A systems approach to dissecting immunity and inflammation
Alan Aderema,∗, Kelly D. Smitha,b
a Institute for Systems Biology, 1441 N. 34th Street, Seattle, WA 98103, USAb Department of Pathology, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA
Keywords: Innate immunity; Inflammation; Systems biology
1. Innate immunity and the inflammatory response
The innate immune system is essential for host defenseand is responsible for early detection and containment ofpathogens[1,2]. Multiple cell types and tissues participatein the ensuing inflammatory response, which includes therecognition of microbes, the activation of anti-microbial de-fenses and the recruitment of circulating inflammatory cells.The nature of the invading pathogen specifies a responsethat provides optimal host defense, but this inflammatory re-sponse is a two edged sword that must be tightly regulated.The complex interactions initiated by the infection set offa wave of events that can lead to multiple outcomes: res-olution of the infection with complete restoration of tissuearchitecture, resolution of the infection and destruction oftissue (scarring), control of the infection with ongoing in-flammation (chronic inflammation), control of the infectionwith initiation of new inflammatory responses (autoimmu-nity), and failure to control the infection. The regulationof the inflammatory response is extraordinarily complicatedand occurs on many levels. Indeed, it is this complexity thatnecessitates a systems approach to the problem[3]. In thisarticle we will focus on one component of the inflammatoryresponse, that of macrophage activation. In particular, theinitial events that are triggered upon pathogen recognitionwill be discussed.
2. Recognition of pathogens and macrophage activation
2.1. Pattern recognition receptors (PRR)
The inflammatory response to infectious agents is acti-vated when the phagocyte recognizes the foreign invadersusing a battery of receptors including the Toll-like receptors(TLRs), scavenger receptors, complement receptors, mem-bers of the C-type lectin receptor family, and integrins. Thesegerm line receptors have evolved to recognize conserved mo-tifs on pathogens that are not found on higher eukaryotes;these structures have essential roles in the biology of the in-vader, and are therefore not subject to high mutation rates.These structural motifs include carbohydrates, glycolipids,proteolipids, glycoproteins and proteins; for example, TLR4recognizes bacterial lipopolysaccharides (LPS), the mannosereceptor binds mannosyl/fucosyl residues, dectin-1 binds�-glucans, and scavenger receptors bind negatively chargedlipids. Pathogens are also opsonized by humoral componentsincluding complement and immunoglobulins, which are inturn recognized by complement- and Fc-receptors, respec-tively.
The molecular mechanism underlying the function ofthese receptors has received intense scrutiny, but cross-talkbetween them has received limited attention. When takentogether with the enormous spectrum of pro- and anti-inflammatory responses induced when the host encountersmicrobial pathogens, a system of extraordinary complexityis revealed.
Fig. 1. A modular view of Toll-like receptor recognition and signaling. Shown are the 10 TLRs with their known agonists, as well as the IL-1 receptor system. The TIR, IFN, Tak1, NF�B, Map kinase,and caspase signaling modules are also shown. Inhibitory molecules are shown in black. Arrows demonstrate the flow of information.
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2.1.1. The Toll-like family of receptorsMammalian TLRs are a family of 10 pattern recognition
receptors that are central to effective innate immunity[4].TLRs recognize a broad spectrum of ligands, includingmodified lipids (LPS and bacterial lipoproteins), proteins(flagellin), and nucleic acids (DNA and double-strandedRNA). Each TLR recognizes specific components of thepathogen, and the specificity of this recognition is shown inFig. 1. It is important to bear in mind that pathogens consistof a complicated cocktail of pathogen-associated molecularpatterns (PAMPs) that stimulate the TLRs in concert, result-ing in the activation of a number of cross-talking signalingpathways. The integration of this information ultimatelygives rise to an appropriate, and measured, immune andinflammatory response.
2.1.2. C type lectinsThe mannose receptor (MR) is the archetypal C type
lectin. It binds mannosy/fucosyl or GlcNAc-glycoconjugateligands on many bacteria, fungi, and protozoan parasites,and initiates a strong proinflammatory response to them.The MR also functions to clear glycosylated endogenousligands, leading to the suggestion that it functions both todetect foreign pathogens and to mediate the clearance of in-jurious self molecules[5]. Dectin-1, another C-type lectin,is the major receptor for�-1,3-glucans; ligand bindinginduces phagocytosis, and reactive oxygen species pro-duction in macrophages. Interestingly, when macrophagesencounter zymosan, dectin-1 cooperates with TLR2/6 topotentiate IL-12 production.
