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• Looking forward
  - Nat Immunol 11(7):545 (2010)
  Nature Immunology | Editorial Looking forward Journal name:Nature ImmunologyVolume:11,Page:545Year published:(2010)DOI:doi:10.1038/ni0710-545 Nature Immunology turns 10 years old in July. What new immunologic insights will the next decade bring? Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In this 10th anniversary issue, we commemorate the launch of Nature Immunology in 2000. This new journal represented a commitment by Nature Publishing Group to provide the immunology community with a journal dedicated to publishing the best immunology research. Ten years later, the aim remains the same: to publish fundamental new insights in all areas of immunology that lead the field significantly forward. How protective immune responses are elicited and how pathologic processes arise because of tolerance breakdown remain core questions in immunology. Answers to these questions will aid in understanding allergies and autoimmunity, inherited defects and environmental triggers that lead to immunodeficiency or cancer. Nature Immunology remains interested in studies that examine these basic concepts. Cellular communication has a key role in orchestrating immune responses. Understanding how communication occurs between innate or parenchymal cells and adaptive immune cells in nonlymphoid tissues and between antigen-presenting cells and lymphocytes to prime antigen-specific responses and engender immunological memory or tolerance is of prime importance. Addressing such questions requires identification of the cell types involved and their location and lineage development, as well as knowledge of the receptors, ligands and signaling pathways that convey information from one cell to an! other. Looking back, we see that tremendous strides have been made in the past decade. It will come as no surprise that among the top-cited papers published in Nature Immunology are those that describe the activation and signaling pathways of Toll-like receptors, the identification of the factors necessary for regulatory T cell generation and function, and the great diversity of helper T cell subsets associated with distinct effector function. More recently, the identification of sensors that detect intracellular pathogens or damage to promote inflammatory responses have sparked interest. Pathogenesis studies have mirrored this trend by identifying the triggers or evasion strategies used by microbes to subvert host immunity. Similarly, the discovery of microRNAs and their role in the post-transcriptional regulation of gene expression has opened new avenues of exploration of their role in regulating immune cell function. Our journal has also placed an emphasis on publishing robust h! uman immunology papers. Like other scientific disciplines, immunology has emerged from an age of identifying and characterizing individual molecules and cell types to one of understanding the roles of each under contextual situations and the regulatory networks that govern interactions of immune cells. Nature Immunology emphasizes the need to demonstrate physiological relevance. Advances in the use of multicolor flow cytometry and the generation of highly specific fluorescent molecular probes have allowed the prospective isolation and analysis of primary cells. Advances in vector-mediated delivery of target genes or short hairpin RNAs to interfere with gene expression have made it feasible, in many cases, to undertake rigorous studies of primary human cells. In animal models, advances in imaging technologies, such as intravital microscopy, have paved the way to allow real-time visualization of immune-cell interactions in living tissues. Advances in automated DNA-sequencing capacities now allow geno! me-wide analysis of chromatin modifications and transcription factor binding by 'ChIP-Seq' approaches. Thus, tools are now in place and continue to be developed that will allow the investigation and manipulation of real immune cells with far greater resolution than was possible even a decade ago. To help celebrate this 10th anniversary issue, we asked several prominent scientists to imagine what the next decade of immunology research might bring. Medzhitov discusses innate immune–recognition pathways. He predicts that as with pathogenic species, the recognition of commensal microorganisms is probably an active process that triggers immunological tolerance rather than immunity. Finlay and colleagues discuss the mucosa as an integrated organ system that is immunologically unique because of its function as a physical barrier. Because many pathogens enter their hosts at mucosal sites, there is great interest in how to elicit protective mucosal antigen-specific immunity. Both Medzhitov and Finlay emphasize that much remains to be learned about the complexity of symbiotic microbial communities and how their interactions with the host organism shape immune responsiveness. Such a systems approach to immunology is also emphasized by Krummel, as autoimmunity or tolerance does not arise from cells acting alone. Instead, immune responses form through the integration of multiple simultaneous and often transient inputs from a suite of participants, including the tissues themselves. As discussed by Krummel, visualization of immune cells in their environment is needed to explore how specific immune responses occur in real time. Tracey analyzes the largely unexplored interactions between cells of the immune and neural systems. These interactions represent a more ancient form of chemical communication that contributes to the regulation of innate immune responses and inflammation. He suggests by studying neural reactions during immune responses, it may be possible one day to therapeutically modulate immunity by targeting neural cells. Finally, Tarakhovsky focuses on how cells epigenetically 'remember things past'. Epigenetic chromatin modification regulates the specification of cellular identity and function. He argues that intracellular pathogens can target the 'readers' of epigenetic information to subvert immune responses. This collection represents but the tip of the iceberg and is not meant to delimit the interests of Nature Immunology, as many other exciting areas remain to be explored. Our motto remains "Immunology. All of it." We are pleased that the immunology community has embraced Nature Immunology and made it a success. We hope you share our enthusiasm and will continue to support the journal during the next 10 years and beyond. Additional data
• Is IL-7 from dendritic cells essential for the homeostasis of CD4+ T cells?
  - Nat Immunol 11(7):547-548 (2010)
  Nature Immunology | Correspondence Is IL-7 from dendritic cells essential for the homeostasis of CD4+ T cells? * Christopher E Martin1 Search for this author in: * NPG journals * PubMed * Google Scholar * David M Kim1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Sprent2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Charles D Surh1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:547–548Year published:(2010)DOI:doi:10.1038/ni0710-547 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To the Editor: In an article published in the February 2009 issue of Nature Immunology1, Guimond et al. offered an interesting explanation for the slower interleukin 7 (IL-7)-driven homeostatic proliferation of naive CD4+ T cells than of CD8+ T cells1, 2. Guimond et al. concluded that CD4+ T cells can respond efficiently only to IL-7 expressed by dendritic cells but not to IL-7 expressed by stromal cells, which are the main IL-7 producers3, 4. As direct evidence of their hypothesis, Guimond et al. showed that polyclonal CD4+ T cells and CD4+ T cells transgenic for the Marilyn T cell antigen receptor (TCR) proliferated abundantly in bone marrow chimeras in which IL-7 was expressed only by bone marrow–derived cells (IL-7-deficient (Il7−/−) hosts given bone marrow from mice deficient in recombination-activating gene 1 (Rag1−/−)). View full text Author information * Author information * Supplementary information Affiliations * Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California, USA. * Christopher E Martin, * David M Kim & * Charles D Surh * Garvan Institute of Medical Research, Sydney, New South Wales, Australia. * Jonathan Sprent * World Class University–Integrative Biosciences and Biotechnology Program, Pohang University of Science and Technology, Pohang, Korea. * Jonathan Sprent & * Charles D Surh Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Charles D Surh (csurh@scripps.edu) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (84K) Supplementary Figure 1 Homeostatic proliferation of polyclonal B6.Thy-1.1+ CD8+ and CD4+ T cells and Thy-1.1+ SMARTA.Rag1−/− TCR transgenic CD4+ T cells in indicated hosts analyzed 1 wk after adoptive transfer. Additional data
• Reply to "Is IL-7 from dendritic cells essential for the homeostasis of CD4+ T cells?"
  - Nat Immunol 11(7):548 (2010)
  Nature Immunology | Correspondence Reply to "Is IL-7 from dendritic cells essential for the homeostasis of CD4+ T cells?" * Crystal L Mackall1 Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Guimond2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume:11,Page:548Year published:(2010)DOI:doi:10.1038/ni0710-548 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Mackall and Guimond respond: In our article1, we concluded that interleukin 7 (IL-7) production is regulated by a simple feedback loop and that IL-7 signaling on antigen-presenting cells controls CD4+ T cell homeostatic expansion. Marilyn HY-reactive T cells proliferated more in IL-7-deficient (Il7−/−) recipients of bone marrow from female mice deficient in recombination-activating gene 1 (Rag1−/−) than in Rag1−/− recipients of such bone marrow (Fig. 3d in ref. 1). Surh et al.2 were surprised by the high degree of proliferation, could not reproduce our findings and assumed that contamination by bone marrow from male mice occurred in our experiments. However, we controlled these experiments by administering the same pool of marrow cells to Rag1−/− recipients, which did not support proliferation, thus ruling out the possibility of contamination by bone marrow from male mice (Supplementary Fig. 1a). Furthermore, Marilyn cells administered to male hosts underwent greater population expansion! and dilution of the cytosolic dye CFSE (Supplementary Fig. 1a) than did those given to Il7−/− recipients. Our original manuscript contained additional data that supported the central model. Marilyn cells proliferated after being transferred into Rag1−/− recipients that received bone marrow from female mice deficient in the common γ-chain but not after being transferred into Rag1−/− recipients that received bone marrow from female Rag1−/− mice (Supplementary Fig. 1b). If contamination by bone marrow from male mice were to explain this, the bone marrow deficient in the common γ-chain (but not the Rag1−/− bone marrow) would have been consistently contaminated across three independent experiments, which seems implausible. Furthermore, Marilyn cells proliferated in female mice deficient in the IL-7 receptor α-chain (Il7r−/−; Supplementary Fig. 1c), which were not chimeric, and we obtained similar results with chimeras generated by the transfer of bone! marrow from female Il7r−/− mice into female Il7r−/− r! ecipient mice (Supplementary Fig. 1d). In Il7r−/− mice and female Il7r−/− chimeras, Marilyn cells show a homeostatic pattern of proliferation, which differed from the rapid proliferation observed in the Il7−/− mouse (Supplementary Fig. 1a). We do not yet understand why the patterns differ, but we observed similar differences with polyclonal T cells (Supplementary Fig. 1e). Together, the multiple models demonstrating enhanced Marilyn cell proliferation when dendritic cells lack IL-7 signaling, the absence of Marilyn cell proliferation in Rag1−/− recipients and the homeostatic proliferation pattern in Il7r−/− recipients refute the notion that contamination by bone marrow from male mice confounded our conclusions1. View full text Author information * Author information * Supplementary information Affiliations * Pediatric Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA. * Crystal L Mackall * Hopital Maisonneuve-Rosemont, University of Montreal, Montreal, Quebec, Canada. * Martin Guimond Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Crystal L Mackall (mackallc@mail.nih.gov) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figure 1 IL7Ra signaling on Bone Marrow Derived Cells Regulates CD4+ Homeostatic Expansion. Additional data
• Tomio Tada 1934–2010
  - Nat Immunol 11(7):549 (2010)
  Tomio Tada died 21 April 2010 of complications of prostate cancer. He was 76 years old.
• Innate immunity: quo vadis?
