Latest Articles Include:
- Detecting oxysterols in the human circulation
- Nat Genet 12(7):577 (2011)
Article preview View full access options Nature Immunology | Correspondence Detecting oxysterols in the human circulation * Ingemar Björkhem1 * Ulf Diczfalusy1 * Tomas Olsson2 * David W Russell3 * Jeffery G McDonald3 * Yuqin Wang4 * William J Griffiths4 * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Page:577Year published:(2011)DOI:doi:10.1038/ni0711-577aPublished online20 June 2011 To the Editor: Oxysterols are oxygenated metabolites of cholesterol. On the basis of in vitro experiments, oxysterols have been ascribed many regulatory roles in connection with inflammation, neurodegeneration and atherosclerosis1. They are present in trace amounts in biological systems, and their analysis represents a challenging analytical problem. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Laboratory Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Huddinge, Sweden. * Ingemar Björkhem & * Ulf Diczfalusy * Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital Solna, Sweden. * Tomas Olsson * Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * David W Russell & * Jeffery G McDonald * Institute of Mass Spectrometry, School of Medicine, Swansea University, Swansea, UK. * Yuqin Wang & * William J Griffiths Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ingemar Björkhem Author Details * Ingemar Björkhem Contact Ingemar Björkhem Search for this author in: * NPG journals * PubMed * Google Scholar * Ulf Diczfalusy Search for this author in: * NPG journals * PubMed * Google Scholar * Tomas Olsson Search for this author in: * NPG journals * PubMed * Google Scholar * David W Russell Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffery G McDonald Search for this author in: * NPG journals * PubMed * Google Scholar * Yuqin Wang Search for this author in: * NPG journals * PubMed * Google Scholar * William J Griffiths Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Reply to "Detecting oxysterols in the human circulation"
- Nat Genet 12(7):577-578 (2011)
Nature Immunology | Correspondence Reply to "Detecting oxysterols in the human circulation" * Mauricio F Farez1 * Roopali Gandhi1 * Francisco Quintana1 * Howard L Weiner1 * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:577–578Year published:(2011)DOI:doi:10.1038/ni0711-577bPublished online20 June 2011 Farez et al. respond: We thank Björkhem and colleagues for their comments on our report of oxysterols in the blood of patients with multiple sclerosis1 and for applying their considerable expertise to this question. We address below the issues they raise about their inability to demonstrate the presence of substantial concentrations of 15-oxygenated C27 steroids in human circulation. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Mauricio F Farez, * Roopali Gandhi, * Francisco Quintana & * Howard L Weiner Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mauricio F Farez Author Details * Mauricio F Farez Contact Mauricio F Farez Search for this author in: * NPG journals * PubMed * Google Scholar * Roopali Gandhi Search for this author in: * NPG journals * PubMed * Google Scholar * Francisco Quintana Search for this author in: * NPG journals * PubMed * Google Scholar * Howard L Weiner Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1.5M) Supplementary Figure 1 and Methods Additional data - Sculpting the immune response to infection
- Nat Genet 12(7):579-582 (2011)
Article preview View full access options Nature Immunology | Meeting Report Sculpting the immune response to infection * Paul J Hertzog1 * Ashley Mansell1 * Ian R van Driel2 * Elizabeth L Hartland3Journal name:Nature ImmunologyVolume: 12,Pages:579–582Year published:(2011)DOI:doi:10.1038/ni0711-579Published online20 June 2011 This report describes advances in the understanding of how microbes elicit and evade immune responses and the sensing of pathogens by host cells that leads to the activation and production of intra- and extracellular signaling molecules. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Paul J. Hertzog and Ashley Mansell are in the Centre for Innate Immunity and Infectious Diseases, Monash Institute of Medical Research, Monash University, Clayton, Victoria, Australia * Ian R. van Driel is with the Department of Biochemistry and Molecular Biology and the Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Victoria, Australia * Elizabeth L. Hartland is in the Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Paul J Hertzog Author Details * Paul J Hertzog Contact Paul J Hertzog Search for this author in: * NPG journals * PubMed * Google Scholar * Ashley Mansell Search for this author in: * NPG journals * PubMed * Google Scholar * Ian R van Driel Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth L Hartland Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - The 50th Midwinter Conference of Immunologists at Asilomar
- Nat Genet 12(7):583-585 (2011)
Article preview View full access options Nature Immunology | Meeting Report The 50th Midwinter Conference of Immunologists at Asilomar * David C Parker1Journal name:Nature ImmunologyVolume: 12,Pages:583–585Year published:(2011)DOI:doi:10.1038/ni0711-583Published online20 June 2011 For 50 years, immunologists have been meeting each winter in California to discuss new findings and theories in immunology. A recurring theme this year was the continuous conversation between the innate and adaptive arms of the immune system. To mark the 50th anniversary of this meeting, some of the speakers took a look back half a century to see how far immunologists have come. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * David C. Parker is in the Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * David C Parker Author Details * David C Parker Contact David C Parker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Innate IL-13 in virus-induced asthma?
- Nat Genet 12(7):587-588 (2011)
Article preview View full access options Nature Immunology | News and Views Innate IL-13 in virus-induced asthma? * Stephania A Cormier1 * Jay K Kolls2 * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:587–588Year published:(2011)DOI:doi:10.1038/ni.2056Published online20 June 2011 Although the role of adaptive immunity in asthma is well characterized, there is relatively little understanding of the contribution of innate immunity to asthma. Studies now suggest that interleukin 13 produced by innate natural helper cells in the lungs has a substantial and underappreciated role in asthma exacerbation. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Stephania A. Cormier is in the Department of Pharmacology & Experimental Therapeutics, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA * Jay K. Kolls is in the Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jay K Kolls Author Details * Stephania A Cormier Search for this author in: * NPG journals * PubMed * Google Scholar * Jay K Kolls Contact Jay K Kolls Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Where, in antiviral defense, does IFIT1 fit?
- Nat Genet 12(7):588-590 (2011)
Article preview View full access options Nature Immunology | News and Views Where, in antiviral defense, does IFIT1 fit? * Andrea Ablasser1 * Veit Hornung1 * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:588–590Year published:(2011)DOI:doi:10.1038/ni.2061Published online20 June 2011 New data show that the interferon-induced gene product IFIT1 is a sensor for 5′-triphosphorylated RNA of viral origin and that it functions within a larger IFIT complex to inhibit viral replication. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Andrea Ablasser and Veit Hornung are with the Institute for Clinical Chemistry and Pharmacology, Unit for Clinical Biochemistry, University Hospital, University of Bonn, Germany. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Veit Hornung Author Details * Andrea Ablasser Search for this author in: * NPG journals * PubMed * Google Scholar * Veit Hornung Contact Veit Hornung Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - An unexpected role for IL-17 in lymphoid organogenesis
- Nat Genet 12(7):590-592 (2011)
Article preview View full access options Nature Immunology | News and Views An unexpected role for IL-17 in lymphoid organogenesis * Tom Cupedo1Journal name:Nature ImmunologyVolume: 12,Pages:590–592Year published:(2011)DOI:doi:10.1038/ni.2058Published online20 June 2011 In response to inhaled pathogens, lymphoid tissues can form in the lung. The driving force behind this organogenic process turns out to be interleukin 17–mediated activation of lung stromal cells. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Tom Cupedo is in the Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Tom Cupedo Author Details * Tom Cupedo Contact Tom Cupedo Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - La(s)t but not least
- Nat Genet 12(7):592-593 (2011)
Article preview View full access options Nature Immunology | News and Views La(s)t but not least * Bernard Malissen1 * Didier Marguet1 * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:592–593Year published:(2011)DOI:doi:10.1038/ni.2054Published online20 June 2011 The T cell antigen receptor is functionally coupled to many kinases and adaptor proteins. Analysis of the spatiotemporal organization of the T cell antigen receptor signaling cascade suggests that adaptor-containing intracellular vesicles are essential for proper signal propagation. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Bernard Malissen and Didier Marguet are at the Centre d'Immunologie de Marseille-Luminy, Université de la Méditerrannée, Institut National de la Santé et de la Recherche Médicale U631, Centre National de la Recherche Scientifique Unité Mixte de Recherche 6102, Marseille, France. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bernard Malissen Author Details * Bernard Malissen Contact Bernard Malissen Search for this author in: * NPG journals * PubMed * Google Scholar * Didier Marguet Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Research Highlights
- Nat Genet 12(7):595 (2011)
Article preview View full access options Nature Immunology | Research Highlights Research Highlights Journal name:Nature ImmunologyVolume: 12,Page:595Year published:(2011)DOI:doi:10.1038/ni0711-595Published online20 June 2011 Complex yet largely ill-defined communication networks exist between the nervous and immune systems. In Science, Sun et al. identify a regulatory circuit in Caenorhabditis elegans whereby the nervous system negatively regulates innate immune responses. Mutant worms lacking expression of OCTR-1, a G protein–coupled catecholamine receptor expressed in sensory neurons, show enhanced resistance to bacterial pathogens. Surprisingly, these worms have higher expression of genes associated with the noncanonical arm of the unfolded protein response associated with cellular stress. This expression occurs in cells that line the intestine and pharynx and involves a pathway that includes the receptor CED-1 and the kinase p38. Wild-type cells likewise upregulate genes encoding molecules of the unfolded protein response in response to infection. How infection triggers OCTR-1 signaling and which neural products activate CED-1 must await future work. LAD Science332, 729–732 (2011) Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Immunology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - T-bet in disease
- Nat Genet 12(7):597-606 (2011)
Nature Immunology | Review T-bet in disease * Vanja Lazarevic1 * Laurie H Glimcher1, 2, 3 * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:597–606Year published:(2011)DOI:doi:10.1038/ni.2059Published online20 June 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The activation of immune-defense mechanisms in response to a microbial attack must be robust and appropriately tailored to fight particular types of pathogens. Infection with intracellular microorganisms elicits a type 1 inflammatory response characterized by mobilization of T helper type 1 (TH1) cells to the site of infection, where they are responsible for the recruitment and activation of macrophages. At the center of the type 1 inflammatory response is the transcription factor T-bet, a critical regulator of the TH1 differentiation program. T-bet induces the production of interferon-γ (IFN-γ) and orchestrates the TH1 cell–migratory program by regulating the expression of chemokines and chemokine receptors. However, tight regulation of the type 1 inflammatory response is essential for the prevention of immunopathology and the development of organ-specific autoimmunity. In this review, we discuss how T-bet expression drives autoaggressive and inflammatory processes and ! how its function in vivo must be delicately balanced to avoid disease. View full text Figures at a glance * Figure 1: Role of T-bet in the differentiation of helper T cells. When naive CD4+ T cells (TH0) are activated in the presence of IFN-γ and IL-12, they differentiate into the TH1 subset. The differentiation of TH1 cells is critically dependent on the transcription factor T-bet. The first wave of T-bet expression in CD4+ T cells is regulated by signaling via the TCR and IFN-γ. T-bet upregulates the gene encoding the IL-12 receptor β2 subunit (Il12rb1) and confers IL-12 responsiveness, which induces the second wave of sustained T-bet expression. T-bet promotes TH1 differentiation not only by upregulating Ifng but also by inducing the expression of genes encoding CXCR3 and chemokines responsible for the mobilization of leukocytes to the site of inflammation. In addition to promoting the TH1 differentiation program, T-bet suppresses commitment to the TH2 or TH17 lineage. T-bet blocks TH2 differentiation by sequestering the TH2-specific transcription factor GATA-3 away from the Il5 and Il13 promoters. T-bet and Runx3 bind to the Il4 silencer ! and prevent Il4 expression. In developing TH17 cells, T-bet binds to Runx1 and blocks expression of the TH17 cell–specific transcription factor RORγt and consequently RORγt target genes (Il23r, Il17a and Il17f). In fully differentiated TH17 cells, T-bet expression is associated with the appearance of repressive epigenetic changes in the Rorc locus, which result in the repression of Rorc expression. In Treg cells, T-bet expression is required for upregulation of the gene encoding CXCR3 and for the recruitment of Treg cells to the site of inflammation. T-bet expression in Treg cells is also essential for their suppressive activity in the scurfy model of autoimmunity but not in most organ-specific inflammatory or autoimmune diseases. * Figure 2: Role of T-bet in immune response to pathogens. () DCs express T-bet in response to signaling via IL-12, IL-18, IFN-γ and Toll-like receptor 9 (TLR9). T-bet expression in DCs is required for activation of the TH1 differentiation program in naive CD4+ T cells. In concert with TCR signaling, IFN-γ and IL-12 derived from mature DCs induce T-bet expression in CD4+ T cells and initiate TH1 differentiation. T-bet regulates the expression of genes encoding CXCR3, CCL3 and CCL4 by TH1 cells. CXCR3 is required for the migration of TH1 cells, whereas CCL3 and CCL4 are responsible for the recruitment of myeloid cells to the site of inflammation. T-bet and eomesodermin (Eomes) have redundant roles in regulating the effector transcriptional program in CD8+ T cells. Both T-bet and eomesodermin control IFN-γ production and expression of the genes encoding granzyme B (Gzmb) and CD122 (the IL-2 and IL-15 receptor (IL-15R) β-subunit; CD122) by CD8+ T cells. Hence, mice deficient in either T-bet or eomesodermin demonstrate partial loss ! of cytotoxicity or partial deficiency in cytokine production relative to that of mice lacking both genes. IFN-γ production and granzyme B expression are essential in immunity to intracellular pathogens, whereas CD122 expression is required for IL-15 responsiveness and the maintenance of memory CD8+ T cell responses in vivo. () IFN-γ and TNF delivered by effector CD4+ and CD8+ T cells activate microbicidal mechanisms in infected macrophages by inducing expression of phagocyte oxidase (Phox) and inducible nitric oxide synthase (iNOS). Reactive oxygen and nitrogen species generated by these two enzymes are responsible for the destruction of intracellular microorganisms. IFN-γR, IFN-γ receptor; TNFR, TNF receptor. * Figure 3: Role of T-bet in the pathogenesis of autoimmune diabetes. T-bet-deficient nonobese diabetic mice are protected from developing type 1 diabetes because of defects in their innate and adaptive immune systems. The priming ability of T-bet-deficient DCs is diminished, which results in the activation of fewer autoreactive TH1 cells. Less cytokine production by T-bet-deficient TH1 cells, which also have defective migration to the pancreas, causes the overall low-grade inflammatory response in the target organ with minimal damage. T-bet expression in CD8+ T cells is required for their pathogenicity in the RIP-LCMV transgenic model of virus-induced type 1 diabetes. T-bet-deficient mice have many fewer CD8+ effector-memory cells that produce IFN-γ and TNF and also have poor migratory potential. * Figure 4: Role of T-bet in the pathogenesis of rheumatoid arthritis. T-bet-deficient mice are protected from developing passive collagen antibody-induced arthritis. In this model, T-bet expression in DCs is required for disease pathogenesis. T-bet-deficient DCs are not efficient antigen-presenting cells and activate TH1 cells poorly. In the absence of T-bet, DCs produce much less IL-1α and CCL3 and recruit fewer leukocytes to the joints. In contrast, the role of T-bet in CD4+ T cells is less straightforward. In certain models, such as collagen- or proteoglycan-induced arthritis, expression of T-bet and IFN-γ may have an immunomodulatory effect on the development of arthritis by constraining the magnitude of TH17 responses. Author information * Abstract * Author information Affiliations * Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA. * Vanja Lazarevic & * Laurie H Glimcher * Ragon Institute of MGH, Harvard and MIT, Boston, Massachusetts, USA. * Laurie H Glimcher * Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. * Laurie H Glimcher Competing financial interests L.H.G. is a member of the board of directors of and holds equity in Bristol Myers Squibb. Corresponding author Correspondence to: * Laurie H Glimcher Author Details * Vanja Lazarevic Search for this author in: * NPG journals * PubMed * Google Scholar * Laurie H Glimcher Contact Laurie H Glimcher Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Tumor necrosis factor induces GSK3 kinase–mediated cross-tolerance to endotoxin in macrophages
