T cells experience varying intensities of tonic or basal TCR signaling in response to self-peptides presented by MHC (self-pMHC) in vivo. We analyzed four subpopulations of mouse naive CD4+ cells that express different levels of Nur77-GFP and Ly6C, surrogate markers that positively and inversely correlate with the strength of tonic TCR signaling, respectively. Adoptive transfer studies suggest that relatively weak or strong Nur77-GFP intensity in thymocytes tends to be maintained in mature T cells. Two-dimensional affinity measurements were lowest for Nur77-GFPloLy6C+ cells and highest for Nur77-GFPhiLy6C cells, highlighting a positive correlation between apparent TCR affinity and tonic TCR signal strength. Despite experiencing the strongest tonic TCR signaling, Nur77-GFPhiLy6C cells were least responsive to multiple concentrations of a cognate or suboptimal pMHC. Gene expression analyses suggest that Nur77-GFPhiLy6C cells induce a gene expression program that has similarities with that of acutely stimulated T cells. However, strong tonic TCR signaling also correlates with increased expression of genes with inhibitory functions, including coinhibitory receptors. Similarly, assay for transposase-accessible chromatin with sequencing analyses suggested that increased tonic TCR signal strength correlated with increased chromatin accessibility associated with genes that have positive and inhibitory roles in T cell activation. Strikingly, Nur77-GFPhiLy6C cells exhibited differential accessibility within regions of Cd200r1 and Tox that were similar in location to differentially accessible regions previously identified in exhausted CD8+ T cells. We propose that constitutive strong tonic TCR signaling triggers adaptations detectable at both the transcriptional and epigenetic levels, ultimately contributing to the tuning of T cell responsiveness.

T cells are positively selected for expressing TCRs that weakly recognize self-peptides presented by MHC (self-pMHC) (1). TCR:self-pMHC interactions stimulate “tonic” or “basal” TCR signaling in mature T cells in vivo (2), resulting in constitutive tyrosine phosphorylation of the TCR complex and association of the tyrosine kinase ZAP70 with the TCR ζ-chain (3, 4). It has been appreciated that the strength of tonic TCR signaling influences T cell activation, although the mechanisms underlying this phenomenon are incompletely understood (5, 6).

Studies of variations in tonic TCR signaling are enabled by surrogate markers of TCR signaling. Such indicators include reporter transgenes of Nr4a1 (encoding Nur77), which are rapidly expressed upon TCR stimulation (7, 8). Upregulation of Nur77-GFP reporter transgene expression is detected in response to self-pMHC during positive selection in the thymus (79). Phenotypically naive CD44loCD4+ T cells located in the spleen and lymph nodes constitutively express Nur77-GFP in MHC class II (MHC-II)–sufficient, but not MHC-II–deficient hosts, suggesting that basal Nur77-GFP expression depends on continuous exposure to self-pMHC-II (7, 10). We showed previously that the intensity of basal TCR ζ-chain phosphorylation is lowest in Nur77-GFPlo T cells and highest in Nur77-GFPhi T cells, suggesting that Nur77-GFP fluorescence intensity can also reflect the intensity of tyrosine phosphorylation of the TCR complex (10). Our previous work also showed that the fluorescence intensity of Nur77-GFP is sensitive to variations in TCR affinity for pMHC and to inhibition of ZAP70, a tyrosine kinase required for activation of the major signal transduction pathways downstream of the TCR (9, 11, 12). Taken together, these findings suggest that Nur77-GFP intensity can reflect relative differences in the strength of TCR signal transduction experienced by T cells in response to self-pMHC or foreign pMHC.

