- Email: [email protected]
How Many Subsets of Innate Lymphoid Cells Do We Need?
Immunity
Previews accumulation, they looked for the source of IL-6 in the gingiva. Surprisingly, they found that IL-6, but not IL-1b, was derived from the epithelium due to tissue damage or mechanical pressure alone, occurring physiologically in the mouth through abrasion and by chewing, thereby enabling the accumulation of Th17 cells. In line with this hypothesis, mice harbored increasing numbers of Th17 cells in the gingiva based on the level of abrasion experienced. Mice placed on soft diet had fewer Th17 cells than those on standard chow, which had fewer Th17 cells than those receiving additional rubbing of the gingiva (Figure 1). Although mechanical pressure may result in expression of chemoattractants, Th17 cells were not selectively recruited to the gingiva. Instead, the presence of IL-6 correlated with increased local proliferation of Th17 cells. This highlights that the cues required to accumulate Th17 cells at barrier sites involve IL-6 but that the induction of this cytokine can be achieved in more ways than via microbial stimulation. It remains to be investigated whether damage or mechanical sensing in the gingiva constitute a tissue-specific cue. The involvement of IL-6 would suggest this could be a general mechanism of relevance at other sites, albeit that the mastication provides continues signal in
the gingiva. In line with this, Dutzan et al. (2017) showed that damage induced in the skin also resulted in the accumulation of Th17 cells, suggesting that microbial induction as well as the tissuedamage response can enhance Th17 cell numbers. IL-17 stimulation of many cell types, such as fibroblasts, macrophages, chondrocytes, and osteocytes, often in synergy with other cytokines, results in the production of IL-6, IL-1b, and TNF, as well as chemokines such as CCL20. This positive-feedback loop would strengthen the accumulation of Th17 cells and recruitment of granulocytes. The results by Dutzan et al. (2017) emphasize the importance of IL-6 in maintaining Th17 cells. The mechanism may find additional relevance in autoimmune disorders such as RA, where continuous bone destruction by Th17 cells is a major symptom of disease, but which may also be directly involved, via the damageinduced secretion of IL-6, in sustaining disease. The work by Dutzan et al. (2017) shows that tissues have the ability to maintain numbers of Th17 cells via different mechanisms. In the case of the gingiva, the maintenance of a resident population of Th17 cells depends on the how well you chew your food.
REFERENCES Berer, K., Mues, M., Koutrolos, M., Rasbi, Z.A., Boziki, M., Johner, C., Wekerle, H., and Krishnamoorthy, G. (2011). Nature 479, 538–541. Conti, H.R., Peterson, A.C., Brane, L., Huppler, A.R., Herna´ndez-Santos, N., Whibley, N., Garg, A.V., Simpson-Abelson, M.R., Gibson, G.A., Mamo, A.J., et al. (2014). J. Exp. Med. 211, 2075–2084. Dutzan, N., Abusleme, L., Bridgeman, H., Greenwell-Wild, T., Zangerle-Murray, T., Fife, M.E., Bouladoux, N., Linley, H., Brenchley, L., Wemyss, K., et al. (2017). Immunity 46, this issue, 133–147. Hirota, K., Duarte, J.H., Veldhoen, M., Hornsby, E., Li, Y., Cua, D.J., Ahlfors, H., Wilhelm, C., Tolaini, M., Menzel, U., et al. (2011). Nat. Immunol. 12, 255–263. Hirota, K., Turner, J.E., Villa, M., Duarte, J.H., Demengeot, J., Steinmetz, O.M., and Stockinger, B. (2013). Nat. Immunol. 14, 372–379. Ivanov, I.I., Frutos, Rde.L., Manel, N., Yoshinaga, K., Rifkin, D.B., Sartor, R.B., Finlay, B.B., and Littman, D.R. (2008). Cell Host Microbe 4, 337–349. Kolls, J.K., McCray, P.B., Jr., and Chan, Y.R. (2008). Nat. Rev. Immunol. 8, 829–835. Li, Y., Innocentin, S., Withers, D.R., Roberts, N.A., Gallagher, A.R., Grigorieva, E.F., Wilhelm, C., and Veldhoen, M. (2011). Cell 147, 629–640. Marks, B.R., Nowyhed, H.N., Choi, J.Y., Poholek, A.C., Odegard, J.M., Flavell, R.A., and Craft, J. (2009). Nat. Immunol. 10, 1125–1132.
How Many Subsets of Innate Lymphoid Cells Do We Need? Kafi N. Ealey1 and Shigeo Koyasu2,* 1Laboratory
for Innate Immune Systems for Immune Cell Systems RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan *Correspondence: [email protected] http://dx.doi.org/10.1016/j.immuni.2016.12.018 2Laboratory
Innate lymphoid cells (ILCs) are composed of three main subsets. In this issue of Immunity, Simoni et al. (2017) show using mass-cytometry that human ILCs are highly heterogeneous between individuals and tissues and lack a previously proposed helper-type ILC1 population. Innate lymphoid cells (ILCs) are composed of cytotoxic natural killer (NK) cells and the more recently discovered
helper-like ILCs. All members of the ILC family lack expression of rearranged antigen receptors and do not display anti-
