InnateDB: facilitating systems-level analyses of the mammalian innate immune response

David J Lynn^1,2, Geoffrey L Winsor¹, Calvin Chan², Nicolas Richard¹, Matthew R Laird¹, Aaron Barsky³, Jennifer L Gardy², Fiona M Roche¹, Timothy H W Chan², Naisha Shah^1,2, Raymond Lo¹, Misbah Naseer², Jaimmie Que², Melissa Yau², Michael Acab¹, Dan Tulpan¹, Matthew D Whiteside¹, Avinash Chikatamarla², Bernadette Mah², Tamara Munzner³, Karsten Hokamp⁴, Robert E W Hancock² & Fiona S L Brinkman¹

Article highlights

Synopsis

The importance of the innate immune response has long been recognized in the front line of defense against invading pathogens. If not tightly regulated, however, an overwhelming immune response can lead to what is sometimes called a cytokine storm. One such out-of-control response, sepsis, results in more than 200 000 deaths a year in the United States alone (Angus et al, 2001). Over the course of the last decade, significant progress has been made in understanding the innate immune response, including the detailed dissection of some of the critical signaling pathways involved (Lang and Mansell, 2007; Matsukawa, 2007). Despite these efforts, many questions remain unanswered including how the innate immune system initiates distinct responses toward particular pathogens. It is becoming increasingly clear that the innate immune response does not involve simple linear pathways but rather complex networks of pathways and interactions, positive and negative feedback loops and multifaceted transcriptional responses (Tegner et al, 2006; Lee and Kim, 2007). To better understand the complexities of the innate immune response and the cross-talk between its components, complementary systems-level analyses and more focused follow-up experimental approaches are now needed.

Recently, researchers have started to apply systems biology approaches to the study of the immune system (Gilchrist et al, 2006; Tegner et al, 2006; Andersen et al, 2008) and bioinformatics resources are now emerging to aid these types of analyses. Despite the enormous efforts of the major publicly available interaction and pathway databases to provide as wide-ranging cover as possible (Salwinski et al, 2004; Alfarano et al, 2005; Joshi-Tope et al, 2005; Breitkreutz et al, 2007; Chatr-aryamontri et al, 2007; Kanehisa et al, 2007; Kerrien et al, 2007), it was quickly apparent to us that currently available bioinformatics resources provided poor coverage and detail of the molecular interactions and pathways relevant to innate immunity, information that is essential for the systems-orientated interpretation of large-scale genomics data.

To overcome these problems and to provide a resource that will enable biologists without a computational background to explore their data in a more systems-oriented manner, we have developed InnateDB. InnateDB (www.innatedb.ca ) is a publicly available database and analysis platform for the genes, proteins, experimentally verified interactions and pathways involved in the human and murine innate immune responses.

One of the primary goals of InnateDB is to provide a manually curated centralized resource for experimentally verified human and mouse protein, gene and RNA molecular interactions involved in the innate immune system. To do this, a dedicated full-time team of curators has been assembled to review the relevant biomedical literature and to submit detailed annotation on these interactions and pathways to InnateDB through customized submission system software. To date, more than 3500 innate immunity-relevant interactions, involving around 1000 genes, have been manually curated through the review of approximately 1000 publications. Only interactions with published direct experimental evidence of a physical or biochemical interaction are submitted to InnateDB. The importance of manual curation is clear, as we are often able to double the number of interactions for a given gene or protein compared to the number currently present in the other interaction databases combined. Furthermore, this detailed manual curation permitted us to richly annotate these interactions and to place them in their relevant context. Interaction data in InnateDB are also curated, stored and downloadable in the Proteomics Standards Initiative Molecular Interaction (PSI-MI) 2.5-compliant XML format (Hermjakob et al, 2004).

In addition to the detailed manual curation of the genes, proteins and their interactions and pathways that are specifically known to have a role in the innate immune response, InnateDB also incorporates data on the entire human and mouse interactomes. To do this, annotation on more than 100 000 human and mouse interactions was integrated from several of the major publicly available interaction databases into InnateDB (Figure 1). To enable the investigation of genes, proteins and their molecular interactions that are relevant to particular pathways, InnateDB also includes cross-references of genes not only to innate immunity-relevant pathways but also to more than 2500 pathways from several of the major publicly available pathway databases (Figure 1). Detailed gene and protein annotation has also been extracted from a variety of other data sources.