2.1.3. The scavenger receptorsThe scavenger receptors (SR) are another example of an
innate immune receptor doubling as a homeostatic receptor[6]. SR-A contributes to resistance to Gram-positive bacte-rial infections, and also appears to regulate LPS-induced in-flammation. This is supported by the observation that SR-Aknockout mice are more susceptible to LPS-induced shock,probably due to an imbalance between SR-A-dependentclearance of LPS and TLR4-dependent secretion of inflam-matory mediators such as TNF�. SR-A also mediates theendocytosis of modified low-density lipoprotein (LDL) bymacrophages, leading to inflammation and foam cell forma-tion. It is not clear how the complex interplay between theimmune and homeostatic functions of SR-A is orchestrated.
2.1.4. IntegrinsComplement receptor 3 (CR3) is a myeloid cell phago-
cytic receptor for complement opsonised particles, and alsofor direct interactions with pathogens such asMycobac-terium tuberculosis and yeast cell wall[7,8]. It is a �2 inte-grin, also known as CD18/CD11b, and it plays a key role inmyelomonocytic cell recruitment to sites of inflammation. Itbinds a wide range of ligands, including ICAM-1, selectedclotting components, senescent platelets, and possibly, de-natured proteins. The opsonic phagocytic mechanism differs
from that mediated by Fc receptors and CR3-mediated up-take by macrophages, in that it does not trigger release ofarachidonate or reactive oxygen metabolites.
2.1.5. Additional relevant receptorsWhile we have primarily focused on the pattern recogni-
tion receptors, it is important to bear in mind that a greatmany additional receptors participate in specifying a spe-cific immune response. Thus,cytokine receptors influencethe nature of the response, for example, a TH1 versus TH2versus tolerogenic response. Chemokine receptors regulate,amongst other things the trafficking of cells to affected tis-sues and to the secondary lymphoid organs. Growth factorreceptors maintain homeostasis and influences differentia-tion, and co-stimulatory receptors positively and negativelyregulate the adaptive immune response. These receptors actin concert with the pattern recognition receptors to orches-trate an appropriate inflammatory response.
3. Signaling pathways regulating inflammation
3.1. Pro-inflammatory pathways
A vast number of signaling pathways regulate inflamma-tory responses, and the translation of information from ex-tracellular signals to intracellular responses is the result ofcomplex integration and interplay between signaling mod-ules. A signaling module refers to an assembly of moleculesthat act in concert as a single functional unit. As an ex-ample, the stimulation of TLRs leads to the assembly ofa Toll-interleukin-1 receptor (TIR) signaling module com-prised of adaptors, such as Myd88, and kinases, such asIRAK-4 (Fig. 1). The TIR signaling module interfaces withother signaling modules, which include the Tak1 module,IFN module, and the caspase module. The Tak1 module ac-tivates both the NF�B and the Map kinase module[9].
The selected group of proximal signaling modules of theTLR pathway, shown inFig. 1, provides a mere glimpse ofthe molecular complexity of TLR-induced responses. Addi-tional complexity arises out of the differential use of indi-vidual components within a signaling module. For example,TLR4 uses the Myd88 adaptor of the TIR signaling moduleto stimulate the production of TNF, while it uses the TRIFadaptor, also within the TIR signaling module, to induce thesecretion of IFN� [O’Neill, 2003 #23]. It is also important toappreciate that the signaling pathways are dynamic, and thatthe specific components of a signaling module will changedue to post-translational modifications, protein–protein in-teractions, subcellular compartmentalization, and differen-tial gene regulation. The other receptor systems, describedabove, are equally complex. Since many of these pathwaysare activated concurrently when a macrophages encountersa single class of bacteria, the cross-talk and integration ofinformation required for an appropriate host response is as-tounding.
