InnateDB – Facilitating Systems Level Analyses of the Mammalian Innate Immune Response

1. InnateDB – Facilitating Systems Level Analyses of the Mammalian Innate Immune Response Lynn et al., Molecular Systems Biology 4:218 (www.nature.com/msb) David Lynn M.Sc., Ph.D., Research Associate, Brinkman Lab., Department of Molecular Biology and Biochemistry, Simon Fraser University & Hancock Lab., Centre for Microbial Diseases & Immunity Research, University of British Columbia, Vancouver, Canada. VanBug Sept. 11th 2008.

2. The Innate Immune Response • Traditionally, the immune response divided into two different branches • the adaptive immune response and the innate immune response. • The Innate immune response is the first line of defence against invading pathogens. research.dfci.harvard.edu/innate/innate.html

3. The Innate Immune Response • In last decade or so, there has been an explosion of interest in the innate immune response. • Now appreciated that most pathogens to which we are exposed are eliminated via the innate immune response without necessarily requiring the activation of adaptive immunity. • Importance of the innate immune response is being recognized in the • initiation of and interplay with the adaptive immune response. • mechanism by which vaccine adjuvants operate in boosting immunity. • Can also be a double-edged sword  If not tightly regulated, 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).

4. Systems Biology Approaches to Investigating the Innate Immune Response: Progress has been made in understanding the innate immune response including the detailed dissection of some of the critical signaling pathways involved. Key Pathways: Toll-like Receptor (TLR) pathways; nucleotide binding and oligomerization domain (NOD)-like receptors (NLRs); retinoic acid-inducible gene 1 (RIG-1)-like receptors (RLRs); NFkB signalling, JAK-STAT, MAPK and Complement pathways. However  now becoming clear that the innate immune response does not involve simple linear pathways but rather complex networks of pathways and interactions, negative feedback loops and multifaceted transcriptional responses. 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.

5. Overview of InnateDB Project (www.innatedb.ca) InnateDB is a molecular interaction and pathway database and analysis platform that has been developed to facilitate systems level analyses of the complex networks of pathways and interactions that govern the innate immune response, the wider immune system and the entire mammalian interactome. To date, more than 5,000 innate immunity relevant interactions have been contextually annotated through the review of 1,400 plus publications. Integrated into InnateDB are novel bioinformatics resources including network visualization software pathway & ontology over-representation analysis orthologous interaction network construction ability to overlay user-supplied gene expression data in an intuitively displayed molecular interaction network and pathway context. Enable biologists without a computational background to explore their data in a more systems-oriented, yet user-friendly, manner.

6. Malaria (Stanford) Cerebral Malaria (IMR, Australia) Typhoid (Vietnam) Salmonella (Sanger & UBC) HIV (Univ. London) TB (AECM) Shigella (Pasteur) + Modulating innate immune response viaNatural HDPsProject-developed synthetic peptides~100 Mouse KOs (Sanger) Integrating Gene Expression Data and Interaction Network Data from a Variety of Infection Models to Uncover Involvement of Common/Different Innate Immunity Pathways & their Central Regulators.

7. The Need for InnateDB & the Manual Curation of Innate Immunity Relevant Molecular Interactions & Pathways. • Despite the enormous efforts of the major publicly available interaction and pathway databases to provide as wide-ranging cover as possible  quickly apparent that currently available resources provided poor coverage and detail of the molecular interactions and pathways relevant to innate immunity. • This information is essential for the systems-orientated interpretation of large scale genomics data. • TLR4  one of the most important molecules in the innate immune response, has relatively few molecular interactions annotated in the major publicly available interaction DBs. • 5 of these DBs combined contained annotated molecular interactions between TLR4 and just 11 other proteins. • Through a review of the literature we have curated, in detail, a further 16 unique interactions, and provided annotation of nearly 60 different lines of evidence supporting these interactions. • Relatively new pathways (NLR, RLR pathways) not annotated at all in major pathway databases.

8. Manual curation > 5,000 innate immune-relevant interactions (human and mouse). Involving 1,700+ genes from review of 1,400+ unique publications. We can often double # of interactions for a given gene. Pathways & interactions are curated with contextual annotations (supporting publication; participant molecules; the species; the interaction detection method; the host system; the interaction type; the cell, cell-line and tissue types etc). Developed InnateDB submission system software to allow submission of interaction annotation in an ontology-controlled and MIMIx & PSI-MI 2.5 compliant manner. Developed curator tool software to allow curators modify existing annotations. Contextually Curating Innate Immunity-Relevant Interactions

9. Contextually Curating Innate Immunity-Relevant Interactions • Curation team  focused on reviewing interactions not annotated in other interaction databases. • Ensured only interactions with published experimental evidence of a direct physical or biochemical interaction are submitted to InnateDB. • Other types of evidence such as co-localization, over-expression, microarray, or other inferences, are not deemed acceptable. • Defining what is an innate immunity relevant interaction  difficult task due to the increasing understanding of the complexity of the response & difficulty in drawing the line between innate and adaptive immunity. • Prioritized systematic curation of molecules that are well described members of key innate immunity signalling pathways. • Then curate experimentally-verified interactions between these molecules and any other molecule, regardless of whether the interacting molecule has any known role in innate immunity. • quickly expands upon the simple linear view of innate immunity pathways into a more comprehensive interaction network perspective.

