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WIREs Syst Biol Med
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Network representations of immune system complexity

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The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Macromolecular Interactions, Methods
Architecture of human gene regulatory and protein–protein interaction networks. (a) Human transcription factor regulatory network (TRN). The human TRN was downloaded from Ref . The network is displayed using the Cytoscape force‐directed layout. Each node in the network is a transcription factor (TF), and edges represent transcriptional regulation. As transcriptional regulation is directed, network edges are directional from a TF to its target. The network has 3107 nodes and 6887 edges. The network clearly shows a hierarchical architecture as observed in Ref . Only a small subset of TFs regulate most of the other TFs, which is obvious from the modularity of the network architecture. The network can be divided into regions that are either autocratic or democratic. In the autocratic regions, a TF is usually regulated by a single TF whereas in the democratic regions a TF is regulated by multiple TFs. Circles highlight some of the autocratic regions. (b) Human protein–protein interaction (PPI) network. The network was downloaded from the Human Protein Reference Database (HPRD) and displayed using the Cytoscape force‐directed layout. Each node in this network is a protein. The edges of the network represent protein–protein associations observed in the literature and manually curated in the database. As PPIs do not have directionality, the network edges are nondirectional. The PPI network has 9251 nodes and 38,869 edges, hence it is much bigger compared with the gene regulatory network (GRN). Unlike the GRN, the PPI network shows lack of hierarchy. (c) A schematic representation of a typical signaling network. Ligands are sensed by specific membrane‐bound receptors (R1 and R2) followed by signal transduction, often mediated by adapter proteins that associate with the effector domains of receptors. The complex signaling circuitry further propagates and processes the signal through multiple steps, including signal integration, amplification [for instance by phosphorylation (P) or dephosphorylation of specific mediators], and noise reduction. Finally, the actuator, usually a TF, directs expression of appropriate target genes based on the processed signal. Different cellular components and their analogs in electric circuits are indicated on the left and right, respectively.
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Illustration of an organ‐level network in adaptive immunity. (a) Generation of an adaptive immune response after exposure to an antigen in the periphery represented as a biological system. Under steady‐state conditions (a, left), T cells enter lymph nodes (LNs) via high endothelial venules (HEVs) and then migrate within LNs in search for antigen. They then exit the LN via efferent lymphatics and eventually return to the systemic circulation. Exposure to antigen in the periphery (a, right), for example, through skin injury, leads to either active transport or passive drainage of antigens into the draining LN. Here, antigens can be taken up, processed, and presented by LN‐resident antigen‐presenting cells (APCs) on major histocompatibility complex (MHC) molecules. Upon recognition of cognate peptide–MHC complex, naïve T cells are activated, proliferate, and subsequently exit the LN via lymphatic conduits. The lymphatic system then connects to the venous circulation and therefore activated T cells have access to perfused peripheral tissues. Activated T cells migrate within the affected tissue and upon receiving appropriate signals secrete effector cytokines. (b) Network rendition of the biological system in (a). Red arrows on the left hand side of the panel represent circulation of naïve T cells within the lymphatic and circulatory system. Dotted edges are potential connections. The color progression from light gray to black indicates the temporal progression of events in the setting of exposure to a pathogen in the periphery leading to the generation of an adaptive response in the draining LN, culminating in the arrival of activated T cells at the site of injury. B, B‐cell follicle.
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A multicellular innate immune network in lymph nodes. An innate immune response circuit in the lymph node (LN) shown as a biological system (left panels) and a network (right panels). Left panels: (a) Innate effector cells are prepositioned in proximity to subcapsular sinus macrophages under steady‐state conditions. (b) Upon exposure to intracellular bacteria draining to the LN through the lymphatic system, macrophages are activated, release cytokines, and engage innate effector cells. Their activation, in turn, leads to cytokine production that enables the macrophages to contain the infection. (c) Exposure to extracellular organisms also leads to macrophage activation. Production of IL‐1β leads indirectly to neutrophil recruitment from the circulation, which leads to containment of the infection. Right panels: Network rendition of the biological system. Dotted edges in (a) represent potential connections. The color progressions of edges from light gray to black in (b) and (c) indicate the temporal progression of events. Thickness of the edges indicates the relative contribution of a particular connection.
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Intracellular molecular networks in innate immunity. Transcriptional, translational, spatial, and functional networks controlling innate immune responses are shown. Pathogens present diverse ligands (1) that are sensed by single or combinations of innate sensors including but not limited to Toll‐like receptors (TLRs), C‐type lectin receptors (CLRs), Nod‐like receptors (NLRs), RIG‐like receptors (RLRs), and AIM2‐like receptors (ALRs) (2). Such sensing triggers differential downstream signaling (3), which in turn can be promoted by preexisting compartmentalization of innate sensors (e.g., TLRs on the plasma membrane or endosomes) or their spatial relocation to membrane‐bound organelles (e.g., relocation of RIG‐I and NLRP3 to mitochondria) that provide suitable platforms for optimal assembly of signaling complexes (4). This leads to activation and/or production of downstream mediators (5) such as transcription factors (e.g., NF‐κB, AP‐1, and IRFs) that translocate to the nucleus to promote transcription of target genes (6) followed by their translation, appropriate protein folding, and post‐translational modifications (7 and 8). Cellular proteins may localize to specific subcellular compartments based on their domain sequences, post‐translational modifications, or association with suitable chaperones (9). Intercellular and intracellular heterogeneity is an important regulator of the innate response (10). Rewiring of the above connections can be expected depending upon the nature of the immune cell encountered (e.g., macrophage, dendritic cell, neutrophil, T cell, or B cell) as well as variations between single cells of seemingly homogeneous immune cell populations (e.g., heterogeneity due to cell state, stochastic nature of molecular interactions, and/or subtle differences in gene or protein expression). Dotted lines indicate indirect connections where the nodes may be separated by more than one degree(s) of freedom.
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