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Additional complexity in signaling is derived from themultifunctional nature of many PRRs[5]. As mentionedabove, the MR both recognizes foreign pathogens and bindsand removes glycosylated hormones from the blood. Simi-larly, SR-A functions as a PRR, and mediates the uptake ofmodified LDL. The CR3 receptor is an innate immune re-ceptor, and has a role in cell motility and extravasation fromthe vascular. Finally, we should also bear in mind that allof these signaling pathways are subject to genetic variationand environmental influences.
3.2. Anti-inflammatory pathways
Although the initiation of the inflammation has beenstudied extensively, less is known about the mechanism bywhich the inflammatory response is dampened. A number ofgeneric inhibitory mechanisms have been delineated. Theseinclude: (A) the secretion of inhibitory molecules; for ex-ample, phagocytosis of apoptotic cells is anti-inflammatorybecause the macrophage is induced to secrete inhibitorycompounds such as prostaglandin E2, IL-10 and TGF�[10]. (B) Varying the ratio of stimulatory and inhibitorymolecules; for example, Fc�RIII contains a stimulatoryITAM motif, whereas Fc�RII contains an inhibitory ITIMmotif [11]. (C) Competition for ligands amongst recep-tors; for example, SR-A knockout mice are hypersensi-tive to LPS because SR-A competes with TLR4 for LPS[6].
Negative regulation of the TLR pathway occurs at a num-ber of levels. First, prolonged exposure with a TLR agonistresults in tolerance to the agonist. Some aspects of toler-ance can be attributed to down modulation of the TLRs, butevidence of cross-tolerance between different TLR agonistsalso indicates that TLR stimulation results in the modulationof signaling components[12]. Several signaling componentsthat inhibit TLR signaling are up regulated after exposureto agonists. These include IRAK-M, SOCS1, a splice vari-ant of Myd88, and SIGIRR, all of which have been impli-cated in negative feedback regulation of the TLR pathway[13].
4. Unraveling complexity using systems biology
Clearly host defense and the inflammatory response areoverwhelmingly complex. Biochemical, cell biological andgenetic approaches have been successful in unraveling, inbroad brushstrokes, some of the functional components ofthese systems. However, the tools of systems biology will beessential in defining, in total, the interactions that underliethis complex biological system.
The science of systems biology has grown directly outof the Human Genome Project. For the first time, the entireparts list of the inflammatory and immune responses havebeen defined and annotated. This, in turn, has permitted thequantitative analysis of all the mRNAs (transcriptome) or
proteins (proteome) present in a particular cell type. The se-quencing effort also includes the introns containing the regu-latory elements, essential for the eventual deciphering of theregulatory code. In addition, the availability of the genomicsequence will reveal polymorphisms within the populationleading to a deeper understanding of the genetic factors in-fluencing disease susceptibility.
Importantly, the genome has catalyzed fundamentalchanges in how we view and practice biology. Thesechanges in paradigm can be summarized as follows. (A)Biology is an informational science. There are two ma-jor types of biological information: the information of ourgenes which encode the molecular machines composedof protein or RNA, and the information of the regulatorynetworks controlling the behavior of the molecular ma-chines. (B) High-throughput biological tools are essentialfor following the flow of information in biological systems.The Human Genome Project has catalyzed the develop-ment of high-throughput DNA sequencing, DNA arrays,genotyping, and proteomics. These tools have permittedglobal studies, the study of behavior of all, or most, of theelements in a system—an essential component of systemsbiology. Many other high-throughput tools will also berequired for systems approaches; these include the visual-ization of biological information in cells, tissues, and evenorganisms. (C) Computer science and applied mathemat-ics are critical tools for deciphering biological informationand for modeling complex biological systems. The datasetgenerated during the analysis of a biological system is sovast that the development of advanced computational andgraphical tools is necessary in order to integrate the datainto informational pathways and networks. It is almostcertain that new types of mathematics will be required forthese challenges. (D) Model organisms can be manipu-lated to provide insights into complex biological systems.In order to study the coordinate behavior of elements ofa biological system, genetic or pharmacological perturba-tions of the system must be carried out in model organ-isms or cell lines, and the flow of information throughthe various hierarchical levels must be captured. For im-munologists, the mouse has been the model organism ofchoice, although tantalizing insights into the innate im-mune system have been and will be gleaned by studyingother organisms, including insects and sea urchins. (E)Comparative genomics permits powerful analyses of devel-opment, physiology, and evolution. Much of the complexityof living organisms stems from complex regulatory net-works, rather than gene diversity. Comparing orthologouschromosomal regions in different species provides pow-erful tools for identifying coding regions and regulatoryelements.
