Cutting Edge: Dynamic Expression of Id3 Defines the Stepwise Differentiation of Tissue-Resident Regulatory T Cells

Jenna M. Sullivan, Barbara Höllbacher and Daniel J. Campbell
J Immunol January 1, 2019, 202 (1) 31-36; DOI: https://doi.org/10.4049/jimmunol.1800917

Abstract

Foxp3+ regulatory T (TR) cells are phenotypically and functionally diverse and broadly distributed in lymphoid and nonlymphoid tissues. However, the pathways guiding the differentiation of tissue-resident TR cell populations have not been well defined. By regulating E-protein function, Id3 controls the differentiation of CD8+ effector T cells and is essential for TR cell maintenance and function. We show that dynamic expression of Id3 helps define three distinct mouse TR cell populations: Id3+CD62LhiCD44lo central TR cells, Id3+CD62LloCD44hi effector TR (eTR) cells, and Id3− eTR cells. Adoptive transfer experiments and transcriptome analyses support a stepwise model of differentiation from Id3+ central TR to Id3+ eTR to Id3− eTR cells. Furthermore, Id3− eTR cells have high expression of functional inhibitory markers and a transcriptional signature of tissue-resident TR cells. Accordingly, Id3− eTR cells are highly enriched in nonlymphoid organs but virtually absent from blood and lymph. Thus, we propose that tissue-resident TR cells develop in a multistep process associated with Id3 downregulation.

Introduction

Several recent studies have highlighted the phenotypic and functional heterogeneity of regulatory T (TR) cells during both steady state and inflammation (1–4). We and others have shown that at steady state in lymphoid organs, TR cells can be broadly divided by expression of CD44 and CD62L into distinct subsets that differ in their localization, dependence on IL-2, and extent of PI3K signaling (2, 5, 6). Moreover, CD44hiCD62Llo effector TR (eTR) cells display diverse expression of transcription factors and chemokine receptors that promote their migration to inflamed tissues and their response to different types of inflammatory signals (1, 7). Accordingly, TR cells found in nonlymphoid tissues have a distinct molecular profile that includes high expression of Gata3 and ST2 (the IL-33R), and are functionally equipped to suppress inflammation at barrier sites (8, 9). Although these data highlight the anatomical, functional, and molecular diversity of TR cells, the pathways by which these TR cell populations differentiate have not been completely defined.

The inhibitor of DNA binding proteins Id2 and Id3 have been extensively studied in lymphocyte development (10, 11). Studies of CD8+ effector T cells revealed that Id2 and Id3 are powerful transcriptional regulators of differentiation that are dynamically regulated during T cell activation and effector/memory T cell development (12, 13). Through their regulation of E-protein function, Id2 and Id3 help control expression of genes essential for CD8+ effector cell differentiation and survival, such as Tcf7, Tbx21, Bcl2, and Klrg1 (14, 15). Although less well studied, inhibitor of DNA binding proteins also have essential roles in CD4+ T cell function. For instance, Id2 and Id3 are essential for TR cell maintenance and function, with TR cells lacking both Id2 and Id3 having impaired proliferation and survival (16). In TR cells, Id3 helps to stabilize Foxp3 through restriction of the E-protein E47 and its downstream targets Spi-B and SOCS3 (17). However, Id3 expression is not uniform in TR cells, and distinct populations of Id3+ and Id3− have been identified (16, 18). In this study, we show that Id3 is dynamically regulated in TR cells and that progressive loss of Id3 correlates with the stepwise differentiation of a highly functional TR cell population localized primarily in nonlymphoid tissues.

Materials and Methods

Mice

C57BL/6, RAG1-deficient, and Foxp3-monomeric red fluorescent protein (mRFP) mice were purchased from The Jackson Laboratory. Id3-GFP mice were a gift from A. Goldrath (12) (University of California, San Diego, La Jolla, CA) and have been previously described (14). Mice were bred and housed under the approval of the Institutional Animal Care and Use Committee of the Benaroya Research Institute.