- Nat Genet 12(7):607-615 (2011)
Nature Immunology | Article Tumor necrosis factor induces GSK3 kinase–mediated cross-tolerance to endotoxin in macrophages * Sung Ho Park1, 2 * Kyung-Hyun Park-Min2 * Janice Chen2 * Xiaoyu Hu2 * Lionel B Ivashkiv1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:607–615Year published:(2011)DOI:doi:10.1038/ni.2043Received18 March 2011Accepted20 April 2011Published online22 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Endotoxin tolerance, a key mechanism for suppressing excessive inflammatory cytokine production, is induced by prior exposure of macrophages to Toll-like receptor (TLR) ligands. Induction of cross-tolerance to endotoxin by endogenous cytokines has not been investigated. Here we show that prior exposure to tumor necrosis factor (TNF) induced a tolerant state in macrophages, with less cytokine production after challenge with lipopolysaccharide (LPS) and protection from LPS-induced death. TNF-induced cross-tolerization was mediated by suppression of LPS-induced signaling and chromatin remodeling. TNF-induced cross-tolerance was dependent on the kinase GSK3, which suppressed chromatin accessibility and promoted rapid termination of signaling via the transcription factor NF-κB by augmenting negative feedback by the signaling inhibitors A20 and IκBα. Our results demonstrate an unexpected homeostatic function for TNF and a GSK3-mediated mechanism for the prevention of prolonged ! and excessive inflammation. View full text Figures at a glance * Figure 1: Pretreatment with TNF suppresses the induction of proinflammatory cytokines on secondary challenge by LPS. () Experimental design: − + −, no treatment; − + L, no pretreatment (1°), followed by LPS challenge (2°); L + L, pretreatment and challenge with LPS; T + L, pretreatment with TNF and challenge with LPS; hCD14+, isolation of monocytes positive for human CD14, and initiation of cultures. () Enzyme-linked immunosorbent assay (ELISA) of IL-6 in culture supernatants of human primary macrophages stimulated for various times (horizontal axis) with LPS (10 ng/ml) or TNF (10 ng/ml) and challenged for 24 h with LPS (10 ng/ml). Data are representative of three experiments (error bars, s.e.m.). () ELISA of IL-6 in culture supernatants of macrophages pretreated for 24 h with LPS or TNF and challenged for 24 h with LPS (10 ng/ml). Each symbol represents an individual sample; small horizontal lines indicate the mean. *P < 0.0001 (paired Student's t-test). Data are from 27 independent experiments with different donors (error bars, s.e.m.). () Real-time PCR analysis of mRNA in human ! primary macrophages stimulated for 24 h with LPS (10 ng/ml) or TNF (10 ng/ml) and challenged for 1 h (TNF, IL-1β, inducible nitric oxide synthase (iNOS) and CCL5 (RANTES)) or 3 h (IL-6 and FPR1) with LPS (10 ng/ml); results are presented relative to baseline expression in unstimulated cells, set as 1. Data are from one representative of four independent experiments with different blood donors (error bars, s.e.m.; cumulative data, Supplementary Fig. 4). * Figure 2: TNF suppresses cytokine production in vivo and protects mice from LPS-induced death. () ELISA of IL-6 in culture supernatants of mouse BMDMs stimulated for 24 h with increasing doses of LPS (1–100 ng/ml) or TNF (1–80 ng/ml) and then challenged with LPS (10 ng/ml). Data are from one representative of three similar experiments (error bars, s.e.m.). () Real-time PCR analysis of IL-6 mRNA in BMDMs pretreated with LPS (100 ng/ml) or TNF (40 ng/ml) and stimulated for 1 h with LPS (10 ng/ml), presented relative to baseline expression in unstimulated cells, set as 1. Data are from one representative of three independent experiments (error bars, s.e.m.). () Real-time PCR analysis of TNF mRNA in BMDMs obtained from mice lacking TNFR1 and TNFR2 (TNFR-KO) or genetically matched control mice (WT), then stimulated for 24 h with LPS (100 ng/ml) or TNF (40 ng/ml) and then challenged for 1 h with LPS (10 ng/ml). *P < 0.0001 (analysis of variance and Tukey's post-hoc test). Data are from four independent experiments (error bars, s.e.m.). () ELISA of serum TNF and IL-6 in ! age- and sex-matched C57BL/6J mice (n = 4 per group) given intraperitoneal or intravenous injection of LPS (100 μg) or TNF (2 μg), respectively, then, 24 h later, given LPS challenge (200 μg) by intraperitoneal injection and assessed after 90 min. *P = 0.002 and **P = 0.0004 (unpaired Student's t-test). Data are from one representative of two additional experiments (with different TNF dosing regimens) with similar results (error bars, s.e.m.). () Survival of mice (n = 4 per group) injected intravenously with PBS or TNF (2 μg), followed by injection of 500 μg of LPS after 24 h, assessed every 6 h for 4 d. Data are from one representative of two additional experiments (with different TNF pretreatment doses) with similar results. * Figure 3: TNF suppresses TLR4 signaling and induces A20 expression. () Immunoblot analysis of total IκBα and phosphorylated (p-) Erk, p38 and Jnk in human primary macrophages stimulated for 24 h with LPS (10 ng/ml) or TNF (10 ng/ml) and challenged for various times (above lanes) with LPS (10 ng/ml). Data are representative of ten experiments. (,) Real-time PCR analysis () and immunoblot analysis () of the expression of SOCS1, SOCS3, IRAK-M, SHIP-1 and A20 in human primary macrophages stimulated for various times (horizontal axes) with LPS or TNF. In , results are presented relative to baseline expression in unstimulated cells, set as 1. Data are representative of four to twelve independent experiments (summary and quantification of pooled results, Supplementary Fig. 6). () ELISA of IL-6 in culture supernatants of human macrophages transfected with control (Ctrl) or A20-specific small interfering RNA (siRNA) and treated for 24 h with LPS or TNF, then challenged with LPS (10 ng/ml). *P < 0.05 (analysis of variance and Tukey's post-hoc test).! Data are from three independent experiments (error bars, s.e.m.). * Figure 4: GSK3 mediates TNF-induced cross-tolerance. () ELISA of IL-6 in culture supernatants of human macrophages treated with vehicle control (dimethyl sulfoxide (0)) or with SB216763 (SB; 1–10 μM) and cultured for 24 h with LPS or TNF, then challenged with LPS (10 ng/ml) and assessed 24 h later. () Immunoblot analysis of whole-cell extracts of human macrophages left untreated or treated with SB216763 (+ SB; 10 μM) and stimulated for various times (above lanes) with LPS or TNF. () Immunoblot analysis of nuclear extracts of human macrophages cultured for 24 h with (TNF) or without (−) TNF and then challenged for various times (above lanes) with LPS (10 ng/ml). () Kinase assay of GSK3β immunoprecipitated (IP) from nuclear extracts of human macrophages stimulated for 24 h with TNF (10 ng/ml). IgG, immunoglobulin G. () ELISA of IL-6 in culture supernatants of human macrophages left untreated (DMSO) or treated with LiCl (20 mM), then cultured for 24 h with LPS or TNF and challenged with LPS (10 ng/ml). () ELISA of IL-6 in ! culture supernatants of human macrophages transfected with control or GSK3β-specific small interfering RNA and treated for 24 h with LPS or TNF, then challenged with LPS (10 ng/ml). Right, immunoblot analysis of GSK3β expression in the cells at left. () ELISA of IL-6 (left) and real-time PCR analysis of TNF mRNA (middle) in BMDMs obtained from mice lacking myeloid GSK3β (Gsk3bfl/fl Cre) or genetically matched control mice (Gsk3b+/+ Cre), then stimulated for 24 h with LPS (100 ng/ml) or TNF (40 ng/ml) and then challenged for 1 h (right) or 24 h (left) with LPS (10 ng/ml); mRNA results are presented relative to baseline expression in unstimulated cells, set as 1. Right, immunoblot analysis of GSK3β expression in the cells at left. Data are representative of three independent experiments (–; error bars, s.e.m.), two independent experiments (; error bars, s.e.m.) or four experiments (,; error bars, s.e.m.; cumulative results, Supplementary Fig. 8). * Figure 5: GSK3 regulates the kinetics of IκBα expression and repression of NF-κB signaling after induction in LPS-stimulated, TNF-tolerized macrophages. (–) Immunoblot analysis of phosphorylated Erk and p38 () and total IκBα (,) in whole-cell lysates of human macrophages pretreated for 24 h with PBS (−) or with TNF (,) or LPS () with or without SB216763 (+SB; 10 μM), then challenged for various times (above lanes) with LPS (10 ng/ml). () Staining of IκBα (green) in human macrophages pretreated with PBS (−), TNF alone (T) or TNF plus SB216763 ((T + SB); 10 μM) and then challenged for various times (below images) with LPS (+ L; 10 ng/ml), with leptomycin B (10 μM) added with LPS to inhibit nuclear export mediated by CRM1 (exportin-1); nuclei are counterstained with the DNA-intercalating dye DAPI (blue). Original magnification, ×40. (,) Chromatin-immunoprecipitation analysis of the recruitment of NF-κB p65 to TNF and IL6 promoters in human macrophages pretreated for 24 h with PBS or TNF alone () or with TNF alone or TNF plus SB216763 () and then stimulated for 1 h (TNF) or 3 h (IL6) with LPS (10 ng/ml). *P < 0.05! and **P < 0.01 (unpaired Student's t-test). Data are representative of three (,,) or ten () experiments or are from four experiments (,; error bars, s.e.m.). * Figure 6: TNF-induced A20 expression is mediated by GSK3. (–) Real-time PCR analysis of IκBα mRNA () and immunoblot analysis of total IκBα and phosphorylated IKKβ () and of total A20, IRAK-M and SHIP-1 () in human macrophages pretreated for 24 h with LPS, TNF alone or TNF plus SB216763 (SB; 10 μM), then challenged for various times (below graph () or above lanes (,)) with LPS (10 ng/ml); mRNA results () are presented relative to baseline expression in unstimulated cells, set as 1. () Immunoblot analysis of A20 expression by human macrophages transfected with control or GSK3-specific small interfering RNA and treated for 24 h with PBS, LPS or TNF. Data are representative of at least three independent experiments (error bars (), s.e.m.). * Figure 7: TNF and GSK3 regulate the accessibility of chromatin at the IL6 promoter. () Proximal IL6 promoter: above, restriction enzyme sites (positions (in kilobases) relative to transcription start site (bent arrow); below, protein-binding sites and probe. () Southern blot analysis of purified genomic DNA from human macrophages pretreated for 24 h with LPS or TNF with or without SB216763 (10 μM), then challenged for 3 h with LPS (10 ng/ml); after limited digestion of nuclei with 50 U BsrBI, DNA was cut to completion with BstX1. Data are representative of three independent experiments. Author information * Abstract * Author information * Supplementary information Affiliations * Graduate Program in Immunology and Microbial Pathogenesis, Weill Cornell Graduate School of Medical Sciences, New York, New York, USA. * Sung Ho Park & * Lionel B Ivashkiv * Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, New York, USA. * Sung Ho Park, * Kyung-Hyun Park-Min, * Janice Chen, * Xiaoyu Hu & * Lionel B Ivashkiv Contributions S.H.P. designed and did experiments and wrote the manuscript; K.-H.P.-M. contributed to the signaling experiments; J.C. contributed to the restriction-enzyme accessibility experiments; X.H. contributed to the in vivo experiments; and L.B.I. designed and supervised the research and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Lionel B Ivashkiv Author Details * Sung Ho Park Search for this author in: * NPG journals * PubMed * Google Scholar * Kyung-Hyun Park-Min Search for this author in: * NPG journals * PubMed * Google Scholar * Janice Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoyu Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Lionel B Ivashkiv Contact Lionel B Ivashkiv Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–11 Additional data - A semi-invariant Vα10+ T cell antigen receptor defines a population of natural killer T cells with distinct glycolipid antigen–recognition properties
- Nat Genet 12(7):616-623 (2011)
Nature Immunology | Article A semi-invariant Vα10+ T cell antigen receptor defines a population of natural killer T cells with distinct glycolipid antigen–recognition properties * Adam P Uldrich1, 7 * Onisha Patel2, 7 * Garth Cameron1 * Daniel G Pellicci1 * E Bridie Day1 * Lucy C Sullivan1 * Konstantinos Kyparissoudis1 * Lars Kjer-Nielsen1 * Julian P Vivian2 * Benjamin Cao3 * Andrew G Brooks1 * Spencer J Williams3 * Petr Illarionov4 * Gurdyal S Besra4 * Stephen J Turner1 * Steven A Porcelli5 * James McCluskey1 * Mark J Smyth1, 6 * Jamie Rossjohn2 * Dale I Godfrey1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature ImmunologyVolume: 12,Pages:616–623Year published:(2011)DOI:doi:10.1038/ni.2051Received24 February 2011Accepted11 May 2011Published online12 June 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Type I natural killer T cells (NKT cells) are characterized by an invariant variable region 14–joining region 18 (Vα14-Jα18) T cell antigen receptor (TCR) α-chain and recognition of the glycolipid α-galactosylceramide (α-GalCer) restricted to the antigen-presenting molecule CD1d. Here we describe a population of α-GalCer-reactive NKT cells that expressed a canonical Vα10-Jα50 TCR α-chain, which showed a preference for α-glucosylceramide (α-GlcCer) and bacterial α-glucuronic acid–containing glycolipid antigens. Structurally, despite very limited TCRα sequence identity, the Vα10 TCR–CD1d–α-GlcCer complex had a docking mode similar to that of type I TCR–CD1d–α-GalCer complexes, although differences at the antigen-binding interface accounted for the altered antigen specificity. Our findings provide new insight into the structural basis and evolution of glycolipid antigen recognition and have notable implications for the scope and immunological role of! glycolipid-specific T cell responses. View full text Figures at a glance * Figure 1: Identification of Jα18−/− T cells reactive to CD1d–α-GalCer. () Flow cytometry of thymocytes and liver lymphocytes isolated from BALB/c and C57BL/6 wild-type (WT), Jα18−/− and Cd1d−/− mice (n = 7–9 mice per genotype) and stained with tetramer loaded with CD1d–α-GalCer and monoclonal antibody to αβTCR; thymocyte populations were also enriched for NKT cells by complement-mediated depletion of CD24+ and CD8+ thymocytes. Numbers above outlined areas indicate percent tetramer-positive αβTCR+ cells. () Expression of CD4 versus CD3, CD8, CD44, CD69 and CD49b (black lines) and isotype-matched control antibody staining (gray shading) on NKT cells reactive to CD1d–α-GalCer (BALB/c wild-type cells) or CD1d–α-GlcCer (BALB/c