Spatio-temporal dynamics of Host-Virus competition: A model study of influenza A.

We present results of a study of the early-time response of the innate immune system to influenza virus infection in an agent-based model (ABM) of epithelial cell layers. We find that the competition between the anti-viral immune response and viral antagonism can lead to viral titers non-monotonic in the initial infection fraction as found in experiments. Our model includes a coarse-grained version of intra-cellular processes and inter-cellular communication via cytokine and virion diffusion. We use ABM to follow the propagation of viral infection in the layer and the increase of the viral load as a function of time for different values of the multiplicity of infection (MOI), the initial number of viruses added per cell. We find that for moderately strong host immune response, the number of infected cells and viral load for a smaller MOI exceeds that for larger MOI, as seen in experiments. We elucidate the mechanism underlying this result as the synergistic action of cytokines secreted by infected cells in controlling viral amplification for larger MOI. We investigate the length and time scales that determine this non-monotonic behavior within the ABM. We study the diffusive spread of virions and cytokines from a single infected cell in an absorbing medium analytically and numerically and deduce the length scale that yields a good estimate of the MOI at which we find non-monotonicity. Detailed computations of the temporal behavior of averaged quantities and spatial measures provide further insights into host-viral interactions and connections to experimental observations.

Modeling and high-throughput experimental data uncover the mechanisms underlying Fshb gene sensitivity to gonadotropin-releasing hormone pulse frequency.

Neuroendocrine control of reproduction by brain-secreted pulses of gonadotropin-releasing hormone (GnRH) represents a longstanding puzzle about extracellular signal decoding mechanisms. GnRH regulates the pituitary gonadotropin's follicle-stimulating hormone (FSH) and luteinizing hormone (LH), both of which are heterodimers specified by unique ß subunits (FSHß/LHß). Contrary to Lhb, Fshb gene induction has a preference for low-frequency GnRH pulses. To clarify the underlying regulatory mechanisms, we developed three biologically anchored mathematical models: 1) parallel activation of Fshb inhibitory factors (e.g. inhibin α and VGF nerve growth factor-inducible), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. growth differentiation factor 9). Simulations with all three models recapitulated the Fshb expression levels obtained in pituitary gonadotrope cells perifused with varying GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration, and pulse frequency revealed that the apparent frequency-dependent pattern of Fshb expression in model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed "true" pulse frequency sensing. To resolve which components of this GnRH signal induce Fshb, we developed a high-throughput parallel experimental system. We analyzed over 4,000 samples in experiments with varying near-physiological GnRH concentrations and pulse patterns. Whereas Egr1 and Fos genes responded only to variations in average GnRH concentration, Fshb levels were sensitive to both average concentration and true pulse frequency. These results provide a foundation for understanding the role of multiple regulatory factors in modulating Fshb gene activity.

Combinatorial actions of Tgfß and Activin ligands promote oligodendrocyte development and CNS myelination.

In the embryonic CNS, development of myelin-forming oligodendrocytes is limited by bone morphogenetic proteins, which constitute one arm of the transforming growth factor-ß (Tgfß) family and signal canonically via Smads 1/5/8. Tgfß ligands and Activins comprise the other arm and signal via Smads 2/3, but their roles in oligodendrocyte development are incompletely characterized. Here, we report that Tgfß ligands and activin B (ActB) act in concert in the mammalian spinal cord to promote oligodendrocyte generation and myelination. In mouse neural tube, newly specified oligodendrocyte progenitors (OLPs) are first exposed to Tgfß ligands in isolation, then later in combination with ActB during maturation. In primary OLP cultures, Tgfß1 and ActB differentially activate canonical Smad3 and non-canonical MAP kinase signaling. Both ligands enhance viability, and Tgfß1 promotes proliferation while ActB supports maturation. Importantly, co-treatment strongly activates both signaling pathways, producing an additive effect on viability and enhancing both proliferation and differentiation such that mature oligodendrocyte numbers are substantially increased. Co-treatment promotes myelination in OLP-neuron co-cultures, and maturing oligodendrocytes in spinal cord white matter display strong Smad3 and MAP kinase activation. In spinal cords of ActB-deficient Inhbb(-/-) embryos, apoptosis in the oligodendrocyte lineage is increased and OLP numbers transiently reduced, but numbers, maturation and myelination recover during the first postnatal week. Smad3(-/-) mice display a more severe phenotype, including diminished viability and proliferation, persistently reduced mature and immature cell numbers, and delayed myelination. Collectively, these findings suggest that, in mammalian spinal cord, Tgfß ligands and ActB together support oligodendrocyte development and myelin formation.