  - Nat Immunol 11(7):551-553 (2010)
  Nature Immunology | Commentary Innate immunity: quo vadis? * Ruslan Medzhitov1 Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature ImmunologyVolume:11,Pages:551–553Year published:(2010)DOI:doi:10.1038/ni0710-551 The next decade will probably witness the development of new concepts that will incorporate the presently unexplained aspects of innate immunity. Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg  "If you don't know where you are going, you will wind up somewhere else." Yogi Berra The field of innate immunity has enjoyed tremendous progress in the past 10 years. This is due in large part to identification of the pathways and mechanisms of innate immune recognition and innate control of adaptive immunity. In the next decade, many developments can be expected that will better define already known aspects of the innate immune system, including the characterization of additional pattern-recognition receptors (PRRs), their signaling pathways and their roles in host defense. Other developments may not be as obvious or may be entirely unexpected. This commentary will discuss a few of the possible future developments of the second type. Microbial sensors: PRRs and beyond Several families of PRRs have been characterized so far, including Toll-like receptors (TLRs), intracellular Nod proteins, Nod-like receptors, RIG-like receptors, dectin proteins and several others1. These receptor families have now been well established to have important roles in pathogen recognition and the activation of different arms of innate and adaptive immunity. Several pathways of pathogen recognition are still not accounted for, including one or more additional cytosolic DNA sensors and cell-intrinsic sensors that detect retroviruses. It can also be expected that innate immune-recognition pathways that are not based on pattern recognition will also be identified over the next decade. One important area of future investigation is whether the innate immune system can detect pathogen-specific features (biochemical activities or particular modes of interactions with the host cells) so that it could distinguish between pathogenic and commensal microorganisms. Such a recognition system has been proposed (as a 'guard theory') to operate in plants, in which dedicated products of resistance genes are thought to monitor key cellular processes that are common targets for pathogens, for example, endosomal trafficking and cytoskeletal dynamics2. Alterations in normal cellular processes are thought to be sensed by such gene products (referred to as 'guard proteins') to initiate host-defense responses in plants. Similar microbe-sensing strategies may operate in mammalian cells. Indeed, the NLRP3 inflammasome seems to sense membrane integrity, as it can be activated by diverse stimuli that can disrupt cellular membranes. Both pore-forming toxins of Gram-positive pathoge! ns and type III secretion systems of Gram-negative pathogens can be detected by NLRP3 inflammasomes, which results in the secretion of members of the interleukin 1 family and many other leaderless proteins by the caspase-1-dependent nonconventional secretory pathway3. Additional mechanisms that can sense pathogen-specific effects on the host probably exist, and they may complement pattern recognition and missing-self recognition. Whether the detection of pathogen-specific activities is essential for pathogen-commensal discrimination remains to be established, however. The argument here can go both ways: on the one hand, the mammalian immune system is widely assumed to be able to distinguish pathogens from commensals to mount immune responses to the former and to avoid responding to the latter; however, commensals do not seem to be intrinsically different from pathogens in terms of their ability to induce an immune response. In fact, commensal-specific innate and adaptive immune responses occur normally and seem to be needed to maintain the normal host-commensal homeostasis4. Thus, the immune system provides protection not only from pathogens but also from commensals. Furthermore, the distinction between commensals and pathogens is operational (that is, whether or not they can cause a disease) and is conditional on the host identity and its immune status. Both commensal and pathogenic microbes use the! host as a niche and use diverse adaptation strategies to establish successful colonization. The full diversity of adaptation strategies used by commensal microbes is unknown at present but it is probably at least as great as the diversity of strategies used by pathogens. The relationship of these strategies to the host's sensing abilities will need to be systematically analyzed in future studies to clarify whether or not the immune system is able to detect some adaptation strategies (for example, virulence activities) but not others. Ultimately, a more biological classification of the microbes that colonize the host may be based on their host-adaptation strategies rather than on their ability to cause a disease. Indeed, virulence factors are nothing other than gene products that evolved for adaptation to a particular niche in the host. Accordingly, the additional pathways of innate immune sensing may best be identified on the basis of common themes in microbial adaptation ! to the host. Effects of microbiota on the host Chris Sharp What does the future hold for innate immunity research? Commensal microflora has long been recognized to have a variety of beneficial effects on host development and physiology. The composition of the microbial communities that colonize the host, particularly in the lower gastrointestinal tract, is difficult to evaluate because many commensal bacteria are unculturable obligatory anaerobes. However, studies have begun to demonstrate remarkably specific and profound effects of seemingly subtle variations in microbial composition on host immunity and susceptibility to diseases5, 6. Many discrepant results obtained in the past in different laboratories can presumably be ascribed to the differences in the composition of commensal communities in different animal facilities throughout the world. Whether just a few bacterial or viral species make all the difference is not yet clear, and it is possible that more complex microbial communities are responsible for the variations in the effects of commensal flora on the host. A major challeng! e for future studies is to identify the full spectrum and mechanisms of the effects of microbiota on the immune system, on susceptibility to infection and autoimmune disease, and on other physiological and pathological processes. Recognition of parasites and allergens One area of investigation in innate immune recognition that has not yet seen much progress is study of the recognition of multicellular parasites. No receptors have yet been clearly demonstrated to be involved in sensing parasite infections, and consequently, the innate control mechanisms of T helper type 2 (TH2) immune responses are not well defined. Indeed, in the field of type 2 immunity, there is no consensus even in terms of very basic questions about the mechanisms involved in the induction of TH2 responses. There is also a major gap in knowledge relating to the mechanisms of innate immune recognition of allergens. Allergens are noninfectious environmental antigens that have immunogenic activities that can trigger TH2–immunoglobulin E immune responses. Why allergens are immunogenic is, in general, not well understood, and the mechanisms of their immunogenicity are largely obscure. Notably, there are several different biochemical classes of allergens, and each probabl! y activates immune responses through distinct mechanisms. Some allergens seem to mimic the biochemical activities of parasite-associated enzymes, for example, proteases. These enzymatic activities may be detected by dedicated sensors of parasitic infection7. This form of innate immune recognition is thus based on the sensing of unique activities of parasites rather than direct molecular (pattern) recognition. Other allergens, specifically those that have lipid-binding properties, are immunogenic because of physical association with lipopolysaccharide and perhaps other TLR ligands8. However, in most cases, the mechanisms of innate immune recognition of allergens, as well as of parasitic worms, are unknown, and this remains a major deficiency in the understanding of innate immune recognition. Immunity versus immunogenicity It is now well appreciated that innate immune recognition, particularly by PRRs, is the basis of immunogenicity of microbial stimuli, such as lipopolysaccharide and nucleic acids. It is also well established that activation of PRRs results in the induction of adaptive immune responses. Although this has been most clearly demonstrated for TLRs, other receptor families are also coupled to the induction of adaptive immunity. Thus, the rules of immunogenicity are more or less understood, but what is not known are the rules for the induction of protective immunity. Indeed, not all immune responses are protective against a given pathogen. It is known that induction of the appropriate effector class is required, but beyond that, the parameters of protective immunity are not fully understood. For example, not all antigen specificities can confer pathogen clearance. Because host protection from a pathogen is what ultimately matters, it is possible that specific mechanisms exist that ! instruct immune responses in a way that maximizes the chances of protection. These mechanisms probably operate at the level of the innate immune system, similar to the innate control mechanisms that determine the choice of effector responses of T cells and B cells. Lack of understanding of the rules of induction of protective immunity explains why the rational design of effective vaccines remains unknown. Such knowledge would clearly be very critical for future efforts in vaccine development. Resistance and tolerance to infections It has been recognized for decades in the field of plant immunity that there are two fundamentally different strategies of host defense from infection: resistance and tolerance9, 10. Mechanisms of resistance diminish pathogen burden through pathogen detection and elimination. Mechanisms of tolerance, however, diminish the negative effects on host fitness of a given amount of pathogen burden. Although the mechanisms of resistance have been the main subject of immunology, with a few notable exceptions11, 12, 13, 14, mechanisms of tolerance to infection have not been studied in animals. A few available studies11, 12, 13, 14 suggest that tolerance has a very important role in host-pathogen interactions, and future research will need to focus on systematic analysis of the mechanisms that allow the host to endure a given amount of pathogen assault. Manipulation of the host tolerance mechanisms may be a valuable therapeutic strategy for some infectious diseases, as exemplified by a! study of tolerance to Plasmodium chabaudi chabaudi infection14. Infection can negatively affect host fitness through two distinct mechanisms. First, pathogens can directly cause tissue damage through their virulence activities. Second, tissue damage can be caused by the immune and inflammatory responses to the pathogen. Thus, host tolerance to infection must come in two forms to enable tolerance to the two sources of tissue damage. The cellular and molecular mechanisms responsible are largely unknown, and these represent important subjects for future research. 'Nonimmune' functions of innate immunity The dominant role of the immune system is to protect the host from infectious agents. However, the immune system may have other roles in mammalian biology. Thus, TLRs have been demonstrated to be involved in tissue repair and homeostasis, at least in tissues colonized by microbes, such as the colon. Available evidence suggests that TLRs and inflammasomes may also have a role in sterile inflammation and have important roles in the pathogenesis of inflammatory diseases3, 15. In addition, the innate immune system may have a role in the defense against noninfectious noxious insults, such as toxic xenobiotics, environmental irritants and venoms. Characterization of the putative functions of the innate immune system in these contexts is an exciting area for future research. Conclusions Despite the tremendous progress made by studies of the innate immune system, many fundamental questions remain and some new questions will surely arise in the near future. The pattern-recognition theory proposed by Janeway over 20 years ago16 has served as a conceptual background for understanding the innate immune system. Additional concepts must now be developed to explain more enigmatic aspects of host-microbe interactions and the functions of the innate immune system in broader physiological contexts. As Yogi Berra pointed out, "It is hard to make predictions, especially about the future," but it is safe to say that exciting new discoveries in the field of innate immunity will continue in the next decade and beyond. References * References * Author information * Takeuchi, O. & Akira, S.Cell140, 805–820 (2010). * ChemPort * PubMed * Article * Dangl, J.L. & Jones, J.D.Nature411, 826–833 (2001). * ChemPort * ADS * ISI * PubMed * Article * Martinon, F., Mayor, A. & Tschopp, J.Annu. Rev. Immunol.27, 229–265 (2009). * ChemPort * PubMed * Article * Shiloh, M.U.et al. Immunity10, 29–38 (1999). * ChemPort * ISI * PubMed * Article * Gaboriau-Routhiau, V.et al. Immunity31, 677–689 (2009). * ChemPort * PubMed * Article * Ivanov, I.I.et al. Cell139, 485–498 (2009). * ChemPort * PubMed * Article * Sokol, C.L., Barton, G.M., Farr, A.G. & Medzhitov, R.Nat. Immunol.9, 310–318 (2008). * ChemPort * PubMed * Article * Trompette, A.et al. Nature457, 585–588 (2009). * ChemPort * ADS * PubMed * Article * Read, A.F., Graham, A.L. & Raberg, L.PLoS Biol.6, e4 (2008). * ChemPort * PubMed * Article * Schneider, D.S. & Ayres, J.S.Nat. Rev. Immunol.8, 889–895 (2008). * ChemPort * PubMed * Article * Ayres, J.S., Freitag, N. & Schneider, D.S.Genetics178, 1807–1815 (2008). * ChemPort * PubMed * Article * Ayres, J.S. & Schneider, D.S.PLoS Biol.7, e1000150 (2009). * ChemPort * PubMed * Article * Raberg, L., Sim, D. & Read, A.F.Science318, 812–814 (2007). * ChemPort * ADS * PubMed * Article * Seixas, E.et al. Proc. Natl. Acad. Sci. USA106, 15837–15842 (2009). * ADS * PubMed * Article * Rock, K.L. & Kono, H.Annu. Rev. Pathol.3, 99–126 (2008). * ChemPort * PubMed * Article * Janeway, C.A. Jr.Cold Spring Harb. Symp. Quant. Biol.54, 1–13 (1989). * ChemPort * PubMed Download references Author information * References * Author information Affiliations * Howard Hughes Medical Institute, Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA. * Ruslan Medzhitov Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Ruslan Medzhitov (ruslan.medzhitov@yale.edu) Additional data
• Illuminating emergent activity in the immune system by real-time imaging
  - Nat Immunol 11(7):554-557 (2010)
  The imaging of tissues and organs as it is now practiced will seem primitive in the coming decade, yet use of this technology will define the origin of emergent activities and drive an era of system integration.
• The future of mucosal immunology: studying an integrated system-wide organ
  - Nat Immunol 11(7):558-560 (2010)
  Over the next 10 years, it will be important to shift the focus of mucosal immunology research to make further advances. Examination of the mucosal immune system as a global organ, rather than as a group of individual components, will identify and characterize relationships between mucosal sites.
• Understanding immunity requires more than immunology
  - Nat Immunol 11(7):561-564 (2010)
  Acetylcholine and related neurotransmitters appeared with unicellular life forms, millions of years before innate immunity. Tools and insights are now available for understanding how the evolving nervous system influenced the development of immunity.