Cell isolation

Unless noted below, single-cell suspensions were isolated from tissues using manual disruption. Peritoneal exudate cells were isolated by injecting sterile PBS into the peritoneal cavity of euthanized mice, performing gentle agitation to dislodge cells, and collection of injected PBS. Intraepithelial lymphocytes and lamina propria lymphocytes were isolated from pooled large and small intestine as previous described (19). Lymphocytes were further purified by resuspension in 44% Percoll (GE Healthcare) layered over 67% Percoll and spun at 2800 rpm for 20 min. Lung and fat were finely minced, digested with 0.26 U/ml Liberase TM (Roche) and 10 U/ml DNAse (Sigma-Aldrich) for 1 h at 37°C and filtered. For skin tissue, ears were processed as above with 0.14 U/ml Liberase TM and 10 U/ml DNAse. For lymph collection, mice were fed 20 ml/kg half and half by oral gavage and sacrificed 2–3 h later. Lymph collected from the cisterna chyli was directly stained for flow cytometry.

Flow cytometry

Single-cell suspensions were stained with fixable Viability Dye eFluor 780 (eBioscience) in PBS for 10 min at room temperature. Cells were stained with directly conjugated Abs in PBS with 0.5% bovine calf serum for 20 min at 4°C. Abs were purchased from BioLegend: CD4 (RM4-5), TCRβ (H57-597), CD44 (IM7), CD62L (MEL-14), CD25 (PC61), ICOS (C398.4A), TIGIT (1G9), CTLA4 (UC10-4B9), GITR (DTA.1), and CD69 (53-7.3). Abs were purchased from eBioscience: KLRG1 (2F1) and CD103 (2E7). Intracellular stains were performed using a FIX & PERM Kit (eBioscience). Data were acquired on an LSR II (BD Biosciences) and analyzed using FlowJo software (Tree Star).

In vitro assays

CD4+ T cells were isolated from spleen and lymph nodes (LNs) using CD4 microbeads (Miltenyi Biotec). Cells (1 × 106) were cultured with plate-bound α-CD3 (2C11) and α-CD28 (37.51) from Bio X Cell at 1 μg/ml each for 48 or 66 h. Inhibitors were purchased and used as follows: ZSTK474 (1 μM; Sigma-Aldrich), rapamycin (10 nM; Selleckchem), NFAT inhibitor (10 μM; Tocris), Mek inhibitor PD0325901 (100 nM; PeproTech), and Erk inhibitor FR180204 (10 μM; Tocris). In vitro TR cell suppression assays were performed as previous described (20). Chemotaxis assays were performed as previously described (21).

In vivo TR cell transfer

TR cells from spleen and LNs were sorted as described for RNA sequencing (RNA-seq). One hundred thousand sorted cells were injected retro-orbitally into RAG1-deficient hosts. Spleen, LNs, and blood of recipient mice were collected 2 wk later and analyzed by flow cytometry.

Statistical analysis

The p values were calculated by Prism Software (GraphPad) using either an unpaired Student t test or one-way ANOVA as indicated. Values <0.05 were considered significant.

RNA-seq

CD4+ T cells were isolated using CD4 microbreads (Miltenyi Biotec) from either peripheral LNs or spleens of three littermate Id3-GFP × Foxp3-mRFP mice. Cells were sorted based on viability and CD4, CD44, CD62L, Id3-GFP, and Foxp3-mRFP expression on a FACSAria II (BD Biosciences). Five hundred cells were sorted directly into SMART-Seq v4 Ultra Low Input RNA Kit (Takara Bio) lysis buffer and cDNA was produced according to manufacturer's protocol. Library construction was performed using a modified protocol of the Nextera XT DNA Sample Preparation Kit (Illumina). Dual-index, single-read sequencing of pooled libraries was run on a HiSeq 2500 sequencer (Illumina) with 58-base reads and an average depth of 8.6 Mio reads per library. Base-calling and demultiplexing were performed automatically on BaseSpace (Illumina) to generate FASTQ files. Details on RNA-seq analysis can be found in the Supplemental Methods. Raw and processed RNA-seq data can be found in Gene Expression Omnibus (GEO) series GSE122593.