Each of these paradigm changes contributes to the modernconcept of systems biology. Thus, all the components of thesystem are defined, the informational pathways within thesystem are elucidated, and mathematical models must bedeveloped that accurately represent the system.
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5. Global technologies for dissecting immunity andinflammation
5.1. Genomics
5.1.1. GenotypingThe understanding of complex biological processes is fa-
cilitated by global analyses of genetic variation and generegulation. The mouse is the best characterized mammalianmodel organism. Several phenotypes have arisen sponta-neously, and have been characterized through breeding, butthe majority of phenotypes have been engineered using genedeletion and transgenic strategies. The sequencing of themouse genome, together with advances in genotyping, per-mits the creation of novel genetic variants and phenotypes,akin to the strategies pioneered withDrosophila. These ap-proaches have recently led to the discovery of thelps2/trifgene, which encodes a novel adaptor belonging to the Myd88family [14,15]. Human genetic variation results in alteredsusceptibility to infectious and inflammatory diseases. Occa-sionally, genetic variation in single genes results in immuneand inflammatory disorders, but more commonly these dis-eases are caused by a complex interplay between a number ofgenes, and exacerbated by environmental influences. Clas-sical genetic studies make use of variable elements withinthe genome that are stably inherited to map phenotypes togenetic loci.
Identification of genes with known biological relevancealso provides the opportunity to address the contributionof genetic variation to disease in the reverse direction.Candidate genes can be interrogated for sequence varia-tion within human populations. Many variants detected bythis strategy constitute single nucleotide polymorphisms(SNPs), which can be tested for their prevalence and as-sociations with disease phenotypes. Such candidate geneanalysis has led to the association of NOD2 with familialand sporadic forms of Crohn’s disease[16], as well asTLR4 with atherosclerosis and bacterial meningitis[17,18].We recently demonstrated that a common variant of TLR5is unable to mediate flagellin signaling, acts in a dominantnegative fashion, and is associated with susceptibility topneumonia caused byLegionella pneumophila, a flagellatedbacterium. The association of these genes with disease issignificant, but, in general, the odds ratios of associationare relatively low. This is to be expected, since humandiseases are complex and multi-factorial, and mutations insingle genes would only partially contribute to the over-all risk. More drastic mutations, resulting in significantsusceptibility to disease, are exceedingly rare, since theyhave been selected against. Clearly, an understanding ofmulti-factorial diseases requires the integration of a vastamount of variables, and can only be attempted usingsystems approaches. This will result in a compendium ofpotential susceptibility loci of genetic disease association,and will the pave the way for predictive and personalizedmedicine.
Fig. 2. Regulation of gene expression by Toll-like receptors. Macrophageswere stimulated with the indicated TLR agonists and the transcriptomewas measured. (A) The proportion of genes induced by individual stimuli,compared with those that were common to all TLR agonists. (B) Thedown-regulated genes.
5.1.2. Transcriptional regulationThe transcriptome refers to the sum of all the expressed
genes in a system, and is commonly measured by microarraytechnologies.
Comparative gene expression studies define global dif-ferences between biological perturbations, which can beany one of a vast number of situations, including stim-ulus, time course, drug treatments, or genetic manipu-lations. We have defined the transcriptome induced inmacrophages by a variety of TLR agonists (Fig. 2). Thedata clearly demonstrates that individual TLRs induce bothcommon and individual regulatory networks within thecell. This suggests a mechanism whereby TLRs translatepathogen identity into an appropriate host inflammatoryresponse.
Transcriptional regulatory networks can be defined byintegrating gene expression data, obtained using microar-rays, with transcription factor binding data, obtained usingchromatin immunoprecipitation andcis-regulatory elementmicroarrays (ChIP-chip technology)[19]. These global ap-proaches have to date been most successful in yeast. How-ever, the availability of the genomic sequences of mouseand human permits the extension of these methods to thedefinition of the gene networks that regulate immune andinflammatory responses.
