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Curating the innate immunity interactome Lynn, David J; Chan, Calvin; Naseer, Misbah; Yau, Melissa; Lo, Raymond; Sribnaia, Anastasia; Ring, Giselle; Que, Jaimmie; Wee, Kathleen; Winsor, Geoffrey L; Laird, Matthew R; Breuer, Karin; Foroushani, Amir K; Brinkman, Fiona S; Hancock, Robert E Aug 20, 2010

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RESEARCH ARTICLE Open AccessCurating the innate immunity interactomeDavid J Lynn1*, Calvin Chan2, Misbah Naseer2, Melissa Yau2, Raymond Lo3, Anastasia Sribnaia2, Giselle Ring2,Jaimmie Que2, Kathleen Wee2, Geoffrey L Winsor3, Matthew R Laird3, Karin Breuer3, Amir K Foroushani1,3,Fiona SL Brinkman3, Robert EW Hancock2AbstractBackground: The innate immune response is the first line of defence against invading pathogens and is regulatedby complex signalling and transcriptional networks. Systems biology approaches promise to shed new light on theregulation of innate immunity through the analysis and modelling of these networks. A key initial step in thisprocess is the contextual cataloguing of the components of this system and the molecular interactions thatcomprise these networks. InnateDB (http://www.innatedb.com) is a molecular interaction and pathway databasedeveloped to facilitate systems-level analyses of innate immunity.Results: Here, we describe the InnateDB curation project, which is manually annotating the human and mouseinnate immunity interactome in rich contextual detail, and present our novel curation software system, which hasbeen developed to ensure interactions are curated in a highly accurate and data-standards compliant manner. Todate, over 13,000 interactions (protein, DNA and RNA) have been curated from the biomedical literature. Here, wepresent data, illustrating how InnateDB curation of the innate immunity interactome has greatly enhanced networkand pathway annotation available for systems-level analysis and discuss the challenges that face such curationefforts. Significantly, we provide several lines of evidence that analysis of the innate immunity interactome has thepotential to identify novel signalling, transcriptional and post-transcriptional regulators of innate immunity.Additionally, these analyses also provide insight into the cross-talk between innate immunity pathways and otherbiological processes, such as adaptive immunity, cancer and diabetes, and intriguingly, suggests links to otherpathways, which as yet, have not been implicated in the innate immune response.Conclusions: In summary, curation of the InnateDB interactome provides a wealth of information to enablesystems-level analysis of innate immunity.BackgroundThe immune system is traditionally divided into two dif-ferent branches - the adaptive immune system, the armof the immune system that mounts a specific responseto foreign antigens, and the innate immune system. Theimportance of the innate immune response is now wellrecognised as the first, and perhaps even the most criti-cal, line of defence against invading pathogens and therehas been an explosion of interest in investigating it.Innate immunity is fast-acting by comparison to theadaptive response, which can take several days torespond, and furthermore, innate immunity instructs,regulates and shapes the subsequent adaptive response[1,2].Despite the lack of antigen specificity present in adap-tive immunity, components of the innate immune systemcan still distinguish between a broad range of pathogensand mount an appropriate response. Receptors of theinnate immune response, known as pathogen recognitionreceptors (PRRs), recognise specific molecular motifs orsignatures (often called pathogen-associated molecularpatterns or PAMPs) expressed by invading pathogens[3], including lipopolysaccharide (LPS), peptidoglycan,lipoteichoic acid, lipopeptides, flagellin, bacterial CpGDNA and viral nucleic acids.The best-studied family of PRRs in humans are theToll-like receptors (TLRs) [4], however, the importanceof other PRRs including the nucleotide-binding oligo-merization domain (NOD)-like receptors (NLRs) [5,6],* Correspondence: david.lynn@teagasc.ie1Animal & Bioscience Research Department, AGRIC, Teagasc, Grange,Dunsany, Co. Meath, IrelandFull list of author information is available at the end of the articleLynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117© 2010 Lynn et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.and the retinoic acid-inducible gene I (RIG-I)-like recep-tors (RLRs) is becoming evident [7,8]. NLRC4, for exam-ple, has recently been shown to be involved in therecognition of components of the bacterial type IIIsecretion system, enabling the discrimination betweenpathogenic and non-pathogenic bacteria [9]; while therecognition of microbiota peptidoglycan by Nod1 hasbeen shown to enhance systemic innate immunity [10].The RIG-I pathway has been shown to have a criticalrole in the response to a range of viral pathogens[11-13].Recently, we have reviewed the complexity of theinnate immune response and have argued that innateimmunity does not involve simple linear pathways, butrather complex networks of molecular interactions andtranscriptional responses [14]. Over the last three years,we have developed InnateDB (http://www.innatedb.com), a database of the molecular interactions and path-ways involved in innate immunity and an analysis plat-form enabling systems-level analysis of the innateimmune response [15]. A key component of the Inna-teDB project is the contextual manual curation of innateimmunity interactions, pathways and their componentmolecules. In our original article on InnateDB, approxi-mately 3,500 molecular interactions had been curated[15]. Currently (July 2010), more than 13,000 interac-tions of relevance to innate immunity have been anno-tated. Given this significant progress, now is anappropriate time to review the InnateDB curation pro-cess and our novel customised software that enablescuration in a data-standards and ontology compliantmanner and to highlight some of the new insights