Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses.

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

Deciphering the combinatorial landscape of immunity.

From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNß controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.

Differential Modulation of Innate Immune Responses in Human Primary Cells by Influenza A Viruses Carrying Human or Avian Nonstructural Protein 1.

The influenza A virus (IAV) nonstructural protein 1 (NS1) contributes to disease pathogenesis through the inhibition of host innate immune responses. Dendritic cells (DCs) release interferons (IFNs) and proinflammatory cytokines and promote adaptive immunity upon viral infection. In order to characterize the strain-specific effects of IAV NS1 on human DC activation, we infected human DCs with a panel of recombinant viruses with the same backbone (A/Puerto Rico/08/1934) expressing different NS1 proteins from human and avian origin. We found that these viruses induced a clearly distinct phenotype in DCs. Specifically, viruses expressing NS1 from human IAV (either H1N1 or H3N2) induced higher levels of expression of type I (IFN-α and IFN-ß) and type III (IFN-λ1 to IFNλ3) IFNs than viruses expressing avian IAV NS1 proteins (H5N1, H7N9, and H7N2), but the differences observed in the expression levels of proinflammatory cytokines like tumor necrosis factor alpha (TNF-α) or interleukin-6 (IL-6) were not significant. In addition, using imaging flow cytometry, we found that human and avian NS1 proteins segregate based on their subcellular trafficking dynamics, which might be associated with the different innate immune profile induced in DCs by viruses expressing those NS1 proteins. Innate immune responses induced by our panel of IAV recombinant viruses were also characterized in normal human bronchial epithelial cells, and the results were consistent with those in DCs. Altogether, our results reveal an increased ability of NS1 from avian viruses to antagonize innate immune responses in human primary cells compared to the ability of NS1 from human viruses, which could contribute to the severe disease induced by avian IAV in humans.IMPORTANCE Influenza A viruses (IAVs) cause seasonal epidemics which result in an important health and economic burden. Wild aquatic birds are the natural host of IAV. However, IAV can infect diverse hosts, including humans, domestic poultry, pigs, and others. IAVs circulating in animals occasionally cross the species barrier, infecting humans, which results in mild to very severe disease. In some cases, these viruses can acquire the ability to be transmitted among humans and initiate a pandemic. The nonstructural 1 (NS1) protein of IAV is an important antagonist of the innate immune response. In this study, using recombinant viruses and primary human cells, we show that NS1 proteins from human and avian hosts show intrinsic differences in the modulation of the innate immunity in human dendritic cells and epithelial cells, as well as different cellular localization dynamics in infected cells.

Innate Immune Response to Influenza Virus at Single-Cell Resolution in Human Epithelial Cells Revealed Paracrine Induction of Interferon Lambda 1.

Early interactions of influenza A virus (IAV) with respiratory epithelium might determine the outcome of infection. The study of global cellular innate immune responses often masks multiple aspects of the mechanisms by which populations of cells work as organized and heterogeneous systems to defeat virus infection, and how the virus counteracts these systems. In this study, we experimentally dissected the dynamics of IAV and human epithelial respiratory cell interaction during early infection at the single-cell level. We found that the number of viruses infecting a cell (multiplicity of infection [MOI]) influences the magnitude of virus antagonism of the host innate antiviral response. Infections performed at high MOIs resulted in increased viral gene expression per cell and stronger antagonist effect than infections at low MOIs. In addition, single-cell patterns of expression of interferons (IFN) and IFN-stimulated genes (ISGs) provided important insights into the contributions of the infected and bystander cells to the innate immune responses during infection. Specifically, the expression of multiple ISGs was lower in infected than in bystander cells. In contrast with other IFNs, IFN lambda 1 (IFNL1) showed a widespread pattern of expression, suggesting a different cell-to-cell propagation mechanism more reliant on paracrine signaling. Finally, we measured the dynamics of the antiviral response in primary human epithelial cells, which highlighted the importance of early innate immune responses at inhibiting virus spread.IMPORTANCE Influenza A virus (IAV) is a respiratory pathogen of high importance to public health. Annual epidemics of seasonal IAV infections in humans are a significant public health and economic burden. IAV also causes sporadic pandemics, which can have devastating effects. The main target cells for IAV replication are epithelial cells in the respiratory epithelium. The cellular innate immune responses induced in these cells upon infection are critical for defense against the virus, and therefore, it is important to understand the complex interactions between the virus and the host cells. In this study, we investigated the innate immune response to IAV in the respiratory epithelium at the single-cell level, providing a better understanding on how a population of epithelial cells functions as a complex system to orchestrate the response to virus infection and how the virus counteracts this system.

