- Email: [email protected]
Dissecting innate immune responses with the tools of systems biology
Dissecting innate immune responses with the tools of systems biology Kelly D Smith1 and Hamid Bolouri2 Systems biology strives to derive accurate predictive descriptions of complex systems such as innate immunity. The innate immune system is essential for host defense, yet the resulting inflammatory response must be tightly regulated. Current understanding indicates that this system is controlled by complex regulatory networks, which maintain homoeostasis while accurately distinguishing pathogenic infections from harmless exposures. Recent studies have used high throughput technologies and computational techniques that presage predictive models and will be the foundation of a systems level understanding of innate immunity. Addresses 1 Department of Pathology, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA e-mail: [email protected] 2 Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA
Because of its complex dynamic behavior, its wealth of intricate intra- and inter-cellular interactions, and its medical significance, the immune system offers an ideal focus for systems biology. The innate immune response is highly context dependent, suggesting unplumbed complexity. Depending on the context, host and pathogen factors can be both protective and injurious. For example, Toll-like receptor (TLR)-2 and myeloid differentiation antigen 88 (MyD88) protect against systemic spread of Group B Streptococcus during low dose challenge; by contrast, TLR2 and MyD88 promote lethality to high doses of the same pathogen [1]. Moreover, all infections and inflammatory disorders are not created equal; TNF antagonism is an efficacious therapeutic target in rheumatoid arthritis [2], but can exacerbate systemic lupus erythematosus [3] and mycobacterial infections [4].
Corresponding author: Bolouri, Hamid ([email protected])
Current Opinion in Immunology 2005, 17:49–54 This review comes from a themed issue on Innate immunity Edited by Alan Aderem and Elena Levashina Available online 8th December 2004 0952-7915/$ – see front matter # 2005 Elsevier Ltd. All rights reserved. DOI 10.1016/j.coi.2004.11.005
Thus, interactions, feedback, dynamic behavior, complexity and context are widespread in, and essential to, innate immunity. These are exactly the issues that systems biology aims to address; raising hopes for early and accurate diagnoses, the instigation of preventative strategies, and the development specific treatments for infectious and inflammatory diseases. Systems biology also presents the opportunity to personalize diagnoses and therapies, taking into account the genetic and environmental factors that make each individual unique.
Abbreviations ChIP chromatin immunoprecipitation IRAK IL-1 receptor-associated kinase MyD88 myeloid differentiation antigen 88 RNAi RNA interference TLR Toll-like receptor
The rest of this review is divided into two sections reviewing the current technologies and computational tools of systems biology with particular emphasis on applicability to innate immune studies. Because of our backgrounds, we have focused on studies in the biology of TLRs as a representative facet of innate immunity.
Introduction
Technologies of systems biology
Systems biology is concerned with understanding the dynamic outcome of molecular interactions between biomolecules (e.g. DNA, RNA, proteins, lipids and metabolites) at the pathway, organelle, cell, organ and organism levels (Figure 1). The methodology of systems biology is an iterative process that begins with the identification of component parts and their respective interactions. This information is then integrated into a predictive (usually mathematical) model of system behavior, which is in essence a hypothesis that can be tested experimentally. The experimental results lead to the refinement of the model and, hence, new hypotheses.
Genomic technologies
www.sciencedirect.com
Genomic technologies aim to define both static and dynamic aspects of genomes within biological systems, including sequence, sequence variation and gene expression. In addition, genomic tools can be used to manipulate genetic sequence and gene expression to help us to better understand biological systems. Transcriptional regulation: gene expression
The most robust technology for global systems measurements is microarray-based analysis of mRNA abundance. Initial studies focused largely on the regulation of gene Current Opinion in Immunology 2005, 17:49–54
50 Innate immunity
Figure 1
Genetic variation Systems biology
Technology
Computation Biology Current Opinion in Immunology
The concept of systems biology. Systems biology is founded on the synergistic interplay between biological, technological and computational sciences. Biological questions drive technological advances, which require new computational tools. Similarly, technological and computational advances provoke biological insight and new models for biological systems.
expression in macrophages and dendritic cells by wellknown TLR agonists and whole microbes [5,6]. These studies identified ‘core’ responses to pathogens, suggesting that at least one part of the innate immune response to pathogens is generic, as the response is similar for pathogens that differ substantially (viruses, bacteria and fungi). This core response exists despite the complex pattern of TLR usage for four different adaptors of the MyD88 family [7], suggesting that different receptors utilize overlapping signaling networks to achieve similar outcomes. TLR-specific responses are also apparent, with the most obvious difference attributable to the unique set of genes induced through TLR3 and TLR4, utilizing the common proximal adaptor TRIF (the Toll/IL-1 receptor (TIR)-domain-containing adapter inducing IFN-b) [8,9]. Transcriptional regulation: protein–DNA interactions
The hybridization of microarrays that probe entire genomic regions with chromatin immunoprecipitation (ChIP) products provides a high-throughput method (ChIP– chip) to quantify the network of protein–DNA interactions (for a review of ChIP methods, see [10]; for a review of design issues, see [11]. This technology has been used to determine NF-kB binding sites on human chromosome 22 in a human cervical carcinoma derived epithelial cell line (HeLa cells) [11], as well as binding sites for cMyc, Sp1 and p53 in a human T cell lymphoma derived cell line (Jurkat) and a human colorectal carcinoma derived cell line (HCT) on chromosomes 21 and 22 [12]. Current Opinion in Immunology 2005, 17:49–54
Genetic variation has been a traditional data source for defining genetic contributions to normal physiology and disease in innate immunity [13–15]. Spontaneous mutations have been aided by chemical mutagenesis and phenotypic screens to identify additional genes that contribute to innate immunity, including TRIF as the proximal adaptor required for MyD88-independent signaling [9], and CD36 as a novel co-receptor required for efficient recognition of diacylated lipopeptides by the TLR2– TLR6 heterodimer [16]. Targeted mutagenesis of genes using traditional and conditional knockout strategies is a low throughput but highly effective means to dissect genetic contributions to innate immunity [7]. The combination of spontaneous, chemically induced and targeted mutants have generated dozens of mice with defects in the TLR signaling pathway, which is a rich source for systems-biology-based experimentation. Insight into the complexity of the TLR regulatory network is beginning to emerge from such studies, and several different genes have been implicated in transcriptionally regulated negative feedback loops, including IL-1 receptor-associated kinase 3 (IRAK-3, also referred to as IRAK-M) [17], suppressor of cytokine signaling 1 (SOCS1) [18], suppression of tumorigenicity 2 (ST2) [19], alternative splice forms of MyD88 [20] and IRAK2 [21], and most recently inositol polyphosphate-5-phosphatase (INPP5D, also known as SHIP) [22]. These studies suggest that there is a higher degree of regulation in TLR signaling than currently appreciated; a property that will only be revealed when these components are analyzed simultaneously as part of a system. Signaling and transcriptional complexities are also emerging, for example IFNb-mediated autocrine amplification of the lipopolysaccharide (LPS)–TLR4 signal [23,24], and IkBz-dependent transcriptional regulation of LPS-induced IL-6 production [25]. RNA interference
RNA interference (RNAi) is the most promising new strategy to systematically perturb gene expression, and to perform saturation mutagenesis in cell-based systems of innate immunity. Using the Drosophila macrophage S2 cell line, Foley and O’Farrel [26] conducted a genomewide RNAi screen with 7216 double-stranded RNAs to identify 121 molecular components that regulate activation of NF-kB and diptericin reporters in response to Gram-negative bacteria. In addition to identifying known components in this pathway, the study also revealed novel feedback interactions upstream of NF-kB activation, as well as novel functional categories of genes that regulate innate immune signaling, such as RNA splicing, Ras signaling and cytoskeleton regulation [26]. The refinement of retroviral methods for RNAi (see the RNAi Codex website; Table 1) permits similar broad screens with high saturation levels in mammalian innate immunity, which have been carried out for the proteasome and p53 [27,28]. www.sciencedirect.com
Innate immune responses and systems biology Smith and Bolouri 51
Table 1 Useful website addresses. Name of site
URL
RNAi Codex The Alliance for Cellular Signaling The Signaling Gateway Biocarta Kyoto Encyclopedia of Genes and Genomes The Pharmacogenetics and Pharmacogenomics Knowledge Base BioCyc Database of Interacting Proteins Biomolecular Interaction Database Human Protein Reference Database Cytoscape Osprey VisANT Gene Ontology Systems Biology Markup Language
http://katahdin.cshl.org/scripts/main.pl http://www.afcs.org http://www.signaling-gateway.org/ http://www.biocarta.com/genes/index.asp http://www.genome.ad.jp/kegg/ http://www.pharmgkb.org/ http://www.biocyc.org/ http://dip.doe-mbi.ucla.edu/ http://bind.ca http://www.hprd.org http://www.cytoscape.org/ http://biodata.mshri.on.ca/osprey/ http://visant.bu.edu/ http://www.geneontology.org http://sbml.org/
Proteomic technologies
Protein microarray technologies
Proteomic technologies aim to capture comprehensive information about protein quantities, protein states and protein interactions within biological systems.
