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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Immunol. Author manuscript; available in PMC 2016 July 1.
Published in final edited form as:
PMCID: PMC4475416
NIHMSID: NIHMS686738

Divergent phenotypes of human regulatory T cells expressing the receptors TIGIT and CD226

Abstract

Regulatory T cells (Treg) play a central role in counteracting inflammation and autoimmunity. A more complete understanding of cellular heterogeneity and the potential for lineage plasticity in human Treg subsets may identify markers of disease pathogenesis and facilitate the development of optimized cellular therapeutics. To better elucidate human Treg subsets, we conducted direct transcriptional profiling of CD4+FOXP3+Helios+ thymic-derived Treg (tTreg) and CD4+FOXP3+Helios T cells, followed by comparison to CD4+FOXP3Helios T conventional (Tconv) cells. These analyses revealed that the coinhibitory receptor T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) was highly expressed on tTreg. TIGIT and the costimulatory factor CD226 bind the common ligand CD155. Thus, we analyzed the cellular distribution and suppressive activity of isolated subsets of CD4+CD25+CD127lo/− T cells expressing CD226 and/or TIGIT. We observed TIGIT is highly expressed and upregulated on Treg following activation and in vitro expansion and is associated with lineage stability and suppressive capacity. Conversely, the CD226+TIGIT population was associated with reduced Treg purity and suppressive capacity following expansion, along with a marked increase in IL-10 and effector cytokine production. These studies provide additional markers to delineate functionally distinct Treg subsets that may help direct cellular therapies and provide important phenotypic markers for assessing the role of Treg in health and disease.

Introduction

The adaptive immune system provides the host with a vast receptor repertoire of T and B cells that facilitate protection from a wide array of pathogenic microbes. One consequence of this incredible diversity is the development of T and B cells specific for self-tissues (1). To counteract this autoreactivity, the immune system employs a number of mechanisms to reinforce peripheral immune tolerance, including a dominant role for a small population of CD4+ regulatory T cells (Treg) (2). The requirement for regulation is most apparent in individuals presenting with a mutation in the Treg lineage transcription factor FOXP3, which results in fatal autoimmune disease (3).

Treg exert their suppressive properties through a host of tolerogenic enzymatic pathways, cytokines, and the expression of multiple negative regulators (4). Of these, CTLA-4 and PD-1 regulate T cell activation through interactions with antigen presenting cells (APC) and host tissues (5). Moreover, it is apparent that Treg, like their TH counterparts, exhibit some level of lineage heterogeneity (68), as well as the potential for cellular plasticity in response to environmental cues (9). Deficiencies in Treg cell frequency and/or function have been associated with autoimmune diseases (10). Of note, we reported an increase in IFNγ+Helios Treg with reduced suppressive capacity in T1D (11). An analogous finding was also reported in patients with MS (12). Unstable function is also been observed at a single-cell level, with IL-17-producing FOXP3+ Treg exhibiting some suppressive capacity that was attenuated in the presence of inflammatory cytokines (13). Collectively, these studies raise the intriguing potential that subsets of antigen-experienced Treg may contribute to defective immune regulation in the context of inflammation and autoimmune disease (9).

The ability to preserve and/or bolster the activity of Treg is now appreciated as being key for inhibiting autoimmune reactivity (14). These studies have generally focused on two subsets of Treg. Thymic-derived Treg (tTreg) express the transcription factors FOXP3 and Helios and are demethylated at the FOXP3-Treg Specific Demethylated Region (TSDR) (15). In contrast, peripherally-induced Treg (pTreg) develop from naïve T cells under tolerogenic conditions and are minimally demethylated at the TSDR (16).

To further characterize the diversity of Treg, we conducted a transcriptional profile of human Tconv, FOXP3+Helios T cells, and FOXP3+Helios+ tTreg subsets. Through this analysis, we demonstrate that the coinhibitory molecule TIGIT is highly expressed in tTreg. TIGIT competes with the costimulatory molecule CD226 for binding to the poliovirus receptor (PVR)/Nectin-like-5/CD155 prominently expressed on APCs (17). CD226 has been identified as an autoimmune susceptibility gene, with a SNP (Gly307Ser; rs763361) linked to type 1 diabetes (T1D), multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) Wegners granulomatosis, celiac disease (CD), and juvenile idiopathic arthritis (JIA)(18, 19). This recently described axis shares many parallel functions with the CD28:CTLA-4 pathway in terms of promoting or inhibiting T cell activation, respectively (2022). Activation of CD226 leads to TCR association and activation that promotes TH1-related signaling (22, 23). Phosphorylation of the tyrosine at residue 322 results in IFNγ secretion and proliferation (24). In contrast, TIGIT contains an inhibitory ITIM that leads to SHIP1 recruitment and downmodulation (20, 25). Ligation of TIGIT with an agonistic antibody has been shown to suppress activation (22).