• Tools and landscapes of epigenetics
  - Nat Immunol 11(7):565-568 (2010)
  Epigenetics studies the phenotypes that are born from past experiences and are kept for life.
• Not a split decision for human hematopoiesis
  - Nat Immunol 11(7):569-570 (2010)
  Hematopoietic lineage schemes commonly show two distinct lymphoid and myeloid branches arising from the hematopoietic stem cell early during blood cell development. A new study of human hematopoiesis demonstrates that, similar to findings in mice, this split is not as dichotomous as is often presented.
• Turning over the Chance card on MS susceptibility
  - Nat Immunol 11(7):570-572 (2010)
  CD8+ T cells expressing two distinct T cell antigen receptors fail to be tolerized and can induce autoimmunity.
• B cell specification from the genome up
  - Nat Immunol 11(7):572-574 (2010)
  Lineage specification and development require a hierarchy of transcription factors. A genome-wide view of transcription factor binding provides new insights into early B lineage development.
• Research Highlights
  - Nat Immunol 11(7):575 (2010)
  
• The many paths to asthma: phenotype shaped by innate and adaptive immunity
  - Nat Immunol 11(7):577-584 (2010)
  Nature Immunology | Review The many paths to asthma: phenotype shaped by innate and adaptive immunity * Hye Young Kim1 Search for this author in: * NPG journals * PubMed * Google Scholar * Rosemarie H DeKruyff1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dale T Umetsu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:577–584Year published:(2010)DOI:doi:10.1038/ni.1892Published online18 June 2010 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 Asthma is a very complex and heterogeneous disease that is characterized by airway inflammation and airway hyper-reactivity (AHR). The pathogenesis of asthma is associated with environmental factors, many cell types, and several molecular and cellular pathways. These include allergic, non-allergic and intrinsic pathways, which involve many cell types and cytokines. Animal models of asthma have helped to clarify some of the underlying mechanisms of asthma, demonstrating the importance of T helper type 2 (TH2)-driven allergic responses, as well as of the non-allergic and intrinsic pathways, and contributing to understanding of the heterogeneity of asthma. Further study of these many pathways to asthma will greatly increase understanding of the distinct asthma phenotypes, and such studies may lead to new therapies for this important public health problem. View full text Figures at a glance * Figure 1: The heterogeneity of asthma. Asthma is a complex disease caused by multiple factors. There are several different forms of asthma (allergic, non-allergic and intrinsic), and in some patients these forms can coexist. Allergic asthma can be induced by allergens and is mediated by TH2 immune responses. Non-allergic asthma can also be caused by several factors, such as air pollution and infection. Non-TH2 cells and various cells of the immune system other than TH2 cells contribute to non-allergic asthma. Some of the many genes involved in development of spontaneous asthma are presented here. * Figure 2: APCs in the lung. () DCs are key APCs in the lung. After antigen challenge, lung DCs process antigen and induce antigen-specific TH2 cell responses. TCR, T cell antigen receptor. () Other cells can also function as APCs to initiate TH2 responses. Basophils, eosinophils, mast cells and natural helper cells express MHC class II and costimulatory molecules. Therefore, these cells of the innate immune system can be the initial sources of TH2 cytokines as well as potential APCs in the lung. SCF, stem cell factor; LTC4, leukotriene C4; Lin, lineage. * Figure 3: Newly identified cells of the innate immune system and pathways in asthma. Although adaptive immunity is critical for asthma pathogenesis, asthma also involves innate, antigen-independent immune responses. IL-25 induces TH2 cytokines such as IL-5 and IL-13 from natural helper cells in the absence of TH2 cells and stimulates NKT cells to produce IL-13, thereby promoting AHR and airway remodeling. IL-33 acts on multiple targets; it stimulates mast cells, eosinophils, basophils, natural helper cells and NKT cells to elicit TH2 cytokines. TSLP activates mast cells and NKT cells to secrete TH2 cytokines. The finding of these cytokines, IL-25, IL-33 and TSLP, and cells of the innate immune system greatly extends understanding of the pathogenesis of asthma. Author information * Abstract * Author information Affiliations * Division of Immunology and Allergy, Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Hye Young Kim, * Rosemarie H DeKruyff & * Dale T Umetsu Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dale T Umetsu (dale.umetsu@childrens.harvard.edu) Additional data
• Revised map of the human progenitor hierarchy shows the origin of macrophages and dendritic cells in early lymphoid development
  Doulatov S Notta F Eppert K Nguyen LT Ohashi PS Dick JE - Nat Immunol 11(7):585-593 (2010)
  Nature Immunology | Article Revised map of the human progenitor hierarchy shows the origin of macrophages and dendritic cells in early lymphoid development * Sergei Doulatov1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Faiyaz Notta1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Kolja Eppert1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Linh T Nguyen3 Search for this author in: * NPG journals * PubMed * Google Scholar * Pamela S Ohashi3 Search for this author in: * NPG journals * PubMed * Google Scholar * John E Dick1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:585–593Year published:(2010)DOI:doi:10.1038/ni.1889Received26 March 2010Accepted18 May 2010Published online13 June 2010 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 The classical model of hematopoiesis posits the segregation of lymphoid and myeloid lineages as the earliest fate decision. The validity of this model in the mouse has been questioned; however, little is known about the lineage potential of human progenitors. Here we provide a comprehensive analysis of the human hematopoietic hierarchy by clonally mapping the developmental potential of seven progenitor classes from neonatal cord blood and adult bone marrow. Human multilymphoid progenitors, identified as a distinct population of Thy-1neg–loCD45RA+ cells in the CD34+CD38− stem cell compartment, gave rise to all lymphoid cell types, as well as monocytes, macrophages and dendritic cells, which indicated that these myeloid lineages arise in early lymphoid lineage specification. Thus, as in the mouse, human hematopoiesis does not follow a rigid model of myeloid-lymphoid segregation. View full text Figures at a glance * Figure 1: Sorting of human progenitors. () Sorting of cord blood (CB) mononuclear Lin− cells stained with anti-CD34, anti-CD38, anti-CD90 (anti-Thy-1), anti-CD135 (anti-Flt3), anti-CD45RA, anti-CD7 and anti-CD10; fractions are labeled A–G as in Table 1. Top, CD34+CD38pos–hi population (CD7+ cells gated out; data not shown), gated on CD135, CD45RA and CD10 to separate CD135+CD45RA− CMPs (fraction D), CD135+CD45RA+CD10− GMPs (fraction E), CD135−CD45RA− MEPs (fraction F) and CD135+CD45RA+CD10+ pre-B cells–NK cells (fraction G). Bottom, separation of the CD34+CD38− compartment on the basis of expression of CD90 and CD45RA, to distinguish CD90+CD45RA− HSCs, CD90−CD45RA− MPPs (fraction A), and CD90neg–loCD45RA+ MLPs; the CD90neg–loCD45RA+ fraction was further subgated on the basis of expression of CD7 and CD10 (fractions B and C). Bottom right, Lin− bone marrow (BM). Numbers in or adjacent to outlined areas indicate percent Lin− cells in each gate. () CD135 (Flt3) expression by human HSCs! , MPPs (fraction A) and MLPs (fractions B and C). Data are representative of at least 20 independent experiments. * Figure 2: Clonal analysis of candidate cord blood and bone marrow progenitor fractions. () Flow cytometry analysis of the multilineage output of individual wells seeded with single cord blood MPPs (fraction A) and cultured for 4 weeks on MS-5 stroma with SCF, TPO, IL-7 and IL-2; only CD45+ events are presented. Numbers in quadrants indicate percent cells in each. (,) Cloning efficiency of myeloid lineages (left) and lymphoid lineages (right) of single cord blood progenitors () or bone marrow progenitors () deposited onto the MS-5 stroma by flow cytometry sorting (fractions A–G, horizontal axes). Bars indicate total cloning efficiency; filled portion indicates the proportion of myeloid potential (myeloid plus mixed colonies) or lymphoid potential (lymphoid plus mixed colonies). Far right (), morphology of cells (fraction B) isolated from single wells confirms lineage assignment. Scale bars, 10 μm. () T cell potential of cord blood progenitors (left; 8 weeks) or bone marrow progenitors (middle; 4 weeks) seeded at limiting dilution on OP9-DL1 stroma. Results ar! e presented as limiting-dilution frequency (error bars, upper limits of 95% confidence interval). Right, marker profiles of T cells cultured on OP9-DL1 stroma; numbers in outlined areas (top) indicate percent CD5+CD45+ cells, and numbers in quadrants (bottom) indicate percent cells in each. () CFU assay (left) of the colony-forming efficiency of myeloid and erythroid lineages of single cord blood and bone marrow progenitors deposited by flow sorting. G, granulocytic; M, macrophage; GM, mixed myeloid; E, erythroid; GEMM, myelo-erythroid. Middle, Giemsa stain of MLP and GMP colonies. Original magnification, ×100. Right, colony-forming efficiency of cord blood MLPs and HSCs cultured for 4 d on OP9 stroma. Data are representative of three independent experiments () or four () or two (–) experiments with independent cord blood samples and over 12 wells for each fraction per experiment (mean and s.e.m., ,,). * Figure 3: Clonal analysis of human MLPs. () Cloning efficiency of myeloid lineages (left) and lymphoid lineages (middle) of single cord blood progenitors deposited onto MS-5 stroma by flow cytometry sorting and cultured for 4 weeks with SCF, TPO, IL-7, IL-2, G-CSF and GM-CSF, with or without M-CSF. Bars indicate total cloning efficiency; filled portion indicates the proportion of myeloid potential (myeloid plus mixed colonies) or lymphoid potential (lymphoid plus mixed colonies). Right, marker profiles of MLP colonies (colony number in parentheses). () Cloning efficiency (right) of T lineages and myeloid lineages of single cord blood or bone marrow MLPs or CMPs deposited onto mixed MS-5 and MS-5–Delta-like 4 stroma by flow cytometry sorting and cultured for 4 weeks. Bars indicate total cloning efficiency; filled portion indicates the proportion of T cell (T cell plus mixed colonies) or myeloid potential (myeloid plus mixed colonies). Left, marker profiles of T cells, myeloid cells and mixed colonies. () Cloning e! fficiency (right) of monocyte and DC lineages of single cord blood progenitors deposited onto OP9 stroma by flow cytometry sorting and cultured for 2 weeks with GM-CSF, M-CSF, IL-4 and IL-6. Bars indicate total cloning efficiency; filled portion indicates the proportion of colonies containing both monocytes and DCs. Left, cell morphology of sorted Giemsa-stained CD14+ and CD1a+ cells. Original magnification, ×100. Middle, marker profiles of MLP colonies (colony number in parentheses). Numbers in quadrants (, right) or outlined areas (, left; , middle) indicate percent cells in each. Data are representative of six () or three (,) experiments with three independent cord blood samples with over 12 wells for each fraction per experiment (mean and s.e.m.). * Figure 4: Differentiation of human progenitors into mature DCs. (,) Phenotypic () and morphological () characterization of DCs derived from differentiated cord blood MLPs, GMPs and PBMs isolated by leukopheresis and matured with IFN-γ and LPS or without TLR ligands (No stim). Numbers in outlined areas () indicate percent CD80+CD83+ cells. Original magnification (), ×100. () Proportion of mature CD80+ CD83+ CD86+ CD40+ DCs in cultures of cord blood MLPs, GMPs and PBMs matured in the presence of various cytokines and TLR ligands (horizontal axis). TNF, tumor necrosis factor; PGE, prostaglandin E; poly(I:C), polyinosinic-polycytidylic acid; CpG, 2′-deoxyribo(cytidine-phosphate-guanosine); LTA, lipoteichoic acid. () Population expansion of cord blood– and bone marrow–derived MLPs and GMPs cultured in DC conditions. () Enzyme-linked immunosorbent assay of the secretion of IL-12 (left) and IL-6 (right) by DCs differentiated from MLPs, GMPs and PBMs. Data are representative of three independent experiments (mean and s.e.m. in –). * Figure 5: In vivo lineage potential of human progenitors. () Human cell engraftment (left) in the injected femur of NSG mice 2 weeks after intrafemoral transplantation of 1 × 103 cord blood MLPs (n = 4 mice) or CMPs (n = 4 mice); right, graft composition, gated on human CD45+ events. () Analysis of the progenitor compartment in NSG mice (n = 4–8) 10 weeks after transplantation with 1 × 105 Lin− cord blood cells; human Lin− cells isolated by column purification from the marrow were stained with the marker panel in Figure 1 without CD135 (thus, CMPs-MEPs appear as a single population). () Cloning efficiency of myeloid lineages (left) and lymphoid lineages (middle) of human progenitor fractions isolated from the bone marrow of NSG mice with human engraftment; single cells (population, horizontal axis) were deposited on MS-5 stroma by flow sorting and were cultured for 4 weeks with SCF, TPO, IL-7 and IL-2 (presented as in Fig. 2b). Numbers in outlined areas (,) indicate percent cells in each. Data are representative of three in! dependent experiments with four to eight mice each and over 12 wells for each fraction per experiment (mean and s.e.m. in ). * Figure 6: Lineage-specific gene expression in human progenitors. Quantitative PCR analysis of the expression of PU.1, C/EBP-α, myeoloperoxidase (MPO), GATA-1, Pax5 and GATA-3 mRNA in progenitor fractions isolated from Lin− cord blood by flow cytometry sorting; results are presented in arbitrary units (AU) relative to the expression of GAPDH (glyceraldehyde phosphate dehydrogenase). Data are combined from two independent experiments (mean and s.e.m.). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE21973 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Sergei Doulatov & * Faiyaz Notta Affiliations * Department of Stem Cell and Developmental Biology, Campbell Family Cancer Research Institute, Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada. * Sergei Doulatov, * Faiyaz Notta, * Kolja Eppert & * John E Dick * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Sergei Doulatov, * Faiyaz Notta, * Kolja Eppert & * John E Dick * Departments of Medical Biophysics and Immunology, Campbell Family Cancer Research Institute, Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada. * Linh T Nguyen & * Pamela S Ohashi Contributions S.D. and F.N. designed and did experiments; S.D. wrote the manuscript; K.E. analyzed microarray data; L.T.N. did DC population expansion experiments; and P.S.O. and J.E.D. supervised the study and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John E Dick (jdick@uhnres.utoronto.ca) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–10 and Supplementary Tables 1–5 Additional data
• Semaphorins guide the entry of dendritic cells into the lymphatics by activating myosin II
  Takamatsu H Takegahara N Nakagawa Y Tomura M Taniguchi M Friedel RH Rayburn H Tessier-Lavigne M Yoshida Y Okuno T Mizui M Kang S Nojima S Tsujimura T Nakatsuji Y Katayama I Toyofuku T Kikutani H Kumanogoh A - Nat Immunol 11(7):594-600 (2010)