Pathway-level information extractor (PLIER) for gene expression data.

A major challenge in gene expression analysis is to accurately infer relevant biological insights, such as variation in cell-type proportion or pathway activity, from global gene expression studies. We present pathway-level information extractor (PLIER) ( https://github.com/wgmao/PLIER and http://gobie.csb.pitt.edu/PLIER ), a broadly applicable solution for this problem that outperforms available cell proportion inference algorithms and can automatically identify specific pathways that regulate gene expression. Our method improves interstudy replicability and reveals biological insights when applied to trans-eQTL (expression quantitative trait loci) identification.

Interpretation of an individual functional genomics experiment guided by massive public data.

A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data. We developed a computational framework, Your Evidence Tailored Integration (YETI; http://yeti.princeton.edu/ ), which creates specialized functional interaction maps from large public datasets relevant to an individual omics experiment. Using this tailored integration, we predicted and experimentally confirmed an unexpected divergence in viral replication after seasonal or pandemic human influenza virus infection.

Single-cell stabilization method identifies gonadotrope transcriptional dynamics and pituitary cell type heterogeneity.

Immediate-early response genes (IEGs) are rapidly and transiently induced following an extracellular signal. Elucidating the IEG response patterns in single cells (SCs) requires assaying large numbers of timed samples at high accuracy while minimizing handling effects. To achieve this, we developed and validated RNA stabilization Buffer for Examination of Single-cell Transcriptomes (RNA-Best), a versatile single-step cell and tissue preservation protocol that stabilizes RNA in intact SCs without perturbing transcription patterns. We characterize for the first time SC heterogeneity in IEG responses to pulsatile gonadotropin-releasing hormone (GnRH) stimuli in pituitary gonadotrope cells. Our study identifies a gene-specific hierarchical pattern of all-or-none transcript induction elicited by increasing concentrations of GnRH. This quantal pattern of gene activation raises the possibility that IEG activation, when accurately resolved at the SC level, may be mediated by gene bits that behave as pure binary switches.

High-density single cell mRNA sequencing to characterize circulating tumor cells in hepatocellular carcinoma.

Patients with hepatocellular carcinoma (HCC) release tumor cells to the bloodstream, which can be detected using cell surface markers. Despite numerous reports suggest a direct correlation between the number of circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough molecular characterization of CTCs. Due to the limited access to tissue samples in patients at advanced stages of HCC, it is crucial to develop new technologies to identify HCC cancer drivers in routine clinical conditions. Here, we describe a method that sequentially combines image flow cytometry and high density single-cell mRNA sequencing to identify CTCs in HCC patients. Genome wide expression profiling of CTCs using this approach demonstrates CTC heterogeneity and helps detect known oncogenic drivers in HCC such as IGF2. This integrated approach provides a novel tool for biomarker development in HCC using liquid biopsy.

Pandemic H1N1 influenza A viruses suppress immunogenic RIPK3-driven dendritic cell death.

The risk of emerging pandemic influenza A viruses (IAVs) that approach the devastating 1918 strain motivates finding strain-specific host-pathogen mechanisms. During infection, dendritic cells (DC) mature into antigen-presenting cells that activate T cells, linking innate to adaptive immunity. DC infection with seasonal IAVs, but not with the 1918 and 2009 pandemic strains, induces global RNA degradation. Here, we show that DC infection with seasonal IAV causes immunogenic RIPK3-mediated cell death. Pandemic IAV suppresses this immunogenic DC cell death. Only DC infected with seasonal IAV, but not with pandemic IAV, enhance maturation of uninfected DC and T cell proliferation. In vivo, circulating T cell levels are reduced after pandemic, but not seasonal, IAV infection. Using recombinant viruses, we identify the HA genomic segment as the mediator of cell death inhibition. These results show how pandemic influenza viruses subvert the immune response.

Different tissue phagocytes sample apoptotic cells to direct distinct homeostasis programs.