thatare being revealed through curation of the innate immu-nity interactome.Why the need for curation?Systems biology approaches reflect the biological realitythat complex cellular processes like the immuneresponse are not regulated by straightforward linearpathways but by networks of complex molecular interac-tions [14]. To undertake systems-level analyses of theinnate immune response, one must first have a catalo-gue of the components of the system and how theyinteract with each other. Generating such a catalogue iscomplicated by the fact that the interactome is adynamic entity, in which the interactions that occur aredependent on their context. Such contextual considera-tions include the cell and/or tissue type, the environ-mental or experimental conditions including thepresence of specific stimuli, the species, the time-point,etc. Additionally, the level of confidence that an interac-tion actually occurs (and has biological relevance) invivo can be dependent on a number of factors. Theseinclude the interaction detection method, whether theinteraction was detected in vitro or in vivo, on addi-tional experimental approaches used to validate theinteraction, and whether the interaction has been inde-pendently reported by other research groups.Several large-scale efforts to identify all possible mole-cular interactions that make up the interactome are wellunder way in several species [16-19], including human[20]. Although these efforts are enormously valuable,they are not without their limitations. Many of theseprojects, for example, are focused on protein-proteininteractions and rely heavily on yeast two-hybridapproaches, which can be associated with high falsepositive and false negative rates [21]. Furthermore, suchapproaches do not provide detailed contextual insightinto which interactions occur under particular condi-tions or in which cell-types.In addition to these large-scale efforts, a large numberof interactions are reported in the biomedical literature.These usually involve relatively low-throughput investi-gations of interactions between a handful of molecules,but are nonetheless, a valuable source of data for defin-ing the interactome. Although there may only be a fewinteractions reported in each publication, there arethousands of such publications. Critically, such publica-tions frequently report rich contextual information onthe interaction, and interactions are often validatedusing several different experimental approaches. Thus,extracting annotation on such interactions from the lit-erature can be extremely valuable. Although literaturemining approaches potentially provide a high-through-put, low cost approach to extracting information andannotation from the literature [22], such approaches canbe highly inaccurate, often rely on text in an abstractrather than the full-text, and do not substitute for cura-tion by a trained curator.Several databases have now been established as reposi-tories for molecular interaction data including the Mole-cular Interaction database (MINT) [23]; the IntActdatabase [24]; the Database of Interacting Proteins (DIP)[25]; the General Repository for Interaction Datasets(BioGRID) [26] and the Biomolecular Interaction Net-work Database (BIND) [27]. Each of these has similarquality and data standards requirements to InnateDBand have been integrated into InnateDB to provide acomprehensive framework of the entire human andmouse interactomes. IntAct, DIP, MINT and BioGRIDhave active literature curation efforts and are membersof the International Molecular Exchange Consortium(IMEx) (http://www.imexconsortium.org/), which aimsto synchronise curation efforts to avoid redundancy.InnateDB is now an observer member of this consor-tium and is working towards full active membership.The sheer scale of the task involved in curating inter-actions from the literature, however, means that even aLynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 2 of 14large consortium, such as IMEx, must focus its efforts toparticular journals and publications. Indeed, several ofthe partner databases concentrate their curation effortson papers published in fewer than ten journals. Impor-tantly from an immunology perspective, neither thejournals that are routinely curated nor the databasesthemselves have a specific focus on the immune system,and in particular, not on the innate immune system.Therefore, the majority of interactions of relevance toinnate immunity are not annotated by these efforts (seeFigure 1 for evidence thereof). Additionally, investigationof the pathways and molecular interactions involved ininnate immunity is a fast-moving field, with an explo-sion of publications in recent years and new interactionsbeing reported on an almost daily basis.To address these issues and to undertake a curationprocess that has a specific interest in the innate immunesystem, the InnateDB project has had a full-time cura-tion team employed for more than three years. As ofFebruary 15th 2010, there were 11,786 InnateDB-curated molecular interactions in InnateDB (>3,000 pub-lished articles reviewed) and an additional 117,066(mostly non-overlapping) interactions integrated fromother databases. This integration of molecular interac-tions from other databases provides broad coverage ofthe entire human and mouse interactomes - the innateimmunity relevant portion of this interactome is thenenriched through curation by the InnateDB team. Cur-rently, InnateDB only curates interactions involvinghuman and mouse molecules, with the majority ofcurated interactions (72% or 8,569 interactions) invol-ving human molecules (although there has been no spe-cific focus on human as opposed to mouse).Additionally, there are 1,005 hybrid interactions invol-ving both human and mouse participants. Curated inter-actions are primarily protein-protein interactions (9,244interactions), however, there are also almost 2,500 pro-tein-DNA interactions and a small, but important, num-ber of RNA interactions (mainly microRNAs).MicroRNAs are now being recognised as key regulatorsof