Cells can be considered as complex circuits of interlinked molecular processes, whose activation or deactivation depends on stimulus-sensing, signaling, and pathway regulation through feedback loops. These processes and relationships can also be described mathematically in biological networks, using the concepts of graph theory, where cellular components such as proteins are represented as nodes or vertices, and biological relationships, for example protein-protein interactions, are indicated by connectors or edges. Recent technical advances that have allowed high-throughput and multivariate investigations of biological processes have generated a wealth of data about relationships in the cell that lends itself to such analyses. Systems biology refers to the study of how interactions between the components of a biological system give rise to the functions and behavior of that system, and can therefore be conceptually defined as a holistic approach to the investigation of biological processes.

Virus infection imposes new variables on the cell circuitry. On the one hand, viral components or ‘patterns’ are sensed by the infected cell, leading to the activation of distinct signaling cascades and alterations in cellular gene expression. In addition, the virus itself specifically targets cellular pathways as it attempts to deflect anti-viral attacks, and subvert the cellular machinery to complete its replication cycle. Such virus-induced modifications thus amount to a re-wiring of the biological circuitry of the host [1,2]. Of note, this re-wiring is often achieved by the virus targeting specific ‘hubs’ within the cell, defined as proteins with many interacting partners and/or that are central to several pathways, and which are identified in network analyses by parameters of connectivity and centrality [3,4]. In this way, even a small number of viral proteins can impact a large number of host processes and produce a complex phenotype that may be difficult to interpret without a systems-level approach.

As a practical consequence of these types of analyses, the modeling of virus-induced perturbations of the host network can be used to identify potential viral targets or hubs, which can then be experimentally tested. For example, network analysis predicted that enoyl-coA isomerase activity was important for HCV infection [5], and this was subsequently confirmed experimentally [6]. Similarly, networks can be used to predict additional consequences of any experimentally observed virus-host interaction, which may in turn provide an explanation for other observed pathogenic effects. In Coxsackie virus B3 infection, the contribution of an autocrine feedback circuit involving the proinflammatory cytokines TNF and IL-1 to virus-induced myocardial damage was initially predicted through network analysis [7]. Ultimately, identifying the most basic or direct levels of the host-virus interface in this way can suggest important targets for the development of antiviral therapies.

The arenaviruses express only 4 proteins: an RNA-dependent RNA polymerase (L) that carries out viral RNA synthesis, a matrix protein (Z) that drives viral budding, a nucleoprotein (NP) that encapsidates viral RNA, and a glycoprotein (GP) that mediates entry. Similar to other viral systems, some of these proteins are multitasking “swiss-army knife” proteins. For example, the Z protein of lymphocytic choriomeningitis virus (LCMV), in addition to its role in viral egress and budding, has been shown to interact with PML [8] to repress EIF4E-dependent translation [9,10], and to associate with the ribosomal protein P0 [11], although the functional relevance of this latter interaction has not yet been determined.

Additionally, certain large-scale analyses of host proteomics [12,13,14], kinomics (study of phosphorylation changes) [15], transcriptomics [16,17,18], and metabolomics (changes in levels of small molecules) [19] have been undertaken to investigate the consequences of arenavirus infections. However, any resulting biological networks that were created only included the results of these individual studies. There is at present no integrated study linking all published findings relating to arenavirus-host interactions, and combining both the results of high throughput studies and more traditional single protein or pathway analyses. This review therefore seeks to summarize and organize current knowledge about arenavirus-host interactions in the context of a network, and to discuss current concepts in the analyses of such networks, in order to provide a “starter kit” systems biology reference as a supporting tool for arenavirus research.