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6. Proteomics
Proteomics can be defined as the systematic analysis of allthe proteins expressed by a cell or a tissue. Quantitative mea-surements are particularly useful for the study of biologicalsystems and pathways, since they reveal dynamic changesin the proteome. The traditional method for quantitative pro-teome analysis combines protein separation by high resolu-tion two-dimensional gel electrophoresis with mass spectro-metric or tandem mass spectrometric analysis (2DE/MS). Inthis method protein quantification is achieved by recordingthe staining intensities of the separated protein species.
Ruedi Aebersold and colleagues from the Institute for Sys-tems Biology recently developed a new experimental strat-egy for quantitative proteomics that alleviates most of thelimitations inherent in the 2DE/MS method[20]. It quantifiesthe relative abundance and identifies every protein present intwo or more samples, even if the proteins are of low abun-dance. This method is based on a class of new chemicalreagents termed isotope coded affinity tags (ICAT), MS/MS,and a suite of software tools for data analysis.
The ICAT strategy is schematically illustrated inFig. 3.Protein mixtures from macrophages, either stimulated withLPS or unstimulated, are treated with the ICATTM reagent(Fig. 2A). The reagent consists of three functional groups.The first is a protein reactive group, which is used to cova-lently attach the reagent at a specific site in the protein. The
Fig. 3. Quantitative analysis of proteomes, using isotope-coded affinity tag (ICATTM) technology. (A) The ICAT reagent structure. (B) The ICATprocedure. (C) Protein reactive group specific reagents.
second is a linker group that exists in two isotopic forms,light (d0) and heavy (d8). The third is an affinity tag, whichis used to selectively extract the reagent–peptide conjugatesfrom the sample mixture. The protein samples to be com-pared are derivatized with either the heavy or light form ofthe ICAT reagent, proteolyzed, and the resulting peptides en-riched on an affinity column (Fig. 2B). These relative abun-dance and identity of each of the peptides are then measuredby mass spectrometry. The procedure can be automated andmultiplexed. A number of ICATTM reagents with differentreactive group specificities have been developed. This per-mits the comparative quantitation of all proteins, phospho-proteins, glycoproteins, and specific proteases (Fig. 2C).
The ICATTM reagents can be applied broadly to analyzeimmunity and inflammation. We are using this method toidentify TLR-dependent changes in secreted proteins, mem-brane proteins, phagosomal proteins, nuclear proteins, sig-naling proteins, and signaling complexes.
6.1. Membrane and secreted proteins
The glyco-ICAT strategy specifically quantifies and iden-tifies secreted and membrane glycoproteins[21]. The car-bohydrate groups of proteins are oxidized and covalentlyattached to hydrazide groups on a solid support. The im-mobilized, purified, glycoproteins are then trypsinized, andthe resulting glycopeptides are labeled with either of the
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heavy or light ICAT reagent. These peptides are released en-zymatically and analyzed using tandem mass spectrometry(MS/MS). This method is particularly important when ana-lyzing secreted proteins since it permits the removal of al-bumin, and other high abundance, non-glycosylated protein,thereby facilitating deeper protein coverage.
6.2. Phosphoproteins and signaling complexes
The phospho-ICAT reagent is particularly useful in delin-eating signaling pathways; the procedure permits the quan-titative comparison and identification of dynamic proteinphosphorylation during macrophage activation[22].
We have used tandem affinity purification together withICAT reagents and mass spectrometry to identify novelinteracting partners within signaling complexes. This strat-egy is appealing since it circumvents the artifacts associ-ated with the yeast 2-hybrid system, and permits dynamicquantification and identification of proteins without needfor gel separation. The experimental approach involvesexpressing epitope-tagged versions of the bait protein inmacrophages, and affinity purifying the bait and associatedproteins after stimulating the cells with an appropriate TLRligand.
Fig. 4. Tandem affinity purification (TAP) procedure. (A) TAP-epitope tagged Myd88 was expressed in RAW 264.7 macrophages. (B) After immuno-precipitation, peptides derived from Myd88-TAP were identified by mass spectrometry. (C) After stimulating macrophages with lipopetide for 10 min,Tirap was identified to co-precipitate with Myd88. (D) Six peptides, corresponding to 38% of the Tirap protein were identified by mass spectrometry.The interaction of Myd88 with Tirap was not detected in unstimulated macrophages.
