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

HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data

Background

Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.

Results

We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.

Conclusion

We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

Oral or intranasal immunization with recombinant Lactobacillus plantarum displaying head domain of Swine Influenza A virus hemagglutinin protects mice from H1N1 virus

BACKGROUND

Swine influenza A virus (swIAV) is a major concern for the swine industry owing to its highly contagious nature and acute viral disease. Currently, most commercial swIAV vaccines are traditional inactivated virus vaccines. The Lactobacillus plantarum-based vaccine platform is a promising approach for mucosal vaccine development. Oral and intranasal immunisations have the potential to induce a mucosal immune response, which confers protective immunity. The aim of this study was to evaluate the probiotic potential and adhesion ability of three L. plantarum strains. Furthermore, a recombinant L. plantarum strain expressing the head domain of swIAV antigen HA1 was constructed and evaluated for its ability to prevent swIAV infection.

RESULTS

The three L. plantarum strains isolated from healthy pig faecal samples maintained the highest survival rate when incubated at pH 3 and at bile salt concentration of 0.3%. They also showed high adherence to intestinal cells. All three L. plantarum strains were monitored in live mice, and no major differences in transit time were observed. Recombinant L. plantarum expressed swIAV HA1 protein (pSIP401-HA1-ZN-3) and conferred effective mucosal, cellular and systemic immune responses in the intestine as well as in the upper respiratory airways of mice. In conclusion, the oral and intranasal administration of L. plantarum strain pSIP401-HA1-ZN-3 in mice induced mucosal immunity and most importantly, provided protection against lethal influenza virus challenge.

CONCLUSION

In summary, these findings suggest that the engineered L. plantarum strain pSIP401-HA1-ZN-3 can be considered as an alternative approach for developing a novel vaccine during an swine influenza A pandemic.

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.

Swine MicroRNAs ssc-miR-221-3p and ssc-miR-222 Restrict the Cross-Species Infection of Avian Influenza Virus

Avian influenza virus (AIV) can cross species barriers to infect humans and other mammals. However, these species-cross transmissions are most often dead-end infections due to host restriction. Current research about host restriction focuses mainly on the barriers of cell membrane, nuclear envelope, and host proteins; whether microRNAs (miRNAs) play a role in host restriction is largely unknown. In this study, we used porcine alveolar macrophage (PAM) cells as a model to elucidate the role of miRNAs in host range restriction. During AIV infection, 40 dysregulation expressed miRNAs were selected in PAM cells. Among them, two Sus scrofa (ssc; swine) miRNAs, ssc-miR-221-3p and ssc-miR-222, could inhibit the infection and replication of AIV in PAM cells by directly targeting viral genome and inducing cell apoptosis via inhibiting the expression of anti-apoptotic protein HMBOX1. Avian but not swine influenza virus caused upregulated expressions of ssc-miR-221-3p and ssc-miR-222 in PAM cells. We further found that NF-κB P65 was more effectively phosphorylated upon AIV infection and that P65 functioned as a transcription activator to regulate the AIV-induced expression of miR-221-3p/222 Importantly, we found that ssc-miR-221-3p and ssc-miR-222 could also be specifically upregulated upon AIV infection in newborn pig tracheal epithelial (NPTr) cells and also exerted anti-AIV function. In summary, our study indicated that miRNAs act as a host barrier during cross-species infection of influenza A virus.IMPORTANCE The host range of an influenza A virus is determined by species-specific interactions between virus and host cell factors. Host miRNAs can regulate influenza A virus replication; however, the role of miRNAs in host species specificity is unclear. Here, we show that the induced expression of ssc-miR-221-3p and ssc-miR-222 in swine cells is modulated by NF-κB P65 phosphorylation in response to AIV infection but not swine influenza virus infection. ssc-miR-221-3p and ssc-miR-222 exerted antiviral function via targeting viral RNAs and causing apoptosis by inhibiting the expression of HMBOX1 in host cells. These findings uncover miRNAs as a host range restriction factor that limits cross-species infection of influenza A virus.

Expression pattern of the interferon regulatory factor family members in influenza virus induced local and systemic inflammatory responses

Type I interferon is considered to be a key cytokine in influenza virus-induced acute lung injury (ALI), in which IRF3 and IRF7 play particularly important roles. However, whether all nine members of IRF family are involved in influenza virus-induced immune response is currently unknown. In this study, we found that all members of IRF family responded to influenza virus. The IRF family expression profile seems to be related to the pathogenicity of the particular influenza virus strain. The influenza virus mainly relies on endosomal TLR signals and the positive feedback loop of IFN-I to cause either direct or indirect different expression of all IRF family members locally or systemically. Interestingly, IRF6 was somewhat different from other IRF family members during influenza virus infection. Overall, the expression profile of the IRF family may be a valuable reference for the prevention and treatment of influenza complications, which encourage further, more in-depth research.

A novel reassortant influenza A (H1N1) virus infection in swine in Shandong Province, eastern China

Influenza A (H1N1) viruses are distributed worldwide and pose a threat to public health. Swine, as a natural host and mixing vessel of influenza A (H1N1) virus, play a critical role in the transmission of this virus to humans. Furthermore, swine influenza A (H1N1) viruses have provided all eight genes or some genes to the genomes of influenza strains that historically have caused human pandemics. Hence, persistent surveillance of influenza A (H1N1) virus in swine herds could contribute to the prevention and control of this virus. Here, we report a novel reassortant influenza A (H1N1) virus generated by reassortment between 2009 pandemic H1N1 viruses and swine viruses. We also found that this virus is prevalent in swine herds in Shandong Province, eastern China. Our findings suggest that surveillance of the emergence of the novel reassortant influenza A (H1N1) virus in swine is imperative.

A Single-Stranded Oligonucleotide Inhibits Toll-Like Receptor 3 Activation and Reduces Influenza A (H1N1) Infection

The initiation of an immune response is dependent on the activation and maturation of dendritic cells after sensing pathogen associated molecular patterns by pattern recognition receptors. However, the response needs to be balanced as excessive pro-inflammatory cytokine production in response to viral or stress-induced pattern recognition receptor signaling has been associated with severe influenza A virus (IAV) infection. Here, we use an inhibitor of Toll-like receptor (TLR)3, a single-stranded oligonucleotide (ssON) with the capacity to inhibit certain endocytic routes, or a TLR3 agonist (synthetic double-stranded RNA PolyI:C), to evaluate modulation of innate responses during H1N1 IAV infection. Since IAV utilizes cellular endocytic machinery for viral entry, we also assessed ssON's capacity to affect IAV infection. We first show that IAV infected human monocyte-derived dendritic cells (MoDC) were unable to up-regulate the co-stimulatory molecules CD80 and CD86 required for T cell activation. Exogenous TLR3 stimulation did not overcome the IAV-mediated inhibition of co-stimulatory molecule expression in MoDC. However, TLR3 stimulation using PolyI:C led to an augmented pro-inflammatory cytokine response. We reveal that ssON effectively inhibited PolyI:C-mediated pro-inflammatory cytokine production in MoDC, notably, ssON treatment maintained an interferon response induced by IAV infection. Accordingly, RNAseq analyses revealed robust up-regulation of interferon-stimulated genes in IAV cultures treated with ssON. We next measured reduced IAV production in MoDC treated with ssON and found a length requirement for its anti-viral activity, which overlapped with its capacity to inhibit uptake of PolyI:C. Hence, in cases wherein an overreacting TLR3 activation contributes to IAV pathogenesis, ssON can reduce this signaling pathway. Furthermore, concomitant treatment with ssON and IAV infection in mice resulted in maintained weight and reduced viral load in the lungs. Therefore, extracellular ssON provides a mechanism for immune regulation of TLR3-mediated responses and suppression of IAV infection in vitro and in vivo in mice.

Executable pathway analysis using ensemble discrete-state modeling for large-scale data

Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data. However, most existing methods do not consider signal integration represented by pathway topology, resulting in enrichment of convergent pathways when downstream genes are modulated. Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes. This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization. Here, we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data. We introduce a new algorithm, Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway's modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies. Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation. Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies. Additionally, BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls. Most interestingly, the two highest impact score nodes identified by BONITA included known drug targets. Thus, BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods. BONITA is available at: https://github.com/thakar-lab/BONITA.

iTRAQ-based proteomic and bioinformatic characterization of human mast cells upon infection by the influenza A virus strains H1N1 and H5N1

Mast cells can support the replication of influenza A virus, although how this occurs is poorly understood. In the present study, using quantitative MS, we analyzed the proteome of human mast cells infected with different influenza A virus strains at 12 h post-infection. Forty-one differentially expressed proteins were identified in human mast cells upon infection by the virulent H5N1 (A/Chicken/Henan/1/04) virus compared to the seasonal H1N1 (A/WSN/33) virus. Bioinformatic analyses confirmed that H1N1 significantly regulates the RNA degradation pathway via up-regulation of CCR4-NOT transcription complex subunit 4, whereas apoptosis could be suppressed by H5N1 via down-regulation of the tumor protein p53 signaling pathway with P ≤ 0.05 at 12 h post-infection. The hypoxia-inducible factor-1 signaling pathway of human mast cells is more susceptible to infection by H5N1 than by H1N1 virus.

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.

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

BACKGROUND

Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated.

METHODS

After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann-Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes.

RESULTS

A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study.

CONCLUSION

This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.

FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis

Motivation

Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing.

Results

We propose an inferential framework for GSEA based on functional data analysis, which utilizes the temporal information based on functional principal component analysis, and disentangles the effects of overlapping genes by a functional extension of the elastic-net regression. Furthermore, the hypothesis testing for the gene sets is performed by an extension of Mann-Whitney U test which is based on weighted rank sums computed from correlated observations. By using both simulated datasets and a large-scale time-course gene expression data on human influenza infection, we demonstrate that our method has uniformly better receiver operating characteristic curves, and identifies more pathways relevant to immune-response to human influenza infection than the competing approaches.

Availability and Implementation

The methods are implemented in R package FUNNEL, freely and publicly available at: https://github.com/yunzhang813/FUNNEL-GSEA-R-Package.

Supplementary information

Supplementary data are available at Bioinformatics online.

Immune responses in influenza A virus and human coronavirus infections: an ongoing battle between the virus and host

Respiratory viruses, especially influenza A viruses and coronaviruses such as MERS-CoV, represent continuing global threats to human health. Despite significant advances, much needs to be learned. Recent studies in virology and immunology have improved our understanding of the role of the immune system in protection and in the pathogenesis of these infections and of co-evolution of viruses and their hosts. These findings, together with sophisticated molecular structure analyses, omics tools and computer-based models, have helped delineate the interaction between respiratory viruses and the host immune system, which will facilitate the development of novel treatment strategies and vaccines with enhanced efficacy.

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

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.

The differences in virus-host interactions resulting in distinct pathogenicity of seasonal and pandemic influenza A viruses (IAV) are not well understood. Here, the authors show that the hemagglutinin segment from pandemic, but not seasonal, IAV suppresses RIPK3-mediated dendritic cell death, thereby reducing T cell activation.

Differential immune response of influenza A virus-infected dendritic cells and association with autophagy

Aim: We sought to study the responses of dendritic cells (DCs) after direct stimulation by different influenza A viruses. Materials & methods: Using bone marrow-derived DCs (BMDCs) as a model, we measured the expression of surface markers, cytokine production and the priming effect on CD4+ naive T cells. Results & conclusion: We found that all of the tested viruses induced BMDC maturation. Cytokine expression assays also demonstrated that activated BMDCs secrete higher levels of cytokines. Similar to the maturation degree, well-stimulated BMDCs induced higher levels of naive CD4+ T-cell activation. Furthermore, we found that the PR8 and WSN influenza A viruse-induced BMDC functional activation was at least partially influenced by autophagy.

Histone Deacetylase 2 Is a Component of Influenza A Virus-Induced Host Antiviral Response

Host cells produce variety of antiviral factors that create an antiviral state and target various stages of influenza A virus (IAV) life cycle to inhibit infection. However, IAV has evolved various strategies to antagonize those antiviral factors. Recently, we reported that a member of class I host histone deacetylases (HDACs), HDAC1 possesses an anti-IAV function. Herein, we provide evidence that HDAC2, another class I member and closely related to HDAC1 in structure and function, also possesses anti-IAV properties. In turn, IAV, like HDAC1, dysregulates HDAC2, mainly at the polypeptide level through proteasomal degradation to potentially minimize its antiviral effect. We found that IAV downregulated the HDAC2 polypeptide level in A549 cells in an H1N1 strain-independent manner by up to 47%, which was recovered to almost 100% level in the presence of proteasome-inhibitor MG132. A further knockdown in HDAC2 expression by up to 90% via RNA interference augmented the growth kinetics of IAV in A549 cells by more than four-fold after 24 h of infection. Furthermore, the knockdown of HDAC2 expression decreased the IAV-induced phosphorylation of the transcription factor, Signal Transducer and Activator of Transcription I (STAT1) and the expression of interferon-stimulated gene, viperin in infected cells by 41 and 53%, respectively. The role of HDAC2 in viperin expression was analogous to that of HDAC1, but it was not in the phosphorylation of STAT1. This indicated that, like HDAC1, HDAC2 is a component of IAV-induced host innate antiviral response and performs both redundant and non-redundant functions vis-a-vis HDAC1; however, IAV dysregulates them both in a redundant manner.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

BACKGROUND

Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.