10 Immunity 46, January 17, 2017 ª 2017 Elsevier Inc.
gen specificity. ILCs play a critical role in the maintenance of tissue homeostasis, early defense against infection,
Immunity
Previews
Figure 1. Heterogeneity of ILCs in Mouse and Human Tissues Simoni et al. (2017) report that ILC1 cells are not present in human tissues and that intraepithelial ILC1-like cells (ieILC1-like) are phenotypically similar to conventional NK cells. Gury-BenAri et al. reported that mouse ILC subsets comprise more heterogeneous populations than previously reported based on single cell RNA-seq analysis (Gury-BenAri et al., 2016). ILC3c is distinct from other ILC3s in that this population expresses a high level of IL-22. ILC3b population seems to correspond to NKp46+ ILC3 and ILC3e population contains lymphoid tissue inducer (LTi)-like cells and cells expressing MHCII, which was not expressed in human ILCs (Simoni et al., 2017) It is not known, however, whether distinct populations found in ILC1 and ILC2 subsets have different in vivo functions in the mouse.
pathophysiology of allergic inflammation, and tissue repair (Artis and Spits, 2015; Morita et al., 2016). Over the past 5–6 years, the helper-like ILCs have been categorized into three main subsets based on their similarity to the T helper cell subsets, with which they share cytokine profiles and transcription factors. Thus, group 1 ILCs are characterized by expression of the transcription factor T-bet and production of interferon-g
(IFN-g), group 2 ILCs secrete T helper-2 (Th2) cell-associated cytokines and are functionally regulated by the transcription factor GATA-3, whereas ILC3s are characterized by RORgt transcription factor expression and secretion of interleukin-17 (IL-17) and IL-22, characteristic of Th17 cells (Artis and Spits, 2015; Morita et al., 2016). Although ILC1, ILC2, and ILC3 cells have been defined based on a panel of cell-surface markers and cyto-
kine profiles, both mouse and human studies continue to reveal previously unrecognized levels of diversity in the expression of surface markers and transcription factors used to characterize these cells. Furthermore, technical limitations have made it difficult to accurately capture the diversity within human ILC subsets, which likely contributes to some of the inconsistencies in reported phenotypes in the literature. In this issue, Simoni et al. (Simoni et al., 2017) used mass-cytometry employing a broad range of surface markers, transcription factors, and functional cellular proteins to characterize human NK and helper-like ILC subsets across nine different healthy tissues and three pathological tissues. In addition, they analyzed the data using t-Distributed Stochastic Neighbor Embedding (t-SNE) which enables 2Dclustering of cell populations according to expression of multiple markers. They identified some clear differences between mouse and human ILCs. An important finding from their analysis of ILCs across a broad range of normal and pathological tissues was the absence of a distinct ILC1 cell population in any of the tissues examined (Figure 1). In both mice and humans, the existence of a distinct subset of helper-type ILC1 cells has been controversial. In most tissues, ILC1 cells can be distinguished from NK cells based on cell-surface expression of CD127 and CD49a, as well as lack of expression of the transcription factor Eomes and lack of perforin and granzyme B (Bernink et al., 2013). However, NK cells and ILC1 cells show overlapping phenotypes in some tissues and these definitions are insufficient to unambiguously discriminate between NK cells and ILC1s in various stages of infection or inflammation in humans (Bjo¨rklund et al., 2016; Robinette et al., 2015). It is interesting that a population of putative ILC1 cells clustered together with T cells and dendritic cells using the t-SNE analysis leading the authors to speculate that there were inaccuracies in the definition of ILC1 cell populations in previous studies (Ve´ly et al., 2016) or contamination with T cells. Robinette et al. (2015) reported closely overlapping phenotypes and functional programs in NK cells and ILC1s, also bringing into Immunity 46, January 17, 2017 11
Immunity
Previews question whether these two cell types are actually just representative subsets within the broad NK spectrum. A subset of ILC1 cells, termed intraepithelial ILC (ieILC), has been previously described in mucosal tissues (Fuchs et al., 2013). This population expresses both Eomes and T-bet, as well as surface NK cell markers including NKp46 and CD49a and is negative for expression of CD127. It has been reported as a distinct ILC subset that expresses CD103, which is absent on NK cells. Simoni et al. (2017) identified the presence of these ieILC1like cells in human non-mucosal pathological tissues. Whereas NKp44 has been reported to be a marker used to characterize ieILC1 (Fuchs et al., 2013), its expression was highly variable in mucosal and non-mucosal pathological tissues in this study. Furthermore, the frequency of these cells was higher in mucosal tissues and pathological tissues compared to non-mucosal tissues and this cell population was strongly associated with various NK cell populations. The functional similarities in responsivity to IL-15 of ieILC-like cells and NK cells provides compelling evidence that this population is also a subset of NK cells rather than a distinct subset of ILCs (Figure 1), raising some interesting questions. What is the role of these cells in non-mucosal pathological tissues where they, together with NK cells, make up at least 90% of ILCs. Further, what