We have found that tandem affinity purification (TAP)pioneered by Bertrand Seraphin and colleagues, is requiredto minimize the noise derived from molecules associatingnon-specifically. The bait and its associated molecules areeluted and the peptides identified and quantitated by theirtandem mass spectra (Fig. 4). We have validated the methodby demonstrating the dynamic association of Myd88 andTirap upon activation of TLR2 (Fig. 4). Studies are un-derway to comprehensively catalog protein signaling com-plexes that are assembled in response to TLR activation.
7. Computational approaches to defining complexinteractions in the immune response
As discussed, macrophage activation is a result of com-plex dynamic behavior. This includes positive and negativefeedback loops, cross-connections between pathways andmodules, kinetic effects such as competitive binding, andgenetic variation. Powerful computational approaches arerequired in order to make sense of this complexity. Inparticular, it is important to display the multiple signalingmodules, and the transcriptional regulatory networks, thatmediate common and distinct TLR-dependent pathways.
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This integrative network analysis defines regulatory con-striction points within the pathways and predicts the effectof perturbation on the network.
To reconstruct and understand both signal transductionand transcriptional regulatory networks, it is necessary todevelop:
• A list of all the components of the system. This step istypically carried out with high-throughput global tech-nologies and the resulting information is stored in a localdatabase; in the case of the ISB it is called SBEAMS.
• A map of the network of interactions among the compo-nents. The information stored within SBEAMS is then an-alyzed, and the network structure can be visualized withgraph handling and analysis software such as Cytoscape(http://www.cytoscape.org).
• An understanding of the nature of interactions among theparts. Such a description can be at various levels of ab-straction, depending on the data available. For instance,one may describe interactions in terms of Boolean logicof the type: “if ligand L is present, then receptor R isactivated” (summarized as “if L then R” in the Booleanformalism). In enzymatic networks, the kinetics of reac-tions frequently affect the behavior of the network as awhole. In such cases, it is necessary to describe the aver-age behavior of chemical reactions using mass action ki-netics. Often, there is good reason to suspect considerablevariability between cells. In such cases, average behaviormodels can be misleading and it may be desirable andnecessary to model the interactions of interest in terms ofindividual, stochastic molecular events.
• A model of how the interactions specified in the abovethree steps, result in overall system behaviors experimen-tally observed in wild-type and perturbed cells.
8. SBEAMS: a systems biology database
SBEAMS is a software and database framework for col-lecting, storing, and accessing different types of experimen-tal data (http://www.sbeams.org/). This system combines arelational database management system (RDBMS) back end,a collection of tools to store, manage, and query experi-ment information and results in the RDBMS, a web frontend for querying the database and providing integrated ac-cess to remote data sources, and an interface to other dataprocessing and analysis programs. All data are organized ina modular schema in the RDBMS using similar designs tosimplify quality control, data analysis, and data integration.Investigators may use web-based tools or custom scripts tocorrelate, explore, and annotate the experimental results.
SBEAMS is a modular framework wherein each modulecan operate independently of the others. The current ma-jor modules provide support for microarrays, proteomics,molecular interactions, histology, phenotyping, genotyping,and EST clustering. A single-user project, as managed by the
core, may include data from one or several of the SBEAMSmodules; for example,Fig. 5 shows the SBEAMS core andthe microarray and proteomics modules.
To manage molecular-interaction information contribut-ing to a systems-level understanding of macrophage ac-tivation, we developed the module SBEAMS-Interactions.This module provides a database and interface for curat-ing protein–protein and other types of interactions obtainedfrom the literature, other databases, ISB experiments, andalgorithms for inferring interactions. The web interface alsofacilitates queries, such as commands to display only thoseinteractions that are of interest at a particular time. Queryresults can be automatically piped to Cytoscape for data in-tegration, graphical visualization and exploration (Fig. 5).