Previous work suggests that Ly6C can also be used as a marker of reactivity to self-pMHC, although its expression inversely correlates with tonic TCR signal strength (13). In combination, Nur77-GFP and Ly6C enable the visualization of a broad range of tonic TCR signal strengths that are experienced by individual naive T cells. Our previous studies investigated the activation of subpopulations of cells across the entire spectrum of Nur77-GFP and Ly6C expression. In those studies, naive Nur77-GFPhiLy6C cells generated fewer IL-2–secreting cells and diminished proliferative responses relative to Nur77-GFPloLy6C+ cells. In the current study, we find that strong tonic TCR signaling is relatively stable and correlates with the relative affinity to pMHC. Cells that experience the strongest tonic TCR signals exhibit gene expression and chromatin accessibility patterns with features similar to both activated T cells and hypofunctional (anergic, exhausted) T cells. These results support a model whereby naive CD4+ T cell responsiveness to TCR stimuli is tuned in response to the strength of tonic TCR signaling.

A mouse strain with the Tg(Nr4a1-EGFP)GY139Gsat transgene, referred to as Nur77-GFP in this study, has been previously described (8). A compound mouse strain with the Nur77-GFP transgene and the targeted Foxp3-IRES-RFP reporter allele, Foxp3tm1Flv/J, has been previously described (10). Mouse strains with a combination of the OT-II TCR transgene (Tg(TcraTcrb)425Cbn), Nur77-GFP transgene, and Foxp3-IRES-RFP reporter allele, with or without the TCRα-chain knockout allele Tcratm1Mom, have been described (10). A strain with the combination of the Tg(TcrAND)53Hed transgene (AND TCR transgene), Nur77-GFP transgene, and Foxp3-IRES-RFP–targeted mutation has been described (10). A strain with the combination of the Tg(Zap70*M413A)2Weis transgene (referred to here as ZAP70 analog-sensitive [ZAP70AS]) and the endogenous Zap70-targeted null mutation Zap70tm1Weis and Nur77-GFP transgene has been described previously (9). Wild-type (WT) CD45.2+ C57BL/6 mice were purchased from The Jackson Laboratory. The mice used in these studies were housed in the Division of Animal Resources at Emory University. Experiments were performed according to protocols approved by the Emory University Institutional Animal Care and Use Committee.

Thymocyte and T cell samples were analyzed using BD LSRFortessa or FACS Symphony analyzers. Abs used for flow cytometry analysis were from either BD Biosciences (CD4 [clone RM4-5], CD44 [IM7], CD127 [SB/199], CD200 [OX-90], Bcl6 [K112-91], Helios [22F6], Ly6C [AL-21], TCR Va2 [B20.1]), BioLegend (CD5 [53-7.3], CD25 [PC61], CD45.1 [A20], CD45.2 [104], CD45RB [c363-16A], CCR7 [4B12], PD-1 [29F.1A12], TCRβ [H57-597], MHC class I [MHC-I, H-2Kb; AF6-88.5]), or eBioscience (CD8α [53-6.7], CD69 [H1.2F3], FR4 [eBio12A5]). Cell viability was assessed by exclusion of LIVE/DEAD fixable yellow or near-infrared stains according to the manufacturer’s protocol. Intracellular staining was performed after fixation and permeabilization using a Foxp3/Transcription Factor staining kit according to the manufacturer’s protocol (Thermo Fisher Scientific). For staining of splenic red pulp cells, 3 μg of anti-CD45.2 allophycocyanin Ab was injected intravascularly 3 min prior to euthanasia, as described (14). Total thymocytes were pre-enriched for CD4 single-positive (CD4SP) thymocytes by negative selection of CD8+ cells by anti–CD8-biotin Abs and streptavidin magnetic beads according to the manufacturer’s protocol (STEMCELL Technologies).

CD4SP cells were enriched from the thymi of Nur77-GFP mice, followed by FACS sorting for CD4+CD8 Nur77-GFPlo or Nur77-GFPhi populations using a FACSAria sorter (BD Biosciences). The sort gates were set on the highest and lowest 15% of cells based on GFP fluorescence intensity. Between 5 × 105 and 7.5 × 105 sorted CD4SP thymocytes were adoptively transferred into congenic WT CD45.1+ hosts by i.v. injection. Naive CD4+ T cells were sorted from the spleens and lymph nodes of OT-II TCR transgenic mice with the phenotype CD4+CD44loCD62LhiFoxp3-RFP, and further sorted into population A (Nur77-GFPloLy6C+), population B (Nur77-GFPmedLy6C+), population C (Nur77-GFPmedLy6C), and population D (Nur77-GFPhiLy6C). A total of 2 × 105 sorted population A–D cells were transferred into WT congenic CD45.2+ mice by i.v. injection.