Jα18−/− cells); right, expression of CD4 and NK1.1 in NKT cells from mice on the C57BL/6 background. Numbers above outlined areas, in quadrants or above bracketed lines, indicate percent positive cells in each area. () Expression of various TCRβ Vβ regions by NKT cells from BALB/c wild-type and J! α18−/− mice. Numbers in quadrants indicate percent positive cells in each. Data are representative of three () or two (,) separate experiments. * Figure 2: Jα18−/− CD1d–α-GalCer+ NKT cells express a semi-invariant Vα10-Jα50–Vβ8+ TCR. () PCR analysis of cDNA isolated from CD1d–α-GalCer-reactive cells sorted from BALB/c Jα18−/− thymuses and amplified with a panel of primers specific for each TCRα V-gene segment or the α-chain constant region (Cα). – (far right), Cα primers with no cDNA. () Single-cell PCR analysis for Vα10 on cDNA isolated from Vβ8.1 and Vβ8.2+ cells positive for CD1d–α-GalCer tetramer (right; Jα18−/− mouse–derived) or Vβ8.1 and Vβ8.2+ CD4+αβTCR+ cells negative for the CD1d–α-GalCer tetramer (left; conventional T cells) sorted from BALB/c Jα18−/− thymuses; n = 16 cells per panel. () Staining of surface TCRβ (top row) and unloaded or α-GalCer-loaded CD1d tetramer (bottom row) on green fluorescent protein–gated human epithelial 293T cells transfected to express full-length rearranged Vα10-Jα50 or Vα14-Jα18 TCR α-chain, plus Vβ8.1, Vβ8.3 or Vβ7 TCR β-chain, and CD3 complex. Isotype, isotype-matched control antibody. Numbers above bracketed l! ines indicate percent-positive cells. Data are from one experiment (,; one for each) or are representative of one (Vβ8.1) or two (Vβ8.3 and Vβ7) experiments (). * Figure 3: Vα10 NKT cells have a unique hierarchy of antigen recognition. () Frequency of NKT cells among thymocyte populations obtained from BALB/c wild-type and Jα18−/− mice, depleted of CD8+ and CD24+ cells and cultured for 72 h with glycolipid-pulsed antigen-presenting cells (Jα18−/− splenocytes), and proliferation of CFSE-labeled type I and Vα10 NKT cells (gated on CD1d–α-GalCer tetramer, with an additional gate to exclude CFSE spectral overlap (not shown)). Numbers above outlined areas or brackets indicate percent positive cells in each; numbers above bracketed lines indicate percent divided cells. Data are from one of two similar experiments. () Proliferation (top) and cytokines in supernatants (below) of NKT cells positive for the CD1d–α-GalCer tetramer sorted from BALB/c wild-type and Jα18−/− mice, then labeled with CFSE and cultured for 72 h (4 × 103 cells per well) in the presence of no glycolipid, α-GalCer (C26:0; 500 ng/ml), α-GlcCer (C20:2; 500 ng/ml), α-GlcA–DAG(mixture of variants; 10 μg/ml), GSL-1 (1 �! �g/ml) or iGb3 (10 μg/ml), plus 20 × 103 sorted splenic CD11c+ dendritic cells. Each symbol shape represents a different experiment. IFN-γ, interferon-γ. Data are from up to four independent experiments (mean and s.e.m. of three to twelve replicates). () Proliferation of gated Vα10 NKT cells positive for the CD1d–α-GalCer tetramer, sorted from BALB/c Jα18−/− thymus, labeled with CFSE and cultured for 72 h in the presence (1 × 103 cells per well) or absence (5 × 103 cells per well) of α-GlcA-DAG (10 μg/ml), plus sorted CD11c+ dendritic cells (20 × 103 per well). Numbers above bracketed lines indicate percent divided cells. Data are from one experiment with two replicates. () Proliferation and cytokine concentrations in supernatants of NKT cells positive for the CD1d–α-GalCer tetramer, sorted from BALB/c wild-type thymus (type I) or Jα18−/− thymus (Vα10), labeled with CFSE and cultured for 72 h (2 × 103 cells per well) with 20 × 103 sorted CD11c+ ! dendritic cells and doubling dilutions of C19:0-C16:0α-GlcA-D! AG. Data are from one of two similar experiments (mean of duplicate cultures). * Figure 4: Vα10 NKT cells have a higher affinity for α-GlcCer and are present in wild-type mice. () Binding of graded concentrations of Vα10 soluble TCR (Vα10–Vβ8.1 (175–0.05 μM)) or type I soluble TCR (Vα14–Vβ8.2 (150–0.04 μM) or Vα14–Vβ7 (200–0.05 μM)) to CD1d–α-GalCer or CD1d–α-GlcCer, after subtraction of results from those of a control flow cell (unloaded CD1d). Far right, saturation plots showing equilibrium binding to immobilized CD1d–α-GalCer or CD1d–α-GlcCer and the equilibrium dissociation constant (Kd) derived by equilibrium analysis. RU, response units. Data are representative of two independent experiments. () Staining profiles of α-GalCer tetramer (left) and α-GlcCer tetramer (middle) and dual tetramer labeling (right) in BALB/c wild-type or Jα18−/− thymocyte populations depleted of CD8+ and CD24+ cells, then simultaneously costained with CD1d tetramers loaded with α-GalCer and α-GlcCer. Numbers adjacent to outlined areas indicate percent cells in each. Far right, single-cell PCR analysis of Vα10 and Vα14 on wil! d-type NKT cells with high expression of the α-GlcCer tetramer (α-GlcCer tethi) or α-GalCer tetramer (α-GalCer tethi). Data are representative of three independent experiments. * Figure 5: Structural comparison of Vα10 NKT cell TCR–CD1d–α-GlcCer and type I NKT cell TCR–CD1d–α-GalCer. () NKT cell Vα10–Vβ8.1 TCR in complex with CD1d–α-GlcCer: magenta, α-GlcCer; gray, CD1d; salmon, Vα10; light green, Vβ8.1; purple, CDR1α; dark green, CDR2α; yellow, CDR3α; teal, CDR1β; ruby, CDR2β; orange, CDR3β. β2m, β2-microglobulin. () Footprint of the NKT cell Vα10–Vβ8.1 TCR on the surface of CD1d–α-GlcCer: spheres indicate α-GlcCer; colors of CD1d and CDR loops as in . () Type I NKT cell Vα14–Vβ8.2 TCR in complex with CD1d–α-GalCer14: blue, α-GalCer; cyan, Vα14; dark green, Vβ8.2; colors of CD1d and CDR loops as in . () Superposition of NKT cell Vα10–Vβ8.1 TCR–CD1d–α-GlcCer and type I NKT cell Vα14–Vβ8.2 TCR–CD1d–α-GalCer (colors as in ,). () Footprint of the type I NKT cell Vα14–Vβ8.2 TCR on the surface of CD1d–α-GalCer: spheres indicate α-GalCer; colors of CD1d, α-GalCer and CDR loops as in ,. * Figure 6: CD1d-mediated interactions with Vα10–Vβ8.1 NKT cell TCR. () Contacts of Vα10 NKT cell TCR CDR1α and CDR2α with CD1d. () Contacts of Vα10 NKT cell TCR CDR3α with CD1d. () Contacts of type I NKT cell TCR CDR3α with CD1d14. () Contacts of Vα10 NKT cell TCR CDR1β and CDR3β with CD1d. () Contacts of Vα10 NKT cell TCR CDR2β with CD1d. Purple, CDR1α; dark green, CDR2α; yellow, CDR3α; teal, CDR1β; ruby, CDR2β; orange, CDR3β; gray, CD1d; black dashed lines, hydrogen bonds and salt-bridge interactions. * Figure 7: Lipid antigen specificity. () Overlay of Vα10 TCR not bound to a ligand and the binary complex of CD1d–PBS-25 (Protein Data Bank accession code, 1Z5L) on the Vα10 TCR–CD1d–α-GlcCer ternary complex: gray, CD1d in the Vα10 complex; magenta, α-GlcCer; salmon, Vα10 TCR in complex; blue, Vα10 TCR not bound to a ligand; light green, CD1d in binary complex; yellow, PBS-25 (α-GalCer analog with a shorter acyl chain). () Interactions with the Vα10 NKT cell TCR mediated by α-GlcCer: purple, CDR1α; dark green, CDR2α; yellow, CDR3α; magenta, α-GlcCer; gray, CD1d; blue, H2O molecules; -OHS, -OH on the sphingosine chain. () Interactions with the type I NKT cell TCR mediated by α-GalCer14: blue, α-GalCer; colors of CD1d and CDR loops as in . Black dashed lines, hydrogen bonds; red dashed lines, van der Waals interactions. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Protein Data Bank * 3HE6 * 1Z5L * 3RUG * 3AXL * 3HE6 * 1Z5L * 3RUG * 3AXL Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Adam P Uldrich & * Onisha Patel Affiliations * Department of Microbiology and Immunology, The University of Melbourne, Parkville, Victoria, Australia. * Adam P Uldrich, * Garth Cameron, * Daniel G Pellicci, * E Bridie Day, * Lucy C Sullivan, * Konstantinos Kyparissoudis, * Lars Kjer-Nielsen, * Andrew G Brooks, * Stephen J Turner, * James McCluskey, * Mark J Smyth & * Dale I Godfrey * ARC Centre of Excellence in Structural and Functional Microbial Genomics, Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia. * Onisha Patel, * Julian P Vivian & * Jamie Rossjohn * School of Chemistry and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia. * Benjamin Cao & * Spencer J Williams * School of Biosciences, University of Birmingham, Edgbaston, Birmingham, UK. * Petr Illarionov & * Gurdyal S Besra * Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA. * Steven A Porcelli * Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia. * Mark J Smyth Contributions A.P.U. identified and carried out cellular and molecular characterization of Vα10 NKT cells and produced protein complexes for crystallographic studies; O.P. solved the crystal structures and did structural analysis; G.C. and K.K. carried out studies of glycolipid specificity and function; L.C.S. did surface plasmon resonance studies; D.G.P., E.B.D., L.K.-N., J.P.V., S.J.T., G.S.B., B.C., A.G.B., S.J.W., P.I., S.A.P., J.M., M.J.S., J.R. and D.I.G. provided intellectual input and key reagents and assisted with experimental design and interpretation and writing of the manuscript; and M.J.S., J.R. and D.I.G. led the investigation together and devised the project and contributed equally to this work. Competing financial interests S.A.P. has received payment as a consultant for Vaccinex for work related to the development of therapeutics based on glycolipids pretreated with CD1d. Corresponding authors Correspondence to: * Jamie Rossjohn or * Dale I Godfrey Author Details * Adam P Uldrich Search for this author in: * NPG journals * PubMed * Google Scholar * Onisha Patel Search for this author in: * NPG journals * PubMed * Google Scholar * Garth Cameron Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel G Pellicci Search for this author in: * NPG journals * PubMed * Google Scholar * E Bridie Day Search for this author in: * NPG journals * PubMed * Google Scholar * Lucy C Sullivan Search for this author in: * NPG journals * PubMed * Google Scholar * Konstantinos Kyparissoudis Search for this author in: * NPG journals * PubMed * Google Scholar * Lars Kjer-Nielsen Search for this author in: * NPG journals * PubMed * Google Scholar * Julian P Vivian Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew G Brooks Search for this author in: * NPG journals * PubMed * Google Scholar * Spencer J Williams Search for this author in: * NPG journals * PubMed * Google Scholar * Petr Illarionov Search for this author in: * NPG journals * PubMed * Google Scholar * Gurdyal S Besra Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Turner Search for this author in: * NPG journals * PubMed * Google Scholar * Steven A Porcelli Search for this author in: * NPG journals * PubMed * Google Scholar * James McCluskey Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Smyth Search for this author in: * NPG journals * PubMed * Google Scholar * Jamie Rossjohn Contact Jamie Rossjohn Search for this author in: * NPG journals * PubMed * Google Scholar * Dale I Godfrey Contact Dale I Godfrey Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (770K) Supplementary Figure 1 and Supplementary Tables 1–2 Additional data - IFIT1 is an antiviral protein that recognizes 5′-triphosphate RNA
- Nat Genet 12(7):624-630 (2011)
Nature Immunology | Article IFIT1 is an antiviral protein that recognizes 5′-triphosphate RNA * Andreas Pichlmair1 * Caroline Lassnig2, 3 * Carol-Ann Eberle1 * Maria W Górna1 * Christoph L Baumann1 * Thomas R Burkard1 * Tilmann Bürckstümmer1 * Adrijana Stefanovic1 * Sigurd Krieger4 * Keiryn L Bennett1 * Thomas Rülicke3, 5 * Friedemann Weber6 * Jacques Colinge1 * Mathias Müller2, 3 * Giulio Superti-Furga1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:624–630Year published:(2011)DOI:doi:10.1038/ni.2048Received04 April 2011Accepted28 April 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Antiviral innate immunity relies on the recognition of microbial structures. One such structure is viral RNA that carries a triphosphate group on its 5′ terminus (PPP-RNA). By an affinity proteomics approach with PPP-RNA as the 'bait', we found that the antiviral protein IFIT1 (interferon-induced protein with tetratricopeptide repeats 1) mediated binding of a larger protein complex containing other IFIT family members. IFIT1 bound PPP-RNA with nanomolar affinity and required the arginine at position 187 in a highly charged carboxy-terminal groove of the protein. In the absence of IFIT1, the growth and pathogenicity of viruses containing PPP-RNA was much greater. In contrast, IFIT proteins were dispensable for the clearance of pathogens that did not generate PPP-RNA. On the basis of this specificity and the great abundance of IFIT proteins after infection, we propose that the IFIT complex antagonizes viruses by sequestering specific viral nucleic acids. View full text Figures at a glance * Figure 1: Identification of an IFN-α/β-induced IFIT-containing complex as a PPP-RNA-binding entity. () Liquid chromatography and tandem mass spectrometry of HEK293 cells left untreated or treated overnight with IFN-β (1,000 U/ml), followed by lysis, precipitation of proteins by incubation with PPP-RNA or OH-RNA coupled to streptavidin beads, elution of bead-associated proteins and separation by one-dimensional SDS PAGE. Proteins identified are presented as dots with detection strength (log of spectral count (log SC)) in OH-RNA precipitation (horizontal axis) and PPP-RNA precipitation (vertical axis), both after IFN-β stimulation. Red dots, no detection in the absence of IFN-β with either OH-RNA and PPP-RNA. Data are from four experiments. () Immunoblot analysis of IFIT1 and tubulin (loading control) in lysates of 1 × 106 HeLa cells treated for 16 h with recombinant IFN-β or of the recombinant IFIT1 standard alone; signals were quantified by infrared imaging. Data are from one of two experiments. (,) Immunoblot analysis of lysates of 293T cells transfected with plasmid! s encoding c-Myc-tagged IFIT proteins () or E. coli–expressed IFIT proteins tagged with histidine (His) and glutathione S-transferase () and subjected to affinity precipitation with PPP-RNA. Data are representative of three experiments. * Figure 2: Formation of a complex containing IFIT proteins. () Size-exclusion chromatography of recombinant IFIT proteins and their binary complexes, presented as overlaid elution profiles (main graph), followed by SDS-PAGE and Coomassie staining of peak fractions A, B and C (inset). A280, absorbance at 280 nm. Data are representative of three experiments. (,) Network analysis of the IFIT protein complex, based on data in Table 1. () Interaction of IFIT proteins (large circles) with other proteins (small circles) in the presence (bottom) or absence (top) of stimulation with IFN-α/β. Red indicates proteins identified by all IFIT proteins. () Protein-interaction network for cells stimulated with IFN-α/β and annotated protein functions with Gene Ontology molecular functions and manual curation. Obvious nonspecific proteins or contaminants have been removed (keratin; albumin from quality-control runs of bovine serum albumin; and MCC12 and PCCAB, which bind to the affinity resin in great abundance). Red indicates shared IFIT partners ! able to bind RNA; green indicates IFIT partners involved in mRNA translation; blue indicates IFIT bait proteins. * Figure 3: Triphosphate-dependent RNA-binding of IFIT1 requires an arginine at position 187. () IFIT1 redistribution (white arrowheads) in IFN-β-treated NIH3T3 cells (n = 100) transfected for 3 h with biotinylated PPP-RNA or OH-RNA (left), presented as percent relocalization of IFIT1 (right). *P < 0.05 (t-test). Data are from two independent experiments (error bars, s.d.). (,) Immunoblot analysis of proteins precipitated from lysates of 293T cells expressing c-Myc–IFIT1 or GFP–RIG-I () or IFN-β-treated HEK293 cells and MEFs () with PPP-RNA or OH-RNA beads (used for affinity purification). Data are representative of at least two independent experiments. () Mobility shift of biotinlyated PPP-RNA or OH-RNA by recombinant histidine- and glutathione S-transferase–tagged versions of either IFIT1 or IFIT3; + AB, addition of antibody to glutathione S-transferase. Left margin: 1, free probe; 2, shifted probe; 3, supershifted probe. Data are representative of three experiments. () Surface charge of an IFIT1 structure model based on O-linked β-N-acetylglucosamine tran! sferase, showing amino acids (one-letter code and position) substituted for functional characterization. Surface color indicates electrostatic potential (red, negative; blue, positive); N, amino terminus; C, carboxyl terminus. () Affinity purification of c-Myc-tagged IFIT1 mutants and hemagglutinin-tagged IFIT3 (HA-IFIT3) coexpressed for 24 h in 293T cells, with PPP-RNA as bait. () Enzyme-linked immunosorbent assay of PPP-RNA or OH-RNA