Computational approaches to understanding dendritic cell responses to influenza virus infection
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- Zaslavsky, E., Hayot, F. & Sealfon, S.C. Immunol Res (2012) 54: 160. doi:10.1007/s12026-012-8322-6
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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/.
KeywordsComputational immunologyToolsModelsDendritic cells
We have undertaken an NIAID-sponsored Modeling Immunity for biodefense project that involves a tight collaboration between experimenters and modelers. The aim is to develop a mechanistic understanding of the initial stages of viral infection, in order to be able to comprehend and predict pathogenicity of newly emerging viruses.
We focus on the innate immune response in dendritic cells (DCs). DCs, as professional antigen presenting cells, contribute to the development of the adaptive immune response tailored to each specific virus . For the in vitro component of our experimental work, the DCs studied are derived from monocytes extracted from human blood. The interaction between viruses and DCs is a complicated dance, where the cells attempt to limit the impact of the virus and the virus attempts to circumvent cellular defenses. The viruses studied are the Newcastle disease virus (NDV) and H1N1 influenza A viruses. NDV, because it is avian, does not counteract the cellular immune response in human DCs, thus allowing a full view of the temporal development of that response . Influenza A H1N1 viruses studied range from PR8 to the 1918 pandemic virus, including seasonal viruses such as Texas/91, New Caledonia/99, and the recent pandemic virus Cal/09, as well as sequence modified viruses to alter their immune antagonists or to incorporate fluorescent reporter proteins. These viruses interfere with the immune responses at many different levels once they have entered the cell . A comparison of their impact on the immune response, both in terms of its dynamics and its strength, is expected to lead to mechanistic insights about the different strategies employed by virus to achieve a successful infection.
Role of computational approaches
Computational approaches serve to organize multiple sets of data in a common framework, to highlight in this way salient features and to illuminate connections between different aspects of the system under study. Once the model is built, it can be used to explore biological regimes not covered by the experiments at hand, to make predictions that are a test of the model, and to lead to new insights about hidden components or unexplored relationships between known ones.
Computational approaches can also be used to improve data acquisition and analysis, such as flow cytometry through flow compensation and clustering algorithms.
Population and single cell experiments
The experiments are meant to probe the early dynamics of the innate immune responses up to 10 h after infection. Measurements (microarray, PCR, multiplex ELISA, and flow cytometry) need therefore be made at a number of time points. They encompass population-wide measurements, which are assumed to describe the behavior of a typical cell, but also single-cell measurements that give insight into cell-to-cell variability under similar conditions of stimulation [4, 5]. Cell-to-cell variability can play a crucial role in cellular response, such as in the case of all-or-none behavior . Extreme variability in the responses of individual cells can misleadingly appear smooth and gradual in biochemical assays that measure the responses of populations of cells.
Diverse computational approaches are useful for immunology, including deterministic differential equation modeling that reflects average cell response, stochastic models that account for single cell variability , data-driven reverse-engineering approaches that predict relationships among entities measured, and hybrid approaches. For time-course microarray data, on top of the usual clustering analysis, we have developed an algorithm (TIDAL) to reconstruct the temporal development of the network of transcription factors active in the immune response . For PCR population studies, we build networks of cellular infection, immune response, and viral antagonism based on gene expression levels, which are derived from a set of chemical reactions. These reactions form the basis of a system of time-dependent ordinary differential equations (ODE) that depend on a number of reaction rate constants that are fitted to the data or extracted from the literature and describe the time evolution in the extracellular medium, intracellular cytoplasm, and nucleus of the measured molecular species. For single-cell measurements where cell-to-cell variability is important, the above mathematical description is no longer appropriate and needs to be replaced by a probabilistic description for which one commonly uses an algorithm proposed by Gillespie . Since paracrine signaling plays an important part in propagating and priming cells for infection, we have constructed an agent-based model (ABM) of individual cells interacting through interferon secretion and diffusion that allows to study whether only a small subset of infected cells initiates the immune response.
We present selected examples of our computational immunology tool development and use of computational approaches in the study of dendritic cell responses to virus. Additional examples of the application of all these approaches to immunology can be accessed at http://tsb.mssm.edu/primeportal.
Allelic imbalance in single cell IFNβ measurements (Fig. 1)
The preceding stochastic model has been extended to include JAK/STAT pathway activation and study the effect of different types of cell heterogeneity on IFN production .
By including the immune response antagonistic actions of viral proteins in the model, as we did with Nipah protein NDV chimeras , the above model can be extended to investigate influenza A viral infections of DCs and predict the varied ways these impact immune response according to how viral protein interferes with the cell’s reaction to virus intrusion.
Connecting the temporal TF profiles into a coherent higher-level cascade, we found a single convergent regulatory network that spans virtually the entire time period analyzed (Fig. 5). The network contains both feed-forward links, which propagate the transcriptional signal through time, and feedback links, where TFs may influence the activity of targets that have previously been up-regulated. Through the combination of computational and experimental validation, we concluded that our network was effective in capturing the underlying biology and produced a pattern that is consistent with stepwise transcriptional signal propagation.
Inferring functional signaling networks from early gene expression measurements (Fig. 6)
PLACA’s methodology relies on availability of data from a series of perturbation experiments. These measure the mean activity and the standard deviation of the activity of all early genes predicted or known to be affected by the signaling components of interest both under normal conditions, and following perturbation of each signaling component. To reverse-engineer the network, a weight matrix describing the connections between genes and signaling components is calculated and used to obtain an estimate of the change in activity of each signaling components following each perturbation. The estimated change in activity is used to infer the interactions between the signaling components by applying a reverse-engineering method .
PLACA was used to reverse-engineer a functional network in the context of an experimental system (see , the gonadotrope signaling network). Here, we show an example network inferred from early gene expression and perturbation experiments generated by a simulation using an arbitrary network model (Fig. 6). Overall, the functional reverse-engineered network shows high similarity to the model that produced the early gene expression data and is robust to experimental noise.
Misty Mountain clustering: application to fast unsupervised flow cytometry gating
To analyze multi-dimensional flow cytometry data, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain , that is based on percolation theory and that efficiently analyzes large datasets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing the cross sections of the histogram only once. Comparison of the performance of this algorithm with other state-of-the-art automated flow cytometry gating methods indicates that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.
The examples described above represent uses of modeling and computational approaches to extend the value of experimental data. The next stage in the evolution of these approaches is to embed these computational approaches within software and web tools that are easily accessible to the general immunology research community. This process is well underway and should make the computational techniques available to researchers who do not have special training in these areas.