innate immunity [28].Results and DiscussionInnateDB Curation Greatly Enhances Innate ImmunityRelevant NetworksThe 11,500+ curated interactions can be grouped into7,985 non-redundant interactions (based on the sameparticipants and interaction type). Of these, 6,882 (86%)were curated only by InnateDB, while 1,103 also havebeen curated by one of the other databases integratedinto InnateDB (Figure 1). As illustrated, without theInnateDB curation efforts there would be a significantpaucity in the innate immunity interactome available forsystems-level analyses.InnateDB also enhances pathway-specific networksproviding a more comprehensive picture of pathway sig-nalling than traditional pathway diagrams. Figure 2 illus-trates this point for the RIG-I signalling pathway, a keypathway in the anti-viral innate immune response [7].The KEGG pathway database [29] depicts RIG-I signal-ling in a clear linear fashion that would be recognisableto most biologists (Figure 2A). If, however, we use Inna-teDB to construct a network of all the possible interac-tions between components of this pathway (Figure 2B),we can see that such pathway diagrams are a convenientsimplification of the inter-connectivity and likely cross-talk between pathway components. Curated InnateDBinformation greatly enhances this network-orientatedperspective of innate immunity signalling pathways.Over half of the interactions illustrated (>200) havebeen curated solely by InnateDB. Furthermore, if weexpand upon this view (Figure 2C) and visualise allpotential molecular interactions involving componentsof this pathway, one can clearly see the potential forFigure 1 The InnateDB-curated innate immunity interactome.A) A network of all interactions in the InnateDB-curated innateimmunity interactome. B) The subset interactions in Figure 1Awhich were curated only by InnateDB in comparison to the BIND,DIP, MINT, IntAct and BioGRID databases (i.e. >80%). C) Interactionsin A which were also curated by the BioGRID, BIND, DIP, MINT orIntACT databases. This figure illustrates how InnateDB curationgreatly enhances our knowledge of innate immunity-relevantinteraction networks, a key step in systems-level analyses.Lynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 3 of 14Figure 2 The RIG-I signalling pathway. A) KEGG pathway diagram of the RIG-I pathway. B) A network of all InnateDB annotated molecularinteractions between components of the RIG-I pathway highlights the additional level of complexity that is not conveyed in the KEGG diagram.Edges coloured red represent phosphorylation interactions; edges coloured blue represent protein-DNA interactions. C) A network of allInnateDB annotated molecular interactions between components of the RIG-I pathway and all other annotated interaction partners reveals thepotential for cross-talk between RIG-I pathway components and many other molecules and pathways. Networks were constructed usingInnateDB (http://www.innatedb.com/batchSearchInit.jsp) and were visualised in Cytoscape 2.6.3 using the Cerebral plugin.Lynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 4 of 14huge complexity in the signalling response and cross-talk and/or interchange between a large number ofother molecules and pathways.Innate Immunity Hub and Bottleneck ProteinsThe network of InnateDB curated human interactionswas analysed using the cytoHubba plugin [30] (http://hub.iis.sinica.edu.tw/cytoHubba/) for Cytoscape 2.6.3[31] to investigate a variety of properties of this networkincluding the identification of network hubs and bottle-necks (see below for definitions), which are likely torepresent the key regulatory nodes in the network. Thetop 50 hubs (i.e. highly connected nodes) in this net-work were identified by using the “Degree” algorithm(Table 1). The hub nodes were, in particular, highlyenriched for proteins involved in the TLR and NFBsignalling pathways [MYD88, TRAF6, IRAK1, CHUK(IKBKA), IKBKB, IKBKG (NEMO), NFKB1, RELA,MAP3K7 (TAK1), etc]. In addition to the NFB tran-scription factor subunits, a number of IRF and STATtranscription factors were identified as hubs. There werealso a number of hub proteins that do not currentlyhave known roles in innate immunity. These providepotentially new regulators of innate immunity that war-rant further investigation.The Hubba software also allows one to predict pro-teins that act as bottlenecks in the network. Bottlenecksare network nodes that are the key connector proteinsin a network and have many “shortest paths” goingthrough them [32]. The majority of hub proteins werealso identified amongst the top 50 bottlenecks (Table 1).Intertwining NetworksThe InnateDB curated interactome includes more than2,000 human genes and more than 1,000 mouse genes.The InnateDB pathway and Gene Ontology tools havebeen used to investigate the pathways and biologicalprocesses which are statistically over-represented in thisdataset. Given that the majority of interactions in Inna-teDB involve human molecules, we have focused theseanalyses on human genes (Additional file 1). Unsurpris-ingly, a range of innate immunity pathways are statisti-cally over-represented in this dataset, including TLR,RIG-I, NLR and other pathways (Additional file 2). Per-haps highlighting an increased appreciation of the linksbetween innate and adaptive immunity [2], several path-ways of relevance to adaptive immunity were also over-represented, including T and B cell receptor signallingpathways. This network of genes and proteins involvedin both innate and adaptive immunity underscores theinterconnectivity of the two systems.Interestingly, the network is also enriched in pathwaysannotated to be involved in cancer (e.g. KEGG pathways- Pathways in cancer; Prostate cancer; Pancreatic cancer;Colorectal cancer; Chronic myeloid leukaemia). Thismay be due to overlap between these cancer pathwayswith apoptosis (also over-represented) and other rele-vant pathways such as TGFb signalling [33]. The impor-tance of apoptosis in the innate immune response iswell known [34,35], however, the connection betweeninnate