Protein microarray technologies are also being used for systematic determination of protein activities at a genomic scale in yeast [35], and the direct identification of protein binding partners in the human basic-region leucine zipper (bZIP) transcription factor family [36]. The major bottleneck is the generation of reagents that specifically and quantitatively identify proteins, which for the most part has relied upon monoclonal antibodies or the creation of labeled recombinant proteins [37].
Yeast two hybrid technology
Yeast two hybrid technology (Y2H) is a mainstay for identifying protein interactions, but is limited by the fact that interactions are typically established in a nonphysiological environment, in the absence of many co-factors and post-translational modifications (for a recent review see [29]). Tandem affinity purification
Tandem affinity purification (TAP) also originated in yeast and has been successfully applied in mammalian cells to identify proteins within molecular complexes [30]. Bouwmeester et al. [30] used TAP to map the TNF signaling network responsible for regulating NF-kB activation in HEK 293 cells. Using this powerful approach, a network of 131 proteins and 221 interactions was created using 32 known components of the TNF signaling pathway, including 80 novel components. As this technology is rather new, we are likely to see it applied to many more regulatory pathways relevant to innate immunity. Mass spectrometry
Mass spectrometry has been applied to quantitative protein measurements of subcellular fractions and specific protein modifications, including phosphorylation and glycosylation. Examples include transmembrane proteins [31], mitochondria [32] and elegant studies of the phagosome [33]. These studies defined a novel mode of phagocytosis, and provided new insights into potential mechanism of cross-presentation of exogenous antigens by class I MHC [34]. www.sciencedirect.com
Protein localization
Protein localization has been systematically applied to yeast using tagged proteins [38,39], and similar studies are under way in mammalian cells (for example see RAW 264.7 image data collected by the Alliance for Cellular Signaling; Table 1. Fluorescence resonance energy transfer
Fluorescence resonance energy transfer (FRET) can localize and quantify protein interactions in living cells (for a recent review, see [40]). This and other related strategies are critical for quantifying real-time molecular information in living biological systems. Metabolomics and lipidomics
The role of small molecules and lipids in innate immunity remains largely uncharted. Lipids, in particular, are known to play a crucial role in the pathogenesis of vascular diseases and also play an important role in establishing membrane subdomains and acting as second messengers in signal transduction. Large-scale lipid analyses have recently been developed using electron ion spray mass spectrometry [41], and large datasets are emerging from studies conducted in the RAW 264.7 murine macrophage cell line (see lipidomics datasets in the Alliance for Cellular Signaling Data Center at The Signaling Gateway (Table 1). Current Opinion in Immunology 2005, 17:49–54
52 Innate immunity
Computational analysis
Computational systems biology spans a very wide range. We focus this review on those aspects that are particularly novel or pertinent to systems biology studies of innate immunity. DNA/protein sequence searching and analysis
DNA and protein sequence searching and analysis have been part and parcel of genomics from its inception (see [42] for a short historical review). Recent efforts have been directed at the identification of cis-regulatory modules and transcription factor binding sites, which are essential components of transcriptional regulatory networks. Although the false positive and miss rates of current algorithms are still short of the ideal, prediction accuracies are improving rapidly (see [43,44] for reviews and on-line tutorials). Lu and Skolnick [45] demonstrated the feasibility of predicting protein–protein interactions on a genomic scale by using the relatively large volume of protein structures solved to date. These methods become increasingly useful when combined with other data using statistical data integration methods [46]. Pathway databases
Pathway databases can provide a network context for data analysis. Among those that we have found most useful are BioCarta, which provides graphical network diagrams
(Table 1), the Kyoto Encyclopedia of Genes and Genomes, which can be accessed both graphically and programmatically (Table 1), The Pharmacogenetics and Pharmacogenomics Knowledge Base (Table 1), and the multispecies, multidatabase resource for metabolic pathways BioCyc (Table 1). There are also now an increasing number of very useful, curated protein interaction databases, such as the Database of Interacting Proteins (Table 1), the Biomolecular Interaction Database (Table 1), and the Human Protein Reference Database (Table 1). Network topology visualization and analysis
The most intuitive way to analyze biomolecular interaction data is through the visualization of networks. Three very useful packages have been developed especially for systems biology: Cytoscape (Table 1), Osprey (Table 1) and VisANT (Table 1). These packages offer a wide range of network analysis tools such as clustering of biomolecules by ontology, expression, or interactions for analysis of high level network organization (see [47] for a review). Integration of genetic data
The integration of genetic data with high throughput assays for gene expression, protein–protein, and proteinDNA interactions, and knowledge bases such as Gene Ontology (Table 1) has so far best been demonstrated in yeast (see for example [48]). However, integrated analysis
Figure 2
(a)
(b)
(c)
(d)
Current Opinion in Immunology
Complex and diverse dynamic behaviors exhibited by a simple network. Even the simplest feedback loop can create complex behaviors. Moreover, the same topology can lead to qualitatively different behaviors depending on kinetic parameters such as transcriptional efficiency and protein half-lives. Here, all four figures model the same simple negative feedback circuit: a gene whose product represses its own transcription. Depending on kinetics, very different behaviors may result. (a) Tuned for regulated level. (b) Single transcriptional pulse. (c) Tuned for rapid response. (d) Tuned for long-lasting oscillation. Graphs show the simulated expression level of the auto-repressive gene for different values of protein half-life and protein–DNA binding equilibrium constant. Current Opinion in Immunology 2005, 17:49–54
www.sciencedirect.com
Innate immune responses and systems biology Smith and Bolouri 53
of this type in metazoans is already providing new insights in human disease (see for example [49]) and is likely to have considerable impact on our current understanding of innate immunity.