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.

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.

Proapoptotic and antiapoptotic actions of Stat1 versus Stat3 underlie neuroprotective and immunoregulatory functions of IL-11.

Current therapies for multiple sclerosis target inflammation but do not directly address oligodendrocyte protection or myelin repair. The gp130 family cytokines ciliary neurotrophic factor, leukemia inhibitory factor, and IL-11 have been identified as oligodendrocyte growth factors, and IL-11 is also strongly immunoregulatory, but their underlying mechanisms of action are incompletely characterized. In this study, we demonstrate that these effects of IL-11 are mediated via differential regulation of apoptosis in oligodendrocytes versus Ag-presenting dendritic cells (DCs), and are dependent on lineage-specific activity of the transcription factors Stat1 versus Stat3. Focal demyelinating lesions induced in cerebral cortices of IL-11Rα(-/-) mice using stereotactic microinjection of lysolecithin were larger than in controls, and remyelination was delayed. In IL-11Rα(-/-) mice, lesions displayed extensive oligodendrocyte loss and axonal transection, and increased infiltration by inflammatory cells including CD11c(+) DCs, CD3(+) lymphocytes, and CD11b(+) phagocytes. In oligodendrocyte progenitor cell (OPC) cultures, IL-11 restricted caspase 9 activation and apoptosis, and it increased myelination in OPC-neuron cocultures. Importantly, siRNA inhibition of Stat1 enhanced the antiapoptotic effects of IL-11 on OPCs, but IL-11 induced apoptosis in the presence of Stat3 silencing. In contrast, IL-11 augmented caspase activation and apoptosis in cultures of CD11c(+) DCs, but not in CD11b(+) or CD3(+) cells. Inhibition of Stat3 exacerbated the proapoptotic effects of IL-11 on DCs, whereas they were ablated in Stat1(-/-) cultures. Collectively, these findings reveal novel mechanisms underlying the actions of a neuroprotective and immunoregulatory member of the gp130 cytokine family, suggesting avenues to enhance oligodendrocyte viability and restrict CNS inflammation in multiple sclerosis.

Novel Nipah virus immune-antagonism strategy revealed by experimental and computational study.

Nipah virus is an emerging pathogen that causes severe disease in humans. It expresses several antagonist proteins that subvert the immune response and that may contribute to its pathogenicity. Studies of its biology are difficult due to its high pathogenicity and requirement for biosafety level 4 containment. We integrated experimental and computational methods to elucidate the effects of Nipah virus immune antagonists. Individual Nipah virus immune antagonists (phosphoprotein and V and W proteins) were expressed from recombinant Newcastle disease viruses, and the responses of infected human monocyte-derived dendritic cells were determined. We developed an ordinary differential equation model of the infectious process that that produced results with a high degree of correlation with these experimental results. In order to simulate the effects of wild-type virus, the model was extended to incorporate published experimental data on the time trajectories of immune-antagonist production. These data showed that the RNA-editing mechanism utilized by the wild-type Nipah virus to produce immune antagonists leads to a delay in the production of the most effective immune antagonists, V and W. Model simulations indicated that this delay caused a disconnection between attenuation of the antiviral response and suppression of inflammation. While the antiviral cytokines were efficiently suppressed at early time points, some early inflammatory cytokine production occurred, which would be expected to increase vascular permeability and promote virus spread and pathogenesis. These results suggest that Nipah virus has evolved a unique immune-antagonist strategy that benefits from controlled expression of multiple antagonist proteins with various potencies.

Immune response modeling of interferon beta-pretreated influenza virus-infected human dendritic cells.