are the tissue environments that enable NK cell subsets to acquire such varied phenotypes? Simoni et al. (2017) identified distinct clusters of ILC3 subsets using the currently adopted panel of cell surface markers and transcription factors that typically delineate these cell populations in most mouse and human studies. However, there was a higher degree of heterogeneity than has previously been reported and a few key differences compared to mice. In contrast to murine cells, in which NKp46 and the chemokine receptor CCR6 expression can be used to segregate the two major ILC3 subsets, clustering analysis revealed variable expression of the natural cytotoxicity receptors (NCRs) NKp30 and NKp46, as well as CCR6 in human ILC3 cells. Nevertheless, across all human tissues examined, it was clear that expression of the NCR NKp44 could serve as a useful marker to distinguish between the two major subsets of ILC3 cells 12 Immunity 46, January 17, 2017
albeit staining of RORgt protein, the ILC3 transcription factor, was weak. Although the ILC2 signature transcription factor GATA3 was highly expressed in ILC2 cells, it was also expressed at lower levels in both NK and ILC3 cells. Therefore, transcription factor staining cannot accurately discern ILC helper subsets in human tissues and a combination of cell surface markers should be used to identify these populations. Compared to NK/ieILC1 and ILC3 subsets, the ILC2 subset was quite homogeneous based on the t-SNE profile. The breadth of diversity within mouse intestinal ILCs was recently identified by Gury-BenAri et al. (2016) using singlecell RNA sequence analysis and mass cytometry analysis. They classified 4–5 subgroups within each of the canonical ILC subsets, grouped together based on functional similarities in transcriptional states and overall gene signatures (Figure 1). Furthermore, alterations in the local tissue environment, including changes in the microbiome, altered ILC transcriptional profiles. The degree of responsiveness of each subgroup was highly distinct and heterogeneous with some ‘‘ILC states’’ responding more than others. It is possible that the heterogeneity observed in the transcriptional states among ILC1 and ILC2 subsets reflects activation and/or maturation stages of these ILCs. In contrast, the heterogeneity observed among ILC3 subsets and differential expression of various cell surface markers as well as cytokines corresponded to previously-defined populations of ILC3s. In human studies, variability in genetics, environmental exposures, and interindividual responses to stimuli can greatly influence the immune cell composition. Furthermore, technical limitations make it difficult to identify phenotypically distinct ILC subsets across a continuum of healthy and diseased states in human tissues. One strength of this study by Simoni et al. (2017) is the comparison of ILC frequencies and phenotypic profiles across many non-pathological and pathological tissues. Under non-pathological conditions, the frequency of ILC2 and ILC3 was very low (< 5%) in non-mucosal and lung tissues, whereas in oral and gastrointestinal mucosal tissues their frequency increased substantially, reflecting their role in mediating innate immune re-
sponses at barrier surfaces. Interestingly, this increased frequency was associated with distinct phenotypic profiles within the ILC helper subsets. The comprehensive and detailed analysis of human ILC subsets in this report supports more recent shifts in our understanding of ILC as innate sensors that exhibit bidirectional plasticity under differing inflammatory conditions (Bal et al., 2016; Bernink et al., 2015). Simoni et al. (2017) found that both ILC2 and ILC3 subsets express a functional receptor for IL-18, which has not been previously identified on these cell types. Signaling through the IL-18R is typically associated with the production of type 1 cytokines. Stimulation with IL-18 resulted in increased production of granulocyte macrophage colony stimulating factor (GM-CSF) and IL-8 in both subsets and in combination with IL-23 elicited IFN-g production from ILC3 cells, yet they maintained their ILC3 phenotype. Interestingly, IL-18 elicited the secretion of type 2 cytokines from ILC2s in a similar manner as IL-33. This phenotype might represent a distinct mechanism by which these cell types can be independently activated under specific inflammatory conditions. More questions remain. Do such highly diversified populations of ILCs reflect their distinct roles in different tissues? Do ILC1 play a specific role in the mouse that is distinct from NK cells? Does IL-18 have similar functions in the mouse? Future studies should answer these questions. REFERENCES Artis, D., and Spits, H. (2015). Nature 517, 293–301. Bal, S.M., Bernink, J.H., Nagasawa, M., Groot, J., Shikhagaie, M.M., Golebski, K., van Drunen, C.M., Lutter, R., Jonkers, R.E., Hombrink, P., et al. (2016). Nat. Immunol. 17, 636–645. Bernink, J.H., Peters, C.P., Munneke, M., te Velde, A.A., Meijer, S.L., Weijer, K., Hreggvidsdottir, H.S., Heinsbroek, S.E., Legrand, N., Buskens, C.J., et al. (2013). Nat. Immunol. 14, 221–229. Bernink, J.H., Krabbendam, L., Germar, K., de Jong, E., Gronke, K., Kofoed-Nielsen, M., Munneke, J.M., Hazenberg, M.D., Villaudy, J., Buskens, C.J., et al. (2015). Immunity 43, 146–160. Bjo¨rklund, A.K., Forkel, M., Picelli, S., Konya, V., Theorell, J., Friberg, D., Sandberg, R., and Mjo¨sberg, J. (2016). Nat. Immunol. 17, 451–460. Fuchs, A., Vermi, W., Lee, J.S., Lonardi, S., Gilfillan, S., Newberry, R.D., Cella, M., and Colonna, M. (2013). Immunity 38, 769–781.