  Nature Immunology | Article Semaphorins guide the entry of dendritic cells into the lymphatics by activating myosin II * Hyota Takamatsu1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Noriko Takegahara1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Yukinobu Nakagawa1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Michio Tomura4 Search for this author in: * NPG journals * PubMed * Google Scholar * Masahiko Taniguchi5 Search for this author in: * NPG journals * PubMed * Google Scholar * Roland H Friedel6 Search for this author in: * NPG journals * PubMed * Google Scholar * Helen Rayburn7 Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Tessier-Lavigne8 Search for this author in: * NPG journals * PubMed * Google Scholar * Yutaka Yoshida9 Search for this author in: * NPG journals * PubMed * Google Scholar * Tatsusada Okuno1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Masayuki Mizui2, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Sujin Kang1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Satoshi Nojima1, 2, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Tohru Tsujimura12 Search for this author in: * NPG journals * PubMed * Google Scholar * Yuji Nakatsuji13 Search for this author in: * NPG journals * PubMed * Google Scholar * Ichiro Katayama3 Search for this author in: * NPG journals * PubMed * Google Scholar * Toshihiko Toyofuku1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Hitoshi Kikutani2, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Atsushi Kumanogoh1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:594–600Year published:(2010)DOI:doi:10.1038/ni.1885Received23 February 2010Accepted10 May 2010Published online30 May 2010 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 The recirculation of leukocytes is essential for proper immune responses. However, the molecular mechanisms that regulate the entry of leukocytes into the lymphatics remain unclear. Here we show that plexin-A1, a principal receptor component for class III and class VI semaphorins, was crucially involved in the entry of dendritic cells (DCs) into the lymphatics. Additionally, we show that the semaphorin Sema3A, but not Sema6C or Sema6D, was required for DC transmigration and that Sema3A produced by the lymphatics promoted actomyosin contraction at the trailing edge of migrating DCs. Our findings not only demonstrate that semaphorin signals are involved in DC trafficking but also identify a previously unknown mechanism that induces actomyosin contraction as these cells pass through narrow gaps. View full text Figures at a glance * Figure 1: Plxna1−/− mice show impaired T cell responses due to defects in the migration of DCs into the lymph nodes. () CFSE dilution by CD4+ OT-II T cells intravenously transferred into wild-type (WT) or Plxna1−/− mice given subcutaneous injection of OVA peptides in complete Freund's adjuvant into the footpads, assessed as antigen-specific T cell responses in the draining lymph nodes (black lines) and nondraining lymph nodes (red lines). Data are representative of three independent experiments. () Two-photon microscopy of CMTMR-labeled wild-type and Plxna1−/− bone marrow–derived DCs (BMDCs; orange) injected into the footpads of wild-type recipient mice that also received CMFDA-labeled CD4+ OT-II T cells (green), showing DC trafficking into popliteal lymph nodes at 24 h after injection. Original magnification, ×40. Results are representative of two experiments. () Trafficking of CFSE-labeled wild-type or Plxna1−/− DCs into the popliteal lymph nodes of wild-type recipient mice after foodpad injection, calculated according to the following equation: (% input cells) = (total ce! lls) × (% CFSE+ cells) / (input cells). *P < 0.01 and **P < 0.001 (Mann-Whitney U-test). Data are representative of three experiments (mean and s.d.). () Absolute number of endogenous DCs isolated from the brachial lymph nodes of wild-type and Plxna1−/− mice at 24 or 48 h after epicutaneous administration of FITC–isomer I to the shoulder skin. Data are representative of three experiments. * Figure 2: Uptake of FITC-dextran and responses to chemokines are not affected in Plxna1−/− DCs. () Uptake of FITC-dextran by wild-type and Plxna1−/− BMDCs at 37 °C for 30 min. Control, cells cultured on ice with FITC-dextran; max, maximum. () Chemotaxis of wild-type and Plxna1−/− DCs toward gradients of CCL19, CCL21 or CXCL12 in Transwell systems (pore size, 5 μm). () Directional sensing of wild-type and Plxna1−/− DCs in response to a CCL21 gradient in a Zigmond chamber, assessed as DC position after 60 min relative to original position and presented as the percentage of cells that ended within a 30° arc facing the CCL21 source. () Expression of CCR7 and CXCR4 in wild-type and Plxna1−/− DCs. Data are representative of three experiments (error bars (), s.d.). * Figure 3: Impaired transmigration of Plxna1−/− DCs across the lymphatics. () Confocal z-stack imaging (top) of CFSE-labeled wild-type and Plxna1−/− BMDCs (green) injected intradermally into the ear tissues of oxazolone-sensitized mice, assessed in whole mounts stained 24 h after transfer with biotinylated anti–LYVE-1 and streptavidin-indocarbocyanine (red). Scale bars, 50 μm. Below, quantification of retained DCs in the fields above; each symbol represents an individual field, and red circles indicate the mean. *P < 0.001 (Mann-Whitney U-test). () Transmigration of wild-type and Plxna1−/− BMDCs across a lymphatic endothelial cell monolayer, assessed by time-lapse video microscopy as interactions recorded every 30 s. Yellow dotted lines indicate junctions of endothelial cells; white arrows indicate DCs in contact with lymphatic endothelial cells; red arrows (top) indicate the transmigration process of wild-type DCs. Scale bars, 50 μm. () Confocal microscopy (left) of wild-type and Plxna1−/− CFSE-labeled DCs added to endothelial cell! monolayers, incubated for 45 min, fixed and then stained with Alexa Fluor 546–conjugated phalloidin; images were obtained with an optical section separation (z-interval) of 0.22 μm. Right, quantification of DC transmigration, presented as transmigrated DCs relative to total DCs. *P < 0.001 (Student's t-test). () Chemotaxis of wild-type or Plxna1−/− DC across Transwell inserts (pore size, 5 μm) layered with lymphatic endothelial cell, in response to a CCL21 gradient. *P < 0.001 (Student's t-test). Data are representative of three experiments (mean and s.d. in ,). * Figure 4: Sema3A–NRP1–plexin-A1 interactions are responsible for DC trafficking. () Trafficking of wild-type DCs in the lymphatics after adoptive transfer into wild-type, Sema3a−/−, Sema6c−/− or Sema6d−/− recipient mice. *P < 0.01 (Mann-Whitney U-test). Data are pooled from three independent experiments (standard error ± 95% confidence interval). () Trafficking of wild-type and Nrp1sema− knock-in (KI) DCs into the lymphatics after adoptive transfer into wild-type recipients (left), and chemotaxis of wild-type and Nrp1sema− knock-in DCs through Transwell inserts (pore size, 5 μm) layered with lymphatic endothelial cells, in response to a CCL21 gradient (right). *P < 0.05 (Mann-Whitney U-test; left) and **P < 0.01 (Student's t-test; right). Data are representative of three experiments (mean and s.d.). () In vitro proliferation of CD4+ T cells in response to keyhole limpet hemocyanin (KLH) after immunization of wild-type, Sema3a−/−, Nrp1sema− knock-in, Sema6c−/− and Sema6d−/− mice with keyhole limpet hemocyanin in complete Fr! eund's adjuvant. *P < 0.01 and **P < 0.001 (Student's t-test). Data are representative of three experiments (mean ± s.d.). * Figure 5: Sema3A acts on the rear side of DCs. () Chemotaxis of DCs in the presence of human immunoglobulin G (IgG) or recombinant Sema3A protein in the lower (left) or upper (right) chamber of a Transwell system, with CCL21 present (CCL21) or absent (−) in the lower chamber. *P < 0.01 (Student's t-test). Data are representative of three experiments (mean and s.d.). () DC speed in a two-dimensional DC chemotaxis assay with Sema3A or human IgG added to the opposite side of CCL21, assessing the frequency distribution (bars) and cumulative frequency distribution (lines) of the instantaneous speed. P < 0.001 (Mann-Whitney U-test). Data are representative of three independent experiments. () Confocal time-lapse video microscopy of BMDCs expressing green fluorescent protein–labeled plexin-A1 (plexin-A1–GFP), treated with lipopolysaccharide, suspended in type I collagen gels and placed into a Zigmond chamber with chemokine gradients; DC locomotion was examined at 1-minute intervals (time (in minutes:seconds), bottom left ! corner). Scale bar, 10 μm. Results are representative of three experiments. () Confocal z-stack imaging (left) of the localization and intensity of plexin-A1 (anti–plexin-A1 plus indocarbocyanine-labeled anti–rabbit IgG; red) and F-actin (Alexa Fluor 488–conjugated phalloidin; green) in DCs. Scale bar, 10 μm. Right, frequency of cells with no colocalization of signals. Data are representative of three independent experiments. * Figure 6: Sema3A induces phosphorylation of MLC and promotes actomyosin contraction. () Confocal z-stack imaging (left) of DCs on fibronectin-coated coverslips treated with human IgG or recombinant Sema3A protein fused with human immunoglobulin Fc portion (Sema3A-Fc) and stained with antibody to phosphorylated MLC (pMLC) plus indocarbocyanine-labeled anti–rabbit IgG (bottom row); eight z-stack images with an optical section separation (z interval) of 0.36 μm were projected onto one single image. DIC (top row), differential interference contrast image. Scale bars, 10 μm. Right, frequency distribution (bars) and cumulative frequency distribution (lines) of the average intensity of dendrite regions in DCs stimulated with human IgG or Sema3A-Fc. P < 0.001 (Mann-Whitney U-test). Data are representative of three independent experiments. () Speed (left) and frequency distribution (bars, right) or cumulative frequency distribution (lines, right) of the instantaneous speed (right) of a single DC in response to chemokines in the presence of human IgG or Sema3A-Fc ! in type I collagen matrices, analyzed by time-lapse microscopy and MetaMorph software. Each symbol (left) represents an individual cell; red horizontal lines indicate the mean. *P < 0.001 (Mann-Whitney U-test). Data are representative of three independent experiments. () Chemotaxis of wild-type or Plxna1−/− DCs in response to CCL21 in the presence of human IgG or Sema3A-Fc; DCs were added to the upper chamber of a Transwell system with type I collagen. NS, not significant; *P < 0.05 (overall difference, one-way analysis of variance (ANOVA); post-hoc multiple comparisons, Tukey's test). Data are representative of three experiments (mean and s.d.). () Chemotaxis of DCs left untreated (No Tx) or treated for 30 min at 37 °C with 50 μM blebbistatin or 30 μM Y-27632 and then added to the upper chamber of a Transwell system (pore size, 5 μm) layered with type I collagen (3 mg/ml; left) and a monolayer of human dermal lymphatic microvascular endothelial cells (right), in re! sponse to CCL21 in the presence of human IgG or Sema3A-Fc in t! he upper chamber. *P < 0.05 and **P < 0.01 (overall difference, one-way ANOVA; post-hoc multiple comparisons, Tukey's test). Data are representative of three experiments (mean and s.d.). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Immunopathology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan. * Hyota Takamatsu, * Noriko Takegahara, * Yukinobu Nakagawa, * Tatsusada Okuno, * Sujin Kang, * Satoshi Nojima, * Toshihiko Toyofuku & * Atsushi Kumanogoh * World Premier International Immunology Frontier Research Center, Osaka University, Suita, Japan. * Hyota Takamatsu, * Noriko Takegahara, * Yukinobu Nakagawa, * Tatsusada Okuno, * Masayuki Mizui, * Sujin Kang, * Satoshi Nojima, * Toshihiko Toyofuku, * Hitoshi Kikutani & * Atsushi Kumanogoh * Department of Dermatology, Osaka University Graduate School of Medicine, Suita, Japan. * Yukinobu Nakagawa & * Ichiro Katayama * Laboratory for Cell Function and Dynamics, Advanced Technology Development Center, Brain Science Institute, RIKEN, Wako, Japan. * Michio Tomura * Department of Biochemistry, Cancer Research Institute, Sapporo Medical University School of Medicine, Sapporo, Japan. * Masahiko Taniguchi * Institute of Developmental Genetics, Helmholtz Center Munich, Neuherberg, Germany. * Roland H Friedel * Department of Developmental Biology, Stanford University, Stanford, California, USA. * Helen Rayburn * Division of Research, Genentech, South San Francisco, California, USA. * Marc Tessier-Lavigne * Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. * Yutaka Yoshida * Department of Molecular Immunology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan. * Masayuki Mizui & * Hitoshi Kikutani * Department of Pathology, Osaka University Graduate School of Medicine, Suita, Japan. * Satoshi Nojima * Department of Pathology, Hyogo College of Medicine, Nishinomiya, Japan. * Tohru Tsujimura * Department of Neurology, Osaka University Graduate School of Medicine, Suita, Japan. * Yuji Nakatsuji Contributions A.K. and H.T. designed the study and wrote the manuscript; H.T. did most of the experiments and analyzed the data with Y. Nakagawa, T.O., M.M., S.K. and S.N.; N.T. produced Sema6d−/− mice, recombinant Sema6D protein and antibody to Sema6D (anti-Sema6D); M. Tomura did two-photon microscopic experiments; M. Tanaguchi produced Sema3a−/− mice; R.H.F., H.R. and M.T.-L. produced Sema6c−/− mice; Y.Y. produced anti-plexin-A1; T. Tsujimura did histological analyses; and Y. Nakatsuji, I.K., T. Toyofuku and H.K. provided collaborative suggestions. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Atsushi Kumanogoh (kumanogo@ragtime.biken.osaka-u.ac.jp) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (2M) Normal to sense direction in response to chemokines in plexin-A1−/− DCs. DCs from wild-type or plexin-A1−/− mice were allowed to adhere to fibronectin-coated cover-slips and placed in a CCL21 gradient in a Zigmond chamber. DC trafficking was recorded every 30 sec by a confocal time-lapse video microscope. * Supplementary Video 2 (4M) Plexin-A1−/− DCs exhibit impaired transmigration across a lymphatic EC-monolayer. BMDCs derived from wild-type or plexin-A1−/− mice were added to lymphatic EC monolayers, and interactions between DCs and the lymphatic ECs were recorded every 30 sec by a time-lapse video microscope. The yellow dotted lines show the cellular junctions of the ECs. White arrows indicate DCs that were contacting the lymphatic ECs. * Supplementary Video 3 (980K) Impaired transmigration in plexin-A1−/− DCs. CFSE-labeled DCs derived from wild-type or plexin-A1−/− mice were added to lymphatic EC monolayers, incubated for 45 min, fixed, and then stained with Alexa 546-conjugated phalloidin. Confocal microscope images were obtained with an optical section separation (Z-interval) of 0.22 μm. Twelve Z-stack images were reconstituted into a 3Dimage using Imaris 3D software. Wild-type DCs penetrated from the apical to basal sides, but plexin-A1−/− DCs could not reach the basal side. * Supplementary Video 4 (980K) Sema3A acts on the rear side of migrating DCs. Two-dimensional DC chemotaxis assays using EZ-TAXIScan were performed, in which recombinant Sema3A or human IgG was applied to the opposite side of CCL21. DC migration was recorded every 30 sec by a time-lapse video microscope. * Supplementary Video 5 (192K) Plexin-A1 localizes to the back of migrating DCs. Plexin-A1-EGFP fusion protein-expressed BMDCs treated with LPS were suspended in type I collagen (3 mg/ml) containing 2% FCS and then placed on one side of the Zigmond chamber to cover the stage with gel. After gel was polymerized, CCL21was added to the other side. DC locomotion was examined at 1-min intervals by a confocal time-lapse video microscope. * Supplementary Video 6 (1M) Sema3A enhances the velocity of DC migration in 3D-collagen matrices. BMDCs treated with LPS for 12 h were suspended in type I collagen (3 mg/ml) containing 2% FCS with either a human IgG or Sema3A-Fc and then placed on one side of the Zigmond chamber to cover the stage with gel. The cells were incubated at 37C for 30 min to polymerize the matrix, and then RPMI containing 0.5% BSA with CCL21 (5 μg/ml) was added to the other chamber. After a 20-min incubation, DC locomotion was examined at 1-min intervals by a confocal time-lapse video microscope. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–7 and Supplementary Methods Additional data