TIGIT has recently been associated with Treg biology through transcriptional profiling of Treg (8, 21, 26, 27), and has also been characterized as highly demethylated in FOXP3+ T cells (28). In addition to intrinsic inhibitory activity, TIGIT binding to CD155 on dendritic cells leads to a reduction in IL-12p40 and a concomitant increase in IL-10 production (29). Importantly, TIGIT attenuates anti-tumor immunity by CD8+ T cells (30), and was recently demonstrated as a mechanism by which Treg exert their suppressive activity (31).

Despite the roles of this costimulatory axis in immunoregulation, little is known about the cellular distribution and phenotype of human Treg expressing CD226 and TIGIT. In this study, we sought to address this knowledge void and specifically understand the role of TIGIT and CD226 in Treg biology. To that end, we isolated subsets of CD4+CD25+CD127−/lo Treg expressing CD226 and/or TIGIT and expanded them ex vivo to assess their phenotype and suppressive capacity. We demonstrate that production of IFNγ within Tconv and Treg is tightly linked to co-expression of CD226. Conversely, selection of CD226 Treg, irrespective of initial TIGIT expression, leads to a suppressive population of tTreg that are demethylated at the TSDR.

Materials and Methods

Sample procurement and processing

Peripheral blood was collected from healthy control donors (median age 28.8, range 22.5–46.7) following informed consent in accordance with the Institutional Review Boards at the University of Florida and University of California, San Francisco or purchased from Life South Blood Centers (median age 23, range 20–26). Venous blood was collected in sodium-heparinized vacutainer tubes (BD Biosciences) or supplied in sodium citrate followed by PBMC isolation by density gradient centrifugation. For FACS experiments, whole blood was pre-enriched by negative selection with RosetteSep (Stemcell) prior to centrifugation.

Transcription factor sorting and expression analysis

CD4+ RosetteSep enriched T cells were stained with anti-CD4, fixed and permeabilized with the FOXP3 Fix/Perm (Biolegend) per manufacturer recommendations and stained for FOXP3 and Helios. tTreg (CD4+FOXP3+Helios+), CD4+FOXP3+Helios T cells and Tconv (CD4+FOXP3Helios) were sorted into RNALater (Life Technologies). RNA was extracted with the RNeasy FFPE Kit (Qiagen) by proteinase K digestion followed by incubation at 80°C. RNA quality was verified on a Bioanalyzer (>300 bp length for over 50% of transcripts) with the RNA Nano Chip (Agilent Technologies). RNA transcripts (100 ng) were directly quantified with the nCounter© and the Human Immunology GX Panel (NanoString Technologies, v1).

Flow cytometry and FACS

All samples were first stained with Fixable Live/Dead Yellow or Near IR (Invitrogen). Surface markers were analyzed immediately following staining or following fixation with BD Cytofix (BD Bioscience). Cells were stained for intracellular proteins in FOXP3 fixation and permeabilization buffers according to manufacturer’s protocol (BioLegend). Antibodies used included CD4-Pacific Blue (RPA-T4), TIGIT-APC or –PerCP-eFluor710 (MBSA43), (eBioscience), CD4-PE-CF594 (RPA-T4), CD45RO-APC (UCHL1) and CD8 (SK1), (BD Bioscience), CXCR3-PerCP-Cy5.5 (G025H7), CCR4-PE-Cy7 (TG6/CCR4), CCR6-BV605 (G034F3), CD226-PE (11A8), CD127-PE or -BV421 (A019D5), CD25-APC or – AlexaFluor (AF)-488 (BC96), IFNγ-PE-Cy7 (4S. B3), Helios-PE or -Pacific Blue, -AF647 (22F6), FOXP3-AF488 and -PE (206D) (BioLegend).

Cytometric analyses were performed on a LSR Fortessa (BD Bioscience). Data were collected using BD DIVA acquisition software and imported into FlowJo (TreeStar Inc., v9.7.5) for analysis. Marker positivity was determined by fluorescence minus one (FMO) method. Expression levels were calculated with geometric mean fluorescence intensity (gMFI).