9. Data visualization using Cytoscape
Visual data integration plays an important role in the fol-lowing components of systems biology. First, it displayscomplex information as an organized representation that isintelligible. Second, the displayed data is interpreted andintegrated with the existing world knowledge base derivedfrom global databases and the literature. This suggests ad-ditional testable hypotheses that generate more data, which,when integrated into the model generates more testable hy-potheses. This leads to an iterative process, in which thechoice of successive experiments is driven by the reevalua-tion of the previous model (Fig. 6). In this way the modelis continuously refined.
We will use the TLR pathway to demonstrate some ofthe functions and capabilities of Cytoscape (http://www.cytoscape.org). Fig. 7A shows a partial interaction network formacrophage activation (approximately 600 interactions areshown). The squares are nodes, and represent individualmolecules, and the lines are edges that represent the inter-actions between them. In this example, we are integratingLPS and CpG induced gene expression data with the inter-action network. We selected the nearest neighbors to TNF;this brings up the nodes and edges in a new window;Fig. 7Bdemonstrates the view for LPS induction, whereasFig. 7Cshows CpG induction. The data shown are relative to un-stimulated macrophages. The colors of the nodes designaterelative changes in gene expression; green designates in-duced genes whereas red designated repressed genes. Thethickness of the edges corresponds to the level of confidencein the interaction (not shown). The edges can be depicted inmany different forms to convey additional information; forexample arrows can show directionality of the interaction.Pathways and molecules that are differentially modulated areeasy to discern. Imbedded within each node and edge is anenormous amount of additional data and links all of whichare available with a click of a mouse, but demonstrating thisis beyond the scope of this review.
A number of additional programs have been written thatinteract with Cytoscape or that serve as plug-ins.
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SEQUEST
SBEAMS Core
mergeCondsVERA/SAMmergeRepspreprocess
dapple
ProteinProphetASAPRatioPeptideProphet
SBEAMS -Proteomics
PathwaysPlugin
BioModulesPlugin
SBEAMS - Microarray
Cytoscape
text
text
Proteomics Pipeline
Microarray Pipeline
extractms
OtherPlugins
Interactive Visualizationand Integration
Data Acquisitionand
Management
Fig. 5. Architecture of SBEAMS and Cytoscape; integration of data acquisition, management and analysis tools. Two modules are demonstrated; theproteomic pipeline and the microarray pipeline. In the proteomics pipeline, Sequest is a high throughput, scalable, customizable sequence database searchengine for tandem mass spectrometry data. Peptide prophet is a statistical program that validates peptide identifications made by tandem mass spectrometry.Protein prophet is a statistical program that validates identification at the protein level. SBEAMS data is the piped into Cytoscape for visualization.
Fig. 6. Network modeling by iterative refinement.
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Fig. 7. Integration of molecular interactions with gene expression data using Cytoscape. (A) Cytoscape view of the macrophage activation interaction network. A close-up of the network (circled region),shows LPS (B) and CpG (C) regulated genes (green: induced, and red: repressed) within the context of the molecular interaction network.
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9.1. Modular structure of bionetworks
Our genome-scale understanding of immunity suggests ahierarchical view of the cell in which groups of interactingmolecules form biological modules, and biological modulesinteract in complex networks that control the properties ofa cell. Biological hypothesis generation is an inherently in-tegrative process in which insight is derived from the globalcontext of interacting biological processes, i.e. modules. Sci-entists use this approach all the time, but their analysis issubjective and reflects their own personal biases. In addition,the human mind is not capable of processing thousand ofvariables. The loose molecular associations in networks canbe identified computationally, thus abstracting the molec-ular network into a modular network[23]. The “modularstructure” plug-in of Cytoscape uses algorithms to extractfrom the data a simplified module that is both unbiased anduseful in that it leads to the generation of hypotheses.
9.2. Gene regulatory network discovery
The enzymatic networks underlying signal transductionand cellular physiology commonly operate on time scalesof the order of seconds. In contrast, the transcriptional reg-ulatory networks that are responsible for changing the stateof a cell, for example during LPS-dependent macrophageactivation, typically operate on time scales that are two tothree orders of magnitude slower.
As a result, viewed from the perspective of genes, signal-ing events often appear instantaneous, while viewed fromthe perspective of enzymatic reactions, changes in gene tran-scription levels happen so slowly as to be insignificant. Thisobservation simplifies modeling of cellular processes by un-coupling the behaviors of enzymatic and genetic networks.