Recognition and removal of apoptotic cells by professional phagocytes, including dendritic cells and macrophages, preserves immune self-tolerance and prevents chronic inflammation and autoimmune pathologies. The diverse array of phagocytes that reside within different tissues, combined with the necessarily prompt nature of apoptotic cell clearance, makes it difficult to study this process in situ. The full spectrum of functions executed by tissue-resident phagocytes in response to homeostatic apoptosis, therefore, remains unclear. Here we show that mouse apoptotic intestinal epithelial cells (IECs), which undergo continuous renewal to maintain optimal barrier and absorptive functions, are not merely extruded to maintain homeostatic cell numbers, but are also sampled by a single subset of dendritic cells and two macrophage subsets within a well-characterized network of phagocytes in the small intestinal lamina propria. Characterization of the transcriptome within each subset before and after in situ sampling of apoptotic IECs revealed gene expression signatures unique to each phagocyte, including macrophage-specific lipid metabolism and amino acid catabolism, and a dendritic-cell-specific program of regulatory CD4+ T-cell activation. A common 'suppression of inflammation' signature was noted, although the specific genes and pathways involved varied amongst dendritic cells and macrophages, reflecting specialized functions. Apoptotic IECs were trafficked to mesenteric lymph nodes exclusively by the dendritic cell subset and served as critical determinants for the induction of tolerogenic regulatory CD4+ T-cell differentiation. Several of the genes that were differentially expressed by phagocytes bearing apoptotic IECs overlapped with susceptibility genes for inflammatory bowel disease. Collectively, these findings provide new insights into the consequences of apoptotic cell sampling, advance our understanding of how homeostasis is maintained within the mucosa and set the stage for development of novel therapeutics to alleviate chronic inflammatory diseases such as inflammatory bowel disease.

RIPK3 Activates Parallel Pathways of MLKL-Driven Necroptosis and FADD-Mediated Apoptosis to Protect against Influenza A Virus.

Influenza A virus (IAV) is a lytic virus in primary cultures of many cell types and in vivo. We report that the kinase RIPK3 is essential for IAV-induced lysis of mammalian fibroblasts and lung epithelial cells. Replicating IAV drives assembly of a RIPK3-containing complex that includes the kinase RIPK1, the pseudokinase MLKL, and the adaptor protein FADD, and forms independently of signaling by RNA-sensing innate immune receptors (RLRs, TLRs, PKR), or the cytokines type I interferons and TNF-α. Downstream of RIPK3, IAV activates parallel pathways of MLKL-driven necroptosis and FADD-mediated apoptosis, with the former reliant on RIPK3 kinase activity and neither on RIPK1 activity. Mice deficient in RIPK3 or doubly deficient in MLKL and FADD, but not MLKL alone, are more susceptible to IAV than their wild-type counterparts, revealing an important role for RIPK3-mediated apoptosis in antiviral immunity. Collectively, these results outline RIPK3-activated cytolytic mechanisms essential for controlling respiratory IAV infection.

Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases.

Many functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases.

Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism.

BACKGROUND: Comparative analysis of genome-wide expression profiles are increasingly being used to study virus-specific host interactions. In order to gain mechanistic insights, gene expression profiles can be combined with information on DNA-binding sites of transcription factors to detect transcription factor activity (by analysis of target gene sets) during viral infections. Here, we apply this approach to study mechanisms of immune antagonism elicited by Influenza A virus (New Caledonia/20/1999) by comparing the transcriptional response with the non-pathogenic Newcastle disease virus (NDV), which lacks human immune antagonism. RESULTS: Existing gene set approaches do not quantify activity in a way that can be statistically compared between responses. We thus developed a new method for Bayesian Estimation of Transcription factor Activity (BETA) that allows for such quantification and comparative analysis across multiple responses. BETA predicted decreased ISGF3 activity during influenza A infection of human dendritic cells (reflected in lower expression of Interferon Stimulated Genes, ISGs). This prediction was confirmed through a combination of mathematical modeling and experiments at different multiplicities of infection to show that ISGs were specifically blocked in infected cells. Suppression of the transcription factor SATB1 was also predicted as a novel effect of influenza-mediated immune antagonism, and validated experimentally. CONCLUSIONS: Comparative analysis of genome-wide transcriptional profiles can reveal new effects of viral immune antagonism. We have developed a computational framework (BETA) that enables quantitative comparative analysis of transcription factor activities. This method will aid future studies to identify mechanistic differences in the host-pathogen interactions. Application of BETA to genome-wide transcriptional profiling data from human DCs identified SATB1 as a novel effect of influenza antagonism.