Cell sorting was conducted on a FACS Aria III (BD Bioscience). Treg (CD4+CD25+CD127) and Tconv (CD4+CD127+) were further enriched based on CD226 and/or TIGIT expression. Post-sort purities were typically greater than 93% (median 93%; range 90%-95%).

In vitro T cell expansion and activation cultures

Treg and Tconv cells were expanded as previously described (32). After expansion, cells were analyzed for intracellular IFNγ by re-activation for 4 h with PMA (10μg/mL) and Ionomycin (500nM) in the presence of GolgiStop (4 μl/6 mL culture; BD Biosciences). For multiplex cytokine detection, cells were activated with anti-CD3 and anti-CD28 coated dynabeads (Life Technologies) according to manufacturer recommendations and supernatants collected at 24, 48, and 72 h.

Suppression assays

Expanded Treg subsets were tested for their ability to suppress autologous T cell proliferation, as described (33), with the following modifications. Treg were labeled with CFSE (0.15 μM), while responding cells were stained with Cell Trace Violet (2.5 μM, Life Technologies) and activated with either autologous APCs or Treg Suppression Inspector beads (Miltenyi Biotec). Triplicate cultures were harvested and pooled following 96 h, stained with live/dead dye, CD4, CD8, CD226 and TIGIT, and proliferation was calculated by division index (DI) of gated live lymphocytes.

Analysis of the FOXP3-TSDR

TSDR demethylation at the conserved non-coding region 2 of FOXP3 is a hallmark of lineage-stable tTreg (34). Nucleotides were isolated with AllPrep DNA/RNA Mini Kit (Qiagen) or DNeasy tissue kit (Qiagen), as appropriate. Bisulfite treatment of genomic DNA was performed on 500ng of DNA with the EZ DNA Methylation Kit (Zymo Research).

Real-time PCR was performed to determine the methylation status at the FOXP3-TSDR for each sample. DNA standards originated from unmethylated bisulfite-converted human EpiTect control DNA (Qiagen) or universally methylated bisulfite-converted human control DNA (Zymo Research). To obtain a large quantity of standard, the TSDR was PCR amplified using the following reaction: 50 μL reaction volume containing 25 μL of ZymoTaq PreMix buffer (Zymo Research) and 0.5 μM each of the primers FOXP3_TSDRfwd (ATATTTTTAGATAGGGATATGGAGATGATTTGTTTGG) and FOXP3_TSDRrev (AATAAACATCACCTACCACATCCACCAACAC). After incubation at 95 °C for 10 min, amplification was performed as follows: 50 cycles at 95 °C for 30 sec, 55 °C for 30 sec, and 72 °C for 1 min. Amplified PCR products were purified with the QIAquick Gel Extraction Kit (Qiagen). The concentration of purified control TSDR DNA was determined with a GE NanoVue spectrophotometer (GE Healthcare Life Sciences).

TSDR real-time PCR was performed with probes that targeted methylated or demethylated target sequences. The reaction was performed in 384-well white trays with a Roche LightCycler 480 system (Roche Diagnostics). Each reaction contained 10 μL Lightcycler 480 Probes Master Mix (Roche), 1 μL bisulfite converted DNA sample or standards, 1 μM of each primer and 150 nM of each probe with a final reaction value of 20 μL. The probes used for amplification were TSDR-Forward GGTTTGTATTTGGGTTTTGTTGTTATAGT and TSDR-Reverse CTATAAAATAAAATATCTACCCTCTTCTCTTCCT. The probes for target sequence detection were FAM-labeled methylated probe, FAM-CGGTCGGATGCGTC-MGB-NFQ, or VIC-labeled unmethylated probe, VIC-TGGTGGTTGGATGTGTTG-MGB-NFQ. All samples were tested in triplicate. The protocol for real-time amplification is as follow: after initial denaturation at 95°C for 10 min, the samples were subjected to 50 cycles at 95°C for 15 sec and at 61°C for 1 min. Fourteen different ratios of fully methylated and demethylated template were used as real-time standards. A 6-order polynomial equation was used to extrapolate the percentage of cells demethylated at the TSDR for each sample.

T Cell Transcriptional Profiling

Using the nSolver Analysis Software (NanoString, Inc.), counts were first normalized to the geometric mean of the positive control spiked into the assay, then normalized to 10 housekeeping genes built into the array. Subsequent analyses were conducted with the Partek Genomic Suite (Partek Inc.). The signal-to-noise ratio was significantly higher in the expanded T cell counts compared to the fixed cell sorted T cell counts. Thus, two different statistical approaches were used.