Sorted population A–D OT-II cells were stimulated with T cell–depleted splenocytes and chicken OVA323–339 peptide (ISQAVHAAHAEINEAGR) or an OVAH331R-altered peptide (ISQAVHAARAEINEAGR) (GenScript). ZAP70AS T cells were stimulated with T cell–depleted splenocytes and 0.1 μg/ml soluble anti-CD3 (clone 145-2C11, BioLegend). Cells were cultured in the presence of varying concentrations of the inhibitor HXJ42, provided by Dr. Kevan Shokat (University of California, San Francisco) (9). For in vivo stimulation, sorted CD45.1+ OT-II population A–D cells were labeled with CellTrace Violet (Thermo Fisher Scientific), and 6 × 105 cells were adoptively transferred into WT CD45.2+ hosts by i.v. injection. The following day, WT splenocytes were pulsed ex vivo with 100 μM OVAH331R peptide at 37°C for 1 h and washed with PBS. A total of 5 × 106 cells were transferred by i.v. injection into the hosts containing OT-II cells. CellTrace dye dilution was detected by flow cytometry 3 d after injection of the peptide-pulsed splenocytes.

CD4+ cells were pre-enriched from the spleen and lymph nodes of OT-II TCRα-deficient Nur77-GFP Foxp3-IRES-RFP mice with CD4+ T cell negative selection kits according to the manufacturer’s protocol (Miltenyi Biotec). Naive CD44loCD62LhiRFP population A–D cells were sorted from enriched CD4+ cells as described above. In these experiments, the T cells were not labeled with anti-CD4 Abs, which may affect the affinity measurement assay. The relative two-dimensional affinities (Ac Ka μm4) were measured using the previously described two-dimensional micropipette adhesion frequency assay (1517). Briefly, human RBCs were coated with I-Ab monomers loaded with WT OVA328–337 peptide, which were obtained from the National Institutes of Health Tetramer Core Facility. Quantification of binding events, pMHC density TCR surface density, and TCR:pMHC affinity calculations were determined as previously described (1517).

RNA sequencing (RNA-seq) libraries were prepared from 1 × 105 CD4+CD44loRFP population A–D cells from the spleen and lymph nodes of Nur77-GFP Foxp3-IRES-RFP mice. Cells were sorted directly into RLT lysis buffer (Qiagen) with 1% 2-ME, sequenced, quality checked, and mapped to the mm10 genome as previously described (18). Genes expressed at 3 reads per million in all samples of one group were considered expressed. Assay for transposase-accessible chromatin with sequencing (ATAC-seq) libraries were prepared from 2 × 104 CD4+CD44loRFP population A–D cells from the spleen and lymph nodes of Nur77-GFP Foxp3-IRES-RFP mice, sequenced, and mapped to the mm10 genome as previously described (18).

Raw fastq files were mapped to a custom mm10 genome containing the GFP sequence using STAR (19) with the GENCODE vM17 reference transcriptome. PCR duplicate reads were marked with Picard MarkDuplicates and removed from downstream analyses. Reads mapping to exons for all unique ENTREZ genes and GFP were summarized using GenomicRanges (20) in R v3.5.2 and normalized to reads per kilobase per million. Genes expressed at 3 reads per million in all samples of one group were considered expressed. Differentially expressed genes were determined using edgeR (21), and genes that displayed an absolute fold change >1.5 and Benjamini–Hochberg false discovery rate (FDR)–corrected p value <0.05 were considered significant.