bound to plates and incubated with recombinant IFIT1 or IFIT1(R187H), followed by detection with secondary reagents; results presented as substrate conversion (absorbance at 450 nm). Data are from one experiment representative of three (error bars, s.d. of triplicates). () Surface plasmon resonance analysis of the affinity of IFIT1 and IFIT1(R187H) for PPP-RNA or OH-RNA, with biotinylated RNA as the immobilized ligand and increasing amounts of recombinant protein; results presented as response units. Data are from two experiments (error bars,! s.d. of duplicates). * Figure 4: IFIT1 sequesters PPP-RNA in vitro. (–) Luciferase activity of rabbit reticulate lysate (RRL) supplemented with RNA template expressing firefly luciferase and recombinant IFIT proteins (70 μM or 35 μM; key) or no protein (0 or No), plus 0.2 μg in vitro–transcribed luciferase-expressing PPP-RNA template (), 0.2 μg or 0.05 μg luciferase-expressing template RNA () or luciferase-expressing RNA left untreated (PPP-luc) or treated with calf intestinal phosphatase (OH-luc), plus 35 μM IFIT1 (). Results are presented as relative light units (RLU). () Translation of luciferase-expressing mRNA template (PPP-luc) in the presence of 35 μM IFIT3, IFIT1 or IFIT1(R187H). () Luciferase activity of wheat-germ extract (WGE) treated and assessed as in . (,) Quantitative RT-PCR analysis of VSV () or influenza A virus () in HEK-Flp-In IFIT1 or HEK-Flp-In GFP cells stimulated for 24 h with doxycycline and infected for 9 h with GFP-tagged VSV or influenza A virus (FluAV), each at a multiplicity of infection of 5, followed! by lysis and precipitation with streptavidin agarose beads (SII-IP). Input, RNA before precipitation. AU, arbitrary units. NS, not significant; *P < 0.05 (t-test). Data are from at least two experiments (–; error bars, s.d. of triplicates) or are from one experiment representative of three () or two (; error bars, s.d. of duplicates). * Figure 5: Influence of RNA-mediated interference of IFIT on virus growth. () Immunoblot analysis of IFIT proteins in 1 × 105 HeLa cells transfected for 48 h with 0.5 μg c-Myc-tagged IFIT expression vector (above lanes) and 5 nM control siRNA (si Ctrl) or siRNA (si) specific for various IFIT proteins (left margin). Data are representative of two experiments. () Immunoblot analysis of IFIT1 or IFIT3 in HeLa cells transfected for 48 h with 5 nM siRNA and stimulated for 16 h with 0.25 μg PPP-RNA. Data are representative of three experiments. (–) Virus accumulation in HeLa cells transfected for 48 h with 5 nM siRNA and infected with VSV (), mutant VSV (with methionine-to-arginine substitution at position 51 of the matrix protein36 (VSV-M2); ), Rift Valley fever virus clone 13 (RVFV; ) or EMCV (), each at a multiplicity of infection of 0.01, assessed at 48 h (,,) or 72 h () after infection as half-maximal tissue culture infectious dose (TCID50). Data are from three independent experiments (error bars, s.d.). () Luciferase activity of HeLa cells tra! nsfected for 48 h with reporter plasmids for firefly luciferase (Pol-I ff-luc; 0.1 μg) and renilla luciferase (pRL-TK; 0.05 μg), plus siRNA, then left uninfected (−) or infected overnight (+) with influenza A virus (multiplicity of infection, 1). Data are from one experiment representative of two (error bars, s.d. of duplicates). () Immunoblot analysis of HeLa cells transfected for 48 h with siRNA, together with plasmid encoding c-Myc-tagged IFIT1 or IFIT1(R187H). () Luciferase activity (as in ) of cells transfected as in . Data are from one experiment representative of three (error bars, s.d. of duplicates). * Figure 6: IFIT1 is needed to contain virus growth and in vivo pathogenicity. () Targeting strategy for mouse Ifit1. lacZ, gene encoding β-galactosidase; PGK, promoter of the gene encoding phosphoglycerate kinase; neo, neomycin-resistance cassette. (,) PCR analysis of Ifit1 and Ifit3 in Ifit1+/+ (+/+) and Ifit1−/− (−/−) MEFs () and immunoblot analysis of IFIT1 in MEFs stimulated for 16 h with mouse IFN-β (mIFN-β; ). () Accumulation of IFN-α/β in MEFs (2 × 105 cells per ml) left unstimulated (Mock) or transfected with PPP-RNA (0.4 μg/ml or 0.08 μg/ml; wedge), viral RNA isolated from VSV particles (vRNA; 0.2 μg/ml), poly(I:C) (1 μg/ml) or poly(dA:dT) (1 μg/ml), assessed in a cell line stably expressing an interferon-stimulated response element–luciferase reporter. Data are representative of four experiments. (,) Accumulation of virus in supernatants of Ifit1+/+ and Ifit1−/− MEFs infected for 48 h with VSV () or EMCV (), each at a multiplicity of infection of 0.01, assessed as half-maximal tissue culture infectious dose. *P < 0.0! 5 (two-way analysis of variance). Data are from two independent experiments (average and s.d. of hexaplicates). () Survival of male IFIT1-deficient (Ifit1−/−) and C57BL/6 (Ifit1+/+) mice (n = 14 per genotype) infected intranasally with VSV (1 × 105 plaque-forming units) and monitored twice daily over a 2-week period. P < 0.01 (Mantel-Cox test). Data are representative of three experiments. () Survival of sex-matched Ifit1−/− and Ifit1+/+ mice (n = 17 per genotype) infected intraperitoneally with EMCV (500 plaque-forming units). Data are representative of three experiments. () Survival of female Ifit1−/− and Ifit1+/+ mice (n = 9 per genotype) infected intraperitoneally with L. monocytogenes (1 × 106 colony-forming units). Data are representative of two experiments. Author information * Abstract * Author information * Supplementary information Affiliations * Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. * Andreas Pichlmair, * Carol-Ann Eberle, * Maria W Górna, * Christoph L Baumann, * Thomas R Burkard, * Tilmann Bürckstümmer, * Adrijana Stefanovic, * Keiryn L Bennett, * Jacques Colinge & * Giulio Superti-Furga * Institute of Animal Breeding and Genetics, University of Veterinary Medicine, Vienna, Austria. * Caroline Lassnig & * Mathias Müller * Biomodels Austria, University of Veterinary Medicine, Vienna, Austria. * Caroline Lassnig, * Thomas Rülicke & * Mathias Müller * Department of Clinical Pathology, Medical University of Vienna, Vienna, Austria. * Sigurd Krieger * Institute of Laboratory Animal Science, University of Veterinary Medicine, Vienna, Austria. * Thomas Rülicke * Institute for Virology, Philipps University Marburg, Germany. * Friedemann Weber Contributions A.P., C.-A.E., M.W.G., C.L.B., A.S., S.K. and F.W. did experiments; C.L. and M.M. did in vivo experiments; T.B. provided reagents; T.R.B. and J.C. did bioinformatic analysis; K.L.B. did mass spectrometry; T.R. generated the IFIT1-deficient mouse; and A.P. and G.S.-F. designed the overall strategy and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Giulio Superti-Furga Author Details * Andreas Pichlmair Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Lassnig Search for this author in: * NPG journals * PubMed * Google Scholar * Carol-Ann Eberle Search for this author in: * NPG journals * PubMed * Google Scholar * Maria W Górna Search for this author in: * NPG journals * PubMed * Google Scholar * Christoph L Baumann Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas R Burkard Search for this author in: * NPG journals * PubMed * Google Scholar * Tilmann Bürckstümmer Search for this author in: * NPG journals * PubMed * Google Scholar * Adrijana Stefanovic Search for this author in: * NPG journals * PubMed * Google Scholar * Sigurd Krieger Search for this author in: * NPG journals * PubMed * Google Scholar * Keiryn L Bennett Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Rülicke Search for this author in: * NPG journals * PubMed * Google Scholar * Friedemann Weber Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Colinge Search for this author in: * NPG journals * PubMed * Google Scholar * Mathias Müller Search for this author in: * NPG journals * PubMed * Google Scholar * Giulio Superti-Furga Contact Giulio Superti-Furga Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–14 and Supplementary Tables 1–2 Additional data - Innate lymphoid cells mediate influenza-induced airway hyper-reactivity independently of adaptive immunity
- Nat Genet 12(7):631-638 (2011)
Nature Immunology | Article Innate lymphoid cells mediate influenza-induced airway hyper-reactivity independently of adaptive immunity * Ya-Jen Chang1, 5 * Hye Young Kim1, 5 * Lee A Albacker1 * Nicole Baumgarth2 * Andrew N J McKenzie3 * Dirk E Smith4 * Rosemarie H DeKruyff1 * Dale T Umetsu1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:631–638Year published:(2011)DOI:doi:10.1038/ni.2045Received15 November 2010Accepted26 April 2011Published online29 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Patients with asthma, a major public health problem, are at high risk for serious disease from influenza virus infection, but the pathogenic mechanisms by which influenza A causes airway disease and asthma are not fully known. We show here in a mouse model that influenza infection acutely induced airway hyper-reactivity (AHR), a cardinal feature of asthma, independently of T helper type 2 (TH2) cells and adaptive immunity. Instead, influenza infection induced AHR through a previously unknown pathway that required the interleukin 13 (IL-13)–IL-33 axis and cells of the non-T cell, non-B cell innate lymphoid type called 'natural helper cells'. Infection with influenza A virus, which activates the NLRP3 inflammasome, resulted in much more production of IL-33 by alveolar macrophages, which in turn activated natural helper cells producing substantial IL-13. View full text Figures at a glance * Figure 1: H3N1 infection causes AHR and inflammation. () Change in lung resistance (RL) in 8-week-old BALB/c mice (n = 7–9 per group; anesthetized, tracheotomized, intubated and mechanically ventilated) challenged with methacholine nebulized into the airways 5 d after infection with H3N1 or allantoic fluid (mock infection control (mock)). *P < 0.01 and **P < 0.001, compared with mock infection (two-way analysis of variance (ANOVA)). () Macrophages (MΦ), neutrophils (Neu), eosinophils (Eos) and lymphocytes (Lym) in BAL fluid 5 d after treatment as in . ND, not detectable. *P < 0.001, compared with mock infection (Student's two-tailed t-test). () Lung sections obtained from mock- or H3N1-infected BALB/c or C57BL/6 mice, stained with hematoxylin and eosin. Scale bars, 200 μm. () Change in lung resistance in 8-week-old C57BL/6 mice (n = 4 per group), treated and assessed as in . *P ≤ 0.05 and **P ≤ 0.001. () Cells in BAL fluid 5 d after treatment as in . *P < 0.001 (Student's two-tailed t-test). (,) AHR in 8-week-old wild-t! ype (WT) or Rag2−/− mice (n = 6–7 per group) 5 d after infection with H3N1 or allantoic fluid, assessed as change in lung resistance () or cells in BAL fluid (), as in ,. *P ≤ 0.01 and **P ≤ 0.001 (two-way ANOVA () or Student's two-tailed t-test ()). Data are representative of three independent experiments (mean and s.e.m.). * Figure 2: ST2-deficient mice fail to develop H3N1-induced AHR. (,) AHR in 8-week-old ST2-deficient (Il1rl1−/−) mice and their heterozygous (Il1rl1+/−) littermates (n = 4–6 per group) 5 d after infection with H3N1 or allantoic fluid, assessed (as in Fig. 1a,b) as change in lung resistance () and cells in BAL fluid (). *P < 0.05, **P < 0.01 and ***P < 0.001 (two-way ANOVA () or Student's two-tailed t-test ()). (,) Change in lung resistance in 8-week-old wild-type mice () and Rag2−/− mice (; n = 3–4 per group) treated with two injections of mAb to ST2 (0.5 mg) or control immunoglobulin G (IgG), on days −1 and day +4, and infected with H3N1 or allantoic fluid on day 0, assessed (as in Fig. 1a) on day +5. *P < 0.001, compared with H3N1 plus IgG (two-way ANOVA). () Change in lung resistance in 8-week-old ST2-deficient mice and their heterozygous littermates (n = 3 per group) sensitized with saline (control) or OVA-alum on day 0, then challenged with OVA on days 7–9 and assessed (as in Fig. 1a) on day 10. *P < 0.05 and **P < ! 0.001, compared with saline (two-way ANOVA). Data are representative of three independent experiments (mean and s.e.m.). * Figure 3: H3N1-induced AHR requires TLR7 and MyD88. () Change in lung resistance in 8-week-old wild-type and Tlr7−/− mice (n = 5–6 per group) 5 d after infection with H3N1 or allantoic fluid (assessed as in Fig. 1a). () Cells in BAL fluid 5 d after treatment as in . () Change in lung resistance in 8-week-old wild-type and Myd88−/− mice (n = 4–6 per group) treated and assessed as in . () Cells in the BAL fluid 5 d after treatment as in . *P < 0.05, **P < 0.01 and ***P < 0.001, compared with H3N1 (two-way ANOVA (,) or Student's two-tailed t-test (,)). Data are representative of two independent experiments (mean and s.e.m.). * Figure 4: H3N1-induced production of IL-33 in alveolar macrophages. () Enzyme-linked immunosorbent assay of IL-33 in lungs of BALB/c mice (n = 3 per group) obtained on days 0, 1, 4 and 7 after infection with H3N1 or mock infection and homogenized in 1 ml PBS. *P < 0.05 and **P < 0.001, compared with mock infection (Student's two-tailed t-test). () Flow cytometry sorting (top) of CD45+ interstitial macrophages (IM; F4/80+CD11c−), alveolar macrophages (AM; F4/80+CD11c+) and DCs (F4/80−CD11c+) from the lungs of BALB/c mice on day 1 after infection with H3N1 or mock infection, followed by staining with mAb to IL-33 or isotype-matched control antibody (below). Numbers adjacent to cell designation (top) or in quadrants (below) indicate percent cells in each. () Absolute number of IL-33+ interstitial macrophages, alveolar macrophages and DCs in the mice in . *P < 0.001, compared with mock infection (Student's two-tailed t-test). () IL-33 expression by CD45− nonhematopoietic lung cells from the mice in . () Quantitative RT-PCR analysis of IL-3! 3 mRNA in alveolar macrophages, bone marrow–derived DCs (BMDC) or the MLE mouse lung epithelial cell line infected for 24 h in vitro with H3N1 at a multiplicity of infection of 5 (top), or in alveolar macrophages infected for 24 h in vitro with H3N1 at various multiplicities of infection (bottom). *P < 0.05 and **P < 0.001, compared with mock infection (Student's two-tailed t-test). () Enzyme-linked immunosorbent assay of IL-33 in supernatants of alveolar macrophages or bone marrow–derived DCs infected for 24 or 96 h in vitro with H3N1 (multiplicity of infection, 5; 5 × 105 cells per well, in 24 well plates) or given mock infection. *P < 0.001, compared with mock infection (Student's two-tailed t-test). Data are representative of three independent experiments (,,; mean and s.e.m.) or three experiments (–; mean and s.e.m. in ). * Figure 5: H3N1 infection results in a greater abundance of natural helper cells in the lungs. () Gating strategy for lung Lin−ST2+ cells among CD45+ cells. Numbers adjacent to outlined areas indicate percent cells in each gate. SSC, side scatter; FSC, forward scatter. (,) Expression of Sca-1 and c-Kit () and of c-Kit, CD90.2 (Thy-1.2), CD25, CD1d and major histocompatibility complex class II (MHCII; ) by lung cells from 8-week-old BALB/c mice 5 d after infection with H3N1 or mock infection, assessed after gating on the Lin−ST2+ subset. Numbers adjacent to outlined areas () indicate percent cells in each. FMO (), fluorescence minus one (control). () Flow cytometry analysis of the expression of c-Kit and Sca-1 (bottom) by lung cells from 8-week-old BALB/c mice (n = 3 per group) 1, 3, 6, 9 or 15 d after infection as in ,, assessed after gating on the Lin−ST2+ subset among CD45+ cells (top). Numbers adjacent to outlined areas (top) or in quadrants (bottom) indicate percent cells in each. (,) Frequency () and absolute number () of CD45+Lin−ST2+c-Kit+Sca-1+ lung ce! lls in . *P < 0.05 and **P < 0.001, compared with day 0 (Student's two-tailed t-test). () Flow cytometry analysis of the expression of c-Kit and Sca-1 (middle) and of CD90.2 (Thy-1.2) and Sca-1 (bottom) by CD45+ lung cells or BAL fluid cells from 8-week-old Rag2−/− mice (n = 3 per group) 5 d after infection as in ,, assessed after gating on the Lin−ST2+ subset (top). Data are representative of or three experiments (mean and s.e.m. in ,). * Figure 6: IL-13 and natural helper cells cause AHR. (,) Change in lung resistance () and cells in BAL fluid () in 8-week-old wild-type and Il13−/− mice after infection with H3N1 or mock infection (n = 5–6 per group; assessed as in Fig. 1a,b). *P ≤ 0.05 and **P < 0.001 (two-way ANOVA () or Student's two-tailed t-test ()). () Quantitative RT-PCR analysis of IL-13 mRNA (top; relative to expression at day 0) and enzyme-linked immunosorbent assay of IL-13 protein (bottom) in homogenates of H3N1-infected BALB/c lungs (n = 3 per group). *P < 0.001, compared with day 0 (Student's two-tailed t-test). () Flow cytometry analysis of intracellular IL-13 and Sca-1 in CD45+Lin−ST2+c-kit+Sca-1+ lung cells left unstimulated (US) or stimulated for 5 h with the phorbol ester PMA plus ionomycin (PMA + iono), assessed after gating (as in top row) on Lin+ST2+ cells (middle row) or Lin−ST2+ cells (bottom row). () Intracellular IL-13 and Sca-1 in lung cells obtained from wild-type or Rag2−/− mice on day 5 d after infection with H3N1 ! or mock infection and stimulated for 5 h with PMA plus ionomycin (bottom), assessed after gating on Lin−ST2+c-Kit+ cells (top). () Absolute number of Lin−ST2+c-Kit+Sca-1+ cells (left) or Lin−ST2+c-Kit+Sca-1+IL-13+ cells (right) in the lungs of mice in . *P ≤ 0.001 (Student's two-tailed t-test). () Flow cytometry (top) of lung interstitial macrophages (F4/80+CD11c−), alveolar macrophages (F4/80+CD11c+) and DCs (F4/80−CD11c+) among lung leukocytes (CD45+) from the mice in ; below, intracellular IL-13 expression in single cells. Data are representative of three experiments (–; mean and s.e.m. in ) or three independent experiments (–; mean and s.e.m. in ). * Figure 7: Natural helper cells are essential for H3N1-induced AHR in Rag2−/− mice. (,) Change in lung resistance () and cells in BAL fluid () in 8-week-old Rag2−/− mice (n = 4 per group) left undepleted (mock and H3N1) or depleted of CD90.2+ (Thy-1.2+) cells by three injections of mAb to CD90.2 (30-H12; 0.5 mg per mouse) on days −3, 0 and +3 (H3N1 + mAb to CD90.2), and infected with H3N1 or mock infected on day 0, analyzed 5 d after infection (as in Fig. 1a,b). *P < 0.05 and **P < 0.001, compared with H3N1 infection (two-way ANOVA () or Student's two-tailed t-test ()). (,) Change in lung resistance () and cells in BAL fluid () in Il13−/− recipients (n = 4 per group) given purified natural helper cells (Lin−ST2+ subsets) from Il13+/+ (Rag2−/−) donors or Il13−/− donors treated intranasally with IL-33 (1 μg) 5 d before adoptive transfer of cells (1× 105 cells/mouse) by intratracheal injection, followed by mock infection or infection of recipients with H3N1 and analysis 5 d after infection (as in Fig. 1a,b). *P ≤ 0.001 (two-way ANOVA ()! or Student's two-tailed t-test ()). Data are representative of two (,) or three (,) independent experiments (mean and s.e.m.). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Ya-Jen Chang & * Hye Young Kim Affiliations * Division of Immunology and Allergy, Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Ya-Jen Chang, * Hye Young Kim, * Lee A Albacker, * Rosemarie H DeKruyff & * Dale T Umetsu * Center for Comparative Medicine, University of California, Davis, California, USA. * Nicole Baumgarth * MRC Laboratory of Molecular Biology, Cambridge, UK. * Andrew N J McKenzie * Department of Inflammation Research, Amgen, Seattle, Washington, USA. * Dirk E Smith Contributions Y.-J.C. designed the study, did experiments, analyzed the data and wrote the manuscript; H.Y.K. did experiments and analyzed the data; L.A.A. did experiments; N.B. provided the H3N1 virus and did experiments. A.N.J.M., D.E.S. and R.H.D. provided reagents and ST2-deficient (Il1rl1−/−) and IL-13 deficient (Il13−/−) mice; and D.T.U. designed the study and wrote the manuscript. Competing financial interests D.E.S. is an employee and shareholder of Amgen. Corresponding author Correspondence to: * Dale T Umetsu Author Details * Ya-Jen Chang Search for this author in: * NPG journals * PubMed * Google Scholar * Hye Young Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Lee A Albacker Search for this author in: * NPG journals * PubMed * Google Scholar * Nicole Baumgarth Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew N J McKenzie Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk E Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Rosemarie H DeKruyff Search for this author in: * NPG journals * PubMed * Google Scholar * Dale T Umetsu Contact Dale T Umetsu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–11 Additional data - The development of inducible bronchus-associated lymphoid tissue depends on IL-17
- Nat Genet 12(7):639-646 (2011)
Nature Immunology | Article The development of inducible bronchus-associated lymphoid tissue depends on IL-17 * Javier Rangel-Moreno1, 5 * Damian M Carragher2, 5 * Maria de la Luz Garcia-Hernandez1 * Ji Young Hwang1 * Kim Kusser1 * Louise Hartson1 * Jay K Kolls3 * Shabaana A Khader4 * Troy D Randall1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:639–646Year published:(2011)DOI:doi:10.1038/ni.2053Received07 April 2011Accepted12 May 2011Published online12 June 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Ectopic or tertiary lymphoid tissues, such as inducible bronchus-associated lymphoid tissue (iBALT), form in nonlymphoid organs after local infection or inflammation. However, the initial events that promote this process remain unknown. Here we show that iBALT formed in mouse lungs as a consequence of pulmonary inflammation during the neonatal period. Although we found CD4+CD3− lymphoid tissue–inducer cells (LTi cells) in neonatal lungs, particularly after inflammation, iBALT was formed in mice that lacked LTi cells. Instead, we found that interleukin 17 (IL-17) produced by CD4+ T cells was essential for the formation of iBALT. IL-17 acted by promoting lymphotoxin-α-independent expression of the chemokine CXCL13, which was important for follicle formation. Our results suggest that IL-17-producing T cells are critical for the development of ectopic lymphoid tissues. View full text Figures at a glance * Figure 1: Development of iBALT in neonatal mice rather than adult mice. (–) Frozen sections of lungs obtained from mice given LPS intranasally starting on day 2 after birth (), 2 weeks after birth () or 3 weeks after birth () and subsequently infected with influenza at 8 weeks of age; lungs collected 3 weeks after infection were probed with anti-B220, anti-CD3 and anti-CD21. Original magnification, ×100. Results are representative of at least three independent experiments with four to eight mice per group. (,) Serial frozen sections of lungs obtained from neonatal mice given PBS () or LPS () intranasally and then given LPS intranasally at 8 weeks of age; lungs collected 3 weeks after the final LPS dose were analyzed by histology (left) or were probed with anti-B220, anti-CD90.1 (Thy-1.2), antibody to peripheral node addressin (PNAd) or anti-CD21 (right). Boxed areas at left indicate areas analyzed by fluorescence microscopy at right; arrows indicate areas of iBALT. Original magnification, ×200 (left) or ×100 (right). Results are representat! ive of two experiments with four to eight mice per group. * Figure 2: Formation of iBALT independently of CCR2 and CCR6. () Flow cytometry of single-cell suspensions of lungs from neonatal C57BL/6 mice given PBS or LPS intranasally; lungs collected 6 h after the final dose were analyzed for B220−CD11c+MHCII+CD11bhiCD103− DCs, B220−CD11c+MHCII+CD11bloCD103+ DCs, CD11b+7/4+Ly6C+Ly6G+ neutrophils and CD11b+7/4+Ly6C+Ly6G− monocytes. Data are representative of three experiments with four to eight mice per group (mean and s.d.). () Frozen sections of lungs from neonatal C57BL/6, Ccr2−/− and Ccr6−/− mice given LPS intranasally; lungs obtained 1 week after the final LPS dose were probed with anti-B220, anti-CD3 and anti-CD21. Original magnification, ×100. Results are representative of three independent experiments with three to five mice per group. () Area of lymphoid follicles in the lungs of LPS-treated mice. Data are representative of three experiments with three to five mice per group and at least one slide per mouse (mean and s.d.). () Quantitative PCR analysis of mRNA among tota! l RNA obtained from the lungs of neonatal mice 6 h after the fifth and final intranasal administration of LPS or PBS; results are presented relative to GAPDH mRNA (glyceraldehyde phosphate dehydrogenase). *P < 0.05, versus LPS-treated C57BL/6 mice. Data are representative of two experiments with four to eight mice per litter (mean and s.d.). * Figure 3: LTi cells are not required for iBALT development. () Flow cytometry of single-cell suspensions of lungs from neonatal mice given PBS or LPS intranasally, assessed 1 week after the fifth and final LPS dose. Lin+ cells were identified with a 'cocktail' of anti-CD3, anti-CD8, anti-CD11b, anti-CD11c, anti-CD19, anti-B220, anti-NK1.1 and anti-Gr-1; numbers in outlined areas indicate percent CD4+Lin− cells. () CD4+Lin− LTi cells and CD4+CXCR5+ Lin− LTi cells in the mice in . () Frozen sections of lungs from neonatal mice given LPS intranasally and then infected with influenza at 8 weeks of age; lungs obtained 3 weeks after infection were probed with anti-B220, anti-Thy-1.2, anti-CD21 and antibody to peripheral node addressin. DKO, CXCL13-, CCL19- and CCL21a-deficient. Original magnification, ×100. Data are representative of two (,) or four () independent experiments with four to eight mice per group (mean and s.d. in ). * Figure 4: Higher expression of IL-17 in neonatal lungs than in adult lungs. Quantitative PCR analysis of mRNA among total RNA extracted from lungs of C57BL/6 mice given PBS or 10 μg LPS intranasally five times (once every other day) starting on day 2, day 14 or 8 weeks after birth; lungs were obtained 6 h after the fifth and final LPS dose. Results are presented relative to GAPDH mRNA. *P < 0.05, versus mice treated with LPS at 0 weeks (unpaired t-test). Data are representative of three experiments with four to eight mice per litter (mean and s.d.). * Figure 5: The development of iBALT requires IL-17. (–) Images of lungs from neonatal C57BL/6 mice (), IL-17RA-deficient (Il17ra−/−) mice (), IL-23p19-deficient (Il23a−/−) mice (), IL-17A-deficient (Il17a−/−) mice () and Lta−/− mice () given LPS intranasally; lungs obtained 1 week after the final LPS dose were probed with anti-B220 and anti-CD21. Original magnification, ×100. Results are representative of three experiments with at least three mice per group. () Area of lymphoid clusters, B220+ B cell follicles and CD21+ FDC networks in –. *P < 0.05, versus C57BL/6 (unpaired t-test). Data are representative of three experiments (mean and s.d. from two slides from at least three mice per group). (,) Quantitative PCR analysis of mRNA among total RNA extracted from lungs of neonatal mice given LPS intranasally; lungs were obtained 6 h after the final intranasal LPS dose. Results are presented relative to GAPDH mRNA. *P < 0.05, versus C57BL/6 (unpaired t-test). Data are representative of two experiments with f! our to eight mice per litter (mean and s.d.). () Quantitative PCR analysis of chemokines in pulmonary fibroblasts obtained from untreated neonatal lungs and stimulated with IL-17A or TNF; results are presented relative to GAPDH mRNA. *P < 0.05, versus C57BL/6 (unpaired t-test). Data are representative of three experiments with similar results (mean and s.d.). * Figure 6: IL-17 acts early in iBALT formation but does not maintain iBALT structure. () Frozen sections of lungs from neonatal mice given LPS intranasally; lungs obtained at various times after LPS treatment alone or LPS treatment plus influenza infection (left margin) were probed with anti-B220, anti-CD3 and anti-CD21. Original magnification, ×100. Results are representative of three experiments with mice from three litters. () Total area of CD21−CD35+ FDC networks in B cell follicles from the mice in . Data are representative of three experiments with at least three litters of mice with four to eight mice per litter (mean and s.d.). () Quantification of follicles and FDC networks (top row) and area of B cell clusters (follicles) or CD21+ FDC networks in neonatal mice given LPS intranasally plus isotype-matched control antibody (Isotype), soluble LTβR–immunoglobulin (sLTβR) or blocking antibody to IL-17 (α-IL-17) intranasally 1 d before or 1 week after the final LPS dose; lungs obtained for histology 1 week after treatment were assessed by immunoflu! orescence microscopy. *P < 0.05, versus C57BL/6 (unpaired t-test). Data are representative of three experiments with at least three mice per group (mean ± s.d. of six slides). * Figure 7: IL-17-producing T cells promote iBALT formation. () Quantitative PCR analysis of IL-17 in CD4+ cells from naive lymph nodes (far left) and αβ T cells, γδ T cells and CD4+ cells from lungs of LPS-treated neonatal mice; results are presented relative to GAPDH expression. (–) Frozen sections of LPS-treated neonatal lungs probed with anti-IL-17A and anti-CD4 () or anti-CD3, anti-CD11c, anti-B220, anti-NKp46 and anti-TCRγδ (,). In : red, CD3+ T cells; violet, CD3+γδ T cells. DAPI, DNA-intercalating dye. () Frozen sections of lungs from neonatal Tcrb−/−Tcrd−/− mice given no cells or CD4+ T cells and given LPS intranasally; lungs obtained 1 week after the fifth and final LPS dose were probed with anti-CD3, anti-CD11c, anti-B220, anti-CD21 and antibody to peanut agglutinin (PNA). () Lungs from Rorc−/− mice treated with anti-CD4 (α-CD4) or isotype-matched control antibody (control) 1 d before the fifth and final LPS administration; lungs obtained 1 week later were probed with anti-CD3, anti-CD11c, anti-B220, ! anti-CD21 and antibody to peanut agglutinin. () Flow cytometry assessing depletion of CD4+αβ T cells and γδ T cells from the mice in . () Morphometric analysis of lymphoid areas in frozen sections of lungs from neonatal LPS-treated Tcrb−/−Tcrd−/− recipients of adoptively transferred γδ T cells or αβ T cells, obtained 1 week after the final LPS administration and probed with anti-CD21, anti-CD3 and anti-B220. () Immunofluorescence analysis (below) of CD21, CD3 and B220 in OVA-specific TFH, TH17 and IL-17-producing TFH (TFH17) cells transferred into neonatal Tcrb−/−Tcrd−/− recipients, which were challenged with LPS and OVA and analyzed 1 week after the final OVA dose; above, flow cytometry analysis of the expression of IL-17 and CXCR5 on cells before transfer. () Total area of B cell follicles in the mice in . *P < 0.05, versus TFH (unpaired t-test). Original magnification, ×400 () or ×100 (–). Data are representative of two experiments with five t! o ten mice () or four to eight mice per litter (–; mean and ! s.d. in ,) or are from one of two experiments with three to seven mice per group (–; mean and s.d. in ). Author information * Abstract * Author information Primary authors * These authors contributed equally to this work. * Javier Rangel-Moreno & * Damian M Carragher Affiliations * Department of Medicine, Division of Allergy Immunology and Rheumatology, University of Rochester Medical Center, Rochester, New York, USA. * Javier Rangel-Moreno, * Maria de la Luz Garcia-Hernandez, * Ji Young Hwang, * Kim Kusser, * Louise Hartson & * Troy D Randall * Trudeau Institute, Saranac Lake, New York, USA. * Damian M Carragher * Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA. * Jay K Kolls * Department of Pediatrics, Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. * Shabaana A Khader Contributions J.R.-M., D.M.C., M.d.l.L.G.-H. and T.D.R. designed the experiments; J.R.-M., D.M.C., M.d.l.L.G.-H., J.Y.H., K.K. and L.H. did the experiments; J.R.-M. and T.D.R. wrote the paper; and J.K.K., S.A.K. and T.D.R. edited the paper and provided the funding. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Troy D Randall Author Details * Javier Rangel-Moreno Search for this author in: * NPG journals * PubMed * Google Scholar * Damian M Carragher Search for this author in: * NPG journals * PubMed * Google Scholar * Maria de la Luz Garcia-Hernandez Search for this author in: * NPG journals * PubMed * Google Scholar * Ji Young Hwang Search for this author in: * NPG journals * PubMed * Google Scholar * Kim Kusser Search for this author in: * NPG journals * PubMed * Google Scholar * Louise Hartson Search for this author in: * NPG journals * PubMed * Google Scholar * Jay K Kolls Search for this author in: * NPG journals * PubMed * Google Scholar * Shabaana A Khader Search for this author in: * NPG journals * PubMed * Google Scholar * Troy D Randall Contact Troy D Randall Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - A cascade of protein kinase C isozymes promotes cytoskeletal polarization in T cells
- Nat Genet 12(7):647-654 (2011)