For the mRNA count data from fixed cells, counts were normalized to the 10 internal housekeeping genes and the average of the background counts (noise) was subtracted with values lower than background set to one. The fold change was calculated by taking the geometric mean of the counts, then dividing one subset by another. A paired ANOVA coupled with the Bonferroni multiple test correction (MTC) was used to determine differentially regulated genes with a significance value of p<0.05. (Gene Expression Omnibus (GEO) accession number: GSE61834; http://www.ncbi.nlm.nih.gov/geo/).

For the mRNA from expanded cells, genes that were below the background threshold (mean of negative controls count + 2 standard deviations) for both Treg and Tconv were removed from the analysis. Because Tconv often have a variance different than Treg at both the RNA and proteins levels, A Welch’s ANOVA with a Bonferroni MTC was used to determine significance (p<0.05). Significantly regulated genes with a false discovery rate below 0.05 were normalized around zero and clustered using the average of the means. 82 genes were identified as being differentially expressed between the populations (GEO accession number: GSE62015).

Isolation of IFNγ producing Treg

Treg and Tconv were FACS isolated from five healthy subjects (median age 26, range 22–30) and sorted into two groups. Briefly, the first group was stimulated for 4 hours with PMA/ionomycin and labeled with the IFNγ cytokine cell-capture reagent (Miltenyi Biotech) followed by FACS isolation of IFNγ and IFNγ+ populations, as previously described (11). The second set was expanded to day 14 prior to reactivation and cytokine cell capture.

For each sample, 25 ng total RNA were amplified using the Ovation® Pico WTA System (NuGen) and labeled with Encore Biotin Module V2 (NuGen). GeneChip® Human Genome U133 Plus 2.0 arrays (Affymetrix) were hybridized to 5 μg labeled, amplified cDNA, washed, stained, and scanned according to the protocol described in the GeneChip Expression analysis manual (GEO accession number: GSE59786). Gene expression profiling data was extracted from the Affymetrix Microarray Suite 5.0 (MAS 5.0) software and used for subsequent statistical analyses.

Multiplex cytokine assay

Cytokine production was determined using the Human TH17 Magnetic Bead Panel (HT17MG-14K-PX25, EMD Millipore) according to manufacturers instructions from culture supernatants collected and run in duplicate. Samples were processed on a Bio-Tek ELx405, detected with MAGPIX system (EMD Millipore), and analyzed with Milliplex Analyst software.

Data Analysis

An ANOVA with a posthoc Tukey multiple test correction was used for analysis of cytometric data utilizing Prism (GraphPad, v6). Geister-Greenhouse variance correction method was applied to the data to account for the difference in variance between Treg and Tconv, with values matched between each individual.

Results

TIGIT is enriched in tTreg

Human Treg display a considerable degree of heterogeneity (15). To limit biases for putatively identified surface markers, we FACS-sorted Treg following intracellular staining for FOXP3 and Helios. We sorted Tconv (FOXP3Helios), FOXP3+Helios,, and tTreg subsets (FOXP3+Helios+) (Figure 1A), which then underwent direct mRNA hybridization that facilitates the direct quantitation of mRNA transcripts without reverse-transcription and amplification. This approach is compatible with partially degraded RNA samples, such as those obtained from fixed samples (35).

Figure 1
TIGIT expression is enriched in FOXP3+Helios+ Tregs

Patient-to-patient variance accounted for 77% of the expression differences observed between the samples, however we identified genes putatively associated with Treg, including CTLA-4 and IL2RA (Supplemental Table 1) (36). Specifically, we found the mRNA levels of TIGIT to be 12.4-times higher in tTreg compared to Tconv (p<0.05; Figure1B). In addition, our analysis demonstrated strong co-expression of TIGIT and Helios in CD4+FOXP3+ T cells (Figure 1C). Our results are in line with prior reports suggesting that TIGIT expression is maintained by FOXP3 (28) and supports the notion that it may play an important role in immune regulation (20, 21, 29, 31).

Given that TIGIT and CD226 compete for binding to the ligand CD155 (21, 22), we analyzed their surface expression on CD4+ T cells in combination with FOXP3 and Helios (Figure 2). Strikingly, tTreg had the highest percentage of TIGIT+ cells (83.4% ± 6.2%) and did not express CD226 in the absence of TIGIT (Figure 2A). In contrast, Tconv cells co-expressed TIGIT only with high CD226 expression, suggesting TIGIT may play an important role in Tconv after activation, as noted previously (22). TIGIT expression by tTreg was increased in frequency and gMFI compared to FOXP3+Helios T cells, and the lowest frequency of TIGIT+ cells and gMFI found in Tconv (Figure 2B, left graphs). In contrast, CD226 expression was higher in Tconv cells followed by FOXP3+Helios, and then tTreg (Figure 2B, right graphs). FOXP3+Helios T cells demonstrated an intermediate phenotype for CD226 and TIGIT expression. Of note, we also analyzed CD96 (TACTILE; T cell activation, increased late expression), a ligand that also binds CD155. CD96 expression positively correlated with CD226 but did not demonstrate the dynamic range observed for CD226 (data not shown). Hence, we limited our downstream analyses to TIGIT and CD226.