Results and Discussion

Id3 is dynamically expressed in TR cells and regulated by TCR signaling

To examine Id3 expression in TR cells, we generated Id3-GFP × Foxp3-mRFP double-reporter mice. In agreement with previously published reports, most CD4+ T cells in spleen and LNs were Id3+ (16, 18). However, we noted a subset of Id3− cells in both Foxp3+ TR cell and Foxp3− conventional CD4+ T cell populations (Fig. 1A). Within TR cells, the Id3− population fell exclusively within the CD44hiCD62Llo eTR cell compartment (5), whereas Id3+ TR cells were found in both the CD44loCD62Lhi central TR (cTR) and eTR cell compartments (Fig. 1B). Thus, in secondary lymphoid organs, TR cells can be divided into three distinct subsets based on Id3, CD62L, and CD44 expression: Id3+ cTR, Id3+ eTR, or Id3− eTR cells. Within the Id3+ TR cell populations, Id3+ cTR cells had higher Id3 expression than Id3+ eTR cells measured by GFP mean fluorescence intensity (Fig. 1B), leading us to hypothesize that TR cells may downregulate Id3 as they transition during their activation and differentiation from cTR cells into eTR cells. Indeed, Id3 expression can be downregulated by TCR signaling (16), and we observed a loss of Id3 expression in the TR cell compartment correlating with the strength of TCR stimulation when cells were activated with different concentrations of plate-bound αCD3/28 (Supplemental Fig. 1). Furthermore, inhibiting either the MAP-kinase/Erk or PI3-kinase/mTOR signaling pathways blocked Id3 downregulation in TR cells (Fig. 1C), consistent with reports that eTR cell development requires TCR stimulation, mTOR signaling, and the PI3-kinase–dependent inactivation of the transcription factor Foxo1 (5, 6, 22).

FIGURE 1.
FIGURE 1.

Id3 is dynamically expressed in TR cells and regulated by TCR signaling. (A) Representative flow cytometry plots of Id3-GFP and Foxp3RFP expression by gated splenic TCRβ+CD4+ T cells. (B) Representative flow cytometry analysis of Id3-GFP expression by splenic CD44loCD62Lhi cTR and CD44hiCD62Llo eTR cells gated as indicated. Bottom left, Graphical analysis of geometric mean fluorescence intensity (gMFI) of Id3-GFP in each of the three gated TR cell populations. (C) Representative flow cytometry plots of Id3-GFP expression by gated splenic TCRβ+CD4+Foxp3+ TR cells 66 h after stimulation of purified CD4+ T cells in the presence or absence of the indicated inhibitors, representative of two independent experiments (expts). (D) Representative flow cytometry plots and graphical analysis of CD44 and Id3-GFP expression by gated TCRβ+CD4+Foxp3+ TR cells recovered from the LNs of RAG1-deficient mice 2 wk after transfer of the indicated TR cell population, summary of three independent expts, three to five mice per group total. Significance determined by one-way ANOVA with Tukey posttest for pairwise comparisons. **p < 0.01, ****p < 0.0001.

To more precisely define the developmental relationship between these TR cell subsets, we used an adaptive transfer model in which TR cell stimulation and expansion depends on TCR/MHC class II interactions (23). For this, we sorted Id3+ cTR, Id3+ eTR, or Id3− eTR cells from spleen and LNs of reporter mice, transferred individual TR cell populations into RAG1-deficient animals, and evaluated the phenotype and expansion of transferred TR cells after 2 wk. Importantly, we did not observe any difference in the extent of Foxp3 expression between the TR cell populations upon their recovery, which varied between ∼30–80% in different experiments (data not shown). Transferred Id3+ cTR cells gave rise to all three subsets, with some cells retaining Id3 and CD62L expression but the majority converting into Id3− eTR cells (Fig. 1D). The bulk of Id3+ eTR cells downregulated Id3, with no cells upregulating CD62L expression, whereas Id3− eTR cells did not give rise to either of the other populations, indicating that these cells are likely a terminal-differentiated population. Thus, Id3− eTR cells appear to develop from Id3+ cTR cells in a stepwise manner during activation, with Id3+ eTR cells acting as an intermediate population.

Id3− eTR cells express inhibitory markers and are highly suppressive

To explore the phenotypic and functional differences between these three subsets of TR cells, we assessed expression of TR cell–associated surface markers on each population of splenic TR cells. In agreement with previously published data, cTR and eTR cells showed distinct phenotypes, with eTR cells having lower expression of the high affinity IL-2 receptor component CD25, but higher levels of the activation and functional surface markers ICOS, KLRG1, TIGIT, GITR, and CTLA4 (Fig. 2A) (5, 6). Moreover, within the eTR cell compartment, the Id3− TR cells had higher expression of activation and inhibitory molecules than their Id3+ TR cell counterparts, but had the lowest expression of CD25. As our laboratory previously described (5), elevated expression of ICOS and diminished expression of CD25 suggests that Id3− eTR cells are less dependent on IL-2 for their homeostatic maintenance and, instead, likely rely on continued ICOS signaling. Additionally, increased expression of these TR cell functional molecules correlated with enhanced in vitro suppressive activity of Id3− eTR cells compared with either Id3+ cTR cells or Id3+ eTR cells (Fig. 2B).