Human Dendritic Cell Response Signatures Distinguish 1918, Pandemic, and Seasonal H1N1 Influenza Viruses.

UNLABELLED: Influenza viruses continue to present global threats to human health. Antigenic drift and shift, genetic reassortment, and cross-species transmission generate new strains with differences in epidemiology and clinical severity. We compared the temporal transcriptional responses of human dendritic cells (DC) to infection with two pandemic (A/Brevig Mission/1/1918, A/California/4/2009) and two seasonal (A/New Caledonia/20/1999, A/Texas/36/1991) H1N1 influenza viruses. Strain-specific response differences included stronger activation of NF-κB following infection with A/New Caledonia/20/1999 and a unique cluster of genes expressed following infection with A/Brevig Mission/1/1918. A common antiviral program showing strain-specific timing was identified in the early DC response and found to correspond with reported transcript changes in blood during symptomatic human influenza virus infection. Comparison of the global responses to the seasonal and pandemic strains showed that a dramatic divergence occurred after 4 h, with only the seasonal strains inducing widespread mRNA loss. IMPORTANCE: Continuously evolving influenza viruses present a global threat to human health; however, these host responses display strain-dependent differences that are incompletely understood. Thus, we conducted a detailed comparative study assessing the immune responses of human DC to infection with two pandemic and two seasonal H1N1 influenza strains. We identified in the immune response to viral infection both common and strain-specific features. Among the stain-specific elements were a time shift of the interferon-stimulated gene response, selective induction of NF-κB signaling by one of the seasonal strains, and massive RNA degradation as early as 4 h postinfection by the seasonal, but not the pandemic, viruses. These findings illuminate new aspects of the distinct differences in the immune responses to pandemic and seasonal influenza viruses.

Understanding multicellular function and disease with human tissue-specific networks.

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.

P2X-selective purinergic antagonists are strong inhibitors of HIV-1 fusion during both cell-to-cell and cell-free infection.

UNLABELLED: Human immunodeficiency virus type 1 (HIV-1) infection is chronic and presently still incurable. Antiretroviral drugs effectively suppress replication; however, persistent activation of inflammatory pathways remains a key cause of morbidity. Recent studies proposed that purinergic signaling is required for HIV-1 infection. Purinergic receptors are distributed throughout a wide variety of tissue types and detect extracellular ATP as a danger signal released from dying cells. We have explored how these pathways are involved in the transmission of HIV-1 from cell to cell through virological synapses. Infection of CD4+ T lymphocytes with HIV-1 in the presence of an inhibitor of P2X receptors effectively inhibited HIV-1 infection through both cell-free and cell-to-cell contact in a dose-dependent manner. Inhibition of direct cell-to-cell infection did not affect the formation of virological synapses or the subsequent cell-to-cell transfer of HIV-1. During both cell-free and cell-to-cell CD4+ T lymphocyte infection, purinergic antagonists blocked infection at the level of viral membrane fusion. During cell-to-cell transmission, we observed CXCR4 colocalization with the newly internalized virus particles within target lymphocytes and found that the purinergic antagonists did not impair the recruitment of the coreceptor CXCR4 to the site of Gag internalization in the target cell. In a screen of a library of purinergic antagonists, we found that the most potent inhibitors of HIV-1 fusion were those that target P2X receptors, while P2Y-selective receptor antagonists or adenosine receptor antagonists were ineffective. Our results suggest that P2X receptors may provide a therapeutic target and that purinergic antagonists may have potent activity against viral infection of CD4+ T lymphocytes by both cell-free and cell-to-cell transmission. IMPORTANCE: This study identifies purinergic antagonists to be potent inhibitors of HIV-1 cell-free and cell-to-cell-mediated infection and provides a stepwise determination of when these compounds inhibit HIV-1 infection. These data provide a rationale for the development of novel antiretroviral therapies that have a dual role in both direct antiviral activity and the reduction of HIV-associated inflammation. Purinergic antagonists are shown here to have equivalent efficacy in inhibiting HIV infection via cell-free and cell-to-cell infection, and it is shown that purinergic receptors could provide an attractive therapeutic anti-HIV target that might avoid resistance by targeting a host signaling pathway that potently regulates HIV infection. The high-throughput screen of HIV-1 fusion inhibitors further defines P2X-selective compounds among the purinergic compounds as being the most potent HIV entry inhibitors. Clinical studies on these drugs for other inflammatory indications suggest that they are safe, and thus, if developed for use as anti-HIV agents, they could reduce both HIV replication and HIV-related inflammation.