Raw ATAC-seq reads were mapped to the mm10 genome using Bowtie2 (22), and enriched accessible regions for each sample were determined using MACS2 (23). A composite set of unique peaks called across all samples was annotated to the nearest gene using HOMER (24), the read depth from each sample was annotated using the GenomicRanges package (20), and data were normalized to reads per peak per million in R v3.5.2. Differentially accessible regions were determined using edgeR (21). Regions that displayed an absolute fold change >2.0 and Benjamini–Hochberg FDR-corrected p value <0.05 were considered significant. Nur77 binding site accessibility was computed by first identifying the location of all Nur77 motifs using the annotatePeaks.pl [DAR file] mm10 -size given -noann -m nur77.motif -mbed nur77.motifs.bed HOMER command. Next, coverage at the 50 bp surrounding each motif was calculated for all samples, and the group mean for each motif was summarized as a boxplot using custom R scripts. Boxplots were generated using the boxplot function in R. Taiji analysis, which was performed using the standard workflow (25).

RNA-seq and ATAC-seq data are available under accession number GSE206074 in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE206074).

In these studies, we analyzed four subpopulations of naive CD4+ cells: population A (Nur77-GFPloLy6C+), population B (Nur77-GFPmedLy6C+), population C (Nur77-GFPmedLy6C), and population D (Nur77-GFPhiLy6C) (Fig. 1A). These four subpopulations were detected within the naive CD4+ cell population, as defined by a CD44loCD25 phenotype that excludes Ag-experienced cells and regulatory T cells (Tregs). The relative expression pattern was skewed differently in Ag-experienced CD44hi cells, with most being Ly6C. CD25+ cells, a population that is enriched for Tregs, included Ly6C and Ly6C+ populations, both of which were skewed toward high Nur77-GFP expression (Fig. 1B). Populations A–D were also detected in naive CD4+ cells as defined by a CD45RBhi phenotype (Fig. 1C). Population A–D cells were also detected using the combination of CD44 and CD62L to define the naive T cell population (10). The percentages of population A–D cells were similar in inguinal/axial lymph nodes, splenic red and white pulp, and peripheral blood (Fig. 1D). Population D was the only subpopulation that exhibited a small but consistent decrease in peripheral blood relative to lymph nodes and spleen. This finding is consistent with previous studies indicating weaker tonic TCR signaling in T cells harvested from peripheral blood relative to T cells from lymph nodes (3).

FIGURE 1.

Strong tonic TCR signal strength is relatively stable.

(A) Schematic shows the gating scheme for populations A–D. (B) Contour plot on the left shows CD44 and CD25 expression by viable CD4+ cells from the spleen of Nur77-GFP mice. The contour plots on the right show the expression of Nur77-GFP and Ly6C for Ag-experienced (CD44hiCD25), naive (CD44loCD25), and Treg (CD25+) populations. Numbers indicate the percentage of cells within each color-coded gate. (C) Histogram on the left shows the expression of CD45RB by viable CD4+ cells. CD45RBlo and CD45RBhi gates are shown. Contour plots on the right show the expression of Nur77-GFP and Ly6C by CD45RBlo and CD45RBhi cells. Numbers indicate the percentage of cells within each gate. For experiments in (B) and (C), n = 3 independent experiments. (D) Bar graph shows the mean percentage ± SEM of population A–D cells in the red pulp (RP) and white pulp (WP) of the spleen, inguinal plus axial lymph nodes (LNs), and peripheral blood (PB). Red pulp cells were identified by positive staining and white pulp cells were identified by the lack of staining following i.v. injection of Nur77-GFP Foxp3-IRES-RFP mice with anti-CD45.2 Abs 3 min prior to euthanasia. n = 3 independent experiments. (E) Contour plot shows CD4 and CD8 expression by total thymocytes from a Nur77-GFP Foxp3-IRES-RFP mouse. Histograms show Nur77-GFP expression by CD4 CD8 double-positive (DP) cells, CD4 single-positive (CD4SP) cells, and CD8 single-positive (CD8SP) cells. (F) Contour plot on the left shows TCRβ and CCR7 expression by total thymocytes from a Nur77-GFP mouse. The arrow indicates that the contour plot to the right is gated on the TCRβ+ CCR7+ population. The contour plot in the middle shows CD69 and MHC-I expression on the TCRβ+CCR7+ populations. CD69+MHC-I semimature (SM), CD69+MHC-I+ mature 1 (M1), and CD69 MHC-I+ populations are indicated. Right, Histograms show Nur77-GFP expression by the indicated populations. For experiments in (E) and (F), n = 3 independent experiments. (G) Histograms show Nur77-GFP expression by total CD4SP thymocytes (top) and FACS-purified Nur77-GFPlo and Nur77-GFPhi CD4SP cells (bottom) from Nur77-GFP mice. (H) Bar graph shows the mean percentage ± SEM of donor cells within the population A–D gates 2 wk after adoptive transfer. For experiments in (G) and (H), n = 3 independent experiments. *p < 0.05, **p < 0.005, ****p < 0.0001 (by Student t test). n.s., not significant. (I) Contour plot shows Nur77-GFP and Ly6C expression by naive CD4+CD44loFoxp3-IRES-RFP cells. Numbers indicate the percentage of cells within the population A–D gates. (J) Graph shows the two-dimensional TCR affinities of naive OT-II population A–D cells for WT OVA228–337 peptide/I-Ab monomers as measured by two-dimensional micropipette adhesion frequency assays. Each symbol represents an individual measurement and cell. Data are from three independent experiments; n = 5 mice. *p < 0.05, ****p < 0.0001 (by ordinary one-way ANOVA Tukey multiple comparison test). n.s., not significant.