Immunity
Previews Gury-BenAri, M., Thaiss, C.A., Serafini, N., Winter, D.R., Giladi, A., Lara-Astiaso, D., Levy, M., Salame, T.M., Weiner, A., David, E., et al. (2016). Cell 166, 1231–1246, e1213. Morita, H., Moro, K., and Koyasu, S. (2016). J. Allergy Clin. Immunol. 138, 1253–1264.
Robinette, M.L., Fuchs, A., Cortez, V.S., Lee, J.S., Wang, Y., Durum, S.K., Gilfillan, S., Colonna, M., and Immunological Genome, C.; Immunological Genome Consortium (2015). Nat. Immunol. 16, 306–317. Simoni, Y., Fehlings, M., Kloverpris, H.N., McGovern, N., Koo, S.L., Loh, C.Y., Lim, S., Kurioka, A.,
Fergusson, J.R., Tang, C.L., et al. (2017). Immunity 46, this issue, 148–161. Ve´ly, F., Barlogis, V., Vallentin, B., Neven, B., Piperoglou, C., Ebbo, M., Perchet, T., Petit, M., Yessaad, N., Touzot, F., et al. (2016). Nat. Immunol. 17, 1291–1299.
T Cells Take on Zika Virus Heather D. Hickman1 and Theodore C. Pierson1,* 1Laboratory of Viral Diseases, National Institutes of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.immuni.2016.12.020
Although CD8+ T cells provide protection against many viral infections, their role in Zika virus (ZIKV) immunity has not been extensively examined. In a recent issue of Cell Host & Microbe, Elong Ngono et al. (2017) define antigenic epitopes determining CD8+ T cell immunity in murine models of ZIKV infection. Zika virus (ZIKV) is a mosquito-born, positive-stranded RNA virus that has skyrocketed into public awareness due to its rapid dissemination among countries of the Western hemisphere. Although ZIKV infection of adults is generally asymptomatic and self-limiting, infection during pregnancy can lead to severe neurodevelopmental birth defects in utero including microcephaly (Hazin et al., 2016). Identifying unique features of ZIKV pathogenesis and strategies to prevent new infections has become a public health priority. Efforts to develop ZIKV vaccines that elicit protective, neutralizing antibodies (Abs) combined with theoretical concerns about the potential enhancement of ZIKV infection by antibodies that cross-react with other flaviviruses, such as dengue virus (DENV), has driven intense examination of the humoral response to ZIKV in both humans and animal models. In contrast, to date, relatively little is known regarding the CD8+ T cell response to ZIKV infection. In a recent issue of Cell Host & Microbe, Elong Ngono et al. (2017) map the antigenic epitopes that elicit murine CD8+ T cell responses during ZIKV infection and demonstrate a protective role for these cells during infection. In contrast to antibodies that bind to virions before they enter cells, CD8+ T cells recognize and eliminate virus-in-
fected cells after recognition of short peptides bound by major histocompatibility class I (MHC) molecules. A multitude of factors influence which specific CD8+ T cells will respond during a viral infection, including but not limited to peptide creation, MHC allelic polymorphism (dictating the peptide repertoire), T cell precursor frequencies, and T cell immunodominance. How these factors act in concert is incompletely understood. Although cytotoxic CD8+ T cells have been shown to play a protective role against DENV and West Nile virus (WNV) (Screaton et al., 2015; Shrestha and Diamond, 2004; Weiskopf and Sette, 2014), their contribution to ZIKV immunity has not been examined. To begin to define the CD8+ response to ZIKV, Elong Ngono et al. (2017) used peptidebinding prediction algorithms to identify peptides encoded by two strains of ZIKV (MR766 and FSS13025) that could be bound by the MHC class I molecules Kb and Db in the commonly used C57BL/6 mouse. These algorithms identified 244 potential ZIKV-derived peptides; 202 of these were shared between the African- and Asian-lineage ZIKV strains studied. To identify which, if any, of these peptides elicited CD8+ T cell responses, Elong Ngono et al. (2017) examined the
CD8+ response in two murine models of ZIKV infection. ZIKV replicates poorly in wild-type mouse strains due to an inability to efficiently antagonize the type I interferon response (Lazear et al., 2016). After infection of mice treated with interferon a/b receptor (IFNAR)-blocking Ab, isolated splenic CD8+ T cells produced IFN-g when stimulated with 26 different peptides from MR766 and 15 peptides from FSS13025 ZIKV strains. To confirm these findings in a more immunocompetent model, Elong Ngono et al. (2017) used H-2Kb mice that lack IFNAR in only a subset of myeloid cells. In these LysMCre+IFNARfl/fl mice, IFN signaling remains intact in T cells, B cells, and dendritic cells (DCs). CD8+ T cells isolated from ZIKV-infected LysMCre+IFNARfl/fl animals responded to the same ZIKV epitopes as CD8+ T cells isolated from mice treated with IFNAR-blocking antibody. Although CD8+ T cell epitopes were identified from almost all of ZIKV’s ten proteins, the response to envelope (E protein)-derived epitopes predominated in both murine models (Figure 1). Interestingly, CD8+ T cells in C57BL/6 mice infected with DENV also possess T cells targeting the structural proteins capsid (C) and E (Yauch et al., 2009). In contrast, DENV-specific human T cells or T cells from DENV-infected HLA-transgenic mice (possessing human MHC alleles)