Figure 2
Differential expression of CD226 and TIGIT on CD4+ T cell subsets

Differentiated T cells concomitantly express high CD226 and chemokine receptors

Prior reports suggest signaling through CD226 suppresses TH2 differentiation and promotes TH1 responses and IFNγ secretion (22). We sought to understand the cellular distribution and expression profiles of CD226 and TIGIT on human naïve, central and effector memory, and effector CD45RA+ (TEMRA) subsets (Figure 3A). While naïve T cells were the most abundant population in our cohort (mean ± SEM, 44.8% ± 14.6), they expressed the lowest surface levels of CD226 (495.4 ± 73.5 gMFI), when compared to effector memory (TEM) and central memory (TCM) subsets (1785 ± 357.2 and 3185 ± 492.6, respectively)(Figure 3B). Furthermore, the cellular distribution of CD226 in the bulk CD4+ T cell population demarcates naïve (CD226−/low) versus CD226hi memory subsets. Few naïve cells express TIGIT (5.3% ± 2.6) compared to the memory subsets (29.0% ± 3.5 for TEM and 32.7% ± 8.2 for TCM). Of note, one subject with a prominent TEMRA population was CD226hiTIGIT (Figure 3A), which coincides with their role as a pro-inflammatory T cell subset (37).

Figure 3
High CD226 expression is associated with a memory CD4+ T cell phenotype

In addition to robust CD226 expression in TEM and TCM, we also noted that chemokine receptors expressed by TH2, TH17, and TH1 cells were co-expressed with high levels of CD226 (Figure 3C; upper plots). TIGIT+ populations also expressed chemokine receptors, albeit to a lesser extent than with CD226 (Figure 3C; lower plots). Increased CD226 and TIGIT on differentiated subsets were observed for both CD4+CD127+ Tconv and CD4+CD25+CD127−/lo Treg (Figure 3D).

CD226 expression is associated with effector cytokine production

CD226 and TIGIT have opposing roles in the regulation of IFNγ (22). We sought to determine how CD226 and TIGIT influenced IFNγ production by PBMC following activation (Figure 3E). IFNγ tracked primarily to the CD226hiTIGIT fraction, with the CD226hiTIGIT+ population consistently containing a minor population of IFNγ+ T cells (64.7% ± 8.7 vs. 24.9 ± 8.3; p=0.011). IFNγ was co-expressed with CD226, as CD226intTIGIT and CD226intTIGIT+ populations contained significantly lower percentages of IFNγ+ cells than their CD226hi counterparts (Figure 3F; 7.03% ± 3.29 and 3.42 ±1.03%, respectively). The CD226 subset was largely devoid of IFNγ+ T cells (0.183% ± 0.053). Overall, these results demonstrate a close association between CD226 and the production of IFNγ by antigen-experienced T cells.

To determine if this association was influenced by TH1-skewing conditions, we activated T cells in the presence of IL-12 (Supplemental Figure 1). While it has been previously shown that both CD226 and TIGIT increase upon T cell activation (22), the change in TIGIT expression by tTreg has not been characterized following culture with IL-12. As shown previously, CD226 expression increased over the 72 hr time course (Supplemental Figure 1A). Likewise, our data demonstrated that TIGIT increases following TCR activation (22). Notably, we observed TIGIT upregulation was attenuated by IL-12 in FOXP3+Helios+ Treg (Supplemental Figure 1B). IL-12 upregulated CD226 and IFNγ as expected (Supplemental Figure 1C–D), however, the proportion of tTreg recovered from the culture decreased in IL-12 (Supplemental Figure 1E)(38). These data suggest a mechanism whereby IL-12 exposure may potentiate Teff cytokine production concomitant with a reduction in both tTreg proliferation and TIGIT expression.