A network is a collection of nodes or vertices connected by edges, which define pairwise relationships between the nodes. Networks are also referred to as graphs, with graph theory designating the mathematical field dedicated to the study of networks properties. Graph theory as a mathematical discipline is generally considered to date back to 1736, with the demonstration by Leonard Euler of the impossibility of finding a non-redundant path allowing the crossing of all seven bridges of Königsberg, which linked two islands to the rest of the city. By reformulating the problem in abstract terms, where land was represented as nodes and bridges as edges, Leonard Euler laid the foundations for graph theory [20]. However, despite attempts during the 20th century to introduce systems-level thinking into biology, notably with the work of Ludwig Von Bertalaffy [21,22], it is only in the early 2000’s with the development of high throughput molecular techniques, that systems biology has emerged as a significant field in biology. Attempts at modeling biological systems using existing mathematical tools revealed that, while some basic mathematical properties of networks could be ascribed biological relevance [23,24,25,26,27,28,29,30], novel analytic tools are needed in order to more aptly deal with the complexity of biological network architecture [31,32,33,34,35,36]. In this section, we will describe general network characteristics and their correlation to biological processes. For further information on systems-level thinking and mathematical concepts in systems biology, we refer the reader to the more comprehensive introductory books by Choi [37] and Alon [38].

In systems-level approaches to investigating cellular processes, the nodes within networks can represent any biological entity, including genes, proteins, complexes or small molecules. Edges represent the relationships between these entities, whether physical, for example receptor-ligand binding, or functional, such as the activation or inhibition of a given protein through phosphorylation (Figure 1A). A network can be composed of several connected components, i.e. groups of nodes can be connected to each other, but bear no common edges with the other subsets of nodes present within the network. A network is said to be fully connected if it is composed of a single connected component. Current knowledge of biological processes does not always allow the inclusion of specific nodes within the largest connected component of the network. These nodes may thus be found as single nodes, when no interaction has been characterized, or within a smaller connected component if information exists about their interaction with other biological elements, which also do not share an edge with any nodes of the largest connected component. It is important to note however, that this absence of connection of specific nodes to the largest connected component may not necessarily reflect biological properties, but rather may result from the lack of experimental data on the entity represented by that node [39].

Local properties in biological networks refer to properties pertaining to a single node within the environment of its immediate neighbors. An elementary measure in networks is that of node degree or connectivity (k), which refers to the number of edges incident to that node (Figure 1B). Biological networks are usually inhomogeneous, and contain highly connected subnetworks [40], which can be defined at local or global levels. Locally, the clustering coefficient or transitivity defines the degree to which neighbors of a specific node are connected to each other [40,41]. A clique, or complete graph, designates a subset of nodes within the network which are fully connected, i.e. there is an edge connecting any two nodes within this subset [42] (Figure 1C). As such, the definition of cliques has been shown to strongly correlate with that of protein complexes or functional modules within networks [26].

These properties describe information about the relative importance of network elements to the structure of the network. A common attribute within the network is the definition of the shortest path or geodesic, which is defined as the minimum distance between any two nodes in the network. From this the mean path length can be calculated by averaging all shortest paths for any two nodes in the network. In biological systems, path length can be used to understand the reactivity of pathways, for example a short path length within a signaling cascade ensures an efficient “information” flow, where only a small number of intermediate steps are required between the initial sensing of a given stimulus and the induction of a biological response, for example induction of gene expression [27].

Centrality or vertex betweenness (b) measures the number of shortest paths that pass through a given vertex (Figure 1C). This property theoretically describes the breadth of pathways a protein is involved with, and nodes with high scores are sometimes referred to as bottlenecks. High-betweenness or bottleneck-ness is a measure of the essentiality of a given protein to the biological system considered [28,29], and has been shown for protein-protein interaction networks to correlate with a regulatory function central to several pathways [30]. Centrality within the network can also be determined through edge betweenness, which calculates the number of shortest paths a specific edge participates in, and similarly to node betweenness, describes the essentiality of the biological relationship specified by the edge to the biological system considered.

The goal of systems biology is to understand how the constitutive components of a network interact with each other. However, one of the main problem in systems biology is the mathematical characterization of such components in a way that is relevant to the elucidation of biological processes. In network theory, there are several ways to define subcomponents of a network, which we will refer to as communities, but which can also be designated as clusters, cohesive groups or modules. Most of the algorithms developed in graph theory rely on the definition of separated communities [43,44,45], which means that any node can only belong to one community. However, a biological element, for example a protein, can be part of several complexes or several pathways. In order to provide a better model for finding communities within biological networks, Palla and coworkers developed a novel algorithm, called the clique percolation method, which allows the identification of overlapping communities [31]. Analysis of a human infectome network using this algorithm revealed the unambiguous assignment of most communities to at least one cellular function [3], however reliable mathematical identification of complex biological associations, for example pathways, remains unresolved. Finally, the identification of shared nodes between communities also led to the characterization of bridging nodes, which, contrary to hubs which may be also shared by communities, display low local connectivity, but high centrality, and provide a link between one or more highly connected components [3] (Figure 1D).