immunity and cancer is now also becoming moreestablished [36,37].Other interesting over-represented pathways includethe Insulin signalling pathway, Wnt signalling, Ubiquitinmediated proteolysis, and Endocytosis among manyothers (Additional file 2). Intriguingly, there is growingevidence of an contribution of a dysregulated innateimmune response to diabetes [38]. Links between Wntsignalling and innate immunity are also becomingapparent [39], while the involvement of ubiquitinmediated proteolysis and endocytosis in innate immu-nity are well known [40,41].The InnateDB curated genes are also over-representedin pathways that do not have well established links toinnate immunity, for example, the neurotrophin path-way. Neurotrophins are a family of proteins involved inneural cell differentiation and survival and may beinvolved in Alzheimer’s disease [42]. So far, there is onlylimited evidence of a relationship between neurotro-phins and inflammation [43]. Although there are likelyto be several reasons why this pathway would be over-represented in the InnateDB curated interactome, it istempting to speculate about links between innate immu-nity and this pathway. The InnateDB interactome pro-vides a wealth of data for further investigation of thelinks between innate immunity and other processes andpathways.Gene Ontology analysis paints a similar picture to thepathway analysis with terms such as innate immuneresponse, inflammatory response, response to virus, apop-tosis, cytokine activity, and signal transduction all beingin the top 20 most statistically significant terms (Addi-tional file 3). Reassuringly, innate immune response isthe most over-represented term (corrected P = 2e-163).Other terms such as protein kinase activity and nucleo-tide binding reflect the large number of phosphorylationand protein-DNA interactions curated by InnateDB.Transcriptional RegulationThe InnateDB curation team has annotated more than2,500 protein-DNA interactions. Aside from thesecurated interactions, we have also investigated whichtranscription factor binding sites are over-represented inthe promoter regions of human genes in the InnateDBcurated interactome (Additional file 4). Perhaps unsur-prisingly, given the central role of NFB in innateimmunity [44], binding sites for its subunits are themost statistically over-represented. The interferonLynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 5 of 14Table 1 Top 50 hub nodes in the InnateDB-curated human innate immunity interactomeGene InnateDB ID Ensembl ID Entrez ID Degree BottleNeckRELA IDBG-57543 ENSG00000173039 5970 104 *CTNNB1 IDBG-27347 ENSG00000168036 1499 92 *IRF1 IDBG-42125 ENSG00000125347 3659 92 *TRAF6 IDBG-40102 ENSG00000175104 7189 90 *STAT1 IDBG-77617 ENSG00000115415 6772 86 *AKT1 IDBG-22709 ENSG00000142208 207 81 *NFKB1 IDBG-31974 ENSG00000109320 4790 75 *IKBKB IDBG-19987 ENSG00000104365 3551 71 *EP300 IDBG-8992 ENSG00000100393 2033 70 *CHUK IDBG-243385 ENSG00000213341 1147 65 *IRAK1 IDBG-90782 ENSG00000184216 3654 55 *MAPK1 IDBG-2147 ENSG00000100030 5594 54 *IRF3 IDBG-63225 ENSG00000126456 3661 51MAP3K7 IDBG-94374 ENSG00000135341 6885 50 *TRAF2 IDBG-92817 ENSG00000127191 7186 49 *ERBB2IP IDBG-24405 ENSG00000112851 55914 48 *SNTA1 IDBG-66573 ENSG00000101400 6640 48 *SQSTM1 IDBG-61811 ENSG00000161011 8878 47 *STAT3 IDBG-50702 ENSG00000168610 6774 46 *IKBKG IDBG-91846 ENSG00000073009 8517 45 *REL IDBG-53133 ENSG00000162924 5966 45NFKBIA IDBG-4758 ENSG00000100906 4792 44 *IRF2 IDBG-46310 ENSG00000168310 3660 42CASP3 IDBG-46394 ENSG00000164305 836 41 *PRKCZ IDBG-86108 ENSG00000067606 5590 41 *BIRC3 IDBG-69045 ENSG00000023445 330 40 *IRF4 IDBG-55681 ENSG00000137265 3662 40 *IRF8 IDBG-45278 ENSG00000140968 3394 40 *MAPK8 IDBG-73479 ENSG00000107643 5599 40 *MTOR IDBG-89258 ENSG00000198793 2475 40 *CASP8 IDBG-78534 ENSG00000064012 841 38 *IL8 IDBG-23954 ENSG00000169429 3576 38 *IRF7 IDBG-17225 ENSG00000185507 3665 38JUN IDBG-99221 ENSG00000177606 3725 38 *MAPK14 IDBG-84613 ENSG00000112062 1432 38 *XIAP IDBG-85142 ENSG00000101966 331 38 *MAVS IDBG-49080 ENSG00000088888 57506 36 *IKBKAP IDBG-79889 ENSG00000070061 8518 35 *TSC1 IDBG-90470 ENSG00000165699 7248 35 *BIRC2 IDBG-69075 ENSG00000110330 329 34RAF1 IDBG-19277 ENSG00000132155 5894 34 *CUL1 IDBG-46918 ENSG00000055130 8454 33 *HRAS IDBG-16878 ENSG00000174775 3265 33 *RIPK1 IDBG-57326 ENSG00000137275 8737 33 *GZMB IDBG-4054 ENSG00000100453 3002 32 *NFKB2 IDBG-87893 ENSG00000077150 4791 32PIK3R1 IDBG-25037 ENSG00000145675 5295 32 *MAPK3 IDBG-25745 ENSG00000102882 5595 31MYD88 IDBG-25713 ENSG00000172936 4615 31PAK1 IDBG-65610 ENSG00000149269 5058 31Lynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 6 of 14regulatory factor, IRF8, is also over-represented [45].Other IRFs, including IRF1, IRF2 and IRF7 are over-represented but these are only statistically significantprior to correction for multiple testing. Similarly, priorto correction for multiple testing, there are many otherwell-known innate immunity relevant transcription fac-tors over-represented including CREB1, CEBPB, AP1and STAT1. In addition to these, there are a number ofother transcription factors that do not have well knownroles in innate immunity and would be potentially inter-esting to investigate in this context. ATF6, for example,does not have a well defined role in innate immunity.This ER stress-regulated transcription factor, however, isa key component of the unfolded protein response(UPR), which is induced in response to and can bemodulated by several viruses and bacterial toxins[46-48]. ATF4, which is also over-represented, is alsoinvolved in this response [49]. A key link between theUPR and innate immunity in C. elegans has veryrecently been demonstrated [50].MicroRNA Regulation of Innate ImmunityThe importance of microRNAs (miRNAs) as regulatorsof innate immunity is now becoming clear [28]. Wehave used the DIANA-mirExTra web server (http://www.microrna.gr/mirextra) [51] to identify miRNA tar-get motifs that are over-represented in our curatedhuman gene dataset. Due to the short size of themiRNA