Immunity 46, January 17, 2017 ª 2017 Published by Elsevier Inc. 13
Previews accumulation, they looked for the source of IL-6 in the gingiva. Surprisingly, they found that IL-6, but not IL-1b, was derived from the epithelium due to tissue damage or mechanical pressure alone, occurring physiologically in the mouth through abrasion and by chewing, thereby enabling the accumulation of Th17 cells. In line with this hypothesis, mice harbored increasing numbers of Th17 cells in the gingiva based on the level of abrasion experienced. Mice placed on soft diet had fewer Th17 cells than those on standard chow, which had fewer Th17 cells than those receiving additional rubbing of the gingiva (Figure 1). Although mechanical pressure may result in expression of chemoattractants, Th17 cells were not selectively recruited to the gingiva. Instead, the presence of IL-6 correlated with increased local proliferation of Th17 cells. This highlights that the cues required to accumulate Th17 cells at barrier sites involve IL-6 but that the induction of this cytokine can be achieved in more ways than via microbial stimulation. It remains to be investigated whether damage or mechanical sensing in the gingiva constitute a tissue-specific cue. The involvement of IL-6 would suggest this could be a general mechanism of relevance at other sites, albeit that the mastication provides continues signal in
the gingiva. In line with this, Dutzan et al. (2017) showed that damage induced in the skin also resulted in the accumulation of Th17 cells, suggesting that microbial induction as well as the tissuedamage response can enhance Th17 cell numbers. IL-17 stimulation of many cell types, such as fibroblasts, macrophages, chondrocytes, and osteocytes, often in synergy with other cytokines, results in the production of IL-6, IL-1b, and TNF, as well as chemokines such as CCL20. This positive-feedback loop would strengthen the accumulation of Th17 cells and recruitment of granulocytes. The results by Dutzan et al. (2017) emphasize the importance of IL-6 in maintaining Th17 cells. The mechanism may find additional relevance in autoimmune disorders such as RA, where continuous bone destruction by Th17 cells is a major symptom of disease, but which may also be directly involved, via the damageinduced secretion of IL-6, in sustaining disease. The work by Dutzan et al. (2017) shows that tissues have the ability to maintain numbers of Th17 cells via different mechanisms. In the case of the gingiva, the maintenance of a resident population of Th17 cells depends on the how well you chew your food.
REFERENCES Berer, K., Mues, M., Koutrolos, M., Rasbi, Z.A., Boziki, M., Johner, C., Wekerle, H., and Krishnamoorthy, G. (2011). Nature 479, 538–541. Conti, H.R., Peterson, A.C., Brane, L., Huppler, A.R., Herna´ndez-Santos, N., Whibley, N., Garg, A.V., Simpson-Abelson, M.R., Gibson, G.A., Mamo, A.J., et al. (2014). J. Exp. Med. 211, 2075–2084. Dutzan, N., Abusleme, L., Bridgeman, H., Greenwell-Wild, T., Zangerle-Murray, T., Fife, M.E., Bouladoux, N., Linley, H., Brenchley, L., Wemyss, K., et al. (2017). Immunity 46, this issue, 133–147. Hirota, K., Duarte, J.H., Veldhoen, M., Hornsby, E., Li, Y., Cua, D.J., Ahlfors, H., Wilhelm, C., Tolaini, M., Menzel, U., et al. (2011). Nat. Immunol. 12, 255–263. Hirota, K., Turner, J.E., Villa, M., Duarte, J.H., Demengeot, J., Steinmetz, O.M., and Stockinger, B. (2013). Nat. Immunol. 14, 372–379. Ivanov, I.I., Frutos, Rde.L., Manel, N., Yoshinaga, K., Rifkin, D.B., Sartor, R.B., Finlay, B.B., and Littman, D.R. (2008). Cell Host Microbe 4, 337–349. Kolls, J.K., McCray, P.B., Jr., and Chan, Y.R. (2008). Nat. Rev. Immunol. 8, 829–835. Li, Y., Innocentin, S., Withers, D.R., Roberts, N.A., Gallagher, A.R., Grigorieva, E.F., Wilhelm, C., and Veldhoen, M. (2011). Cell 147, 629–640. Marks, B.R., Nowyhed, H.N., Choi, J.Y., Poholek, A.C., Odegard, J.M., Flavell, R.A., and Craft, J. (2009). Nat. Immunol. 10, 1125–1132.
How Many Subsets of Innate Lymphoid Cells Do We Need? Kafi N. Ealey1 and Shigeo Koyasu2,* 1Laboratory
for Innate Immune Systems for Immune Cell Systems RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan *Correspondence: [email protected] http://dx.doi.org/10.1016/j.immuni.2016.12.018 2Laboratory
Innate lymphoid cells (ILCs) are composed of three main subsets. In this issue of Immunity, Simoni et al. (2017) show using mass-cytometry that human ILCs are highly heterogeneous between individuals and tissues and lack a previously proposed helper-type ILC1 population. Innate lymphoid cells (ILCs) are composed of cytotoxic natural killer (NK) cells and the more recently discovered
helper-like ILCs. All members of the ILC family lack expression of rearranged antigen receptors and do not display anti-