• An immunoglobulin-like receptor, Allergin-1, inhibits immunoglobulin E–mediated immediate hypersensitivity reactions
  Hitomi K Tahara-Hanaoka S Someya S Fujiki A Tada H Sugiyama T Shibayama S Shibuya K Shibuya A - Nat Immunol 11(7):601-607 (2010)
  Nature Immunology | Article An immunoglobulin-like receptor, Allergin-1, inhibits immunoglobulin E–mediated immediate hypersensitivity reactions * Kaori Hitomi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Satoko Tahara-Hanaoka1 Search for this author in: * NPG journals * PubMed * Google Scholar * Satoru Someya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Akira Fujiki2 Search for this author in: * NPG journals * PubMed * Google Scholar * Hideaki Tada2 Search for this author in: * NPG journals * PubMed * Google Scholar * Tetsuya Sugiyama2 Search for this author in: * NPG journals * PubMed * Google Scholar * Shiro Shibayama2 Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuko Shibuya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Akira Shibuya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature ImmunologyVolume:11,Pages:601–607Year published:(2010)DOI:doi:10.1038/ni.1886Received23 March 2010Accepted13 May 2010Published online06 June 2010 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 Anaphylaxis is a life-threatening immediate hypersensitivity reaction triggered by antigen capture by immunoglobulin E (IgE) bound to the high-affinity IgE receptor (FcεRI) on mast cells. However, the regulatory mechanism of mast cell activation is not completely understood. Here we identify an immunoglobulin-like receptor, Allergin-1, that contains an immunoreceptor tyrosine-based inhibitory motif (ITIM)-like domain, and show it was preferentially expressed on mast cells. Mouse Allergin-1 recruited the tyrosine phosphatases SHP-1 and SHP-2 and the inositol phosphatase SHIP. Coligation of Allergin-1 and FcεRI suppressed IgE-mediated degranulation of bone marrow–derived cultured mast cells. Moreover, mice deficient in Allergin-1 developed enhanced passive systemic and cutaneous anaphylaxis. Thus, Allergin-1 suppresses IgE-mediated, mast cell–dependent anaphylaxis in mice. View full text Figures at a glance * Figure 1: Molecular characteristics of Allergin-1. () Amino acid sequences of mouse Allergin-1 and and human Allergin-1L (L), Allergin-1S1 (S1) and Allergin-1S2 (S2). Numbers in parentheses (left margin) indicate amino acid positions; shading indicates identical amino acid residues; underlining indicates putative leader and transmembrane sequences; red circles outline potential N-linked glycosylation sites in the extracellular domain; * indicates cysteine residues potentially involved in disulfide bonding of the immunoglobulin-like domains; boxes outline ITIM-like motifs. () Mouse and human Allergin-1 proteins. TM, transmembrane domain; black circles indicate potential N-linked glycosylation. The amino acid sequences of the first (blue) and second (orange) immunoglobulin-like domains in human Allergin-1L are identical to those of Allergin-1S1 and Allergin-1S2. () Chromosomal locations of Allergin-1 and ALLERGIN-1. Ch, chromosome. (,) Immunoprecipitation (IP) of lysates of RBL-2H3 cells (RBL) and Ba/F3 cells and their transfe! ctants expressing Flag-tagged mouse Allergin-1 () and BW5147 transfectants (mouse T lymphoma cell line) expressing Flag-tagged human Allergin-1 (), as demonstrated in Figure 3a, with Flag-specific mAb (anti-Flag), followed by no treatment (−) or treatment with N-glycosidase F (+) and immunoblot analysis (IB) with polyclonal anti-Flag in reducing (Red) or nonreducing (NR) conditions. Data are representative of two () or three () independent experiments. * Figure 2: Expression of mouse Allergin-1 protein. () Flow cytometry of Ba/F3 cells (shaded) or Ba/F3 transfectants (5 × 105 cells per experiment) expressing Flag-tagged mouse Allergin-1 (thick lines), stained with biotinylated control mouse immunoglobulin, anti-mouse Allergin-1 (TX83) or mAb to Flag, followed by allophycocyanin (APC)-conjugated streptavidin. (–) Flow cytometry of splenocytes (1 × 107 cells per experiment; ), peritoneal exudate cells (1 × 106 to 5 × 106 cells per experiment; ), BMMCs (5 × 105 cells per experiment; ) and bone marrow cells (1 × 107 cells per experiment; ) from C57BL/6N mice (n = 3), stained with fluorescein isothiocyanate–conjugated (horizontal axes; left) and phycoerythrin-conjugated (vertical axes; left) monoclonal antibodies, or with biotinylated F(ab′)2 fragment of anti-Allergin-1 (open histograms; middle and right) or isotype-matched control antibody (shaded histograms; middle and right), followed by allophycocyanin-conjugated streptavidin. Cells in outlined areas (left) were ! gated and analyzed for Allergin-1 expression (middle and right). B, B cell; T, T cell; NK, natural killer cell; DC, dendritic cell; MΦ, macrophage; Gran, granulocyte; Mast, mast cell; Baso, basophil. Data are representative of three independent experiments. * Figure 3: Expression of human Allergin-1 protein. () Flow cytometry of BW5147 cells transfected with mock vector or cDNA encoding Flag-tagged human Allergin-1, stained with biotinylated control mouse immunoglobulin (shaded), anti–human Allergin-1 (EX33; thick line, bottom) or mAb to Flag (thick line, top), followed by allophycocyanin-conjugated streptavidin. (,) Flow cytometry of peripheral blood mononuclear cells (1 × 106 cells per experiment; ) or cultured mast cells (1 × 105 cells per experiment; ) simultaneously stained with fluorescein isothiocyanate-conjugated (horizontal axes; left) and phycoerythrin-conjugated (vertical axes; left) monoclonal antibodies, or with unlabeled anti–human Allergin-1 (shaded histograms; middle and right) or control immunoglobulin (open histograms; middle and right), followed by Alexa Fluor 647–conjugated anti-human Allergin-1. Cells in outlined areas (left) were gated and analyzed for Allergin-1 expression (middle and right). Mono, monocyte; pDC, plasmacytoid dendritic cell; Neutro! , neutrophil; mDC, monocytic dendritic cell. Data are representative of three independent experiments. * Figure 4: Signal transduction via ITIM-like motifs of mouse Allergin-1. () Wild-type (WT) Allergin-1 (top), with tyrosine residues in the cytoplasmic domain at positions 175, 200, 216, 239 and 241; below, site-specific mutant Allergin-1 constructs with replacement of tyrosine (Y) with phenylalanine (F) at position 216 (FY) or 241 (YF) or both (FF). Boxes outline tyrosine residues in ITIM-like motifs. () Tyrosine phosphorylation (pY) of Allergin-1 and recruitment of phosphatases in RBL-2H3 transfectants expressing wild-type or mutant Allergin-1, left unstimulated (−) or stimulated (+) with pervanadate (VO4) and lysed with 1% digitonin buffer, followed by immunoprecipitation of Allergin-1 with mAb to Flag and immunoblot analysis. Data are representative of five independent experiments. () Release of β-hexosaminidase (β-hex) from RBL-2H3 cells and their transfectants expressing wild-type or mutant Allergin-1, sensitized with mouse IgE anti-TNP and then stimulated TX83 mAb to Allergin-1 (TNP–anti-Allergin-1), presented relative to release from! cells sensitized with mouse IgE anti-TNP and stimulated with TNP-conjugated F(ab′)2 fragments of control immunoglobulin (TNP–control Ig). NS, not significant; *P = 0.001 (unpaired Student's t-test). Data are representative of five independent experiments (mean and s.d. of triplicates). * Figure 5: Normal development of mast cells in Allergin-1-deficient mice. (,) Flow cytometry of peritoneal exudate cells (5 × 106 cells per experiment; ) or BMMCs (5 × 105 cells per experiment; ) derived from wild-type (WT) or Allergin-1−/− (KO) mice and stained with mAb to c-Kit and FcεRI. Numbers in top right quadrants indicate percent mast cells. () Flow cytometry of BMMCs derived from wild-type and Allergin-1−/− mice and stained with TX83 mAb to Allergin-1 (thick lines) or isotype-matched control antibody (shaded histograms). () Release of β-hexosaminidase from BMMCs derived from wild-type and Allergin-1−/− mice, stimulated and presented as described in Figure 4c. *P < 0.001 (unpaired Student's t-test). Data are representative of five independent experiments (error bars (), s.d. of triplicates). * Figure 6: Enhanced systemic anaphylaxis in Allergin-1-deficient mice. () Rectal temperatures of wild-type (Allergin-1+/+) mice and Allergin-1−/− mice (n = 3 per genotype) injected intravenously with 20 μg mouse IgE mAb to TNP, then challenged 24 h later with 1 mg OVA or TNP-OVA, assessed every 6 min. () Rectal temperatures of mast cell–deficient mice given no BMMCs (Wsh/Wsh; n = 2) or given adoptive transfer of BMMCs derived from wild-type mice (WT BMMC→Wsh/Wsh; n = 5) or Allergin-1−/− mice (KO BMMC→Wsh/Wsh; n = 5), then sensitized 3 months later with 20 μg mouse IgE anti-TNP, followed by challenge with 1 mg TNP-OVA. *P < 0.05, **P < 0.01 and ***P < 0.005, wild-type versus Allergin-1−/− mice challenged with TNP-OVA (unpaired Student's t-test). Data are representative of five () or two () independent experiments (mean ± s.e.m.). * Figure 7: Enhanced passive cutaneous anaphylaxis in Allergin-1-deficient mice. () Ear swelling of wild-type (Allergin-1+/+) mice (n = 5) and Allergin-1−/− mice (n = 5) injected intravenously with 2 μg mouse IgE mAb to dinitrophenol, then challenged 24 h later by epicutaneous application of acetone and olive oil alone (right ear) or DNFB in acetone and olive oil (left ear), calculated as the difference between the thickness of the right and left ear. *P < 0.05, **P < 0.001 and ***P < 0.0005, wild-type versus Allergin-1−/− (unpaired Student's t-test). Data are representative of three independent experiments (error bars, s.e.m.). () Histological sections of the right ears (Control) and left ears (DNFB) of wild-type and Allergin-1−/− mice treated as in , stained with hematoxylin and eosin. Scale bar, 0.2 mm. Data are representative of three independent experiments. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * AB542950 * AB542951 * AB542952 * AB542953 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Immunology, Institute of Basic Medical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan. * Kaori Hitomi, * Satoko Tahara-Hanaoka, * Satoru Someya, * Kazuko Shibuya & * Akira Shibuya * Exploratory Research Laboratories, Tsukuba Research Institute, Ono Pharmaceutical, Tsukuba, Japan. * Akira Fujiki, * Hideaki Tada, * Tetsuya Sugiyama & * Shiro Shibayama Contributions K.H., S.S., A.F., H.T. and T.S. did experiments and analyzed data; S.S. and K.S. contributed to experimental design and data interpretation; S.T.-H. designed and did experiments, analyzed data and wrote the paper; and A.S. supervised the overall project and wrote the paper. Competing financial interests A.F., H.T., T.S. and S.S. are an employees of Ono Pharmaceutical. Corresponding authors Correspondence to: * Akira Shibuya (ashibuya@md.tsukuba.ac.jp) or * Satoko Tahara-Hanaoka (tokothr@md.tsukuba.ac.jp) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (568K) Supplementary Figures 1–4 and Table 1 Additional data
• The T helper type 2 response to cysteine proteases requires dendritic cell–basophil cooperation via ROS-mediated signaling
  - Nat Immunol 11(7):608-617 (2010)
  Nature Immunology | Article The T helper type 2 response to cysteine proteases requires dendritic cell–basophil cooperation via ROS-mediated signaling * Hua Tang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Weiping Cao1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sudhir Pai Kasturi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Rajesh Ravindran1 Search for this author in: * NPG journals * PubMed * Google Scholar * Helder I Nakaya1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kousik Kundu2 Search for this author in: * NPG journals * PubMed * Google Scholar * Niren Murthy2 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas B Kepler3 Search for this author in: * NPG journals * PubMed * Google Scholar * Bernard Malissen4 Search for this author in: * NPG journals * PubMed * Google Scholar * Bali Pulendran1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:608–617Year published:(2010)DOI:doi:10.1038/ni.1883Received04 January 2010Accepted03 May 2010Published online23 May 2010 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 The mechanisms that initiate T helper type 2 (TH2) responses are poorly understood. Here we demonstrate that cysteine protease–induced TH2 responses occur via 'cooperation' between migratory dermal dendritic cells (DCs) and basophils positive for interleukin 4 (IL-4). Subcutaneous immunization with papain plus antigen induced reactive oxygen species (ROS) in lymph node DCs and in dermal DCs and epithelial cells of the skin. ROS orchestrated TH2 responses by inducing oxidized lipids that triggered the induction of thymic stromal lymphopoietin (TSLP) by epithelial cells mediated by Toll-like receptor 4 (TLR4) and the adaptor protein TRIF; by suppressing production of the TH1-inducing molecules IL-12 and CD70 in lymph node DCs; and by inducing the DC-derived chemokine CCL7, which mediated recruitment of IL-4+ basophils to the lymph node. Thus, the TH2 response to cysteine proteases requires DC-basophil cooperation via ROS-mediated signaling. View full text Figures at a glance * Figure 1: Vital role of DCs in papain-induced TH2 responses. () Intracellular staining of IL-4 and IFN-γ in CD4+ T cells (left; day 21) and anti-OVA IgE, IgG1 and IgG2b in serum (right; day 21) from mice immunized on days 0, 7 and 14 with CpG or papain. A450, absorbance at 450 nm. () Intracellular staining of IFN-γ and IL-4 in CD4+ T cells from DC-depleted wild-type (WT) and CD11c-DTR mice immunized 4 d earlier with OVA plus papain. Numbers in quadrants (,) indicate percent cells in each. () ELISA of IL-4 in supernatants of draining lymph node cells from wild-type or CD11c-DTR mice given various numbers of CD4+ OT-II T cells 24 h before diphtheria toxin treatment, then immunized with OVA plus papain; 4 d later, cells were restimulated for 4 d ex vivo with OVA peptide (amino acids 323–339). () IL-4-producing CD4+ T cells from 4get mice given no pretreatment (dimethyl sulfoxide (DMSO)) or pretreated with pertussis toxin (PTX) or BW245c, then immunized with OVA plus papain; IL-4 was assessed 4 d later as green fluorescent protein (GF! P). () IL-4-producing CD4+ T cells 4 d after immunization with OVA plus papain, with the site of immunization excised 6 h after immunization. Numbers above outlined areas (,) indicate percent IL-4+CD4+ T cells. () Immunofluorescence microscopy of frozen sections of draining lymph nodes (n = 2) from mice 2 h after injection of Alexa Fluor 488–labeled papain (green). Blue, B220 (B cell–associated marker); red, Thy-1.2 (CD90.2); right, enlargement of area outlined at left. Original magnification, ×5 (left) or ×20 (right). () Production of IL-4 and IFN-γ by CD4+ T cells (left) and anti-OVA IgG1 and IgG2b in serum (right; day 14) from wild-type mice and Langerhans cell–depleted langerin-DTR mice (–LC) after immunization with OVA plus papain, as described in . *, P < 0.05 (t-test). () Uptake of OVA or papain by DC subsets (identified and defined as described in Results) in draining lymph nodes isolated from mice 24 h after subcutaneous immunization with Alexa Fluor 647! –labeled OVA (OVA-A647) plus papain, or Alexa Fluor 488–la! beled papain (Papain-A488) alone. Bottom, proportion of fluorescence-labeled cells in conventional DC (cDC) subsets. SSC, side scatter; LC, Langerhans cell; dDC, dermal DC. () Pooled data from . () Immunostimulatory capacity of the four lymph node DC subsets sorted by flow cytometry from mice immunized 24 h earlier with OVA plus papain or OVA plus LPS, then cultured with OT-II CD4+ T cells; proliferation was assessed by thymidine labeling. *P < 0.05, **P < 0.01 and ***P < 0.001 (analysis of variance). Data are representative of three to