motifs, a large number of miRNAs were identi-fied as over-represented (Additional file 5). Theseinclude miRNAs with known roles in innate immunityor inflammation. miR-105, for example, has been shownto regulate the protein expression of TLR2 in humankeratinocytes [52], while miR-182 expression is a bio-marker for patients with sepsis [53]. Others have rolesin pathways enriched in the InnateDB curated interac-tome, including miR-200 which regulates insulin signal-ling [54], and miR-101 and miR-214 that are involved incancer [55,56]. As with the other preliminary analysesdiscussed above, this dataset provides a wealth of infor-mation to identify new potentially important regulatorsof innate immunity.The Curation ProcessThe goal of manual curation in InnateDB is to accu-rately and richly annotate molecular interactions andpathways of relevance to the innate immune system inhuman and mouse and as demonstrated above this cura-tion process provides an invaluable data source forinvestigating innate immunity. Given that the quality ofthis resource is dependent on our curation process, adiscussion of the InnateDB curation approach and ournovel software, which enables accurate, standardisedcuration, is warranted.Details of molecular interactions are extracted throughreview of relevant publications in the biomedical litera-ture. Curation is primarily carried out in a pathway-centric way, whereby curators systematically review allof the available literature describing interactions thatinvolve members of a particular innate immunity path-way (e.g. RIG-I signalling). Review articles, pathwaydatabases and other sources are used to define the com-ponents of a pathway and then all molecular interac-tions between these genes and their encoded productsand any other molecule (protein, DNA, RNA) arereviewed and curated. Molecular interactions for eachpathway member are systematically curated, althoughpriority is given to publications and experiments thatare not already described in InnateDB (or the otherintegrated databases). Importantly, interactions arecurated between molecules in the pathway and all otherinteractors regardless of whether the interacting mole-cule is a member of the pathway or has any known rolein innate immunity. This allows InnateDB to expand onlinear views of pathways to develop a more comprehen-sive interaction network perspective, highlighting poten-tial cross-talk between pathways and/or prospectivenovel pathway members (Figure 2).This pathway-centric process increases curation effi-ciency as one publication often describes molecularinteractions involving several different pathway mole-cules. Systematically curated pathways are scheduled forfrequent re-curation as the field is moving quickly. Inaddition to this approach, new publications on innateimmunity are also assessed on a daily basis to identifynovel interactions of interest. Priority is given to themost recent publications, ensuring that InnateDB has afast turnaround time for incorporating new informationon the most current research into the database. Further-more, the focus of curation efforts on a specific area (i.e.innate immunity) rather than on curating all molecularinteractions in general is of significant benefit - ensuringthat the curation team develops considerable expertisein assessing the relevant publications and in-depthknowledge of the field.The InnateDB Curation Software SystemThe InnateDB curation system (http://www.innatedb.com/dashboard) is a novel web-based platform that hasbeen designed as part of the curation project to allowthe submission of detailed contextual annotation oneach interaction to the database in a manner that iscompliant with the recently proposed “minimum infor-mation required for reporting a molecular interactionexperiment” (MIMIx) guidelines [57], and in compliancewith the Proteomics Standards Initiative MolecularInteraction (PSI-MI) 2.5 XML format [58]. Such annota-tion includes the supporting publication; the participantLynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 7 of 14molecules; the molecule type; the organism; the biologi-cal role; the interaction detection method; the host sys-tem (in vitro, in vivo, ex vivo); the host organism; theinteraction type; the cell, cell-line and tissue types; cellstatus (primary/cell line); the experimental role; the par-ticipant identification method and sub-cellularlocalisation.The curation system is implemented using the open-source framework CakePHP (http://cakephp.org). Onthe web interface of the system, browser-side scriptingtechnology with JavaScript and JQuery are utilised toprovide a more interactive user experience. Submittedinteractions are stored in a MySQL database and aremigrated to the public database tables on a weekly basis.Note that a user account is required to use the system.The system has been designed to minimise theamount of free-text information that needs to beentered by the curator and instead, it utilises, wherepossible, a series of drop-down menus of PSI-MI [59],Open Biomedical Ontology (OBO) [60] or Gene Ontol-ogy [61] controlled vocabulary terms (Figure 3). Thereare only 4 free-text fields of the 20+ fields that are usedto curate an interaction. Two of these fields relate toadditional comments that curators can record, such asFigure 3 The InnateDB curation system - interaction submission page.Lynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 8 of 14details of any experimental conditions relevant todetecting the interaction. Such comments include, forexample, stimulation with a particular cytokine, infor-mation on mutations, tags, etc. Another free-text field isthe full name for the interaction for which we haveestablished a standard format. The fourth free-text fieldis for the PubMed ID (PMID), however, this must bevalidated before it will be accepted by the system. Whena curator enters a PMID, the abstract for this PMID isautomatically retrieved from NCBI and displayed. Thecurator must then confirm that this is the correctabstract before the PMID will be entered.Interaction