The pretreatment of human dendritic cells with interferon-beta enhances their immune response to influenza virus infection. We measured the expression levels of several key players in that response over a period of 13 h both during pretreatment and after viral infection. Their activation profiles reflect the presence of both negative and positive feedback loops in interferon induction and interferon signaling pathway. Based on these measurements, we have developed a comprehensive computational model of cellular immune response that elucidates its mechanism and its dynamics in interferon-pretreated dendritic cells, and provides insights into the effects of duration and strength of pretreatment.

Chromosome-specific and noisy IFNB1 transcription in individual virus-infected human primary dendritic cells.

The induction of interferon beta (IFNB1) is a key event in the antiviral immune response. We studied the role of transcriptional noise in the regulation of the IFNB1 locus in primary cultures of human dendritic cells (DCs), which are important 'first responders' to viral infection. In single cell assays, IFNB1 mRNA expression in virus-infected DCs showed much greater cell-to-cell variation than that of a housekeeping gene, another induced transcript and viral RNA. We determined the contribution of intrinsic noise by measuring the allelic origin of transcripts in each cell and found that intrinsic noise is a very significant part of total noise. We developed a stochastic model to investigate the underlying mechanisms. We propose that the surprisingly high levels of IFNB1 transcript noise originate from the complexity of IFNB1 enhanceosome formation, which leads to a range up to many minutes in the differences within each cell in the time of activation of each allele.

Noise propagation and scaling in regulation of gonadotrope biosynthesis.

Reproductive physiology depends on the control of biosynthesis in the pituitary gonadotrope by hypothalamic gonadotropin-releasing hormone (GnRH). The responses to GnRH include activation of extracellular signal-regulated kinase (ERK) and induction of Egr1. Using population and single cell signaling assays, we investigated the signal and noise transmission through this signaling and gene circuit, analyzing data obtained from 43,775 individual cells in 40 experiments. After exposure to GnRH, phosphorylated ERK (pERK) is elevated in 50% of the cells at 1.7 (SD = 0.3) min. Studies of the cell-to-cell response showed that for both pERK and for Egr1 protein production the mean response (mu) and standard deviation (sigma) within individual cells were linearly related (sigma = kmu) and had similar values of k. To understand the basis for the scaling observed for noise propagation through this system, we determined the relationship between pERK and egr1 mRNA levels induced at varying concentration of GnRH. While both pERK and egr1 mRNA show a saturating sigmoidal relationship to the concentration of GnRH exposure, egr1 mRNA is linearly related to the levels of pERK. These results explain the basis for variation in cellular responses in an important mammalian signaling pathway leading to gene induction.

Single-cell analysis shows that paracrine signaling by first responder cells shapes the interferon-ß response to viral infection.

Immune responses to viral infection are stochastic processes, which initiate in a limited number of cells that then propagate the response. A key component of the response to viral infection entails the synthesis and secretion of type I interferons (IFNs), including the early induction of the gene encoding IFN-ß (Ifnb1). With single-cell analysis and mathematical modeling, we investigated the mechanisms underlying how increases in the amount of Ifnb1 mRNA per cell and in the numbers of cells expressing Ifnb1 calibrate the response to viral infection. We used single-cell, single-molecule assays to quantify the early induction of Ifnb1 expression (the Ifnb1 response) in human monocyte-derived dendritic cells infected with Newcastle disease virus, thus retaining the physiological stoichiometry of transcriptional regulators to both alleles of the Ifnb1 gene. We applied computational methods to extract the stochastic features that underlie the cell-to-cell variations in gene expression over time. Integration of simulations and experiments identified the role of paracrine signaling in increasing the number of cells that express Ifnb1 over time and in calibrating the immune response to viral infection.

Noise propagation through extracellular signaling leads to fluctuations in gene expression.