FIGURE 1.

Strong tonic TCR signal strength is relatively stable.

(A) Schematic shows the gating scheme for populations A–D. (B) Contour plot on the left shows CD44 and CD25 expression by viable CD4+ cells from the spleen of Nur77-GFP mice. The contour plots on the right show the expression of Nur77-GFP and Ly6C for Ag-experienced (CD44hiCD25), naive (CD44loCD25), and Treg (CD25+) populations. Numbers indicate the percentage of cells within each color-coded gate. (C) Histogram on the left shows the expression of CD45RB by viable CD4+ cells. CD45RBlo and CD45RBhi gates are shown. Contour plots on the right show the expression of Nur77-GFP and Ly6C by CD45RBlo and CD45RBhi cells. Numbers indicate the percentage of cells within each gate. For experiments in (B) and (C), n = 3 independent experiments. (D) Bar graph shows the mean percentage ± SEM of population A–D cells in the red pulp (RP) and white pulp (WP) of the spleen, inguinal plus axial lymph nodes (LNs), and peripheral blood (PB). Red pulp cells were identified by positive staining and white pulp cells were identified by the lack of staining following i.v. injection of Nur77-GFP Foxp3-IRES-RFP mice with anti-CD45.2 Abs 3 min prior to euthanasia. n = 3 independent experiments. (E) Contour plot shows CD4 and CD8 expression by total thymocytes from a Nur77-GFP Foxp3-IRES-RFP mouse. Histograms show Nur77-GFP expression by CD4 CD8 double-positive (DP) cells, CD4 single-positive (CD4SP) cells, and CD8 single-positive (CD8SP) cells. (F) Contour plot on the left shows TCRβ and CCR7 expression by total thymocytes from a Nur77-GFP mouse. The arrow indicates that the contour plot to the right is gated on the TCRβ+ CCR7+ population. The contour plot in the middle shows CD69 and MHC-I expression on the TCRβ+CCR7+ populations. CD69+MHC-I semimature (SM), CD69+MHC-I+ mature 1 (M1), and CD69 MHC-I+ populations are indicated. Right, Histograms show Nur77-GFP expression by the indicated populations. For experiments in (E) and (F), n = 3 independent experiments. (G) Histograms show Nur77-GFP expression by total CD4SP thymocytes (top) and FACS-purified Nur77-GFPlo and Nur77-GFPhi CD4SP cells (bottom) from Nur77-GFP mice. (H) Bar graph shows the mean percentage ± SEM of donor cells within the population A–D gates 2 wk after adoptive transfer. For experiments in (G) and (H), n = 3 independent experiments. *p < 0.05, **p < 0.005, ****p < 0.0001 (by Student t test). n.s., not significant. (I) Contour plot shows Nur77-GFP and Ly6C expression by naive CD4+CD44loFoxp3-IRES-RFP cells. Numbers indicate the percentage of cells within the population A–D gates. (J) Graph shows the two-dimensional TCR affinities of naive OT-II population A–D cells for WT OVA228–337 peptide/I-Ab monomers as measured by two-dimensional micropipette adhesion frequency assays. Each symbol represents an individual measurement and cell. Data are from three independent experiments; n = 5 mice. *p < 0.05, ****p < 0.0001 (by ordinary one-way ANOVA Tukey multiple comparison test). n.s., not significant.