FIGURE 2.
FIGURE 2.

Id3− eTR cells express inhibitory markers and are highly suppressive. (A) Top, Representative flow cytometry histograms. Bottom, Graphical analysis of expression of the indicated markers by gated splenic TR cell populations. (B) Top, Representative flow cytometry analysis of cell proliferation dye (CPD) dilution by CD4+Foxp3− effector T cells stimulated with or without the indicated TR cell populations. Bottom, Graphical analysis of suppression by each of the indicated populations (n = 3). Significance determined by one-way ANOVA with Tukey posttest for pairwise comparisons. *p < 0.05, ***p < 0.001, ****p < 0.0001.

Transcriptional profiling highlights the stepwise differentiation of Id3− eTR cells

To identify and compare their unique transcriptional profiles, we performed RNA-seq on sorted Id3+ cTR, Id3+ eTR, and Id3− eTR cells from spleen or LNs of Id3-GFP × Foxp3-mRFP reporter mice. Although there was little difference between LN and spleen samples, principal component analysis showed that each of the three TR cell populations was transcriptionally distinct (Fig. 3A), and accordingly, we identified 1672 significantly differentially expressed (DE) genes between the three TR cell subsets (Fig. 3B, Supplemental Table I). The largest differences were found between Id3− eTR and Id3+ cTR cells with 1471 DE genes, whereas only 474 genes differed between Id3+ and Id3− eTR cells (Fig. 3B). Interestingly, among the DE genes, we observed a reciprocal increase in Id2 expression as TR cells lose Id3 (Fig. 3C). This reciprocal expression of Id2 and Id3 in TR cells is similar to what occurs in CD8+ T cells, which upregulate Id2 and downregulate Id3 while becoming activated and gaining effector function (12, 13). This suggests that as TR cells downregulate Id3, the closely related Id2 may take over some of its functions while also driving a unique E-protein–dependent signature promoting eTR cell development. Consistent with the stepwise differentiation model we propose for these populations, examination of the 300 most DE genes across the three TR cell subsets (based on highest F-value) showed that both up- and downregulated genes were generally expressed in a gradient fashion, with expression in Id3+ eTR cells falling between that of Id3+ cTR and Id3− eTR cells (Fig. 3D, 3E). Moreover, in accordance with our prior phenotypic analysis and their enhanced suppressive activity, Id3− eTR cells showed elevated expression of known TR cell function genes, including Il10, Ctla4, Pdcd1, Tnfrsf4, Lag3, and Ebi3 (Fig. 3F). To further validate our RNA-seq results, we confirmed differential expression of several TR cell–associated genes by flow cytometry (Supplemental Fig. 2). Gene Ontology term enrichment analysis of genes DE between Id3+ eTR and Id3− eTR cells identified specific molecular pathways altered between these closely related populations (Fig. 3G). The top six enriched categories all related to cytokine and chemokine receptor signaling, with Id3− eTR cells expressing high levels of receptors for IL-1, IL-18, IL-23 and IL-25, indicating that they can tune their activity in response to these key inflammatory cytokines (Fig. 3H). Moreover, among the DE chemokine receptors, Id3− eTR cells had the highest expression and subsequent responsiveness to chemokines that promote lymphocyte migration to inflamed tissues, such as Ccr4 and Cxcr3 (Fig. 3H, 3I) (1). Thus, Id3− eTR cells have a unique molecular profile indicative of their development from Id3+ TR cell precursors, their elevated suppressive function, and altered migratory capacity.

FIGURE 3.
FIGURE 3.