A gene regulatory network is derived from multi-ple data sources[24]. Microarrays are used to defineco-expressed genes; and candidate transcription factors areused for ChIP-chip analysis. A protein–protein interac-tion map is used to augment the protein–DNA map. Toverify DNA–protein interactions determined with globaltechnologies, algorithms have been developed to identifyand compare putative transcription factor binding sitesin co-regulated genes. This structural information is usedto drive “cis-regulatory analysis,” leading to a model oftranscriptional regulation of inflammatory responses.
9.3. Comparative genomics
Comparative genomics is a powerful tool in defininggene function. When complete, this plug-in will comparesequences across species in order to identify orthologs,homologs, conserved domains and genomic regulatory se-quences. These analyses provide three basic insights intothe biology of the system. First is the identification of evo-lutionarily conserved genes and modules within the system.Second, protein function or domain function can be intu-
ited from studies performed in model organisms leading tohypothetical assignment of function. Third, these gene com-parisons will aid in the identification of genomic regulatoryelements that comprise transcriptional regulatory networks.
9.4. Structure explorer
As discussed above, many protein functions and motifscan be assigned by primary sequence analysis, using a vari-ety of different algorithms. However, when the primary se-quence does not suggest functional motifs, we have foundthat predicted structure can lead to the identification of struc-tural homologues that suggest function. Structure exploreruses a combination of a vast library of short known struc-tural motifs and energy minimization principles to predictprotein folds.
9.5. Intelligent integration of global knowledge intoSBEAMS/Cytoscape
It is important to mine relevant data that exists withinthe world literature and in the public databases. For ex-ample, when a particular gene is identified, this plug-inexecutes searches that extract all known information aboutthe gene from public databases, and then integrates the in-formation into the working model generated by Cytoscape.The acquisition of information from database is relativelystraightforward and can be automated. Much more difficultis the extraction of the text based information found withinPubMed. Importantly, this data must be evaluated beforecuration and annotation. Currently this is hand curated, butwith the advent of sophisticated artificial intelligence algo-rithms this task can be partially automated. Critical to thiseffort will be the involvement of the Journals and scientificcommunity. A prerequisite for publication might be theextraction of the relevant data by the authors, editors andreviewers for submission to a central database. For example,the molecule database managed by the Alliance for Cell Sig-naling and hosted by Nature could serve as a model, whichwhen expanded and modified might meet these needs.
10. Concluding remarks
Systems biology approaches are necessary for a completeunderstanding of the innate immune system and the inflam-matory response. These systems wide approaches are nowfeasible due to availability of complete genomic sequencesand high throughput global technologies. Most importantly,new computational strategies need to be developed to makesense of the mountains of data that are generated using thesetechnologies. At the ISB we have developed a suite of pro-grams (Fig. 8) that serve as a starting point to meet this need.These approaches generate complete molecular descriptionof complex biological events and predict the behavior ofthe system. The models lay the foundation for predictive,
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Fig. 8. Computational integration, data analysis, visualization, and model refinement. High-throughput data is processed and transcribed into a structured database (SBEAMS). Database information istranslated into graphical forms suitable for input to human intuition (Cytoscape). Computational analysis of each node within a network adds interpretative value and aids iterative refinement of thenetwork. Comparative genomics and structure explorer enrich functional understanding, as described in the text. Cytoscape, and SBEAMS extract global knowledge to integrate with experimental results.
A. Aderem, K.D. Smith / Seminars in Immunology 16 (2004) 55–67 67
preventive, and personalized medicine. Thus, human geneticvariation predicts susceptibility to disease, and a completeknowledge of the pathways leading to disease will serveto design diagnostics for disease progression and rationallydefine drug targets.
Acknowledgements
The article relies on a large number of scientific contribu-tions and papers. Because of page constraints we have refer-enced review articles, which hopefully, will lead the readerto the appropriate citations. We apologize to those whosework have been referred to, but have not been cited. Wethank our colleagues at the Institute for Systems Biology fortheir helpful discussions and contributions, especially Drs.Hamid Bolouri, Eric Deutsch, Tim Galitski, Adrian Ozin-sky, Javed Roach, Paul Shannon and Vesteinn Thorsson.
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