Close modal

We next analyzed the range of basal Nur77-GFP expression in thymocytes. The mean fluorescence intensity of Nur77-GFP is greater in CD4SP and CD8 single-positive (CD8SP) thymocytes relative to CD4 CD8 double-positive cells (Fig. 1E) (7, 8). Within the CD4SP and CD8SP populations, Nur77-GFP fluorescence intensity spanned more than 2 orders of magnitude, suggesting that a broad range of Nur77-GFP expression is possible at a single-cell level after positive selection. Semimature, mature 1, and mature 2 thymocytes exhibited a broad range of Nur77-GFP, suggesting that heterogeneity of TCR signal strength is detectable throughout maturation (Fig. 1F) (26). To determine whether relatively low or high Nur77-GFP intensity is stable in the transition from CD4SP to mature T cell stages, we sorted CD4SP Nur77-GFPlo or CD4SP Nur77-GFPhi thymocytes and adoptively transferred them into congenic CD45.1 WT hosts (Fig. 1G). At 2 wk posttransfer, most Nur77-GFPlo donor cells exhibited a population A phenotype (Fig. 1H). In contrast, Nur77-GFPhi donor cells were skewed toward the population C phenotype (Fig. 1H). These results suggest that relatively low or high Nur77-GFP expression in positively selected CD4+ T cells tends to persist in the peripheral lymphoid organs.

To determine how T cells may be skewed toward relatively low or high Nur77-GFP expression, we analyzed population A–D cells that express the same transgenic TCR. We previously generated a mouse strain that combines the OT-II TCR transgene, Nur77-GFP transgene, Foxp3-IRES-RFP reporter allele, and the null allele of the endogenous TCR α-chain to preclude expression of endogenously rearranged TCRs (10). Naive OT-II CD44loCD62LhiFoxp3-IRES-RFP cells with population A–D phenotypes were detected (Fig. 1I). These findings suggested that variations in tonic TCR signal strength are possible within populations that express identical transgenic TCRs. Among the variations that could account for the differences in tonic TCR signal strength and intensity of Nur77-GFP expression is the relative TCR:pMHC affinity within the context of a cell–cell interaction. We used two-dimensional micropipette adhesion assays to determine the relative affinities of individual population A–D OT-II cells for OVA228–337 peptide/I-Ab monomers bound to human RBCs (16). The mean apparent affinity to OVA pMHC was lowest for population A, intermediate for populations B and C, and highest for population D, highlighting a marked positive correlation with Nur77-GFP fluorescence intensity (Fig. 1J). These results imply that Nur77-GFP fluorescence intensity in naive T cells, in addition to reflecting the intensity of TCR signal transduction, also reflects the relative affinity of individual T cells for self-antigens.