Transcriptional profiling highlights the stepwise differentiation of Id3− eTR cells. (A) Principal component analysis of RNA-seq data from LN and splenic Id3+ cTR, Id3+ eTR, and Id3− eTR cells populations sorted from three individual mice. (B) Bar graphs showing the number of DE genes (adjusted p value [adj.p.value] < 0.05 and log2 fold change [log2FC] > 1) for each of the indicated pairwise comparisons. (C) Graphical analysis of normalized transcript reads for Id2 or Id3 from RNA-seq data. (D) The 300 most DE genes (determined by F-value) were split into the upregulated (red) and downregulated (blue) fraction–based expression in Id3− eTR versus Id3+ cTR cells. Graph shows the mean log2FC compared with Id3+ cTR cells for both eTR cell populations. Error bars and shaded area represent 1 × SD. (E) Heatmap and hierarchical clustering of splenic RNA-seq samples based on the 300 most differentially expressed genes. (F) Heatmap and hierarchical clustering of splenic RNA-seq samples based on TR cell signature genes identified in reference (8). (G) Gene Ontology (GO) term enrichment analysis for DE genes of Id3− eTR versus Id3+ eTR cells. Dashed lines represent adjusted p values of 0.05 and 0.01. (H) Heatmap and hierarchical clustering of splenic RNA-seq samples based on DE genes found in the top 6 GO functional categories enriched in the comparison of Id3+ and Id3− eTR cells. (I) Graphical analysis of chemotaxis assay.

Id3− TR cells are enriched and resident in nonlymphoid tissues

In contrast to their elevated expression of inflammatory chemokine receptors, Id3− eTR cells had the lowest expression of Ccr7, which promotes TR cell recirculation through secondary lymphoid organs (24) (Fig. 3H). Additionally, expression of CD103 and CD69, which help retain tissue-resident memory (TRM) cells in nonlymphoid sites, was strongly enriched in Id3− eTR cells, suggesting these cells may be tissue resident (Fig. 4A) (25, 26). Indeed, using published gene signatures of CD8+ TRM or circulating memory T cells (27), we found that the CD8+ TRM cell signature gene set was enriched in Id3− eTR cells compared with either Id3+ cTR or Id3+ eTR cells, whereas the circulating memory gene set was enriched in the Id3+ TR cell populations (Fig. 4B). Several groups have recently identified the ST2 (IL-33R)/Gata3 axis as a key determinant of TR cell residency and function in nonlymphoid tissues (8, 9, 28). Accordingly, Id3− eTR cells had the highest expression of all positively associated tissue TR cell genes, such as Il1rl1 (ST2), Gata3, Areg, Irf4, and Rora, whereas Id3+ cTR cells had the lowest expression of these genes, but high expression of negative regulators of tissue residence, such as S1pr1, Tcf7, Klf2, and Lef1, and Id3+ eTR cells displayed intermediate expression for all of these genes (Fig. 4C). Consistent with their TRM cell–like transcriptional signature, Id3− eTR cells were a minority of TR cells in LNs and spleen, but their frequency dramatically increased in nonlymphoid tissues, where they highly expressed the TRM cell surface markers CD103 and CD69 (Fig. 4D–F). Indeed, tissues such as the fat and skin contained almost exclusively Id3− eTR cells. Of particular note, Id3− eTR cells were rarest in the lymph and blood, indicating that Id3− eTR cells do not actively recirculate, but instead are retained as tissue-resident cells in nonlymphoid organs.

FIGURE 4.
FIGURE 4.

Id3− eTR cells are enriched and resident in nonlymphoid tissues. (A) Representative flow cytometry histograms and graphical analysis of expression of the CD69 and CD103 by gated splenic TR cell populations. (B) Enrichment of CD8+ TRM (red) or circulating T cell (blue) gene sets along ranked lists of the indicated pairwise comparisons. (C) Heatmap and hierarchical clustering of splenic RNA-seq samples based on ST2+ tissue TR cell–associated genes. (D) Representative flow cytometry plots of CD103 and CD69 expression by gated TCRβ+CD4+Foxp3+ TR cells in the indicated tissues. (E) Graphical analysis of Id3− eTR cell frequency among total TR cells in various tissues. (F) Representative histograms of GFP expression from various tissues of Id3-GFP × Foxp3-mRFP mice, gated on TCRβ+CD4+Foxp3+ TR cells. Significance determined by one-way ANOVA with Tukey posttest for pairwise comparisons. **p < 0.01, ****p < 0.0001.