References and recommended reading
Quantitative and qualitative modeling of dynamic behavior
1.
Mancuso G, Midiri A, Beninati C, Biondo C, Galbo R, Akira S, Henneke P, Golenbock D, Teti G: Dual role of TLR2 and myeloid differentiation factor 88 in a mouse model of invasive group B streptococcal disease. J Immunol 2004, 172:6324-6329.
2.
Olsen NJ, Stein CM: New drugs for rheumatoid arthritis. N Engl J Med 2004, 350:2167-2179.
3.
Shakoor N, Michalska M, Harris CA, Block JA: Drug-induced systemic lupus erythematosus associated with etanercept therapy. Lancet 2002, 359:579-580.
4.
Wallis RS, Broder MS, Wong JY, Hanson ME, Beenhouwer DO: Granulomatous infectious diseases associated with tumor necrosis factor antagonists. Clin Infect Dis 2004, 38:1261-1265.
5.
Huang Q, Liu D, Majewski P, Schulte LC, Korn JM, Young RA, Lander ES, Hacohen N: The plasticity of dendritic cell responses to pathogens and their components. Science 2001, 294:870-875.
6.
Nau GJ, Richmond JF, Schlesinger A, Jennings EG, Lander ES, Young RA: Human macrophage activation programs induced by bacterial pathogens. Proc Natl Acad Sci USA 2002, 99:1503-1508.
7.
Akira S, Takeda K: Toll-like receptor signaling. Nat Rev Immunol 2004, 4:499-511.
8.
Yamamoto M, Sato S, Hemmi H, Hoshino K, Kaisho T, Sanjo H, Takeuchi O, Sugiyama M, Okabe M, Takeda K et al.: Role of adaptor TRIF in the MyD88-independent toll-like receptor signaling pathway. Science 2003, 301:640-643.
9.
Hoebe K, Du X, Georgel P, Janssen E, Tabeta K, Kim SO, Goode J, Lin P, Mann N, Mudd S et al.: Identification of Lps2 as a key transducer of MyD88-independent TIR signaling. Nature 2003, 424:743-748.
The ultimate goal of systems biology (indeed biology in general) is to deliver predictive models of complex biological systems such as innate immunity. Mathematical modeling provides a framework to systematically explore and analyze biological data while avoiding logical inconsistencies, and is easy to document, communicate, and inspect in detail. Models can be statistical (e.g. based on correlations) or mechanistic (e.g. based on biochemical kinetics). They can also be applied at different levels of resolution (for examples, see [50]). Even the simplest system can have diverse outcomes that can be predicted using formalized mathematical equations, thereby providing unique insights into the molecular and kinetic parameters that control system behavior (Figure 2). At the level of atomic resolution, the structural modeling of drugs, their targets and interactions is relatively common in the pharmaceutical industry (for a review see, [51]). Likewise, at the other end of the spectrum, models of the spread of disease and of patient responses to drugs are well established (see, for example, [52]). At the level of intra- and inter-cellular interaction networks, a wide variety of models of adaptive immunity addressing both overall system dynamics and biochemical kinetics have been developed (for a review, see [53]). By contrast, the innate immune system has received relatively little attention. An exception is NF-kB regulation, which is downstream of several signaling pathways including the TLR– IL-1 and TNF families of receptors. The TNF–NF-kB network has been the subject of several studies, including proteomics mapping [30] and kinetic modeling of response dynamics [54–56]. Some of the leading kinetic simulation and analysis packages are listed on the Systems Biology Markup Language website (Table 1).
Conclusions The systems biology of innate immunity is in its infancy, with many of the efforts still directed at defining the parts lists and connectivity for the regulatory networks that control these responses. The promise of personalized genomics combined with high throughput diagnostics will revolutionize our understanding of innate immunity and the treatment of its disorders. These advances are predicated on further developments in technologies that quantitatively define biological systems, and of data processing, integration and analysis tools and methodologies.
Acknowledgements We thank Adrian Ozinsky, Erica Andersen-Nissen, Colleen Sheridan and Alan Aderem for helpful comments. KS and HB are supported by grants from the National Institutes of Health (U54AI54523 [HB] and R01AI052286 [KS]). www.sciencedirect.com
Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest
10. Weinmann AS, Farnham PJ: Identification of unknown target genes of human transcription factors using chromatin immunoprecipitation. Methods 2002, 26:37-47. 11. Buck MJ, Lieb JD: ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 2004, 83:349-360. 12. Cawley S, Bekiranov S, Ng HH, Kapranov P, Sekinger EA, Kampa D, Piccolboni A, Sementchenko V, Cheng J, Williams AJ et al.: Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 2004, 116:499-509. 13. Beutler B, Rietschel ET: Innate immune sensing and its roots: the story of endotoxin. Nat Rev Immunol 2003, 3:169-176. 14. Hawn TR, Verbon A, Lettinga KD, Zhao LP, Li SS, Laws RJ, Skerrett SJ, Beutler B, Schroeder L, Nachman A et al.: A common dominant TLR5 stop codon polymorphism abolishes flagellin signaling and is associated with susceptibility to legionnaires’ disease. J Exp Med 2003, 198:1563-1572. 15. Picard C, Puel A, Bonnet M, Ku CL, Bustamante J, Yang K, Soudais C, Dupuis S, Feinberg J, Fieschi C et al.: Pyogenic bacterial infections in humans with IRAK-4 deficiency. Science 2003, 299:2076-2079. Epub 2003 Mar 2013. 16. Beutler B: Inferences, questions and possibilities in Toll-like receptor signalling. Nature 2004, 430:257-263. 17. Kobayashi K, Hernandez LD, Galan JE, Janeway CA Jr, Medzhitov R, Flavell RA: IRAK-M is a negative regulator of Toll-like receptor signaling. Cell 2002, 110:191-202. 18. Kinjyo I, Hanada T, Inagaki-Ohara K, Mori H, Aki D, Ohishi M, Yoshida H, Kubo M, Yoshimura A: SOCS1/JAB is a negative regulator of LPS-induced macrophage activation. Immunity 2002, 17:583-591. 19. Brint EK, Xu D, Liu H, Dunne A, McKenzie AN, O’Neill LA, Liew FY: ST2 is an inhibitor of interleukin 1 receptor and Toll-like Current Opinion in Immunology 2005, 17:49–54
54 Innate immunity