Data pertaining to the arenavirus-host interplay was compiled from published studies on arenavirus-host protein-protein interactions [8,9,10,11,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62], viral requirements for specific host factors [63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83], viral inhibition of host proteins [84,85,86], kinomics and proteomics studies of cellular changes in response to infection [12,13,14,15,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107]. Data was also retrieved from studies describing co-localization of viral and host factors [108] or cellular factors that inhibit viral replication [109,110]. In total, 304 cellular proteins were identified as playing a role in arenavirus infection from this literature analysis (through September 2012). To simplify nomenclature, this primary set of 304 host proteins will be referred to as ‘host targets’ in this article. The proteins’ identities, as well as details of the nature of the virus-host interactions, and the specific viral systems in which the interaction was observed, are available in Table S1.

The usefulness of any network will of course only be as good as the primary data used to create it. Therefore, we have carefully curated the primary literature to establish that the experimental data reasonably supported any conclusions made in a publication about interactions, before including it in the dataset. However, we have not assigned any weight to a given interaction. Instead, details are provided that a user can access to determine for his or herself the strength of the data supporting a given interaction, and are available through the Description and References section within Table S1, or through the Description and Reference attribute within the network. In both cases, information is provided about whether the interaction was identified during virus infection, or through a sub-viral system such as the co-expression in cells of specific viral proteins.

A primary network was constructed, organized around the 4 arenaviral proteins and the 304 curated host targets. Protein-protein interactions and functional pathway information was then retrieved for each host target by searching the public databases available through Pathway Commons [111], and was merged with the primary network. Such functional enrichment of the primary network therefore allows a comprehensive overview of the host cell pathways affected by, or required during, arenavirus infection, based on current knowledge curated from the literature. A schematic of the network building process is shown in Figure 2, and the resulting arenavirus-host protein network is available as supplemental data (Supplemental Data—Arenavirus-Host Full Network.cys).

The arenavirus-host network was constructed in Cytoscape 2.8 [112,113], a freely available software for complex network visualization [114]. Retrieval of host protein-protein interactions, as well as pathway enrichment, was performed using the cPath software [115], which enabled access from the Cytoscape platform to the Pathway Commons Web Service [111], a web-based interface that allows the simultaneous mining of biological databases such as Reactome or BioGrid. Network visualization was also carried out in Cytoscape 2.8. Details on how to use the Cytoscape software, as well as links to advanced tutorials, are available in Supplemental File II.

General mathematical properties of the network, such as node connectivity and centrality, were determined using the R package igraph for network analysis [116]. A link to the igraph site, which includes information on how to download and use this software is provided in Supplemental File II.

Any information regarding nodes and edges is encoded within the network as attributes. Different categories of information, such as gene or protein name, role in arenavirus infection, or the pathways the protein is active in, are encoded as individual attributes. For example the gene name of any given node can be found under the attribute “biopax.xref.GENE_SYMBOL”. This information can be retrieved within Cytoscape by directly clicking on the node or edge of interest, which will result in the display of attributes values in the data panel. These attributes can also be used to search for specific elements within the network, such as nodes involved in a given biological process or pathway. For example, the user can configure Cytoscape search options, by clicking on the icon immediately to the right of the search box, to be based on the attribute category “nodes.ListPathway”. The user can then type in the desired request in the search box, such as the term “translation”, which will highlight all nodes involved in this pathway within the network, and provide a list of these nodes in the data panel. Attributes can also be used to specify differential displays within the network (such as the node color or size, but also the layout of the selected nodes), or can be used to create subnetworks through the use of filters (discussed in Section 3.5).