Despite significant interest in tissue-resident TR cells, the mechanisms regulating their differentiation and distribution are still poorly defined. Our data show that TR cells can be subdivided based on Id3 expression and known markers of cTR and eTR cells into distinct populations. Furthermore, our phenotypic, functional, transcriptional, and transfer analyses strongly support a stepwise differentiation model TR cells in which TR cells downregulate Id3 as they progressively gain effector function, tissue homing capacity, and residency in nonlymphoid organs. Similarly, Li and colleagues (28) recently proposed a stepwise model for the differentiation adipose tissue TR cells in which activation in the spleen allowed TR cells to migrate into the adipose tissue, where IL-33 signaling drove their terminal differentiation and functional specialization. High expression of costimulatory receptors, such as ICOS, and receptors for inflammatory cytokines would also allow Id3− eTR cells to respond to inflammatory signals that enhance Foxp3-mediated transcriptional repression and promote eTR cells differentiation and function (29), in part through activation of mTORC1 signaling (22). Although the direct role of Id3 downregulation in the functional differentiation of TR cells is still not established, our data highlight the complexity of tissue TR cell development and identify novel molecular pathways that are modulated during their stepwise differentiation.

Disclosures

The authors have no financial conflicts of interest.

Acknowledgments

We thank A. Goldrath for Id3-GFP mice, K. Arumuganathan and T. Nguyen for help with flow cytometry and cell sorting, V. Gersuk and the Benaroya Research Institute Genomics Core for running RNA-seq samples, and members of the Campbell laboratory for helpful discussions.

Footnotes

  • This work was supported by grants to D.J.C. from the National Institutes of Health (NIH) (AI085130 and AI124693). J.M.S. was supported by NIH National Institute of Allergy and Infectious Diseases T32 Grant AI106677 to the Department of Immunology, University of Washington.

  • The sequence data presented in this article have been submitted to the Gene Expression Omnibus under accession number GSE122593.

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article:

    cTR
    central TR
    DE
    differentially expressed
    eTR
    effector TR
    LN
    lymph node
    mRFP
    monomeric red fluorescent protein
    RNA-seq
    RNA sequencing
    TR
    regulatory T
    TRM
    tissue-resident memory.

  • Received July 5, 2018.
  • Accepted November 7, 2018.