five independent experiments (mean and s.e.m.). * Figure 2: DCs and basophils act in concert to drive TH2 responses. () Production of IFN-γ and IL-4 by OT-II CD4+ T cells after culture with CD11c+ lymph node DCs from mice immunized 24 h before with OVA plus papain or OVA plus CpG. () Flow cytometry analysis of IL-4 expression (middle) by IgE+DX5+ basophils (blue line) and nonbasophils (red line) sorted (left) from draining lymph nodes of 4get mice immunized subcutaneously 3 d earlier with papain, and ELISA of IL-4 production by flow cytometry–sorted basophils from mice immunized subcutaneously with papain plus OVA (right). Med, well with medium only. Number above outlined area (left) indicates percent IgE+DX5+ cells. () IL-4 production by cocultures of OT-II CD4+ T cells and CD11c+ DCs, basophils or a combination of DCs plus basophils isolated from mice immunized with OVA plus papain. () Intracellular flow cytometry analysis of IL-4-producing OT-II CD4+ T cells cultured as in . Numbers adjacent to outlined areas indicate percent IL-4+CD4+ cells. () Proliferation of OT-II CD4+ T cells st! imulated in vitro with various numbers of lymph node CD11c+ DCs or basophils isolated from mice immunized with OVA plus papain, with no exogenous OVA added, assessed by [3H]thymidine incorporation. () Proliferation of OT-II cells (labeled with the cytosolic dye CFSE) from unimmunized mice (Naive) or mice immunized with OVA plus papain and assessed with no further treatment (PBS), after ablation of skin-derived DCs by ear excision 6 h after immunization, or after depletion of basophils with MAR-1. Numbers above bracketed lines indicate percent CSFE+ (dividing) cells. () Flow cytometry analysis of IL-4 expression in CD4+ T cells in 4get mice, assessed (as green fluorescent protein) after basophil depletion and immunization as in . Numbers above outlined areas indicate percent IL-4+CD4+ cells. *P < 0.05 and **P < 0.01 (t-test). Data are representative of three independent experiments (error bars (–,), s.e.m.). * Figure 3: ROS production by papain-activated DCs is critical for TH2 differentiation. () Microarray analysis of gene expression in lymph node DCs stimulated for 4 or 17 h in vitro with papain or LPS. (,) Flow cytometry analysis of ROS production by untreated and papain-stimulated DCs in vitro () and in vivo (). () Flow cytometry analysis of intracellular IFN-γ production by OT-II T cells stimulated for 72 h with lymph node DCs pulsed with OVA peptide, amino acids 323–339, alone (Med) or together with papain, LPS, or LPS plus papain. () IFN-γ production assessed as in but for cells pulsed with papain alone (far left) or with papain plus NAC in the presence of neutralizing anti-CD70 (α-CD70) or anti-IL-12 (α-IL-12) or isotype-matched control antibody (Isotype). (,) Flow cytometry analysis of IL-4 expression (as green fluorescent protein) in CD4+ T cells from draining lymph nodes of 4get mice immunized with papain, with no pretreatment (PBS () or Blank ()) or after pretreatment with NAC () or microparticle-encapsulated tempol (). Numbers above outlined are! as indicate percent CD4+IFN-γ+ cells (,) or IL-4+CD4+ cells (,). Data are representative of one experiment () or two to five experiments (–). * Figure 4: TSLP production in skin in response to immunization with papain is dependent on ROS. () Quantitative RT-PCR analysis of TLSP mRNA expression in ears of mice injected with papain, presented relative to the expression of GAPDH mRNA ('housekeeping' gene encoding glyceraldehyde phosphate dehydrogenase). () Immunofluorescence confocal microscopy of frozen ear sections from mice immunized with OVA plus papain. Blue, DAPI (DNA-intercalating dye); green, TSLP. Original magnification, ×20. () Quantitative RT-PCR analysis of HO-1 mRNA expression in ears of mice injected with papain, presented relative to GAPDH mRNA expression. () Immunofluorescence confocal microscopy of the site of immunization with OVA plus papain, stained for DAPI (blue), hydro-Cy5 (red) and CD11c (green) to assess ROS activity. Far right, enlargement of area outlined at left; arrows indicate some hydro-Cy5 staining in DCs. Original magnification, ×20 (main images). () Quantitative RT-PCR analysis of TSLP mRNA expression in ears of mice injected with papain, with (WT-NAC) or without (WT) pretreat! ment with NAC, presented relative to GAPDH mRNA expression. () Flow cytometry analysis of expression of the TSLP receptor (TLSPR; blue lines) on CD11c+ and CD11c− dermal hematopoietic cells (sorted as shown at left), including dermal DCs. Red lines, isotype-matched control antibody. Numbers adjacent to outlined areas (left) indicate percent CD11c+CD45+ cells (top) or CD11c−CD45+ cells (bottom); MFI (right), mean fluorescent intensity. NS, not significant; *P < 0.05, **P < 0.01 and ***P < 0.001 (t-test). Data are representative of two to three independent experiments (error bars (,,), s.e.m.). * Figure 5: Papain-induced TH2 responses are dependent on TLR4-TRIF signaling. () Flow cytometry of intracellular staining for IL-4 and IFN-γ in CD4+ T cells from draining lymph nodes (left) and OVA-specific antibody titers (right) of wild-type or Tlr4−/− mice immunized with OVA plus papain or OVA plus LPS. Numbers in quadrants (left) indicate percent cells in each. () Flow cytometry of intracellular IL-4 staining in CD4+ T cells from draining lymph nodes (above) and OVA-specific antibody titers (below) of wild-type and Trif−/− mice immunized as in . Numbers above outlined areas (top) indicate percent IL-4+CD4+ cells. () Immunofluorescence microscopy of frozen tissue sections of skin at the site of immunization, obtained from C57BL/6 mice injected with PBS or papain, fixed and stained with the EO6 antibody specific for OxPLs. Far right, enlargement of area outlined at left. Original magnification, ×20 (main images). () Flow cytometry analysis of the expression of OxPLs in draining lymph node CD11c+ DCs from mice injected with PBS (red line) o! r papain (blue line). () Quantitative RT-PCR analysis of TSLP mRNA expression in skin tissue derived from the site of immunization of papain-injected wild-type, TLR4-deficient or TRIF-deficient mice, presented relative to GAPDH mRNA expression. *P < 0.05, **P < 0.01 and ***P < 0.001 (t-test). Data are representative of two to three independent experiments (error bars (,,), s.e.m.). * Figure 6: Regulation of basophil migration by ROS, TLR4 and TRIF signaling in DCs. () RT-PCR analysis of CCL7 mRNA expression by lymph node DCs isolated from unimmunized mice (Naive; left) or mice immunized subcutaneously with papain with (right) or without (middle) NAC pretreatment. () CCL7 mRNA expression by lymph node DCs from wild-type, TLR4-deficient or TRIF-deficient mice left unimmunized or immunized with papain. Results in , are presented relative to GAPDH mRNA expression. () Recruitment of basophils to the lymph nodes in CD11c-DTR mice left undepleted (no DT) or depleted of DCs (DT) and then immunized subcutaneously 1 d later with papain and evaluated 3 d later. LN, lymph node. () Recruitment of basophils to the lymph nodes in wild-type mice immunized with papain, with (NAC) or without (PBS) pretreatment with NAC. () Recruitment of basophils to the lymph nodes in unimmunized mice and wild-type, Tlr4−/− and Trif−/− mice immunized with papain. *P < 0.05 (t-test). Data are representative of two to three independent experiments. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE21602 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Emory Vaccine Center, Atlanta, Georgia, USA. * Hua Tang, * Weiping Cao, * Sudhir Pai Kasturi, * Rajesh Ravindran, * Helder I Nakaya & * Bali Pulendran * Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. * Kousik Kundu & * Niren Murthy * Center for Computational Immunology, Duke University, Durham, North Carolina, USA. * Thomas B Kepler * Centre d'Immunologie de Marseille-Luminy, Institut National de la Santé et de la Recherche Médicale U631, Centre National de la Recherche Scientifique Unité Mixte de Recherche 6102, Université de la Méditerranée, Marseille, France. * Bernard Malissen * Department of Pathology, Emory University, Atlanta, Georgia, USA. * Bali Pulendran Contributions H.T. and B.P. designed experiments; H.T. did experiments; R.R. and W.C. assisted with experiments; S.P.K. K.K. and N.M. designed microparticles and assisted with ROS imaging; T.B.K. and H.L.N. assisted with data analyses.; B.M. provided mice; and H.T. and B.P. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bali Pulendran (bpulend@emory.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–19 Additional data
• Foxo proteins cooperatively control the differentiation of Foxp3+ regulatory T cells
  Ouyang W Beckett O Ma Q Paik JH Depinho RA Li MO - Nat Immunol 11(7):618-627 (2010)
  Nature Immunology | Article Foxo proteins cooperatively control the differentiation of Foxp3+ regulatory T cells * Weiming Ouyang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Omar Beckett1 Search for this author in: * NPG journals * PubMed * Google Scholar * Qian Ma1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ji-hye Paik2 Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald A DePinho2 Search for this author in: * NPG journals * PubMed * Google Scholar * Ming O Li1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:618–627Year published:(2010)DOI:doi:10.1038/ni.1884Received08 March 2010Accepted07 May 2010Published online13 May 2010Corrected online18 May 2010,18 May 2010 Abstract * Abstract * Accession codes * Change history * 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 CD4+ regulatory T cells (Treg cells) characterized by expression of the transcription factor Foxp3 have a pivotal role in maintaining immunological tolerance. Here we show that mice with T cell–specific deletion of both the Foxo1 and Foxo3 transcription factors (collectively called 'Foxo proteins' here) developed a fatal multifocal inflammatory disorder due in part to Treg cell defects. Foxo proteins functioned in a Treg cell–intrinsic manner to regulate thymic and transforming growth factor-β (TGF-β)-induced Foxp3 expression, in line with the ability of Foxo proteins to bind to Foxp3 locus and control Foxp3 promoter activity. Transcriptome analyses showed that Foxo proteins regulated the expression of additional Treg cell–associated genes and were essential for inhibiting the acquisition of effector T cell characteristics by Treg cells. Thus, Foxo proteins have crucial roles in specifying the Treg cell lineage. View full text Figures at a glance * Figure 1: The development of a multifocal inflammatory disorder in mice with T cell–specific deletion of Foxo1 and Foxo3. () Hematoxylin and eosin staining of colon, lung, liver and pancreas sections from wild-type, Foxo1−/−Foxo3+/−, Foxo1+/−Foxo3−/− and Foxo1−/−Foxo3−/− mice at 8 weeks of age. Original magnification, ×20. Results are representative of two experiments with three mice per group. () Titers of dsDNA antibody and nuclear antibody in the serum from wild-type, Foxo1−/−Foxo3+/−, Foxo1+/−Foxo3−/− and Foxo1−/−Foxo3−/− mice 6–8 weeks of age (n = 8–14 mice per genotype). Each symbol represents an individual mouse; small horizontal lines indicate the mean. A450, absorbance at 450 nm. *P ≤ 0.05, **P ≤ 0.01 and ***P ≤ 0.001 (Student's t-test). Data are pooled from eight experiments. * Figure 2: T cell activation and differentiation in the absence of Foxo1 and Foxo3. () CD4+ T cell numbers in the spleen, peripheral lymph nodes (pLN; inguinal, axillary, brachial and cervical lymph nodes) and mesenteric lymph nodes (mLN) of 6- to 8-week-old wild-type, Foxo1−/−Foxo3+/−, Foxo1+/−Foxo3−/− and Foxo1−/−Foxo3−/− mice (n = 7–12 per genotype). Each symbol represents an individual mouse; small horizontal lines indicate the mean. *P ≤ 0.05, **P ≤ 0.01 and ***P ≤ 0.001 (Student's t-test). Data are pooled from seven experiments. () Flow cytometry analysis of the expression of CD44 and CD62L in splenic and peripheral and mesenteric lymph node CD4+ T cells from 6-week-old wild-type, Foxo1−/−Foxo3+/−, Foxo1+/−Foxo3−/− and Foxo1−/−Foxo3−/− mice. Numbers in quadrants indicate percent cells in each. Data are representative of four experiments with six mice per group. () Intracellular staining analysis of the expression of IFN-γ and IL-17 in splenic and peripheral and mesenteric lymph node CD4+ T cells obtaine! d from 6-week-old wild-type, Foxo1−/−Foxo3+/−, Foxo1+/−Foxo3−/− and Foxo1−/−Foxo3−/− mice and stimulated for 4 h with PMA and ionomycin. Numbers in quadrants indicate percent cells in each. Data are from one representative of five independent experiments. * Figure 3: Treg cell development and function in the absence of Foxo1 and Foxo3. () Flow cytometry analysis of Foxp3 expression (left) in thymic () and splenic () CD4+ T cells from 3-week-old wild-type, Foxo1−/−Foxo3+/−, Foxo1+/−Foxo3−/− and Foxo1−/−Foxo3−/− mice, and frequency (middle) and number (right) of Treg cells (n = 5 mice per group). Numbers above outlined areas (left) indicate percent Foxp3+CD4+ cells; each symbol (middle, right) represents an individual mouse and small horizontal lines indicate the mean. *P ≤ 0.05, **P ≤ 0.01 and ***P ≤ 0.001 (Student's t-test). Data are representative of four experiments. () Flow cytometry analysis of the expression of Foxp3, CTLA-4, CD25 and GITR in thymic and splenic Treg cells from 3-week-old wild-type and Foxo1−/−Foxo3−/− mice. Data are from one representative of five independent experiments. () Suppression of wild-type naive (CD44loFoxp3−) CD4+ T cells, labeled with the cytosolic dye CFSE (responding T cells (Tresp)), by wild-type and Foxo1−/−Foxo3−/− Treg cell! s, presented as percent undivided responding T cells versus the Treg cell/Tresp cell ratio (top), and CFSE dilution in responding T cells cultured with Treg cells at a ratio of 1:1 or without Treg cells (Tresp only; bottom). *P ≤ 0.05 (Student's t-test). Data are from one representative of three independent experiments (mean ± s.e.m.). () Change in body weight of Rag1−/− mice given naive T cells (Tn) alone or in combination with Treg cells from 3-week-old wild-type or Foxo1−/−Foxo3−/− mice. Data are from two independent experiments with five mice per group (mean ± s.e.m.). () Hematoxylin and eosin staining of colon sections from Rag1−/− mice at 6 weeks after the transfer in . Original magnification, ×20. Results are from one representative of two independent experiments. * Figure 4: Gene expression and cytokine production in Foxo1−/−Foxo3−/− Treg cells. () Expression of Treg cell signature genes (presented as protein designations) in wild-type (WT) and Foxo1−/−Foxo3−/− (double-knockout (DKO)) Treg cells, partitioned into seven clusters. Red, expression correlation >0.5; green, expression correlation <–0.5 (key). Data are representative of two experiments. () Expression of Foxo1 and Foxo3 target genes that do not belong to the Treg cell signature, presented as in . Data are representative of two experiments. () Intracellular staining analysis of the expression of IFN-γ and IL-17 in CD4+Foxp3+ Treg cells obtained from 6-week-old wild-type (Foxo1+/+Foxo3+/+) and Foxo1−/−Foxo3−/− mice and stimulated for 4 h with PMA and ionomycin. Numbers in quadrants indicate percent cells in each. Results are from one representative of five independent experiments. * Figure 5: Wild-type Treg cells correct the inflammatory disorder of Foxo1−/−Foxo3−/− mice. () Flow cytometry analysis of the expression of CD45.1 and Foxp3 in CD4+ T cells from the spleens of Foxo1−/−Foxo3−/− mice with (Treg transfer) or without (Control) transfer of Treg cells. () CD4+ T cells in the spleens, peripheral and mesenteric lymph nodes of 8-week-old Foxo1−/−Foxo3−/− mice (n = 4) with or without transfer of Treg cells. Each symbol represents an individual mouse; small horizontal lines indicate the mean. *P ≤ 0.05 (Student's t-test). () Flow cytometry analysis of the expression of CD44 and CD62L by splenic CD4+ T cells from 8-week-old Foxo1−/−Foxo3−/− mice with or without transfer of Treg cells. () Expression of IFN-γ and IL-17 in