Our prior studies demonstrated elevated IFNγ+ Treg in patients with T1D (11). To further characterize this subset, we isolated IFNγ+ or IFNγ Treg and Tconv subsets conducted a transcriptional profile of the FACS isolated subsets. Principal component analysis (PCA) indicated clear divergence of Treg and Tconv populations, with further discordance in IFNγ+ and IFNγ Treg (Figure 3G). Importantly, both freshly isolated and expanded IFNγ+ Treg express significantly more CD226 than the IFNγ Treg subset (Supplemental Figure 2A). Moreover, TIGIT expression was increased in IFNγ Tregs compared to IFNγ+ Tregs, while the inverse is true for Tconv (Supplemental Figure 2B).

CD226 and TIGIT identify functionally distinct subpopulations of Treg

Human CD4+CD25hiCD127 Tregs contain a significant degree of heterogeneity in terms of lineage diversity and antigen exposure. We subdivided CD4+CD25hiCD127 Tregs (42) based on CD226 and TIGIT expression (Figure 4A, right plot). FOXP3-TSDR demethylation at the conserved non-coding sequence 2 (CNS2) has been previously associated with Treg stability (34, 43). Hence, we measured the percentage of cells demethylated at the TSDR in our subsets. When analyzed directly ex vivo, we observed CD226+TIGIT T cells were reduced in TSDR demethylation (30.0% ± 8.3), in comparison to the other Treg subsets and Tconv cells (Figure 4B). Despite this reduction in TSDR-demethylation, the resulting population suppressed to comparable levels when compared to the other freshly isolated CD4+CD25hiCD127 Treg populations (Figure 4C). Dye dilution analysis facilitated further analysis of Treg proliferation and viability. We noted increased proliferation of both the CD226TIGIT (naïve) and CD2226+TIGIT populations. Interestingly, we noted increased IL-10 in the suppression assay co-culture with CD2226+TIGIT Treg. We also observed a trend toward more CD2226+TIGIT+ cell death; however, this did not reach statistical significance for any Treg subset (data not shown). Thus, freshly isolated CD226+TIGIT T cells exhibit comparable ex vivo suppression when freshly isolated, yet differ in FOXP3-TSDR demethylation and cytokine production.

Figure 4
Direct ex vivo analysis of TIGIT and CD226 Treg

Protocols to generate expanded human Treg are susceptible to outgrowth of non-Treg and the potential for lineage instability (15, 3941). Therefore, we analyzed the purity and suppressive activity of Tregs post in vitro expansion based on CD226 and TIGIT expression. (Figure 5A). While the TIGIT+ fractions constituted the majority of Treg isolated from PBMC, we observed these cells were highly refractory to expansion, limiting the overall Treg yield (Figure 5A). This decrease in proliferative capacity of the sorted TIGIT+ Treg supports the role of TIGIT as an intrinsic negative regulator (22), and also reflects an enrichment of antigen-experienced (CD45RO+) cells with limited expansion capacity (32). In contrast, the TIGIT Treg fraction expanded robustly from a naïve state.

Figure 5
In vitro Expansion of Tregs expressing TIGIT and/or CD226 yield subpopulations differing in proliferative ability, purity, and suppressive capacity

To further assess the purity and functional capacity of these subsets, we analyzed FOXP3 and Helios following expansion (Figure 5B,C). The highest purity of post-expansion Treg originated from cells lacking CD226 expression at the time of the initial sort (i.e., prior to in vitro expansion). The highest percentage of FOXP3+Helios+ cells were observed in the CD226TIGIT+ population, followed by CD226TIGIT and CD226+TIGIT+ cultures (90.73% ± 3.7, 83.69 ± 8.69, and 74.30 ± 10.13, respectively). In contrast, the CD226+TIGIT population contained the least FOXP3+Helios+ cells post expansion (38.18 ± 11.70). Tconv expanded with very little FOXP3 and Helios co-expression (Figure 5B). In terms of surface CD226 and TIGIT expression, TIGIT was maintained or upregulated on TIGIT+ and TIGITCD226 cells following expansion, but remained low on TIGITCD226+ Treg and Tconv populations (Figure 5D,E).

We previously demonstrated that expanded Treg from T1D subjects were enriched in FOXP3+Helios cells with the capacity to produce IFNγ (32). Accordingly, CD226+ Treg have increased capacity to produce IFNγ upon stimulation, while CD226 Treg were almost completely devoid of IFNγ (Figure 5F,G). CD226 Treg (TIGIT+ or TIGIT) were both able to potently suppress autologous CD4 and CD8 T cell proliferation, with some diminution observed in the CD226+ expanded subset (Figure 5H). The CD226TIGIT and CD226TIGIT+ subsets were almost completely demethylated at the TSDR (92.0% ± 3.13% and 94.5% ± 3.71, respectively, Figure 5I). The degree of demethylation at the TSDR correlated strongly with FOXP3 and Helios co-expression determined by FACS (Figure 5I; R2=0.94, p<0.0001).