Nature Immunology | Article A cascade of protein kinase C isozymes promotes cytoskeletal polarization in T cells * Emily J Quann1 * Xin Liu1 * Grégoire Altan-Bonnet2 * Morgan Huse1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:647–654Year published:(2011)DOI:doi:10.1038/ni.2033Received07 December 2010Accepted05 April 2011Published online22 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Polarization of the T cell microtubule-organizing center (MTOC) toward the antigen-presenting cell (APC) is driven by the accumulation of diacylglycerol (DAG) at the immunological synapse (IS). The mechanisms that couple DAG to the MTOC are not known. By single-cell photoactivation of the T cell antigen receptor (TCR), we found that three distinct isoforms of protein kinase C (PKC) were recruited by DAG to the IS in two steps. PKC-ε and PKC-η accumulated first in a broad region of membrane, whereas PKC-θ arrived later in a smaller zone. Functional experiments indicated that PKC-θ was required for MTOC reorientation and that PKC-ε and PKC-η operated redundantly to promote the recruitment of PKC-θ and subsequent polarization responses. Our results establish a previously uncharacterized role for PKC proteins in T cell polarity. View full text Figures at a glance * Figure 1: PKC-θ, PKC-ε and PKC-η accumulate at the region of TCR activation before MTOC reorientation. TIRF microscopy (PKC) and epifluorescence microscopy (tubulin) of 5C.C7 T cells expressing RFP-tubulin together with GFP-labeled PKC-θ (), PKC-ε (), PKC-η () or PKC-δ () on coverslips coated with peptide-MHC that can be photoactivated; cells were stimulated in defined regions with a pulse of ultraviolet light (red oval (left) or purple line (right)). Left, time-lapse images (interval, ~30 s); right, PKC accumulation (presented as normalized mean fluorescence intensity (ΔF/F) in the irradiated region) and MTOC reorientation (presented as root mean square distance (r.m.s.d.) between the MTOC and the irradiated region). Scale bars (left), 5 μm; time (in images), minutes:seconds. Data are representative of at least two independent experiments with six or more cells each. * Figure 2: Distinct accumulation patterns and kinetics of nPKC proteins. () TIRF microscopy (interval, ~30 s) of 5C.C7 T cells expressing all three pairwise combinations of PKC-θ, PKC-ε and PKC-η, shown responding to ultraviolet irradiation (magenta ovals) on coverslips containing peptide-MHC that can be photoactivated; each pair of proteins was imaged in two fluorescence configurations (for example, PKC-θ–GFP with PKC-η–RFP, and PKC-η–GFP with PKC-θ–RFP). Scale bars, 5 μm; time (between rows), minutes:seconds (m:s). () Width ratio for each PKC protein pair in , after autocorrelation analysis: left, average ratio over an entire time lapse (160 time points) for one cell (n = 12 cells per data set); right, all ratios from the entire data set. *P < 0.0001 (Student's t-test). () Offset times separating the recruitment of PKC-θ, PKC-ε and PKC-η from MTOC reorientation, for cells expressing GFP-labeled PKC proteins with RFP-tubulin. () Offset times separating the recruitment of PKC-ε and PKC-η from the recruitment of PKC-θ in cell! s expressing either labeled PKC-ε or PKC-η together with labeled PKC-θ. () Offset time separating PKC-ε recruitment from PKC-η recruitment in cells expressing labeled PKC-ε with labeled PKC-η. Offset times were calculated with cross-correlation curves from at least 15 paired responses. Data are representative of at least two independent experiments (error bars (–), s.e.m.). * Figure 3: Recruitment of nPKC proteins correlates with more PKC activity. () Microscopy of 5C.C7 T cells (intervals, ~20 s) expressing Marcksl1-GFP (TIRF microscopy) and RFP-tubulin (epifluorescence microscopy), stimulated with ultraviolet irradiation (red oval) on coverslips containing peptide-MHC that can be photoactivated. Scale bars, 5 μm. () Quantification of the response in , showing depletion of Marcksl1-GFP together with MTOC reorientation (presented as in Fig. 1). () Offset times separating the recruitment of PKC-η–GFP and PKC-θ–GFP from depletion of Marcksl1-RFP (time 0), determined by cross-correlation analysis of at least 12 paired responses. Data are representative of at least two independent experiments with six or more cells each (error bars (), s.e.m.). * Figure 4: PKC activity is required for MTOC polarization. () Distance (r.m.s.d.) between the MTOC and the irradiated region (average path) over time in GFP-tubulin–expressing 5C.C7 T cells (n = 20 cells per curve) photoactivated after 50 s by ultraviolet irradiation (purple line) in the presence (Gö6983) or absence (Control) of 500 nM Gö6983 (left), and distribution of distances between the MTOC and the center of the irradiated region for all observations after 4 min (polarization histogram; right); each experiment was 8 min in length. () Time-lapse images (intervals, 0.5 min) of GFP–centrin-2–expressing 5C.C7 T cells loaded with the calcium indicator Fura-2AM and mixed with APCs in the presence of 500 nM Gö6983 or vehicle control (dimethyl sulfoxide (DMSO)); each GFP–centrin-2 image (GFP Cent; relevant APC is blue) is paired with its corresponding differential interference contrast image with a ratiometric Fura-2 signal overlaid on it (DIC Fura; 'warmer' colors (orange and red) indicate more intracellular Ca2+). () Quan! tification of MTOC polarization in ; for each observation, the MTOC was assigned to one of four positional bins (left), which generated a distribution of MTOC positions for the entire data set (right). Data are representative of two independent experiments (; average and s.e.m.) or one experiment (,) with fifteen DMSO-treated control cells (287 observations) and nine Gö6983-treated cells (155 observations). * Figure 5: PKC-θ and either PKC-ε or PKC-η are required for MTOC polarization. () Immunoblot analysis of PKC-θ, PKC-ε and PKC-η in 5C.C7 T cells expressing GFP–centrin-2, transfected by nucleofection with nontargeting control siRNA (NT) or siRNA specific for various PKC proteins (above lanes). Actin serves as a loading control throughout. (,) MTOC polarization in cells (n ≥ 12 cells per curve) treated as in with siRNA specific for PKC-θ () or for PKC-ε or PKC-η alone or PKC-ε and PKC-η together (); results are presented as in Figure 4a. Data are representative of at least three independent experiments (error bars (,), s.e.m.). * Figure 6: Defective MTOC polarization in PKC-θ-deficient T cells. () TCR photoactivation of GFP-tubulin–expressing 5C.C7 T cells obtained from PKC-θ-deficient (Prkcq−/−) mice and their heterozygous Prkcq+/− littermates, and wild-type (WT) mice; cells were stimulated with ~50% less ultraviolet light than in typical photoactivation studies (assessed as in Fig. 4a). Data are representative of two independent experiments with at least 17 cells (error bars, s.e.m.). () MTOC polarization in 5C.C7 T cells (genotype, above) left untransduced (Control) or transduced with exogenous PKC-θ–RFP (Exo PKC-θ–RFP+), followed by TCR photoactivation (assessed as in Fig. 4a). Data are representative of three independent experiments with at least 21 cells (error bars, s.e.m.). * Figure 7: PKC-θ recruitment requires PKC-ε and PKC-η. (–) PKC accumulation (left; presented as in Fig. 1) in 5C.C7 T cells expressing PKC-θ–GFP (), PKC-η–GFP (), or the C1 region of PKC-θ fused to GFP (C1-GFP; ), transfected by nucleofection with siRNA specific for various PKC proteins (keys), followed by TCR photoactivation (purple line); right, immunoblot analysis of PKC isoforms in the cells at left (as in Fig. 5a). Data are representative of at least two independent experiments with at least 13 cells (error bars, s.e.m.). * Figure 8: TCR-induced cytokine production requires PKC-θ, PKC-ε and PKC-η. () Flow cytometry analysis of IL-2 (top) and IFN-γ (bottom) in 5C.C7 T cell blasts transfected by nucleofection with siRNA (key) and then left unstimulated (shaded curves) or stimulated with immobilized MCC–I-Ek (1 ng/ml), quantified by intracellular cytokine staining (left); right, frequency of IL-2+ cells (top) and IFN-γ+ cells (bottom) as a function of the concentration of MCC–I-Ek used for preparation of the stimulatory surface. () Immunoblot analysis of PKC isoforms in the cells in (as in Fig. 5a). Data are representative of at least two independent experiments. Author information * Abstract * Author information * Supplementary information Affiliations * Immunology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Emily J Quann, * Xin Liu & * Morgan Huse * Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Grégoire Altan-Bonnet Contributions E.J.Q. and M.H. designed the experiments; E.J.Q. collected and analyzed the data; X.L. did the Marcksl1 experiments; G.A.-B. assisted with MATLAB programming and data analysis; and M.H. wrote the manuscript (with help from E.J.Q. and G.A.-B.). Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Morgan Huse Author Details * Emily J Quann Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Grégoire Altan-Bonnet Search for this author in: * NPG journals * PubMed * Google Scholar * Morgan Huse Contact Morgan Huse Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (3M) PKC-θ accumulates in the region of TCR stimulation prior to MTOC reorientation. * Supplementary Video 2 (3M) PKC-ε accumulates in the region of TCR stimulation prior to MTOC reorientation. * Supplementary Video 3 (2M) PKC-η accumulates in the region of TCR stimulation prior to MTOC reorientation. * Supplementary Video 4 (6M) PKC-η and PKC-θ display distinct spatiotemporal patterns of recruitment. * Supplementary Video 5 (3M) PKC-ε and PKC-θ display similar spatiotemporal patterns of recruitment. * Supplementary Video 6 (3M) Marcksl1 is depleted from the region of TCR stimulation prior to MTOC reorientation. PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–10 Additional data - Pre-existing clusters of the adaptor Lat do not participate in early T cell signaling events
- Nat Genet 12(7):655-662 (2011)
Nature Immunology | Article Pre-existing clusters of the adaptor Lat do not participate in early T cell signaling events * David J Williamson1, 3 * Dylan M Owen1, 3 * Jérémie Rossy1, 3 * Astrid Magenau1 * Matthias Wehrmann2 * J Justin Gooding2 * Katharina Gaus1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:655–662Year published:(2011)DOI:doi:10.1038/ni.2049Received25 February 2011Accepted05 May 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Engaged T cell antigen receptors (TCRs) initiate signaling through the adaptor protein Lat. In quiescent T cells, Lat is segregated into clusters on the cell surface, which raises the question of how TCR triggering initiates signaling. Using super-resolution fluorescence microscopy, we found that pre-existing Lat domains were neither phosphorylated nor laterally transported to TCR activation sites, which suggested that these clusters do not participate in TCR signaling. Instead, TCR activation resulted in the recruitment and phosphorylation of Lat from subsynaptic vesicles. Studies of Lat mutants confirmed that recruitment preceded and was essential for phosphorylation and that both processes were independent of surface clustering of Lat. Our data suggest that TCR ligation preconditions the membrane for vesicle recruitment and bulk activation of the Lat signaling network. View full text Figures at a glance * Figure 1: Mapping of single Lat molecules in resting and activated T cells. (–) TIRF images (,) and single-molecule PALM images (,) of adherent Lat-deficient JCaM2 cells transfected with a plasmid expressing Lat-mEos2 and fixed in suspension (Resting) or exposed for 10 min to glass coverslips coated with anti-CD3 and anti-CD28 (Activated). Scale bar, 2 μm. () Ripley's K-function analysis of the molecules in regions outlined in red in ,; L(r)—r reports the degree of clustering relative to a random distribution (indicated by the 95% confidence interval (CI)), and r is the radial scale. () Lat cluster maps of the regions outlined in ,, generated from local point-pattern analysis. () Ripley's K-function analysis as in for regions outlined in red in ,. () Lat cluster maps as in , for regions outlined in ,. Color (,) indicates clustering (L(r)), from low (blue) to high (red); scale bar, 1 μm. () Map of regions outlined in red in , containing all Lat molecules and Lat clusters identified after application of a clustering threshold. () Map of clustere! d Lat molecules in ,; colors indicate photons emitted per molecule. () Map of Lat molecules and clusters as in , for regions in ,. () Maps of clustered Lat molecules as in , for regions in ,. Inset (,), clusters of Lat (dashed outline in main image) at fourfold higher magnification; main image size (–), 3 μm × 3 μm. Data are representative of 20–25 experiments. * Figure 2: Quantitative statistical analysis of Lat clustering in resting and activated T cells. (–) Lat clusters per μm2 (), frequency of Lat in clusters () and total density of Lat () obtained from PALM data after local point-pattern analysis and application of a cluster threshold in resting and activated JCaM2 cells expressing Lat-mEos2. Each symbol represents one image region; horizontal bars and error bars indicate mean and s.d., respectively (throughout). *P < 0.0001 (Student's t-test). (,) Distribution of the size of Lat clusters () and of photons emitted from each Lat molecule () obtained by analysis PALM image regions with local point-pattern analysis and application of a cluster threshold in resting (n = 38 regions) and activated (n = 21 regions) JCaM2 cells expressing Lat-mEos2. In , distributions were analyzed by nonlinear curve fitting (solid lines) with 95% confidence intervals (dashed lines); shaded tan area indicates significant difference. Inset (), Difference between resting and activated cells in cluster size distribution. *P < 0.01 and **P < 0.000! 1 (Student's t-test). Data are representative of 20–25 experiments. * Figure 3: Clustering of endogenous Lat and phosphorylated Lat in activated wild-type Jurkat cells and primary mouse T cells. (,) Cluster maps () and Ripley's K-function plots () of dSTORM images of endogenous Lat and phosphorylated Lat (p-Lat) in activated Jurkat cells (top) and primary mouse T cells (bottom), obtained by local point-pattern analysis. Scale bar (), 1 μm. (,) Quantification of the frequency of Lat and phosphorylated Lat in clusters () and cluster counts per μm2 () in Jurkat cells, obtained by analysis of dSTORM image regions with local point-pattern analysis and application of a cluster threshold. *P < 0.0001 (Student's t-test). () Distribution of cluster sizes for Lat (n = 11) and phosphorylated Lat (n = 12) in image regions in activated Jurkat cells, obtained as in ,. Shaded tan area indicates the size range in which new Lat clusters appeared after TCR activation, as detected by PALM in JCaM2 cells expressing Lat-mEos2. NS, not significant (P > 0.05). () Dual-channel analysis of Lat (PALM; green in merged image) and phosphorylated Lat (dSTORM; red in merged image) in an image r! egion in an activated wild-type Jurkat cell. Dashed white circles outline regions in which Lat and phosphorylated Lat clusters overlap. Scale bar, 1 μm. Data are representative of 10–15 experiments. * Figure 4: Localized activation of T cells on micro-patterned surfaces. () TIRF and brightfield images of wild-type Jurkat cells incubated for 10 min with glass surfaces onto which Alexa Fluor 488 (AF488)-conjugated anti-CD3 and anti-CD28 were micro-patterned, followed by immunostaining with DyLight649-conjugated antibody to phosphorylated Lat or to total Lat. Scale bar, 10 μm. (–) Quantification of Lat clusters per μm2 (), Lat molecules per cluster () and total Lat density () in nonactivated and activated regions from the wild-type Jurkat cells in , obtained by analysis of dSTORM image regions by local point-pattern analysis and application of a cluster threshold. *P < 0.001 (Student's t-test). Data are representative of five to eight experiments. * Figure 5: Live-cell PALM imaging of Lat in activated T cells. (,) Lifetime of a single, relatively immobile cluster of Lat () from an image sequence (at intervals of 2.9 s) of a cluster map 'movie' () of a Lat-mEos2–expressing JCaM2 cell imaged while interacting with an activating surface. Scale bar, 1 μm. () Lifetime of individual clusters of Lat in activated Lat-mEos2–expressing JCaM2 cells from the movie in . () Density of total Lat or clustered Lat and number of Lat clusters over the entire plasma membrane and the 10-minute activation course in the movie in . Data are representative of three experiments. * Figure 6: Crosslinking of Lat at the cell surface. () TIRF and epifluorescence (EPI) images of JCaM2 cells expressing biotinylated AP-Lat, assessed without (No crosslinking) and with (Crosslinking) incubation with streptavidin-coated beads (SA beads; purple circles); PALM images were converted to Lat cluster maps by local point-pattern analysis. Yellow dashed lines indicate cell outlines. Scale bars, 10 μm (TIRF and EPI) or 1 μm (cluster map). (–) Lat density in the TIRF zone in resting and activated JCaM2 cells expressing AP-Lat (), and density of Lat clusters () and phosphorylated Lat clusters () in the TIRF zone in activated JCaM2 cells expressing AP-Lat, assessed before and after crosslinking of AP-Lat. *P < 0.0001 (Student's t-test). (,) Distribution of sizes of clusters of Lat (n = 15; ) and phosphorylated Lat (n = 22; ) in activated JCaM2 cells, obtained by analysis of PALM image regions by local point-pattern analysis and application of a cluster threshold. () Quantification of immunoblot analysis of phosphorylat! ed Lat and total Lat in resting and activated JCaM2 cells before and after crosslinking of AP-Lat. Data are representative of three experiments (mean and s.d. in –,). * Figure 7: Cluster analysis of Lat mutants in resting and activated T cells. () Cluster maps of resting and activated JCaM2 cells expressing wild-type Lat (WT Lat), YF Lat or CS Lat, obtained by analysis of PALM image regions with local-point pattern analysis. Scale bar, 1 μm. () Density of clusters of wild-type Lat, YF Lat and CS Lat in resting and activated JCaM2 cells generated as in , after application of a clustering threshold. *P < 0.0001 (Student's t-test). (,) Frequency of cluster sizes of YF Lat (n = 19 regions; ) and CS Lat (n = 9 regions; ) in resting and activated JCaM2 cells and frequency of cluster sizes of wild-type Lat in activated JCaM2 cells (,), obtained as in . Data are representative of three experiments. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * David J Williamson, * Dylan M Owen & * Jérémie Rossy Affiliations * Centre for Vascular Research, University of New South Wales, Sydney, Australia. * David J Williamson, * Dylan M Owen, * Jérémie Rossy, * Astrid Magenau & * Katharina Gaus * School of Chemistry and the Australian Centre for Nanomedicine, University of New South Wales, Sydney, Australia. * Matthias Wehrmann & * J Justin Gooding Contributions D.J.W., molecular biology, PALM, crosslinking experiments and analysis, and manuscript preparation; D.M.O., conceptualization and PALM-STORM analysis; J.R., STORM experiments and analysis; A.M., PALM experiments and analysis of surface-patterning experiments; M.W., surface-patterning experiments; J.J.G., conceptualization of surface patterning; and K.G., conceptualization and manuscript preparation. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Katharina Gaus Author Details * David J Williamson Search for this author in: * NPG journals * PubMed * Google Scholar * Dylan M Owen Search for this author in: * NPG journals * PubMed * Google Scholar * Jérémie Rossy Search for this author in: * NPG journals * PubMed * Google Scholar * Astrid Magenau Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias Wehrmann Search for this author in: * NPG journals * PubMed * Google Scholar * J Justin Gooding Search for this author in: * NPG journals * PubMed * Google Scholar * Katharina Gaus Contact Katharina Gaus Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (8M) Lat cluster map movie. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–7 and Supplementary Methods Additional data - Transcription factor T-bet represses expression of the inhibitory receptor PD-1 and sustains virus-specific CD8+ T cell responses during chronic infection
- Nat Genet 12(7):663-671 (2011)
Nature Immunology | Article Transcription factor T-bet represses expression of the inhibitory receptor PD-1 and sustains virus-specific CD8+ T cell responses during chronic infection * Charlly Kao1 * Kenneth J Oestreich2 * Michael A Paley1 * Alison Crawford1 * Jill M Angelosanto1 * Mohammed-Alkhatim A Ali1 * Andrew M Intlekofer3 * Jeremy M Boss4 * Steven L Reiner3 * Amy S Weinmann2 * E John Wherry1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:663–671Year published:(2011)DOI:doi:10.1038/ni.2046Received24 September 2010Accepted02 May 2011Published online29 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg T cell exhaustion has a major role in failure to control chronic infection. High expression of inhibitory receptors, including PD-1, and the inability to sustain functional T cell responses contribute to exhaustion. However, the transcriptional control of these processes remains unclear. Here we demonstrate that the transcription factor T-bet regulated the exhaustion of CD8+ T cells and the expression of inhibitory receptors. T-bet directly repressed transcription of the gene encoding PD-1 and resulted in lower expression of other inhibitory receptors. Although a greater abundance of T-bet promoted terminal differentiation after acute infection, high T-bet expression sustained exhausted CD8+ T cells and repressed the expression of inhibitory receptors during chronic viral infection. Persistent antigenic stimulation caused downregulation of T-bet, which resulted in more severe exhaustion of CD8+ T cells. Our observations suggest therapeutic opportunities involving higher T-be! t expression during chronic infection. View full text Figures at a glance * Figure 1: Downregulation of T-bet during chronic relative to its expression during acute LCMV infection. () Intracellular T-bet in CD8+ H-2Dbgp33-specific T cells from C57BL/6 (B6) mice left undepleted or B6 mice depleted CD4+ T cells (CD4 depletion), assessed after infection with Arm or clone 13: above, average mean fluorescence intensity (MFI) of each group for all time points; below, MFI for individual mice at day 8 (left) or day 39 (right) after infection (each symbol represents an individual mouse, and small horizontal lines indicate the mean, for all similar plots throughout). *P < 0.05 and **P < 0.001 (two-tailed unpaired Student's t-test). () Intracellular T-bet in the cells in at various times after infection (left margin); numbers in plots indicate MFI (above) and results relative to MFI at day 8 after infection (in parentheses below); gray filled curves on day 8 are CD44loCD8+ cells. Data are representative of at least three independent experiments with at least three mice per group (error bars (), s.e.m.). * Figure 2: Ablation of T-bet results in loss of antigen-specific CD8+ T cells and impairs viral control during chronic infection. () Flow cytometry analysis of tetramer staining and CD44 expression of CD8+ T cells in spleens from wild-type (WT), cHet and cKO mice on days 9 and 37 after infection with clone 13; plots are gated on CD8+ splenocytes. Numbers above outlined areas indicate percent H-2Dbgp33 (left) or H-2Dbgp276 (right) tetramer-positive CD8+ cells. (,) H-2Dbgp276-specific CD8+ T cells in wild-type, cHet and cKO mice in the spleen () and in the liver, bone marrow (one femur), brain, lymph nodes (LN) and blood (per 1 × 106 peripheral blood mononuclear cells (PBMC); ) on days 8 and 30 () or on day 30 () after infection with clone 13. () Longitudinal monitoring of viral titers in the serum of wild-type, cHet and cKO mice after infection with clone 13. () Viral titers at day 30 after infection as in . PFU, plaque-forming units. NS, not significant (P > 0.05); *P < 0.05, **P < 0.005 and ***P < 0.001 (two-tailed unpaired Student's t-test). Data are representative of at least three independent expe! riments with at least seven mice per group (error bars (), s.e.m.). * Figure 3: Ablation of T-bet impairs the function of antigen-specific CD8+ T cells later but not early during chronic infection. () Production of IFN-γ and CCL3 (MIP-1α) in wild-type, cHet and cKO CD8+ splenocytes restimulated with a pool of 20 LCMV-specific peptides on days 8 and 30 after infection with clone 13; numbers in quadrants indicate percent IFN-γ+CCL3+ cells (top right) or IFN-γ−CCL3+ cells (bottom right) among total CD8+ cells. () Total antigen-specific (CCL3+) CD8+ T cells in the spleens of wild-type, cHet and cKO mice at day 8 (left) and day 30 (right) after infection with clone 13, identified by peptide restimulation by the gating strategy in (above), and polyfunctional dual-positive (IFN-γ+CCL3+) antigen-specific cells (below). *P < 0.05, **P < 0.005 and ***P < 0.001 (two-tailed unpaired Student's t-test). Data are representative of at least three independent experiments with at least four mice per group. * Figure 4: Inverse correlation of the expression of T-bet and PD-1 during chronic infection. () Flow cytometry of CD8+ cells in the spleens of B6 mice (with or without depletion of CD4+ T cells) on 26 after infection with Arm or clone 13. Numbers above outlined areas indicate percent antigen-specific (H-2Dbgp33+) CD8+ cells. () Costaining for surface PD-1 and intracellular T-bet in antigen-specific (H-2Dbgp33+) CD8+ T cells on days 8 and 26 after infection as in . Numbers in outlined areas indicate percent PD-1hiT-betlo cells (top left) or PD-1lo–intT-bethi cells (bottom right). () PD-1 expression on antigen-specific CD8+ (H-2Dbgp276+) and CD4+ (positive for the tetramer of I-Ab and LCMV glycoprotein epitope of amino acids 66–77 (I-Abgp61+)) T cells in spleens of wild-type, cHet and cKO mice at day 30 after infection with clone 13. Data are representative of at least three independent experiments with at least three mice per group. * Figure 5: Downregulation of T-bet during chronic infection is due to persistent antigen. () Expression of PD-1 and T-bet in donor H-2Dbgp33-specific and H-2Dbgp276-specific CD8+ T cells in the spleens of congenic recipient (CD45.1+) mice 8 d after adoptive transfer of CD8+ splenocytes from donor (CD45.2+) mice; donor mice were infected with clone 13 8 d before isolation of cells for transfer, and recipients were infected with wild-type or V35A clone 13 8 d before receiving transferred cells. Cognate peptide for the CD8+ T cell population analyzed was present (+Ag) or absent (−Ag) in the recipient mouse. Numbers above outlined areas (far left) indicate percent CD45.2+ cells; numbers in quadrants (right) indicate percent PD-1hiT-betlo cells (top left) or PD-1lo–intT-bethi cells (bottom right). () Quantitative analysis of T-bet expression in donor H-2Dbgp33-specific and H-2Dbgp276-specific CD8+ cells from . *P < 0.005 (two-tailed Student's t-test). Data are representative of two independent experiments with similar results (four to six mice per group). * Figure 6: Cell-intrinsic role for T-bet in the repression of PD-1, demonstrated by peripheral chimeras and mixed transfer of P14 cells. () PD-1 expression (bottom) on gated host and donor H-2Dbgp33-specific CD8+ peripheral blood mononuclear cells in blood from chimeras generated by intravenous transfer of ~25 × 106 CD8+ cKO splenocytes into CD45.1+ wild-type hosts, followed by infection of hosts with Arm or clone 13 the next day. *P < 0.05 and **P < 0.005 (two-tailed paired Student's t-test). () Expression of T-bet and PD-1 by gated cHet donor (top) and wild-type host (bottom) antigen-specific CD8+ splenocytes in chimeras generated by intravenous transfer of ~25 × 106 CD8+ cHet splenocytes into CD45.1+ wild-type hosts, followed by infection of hosts with Arm or clone 13 the next day and analysis 42 d later. () PD-1 expression on donor wild-type and Tbx21+/− P14 CD8+ T cells from chimeras generated by intravenous transfer of 1 × 103 CD8+ wild-type (CD45.2+) P14 cells and 1 × 103 CD8+Tbx21+/− (CD45.1+CD45.2+) P14 cells into CD45.1+ wild-type hosts, followed by infection of hosts with Arm or clone 13 th! e next day and analysis 40 d later. Numbers above outlined areas and in quadrants indicate percent cells in each. Data are representative of two to four independent experiments with similar results (at least three mice per group). * Figure 7: T-bet overexpression downregulates PD-1 expression and improves the durability of exhausted antigen-specific CD8+ T cells. () Efficiency of transduction of CD8+ antigen-specific P14 cells with empty retrovirus (Empty RV) or T-bet-expressing retrovirus (T-bet RV), assessed in vitro 4 d after activation with antigen (3 d after transduction). Numbers above bracketed lines indicate percent GFP+ (transduced) cells. () Expression of IL-7Rα (top right) and PD-1 (bottom right) on GFP+ and GFP− donor (CD45.2+) antigen-specific P14 cells transduced as in and gated (left) as GFP+ (green) or GFP− (red), assessed on days 41–43 after infection of hosts with Arm or clone 13. Numbers in plots indicate percent cells in outlined areas (far left) or gated areas (middle left). *P = 0.046 and **P < 0.005 (two-tailed paired Student's t-test). () Frequency of GFP+ cells among the transferred P14 population in , tracked longitudinally in peripheral blood mononuclear cells from hosts infected with Arm or clone 13. Data are representative of at least two independent experiments with similar results (error bars (),! s.e.m.). * Figure 8: T-bet regulates the expression of many cell-surface inhibitory receptors during chronic infection. () Expression of inhibitory receptors (vertical axes) and PD-1 by virus-specific CD8+ T cells in spleens from wild-type and cHet mice at day 30 after infection with clone 13. Numbers in quadrants indicate percent cells in each. () Expression of inhibitory receptors by donor wild-type and Tbx21+/− P14 CD8+ T cells from chimeras generated as described in Figure 6c, followed by infection of hosts with clone 13 the next day and analysis 40 d later. () Expression of inhibitory receptors on GFP+ and GFP− P14 CD8+ T cells generated by the retroviral overexpression, transfer and gating strategy in Figure 6a,c, followed by infection of hosts with clone 13. Numbers in quadrants indicate percent cells expressing inhibitory receptors among transduced cells (GFP+; top) and untransduced cells (GFP−; bottom). Data are representative of at least three independent experiments with similar results (at least three mice per group in each). * Figure 9: T-bet is a direct transcriptional repressor of the Pdcd1. () PD-1 expression on EL4 cells; A20 mouse B lymphoma cells serve as a negative control. () Expression of T-bet and PD-1 on EL4 cells transduced with empty or T-bet–expressing retrovirus. Numbers in quadrants (top row) indicate percent T-bet+GFP+ cells (top right) or T-bet−GFP+ cells (bottom right); numbers above outlined areas (bottom row) indicate PD-1 MFI for GFP− cells (untransduced; left) or GFP+ cells (transduced; right) () PD-1 expression in the transduced (GFP+) cells in , presented relative to that of the untransduced (GFP−) population. () Luciferase activity (top) of EL4 cells transfected with a PD-1 luciferase reporter (below), plus an empty construct (pEF1) or one containing wild-type T-bet (T-bet (WT)) or T-bet with point substitutions that abrogate binding to DNA (T-bet (DBM)) and activated with PMA and ionomycin; results are presented relative to those of cells transfected with the empty construct without stimulation by PMA and ionomycin. Below, PD-1 r! egulatory region, containing two highly conserved regions (B and C), cloned into a pGL3 luciferase reporter (LUC). (,) Expression of PD-1 by CD8+ P14 cells activated in vitro and transduced with empty or T-bet–expressing retrovirus (presented as in ,). () ChIP analysis of T-bet in in vitro–activated P14 CD8+ T cells (In vitro P14; left), P14 CD8+ T cells 24 d after Arm infection in vivo (Memory; middle) and CD44hiCD62Llo CD8+ T cells (all PD-1int or PD-1hi) 40 d after infection with clone 13 (Exhausted; right), followed by PCR amplification of regions of Pdcd1 located −0.5 and −1.5 kilobases from the transcription start site and promoter regions of Ifng and Hprt1 (encoding hypoxanthine guanine phosphoribosyl transferase). () ChIP analysis of T-bet in in vitro–activated, T helper type 1 (TH1)-polarized wild-type or Tbx21−/− CD4+ T cells, followed by quantitative PCR analysis of Pdcd1. *P < 0.005 and **P < 0.001 (two-tailed Student's t-test). Data are representa! tive of at least two to five independent experiments with simi! lar results (error bars, s.e.m.). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Microbiology and Institute for Immunology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Charlly Kao, * Michael A Paley, * Alison Crawford, * Jill M Angelosanto, * Mohammed-Alkhatim A Ali & * E John Wherry * Department of Immunology, University of Washington School of Medicine, Seattle, Washington, USA. * Kenneth J Oestreich & * Amy S Weinmann * Department of Medicine and Institute for Immunology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Andrew M Intlekofer & * Steven L Reiner * Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, USA. * Jeremy M Boss Contributions C.K. and E.J.W. designed the experiments and analyzed the data; K.J.O. did ChIP for CD4+ T cells and EL4 luciferase assays; A.C., J.M.A., M.A.P., M.-A.A., and A.M.I. assisted in doing and analyzing experiments; J.M.B., S.L.R. and A.S.W. assisted with the design of the experiments and provided constructs, reagents and mouse strains; and C.K. and E.J.W. wrote the manuscript. Competing financial interests E.J.W. has a patent licensing agreement on the PD-1 pathway. Corresponding author Correspondence to: * E John Wherry Author Details * Charlly Kao Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth J Oestreich Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Paley Search for this author in: * NPG journals * PubMed * Google Scholar * Alison Crawford Search for this author in: * NPG journals * PubMed * Google Scholar * Jill M Angelosanto Search for this author in: * NPG journals * PubMed * Google Scholar * Mohammed-Alkhatim A Ali Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew M Intlekofer Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy M Boss Search for this author in: * NPG journals * PubMed * Google Scholar * Steven L Reiner Search for this author in: * NPG journals * PubMed * Google Scholar * Amy S Weinmann Search for this author in: * NPG journals * PubMed * Google Scholar * E John Wherry Contact E John Wherry Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (9M) Supplementary Figures 1–11 and Supplementary Table 1 Additional data - The sphingosine 1-phosphate receptor S1P2 maintains the homeostasis of germinal center B cells and promotes niche confinement