receptor 4 signaling and maintains endotoxin tolerance. Nat Immunol 2004, 5:373-379. Epub 2004 Mar 2007. 20. Janssens S, Burns K, Vercammen E, Tschopp J, Beyaert R: MyD88S, a splice variant of MyD88, differentially modulates NF-kappaB- and AP-1-dependent gene expression. FEBS Lett 2003, 548:103-107. 21. Hardy MP, O’Neill LA: The murine IRAK2 gene encodes four alternatively spliced isoforms, two of which are inhibitory. J Biol Chem 2004, 279:27699-27708. Epub 22004 Apr 27613. 22. Sly LM, Rauh MJ, Kalesnikoff J, Song CH, Krystal G: LPS-Induced Upregulation of SHIP Is Essential for Endotoxin Tolerance. Immunity 2004, 21:227-239. 23. Hoshino K, Kaisho T, Iwabe T, Takeuchi O, Akira S: Differential involvement of IFN-beta in Toll-like receptor-stimulated dendritic cell activation. Int Immunol 2002, 14:1225-1231. 24. Nathan D, Sterner DE, Berger SL: Histone modifications: Now summoning sumoylation. Proc Natl Acad Sci USA 2003, 100:13118-13120. 25. Yamamoto M, Yamazaki S, Uematsu S, Sato S, Hemmi H, Hoshino K, Kaisho T, Kuwata H, Takeuchi O, Takeshige K et al.: Regulation of Toll/IL-1-receptor-mediated gene expression by the inducible nuclear protein IkappaBzeta. Nature 2004, 430:218-222. 26. Foley E, O’Farrell PH: Functional Dissection of an Innate Immune Response by a Genome-Wide RNAi Screen. PLoS Biol 2004, 2:E203. Epub 2004 Jun 2022. 27. Paddison PJ, Silva JM, Conklin DS, Schlabach M, Li M, Aruleba S, Balija V, O’Shaughnessy A, Gnoj L, Scobie K et al.: A resource for large-scale RNA-interference-based screens in mammals. Nature 2004, 428:427-431. See annotation to [28]. 28. Berns K, Hijmans EM, Mullenders J, Brummelkamp TR, Velds A, Heimerikx M, Kerkhoven RM, Madiredjo M, Nijkamp W, Weigelt B et al.: A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 2004, 428:431-437. This reference, together with [27] describe the development and feasibility of retrovirus based tools for RNAi screens in human and mouse cell lines. This technology couples high levels of saturation with easy target identification, and promises to be broadly applicable to studying mammalian biological systems, including innate immunity. 29. Miller J, Stagljar I: Using the yeast two-hybrid system to identify interacting proteins. Methods Mol Biol 2004, 261:247-262. 30. Bouwmeester T, Bauch A, Ruffner H, Angrand PO, Bergamini G, Croughton K, Cruciat C, Eberhard D, Gagneur J, Ghidelli S et al.: A physical and functional map of the human TNF-alpha/NF-kappa B signal transduction pathway. Nat Cell Biol 2004, 6:97-105. This article demonstrates the utility of tandem affinity purification technology to define the TNF signaling pathway and how it changes during activation. The strategy dramatically expanded our understanding of TNF signaling, and established a paradigm for future proteomic analyses of signaling pathways.
36. Newman JR, Keating AE: Comprehensive identification of human bZIP interactions with coiled-coil arrays. Science 2003, 300:2097-2101. 37. Michaud GA, Salcius M, Zhou F, Bangham R, Bonin J, Guo H, Snyder M, Predki PF, Schweitzer BI: Analyzing antibody specificity with whole proteome microarrays. Nat Biotechnol 2003, 21:1509-1512. Epub 2003 Nov 1509. 38. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O’Shea EK, Weissman JS: Global analysis of protein expression in yeast. Nature 2003, 425:737-741. 39. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O’Shea EK: Global analysis of protein localization in budding yeast. Nature 2003, 425:686-691. 40. Weijer CJ: Visualizing signals moving in cells. Science 2003, 300:96-100. 41. Forrester JS, Milne SB, Ivanova PT, Brown HA: Computational lipidomics: a multiplexed analysis of dynamic changes in membrane lipid composition during signal transduction. Mol Pharmacol 2004, 65:813-821. 42. Kanehisa M, Bork P: Bioinformatics in the post-sequence era. Nat Genet 2003, 33(Suppl):305-310. 43. Wasserman WW, Sandelin A: Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet 2004, 5:276-287. 44. Nardone J, Lee DU, Ansel KM, Rao A: Bioinformatics for the ‘bench biologist’: how to find regulatory regions in genomic DNA. Nat Immunol 2004, 5:768-774. 45. Lu L, Arakaki AK, Lu H, Skolnick J: Multimeric threading-based prediction of protein-protein interactions on a genomic scale: application to the Saccharomyces cerevisiae proteome. Genome Res 2003, 13:1146-1154. 46. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302:449-453. 47. Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5:101-113. 48. Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M et al.: Global mapping of the yeast genetic interaction network. Science 2004, 303:808-813. 49. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G et al.: Genetics of gene expression surveyed in maize, mouse and man. Nature 2003, 422:297-302. 50. Bower JM, Bolouri H: Computational Modeling of Genetic and Biochemical Networks. Cambridge, MA: The MIT Press; 2001. 51. Jorgensen WL: The many roles of computation in drug discovery. Science 2004, 303:1813-1818.
31. Wu CC, MacCoss MJ, Howell KE, Yates JR III: A method for the comprehensive proteomic analysis of membrane proteins. Nat Biotechnol 2003, 21:532-538.
52. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DD: HIV-1 dynamics in vivo: virion clearance rate, infected cell lifespan, and viral generation time. Science 1996, 271:1582-1586.
32. Mootha VK, Bunkenborg J, Olsen JV, Hjerrild M, Wisniewski JR, Stahl E, Bolouri MS, Ray HN, Sihag S, Kamal M et al.: Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell 2003, 115:629-640.
53. Goldstein B, Faeder JR, Hlavacek WS: Mathematical and computational models of immune-receptor signaling. Nat Rev Immunol 2004, 4:445-456. This is an excellent overview and introduction to biochemical kinetics modeling.
33. Gagnon E, Duclos S, Rondeau C, Chevet E, Cameron PH, Steele-Mortimer O, Paiement J, Bergeron JJ, Desjardins M: Endoplasmic reticulum-mediated phagocytosis is a mechanism of entry into macrophages. Cell 2002, 110:119-131. 34. Houde M, Bertholet S, Gagnon E, Brunet S, Goyette G, Laplante A, Princiotta MF, Thibault P, Sacks D, Desjardins M: Phagosomes are competent organelles for antigen cross-presentation. Nature 2003, 425:402-406. 35. Zhu H, Bilgin M, Bangham R, Hall D, Casamayor A, Bertone P, Lan N, Jansen R, Bidlingmaier S, Houfek T et al.: Global analysis of protein activities using proteome chips. Science 2001, 293:2101-2105.