BACKGROUND: Cell-to-cell variability in mRNA and proteins has been observed in many biological systems, including the human innate immune response to viral infection. Most of these studies have focused on variability that arises from (a) intrinsic stochastic fluctuations in gene expression and (b) extrinsic sources (e.g. fluctuations in transcription factors). The main focus of our study is the effect of extracellular signaling on enhancing intrinsic stochastic fluctuations. As a new source of noise, the communication between cells with fluctuating numbers of components has received little attention. We use agent-based modeling to study this contribution to noise in a system of human dendritic cells responding to viral infection. RESULTS: Our results, validated by single-cell experiments, show that in the transient state cell-to-cell variability in an interferon-stimulated gene (DDX58) arises from the interplay between the spatial randomness of the cellular sources of the interferon and the temporal stochasticity of its own production. The numerical simulations give insight into the time scales on which autocrine and paracrine signaling act in a heterogeneous population of dendritic cells upon viral infection. We study the effect of different factors that influence the magnitude of the cell-to-cell-variability of the induced gene, including the cell density, multiplicity of infection, and the time scale over which the cellular sources begin producing the cytokine. CONCLUSIONS: We propose a mechanism of noise propagation through extracellular communication and establish conditions under which the mechanism is operative. The cellular stochasticity of gene induction, which we investigate, is not limited to the specific interferon-induced gene we have studied; a broad distribution of copy numbers across cells is to be expected for other interferon-stimulated genes. This can lead to functional consequences for the system-level response to a viral challenge.

Computational approaches to understanding dendritic cell responses to influenza virus infection.

The evolution of immunology research from measurements of single entities to large-scale data-intensive assays necessitates the integration of experimental work with bioinformatics and computational approaches. The introduction of physics into immunology has led to the study of new phenomena, such as cellular noise, which is likely to prove increasingly important to understand immune system responses. The fusion of "hard science" and biology is also leading to a re-examination of data acquisition, analysis, and statistical validation and is resulting in the development of easy-to-access tools for immunology research. Here, we review some of our models, computational tools, and results related to studies of the innate immune response of human dendritic cells to viral infection. Our project functions on an open model across institutions with electronic record keeping and public sharing of data. Our tools, models, and data can be accessed at http://tsb.mssm.edu/primeportal/ .

Simulations of stochastic biological phenomena.

This Teaching Resource provides lecture notes, slides, and a student assignment for a two-part lecture that introduces stochastic modeling of biological systems. The first lecture uses biological examples to present the concept of cell-to-cell variability and makes the connection between the variability of single-cell measurements and concepts from statistical mechanics and probability theory. This section makes the point that for low copy number of a species, the usual differential equation formalism is no longer applicable and needs to be replaced by a probabilistic approach based on the so-called Master Equation. As an example, a simple model of gene transcription is discussed in detail, the different contributions to the relevant Master Equation are highlighted, and the equation itself is derived. The second lecture describes how, for more complex and biologically interesting applications, direct solution of the Master Equation becomes difficult. Gillespie's algorithm, which is used in most cases of biological interest, is then introduced as a practical alternative. The lecture delves into the crux of Gillespie's algorithm, which entails the drawing of two random numbers at each time step. It establishes the corresponding formalism, details the connection between chemical rate constants and Gillespie rates, and culminates in a description and explanation of a core MATLAB program for the transcriptional model considered in the first lecture. This core program, written for a single cell, is expanded by the students in the homework assignment to consider both transcription and translation.

Multi-scale stochastic simulation of diffusion-coupled agents and its application to cell culture simulation.

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems.

Role of cell-to-cell variability in activating a positive feedback antiviral response in human dendritic cells.

In the first few hours following Newcastle disease viral infection of human monocyte-derived dendritic cells, the induction of IFNB1 is extremely low and the secreted type I interferon response is below the limits of ELISA assay. However, many interferon-induced genes are activated at this time, for example DDX58 (RIGI), which in response to viral RNA induces IFNB1. We investigated whether the early induction of IFNBI in only a small percentage of infected cells leads to low level IFN secretion that then induces IFN-responsive genes in all cells. We developed an agent-based mathematical model to explore the IFNBI and DDX58 temporal dynamics. Simulations showed that a small number of early responder cells provide a mechanism for efficient and controlled activation of the DDX58-IFNBI positive feedback loop. The model predicted distributions of single cell responses that were confirmed by single cell mRNA measurements. The results suggest that large cell-to-cell variation plays an important role in the early innate immune response, and that the variability is essential for the efficient activation of the IFNB1 based feedback loop.

Coregulation mapping based on individual phenotypic variation in response to virus infection.

