Sex-based differences in persistent lung inflammation following influenza infection of juvenile outbred mice.

Children are susceptible to influenza infections and can experience severe disease presentation due to a lack of or limited pre-existing immunity. Despite the disproportionate impact influenza has on this population, there is a lack of focus on pediatric influenza research, particularly when it comes to identifying the pathogenesis of long-term outcomes that persist beyond the point of viral clearance. In this study, juvenile outbred male and female mice were infected with influenza and analyzed following viral clearance to determine how sex impacts the persistent inflammatory responses to influenza. It was found that females maintained a broader cytokine response in the lung following clearance of influenza, with innate, type I and type II cytokine signatures in almost all mice. Males, on the other hand, had higher levels of IL-6 and other macrophage-related cytokines, but no evidence of a type I or type II response. The immune landscape was similar in the lungs between males and females postinfection, but males had a higher regulatory T cell to TH1 ratio compared with female mice. Cytokine production positively correlated with the frequency of TH1 cells and exudate macrophages, as well as the number of cells in the bronchoalveolar lavage fluid. Furthermore, female lungs were enriched for metabolites involved in the glycolytic pathway, suggesting glycolysis is higher in female lungs compared with males after viral clearance. These data suggest juvenile female mice have persistent and excessive lung inflammation beyond the point of viral clearance, whereas juvenile males had a more immunosuppressive phenotype.NEW & NOTEWORTHY This study identifies sex-based differences in persistent lung inflammation following influenza infection in an outbred, juvenile animal model of pediatric infection. These findings indicate the importance of considering sex and age as variable in infectious disease research.

Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNß production.

MOTIVATION: The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNß). Experimental evidence shows that IFNß signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNß production, an agent-based model is developed to simulate both DNA transfection and viral infection. RESULTS: We show that heterogeneity can enhance IFNß responses during infection. Furthermore, by varying the degree of IFNß stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection. AVAILABILITY AND IMPLEMENTATION: Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.

Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking epithelial cell signaling to systemic immune models.

DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks.

BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .

Repurposing the psoriasis drug Oxarol to an ointment adjuvant for the influenza vaccine.

Aluminum precipitates have long been used as adjuvants for human vaccines, but there is a clear need for safer and more effective adjuvants. Here we report in a mouse model that the psoriasis drug Oxarol ointment is a highly effective vaccine adjuvant. By applying Oxarol ointment onto skin, humoral responses and germinal center (GC) reactions were augmented, and the treated mice were protected from death caused by influenza virus infection. Keratinocyte-specific vitamin D3 receptor (Vdr) gene expression was required for these responses through induction of the thymic stromal lymphopoietin (Tslp) gene. Experiments involving administration of recombinant TSLP or, conversely, anti-TSLP antibody demonstrated that TSLP plays a key role in the GC reactions. Furthermore, cell-type-specific Tslpr gene deletion or diphtheria toxin-mediated deletion of specific cell types revealed that CD11c+ cells excluding Langerhans cells were responsible for the Oxarol-mediated GC reactions. These results indicate that active vitamin D3 is able to enhance the humoral response via Tslp induction in the skin and serves as a new vaccine adjuvant.

Pathogenesis of Influenza A(H7N9) Virus in Aged Nonhuman Primates.

The avian influenza A(H7N9) virus has caused high mortality rates in humans, especially in the elderly; however, little is known about the mechanistic basis for this. In the current study, we used nonhuman primates to evaluate the effect of aging on the pathogenicity of A(H7N9) virus. We observed that A(H7N9) virus infection of aged animals (defined as age 20-26 years) caused more severe symptoms than infection of young animals (defined as age 2-3 years). In aged animals, lung inflammation was weak and virus infection was sustained. Although cytokine and chemokine expression in the lungs of most aged animals was lower than that in the lungs of young animals, 1 aged animal showed severe symptoms and dysregulated proinflammatory cytokine and chemokine production. These results suggest that attenuated or dysregulated immune responses in aged animals are responsible for the severe symptoms observed among elderly patients infected with A(H7N9) virus.

TREAP: A New Topological Approach to Drug Target Inference.

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug's mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA's accuracy. We find that network topology predominantly determines the accuracy of ProTINA's predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection.

Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein-gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene network's dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection.

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery.

BACKGROUND: Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates. RESULTS: Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. CONCLUSIONS: Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.

Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback.

Cyclic GMP-AMP synthase (cGAS) has recently been identified as the primary protein that detects cytosolic double stranded DNA to invoke a type I interferon response. The cGAS pathway is vital in the recognition of DNA encoded viruses as well as self-DNA leaked from the nucleus of damaged cells. Currently, the dynamics regulating the cGAS pathway are poorly understood; limiting our knowledge of how DNA-induced immune responses are regulated. Using systems biology approaches, we formulated a mathematical model to describe the dynamics of this pathway and examine the resulting system-level emergent properties. Unknown model parameters were fit to data compiled from literature using a Parallel Tempering Markov Chain Monte Carlo (PT-MCMC) approach, resulting in an ensemble of parameterized models. A local sensitivity analysis demonstrated that parameter sensitivity trends across model ensembles were independent of the select parameterization. An in-silico knock-down of TREX1 found that the interferon response is highly robust, showing that complete inhibition is necessary to induce chemical conditions consistent with chronic inflammation. Lastly, we demonstrate that the model recapitulates interferon expression data resulting from small molecule inhibition of cGAS. Overall, the importance of this model is exhibited in its capacity to identify sensitive components of the cGAS pathway, generate testable hypotheses, and confirm experimental observations.