RESULTS

We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.

CONCLUSION

We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

PathCellNet: Cell-type specific pathogen-response network explorer

Pathogen specific immune response is a complex interplay between several innate and adaptive immune cell-types. Innate immune cells play a critical role in pathogen recognition and initiating the antigen specific adaptive immune response. Despite specific functional roles of the innate immune cells, they share several anti-viral pathways. The question then becomes, what is the overlap in the transcriptional changes induced upon viral infections across different cell-types? Here we investigate the extent to which gene signatures are conserved across innate immune cell-types by performing a comparative analysis of transcriptomic data. Particularly, we integrate transcriptomic datasets measuring response of two innate immune cells (epithelial and dendritic cells) to influenza virus. The study reveals cell-type specific regulatory genes and a conserved network between the two cell-types. Additionally, novel functionally associated gene clusters are identified which are robustly defined across multiple independent studies. These gene clusters can be used in future investigation, and to facilitate their use we release PathCellNet (version 0), a cloud based tool to explore cell-type specific connectivity of user-defined genes. In the future, expansion of PathCellNet will allow exploration of cell-type specific responses across a variety of pathogens and cell-types.

Caspase-mediated degradation of host cortactin that promotes influenza A virus infection in epithelial cells

Influenza A virus (IAV) is well-known to exploit host factors to its advantage. Here, we report that IAV exploits host cortactin, an actin filament-stabilising protein for infection in epithelial cells. By using RNA interference-mediated knockdown and overexpression approach, we demonstrate that cortactin promotes IAV infection. However, cortactin polypeptide undergoes the degradation during late IAV infection. By perturbing the lysosome and proteasome, two main compartments governing the degradation of mammalian proteins, we demonstrate that a lysosome-associated apoptotic pathway mediates the degradation of cortactin in IAV-infected cells. However, we could not detect cleaved cortactin fragments by western blotting using the antibodies recognising either N-terminal/Central or C-terminal cortactin regions, which suggested the presence of multiple caspase cleavage sites. Indeed, CaspDB, a recently-described database predicted up to 35 caspase cleavage motifs present across cortactin polypeptide. The data presented indicate that host cortactin potentially has a dual but contrasting role during IAV infection.

Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection

Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection.

Systems biology: A tool for charting the antiviral landscape

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Conventional approaches overlook the complexity of the antiviral response.

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Systems biology approaches provide a comprehensive and unbiased analysis.

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The Collaborative Cross studies how host genetics influences antiviral immunity.

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Transcriptomics is a powerful tool to study tissue and cellular antiviral responses.

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Single cell analysis allows for discrimination between bystander and infected cells.

The host antiviral programs that are initiated following viral infection form a dynamic and complex web of responses that we have collectively termed as “the antiviral landscape”. Conventional approaches to studying antiviral responses have primarily used reductionist systems to assess the function of a single or a limited subset of molecules. Systems biology is a holistic approach that considers the entire system as a whole, rather than individual components or molecules. Systems biology based approaches facilitate an unbiased and comprehensive analysis of the antiviral landscape, while allowing for the discovery of emergent properties that are missed by conventional approaches. The antiviral landscape can be viewed as a hierarchy of complexity, beginning at the whole organism level and progressing downward to isolated tissues, populations of cells, and single cells. In this review, we will discuss how systems biology has been applied to better understand the antiviral landscape at each of these layers. At the organismal level, the Collaborative Cross is an invaluable genetic resource for assessing how genetic diversity influences the antiviral response. Whole tissue and isolated bulk cell transcriptomics serves as a critical tool for the comprehensive analysis of antiviral responses at both the tissue and cellular levels of complexity. Finally, new techniques in single cell analysis are emerging tools that will revolutionize our understanding of how individual cells within a bulk infected cell population contribute to the overall antiviral landscape.