ParticipantsAn interaction may have two participants, in the case ofbinary interactions, or multiple participants in the caseof complexes. Self interactions are annotated as binaryinteractions with the same participant. Network andpathway visualisation in InnateDB is carried out usingCerebral (Cell Region-Based Rendering And Layout)[62]. Cerebral is a plugin for the Cytoscape biomolecularinteraction viewer [31] that generates more biologicallyintuitive pathway-like layouts of networks using subcel-lular localisation and other annotation. In the version ofCerebral launched from InnateDB, complexes are dis-played as separate nodes with each participant shown asan interaction with the complex. Such edges are labelled‘X is part of complex Y’. In this way, nodes representingcomplexes can be linked to other interactions in thenetwork without inferring binary interactions betweenall participants in a complex.Each interaction participant is linked to InnateDB viaa unique, stable, InnateDB molecule ID, which mapsone-to-one with identifiers from the Ensembl database(http://www.ensembl.org). When a curator adds a parti-cipant, they enter the gene/protein name into a searchfield, InnateDB is then searched for all matching gene/protein synonyms (both symbols and full names aresearched). Although HGNC (HUGO Gene Nomencla-ture Committee) symbols are used for human partici-pants [63] and Mouse Genome Database (MGD)symbols for mouse participants [64], all known syno-nyms, full-names and other details for the participantare displayed for the curator. This reduces incidences ofconfusing alternative gene names. InnateDB also pro-vides extensive cross-references to other major databases(CCDS, EMBL, Ensembl, Entrez Gene, HPRD, HUGO,OMIM, RefSeq, UniProt).As mentioned, InnateDB currently only includes inter-actions involving human or mouse molecules. Hybridinteractions involving human and mouse participantsare allowed. If no information about the participant spe-cies can be gathered from the paper or in otherreferences, the authors of the paper are contacted toprovide this information.Interaction TypesThe most common interaction type among curatedinteractions is “physical association”, however, there arealso many more specific interaction types including over700 phosphorylation interactions, more than 300 clea-vage interactions, 85 ubiquitination interactions, andsmaller numbers of other biochemical interactionsincluding sumoylation, methylation, and acetylationinteractions. There are also over 300 transcriptional reg-ulation interactions in InnateDB. These interactionsmust be supported by evidence showing physical pro-tein-DNA binding and evidence that this binding alterstranscription, for example, through a luciferase assay.Interaction EvidenceEach interaction, which is defined by the participantmolecules and the interaction type, may have multiplelines of interaction evidence associated with it. Interac-tion evidence refers to the experimental procedures andconditions that were reported to support the interaction.The same interaction may be supported by multiple dif-ferent publications or different experiments reported inthe same publication. For convenience, interactions withmultiple lines of evidence are grouped into a single non-redundant entry on the InnateDB website. For detaileddiscussion of how evidence is curated in InnateDBplease see the curation manual (http://www.innatedb.com/doc/InnateDB_2010_curation_guide.pdf).Interaction Evidence - which journals are curated?To date, more than 3,000 journal articles have beencurated by InnateDB curators (see http://www.innatedb.com/statistics.jsp for up-to-date statistics). The curationteam does not focus their efforts to any specific journals -relevant articles are curated regardless of the journal inwhich they are published as long as they meet the appro-priate quality standards for the interaction evidence.Indeed, at least one article has been curated from >200different journals. That said, more than 70% of curatedarticles have come from 20 journals (Figure 4). It is worthnoting that many of the journals in this top 20 would notbe considered to be immunology journals, underscoringthe importance of not limiting curation efforts to journalsperceived as “relevant”. More than 800 articles, for exam-ple, have been curated from the Journal of BiologicalChemistry.The majority of curated articles have been publishedin the last decade (>80%), with no particular year beingparticularly over-represented in this time-frame (200-300 curated articles in each year from 2000 - 2009).Lynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 9 of 14Almost all other curated articles were published in thelate 1990’s.Interaction Evidence - Cell & Tissue TypesThe interactome is not a single static entity and is verymuch dependent on the context of the particular cell-type under investigation, thus detailed contextual anno-tation of interactions has the potential to be veryvaluable. Although curated interactions in InnateDB areannotated in a wide range of cell and tissue types, themajority of these interactions stem from studies invol-ving cell lines (87%) rather than primary cells. For pri-mary cell interactions, macrophages represent the mostprevalent cell-type, although less than 200 interactionshave been recorded. Epithelial cell derived lines are themost abundant cell line (~30%). Additionally, there areapproximately 300 macrophage cell line interactions.What is clear is that cell-type specific interaction mapsare not currently feasible from this type of data andlarge-scale efforts to map the interactomes of particularcell-types are urgently required.Interaction Evidence - Interaction Detection MethodsCurated interactions in InnateDB are supported by abroad range of interaction detection methods, includingX-ray crystallography, yeast two-hybrids and GST pull-downs. The most abundant detection method, however,is coimmunoprecipitation which accounts for nearly halfof all evidence.Annotating Innate Immunity GenesAside from annotating innate