10. Going Beyond Innate Immunity – A Centralized Resource for Interactions & Pathways • Aside from the well known signalling pathways  a range of other disparate processes, including apoptosis, ubiquitination, endocytosis, cell activation and recruitment  all required to mount effective innate immune response. • Adding to this complexity  borders between the innate and adaptive immune responses are becoming increasingly blurred. • Furthermore, if we hope to identify new networks or pathways involved in innate immunity, analyzes must include genes and proteins that are, as yet, not known to play specific roles in the innate immune response. • To address these issues  InnateDB also incorporates data on the entire human and mouse interactomes.

11. >105,000 human and mouse interactions extracted & loaded from BIND, INTACT, DIP, BIOGRID & MINT DBs. Cross-referenced genes to > 2,500 pathways from KEGG, PID, BIOCARTA, INOH, NetPath & Reactome DBs. Allows one to visualize/analyze interactions associated with specific pathway. Pathway ORA. Annotation from Ensembl provides details of human & mouse genes, transcripts and proteins. UniProt, Entrez, Gene Ontology  rich protein & gene annotation. Going Beyond Innate Immunity – An Integrative Biology Resource

12. Through manual curation & integration of existing data from publicly available databases we can greatly increase innate immunity relevant networks TLR4 direct and secondary interactions annotated by InnateDB TLR4 direct and secondary interactions annotated by MINT Database

13. Direct and Secondary Interactions of TLR4 in InnateDB(~20% of these interactions unique to InnateDB)

14. Robust Orthology & Gene Order Predictions – Facilitating Comparative Analysis Majority of mammalian interaction data available in InnateDB and other interaction databases primarily refers to human genes and proteins. To facilitate comparative network-based analysis of the human, mouse and bovine interactomes, detailed orthology predictions have been integrated into InnateDB. Orthology predictions generated using an in-house method, Ortholuge, which provides accurate predictions of orthology using a phylogenetic distance-based approach. Orthology predictions are further supported through the development of a human and mouse gene order and synteny browser.

16. Search using a wide variety of terms Construct more complex queries using Boolean operators Advanced Search for Genes & Proteins

17. Return direct interactors or their secondary interacting neighbors. Allows users search for interactions in particular cell/tissue types, particular interaction types e.g. phosphorylation, or experimental type e.g. co-immunoprecipitation. Advanced Search for Interactions To reduce redundancy, interactions in InnateDB that have the same participants and interaction type are grouped together by default. Choose 'No' to return all redundant interactions separately.

18. Select a pathway from the list or search more than 2,500 pathways by typing the pathway name in the box. Pathways and the interactions in them are species specific. By default, a list of molecular interactions are returned which is restricted to interactions only between annotated members of the pathway. If "No" is selected, a more comprehensive list of interactions is returned, displaying interactions between pathway members and all other molecules with which they interact.. Search for Particular Interactions or Genes that are in a Specific Pathway

19. View interactions involving this gene and its encoded proteins Search Results: searching for genes of interest e.g. IRAK

20. View evidence supporting this interaction & contextual details e.g. cell type etc Interaction Results Page.

21. View evidence supporting this interaction & contextual details e.g. cell type etc There may be multiple evidence references for an interaction.

22. Network/Pathway Visualization using Cerebral and Cytoscape Barsky A, Gardy JL, Hancock REW, and Munzner T. (2007) Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics 23(8):1040-2.

23. How a biologist thinks of a pathway ….

24. Pathway Visualization in Cytoscape

25. Pathway Visualization using Cerebralwww.pathogenomics.ca/cerebral (Bioinformatics 2007)

26. Click here to visualize interactions in Cerebral You must have a recent version of Java installed. Visualize Interactions in a subcellular localization-based layout using the Cerebral plugin for Cytoscape.

27. Network Visualization using Cerebral Plugin for Cytoscapewww.pathogenomics.ca/cerebral (Bioinformatics 2007)

28. InnateDB Web Start Version of Cerebral • Cerebral uses subcellular localization annotations to provide more biologically intuitive pathway-like lay-outs of interaction networks. • Note: the subcellular localizations in Cerebral should only be used as a guide. • There are many proteins with no annotated subcellular localizations and many others that have multiple possible localizations (only 1 will be shown, nuclear, extracellular and membrane localizations will take precedence over cytoplasm if there are multiple). • Localizations are inferred from manual curation or Gene Ontology annotation in InnateDB  either directly based on cellular compartment annotation or indirectly via functional annotation e.g. proteins annotated with term transcription factor activity will be placed in nucleus. • Details of all annotated subcellular localizations are provided in the node attribute browser tab. • Lines connecting nodes represent interactions. Dashed lines have only 1 supporting publication in InnateDB. The thicker the line the more publications support the interaction.