CD4+Foxp3− conventional T cells obtained from 8-week-old Foxo1−/−Foxo3−/− mice with or without transfer of Treg cells, then stimulated for 4 h with PMA and ionomycin. Numbers in top right quadrants indicate percent IFN-γ+ cells (left column) or IL-17+ cells (right column). () Expression of ! IFN-γ and IL-17 in Treg cells from 8-week-old Foxo1−/−Foxo3−/− mice with or without transfer of wild-type CD45.1+ Treg cells. Numbers in quadrants (,,) indicate percent cells in each. Data are representative of two (,) or three (,) experiments or are pooled from four experiments (). * Figure 6: Foxo1−/−Foxo3−/− Treg cells do not correct the scurfy-induced immune disorder. () Survival of chimeras generated by reconstitution of Rag1−/− recipients with bone marrow from scurfy mice (Foxp3sf; n = 9) or a mixture of equal amounts of bone marrow from scurfy and wild-type mice (Foxp3sf + WT; n = 12) or scurfy and Foxo1−/−Foxo3−/− mice (Foxp3sf + DKO; n = 12). () Foxp3 expression in thymic and splenic CD4+ T cells from the chimeras in . Numbers above outlined areas indicate percent Foxp3+CD4+ cells. () Expression of CD44 and CD62L in splenic CD4+ and CD8+ T cells from the chimeras in . Numbers in quadrants indicate percent cells in each. Data are representative of three independent experiments () or two experiments (). * Figure 7: A cell-intrinsic role for Foxo1 and Foxo3 in the control of Foxp3 expression. () Foxp3 expression in thymic TCR-βhiCD4+ T cells from chimeras generated by reconstitution of Rag1−/− recipients with wild-type (Foxo1+/+Foxo3+/+) or Foxo1−/−Foxo3−/− bone marrow (Chimera) or a mixture of equal amounts of bone marrow from wild-type and Foxo1−/−Foxo3−/− mice (Mixed chimera; left), and frequency of Treg cells among thymic TCR-βhiCD4+ T cells (right; n = 5 chimeras per group). Numbers above outlined areas (left) indicate percent Foxp3+CD4+ cells; each symbol (right) represents an individual mouse and small horizontal lines indicate the mean. *P ≤ 0.001 (Student's t-test). (,) Expression of Foxp3 protein () and Foxp3 mRNA () in thymic CD4+ T cells from wild-type and Foxo1−/−Foxo3−/− mice left unstimulated (Naive), stimulated for 54 h with anti-CD3 and anti-CD28 (Activ) without rest, or stimulated for 18 h with anti-CD3 and anti-CD28 followed by 36 h of rest (Activ + rest). Numbers above bracketed lines () indicate percent Foxp3+ ! cells; mRNA results () are presented relative to β-actin expression. (,) Expression of Foxp3 protein () and Foxp3 mRNA () in peripheral naive CD4+ T cells from wild-type and Foxo1−/−Foxo3−/− mice stimulated for 54 h with CD3-CD28 Dynalbeads in the presence or absence of TGF-β. Numbers above outlined areas () indicate percent Foxp3+CD4+ cells; mRNA results () are presented relative to β-actin expression. Data are representative of three (,), or two (,) experiments (mean ± s.e.m. in ,). * Figure 8: Regulation of Foxp3 transcription by Foxo1 and Foxo3. () Putative Foxo-binding sites in CNS1 () and CNS3 () of mouse Foxp3. +1 indicates the transcription start site (TSS). () Immunoprecipitation of chromatin from the mouse Foxp3 locus in naive CD4+ T cells and Treg cells with anti-Foxo1 () or anti-Foxo3 (), followed by quantitative PCR analysis of immunoprecipitates; results are presented relative to enrichment by immunoprecipitation with isotype-matched control antibody. Dashed lines indicate control antibody binding, set as 1. Data are representative of three independent experiments (mean and s.d. of triplicates). () Alignment of the conserved putative Foxo-binding sites (FBS1 and FBS2) and mutated Foxo-binding sites (FBS1mut and FBS2mut) in mouse Foxp3 and human FOXP3 promoter regions (top): red, consensus Foxo-binding sequence; blue, replacement of TGT in the core sequence with CAC; nucleotides numbered relative to the transcription start site; *, conserved nucleotides. Below, insertion of wild-type CNS1 plus mutated Foxo-! binding sites (described above) into the pGL4-Basic luciferase (Luc) reporter plasmid. () Luciferase activity of CD4+ T cells or TGF-β-induced Treg cells transfected with the reporter constructs in , assessed 16 h after transfection and presented relative to the luciferase activity of cells transfected with the pGL4-Basic plasmid alone, set as 1. *P ≤ 0.001 (Student's t-test). Data are representative of three independent experiments. Accession codes * Abstract * Accession codes * Change history * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE21678 Change history * Abstract * Accession codes * Change history * Author information * Supplementary informationCorrected online 18 May 2010In the version of this article initially published online, the final sentence of the "Chromatin immunoprecipitation" paragraph of the Online Methods section was incorrect. The correct sentence is "Primers for analysis of the binding of Foxo1 and Foxo3 to the Foxp3 locus are in Supplementary Table 5." The error has been corrected for the PDF and HTML versions of this article.Corrected online 18 May 2010In the version of this supplementary file originally posted online, the Supplementary Information file was incorrect. The error has been corrected in this file as of 18 May 2010. Author information * Abstract * Accession codes * Change history * Author information * Supplementary information Affiliations * Immunology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Weiming Ouyang, * Omar Beckett, * Qian Ma & * Ming O Li * Belfer Institute for Applied Cancer Science, Department of Medical Oncology, Department of Medicine and Department of Genetics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. * Ji-hye Paik & * Ronald A DePinho Contributions W.O. and M.O.L. designed the research and analyzed the data; W.O., O.B. and Q.M. did experiments; R.A.D. and J.-h.P. provided the mouse strain with floxed Foxo3 and feedback on the manuscript; and W.O. and M.O.L. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ming O Li (lim@mskcc.org) Supplementary information * Abstract * Accession codes * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–14, Tables 1–5 Additional data
• Viral infection triggers central nervous system autoimmunity via activation of CD8+ T cells expressing dual TCRs
  Ji Q Perchellet A Goverman JM - Nat Immunol 11(7):628-634 (2010)
  Nature Immunology | Article Viral infection triggers central nervous system autoimmunity via activation of CD8+ T cells expressing dual TCRs * Qingyong Ji1 Search for this author in: * NPG journals * PubMed * Google Scholar * Antoine Perchellet1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Joan M Goverman1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature ImmunologyVolume:11,Pages:628–634Year published:(2010)DOI:doi:10.1038/ni.1888Received30 March 2010Accepted13 May 2010Published online06 June 2010 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 Multiple sclerosis is an inflammatory, demyelinating, central nervous system disease mediated by myelin-specific T cells. Environmental triggers that cause the breakdown of myelin-specific T cell tolerance are unknown. Here we found that CD8+ myelin basic protein (MBP)-specific T cell tolerance was broken and autoimmunity was induced by infection with a virus that did not express MBP cross-reactive epitopes and did not depend on bystander activation. Instead, the virus activated T cells expressing dual T cell antigen receptors (TCRs) that were able to recognize both MBP and viral antigens. Our results demonstrate the importance of dual TCR–expressing T cells in autoimmunity and suggest a mechanism by which a ubiquitous viral infection could trigger autoimmunity in a subset of infected people, as suggested by the etiology of multiple sclerosis. View full text Figures at a glance * Figure 1: Infection with wild-type vaccinia virus induces autoimmune disease in 8.8 mice. () Weight change in Mbp+/+ 8.8, Mbp−/− 8.8 and Mbp+/+ wild-type (WT) mice (n = 7–11 mice per group) infected on day 0 with Vac-MBP or wild-type vaccinia virus (WT Vac), presented relative to weight on day 0. P < 0.001, Mbp+/+ 8.8 versus Mbp−/− 8.8, and P < 0.0001, Mbp+/+ 8.8 versus wild-type mice, after infection with wild-type vaccinia virus (Student's t-test). () Flow cytometry of splenocytes obtained from naive or infected 8.8 mice (7 d after infection with wild-type vaccinia virus), stained with MBP(79–87)–H-2Kk tetramer (MBP tetramer) and anti-CD8, anti-CD44 and anti-CD62L and gated on CD8+ cells. () Flow cytometry of CFSE-labeled cells from naive or infected 8.8 mice (7 d after infection with wild-type vaccinia virus), injected with equal numbers of wild-type splenocytes pulsed with MBP(79–87) (CSFEbright) and unpulsed splenocytes (CFSEdim) and analyzed 20 h later. () Flow cytometry of mononuclear CNS cells isolated from Mbp+/+ and Mbp−/− 8.8 mice 7! d after infection with wild-type vaccinia virus, then stained with MBP(79–87)–H-2Kk tetramer and anti-CD8. Numbers in quadrants (,) indicate percent cells in each; numbers above bracketed lines () indicate percent CFSEdim cells (left) or CSFEbright cells (right). Data were compiled from three independent experiments (; mean ± s.e.m.) or are representative of two experiments (–). * Figure 2: Wild-type vaccinia virus does not stimulate 8.8 T cells via bystander activation. () Flow cytometry of splenocytes from Mbp−/− 8.8 mice, either naive or infected 7 d earlier with wild-type vaccinia virus, stained with MBP(79–87)–H-2Kk tetramer and anti-CD8, anti-CD44 and anti-CD62L and gated on CD8+ cells. Numbers in quadrants indicate percent cells in each. Data are representative of two experiments. () Flow cytometry of CFSE-labeled cells from naive Mbp−/− 8.8 mice and Mbp−/− 8.8 mice infected with wild-type vaccinia virus (7 d after infection), injected with equal numbers of wild-type splenocytes pulsed with MBP(79–87) (CFSE bright) and unpulsed splenocytes (CFSE dim) and analyzed 20 h later. Numbers above bracketed lines indicate percent CFSEdim cells (left) or CSFEbright cells (right). Data are representative of three experiments. * Figure 3: The 8.8 TCR is not cross-reactive to wild-type vaccinia virus epitopes. Flow cytometry of splenocytes from Thy-1.2+Rag2−/− 8.8 mice given nontransgenic splenocytes (2.5 × 106) from Thy-1.1+ C3HeB/Fej mice and then infected with wild-type vaccinia virus 2 weeks later; cells collected 7 d after infection were stimulated in vitro for 18 h with either vaccinia virus–infected Thy-1.1+ splenocytes (Vac targets) or MBP(79–87)-pulsed Thy-1.1+ splenocytes (MBP targets) and stained with anti-Thy-1.2 and anti-CD8 (left), then were made permeable and stained with anti-IFN-γ (right). Numbers adjacent to outlined areas (left) indicate percent Thy-1.2+CD8+ cells (top) or Thy-1.2−CD8+ cells (bottom); numbers in quadrants (right) indicate percent IFN-γ+CD8+ cells (top right) or IFN-γ−CD8+ cells (bottom right). Data are representative of two experiments. * Figure 4: Activation of Rag2+/+ 8.8 T cells by wild-type vaccinia virus requires expression of endogenous TCR chains. () Weight change in Rag2+/+ and Rag2−/− 8.8 mice (n = 5 mice per group) given splenocytes (2.5 × 106) from wild-type mice and then infected with wild-type vaccinia virus 2 weeks later. P < 0.0001 (Student's t-test). () Lysis of MBP peptide–pulsed splenocytes in vivo by Rag2+/+ and Rag2−/− 8.8 mice infected with wild-type vaccinia virus, assessed as described in Figure 1c. Data are representative of four experiments (mean ± s.e.m. in ). * Figure 5: Infection of 8.8 mice with wild-type vaccinia virus 'preferentially' expands CD8+ Vβ6+Vβ8+ T cell populations that respond to vaccinia virus epitopes. () Flow cytometry analysis of the expression of Vβ6 and Vβ8 by splenocytes collected from naive or infected Rag2+/+ 8.8 mice (7 d after infection with wild-type vaccinia virus), gated on CD8+ cells. Far left, staining of splenocytes from naive Rag2+/+ 8.8 mice with isotype-matched control antibodies for anti-Vβ6 and anti-Vβ8. () Expression of CD44 and CD62L by the CD8+ Vβ6−Vβ8+ and CD8+ Vβ6+Vβ8+ cells identified in the vaccinia virus–infected mice in . () Flow cytometry analysis of the expression of Vβ6 and IFN-γ by splenocytes obtained from the infected mice in , cultured in vitro overnight with unmanipulated (Media), vaccinia virus–infected (Vac targets) or MBP peptide-pulsed (MBP targets) splenocytes from Thy-1.1 C3HeB/Fej mice, and gated on CD8+Thy-1.2+ cells. Numbers in quadrants indicate percent cells in each. Data are representative of five experiments with more than ten mice (,) or are representative of three experiments (). * Figure 6: Vac-MBP activates Rag2−/− 8.8 T cells to induce autoimmunity but wild-type vaccinia virus does not. () CFSE dilution by CD8+Thy-1.2+Vα8+Vβ8+ cells among CFSE-labeled Rag2−/− 8.8 splenocytes (2 × 106) transferred into Mbp−/− mice that were left uninfected (gray lines) or infected 1 d earlier with either wild-type vaccinia virus or Vac-MBP (black lines); splenocytes collected 3 d later were stained with anti-CD8, anti-Thy-1.2, anti-Vα8 and anti-Vβ8. Data are representative of two experiments. () Weight change of Rag2+/+ and Rag2−/− 8.8 mice reconstituted with 2.5 × 106 splenocytes from naive wild-type mice and infected with Vac-MBP virus 2 weeks later (monitored as described in Fig. 1a). Numbers in parentheses represent disease incidence (diseased mice / total mice). Data are representative of two experiments (mean ± s.e.m.). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Immunology, University of Washington, Seattle, Washington, USA. * Qingyong Ji, * Antoine Perchellet & * Joan M Goverman * Present address: Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, Kansas, USA. * Antoine Perchellet Contributions Q.J. did most of the experiments and analyzed the data; A.P. did the initial disease-induction experiments and critiqued the manuscript; Q.J. and J.M.G. designed the study and wrote the manuscript; and J.M.G. secured the funding. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Joan M Goverman (goverman@u.washington.edu) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (3M) EAE in 8.8 mice after wild-type vaccinia infection. Female 8.8 mice were intraperitoneally infected with 5×106 pfu of wild-type vaccinia virus. This video was taken one month after vaccinia infection. PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–5 and Table 1 Additional data
• A global network of transcription factors, involving E2A, EBF1 and Foxo1, that orchestrates B cell fate
  Lin YC Jhunjhunwala S Benner C Heinz S Welinder E Mansson R Sigvardsson M Hagman J Espinoza CA Dutkowski J Ideker T Glass CK Murre C - Nat Immunol 11(7):635-643 (2010)