immunity interactions andpathways, the InnateDB curation team has also begun toannotate which genes have a role in the innate immuneresponse. This was initiated because Gene Ontologyannotation [61] of the innate immune response is lim-ited to a quite small number of genes, and our effortreflects a desire in the research community to have adefined list of innate immune genes. For innate immunegene annotation, curators employ an internal annotationtool in the InnateDB curation system to associate rele-vant genes with publications that provide evidence of arole of a given gene in innate immunity. In addition toa link to the relevant publication(s), the curators providea one-line summary of the role, similar to Entrez Gen-eRIFs (http://www.ncbi.nlm.nih.gov/projects/GeneRIF/GeneRIFhelp.html). Such genes are also automaticallyassociated/tagged with the Gene Ontology term “innateimmune response” in InnateDB, providing a more com-prehensive list of such genes for use by the InnateDBGene Ontology over-representation analysis tool. This isan on-going process but, to date, more than 500 geneshave been annotated. It is not intended for InnateDB tocomprehensively annotate all of the roles of a given02004006008001000J BiolChemMol CellBiolJ ImmunolProcNatl Acad SciUSAEMBOJBiochemBiophysRes CommunOncogeneMol CellBloodFEBSLettNatureNat ImmunolCellBiochemJJ ExpMedCancerResImmunityScienceJ CellBiolCell SignalJournal Names#ArticlesCuratedFigure 4 Number of articles curated by the InnateDB curation team in the top 20 journals.Lynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 10 of 14gene, but rather to provide a brief indication as towhether the gene has a role in innate immunity.Reliability of Manual CurationIt has been suggested that curation of protein interac-tion datasets “can be error prone and possibly of lowerquality than commonly assumed” [65]. This assertionappears to be based largely on subjective reliability cri-teria such as the low overlap between curated datasetsin various different databases. In response to this asser-tion, members of the IMEx consortium have pointedout that the low overlap between databases in this con-sortium is quite intentional [66]. To avoid unnecessaryredundancy, several of these databases coordinate theircuration efforts. Furthermore, the IMEx consortiumshowed that curation error rates in their databases arein the region of 2-9% in comparison to the close to 50%error rate suggested by Cusick et al [65].Similarly, the InnateDB curation team focuses oninteractions that have not already been curated in any ofthe databases integrated into InnateDB, unless thoseinteractions are supported by an additional un-reviewedarticle or there is additional annotation that could beadded. Therefore, the limited overlap between InnateDBand other databases is intentional, avoids redundancyand reflects the database’s focus on innate immunity(Figure 1). Consistent with the IMEx consortium cura-tion process, InnateDB aims to accurately represent dataon interactions presented in the literature. The curationteam avoids, as much as possible, subjective calls on thequality of the evidence supporting an interaction unlessthat evidence is clearly insufficient to support the claimsin the publication or does not support a direct physicalor biochemical interaction.Conclusions and MethodsChallenges of CurationThe process of experimentally verifying molecular inter-actions can offer many challenges in completing fullMIMIx-compliant annotation for each InnateDB sub-mission. The absence of key information from publica-tions often impedes the curation procedure, reducingthe annotation available to accurately portray a molecu-lar interaction. The incorrect or absent identification ofthe source organism of a participant molecule wasrecently reported as a common error in many externalinteraction databases [65]. In particular, many publica-tions describing molecular interactions do not clarifywhether they are referring to a human or to a mousegene/protein. Over the approximately 90 million yearsthat evolutionarily separate human and mouse [67],there have been substantial changes to their respectivesignalling networks, and an interaction in one speciesdoes not guarantee it will occur in the other. Databaseslike InnateDB, therefore, must distinguish betweenhuman and mouse molecules. In many cases, informa-tion regarding the organism in question is reported inthe supplemental data or in referenced material, requir-ing a great deal of effort to track down. In a number ofcases, direct correspondence with the authors is theonly option available to the curators to verify such infor-mation. Thankfully, most authors are more than willingto reply. It is not uncommon, however, for authors tobe themselves uncertain. Journal editors and peerreviewers must be encouraged to ensure that suchdetails are clearly specified in papers.An important step in the right direction in this regardis the collaboration between the MINT database and theFEBS Letters journal [68,69]. This collaboration involvesthe processing of accepted articles prior to publicationby MINT curators to create a structured digital abstract,which describes the interactions in the paper in detail.This process involves the manuscript authors in thecuration process.Another key challenge for curation is the fact thatmolecules can have several common names, which canlead to ambiguity in annotating the participant mole-cules in an interaction. A prominent example in theinnate immunity area is the gene encoding the TLRadaptor protein, TIRAP. This gene is also frequentlyknown as MAL. The official HGNC name [63] for thisgene is TIRAP, however, there is another completely dif-ferent gene with the HGNC name, MAL. One can seethe potential for confusion. If provided in the paper, thecurators use gene/protein accession numbers to confirmthe gene in question - this should be strongly encour-aged by journal editors and reviewers. As discussedabove, the curation system also displays all synonyms,full-names and other details for a curator to