10 Immunity 46, January 17, 2017 ª 2017 Elsevier Inc.
gen specificity. ILCs play a critical role in the maintenance of tissue homeostasis, early defense against infection,
Immunity
Previews
Figure 1. Heterogeneity of ILCs in Mouse and Human Tissues Simoni et al. (2017) report that ILC1 cells are not present in human tissues and that intraepithelial ILC1-like cells (ieILC1-like) are phenotypically similar to conventional NK cells. Gury-BenAri et al. reported that mouse ILC subsets comprise more heterogeneous populations than previously reported based on single cell RNA-seq analysis (Gury-BenAri et al., 2016). ILC3c is distinct from other ILC3s in that this population expresses a high level of IL-22. ILC3b population seems to correspond to NKp46+ ILC3 and ILC3e population contains lymphoid tissue inducer (LTi)-like cells and cells expressing MHCII, which was not expressed in human ILCs (Simoni et al., 2017) It is not known, however, whether distinct populations found in ILC1 and ILC2 subsets have different in vivo functions in the mouse.
pathophysiology of allergic inflammation, and tissue repair (Artis and Spits, 2015; Morita et al., 2016). Over the past 5–6 years, the helper-like ILCs have been categorized into three main subsets based on their similarity to the T helper cell subsets, with which they share cytokine profiles and transcription factors. Thus, group 1 ILCs are characterized by expression of the transcription factor T-bet and production of interferon-g
(IFN-g), group 2 ILCs secrete T helper-2 (Th2) cell-associated cytokines and are functionally regulated by the transcription factor GATA-3, whereas ILC3s are characterized by RORgt transcription factor expression and secretion of interleukin-17 (IL-17) and IL-22, characteristic of Th17 cells (Artis and Spits, 2015; Morita et al., 2016). Although ILC1, ILC2, and ILC3 cells have been defined based on a panel of cell-surface markers and cyto-
kine profiles, both mouse and human studies continue to reveal previously unrecognized levels of diversity in the expression of surface markers and transcription factors used to characterize these cells. Furthermore, technical limitations have made it difficult to accurately capture the diversity within human ILC subsets, which likely contributes to some of the inconsistencies in reported phenotypes in the literature. In this issue, Simoni et al. (Simoni et al., 2017) used mass-cytometry employing a broad range of surface markers, transcription factors, and functional cellular proteins to characterize human NK and helper-like ILC subsets across nine different healthy tissues and three pathological tissues. In addition, they analyzed the data using t-Distributed Stochastic Neighbor Embedding (t-SNE) which enables 2Dclustering of cell populations according to expression of multiple markers. They identified some clear differences between mouse and human ILCs. An important finding from their analysis of ILCs across a broad range of normal and pathological tissues was the absence of a distinct ILC1 cell population in any of the tissues examined (Figure 1). In both mice and humans, the existence of a distinct subset of helper-type ILC1 cells has been controversial. In most tissues, ILC1 cells can be distinguished from NK cells based on cell-surface expression of CD127 and CD49a, as well as lack of expression of the transcription factor Eomes and lack of perforin and granzyme B (Bernink et al., 2013). However, NK cells and ILC1 cells show overlapping phenotypes in some tissues and these definitions are insufficient to unambiguously discriminate between NK cells and ILC1s in various stages of infection or inflammation in humans (Bjo¨rklund et al., 2016; Robinette et al., 2015). It is interesting that a population of putative ILC1 cells clustered together with T cells and dendritic cells using the t-SNE analysis leading the authors to speculate that there were inaccuracies in the definition of ILC1 cell populations in previous studies (Ve´ly et al., 2016) or contamination with T cells. Robinette et al. (2015) reported closely overlapping phenotypes and functional programs in NK cells and ILC1s, also bringing into Immunity 46, January 17, 2017 11
Immunity
Previews question whether these two cell types are actually just representative subsets within the broad NK spectrum. A subset of ILC1 cells, termed intraepithelial ILC (ieILC), has been previously described in mucosal tissues (Fuchs et al., 2013). This population expresses both Eomes and T-bet, as well as surface NK cell markers including NKp46 and CD49a and is negative for expression of CD127. It has been reported as a distinct ILC subset that expresses CD103, which is absent on NK cells. Simoni et al. (2017) identified the presence of these ieILC1like cells in human non-mucosal pathological tissues. Whereas NKp44 has been reported to be a marker used to characterize ieILC1 (Fuchs et al., 2013), its expression was highly variable in mucosal and non-mucosal pathological tissues in this study. Furthermore, the frequency of these cells was higher in mucosal tissues and pathological tissues compared to non-mucosal tissues and this cell population was strongly associated with various NK cell populations. The functional similarities in responsivity to IL-15 of ieILC-like cells and NK cells provides compelling evidence that this population is also a subset of NK cells rather than a distinct subset of ILCs (Figure 1), raising some interesting questions. What is the role of these cells in non-mucosal pathological tissues where they, together with NK cells, make up at least 90% of ILCs. Further, what are the tissue environments that enable NK cell subsets to acquire such varied phenotypes? Simoni et al. (2017) identified distinct clusters of ILC3 subsets using the currently adopted panel of cell surface markers and transcription factors that typically delineate these cell populations in most mouse and human studies. However, there was a higher degree of heterogeneity than has previously been reported and a few key differences compared to mice. In contrast to murine cells, in which NKp46 and the chemokine receptor CCR6 expression can be used to segregate the two major ILC3 subsets, clustering analysis revealed variable expression of the natural cytotoxicity receptors (NCRs) NKp30 and NKp46, as well as CCR6 in human ILC3 cells. Nevertheless, across all human tissues examined, it was clear that expression of the NCR NKp44 could serve as a useful marker to distinguish between the two major subsets of ILC3 cells 12 Immunity 46, January 17, 2017