We next investigated how populations A–D respond to variations in pMHC concentration or affinity of an altered peptide ligand. Naive CD4+CD44loFoxp3 population A–D cells were sorted from OT-II TCRα-deficient Nur77-GFP Foxp3-IRES-RFP TCR transgenic mice. Sorted T cells were stimulated with APCs plus WT OVA323–339 peptide or OVAH331R altered peptide ligand, a less potent altered peptide (27). In response to high concentrations of OVA323–339 (≥10 μM), the percentages of population A–D cells that expressed CD25 and CD69 were similar (Fig. 2A, 2B). However, low concentrations of OVA323–339 ≤1 μM revealed graded responses where the strongest response was by population A cells and decreased stepwise to populations B, C, and D. Graded responses to OVAH331R peptide were detected at all concentrations of peptide that were tested (Fig. 2C, 2D). We next aimed to determine the responsiveness of population A–D OT-II cells to the altered peptide ligand in vivo. Population A–D OT-II cells were labeled with CellTrace Violet and adoptively transferred to congenic WT hosts that were subsequently challenged with splenocytes pulsed with OVAH331R peptide. After 72 h, most population A and B cells had undergone multiple rounds of cell division, whereas most population C and D cells remained undivided (Fig. 2E). These results are consistent with in vitro studies and highlight the hyporesponsiveness of naive T cells that experience strong tonic TCR signaling.

FIGURE 2.

Strong tonic TCR signaling correlates with hyporesponsiveness to TCR stimulation.

(A) Bar graph shows the mean percentage ± SEM of OT-II population A–D cells expressing CD25 and CD69 after 24 h of stimulation with T cell–depleted splenocytes pulsed with the indicated concentrations of WT OVA peptide. CD4+CD44loFoxp3-IRES-RFP population A–D cells were sorted from the spleen and lymph nodes of OT-II Nur77-GFP Foxp3-IRES-RFP Tcra−/− mice. (B) Histograms show the expression of Nur77-GFP, CD69, and CD25 by OT-II population A–D cells after stimulation with T cell–depleted splenocytes plus the indicated concentrations of WT OVA peptide. (C and D) Similar to (A) and (B), except OVAH331R peptide was added to the cells. For experiments in (A)–(D), n = 3 independent experiments. (E) Overlaid contour plot shows Nur77-GFP and Ly6C expression by CD4+CD44loFoxp3-IRES-RFP population A–D cells sorted from OT-II Nur77-GFP Foxp3-IRES-RFP mice prior to adoptive transfer into congenic WT CD45.2+ hosts. The dot plots show CellTrace Violet and Nur77-GFP fluorescence of population A–D cells 72 h after adoptive transfer of splenocytes that were pulsed with 100 μM OVAH331R peptide. n = 2 independent experiments. (F) Histograms show the expression of Nur77-GFP, CD69, and CD25 by population A–D cells sorted from ZAP70AS mice, following stimulation with T cell–depleted splenocytes and anti-CD3ε Abs and in the presence of the indicated concentrations of HXJ42 inhibitor. (G) Bar graph shows the mean percentage ± SEM of population A–D cells from ZAP70AS mice expressing both CD25 and CD69 after 24 h of stimulation. The concentration of HXJ42 in each condition is indicated. (H) Bar graph shows the mean percentage ± SEM of total CD4+ cells from control Zap70+/− mice expressing both CD25 and CD69 following stimulation with T cell–depleted splenocytes and anti-CD3ε Abs for 24 h in the presence of the indicated concentrations of HXJ42. n = 3 independent experiments. Statistical significance, *p < 0.05, **p < 0.005, ****p < 0.0001 (by Student t test). n.s., not significant.

FIGURE 2.

Strong tonic TCR signaling correlates with hyporesponsiveness to TCR stimulation.