Current Opinion in Immunology 2005, 17:49–54
54. Hoffmann A, Levchenko A, Scott ML, Baltimore D: The IkappaBNF-kappaB signaling module: temporal control and selective gene activation. Science 2002, 298:1241-1245. 55. Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M: Mathematical model of NF-kappaB regulatory module. J Theor Biol 2004, 228:195-215. 56. Nelson DE, Ihekwaba AEC, Elliott M, Johnson JR, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG et al.: Oscillations in NF-kB signaling control the dynamics of gene expression leading to export of the complex to the cytoplasm. Science 2004, 306:704-708.
www.sciencedirect.com
Because of its complex dynamic behavior, its wealth of intricate intra- and inter-cellular interactions, and its medical significance, the immune system offers an ideal focus for systems biology. The innate immune response is highly context dependent, suggesting unplumbed complexity. Depending on the context, host and pathogen factors can be both protective and injurious. For example, Toll-like receptor (TLR)-2 and myeloid differentiation antigen 88 (MyD88) protect against systemic spread of Group B Streptococcus during low dose challenge; by contrast, TLR2 and MyD88 promote lethality to high doses of the same pathogen [1]. Moreover, all infections and inflammatory disorders are not created equal; TNF antagonism is an efficacious therapeutic target in rheumatoid arthritis [2], but can exacerbate systemic lupus erythematosus [3] and mycobacterial infections [4].
Corresponding author: Bolouri, Hamid ([email protected])
Current Opinion in Immunology 2005, 17:49–54 This review comes from a themed issue on Innate immunity Edited by Alan Aderem and Elena Levashina Available online 8th December 2004 0952-7915/$ – see front matter # 2005 Elsevier Ltd. All rights reserved. DOI 10.1016/j.coi.2004.11.005
Thus, interactions, feedback, dynamic behavior, complexity and context are widespread in, and essential to, innate immunity. These are exactly the issues that systems biology aims to address; raising hopes for early and accurate diagnoses, the instigation of preventative strategies, and the development specific treatments for infectious and inflammatory diseases. Systems biology also presents the opportunity to personalize diagnoses and therapies, taking into account the genetic and environmental factors that make each individual unique.
Abbreviations ChIP chromatin immunoprecipitation IRAK IL-1 receptor-associated kinase MyD88 myeloid differentiation antigen 88 RNAi RNA interference TLR Toll-like receptor
The rest of this review is divided into two sections reviewing the current technologies and computational tools of systems biology with particular emphasis on applicability to innate immune studies. Because of our backgrounds, we have focused on studies in the biology of TLRs as a representative facet of innate immunity.
Introduction
Technologies of systems biology
Systems biology is concerned with understanding the dynamic outcome of molecular interactions between biomolecules (e.g. DNA, RNA, proteins, lipids and metabolites) at the pathway, organelle, cell, organ and organism levels (Figure 1). The methodology of systems biology is an iterative process that begins with the identification of component parts and their respective interactions. This information is then integrated into a predictive (usually mathematical) model of system behavior, which is in essence a hypothesis that can be tested experimentally. The experimental results lead to the refinement of the model and, hence, new hypotheses.
Genomic technologies
www.sciencedirect.com
Genomic technologies aim to define both static and dynamic aspects of genomes within biological systems, including sequence, sequence variation and gene expression. In addition, genomic tools can be used to manipulate genetic sequence and gene expression to help us to better understand biological systems. Transcriptional regulation: gene expression
The most robust technology for global systems measurements is microarray-based analysis of mRNA abundance. Initial studies focused largely on the regulation of gene Current Opinion in Immunology 2005, 17:49–54
50 Innate immunity
Figure 1
Genetic variation Systems biology
Technology
Computation Biology Current Opinion in Immunology
The concept of systems biology. Systems biology is founded on the synergistic interplay between biological, technological and computational sciences. Biological questions drive technological advances, which require new computational tools. Similarly, technological and computational advances provoke biological insight and new models for biological systems.
expression in macrophages and dendritic cells by wellknown TLR agonists and whole microbes [5,6]. These studies identified ‘core’ responses to pathogens, suggesting that at least one part of the innate immune response to pathogens is generic, as the response is similar for pathogens that differ substantially (viruses, bacteria and fungi). This core response exists despite the complex pattern of TLR usage for four different adaptors of the MyD88 family [7], suggesting that different receptors utilize overlapping signaling networks to achieve similar outcomes. TLR-specific responses are also apparent, with the most obvious difference attributable to the unique set of genes induced through TLR3 and TLR4, utilizing the common proximal adaptor TRIF (the Toll/IL-1 receptor (TIR)-domain-containing adapter inducing IFN-b) [8,9]. Transcriptional regulation: protein–DNA interactions
The hybridization of microarrays that probe entire genomic regions with chromatin immunoprecipitation (ChIP) products provides a high-throughput method (ChIP– chip) to quantify the network of protein–DNA interactions (for a review of ChIP methods, see [10]; for a review of design issues, see [11]. This technology has been used to determine NF-kB binding sites on human chromosome 22 in a human cervical carcinoma derived epithelial cell line (HeLa cells) [11], as well as binding sites for cMyc, Sp1 and p53 in a human T cell lymphoma derived cell line (Jurkat) and a human colorectal carcinoma derived cell line (HCT) on chromosomes 21 and 22 [12]. Current Opinion in Immunology 2005, 17:49–54
Genetic variation has been a traditional data source for defining genetic contributions to normal physiology and disease in innate immunity [13–15]. Spontaneous mutations have been aided by chemical mutagenesis and phenotypic screens to identify additional genes that contribute to innate immunity, including TRIF as the proximal adaptor required for MyD88-independent signaling [9], and CD36 as a novel co-receptor required for efficient recognition of diacylated lipopeptides by the TLR2– TLR6 heterodimer [16]. Targeted mutagenesis of genes using traditional and conditional knockout strategies is a low throughput but highly effective means to dissect genetic contributions to innate immunity [7]. The combination of spontaneous, chemically induced and targeted mutants have generated dozens of mice with defects in the TLR signaling pathway, which is a rich source for systems-biology-based experimentation. Insight into the complexity of the TLR regulatory network is beginning to emerge from such studies, and several different genes have been implicated in transcriptionally regulated negative feedback loops, including IL-1 receptor-associated kinase 3 (IRAK-3, also referred to as IRAK-M) [17], suppressor of cytokine signaling 1 (SOCS1) [18], suppression of tumorigenicity 2 (ST2) [19], alternative splice forms of MyD88 [20] and IRAK2 [21], and most recently inositol polyphosphate-5-phosphatase (INPP5D, also known as SHIP) [22]. These studies suggest that there is a higher degree of regulation in TLR signaling than currently appreciated; a property that will only be revealed when these components are analyzed simultaneously as part of a system. Signaling and transcriptional complexities are also emerging, for example IFNb-mediated autocrine amplification of the lipopolysaccharide (LPS)–TLR4 signal [23,24], and IkBz-dependent transcriptional regulation of LPS-induced IL-6 production [25]. RNA interference
RNA interference (RNAi) is the most promising new strategy to systematically perturb gene expression, and to perform saturation mutagenesis in cell-based systems of innate immunity. Using the Drosophila macrophage S2 cell line, Foley and O’Farrel [26] conducted a genomewide RNAi screen with 7216 double-stranded RNAs to identify 121 molecular components that regulate activation of NF-kB and diptericin reporters in response to Gram-negative bacteria. In addition to identifying known components in this pathway, the study also revealed novel feedback interactions upstream of NF-kB activation, as well as novel functional categories of genes that regulate innate immune signaling, such as RNA splicing, Ras signaling and cytoskeleton regulation [26]. The refinement of retroviral methods for RNAi (see the RNAi Codex website; Table 1) permits similar broad screens with high saturation levels in mammalian innate immunity, which have been carried out for the proteasome and p53 [27,28]. www.sciencedirect.com
Innate immune responses and systems biology Smith and Bolouri 51
Table 1 Useful website addresses. Name of site
URL
RNAi Codex The Alliance for Cellular Signaling The Signaling Gateway Biocarta Kyoto Encyclopedia of Genes and Genomes The Pharmacogenetics and Pharmacogenomics Knowledge Base BioCyc Database of Interacting Proteins Biomolecular Interaction Database Human Protein Reference Database Cytoscape Osprey VisANT Gene Ontology Systems Biology Markup Language
http://katahdin.cshl.org/scripts/main.pl http://www.afcs.org http://www.signaling-gateway.org/ http://www.biocarta.com/genes/index.asp http://www.genome.ad.jp/kegg/ http://www.pharmgkb.org/ http://www.biocyc.org/ http://dip.doe-mbi.ucla.edu/ http://bind.ca http://www.hprd.org http://www.cytoscape.org/ http://biodata.mshri.on.ca/osprey/ http://visant.bu.edu/ http://www.geneontology.org http://sbml.org/
Proteomic technologies
Protein microarray technologies
Proteomic technologies aim to capture comprehensive information about protein quantities, protein states and protein interactions within biological systems.
