cultured cells were infected at an MOI of 25 with WNV-NY. Total RNA was harvested at 0, 6, 12, and 24 hours post-infection with an RNeasy Mini Kit (Qiagen). RNA was treated with DNase prior to cDNA generation. Gene expression was assayed on Illumina microarray chips. Microarray datasets were processed by quantile normalization and annotated using the illuminaMousev2.db R package version 1.10.0. Data were assessed by linear modeling with the limma package [81]. Differentially expressed genes were identified as those with at least a 1.5-fold change as compared to controls and a P-value<0.05 without correction for false discovery. WNV-infected samples were first compared with mock-infected controls. Microarray data have been deposited in GeoArchive, series number GSE42232. Transfection and ectopic expression MEFs prepared from WT, DKO, or TKO mice were immortalized after transfection with the plasmid pSV2, which encodes for the large T antigen of SV40. MEFs were transfected using Lipofectamine 2000 (Invitrogen) with plasmids expressing myc-tagged forms of murine IRF-5 (Origene) or residues 1–229 of human RIG-I (N-RIG) [82]. Cells were lysed 24 hours post-transfection and analyzed by qRT-PCR and western blotting. Western blotting Macrophages and mDC were lysed in RIPA buffer (10 mM Tris, 150 mM NaCl, 0.02% sodium azide, 1% sodium deoxycholate, 1% Triton X-100, 0.1% SDS, pH 7.4), with protease inhibitors (Sigma). Samples (20 µg) were resolved by electrophoresis on 10% SDS-polyacrylamide gels. MEFs were lysed in RIPA buffer and lysates were separated by electrophoresis on 4–12% SDS-polyacrylamide gels. Following transfer of proteins, membranes were blocked with 5% non-fat dried milk and probed with the following panel of primary antibodies: rabbit anti-IFIT2 and -IFIT3 (provided by Dr. G. Sen, [83]); rabbit anti-RIG-I and anti-MDA5 (IBL); mouse anti-tubulin (Sigma); rabbit anti-GAPDH (Santa Cruz); rabbit anti-STAT1 (Cell Signaling); goat-anti WNV NS3 (R&D Systems); mouse anti-myc (Santa Cruz). Western blots were incubated with peroxidase-conjugated secondary antibodies (Jackson Immunoresearch and Sigma) and visualized using ECL reagents (Amersham Biosciences and Pierce). Measurement of ISG expression by qRT-PCR mDCs were treated for 24 hours with 500 IU/ml of IFN-β (PBL Interferon Source), 50 µg/ml of poly(I∶C) (InvivoGen), or 5 µg/ml of LPS (List Biological Laboratories). Macrophages and mDC were infected with WNV-NY at an MOI 0.1 for 24 hours. MEFs were harvested 24 hours after transfection. Total RNA was extracted using the E.Z.N.A. total RNA kit (Omega Bio-tek) or RNeasy kit (Qiagen) and treated with DNase. Fluorogenic qRT-PCR was performed using One-Step RT-PCR Master Mix and a 7500 Fast Real-Time PCR System (Applied Biosystems) with the indicated Taqman primers and probes ( Table S4 ). Gene induction was normalized to Gapdh levels and expressed on a log2 scale as fold increase over mock according to the ΔΔCt method [84]. Statistical analysis Data were analyzed with GraphPad Prism software. Viral burdens were compared using the Mann-Whitney test. Serum type I IFN levels, viral growth curves and qRT-PCR were compared using a 2-way ANOVA. Kaplan-Meier survival curves were analyzed by the log rank test and mean times to death were compared by Student's T-test. Supporting Information Figure S1 Genotyping of TKO mice. DNA from the tails of the indicated mice was amplified by PCR using primers specific for IRF-3, IRF-5, or IRF-7 and separated by agarose gel electrophoresis. The band sizes confirmed the genotypes of the knockout mice. (TIF) Click here for additional data file. Table S1 Gene induction in WNV-NY infected mDC. All genes (445) for which expression level in at least one genotype was ≥1.5-fold changed at 24 hours after WNV infection (P<0.05, without correction for false discovery). Values represent the mean of three independent samples for each genotype. “Fold change” refers to the relative fold change of expression in WNV-infected mDC compared with mock-infected controls of the same genotype. DKO: Irf3−/− ×Irf7−/− ; TKO: Irf3−/− ×Irf 5−/− ×Irf7−/− . (DOCX) Click here for additional data file. Table S2 IFN-independent gene induction. Genes are shown for which expression level in Ifnar−/− mDC was ≥1.5-fold changed at 24 hours after WNV infection (P<0.05, without correction for false discovery). Values represent the mean of three independent samples for each genotype. “Fold change” refers to the relative fold change of expression in WNV-infected mDC compared with mock-infected controls of the same genotype. DKO: Irf3−/− ×Irf7−/− ; TKO: Irf3−/− ×Irf 5−/− ×Irf7−/− . (DOCX) Click here for additional data file. Table S3 Genes induced in IFNAR and DKO, but not TKO mDC. Genes are shown for which expression level in Ifnar−/− and DKO mDC was ≥1.5-fold changed at 24 hours after WNV infection (P<0.05), but which fell short of these cutoffs in TKO cells. Values represent the mean of three independent samples for each genotype. “Fold change” refers to the relative fold change of expression in WNV-infected mDC compared with mock-infected controls of the same genotype. DKO: Irf3−/− ×Irf7−/− ; TKO: Irf3−/− ×Irf 5−/− ×Irf7−/− . (DOCX) Click here for additional data file. Table S4 Primers and probes used for quantitative RT-PCR. (DOCX) Click here for additional data file.

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Journal

Journal ID (nlm-ta): G3 (Bethesda)

Journal ID (iso-abbrev): Genetics

Journal ID (hwp): G3: Genes, Genomes, Genetics

Journal ID (pmc): G3: Genes, Genomes, Genetics

Journal ID (publisher-id): G3: Genes, Genomes, Genetics

Title: G3: Genes|Genomes|Genetics

Publisher: Genetics Society of America

ISSN (Electronic): 2160-1836

Publication date (Electronic): 05 June 2017

Publication date Collection: June 2017

Volume: 7

Issue: 6

Pages: 1665-1682

Affiliations

[* ]Department of Immunology, University of Washington, Seattle, Washington 98109

[† ]Center for Innate Immunity and Immune Disease (CIIID), University of Washington, Seattle, Washington 98109

[‡ ]OHSU Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon

[§ ]Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon

[** ]Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon 97239

[†† ]Department of Genetics, University of North Carolina at Chapel Hill, North Carolina

[‡‡ ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, North Carolina 27514

Author notes

[1 ]Corresponding author: Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109. E-mail: mgale@ 123456uw.edu

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Richard Green http://orcid.org/000-0002-6332-7436

Article

Publisher ID: GGG_041624

DOI: 10.1534/g3.117.041624

PMC ID: 5473748

PubMed ID: 28592649

SO-VID: 05a4f847-9b73-48eb-8ce9-7f54c9377e9c

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This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Date received : 04 January 2017

Date accepted : 24 March 2017

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Figures: 12, Tables: 2, Equations: 1, References: 61, Pages: 18

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