CD226+TIGIT T cells express an activated effector gene expression profile and produce IL-10 and effector cytokines

Gene expression profiles provide a powerful signature of the regulatory and effector mechanisms employed by T cells (8). Given that most Treg therapies will require some form of expansion, we conducted a gene profile on in vitro expanded Tconv (CD4+CD25CD127+) or Treg (CD4+CD25+CD127−/lo) that were further sub-divided based on initial TIGIT and CD226 expression (Figure 6A). From this analysis, we found 159 genes differentially expressed between the groups (p<0.05). This data is summarized in the heatmap and dendogram (Figure 6A and Supplemental Table 2). The normalized transcript counts were used to cluster the genes. 18.6% of the variance observed was attributable to individual subject variance, while the remainder segregated based on the initial sorted populations. Tconv showed a clear demarcation from Treg, with the CD226+TIGIT population demonstrating an intermediate expression profile between Treg and Tconv (Figure 6A, green bars). Interestingly, the CD226+TIGIT+ population has a Treg signature highly enriched in negative regulators and immunoregulatory pathways (Figure 6B–E). Despite this regulatory signature, it did not completely correlate with their suppressive capacity following in vitro expansion (Figure 5H). This may imply that CD226 negatively impacts suppression, or may reflect a preferential outgrowth of non-Treg by d14, as indicated by the TSDR results (Figure 5I). PCA analysis of Treg subsets demonstrated the CD226+TIGIT population shared some common features with Tconv cells (Figure 6F). An extensive multiplex cytokine profile of freshly isolated Tregs further confirmed the capacity of CD226+TIGIT T cells to produce a broad array of cytokines (Figure 6G,H).

Figure 6
CD226+TIGIT Tregs display an intermediate effector gene expression profile

Exclusion of CD226+ eliminates unstable Treg following in vitro expansion

Improving Treg stability and limiting contamination non-tTreg may be critical for future Treg therapies (44). Prior efforts demonstrated that CD226 expression, irrespective of initial TIGIT expression, resulted in a sizable fraction of cells that were reduced in suppressive activity and methylated at the TSDR (Figure 5). In addition, we noted that the TIGIT+ population was highly refractory to expansion. Thus, we hypothesized that eliminating CD226+ cells during the initial sort would increase purity without significantly constraining the final cell yield (Figure 7A). We observed that total Treg and CD226 Treg expanded with comparable kinetics (Figure 7B). Importantly, the purity of the Treg subsets (FOXP3+Helios+) was consistently higher in the CD226 Treg subset compared to the CD226+ Treg fraction (89.33 ± 3.45 vs. 57.2 ± 5.96, respectively) (Figure 7C–D). As noted previously, we again observed that CD226 expression correlated with IFNγ (Figure 7E–F). This observation was inversely related to TIGIT expression following culture (Figure 7G,H). In terms of suppressive capacity, CD226 Treg were consistently more able to suppress CD4+ and CD8+ responder T cells than their CD226+ Treg counterparts (Figure 7I). Indeed, an elevated ratio of TIGIT to CD226 expression on the tTreg population following an in vitro suppression assay was associated with increased suppressive activity and TSDR demethylation (Figure 7I–K). Taken together, these results suggest that elimination of cells expressing CD226 provides an effective means to further enrich a stable population of human Treg.

Figure 7
Exclusion of CD226+ Tregs reduces IFNγ production and augments the in vitro suppressive capacity of Tregs

Discussion

Prior efforts to define the transcriptional profile of human Treg have relied primarily upon the use of surrogate surface markers for isolation. This methodology is subject to alterations in surface marker expression following antigen exposure and cellular activation (particularly for CD45RA, CD25, and CD127). In our study, we conducted a direct transcriptional profile of Treg by FACS sorting cells based on the transcription factors FOXP3 and Helios. This analysis identified TIGIT, an important negative regulator, as highly expressed on tTreg relative to Tconv or FOXP3+Helios T cells.