  Nature Immunology | Resource A global network of transcription factors, involving E2A, EBF1 and Foxo1, that orchestrates B cell fate * Yin C Lin1, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Suchit Jhunjhunwala1, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Benner2 Search for this author in: * NPG journals * PubMed * Google Scholar * Sven Heinz2 Search for this author in: * NPG journals * PubMed * Google Scholar * Eva Welinder1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Mansson1 Search for this author in: * NPG journals * PubMed * Google Scholar * Mikael Sigvardsson4 Search for this author in: * NPG journals * PubMed * Google Scholar * James Hagman5 Search for this author in: * NPG journals * PubMed * Google Scholar * Celso A Espinoza6 Search for this author in: * NPG journals * PubMed * Google Scholar * Janusz Dutkowski7, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Trey Ideker7, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher K Glass2 Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelis Murre1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature ImmunologyVolume:11,Pages:635–643Year published:(2010)DOI:doi:10.1038/ni.1891Received08 March 2010Accepted19 May 2010Published online13 June 2010 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 It is now established that the transcription factors E2A, EBF1 and Foxo1 have critical roles in B cell development. Here we show that E2A and EBF1 bound regulatory elements present in the Foxo1 locus. E2A and EBF1, as well as E2A and Foxo1, in turn, were wired together by a vast spectrum of cis-regulatory sequences. These associations were dynamic during developmental progression. Occupancy by the E2A isoform E47 directly resulted in greater abundance, as well as a pattern of monomethylation of histone H3 at lysine 4 (H3K4) across putative enhancer regions. Finally, we divided the pro-B cell epigenome into clusters of loci with occupancy by E2A, EBF and Foxo1. From this analysis we constructed a global network consisting of transcriptional regulators, signaling and survival factors that we propose orchestrates B cell fate. View full text Figures at a glance * Figure 1: E2A occupancy and epigenetic marking in cultured EBF1-deficient pre-pro-B cells and RAG-1-deficient pro-B cells. () E2A occupancy (blue) and H3K4me1, H3K4me2 and H3K4me3 regions (green) in the Cd19 and Cd79a loci, identified by ChIP-Seq. Red arrows indicate TSS; numbers in top left corners indicate tags observed. () Cis-regulatory sequences associated with E2A occupancy in pre-pro-B cells and pro-B cells, identified by comparison of enriched peaks with randomly selected genomic DNA sequences. Letter size indicates nucleotide frequency. Right margin (red), log P values; smaller values indicate greater enrichment for a given motif than for randomly selected regions. () Frequency of EBF, ETS and RUNX consensus binding sites located in close genomic proximity to E2A-bound regions. Data are from five independent experiments. * Figure 2: E2A occupancy and H3K4 methylation in cultured RAG-1-deficient pro-B cells. (,) H3K4 methylation (H3K4me1, H3K4me2 and H3K4me3) plotted as a function of genomic separation from promoter-distal E2A-bound sites (>3 kb from nearby TSSs; ) or promoter-proximal E2A-bound sites (<3 kb from nearby TSSs; ). (,) Heat map of E2A occupancy and H3K4me1, H3K4me2 and H3K4me3 patterns for promoter-distal DNA sequences (>3 kb from nearby TSSs; ) or promoter-proximal regions (<3 kb from nearby TSSs; ). Each row indicates an E2A-bound DNA fragment. Colors indicate enrichment for E2A binding and H3K4 methylation (scale below). Data are representative of four independent experiments. * Figure 3: E2A occupancy and patterns of H3K4 methylation in cultured EBF1-deficient pre-pro-B cells and RAG-1-deficient pro-B cells. Heat map of E2A occupancy and distribution of H3K4me1, H3K4me2 and H3K4me3 for promoter-distal regions (>3 kb from nearby TSS; 7,296 sites) in pre-pro-B cells (left) and pro-B cells (right); cistronic elements were grouped by the Ward hierarchical clustering method. Each row represents an E2A-bound DNA fragment. Colors (far left) identify clusters. Clusters I, II, III, IV, V and VI (left margin) represent groups of 785, 1,083, 1,140, 1,940, 824 and 1,524 binding sites, respectively, more closely related to each other than those assigned to different clusters. Data are from eight independent experiments. * Figure 4: Distinct cis-regulatory DNA sequences associate with E2A occupancy in cultured RAG-1-deficient pro-B cells. Cis-regulatory sequences associated with E2A occupancy and patterns of H3K4me1 identified by comparison of enriched binding sites (clusters as described in Fig. 3) to randomly selected genomic DNA sequences, gated on DNA sequences located 100 bp upstream or downstream of the E2A-binding sites; sequences distinct from each cluster were pooled and analyzed by the MEME algorithm. Letter size indicates frequency. Right, ranking of the top six scoring motifs (by log P value (red; HOMER)), showing the transcription factors associated with cis-regulatory sequences (CTCF, ETS, EBF, Pu.1, Foxo and Runx); numbers in black indicate nonredundant motif occurrences. Data are from one experiment. * Figure 5: Distinct patterns of H3K4 monomethylation are associated with coordinated occupancy by E2A and EBF1 and Foxo1. () ChIP-Seq analysis of cis-regulatory sequences associated with occupancy by EBF1 or Foxo1 in lysates of RAG-1-deficient pro-B cells immunoprecipitated (IP) with anti-EBF1 (left) or anti-Foxo1 (right); motifs with E2A occupancy were identified by comparison of enriched peaks to randomly selected genomic DNA sequences. Letter size indicates frequency of detection. Numbers at right (red) indicate log P values; smaller values represent greater enrichment than that of randomly selected regions. () Heat map of Foxo1 occupancy and H3K4me1, H3K4me2 and H3K4me3 patterns centered on 1,118 promoter-distal Foxo1-binding sites, showing enrichment for binding of Foxo1 and E2A as well as H3K4 methylation. Each row represents a 6-kb genomic region centered on Foxo1 occupancy. Data are from ten independent experiments. * Figure 6: Coordinated binding of E2A, EBF and Foxo1 is associated with a B lineage–specific program of gene expression. () Distribution of RNA expression in pre-pro-B and pro-B cells. Genes closest to the binding sites (measured from the TSS) were categorized (left graph) as those with E2A- and EBF1-binding sites located within 150 bp of each other (right two bars); those in the vicinity of EBF1-binding sites with no E2A peak within 150 bp (middle two bars); and those in the vicinity of E2A-binding sites with no EBF1 peak within 150 bp (left two bars). Middle and right graphs, same analysis of E2A- and Foxo1-binding sites (middle) and E2A- and CTCF-binding sites (right). Bottom, P values (paired t-test). Data are from one experiment. () Flow cytometry analysis of the expression of B220 and CD19 (left) in wild-type (WT), Foxo1f/+–ER-Cre, E2A+/− and E2A+/−Foxo1f/+–ER-Cre bone marrow (n = 4 mice per group), and absolute number of pro-B cells, pre-B cells and immature (Imm) B cells in the bone marrow (right). Numbers adjacent to outlined areas (left) indicate percent cells in each. Data a! re from one representative of two independent experiments with similar results (error bars, s.d.). () Enhancer activity of H3K4me1 DNA segments with coordinated binding of E2A and EBF1 in the pro-B cell line 22D6 and the T cell lines A12 and 166, assessed for genes closest to the respective enhancer elements (measured from the TSS; horizontal axis; n =24); results were normalized to renilla activity and are presented relative to basal promoter activity (logarithmic scale). Data are representative of at least two independent experiments. * Figure 7: Binding of E47 to DNA alters the pattern of H3K4 monomethylation. () Distribution of H3K4me1 across E2A occupancy in E2A-deficient pre-pro-B cells transduced for 21 h with E47-ER or bHLH-ER (control), then incubated for 1 or 6 h with tamoxifen to induce E47 activity, crosslinked with formaldehyde, immunoprecipitated with anti-E2A or anti-H3K4me1 and analyzed by ChIP-Seq; results are presented as individual immunoprecipitated mapped reads (or tags) per base pair. () ChIP-Seq analysis of E2A-deficient A12 cells transduced as described in . Data are from two independent experiments. * Figure 8: Regulatory network that links the activities of an ensemble of transcriptional regulators, signaling components and survival factors in developing B cells into a common pathway. Transcriptional regulatory targets of E2A and EBF1, as well as genes bound by Foxo1, identified by integrative genome-wide analysis of protein-DNA binding, are separated into eight groups on the basis of positive or negative changes change in transcript amount in pro-B cells relative to that in E2A-deficient pre-pro-B cells or EBF1-deficient pre-pro-B cells. Genes with critical roles in B cell commitment or potential regulators are indicated; all other genes are grouped into rings (ring size indicates the relative number of members). Darker colors indicate loci with Foxo1 occupancy. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE21978 * GSE21512 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Yin C Lin & * Suchit Jhunjhunwala Affiliations * Department of Molecular Biology, University of California, San Diego, La Jolla, California, USA. * Yin C Lin, * Suchit Jhunjhunwala, * Eva Welinder, * Robert Mansson & * Cornelis Murre * Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California, USA. * Christopher Benner, * Sven Heinz & * Christopher K Glass * Center for Stem Cell Biology and Cell Therapy, Lund University, Lund, Sweden. * Eva Welinder * Department for Biomedicine and Surgery, Linkoping University, Linkoping, Sweden. * Mikael Sigvardsson * Integrated Department of Immunology, National Jewish Health, Denver, Colorado, USA. * James Hagman * Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, California, USA. * Celso A Espinoza * Department of Bioengineering and Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California, USA. * Janusz Dutkowski & * Trey Ideker * The Institute for Genomic Medicine, University of California, San Diego, La Jolla, California, USA. * Janusz Dutkowski & * Trey Ideker Contributions Y.C.L. designed and did experiments, analyzed data and wrote the manuscript; S.J. and C.B. wrote programs and analyzed data; S.H. did CTCF ChIP-Seq and monomethylation of H3K4 in RAG-deficient pro-B cells; J.H. generated EBF-deficient pre-pro-B cells; M.S. provided anti-EBF; E.W. and R.M. analyzed E2A-Foxo1–deficient mice; C.A.E. did ChIP-Seq experiments during the initial phase of the study; J.D. and T.I. applied computational approaches to generate a global network; C.K.G. analyzed data and edited the manuscript; and C.M. designed experiments, analyzed data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Christopher K Glass (cglass@ucsd.edu) or * Cornelis Murre (murre@biomail.ucsd.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–9 and Supplementary Tables 1–7 Additional data
• Corrigendum: Dock8 mutations cripple B cell immunological synapses, germinal centers and long-lived antibody production
  - Nat Immunol 11(7):644 (2010)
  Nature Immunology | Corrigendum Corrigendum: Dock8 mutations cripple B cell immunological synapses, germinal centers and long-lived antibody production * Katrina L Randall Search for this author in: * NPG journals * PubMed * Google Scholar * Teresa Lambe Search for this author in: * NPG journals * PubMed * Google Scholar * Andy Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Bebhinn Treanor Search for this author in: * NPG journals * PubMed * Google Scholar * Edyta Kucharska Search for this author in: * NPG journals * PubMed * Google Scholar * Heather Domaschenz Search for this author in: * NPG journals * PubMed * Google Scholar * Belinda Whittle Search for this author in: * NPG journals * PubMed * Google Scholar * Lina E Tze Search for this author in: * NPG journals * PubMed * Google Scholar * Anselm Enders Search for this author in: * NPG journals * PubMed * Google Scholar * Tanya L Crockford Search for this author in: * NPG journals * PubMed * Google Scholar * Tiphaine Bouriez-Jones Search for this author in: * NPG journals * PubMed * Google Scholar * Duncan Alston Search for this author in: * NPG journals * PubMed * Google Scholar * Jason G Cyster Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Lenardo Search for this author in: * NPG journals * PubMed * Google Scholar * Fabienne Mackay Search for this author in: * NPG journals * PubMed * Google Scholar * Elissa K Deenick Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart G Tangye Search for this author in: * NPG journals * PubMed * Google Scholar * Tyani D Chan Search for this author in: * NPG journals * PubMed * Google Scholar * Tahra Camidge Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Brink Search for this author in: * NPG journals * PubMed * Google Scholar * Carola G Vinuesa Search for this author in: * NPG journals * PubMed * Google Scholar * Facundo D Batista Search for this author in: * NPG journals * PubMed * Google Scholar * Richard J Cornall Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher C Goodnow Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature ImmunologyVolume:11,Page:644Year published:(2010)DOI:doi:10.1038/ni0710-644a Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Immunol.10, 1283–1291 (2009); published online 8 November 2009; corrected after print 4 December 2009 In the version of this article initially published, the third author's name is missing the middle initial. The correct name is Andy L Johnson. The error has been corrected in the HTML and PDF versions of the article. Additional data
• Erratum: An essential role for the transcription factor HEB in thymocyte survival, Tcra rearrangement and the development of natural killer T cells
  - Nat Immunol 11(7):644 (2010)
  Nature Immunology | Erratum Erratum: An essential role for the transcription factor HEB in thymocyte survival, Tcra rearrangement and the development of natural killer T cells * Louise M D'Cruz Search for this author in: * NPG journals * PubMed * Google Scholar * Jamie Knell Search for this author in: * NPG journals * PubMed * Google Scholar * Jessica K Fujimoto Search for this author in: * NPG journals * PubMed * Google Scholar * Ananda W Goldrath Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature ImmunologyVolume:11,Page:644Year published:(2010)DOI:doi:10.1038/ni0710-644b Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Immunol.11, 240–249 (2010); published online 14 February 2010; corrected after print 12 March 2010 In the version of this article initially published, references 35–41 were cited out of order in the text. The citations on page 247 should be as follows: column one, first paragraph, second full sentence, "RAG-2 recombinase protein35,36, which could in turn impair rearrangements37"; column two, middle paragraph, final two sentences, "iNKT development20,21,22,23,28,29,38, 39....from stage 0 through stage 2 (refs. 20, 38)"; column two, final paragraph, second sentence, "RORγt-deficient thymocytes40"; and column two, final paragraph, fourth sentence, "Tcra rearrangements33,34,41." The error has been corrected in the HTML and PDF versions of the article. The reference list should be as follows: References * Yannoutsos, N.et al. The role of recombination activating gene (RAG) reinduction in thymocyte development in vivo. J. Exp. Med.194, 471–480 (2001). * ChemPort * ISI * PubMed * Article * Nichols, K.E.et al. Regulation of NKT cell development by SAP, the protein defective in XLP. Nat. Med.11, 340–345 (2005). * ChemPort * ISI * PubMed * Article * Savage, A.K.et al. The transcription factor PLZF directs the effector program of the NKT cell lineage. Immunity29, 391–403 (2008). * ChemPort * PubMed * Article * Benlagha, K., Wei, D.G., Veiga, J., Teyton, L. & Bendelac, A.Characterization of the early stages of thymic NKT cell development. J. Exp. Med.202, 485–492 (2005). * ChemPort * PubMed * Article * Sun, Z.et al. Requirement for RORγ in thymocyte survival and lymphoid organ development. Science288, 2369–2373 (2000). * ChemPort * ADS * ISI * PubMed * Article Download references Additional data
• Erratum: Temporal changes in dendritic cell subsets, cross-priming and costimulation via CD70 control CD8+ T cell responses to influenza
  - Nat Immunol 11(7):644 (2010)
  Nature Immunology | Erratum Erratum: Temporal changes in dendritic cell subsets, cross-priming and costimulation via CD70 control CD8+ T cell responses to influenza * André Ballesteros-Tato Search for this author in: * NPG journals * PubMed * Google Scholar * Beatriz León Search for this author in: * NPG journals * PubMed * Google Scholar * Frances E Lund Search for this author in: * NPG journals * PubMed * Google Scholar * Troy D Randall Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature ImmunologyVolume:11,Page:644Year published:(2010)DOI:doi:10.1038/ni0710-644c Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Immunol.11, 216–224 (2010); published online 24 January 2010; corrected after print 7 May 2010 In the version of this article initially published, the label along the vertical axis of Figure 6h was incorrect. The correct label is "OT-II proliferating cells (×102)." The error has been corrected in the HTML and PDF versions of the article. Additional data