view whenannotating a participant molecule. This approach high-lights cases where there are two or more genes withsimilar/same names, allowing curators to review care-fully which gene they are referring to. Another relatedissue is identifying which specific protein isoform isdescribed in an experiment. At present, this is oftenimpossible to tell. Therefore, all interactions in Inna-teDB are mapped back to the parent gene ID, withannotation on the molecule type (e.g. protein) involved.Other challenges to curation include evolving stan-dards. PSI-MI [59] and OBO terms [60], describinginteraction types, detection methods, cell-types, etc, arenot static and a term that is valid today may be depre-cated or replaced in the future. Similarly, not all relevantterms have been described in ontologies yet; new inter-action detection methods, for example, may not be spe-cified. Additionally, not all fields have standardisedontologies. Cell lines, for example, do not have a stan-dardised OBO ontology. InnateDB adheres to using cellLynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 11 of 14line names from the American Type Culture Collection(http://www.atcc.org) where possible, however, this list-ing is not comprehensive. An additional issue regardingcell lines include cases where different cell lines mayhave the same or very similar names.While these and other issues provide notable chal-lenges to the curation team, the InnateDB curation sys-tem, its detailed guide on the curation process, andregular meetings to discuss potential pitfalls, ensuresthat InnateDB has a very high standard of curation. Asdiscussed, InnateDB curation of innate immunity rele-vant interactions, pathways and genes is providing themost comprehensive picture yet of the innate immuneinteractome, and promises to shed new light into itsregulation and how pathogens can evolve to subvert it.Additional materialAdditional file 1: Details of the 2089 human genes which areinteraction participants in the InnateDB curated interactome.Additional file 2: Pathway analysis of the 2089 human genes whichare interaction participants in the InnateDB curated interactomerevealing which pathways are statistically over-represented in theinnate immunity interactome.Additional file 3: Gene Ontology analysis of the 2089 human geneswhich are interaction participants in the InnateDB curatedinteractome revealing which GO terms are statistically over-represented in the innate immunity interactome.Additional file 4: Transcription factor binding site analysis of the2089 human genes which are interaction participants in theInnateDB curated interactome revealing which transcription factorbinding sites are statistically over-represented in the promoterregions of these genes.Additional file 5: MicroRNA target motifs which are statisticallyover-represented in the 2089 human genes which are interactionparticipants in the InnateDB curated interactome.AbbreviationsBIND: (Biomolecular Interaction Network Database); BioGRID: (GeneralRepository for Interaction Datasets); Cerebral: (Cell Region-Based RenderingAnd Layout); DIP: (Database of Interacting Proteins); HGNC: (HUGO GeneNomenclature Committee); IMEx: (International Molecular ExchangeConsortium); LPS: (lipopolysaccharide); MIMIx: (minimum informationrequired for reporting a molecular interaction experiment); MINT: (MolecularInteraction database); miRNAs: (microRNAs); (MGD): Mouse GenomeDatabase; NLRs: (nucleotide-binding oligomerization domain (NOD)-likereceptors); OBO: (Open Biomedical Ontology); PAMPS: (pathogen-associatedmolecular patterns); PMID: (PubMed ID); PRRs: (pathogen recognitionreceptors); PSI-MI: (Proteomics Standards Initiative Molecular Interaction);RLRs: (retinoic acid-inducible gene I (RIG-I)-like receptors); TLRs: (Toll-likereceptors); UPR: (unfolded protein response).AcknowledgementsWe wish to thank Eddie Yuen, Patrick Taylor, Sheena Tam, Tom Yang, TraceeWee, and other members of the Pathogenomics of Innate Immunity projectfor their assistance in manual curation of InnateDB. We would also like tothank the various interaction, pathway and annotation databases that havebeen integrated into InnateDB for freely providing their data to the public.Grateful thanks also go to the many researchers who have taken the time torespond to our queries regarding curation of their publications.FundingThis work was supported by Genome BC through the Pathogenomics ofInnate Immunity (PI2) project and by the Foundation for the NationalInstitutes of Health and the Canadian Institutes of Health Research under theGrand Challenges in Global Health Research Initiative (Grand Challenges ID:419). DJL was funded in part during this project by a postdoctoral traineeaward from the Michael Smith Foundation for Health Research (MSFHR).FSLB is a Canadian Institutes of Health Research (CIHR) New Investigator anda MSFHR Senior Scholar. REWH holds a Canada Research Chair (CRC).Funding to enable bovine systems biology in InnateDB is provided byTeagasc.Author details1Animal & Bioscience Research Department, AGRIC, Teagasc, Grange,Dunsany, Co. Meath, Ireland. 2Centre for Microbial Diseases and ImmunityResearch, 232 - 2259 Lower Mall, University of British Columbia, Vancouver,British Columbia, V6T 1Z4, Canada. 3Department of Molecular Biology andBiochemistry, 8888 University Drive, Simon Fraser University, Burnaby, BritishColumbia, V5A 1S6, Canada.Authors’ contributionsDJL wrote the paper, with input from other authors, oversees the curationeffort with REWH and FSLB, and carried out the analyses in the paper. CCdesigned the InnateDB curation software, with input from DJL. MN, MY, RL,AS, GR, KW and JQ all have worked as curators on the project. GLW, MRL,KB, AKF are database and software developers for InnateDB. 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BMC Systems Biology 2010 4:117.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitLynn et al. BMC Systems Biology 2010, 4:117http://www.biomedcentral.com/1752-0509/4/117Page 14 of 14

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