Combinatorial cytokine code generates anti-viral state in dendritic cells.

The physiological function of the immune system and the response to therapeutic immunomodulators may be sensitive to combinatorial cytokine micro-environments that shape the responses of specific immune cells. Previous work shows that paracrine cytokines released by virus-infected human dendritic cells (DC) can dictate the maturation state of naïve DCs. To understand the effects of paracrine signaling, we systematically studied the effects of combinations cytokines in this complex mixture in generating an anti-viral state. After naïve DCs were exposed to either IFNß or to paracrine signaling released by DCs infected by Newcastle disease virus (NDV), microarray analysis revealed a large number of genes that were differently regulated by the DC-secreted paracrine signaling. In order to identify the cytokine mechanisms involved, we identified 20 cytokines secreted by NDV infected DCs for which the corresponding receptor gene is expressed in naïve DCs. By exposing cells to all combinations of 19 cytokines (leave-one-out studies), we identified five cytokines (IFNß, TNFα, IL-1ß, TNFSF15, and IL28) as candidates for regulating DC maturation markers. Subsequent experiments identified IFNß, TNFα, and IL1ß as the major contributors to this anti-viral state. This finding was supported by infection studies in vitro, by T-cell activation studies and by in vivo infection studies in mouse. Combination of cytokines can cause response states in DCs that differ from those achieved by the individual cytokines alone. These results suggest that the cytokine microenvironment may act via a combinatorial code to direct the response state of specific immune cells. Further elucidation of this code may provide insight into responses to infection and neoplasia as well as guide the development of combinatorial cytokine immunomodulation for infectious, autoimmune, and immunosurveillance-related diseases.

Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection.

BACKGROUND: H1N1 influenza viruses were responsible for the 1918 pandemic that caused millions of deaths worldwide and the 2009 pandemic that caused approximately twenty thousand deaths. The cellular response to such virus infections involves extensive genetic reprogramming resulting in an antiviral state that is critical to infection control. Identifying the underlying transcriptional network driving these changes, and how this program is altered by virally-encoded immune antagonists, is a fundamental challenge in systems immunology. RESULTS: Genome-wide gene expression patterns were measured in human monocyte-derived dendritic cells (DCs) infected in vitro with seasonal H1N1 influenza A/New Caledonia/20/1999. To provide a mechanistic explanation for the timing of gene expression changes over the first 12 hours post-infection, we developed a statistically rigorous enrichment approach integrating genome-wide expression kinetics and time-dependent promoter analysis. Our approach, TIme-Dependent Activity Linker (TIDAL), generates a regulatory network that connects transcription factors associated with each temporal phase of the response into a coherent linked cascade. TIDAL infers 12 transcription factors and 32 regulatory connections that drive the antiviral response to influenza. To demonstrate the generality of this approach, TIDAL was also used to generate a network for the DC response to measles infection. The software implementation of TIDAL is freely available at http://tsb.mssm.edu/primeportal/?q=tidal_prog. CONCLUSIONS: We apply TIDAL to reconstruct the transcriptional programs activated in monocyte-derived human dendritic cells in response to influenza and measles infections. The application of this time-centric network reconstruction method in each case produces a single transcriptional cascade that recapitulates the known biology of the response with high precision and recall, in addition to identifying potentially novel antiviral factors. The ability to reconstruct antiviral networks with TIDAL enables comparative analysis of antiviral responses, such as the differences between pandemic and seasonal influenza infections.

Mouse dendritic cell (DC) influenza virus infectivity is much lower than that for human DCs and is hemagglutinin subtype dependent.

We show that influenza A H1N1 virus infection leads to very low infectivity in mouse dendritic cells (DCs) in vitro compared with that in human DCs. This holds when H3 or H5 replaces H1 in recombinant viruses. Viruslike particles confirm the difference between mouse and human, suggesting that reduced virus entry contributes to lower mouse DC infectivity. Low infectivity of mouse DCs should be considered when they are used to study responses of DCs that are actually infected.