29. Integrating Gene Expression Data in a Molecular Interaction Network and Pathway Context

30. InnateDB – Integrating Gene Expression Data in a Molecular Interaction Network and Pathway Context

31. InnateDB Data Analysis Page – File Upload Specify column in your file containing external database IDs you wish to use to cross-reference to InnateDB. Ensembl, Entrez Gene, RefSeq and UniProt IDs are all accepted.

32. Choose Interactors to return list of molecules interacting with genes in uploaded list. Choose Pathways to return associated pathways and do pathway over-representation analysis InnateDB Data Analysis Page – Return associated Pathways, Genes, Interactors or Interactions Choose Genes to return associated gene annotation for genes in uploaded list. Useful for finding gene ontology annotation, orthologs and other annotation.

33. Integrate Gene Expression Data with Gene, Pathway and Interaction Annotation in InnateDB

34. Pathway & Gene Ontology Over-representation Analysis

35. Pathway & Gene Ontology Over-representation Analysis • Several different statistical methods are available to determine if particular pathways/GO terms are significantly associated with DE genes - Hypergeometric, Fisher & Chi Square. • Statistics are returned individually for up-regulated and down-regulated pathways/terms. • Two options to correct for multiple testing are included - The Benjamini & Hochberg correction for the FDR and the more conservative Bonferroni correction. • Pathway analysis tool  expected to be more powerful than many of the existing tools that tend to use only one or two sources of pathway annotation for analysis. • Recommend that All genes from an array dataset not just differentially expressed (DE) genes are uploaded. • InnateDB uses the proportion of DE genes on the whole array to more accurately determine if a particular pathway is significant. • Also option to carry out pathway/ontology ORA on a subset of genes • Uses slightly different algorithm that does not take gene expression values into account. • If there are multiple probes for the same gene these values will be averaged for the purposes of the pathway and ontology ORA.

36. Click here to do pathway over-representation analysis. Pathways Associated with Uploaded List

37. Choose Parameters for Pathway ORA

38. Display Pathways significantly associated with down-regulated genes. Click here to see a summary of the pathway and what genes are DE. Pathways significantly associated with up-regulated genes.

39. Pathway Summary Page for All Conditions

40. Constructing & Analysing Molecular Interaction Networks

41. Constructing & Analyzing Networks Using InnateDB • Pathway analysis can be very powerful in determining which annotated pathways are most significantly associated with DE genes. • Pathway analysis, however, relies on using the association of DE genes to known biological pathways, which are often annotated as relatively simple linear cascades. • Network analysis  move from this simple view of the signaling response to a more comprehensive analysis of the molecular interactions between DE genes and their encoded proteins & RNAs. • Potentially uncover as yet unknown signaling cascades or pathways, functionally relevant sub-networks and the central molecules, or hubs, of these networks. • All Interaction networks in InnateDB can be constructed with integrated gene expression or other quantitative data from up to 10 conditions. • Networks can be interactively visualized using Cerebral plugin in Cytoscape or the Cerebral layout may be deleted and any other Cytoscape plugin used for layout and analysis.

42. Constructing & Analyzing Networks Using InnateDB • By default if one uploads a list of genes and returns a list of interactions • Network of all interactions both between genes in list & any other molecules with which they interact are returned. • Useful to identify components of network that interact with DE genes or other genes of interest. • Potential to identify key regulators of gene expression, even though these regulators themselves may not be differentially expressed.

43. Constructing & Analyzing Networks Using InnateDB • Options to reduce complexity of network: • Limit network to show only interactions between molecules in uploaded list. • Useful, for example, to visualize and analyze network of DE genes.

44. Constructing & Analyzing Networks Using InnateDB • Options: Limit network to show only interactions between molecules annotated to belong to particular pathway.

45. Click here to visualize interactions in Cerebral with overlaid gene expression data. Results: Visualize Gene Expression Data in an Interaction Network Context

46. Multi-experiment View in Cerebral

47. Click on these buttons in 2 different mini-windows to display changes in gene expression from 1 condition to another in the bigger window. Click “Node Attribute Browser” and select from list to see node attributes. Cerebral – Multi-Array Viewer

48. Click here to display graphs of change in expression for each gene. Clicking a line highlights that gene in the network. K-means clusters of genes with similar patterns of gene expression. Cerebral – Multi-Array Viewer

49. Interactively Link back to InnateDB to Look up Information on Particular Genes/Interactions of Interest.

50. Future Plans for InnateDB • Continued manual curation of innate immune relevant interactions. • Community participation. • Pathway curation – BIOPAX compliance – working on new pathway curation tool. • Improved functionality & performance on InnateDB website. • Improving ontology ORA & Transcription factor over-representation tools, Network analysis tools. • Working with other DBs e.g. ISB, Immport, pathway DBs for better integration and coordinating curation efforts.