References

  1. ↵
    1. Campbell, D. J.
    2015. Control of regulatory T cell migration, function, and homeostasis. J. Immunol. 195: 2507–2513.
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Smigiel, K. S.,
    2. S. Srivastava,
    3. J. M. Stolley,
    4. D. J. Campbell
    . 2014. Regulatory T-cell homeostasis: steady-state maintenance and modulation during inflammation. Immunol. Rev. 259: 40–59.
    OpenUrlCrossRefPubMed
    1. Dominguez-Villar, M.,
    2. D. A. Hafler
    . 2018. Regulatory T cells in autoimmune disease. Nat. Immunol. 19: 665–673.
    OpenUrl
  4. ↵
    1. Josefowicz, S. Z.,
    2. L. F. Lu,
    3. A. Y. Rudensky
    . 2012. Regulatory T cells: mechanisms of differentiation and function. Annu. Rev. Immunol. 30: 531–564.
    OpenUrlCrossRefPubMed
  5. ↵
    1. Smigiel, K. S.,
    2. E. Richards,
    3. S. Srivastava,
    4. K. R. Thomas,
    5. J. C. Dudda,
    6. K. D. Klonowski,
    7. D. J. Campbell
    . 2014. CCR7 provides localized access to IL-2 and defines homeostatically distinct regulatory T cell subsets. J. Exp. Med. 211: 121–136.
    OpenUrlAbstract/FREE Full Text
  6. ↵
    1. Luo, C. T.,
    2. W. Liao,
    3. S. Dadi,
    4. A. Toure,
    5. M. O. Li
    . 2016. Graded Foxo1 activity in Treg cells differentiates tumour immunity from spontaneous autoimmunity. Nature 529: 532–536.
    OpenUrlCrossRefPubMed
  7. ↵
    1. Gratz, I. K.,
    2. D. J. Campbell
    . 2014. Organ-specific and memory treg cells: specificity, development, function, and maintenance. Front. Immunol. 5: 333.
    OpenUrlPubMed
  8. ↵
    1. Delacher, M.,
    2. C. D. Imbusch,
    3. D. Weichenhan,
    4. A. Breiling,
    5. A. Hotz-Wagenblatt,
    6. U. Träger,
    7. A. C. Hofer,
    8. D. Kägebein,
    9. Q. Wang,
    10. F. Frauhammer, et al
    . 2017. Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues. Nat. Immunol. 18: 1160–1172.
    OpenUrlCrossRefPubMed
  9. ↵
    1. Wohlfert, E. A.,
    2. J. R. Grainger,
    3. N. Bouladoux,
    4. J. E. Konkel,
    5. G. Oldenhove,
    6. C. H. Ribeiro,
    7. J. A. Hall,
    8. R. Yagi,
    9. S. Naik,
    10. R. Bhairavabhotla, et al
    . 2011. GATA3 controls Foxp3+ regulatory T cell fate during inflammation in mice. J. Clin. Invest. 121: 4503–4515.
    OpenUrlCrossRefPubMed
  10. ↵
    1. Kee, B. L.
    2009. E and ID proteins branch out. Nat. Rev. Immunol. 9: 175–184.
    OpenUrlCrossRefPubMed
  11. ↵
    1. Quong, M. W.,
    2. W. J. Romanow,
    3. C. Murre
    . 2002. E protein function in lymphocyte development. Annu. Rev. Immunol. 20: 301–322.
    OpenUrlCrossRefPubMed
  12. ↵
    1. Yang, C. Y.,
    2. J. A. Best,
    3. J. Knell,
    4. E. Yang,
    5. A. D. Sheridan,
    6. A. K. Jesionek,
    7. H. S. Li,
    8. R. R. Rivera,
    9. K. C. Lind,
    10. L. M. D’Cruz, et al
    . 2011. The transcriptional regulators Id2 and Id3 control the formation of distinct memory CD8+ T cell subsets. Nat. Immunol. 12: 1221–1229.
    OpenUrlCrossRefPubMed
  13. ↵
    1. Omilusik, K. D.,
    2. L. A. Shaw,
    3. A. W. Goldrath
    . 2013. Remembering one’s ID/E-ntity: E/ID protein regulation of T cell memory. Curr. Opin. Immunol. 25: 660–666.
    OpenUrlCrossRefPubMed
  14. ↵
    1. Miyazaki, M.,
    2. R. R. Rivera,
    3. K. Miyazaki,
    4. Y. C. Lin,
    5. Y. Agata,
    6. C. Murre
    . 2011. The opposing roles of the transcription factor E2A and its antagonist Id3 that orchestrate and enforce the naive fate of T cells. Nat. Immunol. 12: 992–1001.
    OpenUrlCrossRefPubMed
  15. ↵
    1. Omilusik, K. D.,
    2. M. S. Nadjsombati,
    3. L. A. Shaw,
    4. B. Yu,
    5. J. J. Milner,
    6. A. W. Goldrath
    . 2018. Sustained Id2 regulation of E proteins is required for terminal differentiation of effector CD8+ T cells. J. Exp. Med. 215: 773–783.
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Miyazaki, M.,
    2. K. Miyazaki,
    3. S. Chen,
    4. M. Itoi,
    5. M. Miller,
    6. L. F. Lu,
    7. N. Varki,
    8. A. N. Chang,
    9. D. H. Broide,
    10. C. Murre