Protein microarray technologies are also being used for systematic determination of protein activities at a genomic scale in yeast [35], and the direct identification of protein binding partners in the human basic-region leucine zipper (bZIP) transcription factor family [36]. The major bottleneck is the generation of reagents that specifically and quantitatively identify proteins, which for the most part has relied upon monoclonal antibodies or the creation of labeled recombinant proteins [37].
Yeast two hybrid technology
Yeast two hybrid technology (Y2H) is a mainstay for identifying protein interactions, but is limited by the fact that interactions are typically established in a nonphysiological environment, in the absence of many co-factors and post-translational modifications (for a recent review see [29]). Tandem affinity purification
Tandem affinity purification (TAP) also originated in yeast and has been successfully applied in mammalian cells to identify proteins within molecular complexes [30]. Bouwmeester et al. [30] used TAP to map the TNF signaling network responsible for regulating NF-kB activation in HEK 293 cells. Using this powerful approach, a network of 131 proteins and 221 interactions was created using 32 known components of the TNF signaling pathway, including 80 novel components. As this technology is rather new, we are likely to see it applied to many more regulatory pathways relevant to innate immunity. Mass spectrometry
Mass spectrometry has been applied to quantitative protein measurements of subcellular fractions and specific protein modifications, including phosphorylation and glycosylation. Examples include transmembrane proteins [31], mitochondria [32] and elegant studies of the phagosome [33]. These studies defined a novel mode of phagocytosis, and provided new insights into potential mechanism of cross-presentation of exogenous antigens by class I MHC [34]. www.sciencedirect.com
Protein localization
Protein localization has been systematically applied to yeast using tagged proteins [38,39], and similar studies are under way in mammalian cells (for example see RAW 264.7 image data collected by the Alliance for Cellular Signaling; Table 1. Fluorescence resonance energy transfer
Fluorescence resonance energy transfer (FRET) can localize and quantify protein interactions in living cells (for a recent review, see [40]). This and other related strategies are critical for quantifying real-time molecular information in living biological systems. Metabolomics and lipidomics
The role of small molecules and lipids in innate immunity remains largely uncharted. Lipids, in particular, are known to play a crucial role in the pathogenesis of vascular diseases and also play an important role in establishing membrane subdomains and acting as second messengers in signal transduction. Large-scale lipid analyses have recently been developed using electron ion spray mass spectrometry [41], and large datasets are emerging from studies conducted in the RAW 264.7 murine macrophage cell line (see lipidomics datasets in the Alliance for Cellular Signaling Data Center at The Signaling Gateway (Table 1). Current Opinion in Immunology 2005, 17:49–54
52 Innate immunity
Computational analysis
Computational systems biology spans a very wide range. We focus this review on those aspects that are particularly novel or pertinent to systems biology studies of innate immunity. DNA/protein sequence searching and analysis
DNA and protein sequence searching and analysis have been part and parcel of genomics from its inception (see [42] for a short historical review). Recent efforts have been directed at the identification of cis-regulatory modules and transcription factor binding sites, which are essential components of transcriptional regulatory networks. Although the false positive and miss rates of current algorithms are still short of the ideal, prediction accuracies are improving rapidly (see [43,44] for reviews and on-line tutorials). Lu and Skolnick [45] demonstrated the feasibility of predicting protein–protein interactions on a genomic scale by using the relatively large volume of protein structures solved to date. These methods become increasingly useful when combined with other data using statistical data integration methods [46]. Pathway databases
Pathway databases can provide a network context for data analysis. Among those that we have found most useful are BioCarta, which provides graphical network diagrams
(Table 1), the Kyoto Encyclopedia of Genes and Genomes, which can be accessed both graphically and programmatically (Table 1), The Pharmacogenetics and Pharmacogenomics Knowledge Base (Table 1), and the multispecies, multidatabase resource for metabolic pathways BioCyc (Table 1). There are also now an increasing number of very useful, curated protein interaction databases, such as the Database of Interacting Proteins (Table 1), the Biomolecular Interaction Database (Table 1), and the Human Protein Reference Database (Table 1). Network topology visualization and analysis
The most intuitive way to analyze biomolecular interaction data is through the visualization of networks. Three very useful packages have been developed especially for systems biology: Cytoscape (Table 1), Osprey (Table 1) and VisANT (Table 1). These packages offer a wide range of network analysis tools such as clustering of biomolecules by ontology, expression, or interactions for analysis of high level network organization (see [47] for a review). Integration of genetic data
The integration of genetic data with high throughput assays for gene expression, protein–protein, and proteinDNA interactions, and knowledge bases such as Gene Ontology (Table 1) has so far best been demonstrated in yeast (see for example [48]). However, integrated analysis
Figure 2
(a)
(b)
(c)
(d)
Current Opinion in Immunology
Complex and diverse dynamic behaviors exhibited by a simple network. Even the simplest feedback loop can create complex behaviors. Moreover, the same topology can lead to qualitatively different behaviors depending on kinetic parameters such as transcriptional efficiency and protein half-lives. Here, all four figures model the same simple negative feedback circuit: a gene whose product represses its own transcription. Depending on kinetics, very different behaviors may result. (a) Tuned for regulated level. (b) Single transcriptional pulse. (c) Tuned for rapid response. (d) Tuned for long-lasting oscillation. Graphs show the simulated expression level of the auto-repressive gene for different values of protein half-life and protein–DNA binding equilibrium constant. Current Opinion in Immunology 2005, 17:49–54
www.sciencedirect.com
Innate immune responses and systems biology Smith and Bolouri 53
of this type in metazoans is already providing new insights in human disease (see for example [49]) and is likely to have considerable impact on our current understanding of innate immunity.