An Ultrasensitive Mechanism Regulates Influenza Virus-Induced Inflammation.

Influenza viruses present major challenges to public health, evident by the 2009 influenza pandemic. Highly pathogenic influenza virus infections generally coincide with early, high levels of inflammatory cytokines that some studies have suggested may be regulated in a strain-dependent manner. However, a comprehensive characterization of the complex dynamics of the inflammatory response induced by virulent influenza strains is lacking. Here, we applied gene co-expression and nonlinear regression analysis to time-course, microarray data developed from influenza-infected mouse lung to create mathematical models of the host inflammatory response. We found that the dynamics of inflammation-associated gene expression are regulated by an ultrasensitive-like mechanism in which low levels of virus induce minimal gene expression but expression is strongly induced once a threshold virus titer is exceeded. Cytokine assays confirmed that the production of several key inflammatory cytokines, such as interleukin 6 and monocyte chemotactic protein 1, exhibit ultrasensitive behavior. A systematic exploration of the pathways regulating the inflammatory-associated gene response suggests that the molecular origins of this ultrasensitive response mechanism lie within the branch of the Toll-like receptor pathway that regulates STAT1 phosphorylation. This study provides the first evidence of an ultrasensitive mechanism regulating influenza virus-induced inflammation in whole lungs and provides insight into how different virus strains can induce distinct temporal inflammation response profiles. The approach developed here should facilitate the construction of gene regulatory models of other infectious diseases.

C646, a Novel p300/CREB-Binding Protein-Specific Inhibitor of Histone Acetyltransferase, Attenuates Influenza A Virus Infection.

New strategies to develop novel broad-spectrum antiviral drugs against influenza virus infections are needed due to the emergence of antigenic variants and drug-resistant viruses. Here, we evaluated C646, a novel p300/CREB-binding protein-specific inhibitor of histone acetyltransferase (HAT), as an anti-influenza virus agent in vitro and in vivo and explored how C646 affects the viral life cycle and host response. Our studies highlight the value of targeting HAT activity for anti-influenza drug development.

Disease severity is associated with differential gene expression at the early and late phases of infection in nonhuman primates infected with different H5N1 highly pathogenic avian influenza viruses.

UNLABELLED: Occasional transmission of highly pathogenic avian H5N1 influenza viruses to humans causes severe pneumonia with high mortality. To better understand the mechanisms via which H5N1 viruses induce severe disease in humans, we infected cynomolgus macaques with six different H5N1 strains isolated from human patients and compared their pathogenicity and the global host responses to the virus infection. Although all H5N1 viruses replicated in the respiratory tract, there was substantial heterogeneity in their replicative ability and in the disease severity induced, which ranged from asymptomatic to fatal. A comparison of global gene expression between severe and mild disease cases indicated that interferon-induced upregulation of genes related to innate immunity, apoptosis, and antigen processing/presentation in the early phase of infection was limited in severe disease cases, although interferon expression was upregulated in both severe and mild cases. Furthermore, coexpression analysis of microarray data, which reveals the dynamics of host responses during the infection, demonstrated that the limited expression of these genes early in infection led to a failure to suppress virus replication and to the hyperinduction of genes related to immunity, inflammation, coagulation, and homeostasis in the late phase of infection, resulting in a more severe disease. Our data suggest that the attenuated interferon-induced activation of innate immunity, apoptosis, and antigen presentation in the early phase of H5N1 virus infection leads to subsequent severe disease outcome. IMPORTANCE: Highly pathogenic avian H5N1 influenza viruses sometimes transmit to humans and cause severe pneumonia with ca. 60% lethality. The continued circulation of these viruses poses a pandemic threat; however, their pathogenesis in mammals is not fully understood. We, therefore, investigated the pathogenicity of six H5N1 viruses and compared the host responses of cynomolgus macaques to the virus infection. We identified differences in the viral replicative ability of and in disease severity caused by these H5N1 viruses. A comparison of global host responses between severe and mild disease cases identified the limited upregulation of interferon-stimulated genes early in infection in severe cases. The dynamics of the host responses indicated that the limited response early in infection failed to suppress virus replication and led to hyperinduction of pathological condition-related genes late in infection. These findings provide insight into the pathogenesis of H5N1 viruses in mammals.

Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection.

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.

Background: The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza. Methods: Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network). Results: We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection. Conclusions: This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data.

BACKGROUND: Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics. RESULTS: CTen (cell type enrichment) is a web-based analytical tool which uses our highly expressed, cell specific (HECS) gene database to identify enriched cell types in heterogeneous microarray data. The web interface is designed for differential expression and gene clustering studies, and the enrichment results are presented as heatmaps or downloadable text files. CONCLUSIONS: In this work, we use an independent, cell-specific gene expression data set to assess CTen's performance in accurately identifying the appropriate cell type and provide insight into the suggested level of enrichment to optimally minimize the number of false discoveries. We show that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool. Furthermore, we discuss the strong implications cell type enrichment has in the design of effective microarray workflow strategies and show that, by combining CTen with gene expression clustering, we may be able to determine the relative changes in the number of key cell types.CTen is available at http://www.influenza-x.org/~jshoemaker/cten/

Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection.

The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains.