Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks

Introduction

Network analysis has emerged as a powerful approach to elucidate biological and disease processes¹. Biological networks (and many other types of networks) have been shown to have a power law distribution of node connectivity, with most nodes having few connections and a few nodes being highly connected². The identification of such highly connected nodes, termed hubs, is often of interest as hubs have been shown to be topologically and functionally important. The deletion of genes encoding hub proteins, for example, has been shown to correlate with lethality in yeast (the centrality-lethality rule)³. Hubs have also been found to be preferentially targeted by both bacterial and viral pathogens⁴ and may be master regulators of biological processes⁵. Biological networks, such as the human interactome, however, are not static entities⁶, and the extent to which a node acts as a hub can change depending on the biological context e.g. the network present in a specific cell type at a particular point in time^7,8. Integrating contextual information, such as gene or protein expression data, with standard network analysis can provide insight into what are the most relevant network features in a particular study or context^9–11.

Cytoscape has a number of applications to identify hubs in networks including cytoHubba¹², APID2Net¹³, PinnacleZ¹⁴, NetworkAnalyzer^15,16 and CentiScaPe¹⁷, however, only the latter two are compatible with Cytoscape 3+. All of the applications available to date identify hubs based on node connectivity (degree) in a network of interest. To construct a network, users frequently query interaction databases to identify the interactors of a list of genes of interest, e.g. differentially expressed genes, and then identify the high degree nodes in this network. This approach to constructing a network is useful because it identifies a more fully connected network for analysis than would be the case if one restricted interactions to only those that occur between nodes in the gene list. Analysis of these networks can, for example, identify subnetworks that are enriched in (but do not exclusively consist of) differentially expressed genes, or identify non-differentially expressed nodes that are topologically important in the network, both of which would otherwise not be identified. Identifying hubs in these networks, however, is biased towards identifying nodes that are highly connected in general such as promiscuous, ubiquitous or well-studied nodes, because nodes with many interactions in the query database have a higher probability of being included in the network by chance alone. Analysis of these degree-based hubs, for example identifying what biological processes or pathways these nodes are enriched in, tells us little about the experimental context of interest and more about the properties of highly connected nodes in general. A more appropriate analysis is to determine which nodes interact with relevant nodes in the network (which we term contextual nodes) more than is statistically expected.

Here, we introduce the Contextual Hub Analysis Tool (CHAT), a Cytoscape App that identifies hub nodes that interact with more "contextual" nodes (e.g. differentially expressed genes or proteins) than statistically expected in networks integrated with user-supplied contextual data (e.g. gene expression data). We term these nodes contextual hubs. We show that such contextual hubs are considerably more relevant than degree-based hubs to the specific experimental context under investigation. As such, these nodes are promising candidates for further functional validation studies and potentially represent important points in the network for drug targeting.

Methods

Operation

The identification of the top contextual hubs consists of three primary steps: 1) input of a user-supplied gene list and contextual data, 2) network construction and statistical analysis to identify nodes that preferentially interact with contextual nodes and 3) visualization of the top contextual hubs and their interactions and comparison to the top degree-based hubs. To construct a network using CHAT, the user must provide a list of gene identifiers and associated numerical or categorical attributes in the text box in tab-delimited format, or upload the data as a csv or tab-delimited file via the upload button (Figure 1) (.csv or .txt file types). The user can then specify which genes in the uploaded list are contextually important based on the user-provided contextual data (e.g. genes with > 2 fold-change in expression). The user then selects one of the databases in the PSICQUIC registry to query, and specifies the relevant species, ID type and interaction type for the query. The user can then choose to visualize the network using any of the layout algorithms available in Cytoscape. Clicking the OK button creates the network and a new tab in the results panel, which allows the user to visualize the network and to analyze the results further (Figure 2). The results panel is split into several parts. In the first part, the parameters used to generate the network (database, species, id type and interaction type(s)) are displayed. The second panel allows the user to compare the top contextual hubs and the top degree-based hubs at the click of a button. By default, node size and node color are proportional to the node’s corrected p-value calculated by CHAT, such that the smaller the p-value (i.e. more statistically significant), the larger the node size and the darker the red coloring of the node. The user can customize the color scheme, however. In contrast, if the users selects “Show degree hubs”, the visualization changes and the node size and coloring will now be proportional to each node’s degree in the selected database. By default, CHAT displays the top 20 contextual hubs but the user can adjust this by using the slider provided. To investigate a single node in detail the user can employ CHAT’s “Node Analyzer” by right clicking on a node. This will limit the network view to show only the selected node and its interactors and will display a table at the bottom of the results panel tab with information on the node’s name, p-value and its interactors.