Figure 1

Integration of publicly available resources as a foundation for InnateDB. More than 100 000 interactions were integrated into InnateDB from the Molecular Interaction (MINT) database (Chatr-aryamontri et al, 2007); the IntAct database (Kerrien et al, 2007); the Database of Interacting Proteins (DIP) (Salwinski et al, 2004); the General Repository for Interaction Datasets (BioGRID) (Breitkreutz et al, 2007) and the Biomolecular Interaction Network Database (BIND) (Alfarano et al, 2005). Cross-references to more than 2500 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (Kanehisa et al, 2007), the NCI-Nature Pathway Interaction Database (PID) (http://pid.nci.nih.gov), Integrating Network Objects with Hierarchies (INOH) pathway database (http://www.inoh.org/), NetPath (http://www.netpath.org) and the Reactome database (Joshi-Tope et al, 2005) were also incorporated. Up-to-date versions of Ensembl (www.ensembl.org) provide details of human and mouse genes, transcripts and proteins along with rich protein and gene annotation from the Universal Protein Resource (UniProt) (The UniProt Consortium, 2007), Gene Ontology (Ashburner et al, 2000) and Entrez Gene (http://www.ncbi.nlm.nih.gov/).

Full figure and legend (212K)Figures & Tables index

Specific interactions, pathways and genes or proteins of interest can be interactively searched for in InnateDB through the flexible web-based search interface of the database, providing a knowledge base for the community, whereas the bioinformatics and network visualization tools incorporated into InnateDB elevate the system from database to robust analysis platform. InnateDB allows one to integrate quantitative data (such as differential gene expression) into a molecular interaction network and pathway context, enabling the interrogation of such data in novel and insightful ways. Investigating differentially expressed molecular interaction networks may identify subnetworks or as-yet unidentified pathways as being significantly involved in the response to a particular stimulus. By incorporating Cytoscape into InnateDB, investigators are able to take a closer look at the interactions involved in these pathways or subnetworks, potentially identifying cross-talk between key pathways, and highlighting the molecules that are the hubs of these networks. Our Cerebral plugin allows one to further extend this experience, visually interrogating quantitative data across multiple conditions in more biologically intuitive pathway-like layouts of networks, which are generated using subcellular localization information.

Integrated pathway over-representation analysis can identify those pathways that are significantly associated with differentially regulated genes, highlighting those pathways that are significantly altered in their gene expression. Through such pathway analysis, it is possible to identify common pathways that are involved in the innate immune response to particular infections, and to identify the common central regulators of these pathways as attractive targets for immune modulation. (Figure 4).

Figure 4

Cerebral enables the overlay of quantitative data in a molecular interaction network context. To facilitate the side-by-side comparison of specific experimental conditions, Cerebral uses a series of small, linked views to visualize quantitative data (gene expression values, for example) across multiple conditions simultaneously, alongside a larger central window, which permits more detailed investigation of particular conditions or regions of the network. Nodes (i.e. molecules) are colored according to user-defined thresholds of fold change in gene expression (red, significantly upregulated; green, significantly downregulated). The small multiple windows and the larger overview window are linked––if one zooms in or out, pans, mouses over or selects a node in one of the views, the same action will be perpetuated across all views. By selecting one of the small multiple windows, the expression values for that condition will be promoted to the larger overview window. If two small multiple windows are selected simultaneously, the difference in gene expression in the two conditions is computed and displayed in the main window. In this view, the nodes in the main window are colored according to the magnitude of the change between the two conditions. InnateDB uses the number of pieces of evidence supporting an interaction, usually separate publications or experiments, as a measure of confidence in the interaction. Cerebral uses weighted lines in its display to represent these confidence scores (heavier weighted lines=higher confidence). By right clicking on a node or edge, one may interactively link to the relevant pages in InnateDB for more detailed annotation regarding the gene, protein or interaction of interest.

Full figure and legend (523K)Figures & Tables index

InnateDB, along with other emerging resources for bioinformatics and systems-level analysis of immunology (Kelley et al, 2005; Ortutay and Vihinen, 2006; Hijikata et al, 2007; Korb et al, 2008), will undoubtedly lead to novel and much deeper insights into the innate immune response to particular pathogens.

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