BACKGROUND: Gene coregulation across a population is an important aspect of the considerable variability of the human immune response to virus infection. Methodology to investigate it must rely on a number of ingredients ranging from gene clustering to transcription factor enrichment analysis. RESULTS: We have developed a methodology to investigate the gene to gene correlations for the expression of 34 genes linked to the immune response of Newcastle Disease Virus (NDV) infected conventional dendritic cells (DCs) from 145 human donors. The levels of gene expression showed a large variation across individuals. We generated a map of gene co-expression using pairwise correlation and multidimensional scaling (MDS). The analysis of these data showed that among the 13 genes left after filtering for statistically significant variations, two clusters are formed. We investigated to what extent the observed correlation patterns can be explained by the sharing of transcription factors (TFs) controlling these genes. Our analysis showed that there was a significant positive correlation between MDS distances and TF sharing across all pairs of genes. We applied enrichment analysis to the TFs having binding sites in the promoter regions of those genes. This analysis, after Gene Ontology filtering, indicated the existence of two clusters of genes (CCL5, IFNA1, IFNA2, IFNB1) and (IKBKE, IL6, IRF7, MX1) that were transcriptionally co-regulated. In order to facilitate the use of our methodology by other researchers, we have also developed an interactive coregulation explorer web-based tool called CorEx. It permits the study of MDS and hierarchical clustering of data combined with TF enrichment analysis. We also offer web services that provide programmatic access to MDS, hierarchical clustering and TF enrichment analysis. CONCLUSIONS: MDS mapping based on correlation in conjunction with TF enrichment analysis represents a useful computational method to generate predictions underlying gene coregulation across a population.

Stimulus size dependence of information transfer from retina to thalamus.

Relay cells in the mammalian lateral geniculate nucleus (LGN) are driven primarily by single retinal ganglion cells (RGCs). However, an LGN cell responds typically to less than half of the spikes it receives from the RGC that drives it, and without retinal drive the LGN is silent (Kaplan and Shapley, 1984). Recent studies, which used stimuli restricted to the receptive field (RF) center, show that despite the great loss of spikes, more than half of the information carried by the RGC discharge is typically preserved in the LGN discharge (Sincich et al., 2009), suggesting that the retinal spikes that are deleted by the LGN carry less information than those that are transmitted to the cortex. To determine how LGN relay neurons decide which retinal spikes to respond to, we recorded extracellularly from the cat LGN relay cell spikes together with the slow synaptic ('S') potentials that signal the firing of retinal spikes. We investigated the influence of the inhibitory surround of the LGN RF by stimulating the eyes with spots of various sizes, the largest of which covered the center and surround of the LGN relay cell's RF. We found that for stimuli that activated mostly the RF center, each LGN spike delivered more information than the retinal spike, but this difference was reduced as stimulus size increased to cover the RF surround. To evaluate the optimality of the LGN editing of retinal spikes, we created artificial spike trains from the retinal ones by various deletion schemes. We found that single LGN cells transmitted less information than an optimal detector could.

A simple model of retina-LGN transmission.

To gain a deeper understanding of the transmission of visual signals from retina through the lateral geniculate nucleus (LGN), we have used a simple leaky integrate and-fire model to simulate a relay cell in the LGN. The simplicity of the model was motivated by two questions: (1) Can an LGN model that is driven by a retinal spike train recorded as synaptic ('S') potentials, but does not include a diverse array of ion channels, nor feedback inputs from the cortex, brainstem, and thalamic reticular nucleus, accurately simulate the LGN discharge on a spike-for-spike basis? (2) Are any special synaptic mechanisms, beyond simple summation of currents, necessary to model experimental recordings? We recorded cat relay cell responses to spatially homogeneous small or large spots, with luminance that was rapidly modulated in a pseudo-random fashion. Model parameters for each cell were optimized with a Simplex algorithm using a short segment of the recording. The model was then tested on a much longer, distinct data set consisting of responses to numerous repetitions of the noisy stimulus. For LGN cells that spiked in response to a sufficiently large fraction of retinal inputs, we found that this simplified model accurately predicted the firing times of LGN discharges. This suggests that modulations of the efficacy of the retino-geniculate synapse by pre-synaptic facilitation or depression are not necessary in order to account for the LGN responses generated by our stimuli, and that post-synaptic summation is sufficient.