Figure 1. CHAT network analysis.

To construct a network using CHAT, the user provides a list of gene identifiers and associated numerical or categorical attributes relevant in the context of interest.

Figure 2. Network visualization.

CHAT provides a number of options to customize the network visualization.

Use case

As a demonstration of its potential utility and as validation, CHAT was used to construct a network using a dataset of 462 genes that have been reported to be up-regulated during Dengue fever, a mosquito-borne viral infection²² (Ensembl gene IDs for these 462 genes are provided in Dataset 1). These 462 genes represent the contextual data for this case study. CHAT was used to construct a network of these genes and their first neighbor interactors using interaction data that was sourced from InnateDB^23,24 via the PSICQUIC web service (InnateDB-All). A network of 4,910 nodes was generated. CHAT was then used to identify the top 20 conventional hub nodes (based solely on degree) and the top 20 contextual hub nodes in the network (Figure 3). No nodes were in common in the two top 20 lists. InnateDB pathway analysis^23,24 revealed that the top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer (Supplementary Table 1), which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison to degree-based hubs, the top 20 contextual hubs were statistically enriched in pathways related to the immune response to viral infection, such as the interferon signaling pathway; the Retinoic acid inducible gene-I (RIG-I) pathway; the Toll-like receptor (TLR) pathway; and the Janus kinase (JAK) - Signal Transducer and Activator of Transcription (STAT) pathway (Supplementary Table 2). All of these pathways have been shown to play key roles in the host response to Dengue infection^25,26. Indeed, many of the top 20 contextual hubs (but not degree-based hubs) were well-known transcription factors involved in the host interferon response including STAT1, STAT2 and the interferon regulatory factors (IRFs); IRF1, 3, 8 and 9, which is a key cellular response to viral infection including Dengue^27,28. Another gene identified in the contextual hub analysis but not the degree-based analysis was interferon-stimulated gene 15 (ISG15). Cells in which ISG15 has been silenced have been shown to have significantly higher Dengue viral loads²⁹. The results of the pathway analysis were reinforced by a Gene Ontology analysis using innatedb.com^23,24, which identified terms including cytokine-mediated signaling pathway, type I interferon signaling pathway, and innate immune response among the top 10 enriched terms (FDR < 0.05) for the contextual hubs but not the degree-based hubs (Supplementary Table 3 and Supplementary Table 4).

Figure 3. Visualization of a Dengue gene expression dataset.

A CHAT network visualization comparing contextual hubs (A) to degree-based hubs (B) in a network constructed using InnateDB^23,24.

Conclusion

Through the integration of contextual information, such as gene or protein expression, contextual hub analysis as implemented in CHAT can identify context-specific hubs more relevant to the biological context under study, such as disease, treatment or cellular state. As shown in the above case study, these hubs are of more functional relevance than genes found through analysis based on degree only. Given the current emphasis on the importance of considering the network model of biological pathways and the ever-increasing abundance of high-throughput data, CHAT provides a valuable addition to the biologists’ computational toolkit in using a network-based approach to help prioritize genes of interest for further investigation or drug discovery. In the future, CHAT can be extended to include the contextual analysis of other network features such as network bottlenecks.

Data availability

F1000Research: Dataset 1. Use case data: 462 genes that have been reported to be up-regulated during Dengue fever infection, 10.5256/f1000research.9118.d128126³⁰

Software availability

Software available from: http://apps.cytoscape.org/apps/chat

Latest source code: https://bitbucket.org/dynetteam/chat

Archived source code at time of publication: http://www.dx.doi.org/10.5281/zenodo.56496³¹

Manual/Tutorial: https://bitbucket.org/dynetteam/chat/downloads

License: Lesser GNU Public License 3.0