albeit staining of RORgt protein, the ILC3 transcription factor, was weak. Although the ILC2 signature transcription factor GATA3 was highly expressed in ILC2 cells, it was also expressed at lower levels in both NK and ILC3 cells. Therefore, transcription factor staining cannot accurately discern ILC helper subsets in human tissues and a combination of cell surface markers should be used to identify these populations. Compared to NK/ieILC1 and ILC3 subsets, the ILC2 subset was quite homogeneous based on the t-SNE profile. The breadth of diversity within mouse intestinal ILCs was recently identified by Gury-BenAri et al. (2016) using singlecell RNA sequence analysis and mass cytometry analysis. They classified 4–5 subgroups within each of the canonical ILC subsets, grouped together based on functional similarities in transcriptional states and overall gene signatures (Figure 1). Furthermore, alterations in the local tissue environment, including changes in the microbiome, altered ILC transcriptional profiles. The degree of responsiveness of each subgroup was highly distinct and heterogeneous with some ‘‘ILC states’’ responding more than others. It is possible that the heterogeneity observed in the transcriptional states among ILC1 and ILC2 subsets reflects activation and/or maturation stages of these ILCs. In contrast, the heterogeneity observed among ILC3 subsets and differential expression of various cell surface markers as well as cytokines corresponded to previously-defined populations of ILC3s. In human studies, variability in genetics, environmental exposures, and interindividual responses to stimuli can greatly influence the immune cell composition. Furthermore, technical limitations make it difficult to identify phenotypically distinct ILC subsets across a continuum of healthy and diseased states in human tissues. One strength of this study by Simoni et al. (2017) is the comparison of ILC frequencies and phenotypic profiles across many non-pathological and pathological tissues. Under non-pathological conditions, the frequency of ILC2 and ILC3 was very low (< 5%) in non-mucosal and lung tissues, whereas in oral and gastrointestinal mucosal tissues their frequency increased substantially, reflecting their role in mediating innate immune re-
sponses at barrier surfaces. Interestingly, this increased frequency was associated with distinct phenotypic profiles within the ILC helper subsets. The comprehensive and detailed analysis of human ILC subsets in this report supports more recent shifts in our understanding of ILC as innate sensors that exhibit bidirectional plasticity under differing inflammatory conditions (Bal et al., 2016; Bernink et al., 2015). Simoni et al. (2017) found that both ILC2 and ILC3 subsets express a functional receptor for IL-18, which has not been previously identified on these cell types. Signaling through the IL-18R is typically associated with the production of type 1 cytokines. Stimulation with IL-18 resulted in increased production of granulocyte macrophage colony stimulating factor (GM-CSF) and IL-8 in both subsets and in combination with IL-23 elicited IFN-g production from ILC3 cells, yet they maintained their ILC3 phenotype. Interestingly, IL-18 elicited the secretion of type 2 cytokines from ILC2s in a similar manner as IL-33. This phenotype might represent a distinct mechanism by which these cell types can be independently activated under specific inflammatory conditions. More questions remain. Do such highly diversified populations of ILCs reflect their distinct roles in different tissues? Do ILC1 play a specific role in the mouse that is distinct from NK cells? Does IL-18 have similar functions in the mouse? Future studies should answer these questions. REFERENCES Artis, D., and Spits, H. (2015). Nature 517, 293–301. Bal, S.M., Bernink, J.H., Nagasawa, M., Groot, J., Shikhagaie, M.M., Golebski, K., van Drunen, C.M., Lutter, R., Jonkers, R.E., Hombrink, P., et al. (2016). Nat. Immunol. 17, 636–645. Bernink, J.H., Peters, C.P., Munneke, M., te Velde, A.A., Meijer, S.L., Weijer, K., Hreggvidsdottir, H.S., Heinsbroek, S.E., Legrand, N., Buskens, C.J., et al. (2013). Nat. Immunol. 14, 221–229. Bernink, J.H., Krabbendam, L., Germar, K., de Jong, E., Gronke, K., Kofoed-Nielsen, M., Munneke, J.M., Hazenberg, M.D., Villaudy, J., Buskens, C.J., et al. (2015). Immunity 43, 146–160. Bjo¨rklund, A.K., Forkel, M., Picelli, S., Konya, V., Theorell, J., Friberg, D., Sandberg, R., and Mjo¨sberg, J. (2016). Nat. Immunol. 17, 451–460. Fuchs, A., Vermi, W., Lee, J.S., Lonardi, S., Gilfillan, S., Newberry, R.D., Cella, M., and Colonna, M. (2013). Immunity 38, 769–781.
Immunity
Previews Gury-BenAri, M., Thaiss, C.A., Serafini, N., Winter, D.R., Giladi, A., Lara-Astiaso, D., Levy, M., Salame, T.M., Weiner, A., David, E., et al. (2016). Cell 166, 1231–1246, e1213. Morita, H., Moro, K., and Koyasu, S. (2016). J. Allergy Clin. Immunol. 138, 1253–1264.