The arenaviruses are distinguished serologically as belonging to either the Old World (LCMV complex) or the New World (Tacaribe complex) viruses, with the New World viruses being further divided into clades, A, B and C. The members of these groups have both common and distinct features, and pathogenic and non-pathogenic viruses are present in both of the two major serological complexes. The network we built includes all relevant information about the arenavirus-host relationships that we obtained from the literature, and includes observations about the following viruses: Old World (LASV, LCMV, MOPV, MOBV), New World clade A (PICV), clade B (JUNV, MACV, GTOV, AMAV, TCRV, SABV), clade C (OLVV, LATV), and clade A/B (WWAV). The network also contains information, where available, about different strains of viruses used, with human pathogenic strains denoted with a subscript ‘v’ and non-pathogenic strains with the subscript ‘a’, for example the attenuated PICV variant P2 is denoted PICV_a, and the virulent PICV variant P18 is denoted as PICV_v [117,118]. The information for each investigated virus is reported as string values (TRUE/ FALSE/ Not tested), indicating whether the interaction or the node has been confirmed for the specific virus indicated, or if no studies have yet evaluated the role of a host protein interaction for that arenavirus family member.

Additional information linking the interaction to a summary of findings, and a reference to the original published report for a specific virus-host relationship, is also accessible within the network through the attribute “nodes.Description and References”.

The advantage of building a full network, that includes information from all published reports, is that the information pertaining to all arenaviruses is encoded in one single file. This full or master network should be considered a work in progress, and future experimental data will both confirm and add to the value of the relationships embedded in the present version (September 2012). The network is also paradoxical, since it includes data that is both true and false for different viral systems. For example, the binding of GP to two known receptors for the arenaviruses, human transferrin receptor 1 and alpha-dystroglycan [50,51,52,53,54,55,56,57], is both true and false, depending on the virus strain. Therefore, in order to perform relevant systems-level analyses for the arenaviruses, the network should be customized through the use of filters, from which a relevant subnetwork can be derived through user-driven curation, and depending on investigative needs.

Filters can be set within from within the Cytoscape software. The available filters can be applied to any attribute that is assigned to either nodes or edges (for example, to select only viral host targets), as well as topology (for example, to select neighbors at a given distance from nodes of interest). Multiple filters can be combined through the use of Boolean links: AND (–node or edge selected must pass both filters), OR (–node or edge selected must pass at least one filter) and NOT (–to exclude nodes or edges).

As a practical example, if a user wanted to generate a network displaying only the proteins that have been shown to play a role during infection by the pathogenic strains of JUNV, the following filter would be applied: nodes. JUNV_v > TRUE. However, since this filter will only select a subset of the host target proteins, information and context could be lost. To put this data into a more relevant context, we have encoded a “Connect pathways” attribute. In this case, the user can select both the subset of viral host targets that are specific to pathogenic JUNV strains, as well as maintaining the integrity of the pathways that these proteins are involved in, by applying the following filter: nodes.Connect pathways >JUNV_v. Finally, the specific subnetwork can be created and saved as a separate network through the path: File > New > Network > From selected nodes, All edges, and further analyzed using Cytoscape Network Analyzer, or other network analysis tools such as igraph.

Using the tools described above, we generated a subnetwork to investigate common viral host targets in arenavirus infection, and to analyze the role of these proteins in the context of the pathways they contribute to. As highlighted previously, viruses tend to target highly connected nodes within networks [3,4]. This can explain how viruses expressing only a small subset of proteins can subvert major cellular pathways, through the strategic targeting of these central elements. In order to identify potential hubs within the network, we plotted centrality values vs connectivity values for all nodes present in the common subnetwork (Figure 3B). The node with the highest connectivity value was found to be p53, a master regulator of cellular processes, whose steady-state levels are decreased during arenavirus infection [15], although no mechanism for this observation has yet been identified. Other hubs, which have already been identified as arenaviral host targets, include nucleophosmin [15], involved in ribosome biogenesis, and cytoskeletal elements such as vimentin [15], tubulin [70,71,72,77] and actin [13,70,71,72]. Amongst hub proteins that have not been previously identified as viral host targets, we found general adapters in signal transduction pathways such as YWHAZ or SHC1, as well as the master regulator AKT1 kinase, which is involved in many cellular processes such as metabolism, proliferation, cell survival and growth, and angiogenesis. These provide interesting leads for further investigation, since any direct effect on these proteins could potentially explain downstream phenotypes previously reported.