To further investigate this axis, we characterized TIGIT expression on Treg in the context of the competing costimulatory molecule CD226. This analysis identified four distinct subpopulations of cells based on their surface expression of these receptors. Our results demonstrated CD226 expression marks both TCM and TEM and Treg subsets capable of producing IFNγ and IL-10 for Treg. Interestingly, TIGIT expression was stable or upregulated on Treg following in vitro expansion. An incipient concept in Treg biology relates to the ability of Treg to co-opt the transcription programs of the TH cells they are posed with suppressing (e.g., Tbet+ Treg suppress TH1 immunity (45), TH2-Treg suppress humoral responses (46), etc). It is intriguing to think this may also be the case for antigen-experienced Treg that are CD226+TIGIT+. In support of this, we noted lineage associated chemokine receptor expression on both CD226 and TIGIT expressing Treg.

TIGIT+CD226 Treg expressed high levels of FOXP3 and Helios and were demethylated at the TSDR. Moreover, data from in vitro suppression assays indicated TIGIT expression on Treg was associated with robust suppressive activity. We would speculate the relative ratio of these receptors might provide an informative biomarker. These findings are particularly timely given the genetic associations of CD226 in autoimmune diseases (11, 12, 18) and multiple reports of Treg functional defects and effector cytokine production by Treg (e.g., IFNγ and IL-17). Interestingly, analysis of IL-10 producing T regulatory-type 1 (TR1) cells also reported high CD226 expression, in addition to CD49b and LAG3 (47).

Our findings for TIGIT+ Treg draw some distinctions from those recently reported by Joller et al., who suggested TIGIT+ Treg share features with T cells of a proinflammatory lineage (31). Our results indicate this phenotype may be more representative of cells co-expressing CD226 and TIGIT, as few cytokines or effector genes were upregulated in TIGIT single positive populations. In fact, IL-10 expression and the IL-10-associated transactivator PRDM1(48) were only discernible in the CD226+TIGIT+ Treg population. Moreover, the shift toward an effector-like lineage was most prominent in the CD226+TIGIT population.

Immunotherapeutics targeting coinhibitory molecules such as CTLA-4 and PD-1 have garnered increasing interest following notable clinical successes (49). Our findings raise important implications for future therapies that may seek to target the CD226/TIGIT axis. Our studies support prior reports that CD226 is associated with proinflammatory Teff (21, 50). In addition, our data clearly demonstrate CD226 is also expressed at low to intermediate levels on naïve T cells and may play a key role in IL-10 producing Treg. Moreover, CD226 is upregulated on the majority of tTreg following activation. Thus, therapies seeking to block CD226 to attenuate Teff activity must be carefully dosed to target CD226hi expressing Teff, while preserving naïve T cells and IL-10 producing TR1. Our results also suggest the CD226/TIGIT axis may be susceptible to control by innate inflammatory cytokines, as we demonstrated for IL-12. These findings suggest that one potential benefit of anti-IL-12 antibody therapy may be the preservation of TIGIT expression on tTreg. Finally, our study has implications for Treg adoptive cell therapies that are currently progressing in clinical trials for a number of autoimmune conditions (44). Overall, our studies continue to support the notion that CD4+CD25+CD127−/lo Tregs maintain a high degree of purity following expansion over a period of 14 d. Extending these findings, we demonstrated that the selection of the TIGIT+ Treg population resulted in a highly enriched population, but this came at the cost of initial Treg recovery and resulted in a highly refractory population limiting the overall yield. However, our data also suggests the isolation of CD226 Treg, irrespective of initial TIGIT expression, results in a highly pure and potent population of TIGIT+ Treg for use in cell therapies. In sum, these data provide a biological context in which the autoimmune candidate gene CD226 may modulate T cell biology. Moreover, these studies provide markers to identify highly suppressive Treg for use in cell therapies.

Supplementary Material

Acknowledgments

Funding This work was supported, in part, by grants from the National Institutes of Health (P01 grant-AI42288) awarded to M.A.A. and T.M.B, a Career Development Award from the Juvenile Diabetes Research Foundation (JDRF) to T.M.B. (2-2012-280), a Basic Science Award from the American Diabetes Association to T.M.B. (7-13-BS-022), a Pfizer Aspire Award to TMB, and a U01 (AI102011) from NIAID and NIDDK to J.A.B.

We thank the blood donors who graciously participated in these studies. We are grateful for the efforts of the Diabetes Institute physicians and administrators who facilitate human sample research, specifically, Drs. Desmond Schatz and Michael Haller and Kimberly Young. The authors wish to thank members of the Brusko lab for helpful discussions and Phillip Lichlyter, Amy Patel, Kristi Balavage, and Alton Stone for technical assistance. We thank Drs. Mark Wallet, Maigan Hulme, and Clive Wasserfall for critical review of the manuscript.

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