References and recommended reading
Quantitative and qualitative modeling of dynamic behavior
1.
Mancuso G, Midiri A, Beninati C, Biondo C, Galbo R, Akira S, Henneke P, Golenbock D, Teti G: Dual role of TLR2 and myeloid differentiation factor 88 in a mouse model of invasive group B streptococcal disease. J Immunol 2004, 172:6324-6329.
2.
Olsen NJ, Stein CM: New drugs for rheumatoid arthritis. N Engl J Med 2004, 350:2167-2179.
3.
Shakoor N, Michalska M, Harris CA, Block JA: Drug-induced systemic lupus erythematosus associated with etanercept therapy. Lancet 2002, 359:579-580.
4.
Wallis RS, Broder MS, Wong JY, Hanson ME, Beenhouwer DO: Granulomatous infectious diseases associated with tumor necrosis factor antagonists. Clin Infect Dis 2004, 38:1261-1265.
5.
Huang Q, Liu D, Majewski P, Schulte LC, Korn JM, Young RA, Lander ES, Hacohen N: The plasticity of dendritic cell responses to pathogens and their components. Science 2001, 294:870-875.
6.
Nau GJ, Richmond JF, Schlesinger A, Jennings EG, Lander ES, Young RA: Human macrophage activation programs induced by bacterial pathogens. Proc Natl Acad Sci USA 2002, 99:1503-1508.
7.
Akira S, Takeda K: Toll-like receptor signaling. Nat Rev Immunol 2004, 4:499-511.
8.
Yamamoto M, Sato S, Hemmi H, Hoshino K, Kaisho T, Sanjo H, Takeuchi O, Sugiyama M, Okabe M, Takeda K et al.: Role of adaptor TRIF in the MyD88-independent toll-like receptor signaling pathway. Science 2003, 301:640-643.
9.
Hoebe K, Du X, Georgel P, Janssen E, Tabeta K, Kim SO, Goode J, Lin P, Mann N, Mudd S et al.: Identification of Lps2 as a key transducer of MyD88-independent TIR signaling. Nature 2003, 424:743-748.
The ultimate goal of systems biology (indeed biology in general) is to deliver predictive models of complex biological systems such as innate immunity. Mathematical modeling provides a framework to systematically explore and analyze biological data while avoiding logical inconsistencies, and is easy to document, communicate, and inspect in detail. Models can be statistical (e.g. based on correlations) or mechanistic (e.g. based on biochemical kinetics). They can also be applied at different levels of resolution (for examples, see [50]). Even the simplest system can have diverse outcomes that can be predicted using formalized mathematical equations, thereby providing unique insights into the molecular and kinetic parameters that control system behavior (Figure 2). At the level of atomic resolution, the structural modeling of drugs, their targets and interactions is relatively common in the pharmaceutical industry (for a review see, [51]). Likewise, at the other end of the spectrum, models of the spread of disease and of patient responses to drugs are well established (see, for example, [52]). At the level of intra- and inter-cellular interaction networks, a wide variety of models of adaptive immunity addressing both overall system dynamics and biochemical kinetics have been developed (for a review, see [53]). By contrast, the innate immune system has received relatively little attention. An exception is NF-kB regulation, which is downstream of several signaling pathways including the TLR– IL-1 and TNF families of receptors. The TNF–NF-kB network has been the subject of several studies, including proteomics mapping [30] and kinetic modeling of response dynamics [54–56]. Some of the leading kinetic simulation and analysis packages are listed on the Systems Biology Markup Language website (Table 1).
Conclusions The systems biology of innate immunity is in its infancy, with many of the efforts still directed at defining the parts lists and connectivity for the regulatory networks that control these responses. The promise of personalized genomics combined with high throughput diagnostics will revolutionize our understanding of innate immunity and the treatment of its disorders. These advances are predicated on further developments in technologies that quantitatively define biological systems, and of data processing, integration and analysis tools and methodologies.
Acknowledgements We thank Adrian Ozinsky, Erica Andersen-Nissen, Colleen Sheridan and Alan Aderem for helpful comments. KS and HB are supported by grants from the National Institutes of Health (U54AI54523 [HB] and R01AI052286 [KS]). www.sciencedirect.com
Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest
10. Weinmann AS, Farnham PJ: Identification of unknown target genes of human transcription factors using chromatin immunoprecipitation. Methods 2002, 26:37-47. 11. Buck MJ, Lieb JD: ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 2004, 83:349-360. 12. Cawley S, Bekiranov S, Ng HH, Kapranov P, Sekinger EA, Kampa D, Piccolboni A, Sementchenko V, Cheng J, Williams AJ et al.: Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 2004, 116:499-509. 13. Beutler B, Rietschel ET: Innate immune sensing and its roots: the story of endotoxin. Nat Rev Immunol 2003, 3:169-176. 14. Hawn TR, Verbon A, Lettinga KD, Zhao LP, Li SS, Laws RJ, Skerrett SJ, Beutler B, Schroeder L, Nachman A et al.: A common dominant TLR5 stop codon polymorphism abolishes flagellin signaling and is associated with susceptibility to legionnaires’ disease. J Exp Med 2003, 198:1563-1572. 15. Picard C, Puel A, Bonnet M, Ku CL, Bustamante J, Yang K, Soudais C, Dupuis S, Feinberg J, Fieschi C et al.: Pyogenic bacterial infections in humans with IRAK-4 deficiency. Science 2003, 299:2076-2079. Epub 2003 Mar 2013. 16. Beutler B: Inferences, questions and possibilities in Toll-like receptor signalling. Nature 2004, 430:257-263. 17. Kobayashi K, Hernandez LD, Galan JE, Janeway CA Jr, Medzhitov R, Flavell RA: IRAK-M is a negative regulator of Toll-like receptor signaling. Cell 2002, 110:191-202. 18. Kinjyo I, Hanada T, Inagaki-Ohara K, Mori H, Aki D, Ohishi M, Yoshida H, Kubo M, Yoshimura A: SOCS1/JAB is a negative regulator of LPS-induced macrophage activation. Immunity 2002, 17:583-591. 19. Brint EK, Xu D, Liu H, Dunne A, McKenzie AN, O’Neill LA, Liew FY: ST2 is an inhibitor of interleukin 1 receptor and Toll-like Current Opinion in Immunology 2005, 17:49–54
54 Innate immunity