Robinette, M.L., Fuchs, A., Cortez, V.S., Lee, J.S., Wang, Y., Durum, S.K., Gilfillan, S., Colonna, M., and Immunological Genome, C.; Immunological Genome Consortium (2015). Nat. Immunol. 16, 306–317. Simoni, Y., Fehlings, M., Kloverpris, H.N., McGovern, N., Koo, S.L., Loh, C.Y., Lim, S., Kurioka, A.,
Fergusson, J.R., Tang, C.L., et al. (2017). Immunity 46, this issue, 148–161. Ve´ly, F., Barlogis, V., Vallentin, B., Neven, B., Piperoglou, C., Ebbo, M., Perchet, T., Petit, M., Yessaad, N., Touzot, F., et al. (2016). Nat. Immunol. 17, 1291–1299.
T Cells Take on Zika Virus Heather D. Hickman1 and Theodore C. Pierson1,* 1Laboratory of Viral Diseases, National Institutes of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.immuni.2016.12.020
Although CD8+ T cells provide protection against many viral infections, their role in Zika virus (ZIKV) immunity has not been extensively examined. In a recent issue of Cell Host & Microbe, Elong Ngono et al. (2017) define antigenic epitopes determining CD8+ T cell immunity in murine models of ZIKV infection. Zika virus (ZIKV) is a mosquito-born, positive-stranded RNA virus that has skyrocketed into public awareness due to its rapid dissemination among countries of the Western hemisphere. Although ZIKV infection of adults is generally asymptomatic and self-limiting, infection during pregnancy can lead to severe neurodevelopmental birth defects in utero including microcephaly (Hazin et al., 2016). Identifying unique features of ZIKV pathogenesis and strategies to prevent new infections has become a public health priority. Efforts to develop ZIKV vaccines that elicit protective, neutralizing antibodies (Abs) combined with theoretical concerns about the potential enhancement of ZIKV infection by antibodies that cross-react with other flaviviruses, such as dengue virus (DENV), has driven intense examination of the humoral response to ZIKV in both humans and animal models. In contrast, to date, relatively little is known regarding the CD8+ T cell response to ZIKV infection. In a recent issue of Cell Host & Microbe, Elong Ngono et al. (2017) map the antigenic epitopes that elicit murine CD8+ T cell responses during ZIKV infection and demonstrate a protective role for these cells during infection. In contrast to antibodies that bind to virions before they enter cells, CD8+ T cells recognize and eliminate virus-in-
fected cells after recognition of short peptides bound by major histocompatibility class I (MHC) molecules. A multitude of factors influence which specific CD8+ T cells will respond during a viral infection, including but not limited to peptide creation, MHC allelic polymorphism (dictating the peptide repertoire), T cell precursor frequencies, and T cell immunodominance. How these factors act in concert is incompletely understood. Although cytotoxic CD8+ T cells have been shown to play a protective role against DENV and West Nile virus (WNV) (Screaton et al., 2015; Shrestha and Diamond, 2004; Weiskopf and Sette, 2014), their contribution to ZIKV immunity has not been examined. To begin to define the CD8+ response to ZIKV, Elong Ngono et al. (2017) used peptidebinding prediction algorithms to identify peptides encoded by two strains of ZIKV (MR766 and FSS13025) that could be bound by the MHC class I molecules Kb and Db in the commonly used C57BL/6 mouse. These algorithms identified 244 potential ZIKV-derived peptides; 202 of these were shared between the African- and Asian-lineage ZIKV strains studied. To identify which, if any, of these peptides elicited CD8+ T cell responses, Elong Ngono et al. (2017) examined the
CD8+ response in two murine models of ZIKV infection. ZIKV replicates poorly in wild-type mouse strains due to an inability to efficiently antagonize the type I interferon response (Lazear et al., 2016). After infection of mice treated with interferon a/b receptor (IFNAR)-blocking Ab, isolated splenic CD8+ T cells produced IFN-g when stimulated with 26 different peptides from MR766 and 15 peptides from FSS13025 ZIKV strains. To confirm these findings in a more immunocompetent model, Elong Ngono et al. (2017) used H-2Kb mice that lack IFNAR in only a subset of myeloid cells. In these LysMCre+IFNARfl/fl mice, IFN signaling remains intact in T cells, B cells, and dendritic cells (DCs). CD8+ T cells isolated from ZIKV-infected LysMCre+IFNARfl/fl animals responded to the same ZIKV epitopes as CD8+ T cells isolated from mice treated with IFNAR-blocking antibody. Although CD8+ T cell epitopes were identified from almost all of ZIKV’s ten proteins, the response to envelope (E protein)-derived epitopes predominated in both murine models (Figure 1). Interestingly, CD8+ T cells in C57BL/6 mice infected with DENV also possess T cells targeting the structural proteins capsid (C) and E (Yauch et al., 2009). In contrast, DENV-specific human T cells or T cells from DENV-infected HLA-transgenic mice (possessing human MHC alleles)
Immunity 46, January 17, 2017 ª 2017 Published by Elsevier Inc. 13