    . 2014. Id2 and Id3 maintain the regulatory T cell pool to suppress inflammatory disease. Nat. Immunol. 15: 767–776.
    OpenUrlCrossRefPubMed
  17. ↵
    1. Rauch, K. S.,
    2. M. Hils,
    3. E. Lupar,
    4. S. Minguet,
    5. M. Sigvardsson,
    6. M. E. Rottenberg,
    7. A. Izcue,
    8. C. Schachtrup,
    9. K. Schachtrup
    . 2016. Id3 maintains Foxp3 expression in regulatory T cells by controlling a transcriptional network of E47, Spi-B, and SOCS3. Cell Rep. 17: 2827–2836.
    OpenUrl
  18. ↵
    1. Rauch, K. S.,
    2. M. Hils,
    3. A. J. Menner,
    4. M. Sigvardsson,
    5. S. Minguet,
    6. P. Aichele,
    7. C. Schachtrup,
    8. K. Schachtrup
    . 2017. Regulatory T cells characterized by low Id3 expression are highly suppressive and accumulate during chronic infection. Oncotarget 8: 102835–102851.
    OpenUrl
  19. ↵
    1. Couter, C. J.,
    2. N. K. Surana
    . 2016. Isolation and flow cytometric characterization of murine small intestinal lymphocytes. J. Vis. Exp. 111: e54114.
    OpenUrl
  20. ↵
    1. Srivastava, S.,
    2. M. A. Koch,
    3. M. Pepper,
    4. D. J. Campbell
    . 2014. Type I interferons directly inhibit regulatory T cells to allow optimal antiviral T cell responses during acute LCMV infection. J. Exp. Med. 211: 961–974.
    OpenUrlAbstract/FREE Full Text
  21. ↵
    1. Koch, M. A.,
    2. G. Tucker-Heard,
    3. N. R. Perdue,
    4. J. R. Killebrew,
    5. K. B. Urdahl,
    6. D. J. Campbell
    . 2009. The transcription factor T-bet controls regulatory T cell homeostasis and function during type 1 inflammation. Nat. Immunol. 10: 595–602.
    OpenUrlCrossRefPubMed
  22. ↵
    1. Sun, I. H.,
    2. M. H. Oh,
    3. L. Zhao,
    4. C. H. Patel,
    5. M. L. Arwood,
    6. W. Xu,
    7. A. J. Tam,
    8. R. L. Blosser,
    9. J. Wen,
    10. J. D. Powell
    . 2018. mTOR complex 1 signaling regulates the generation and function of central and effector Foxp3(+) regulatory T cells. J. Immunol. 201: 481–492.
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Gavin, M. A.,
    2. S. R. Clarke,
    3. E. Negrou,
    4. A. Gallegos,
    5. A. Rudensky
    . 2002. Homeostasis and anergy of CD4(+)CD25(+) suppressor T cells in vivo. Nat. Immunol. 3: 33–41.
    OpenUrlCrossRefPubMed
  24. ↵
    1. Lee, J. H.,
    2. S. G. Kang,
    3. C. H. Kim
    . 2007. FoxP3+ T cells undergo conventional first switch to lymphoid tissue homing receptors in thymus but accelerated second switch to nonlymphoid tissue homing receptors in secondary lymphoid tissues. J. Immunol. 178: 301–311.
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Mackay, L. K.,
    2. A. Rahimpour,
    3. J. Z. Ma,
    4. N. Collins,
    5. A. T. Stock,
    6. M. L. Hafon,
    7. J. Vega-Ramos,
    8. P. Lauzurica,
    9. S. N. Mueller,
    10. T. Stefanovic, et al
    . 2013. The developmental pathway for CD103(+)CD8+ tissue-resident memory T cells of skin. Nat. Immunol. 14: 1294–1301.
    OpenUrlCrossRefPubMed
  26. ↵
    1. Schenkel, J. M.,
    2. D. Masopust
    . 2014. Tissue-resident memory T cells. Immunity 41: 886–897.
    OpenUrlCrossRefPubMed
  27. ↵
    1. Milner, J. J.,
    2. C. Toma,
    3. B. Yu,
    4. K. Zhang,
    5. K. Omilusik,
    6. A. T. Phan,
    7. D. Wang,
    8. A. J. Getzler,
    9. T. Nguyen,
    10. S. Crotty, et al
    . 2017. Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours. Nature 552: 253–257.
    OpenUrlCrossRef
  28. ↵
    1. Li, C.,
    2. J. R. DiSpirito,
    3. D. Zemmour,
    4. R. G. Spallanzani,
    5. W. Kuswanto,
    6. C. Benoist,
    7. D. Mathis
    . 2018. TCR transgenic mice reveal stepwise, multi-site acquisition of the distinctive fat-treg phenotype. Cell 174: 285–299.e12.
    OpenUrl
  29. ↵
    1. Arvey, A.,
    2. J. van der Veeken,
    3. R. M. Samstein,
    4. Y. Feng,
    5. J. A. Stamatoyannopoulos,
    6. A. Y. Rudensky
    . 2014. Inflammation-induced repression of chromatin bound by the transcription factor Foxp3 in regulatory T cells. Nat. Immunol. 15: 580–587.
    OpenUrlCrossRefPubMed
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