receptor 4 signaling and maintains endotoxin tolerance. Nat Immunol 2004, 5:373-379. Epub 2004 Mar 2007. 20. Janssens S, Burns K, Vercammen E, Tschopp J, Beyaert R: MyD88S, a splice variant of MyD88, differentially modulates NF-kappaB- and AP-1-dependent gene expression. FEBS Lett 2003, 548:103-107. 21. Hardy MP, O’Neill LA: The murine IRAK2 gene encodes four alternatively spliced isoforms, two of which are inhibitory. J Biol Chem 2004, 279:27699-27708. Epub 22004 Apr 27613. 22. Sly LM, Rauh MJ, Kalesnikoff J, Song CH, Krystal G: LPS-Induced Upregulation of SHIP Is Essential for Endotoxin Tolerance. Immunity 2004, 21:227-239. 23. Hoshino K, Kaisho T, Iwabe T, Takeuchi O, Akira S: Differential involvement of IFN-beta in Toll-like receptor-stimulated dendritic cell activation. Int Immunol 2002, 14:1225-1231. 24. Nathan D, Sterner DE, Berger SL: Histone modifications: Now summoning sumoylation. Proc Natl Acad Sci USA 2003, 100:13118-13120. 25. Yamamoto M, Yamazaki S, Uematsu S, Sato S, Hemmi H, Hoshino K, Kaisho T, Kuwata H, Takeuchi O, Takeshige K et al.: Regulation of Toll/IL-1-receptor-mediated gene expression by the inducible nuclear protein IkappaBzeta. Nature 2004, 430:218-222. 26. Foley E, O’Farrell PH: Functional Dissection of an Innate Immune Response by a Genome-Wide RNAi Screen. PLoS Biol 2004, 2:E203. Epub 2004 Jun 2022. 27. Paddison PJ, Silva JM, Conklin DS, Schlabach M, Li M, Aruleba S, Balija V, O’Shaughnessy A, Gnoj L, Scobie K et al.: A resource for large-scale RNA-interference-based screens in mammals. Nature 2004, 428:427-431. See annotation to [28]. 28. Berns K, Hijmans EM, Mullenders J, Brummelkamp TR, Velds A, Heimerikx M, Kerkhoven RM, Madiredjo M, Nijkamp W, Weigelt B et al.: A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 2004, 428:431-437. This reference, together with [27] describe the development and feasibility of retrovirus based tools for RNAi screens in human and mouse cell lines. This technology couples high levels of saturation with easy target identification, and promises to be broadly applicable to studying mammalian biological systems, including innate immunity. 29. Miller J, Stagljar I: Using the yeast two-hybrid system to identify interacting proteins. Methods Mol Biol 2004, 261:247-262. 30. Bouwmeester T, Bauch A, Ruffner H, Angrand PO, Bergamini G, Croughton K, Cruciat C, Eberhard D, Gagneur J, Ghidelli S et al.: A physical and functional map of the human TNF-alpha/NF-kappa B signal transduction pathway. Nat Cell Biol 2004, 6:97-105. This article demonstrates the utility of tandem affinity purification technology to define the TNF signaling pathway and how it changes during activation. The strategy dramatically expanded our understanding of TNF signaling, and established a paradigm for future proteomic analyses of signaling pathways.
36. Newman JR, Keating AE: Comprehensive identification of human bZIP interactions with coiled-coil arrays. Science 2003, 300:2097-2101. 37. Michaud GA, Salcius M, Zhou F, Bangham R, Bonin J, Guo H, Snyder M, Predki PF, Schweitzer BI: Analyzing antibody specificity with whole proteome microarrays. Nat Biotechnol 2003, 21:1509-1512. Epub 2003 Nov 1509. 38. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O’Shea EK, Weissman JS: Global analysis of protein expression in yeast. Nature 2003, 425:737-741. 39. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O’Shea EK: Global analysis of protein localization in budding yeast. Nature 2003, 425:686-691. 40. Weijer CJ: Visualizing signals moving in cells. Science 2003, 300:96-100. 41. Forrester JS, Milne SB, Ivanova PT, Brown HA: Computational lipidomics: a multiplexed analysis of dynamic changes in membrane lipid composition during signal transduction. Mol Pharmacol 2004, 65:813-821. 42. Kanehisa M, Bork P: Bioinformatics in the post-sequence era. Nat Genet 2003, 33(Suppl):305-310. 43. Wasserman WW, Sandelin A: Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet 2004, 5:276-287. 44. Nardone J, Lee DU, Ansel KM, Rao A: Bioinformatics for the ‘bench biologist’: how to find regulatory regions in genomic DNA. Nat Immunol 2004, 5:768-774. 45. Lu L, Arakaki AK, Lu H, Skolnick J: Multimeric threading-based prediction of protein-protein interactions on a genomic scale: application to the Saccharomyces cerevisiae proteome. Genome Res 2003, 13:1146-1154. 46. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302:449-453. 47. Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5:101-113. 48. Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M et al.: Global mapping of the yeast genetic interaction network. Science 2004, 303:808-813. 49. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G et al.: Genetics of gene expression surveyed in maize, mouse and man. Nature 2003, 422:297-302. 50. Bower JM, Bolouri H: Computational Modeling of Genetic and Biochemical Networks. Cambridge, MA: The MIT Press; 2001. 51. Jorgensen WL: The many roles of computation in drug discovery. Science 2004, 303:1813-1818.
31. Wu CC, MacCoss MJ, Howell KE, Yates JR III: A method for the comprehensive proteomic analysis of membrane proteins. Nat Biotechnol 2003, 21:532-538.
52. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DD: HIV-1 dynamics in vivo: virion clearance rate, infected cell lifespan, and viral generation time. Science 1996, 271:1582-1586.
32. Mootha VK, Bunkenborg J, Olsen JV, Hjerrild M, Wisniewski JR, Stahl E, Bolouri MS, Ray HN, Sihag S, Kamal M et al.: Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell 2003, 115:629-640.
53. Goldstein B, Faeder JR, Hlavacek WS: Mathematical and computational models of immune-receptor signaling. Nat Rev Immunol 2004, 4:445-456. This is an excellent overview and introduction to biochemical kinetics modeling.
33. Gagnon E, Duclos S, Rondeau C, Chevet E, Cameron PH, Steele-Mortimer O, Paiement J, Bergeron JJ, Desjardins M: Endoplasmic reticulum-mediated phagocytosis is a mechanism of entry into macrophages. Cell 2002, 110:119-131. 34. Houde M, Bertholet S, Gagnon E, Brunet S, Goyette G, Laplante A, Princiotta MF, Thibault P, Sacks D, Desjardins M: Phagosomes are competent organelles for antigen cross-presentation. Nature 2003, 425:402-406. 35. Zhu H, Bilgin M, Bangham R, Hall D, Casamayor A, Bertone P, Lan N, Jansen R, Bidlingmaier S, Houfek T et al.: Global analysis of protein activities using proteome chips. Science 2001, 293:2101-2105.
Current Opinion in Immunology 2005, 17:49–54
54. Hoffmann A, Levchenko A, Scott ML, Baltimore D: The IkappaBNF-kappaB signaling module: temporal control and selective gene activation. Science 2002, 298:1241-1245. 55. Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M: Mathematical model of NF-kappaB regulatory module. J Theor Biol 2004, 228:195-215. 56. Nelson DE, Ihekwaba AEC, Elliott M, Johnson JR, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG et al.: Oscillations in NF-kB signaling control the dynamics of gene expression leading to export of the complex to the cytoplasm. Science 2004, 306:704-708.
www.sciencedirect.com