- Nat Genet 12(7):672-680 (2011)
Nature Immunology | Article The sphingosine 1-phosphate receptor S1P2 maintains the homeostasis of germinal center B cells and promotes niche confinement * Jesse A Green1 * Kazuhiro Suzuki1, 5, 6 * Bryan Cho1, 2, 6 * L David Willison3, 5, 6 * Daniel Palmer3 * Christopher D C Allen1, 5 * Timothy H Schmidt1 * Ying Xu1 * Richard L Proia4 * Shaun R Coughlin3 * Jason G Cyster1 * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume: 12,Pages:672–680Year published:(2011)DOI:doi:10.1038/ni.2047Received14 March 2011Accepted02 May 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Mice deficient in sphingosine 1-phosphate receptor type 2 (S1P2) develop diffuse large B cell lymphoma. However, the role of S1P2 in normal germinal center (GC) physiology is unknown. Here we show that S1P2-deficient GC B cells outgrew their wild-type counterparts in chronically established GCs. We found that antagonism of the kinase Akt mediated by S1P2 and its downstream mediators Gα12, Gα13 and p115RhoGEF regulated cell viability and was required for growth control in chronically proliferating GCs. Moreover, S1P2 inhibited GC B cell responses to follicular chemoattractants and helped confine cells to the GC. In addition, S1P2 overexpression promoted the centering of activated B cells in the follicle. We suggest that by inhibiting Akt activation and migration, S1P2 helps restrict GC B cell survival and localization to an S1P-low niche at the follicle center. View full text Figures at a glance * Figure 1: Growth advantage of GC B cells deficient in S1P2, Gα12-Gα13 or p115RhoGEF in chronic GCs. () Quantitative PCR analysis of the transcript abundance of S1pr1 and S1pr2 in follicular (FO) and GC B cells, presented relative to the abundance of Hprt1 (encoding hypoxanthine guanine phosphoribosyl transferase). (,) GC B cells in spleen and mLNs () and immunohistochemical staining of mLNs () from 1-year-old S1pr2+/− and S1pr2−/− mice. Scale bar (), 500 μm. () Flow cytometry of mLN cells (top left), follicular B cells (top right), IgDlo B cells (bottom left) and GC B cells (bottom right) from mixed–bone marrow chimeras generated with 60% wild-type (CD45.1+) cells plus 40% S1pr2+/+ or S1pr2−/− (CD45.2+) cells, reconstituted for at least 8 weeks and immunized intraperitoneally with SRBCs 6–8 d before analysis. Numbers adjacent to outlined areas indicate percent cells in each gate. (,) Contribution of CD45.2+ cells to follicular and GC populations in the spleen and mLNs of mixed–bone marrow chimeras generated with a mixture of CD45.1+ bone marrow plus CD45.2! + bone marrow at a ratio of 60:40 () or 90:10 (), reconstituted and immunized as in . () Contribution of S1pr2−/− (CD45.2+) GC B cells to GC and follicular populations in mixed–bone marrow chimeras: Sphk1f/−Mx1-Cre Sphk2−/− or Sphk1f/fMx1-Cre Sphk2−/− (SphK-deficient) hosts, or hosts retaining at least one wild-type allele of Sphk1 (Control). (,) Contribution of CD45.2+ cells to follicular and GC populations in the spleen, mLNs and Peyer's patches (PP; ) or mLNs () of mixed–bone marrow chimeras generated with mixtures of wild-type CD45.1+ cells plus CD45.2+ cells from either littermate control mice (Gα12-deficient (G12-KO) in ; Arhgef1+/+ in ) or mice deficient in Gα12 and Gα13 (G12-G13–DKO; ) or p115RhoGEF (Arhgef1−/−; ), at a ratio of ~60:40, reconstituted and immunized as in . Each symbol (,–) represents an individual mouse (throughout); small horizontal lines () indicate the mean. *P ≤ 0.01, **P ≤ 0.001 and ***P ≤ 0.0001 (Student's t-t! est). Data are representative of three experiments (), four ex! periments with 11–13 mice (,), more than five experiments with 19–23 mice (,) or three experiments with five mice (), or are from three experiments with six mice () or one representative of two similar experiments (,). * Figure 2: Resistance to apoptosis and greater Akt activation in GC B cells deficient in S1P2, Gα12-Gα13 or p115RhoGEF. (,) Frequency of GC B cells with activated caspase-3 (Active Csp3+; ) and TUNEL assay of fragmented DNA () in chimeras reconstituted with mixtures of S1pr2+/+ or S1pr2−/− (CD45.2+) plus wild-type (CD45.1+) bone marrow (horizontal axes), then immunized with SRBCs and analyzed after 6–8 d. () Flow cytometry analysis of Akt phosphorylated at Thr308 (p-Akt(T308)) in follicular and GC B cells from mixed chimeras as in ,. Right, mean fluorescence intensity (MFI) in various GC B cell populations. () Analysis of Akt phosphorylated at Thr308 in wild-type GC B cells from spleen suspensions left untreated (UT) or treated with JTE-013 alone or JTE-013 and wortmannin (JTE-013 + WMN) for 30 min immediately after isolation. () Mean fluorescence intensity of Akt phosphorylated at Thr308 in mLN GC B cells from Sphk1f/−Mx1-CreSphk2−/− or Sphk1f/fMx1-CreSphk2−/− (S1P-deficient) mice or mice retaining at least one wild-type allele of Sphk1 (Control (Ctrl)). () Flow cytometry ana! lysis of Akt phosphorylated at Thr308 in GC B cells deficient in both Gα12 and Gα13 (G12-G13–DKO) or in p115RhoGEF (Arhgef1−/−), and control cells from littermates (G12-KO or Arhgef1+/+, respectively) and wild-type (CD45.1+) mice (all mixed–bone marrow chimeras). Right, summary of mean fluorescence intensities. () Immunoblot analysis of Akt phosphorylated at Ser473 (p-Akt(S473)) in Ramos cells left untreated or treated for 5 min with S1P in the presence or absence of JTE-013. () Analysis of Akt phosphorylated at Thr308 in Ramos cells left untreated or treated for 5 min with S1P (10 nm) alone or in the presence of Y27632 (10 μM), JTE-013 (10 μM) or bpV(pic) (500 nM). () Phosphorylation of Akt at Thr308 in Ramos cells treated with S1P alone or in the presence of JTE-013 or Y27632, presented relative to results obtained with untreated cells, set as 1 (dashed line). †P ≤ 0.05, *P ≤ 0.01, **P ≤ 0.001 and ***P ≤ 0.0001 (Student's t-test). Data are from three! experiments with eight to nine mice (,), six experiments with! twelve to thirteen mice () or eight experiments with eight mice (), or are representative of four experiments with seven mice () or three mice (), or two () or three () experiments. * Figure 3: Akt activation confers an advantage to mucosal GCs, and S1P2 regulates translation in GC cells. () Flow cytometry of mLN cells from irradiated mice reconstituted with Cr2-Cre–transgenic bone marrow transduced with retrovirus expressing the Thy-1.1 reporter alone (vector) or the reporter and myr-Akt. Numbers adjacent to outlined areas indicate percent IgDloFas+ GC B cells (bottom right) or IgDhi follicular B cells (top left). Right, contribution of transduced (Thy-1.1+) cells to follicular and GC B cell populations. () TUNEL assay of mLN GC B cells from mice as in . () Phosphorylated (p-) 4E-BP1 in follicular and GC B cells from mixed–bone marrow chimeras containing wild-type (CD45.1+) cells and either S1pr2+/+ or S1pr2−/− (CD45.2+) cells. Right, mean fluorescence intensity of phosphorylated 4E-BP1 in GC populations. () Incorporation of 35S-labeled cysteine and methionine within 30 min by Ramos cells cultured in medium containing S1P (S1P), lacking S1P (No S1P), containing both S1P and JTE-013 (S1P + JTE-013) or lacking S1P and containing rapamycin (No S1P + rap! amycin); results were divided by the mean of the S1P group. †P ≤ 0.05, *P ≤ 0.01, **P ≤ 0.001 and ***P ≤ 0.0001 (Student's t-test). Data are from two experiments with seven mice (), two experiments with six mice () or four experiments with four mice () or are representative of three experiments with triplicate measurements (). * Figure 4: Regulation of the migration and positioning of GC B cells by S1P2. () Transwell assay of the migration of wild-type Bcl2-transgenic follicular and GC B cells toward CXCL12 (0.3 μg/ml) or CXCL13 (1 μg/ml) in the presence or absence of S1P and/or JTE-013. () Transwell assay of the migration of S1pr2+/− or S1pr2−/−Bcl2-transgenic GC B cells toward CXCL12 in the presence or absence of S1P. () Immunohistochemical analysis of splenic GL7+ GCs from an S1pr2+/− mouse and an S1pr2−/− mouse immunized with SRBCs. Scale bar, 200 μm. () Microscopy of lymph node sections from recipient (CD45.1+) mice given lysozyme-specific S1pr2+/+ or S1pr2−/− Hy10 (CD45.2+) B cells plus wild-type Hy10 (CD45.1+) B cells and OT-II (CD45.1+) T cells, then immunized with duck egg lysozyme–ovalbumin; sections were stained for CD45.2+ Hy10 GC B cells (blue) and follicular B cells (IgD; brown) at day 14. Scale bar, 200 μm. () Analysis of the velocity of S1pr2+/+ and S1pr2−/− GC B cells, assessed by real-time two-photon microscopy of the migration of ! fluorescence-labeled GC B cells in intact lymph nodes (Supplementary Videos 1 and 2). () Confinement of wild-type and S1pr2−/− GC B cells, assessed as in . () GC surface (red; Supplementary Fig. 6) with tracks of wild-type GC B cells (positive for cyan fluorescent protein) and S1pr2−/− GC B cells (positive for green fluorescent protein green; Supplementary Videos 3,4,5,6). Original magnification, ×20. () Analysis of the velocity of GC B cells during migration inside or outside the GC surface. Downward arrows (,) indicate average value. NS, not significant (P = 0.73); *P ≤ 0.01, **P ≤ 0.001 and ***P ≤ 0.0001 (Student's t-test (,) or Mann-Whitney U-test (,,)). Data are representative of at least five experiments (), two (,) or three (,) experiments, or two experiments with four GCs (,), or are from one experiment (). * Figure 5: S1P2 acts together with CXCR5 and FDCs to promote GC B cell clustering. () Immunohistochemical analysis of the distribution of GL7+ GC B cells and IgD+ B cells in mLN sections from a CXCL13-deficient mouse reconstituted with bone marrow from S1pr2+/− or S1pr2−/− donors. Scale bar, 100 μm. Right, GC B cells per mLN. Data are from three experiments with eight mice. () Immunohistochemical analysis of spleen sections from an S1pr2+/− mouse and an S1pr2−/− mouse treated for 4 weeks with lymphotoxin-α1β2 antagonist, then (at the fourth injection) immunized intraperitoneally with SRBCs, followed by analysis 7 d later. Scale bar, 100 μm. Right, GC B cells in spleen. Data are from three experiments with eight mice. * Figure 6: Degradation of S1P by B cells. () Surface abundance of S1P1 on follicular B cells from the peripheral lymph nodes (pLN), blood and spleens of wild-type control mice (Ctrl), S1pr1-deficient mice (S1P1-KO) and Sphk1f/−Mx1-CreSphk2−/− or Sphk1f/fMx1-CreSphk2−/− mice (S1P-deficient). () Abundance of transcripts encoding S1P lyase (Sgpl1), sphingosine 1-phosphate phosphatase 1 (Sgpp1) and lipid phosphate phosphatases (Lpp1–Lpp3) in B cells and T cells, presented relative to Hprt1 transcript abundance. () S1P remaining in culture supernatants before (S1P alone) and after (S1P +) incubation for 30–180 min (horizontal axis) with B cells or T cells, assessed as staining of Flag-tagged S1P1 and presented relative to results of reporter cells not exposed to S1P (No S1P). Data are representative of three experiments with more than three mice of each type () or two experiments () or are from three experiments (). * Figure 7: S1P2 directs activated B cells to the GC and the center of the follicle. () Immunohistochemical staining of GC-containing follicles in splenic sections from recipient mice immunized with SRBCs and then, 6 d later, given immunoglobulin-transgenic B cells transduced with retroviral vector encoding S1P2 or a control surface receptor (truncated Ngfr), as well as a human CD4 (hCD4) reporter, assessed 24 h after cell transfer. () Immunohistochemical staining of primary follicles in unimmunized recipients of Gpr183+/− immunoglobulin-transgenic B cells transduced as in . () Immunohistochemical staining of primary follicles in an Sphk1f/−Mx1-CreSphk2−/− mouse (S1P-deficient recipient) and an Sphk2−/− littermate (Control recipient) given Gpr183+/− immunoglobulin-transgenic B cells transduced as in . Scale bars, 200 μm. Data are representative of three independent experiments. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Kazuhiro Suzuki, * Bryan Cho & * L David Willison Affiliations * Howard Hughes Medical Institute and Department of Microbiology and Immunology, San Francisco, California, USA. * Jesse A Green, * Kazuhiro Suzuki, * Bryan Cho, * Christopher D C Allen, * Timothy H Schmidt, * Ying Xu & * Jason G Cyster * Department of Dermatology, University of California San Francisco, San Francisco, California, USA. * Bryan Cho * Cardiovascular Research Institute, University of California San Francisco, San Francisco, California, USA. * L David Willison, * Daniel Palmer & * Shaun R Coughlin * Genetics of Development and Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA. * Richard L Proia * Present addresses: World Premier International Immunology Frontier Research Center, Osaka University, Osaka, Japan (K.S.), Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA (L.D.W.) and Sandler-Newmann Foundation, Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA (C.D.C.A.). * Kazuhiro Suzuki, * L David Willison & * Christopher D C Allen Contributions J.A.G., B.C. and J.G.C. designed the experiments; J.A.G. did most of the experiments; B.C. did some of the early experiments; K.S. and J.A.G. did the imaging experiments; C.D.C.A. and Y.X. did the initial gene-expression analysis; T.H.S. and J.A.G. did the experiments with PTEN inhibitor; L.D.W. and S.R.C. generated Gα12-deficient mice; D.P. and S.R.C. generated Gna12−/−Gna13f/fMx1-Cre bone marrow; R.L.P. generated S1P2-deficient mice; J.A.G., K.S., B.C. and J.G.C. analyzed the data; and J.A.G. and J.G.C. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jason G Cyster Author Details * Jesse A Green Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuhiro Suzuki Search for this author in: * NPG journals * PubMed * Google Scholar * Bryan Cho Search for this author in: * NPG journals * PubMed * Google Scholar * L David Willison Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Palmer Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher D C Allen Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy H Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Ying Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Richard L Proia Search for this author in: * NPG journals * PubMed * Google Scholar * Shaun R Coughlin Search for this author in: * NPG journals * PubMed * Google Scholar * Jason G Cyster Contact Jason G Cyster Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (6M) Movement of S1pr2+/+ Hy10 GC B cells within FDC network. * Supplementary Video 2 (6M) Movement of S1pr2−/− Hy10 GC B cells around the perimeter of the FDC network. * Supplementary Video 3 (21M) Tracks of wild-type and S1pr2−/− Hy10 GC B cells in and around PE-IC-labeled FDC network. * Supplementary Video 4 (10M) Movement of S1pr2+/+ Hy10 GC B cells within a GC surrounded by an excess of labeled follicular B cells. * Supplementary Video 5 (9M) Movement of S1pr2−/− Hy10 GC B cells around the perimeter of a GC surrounded by an excess of labeled follicular B cells. * Supplementary Video 6 (26M) Tracks of wild-type and S1pr2−/− Hy10 GC B cells with respect to the surface of the GC, defined by the distribution of wild-type follicular and GC B cells. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–8 Additional data
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