(A) Bar graph shows the mean percentage ± SEM of OT-II population A–D cells expressing CD25 and CD69 after 24 h of stimulation with T cell–depleted splenocytes pulsed with the indicated concentrations of WT OVA peptide. CD4+CD44loFoxp3-IRES-RFP population A–D cells were sorted from the spleen and lymph nodes of OT-II Nur77-GFP Foxp3-IRES-RFP Tcra−/− mice. (B) Histograms show the expression of Nur77-GFP, CD69, and CD25 by OT-II population A–D cells after stimulation with T cell–depleted splenocytes plus the indicated concentrations of WT OVA peptide. (C and D) Similar to (A) and (B), except OVAH331R peptide was added to the cells. For experiments in (A)–(D), n = 3 independent experiments. (E) Overlaid contour plot shows Nur77-GFP and Ly6C expression by CD4+CD44loFoxp3-IRES-RFP population A–D cells sorted from OT-II Nur77-GFP Foxp3-IRES-RFP mice prior to adoptive transfer into congenic WT CD45.2+ hosts. The dot plots show CellTrace Violet and Nur77-GFP fluorescence of population A–D cells 72 h after adoptive transfer of splenocytes that were pulsed with 100 μM OVAH331R peptide. n = 2 independent experiments. (F) Histograms show the expression of Nur77-GFP, CD69, and CD25 by population A–D cells sorted from ZAP70AS mice, following stimulation with T cell–depleted splenocytes and anti-CD3ε Abs and in the presence of the indicated concentrations of HXJ42 inhibitor. (G) Bar graph shows the mean percentage ± SEM of population A–D cells from ZAP70AS mice expressing both CD25 and CD69 after 24 h of stimulation. The concentration of HXJ42 in each condition is indicated. (H) Bar graph shows the mean percentage ± SEM of total CD4+ cells from control Zap70+/− mice expressing both CD25 and CD69 following stimulation with T cell–depleted splenocytes and anti-CD3ε Abs for 24 h in the presence of the indicated concentrations of HXJ42. n = 3 independent experiments. Statistical significance, *p < 0.05, **p < 0.005, ****p < 0.0001 (by Student t test). n.s., not significant.

Close modal

To determine the sensitivity of population A–D cells to incremental decreases in TCR signal strength, we used a specific inhibitor of ZAP70, a tyrosine kinase required for activation of signaling downstream of the TCR. Titration of ZAP70 catalytic activity can be achieved with a compound mouse strain that has a transgene encoding a methionine 413 to alanine mutant of ZAP70 and is on a Zap70 knockout background (9). T cells from this strain express only the M413A mutant ZAP70, which is catalytically active but gains sensitivity to the compound HXJ42, an analog of the kinase inhibitor PP1. Purified population A–D cells expressing the ZAP70AS mutant were stimulated with APCs and the same concentration of anti-CD3 in the presence of varying concentrations of HXJ42. Following stimulation of population A–D cells in the absence of inhibitor, the percentage of CD25- and CD69-expressing cells progressively decreased from population A to population D (Fig. 2F, 2G). Increases in inhibitor concentration correlated with reductions in the responses of each population. However, the pattern of graded decreases in CD25 and CD69 expression from population A to population D was consistently detected. The concentrations of HXJ42 used in these experiments (≤0.5 μM) did not result in significant reductions in the upregulation of CD25 and CD69 by control Zap70+/− T cells, which express the WT, HXJ42-insensitive, ZAP70 protein (Fig. 2H). This result suggested that the effects of HXJ42 inhibition were specific to inhibition of the ZAP70AS mutant kinase. Taken together, these results suggest that as tonic TCR signaling increases, responsiveness to TCR stimuli decreases, the end result of which renders population D cells most susceptible to inhibition of TCR signal transduction.

To determine whether population A–D cells exhibit distinct gene expression patterns, we performed bulk RNA-seq analysis of naive CD4+CD44loFoxp3-IRES-RFP cells sorted from three non-TCR transgenic mice. A total of 232 genes were differentially expressed (>1.5-fold) between any two populations. Principal component analysis (PCA) of all differentially expressed genes (DEGs) revealed that populations A and C were more similar to each other in principal component 1 (PC1), whereas populations B and D were most similar to each other, also in PC1 (Fig. 3A). This pattern may reflect the relatively high levels of Nur77-GFP expression by populations B and D among the Ly6C+ cells and Ly6C cells, respectively. The comparison between populations C and D yielded the most DEGs (n = 174), 144 of which were upregulated in population D (Fig. 3B, 3C). We next performed pathway analysis of the DEGs identified for each pairwise comparison. For the population D versus population A and population C versus population B comparisons, there were no statistically significant Gene Ontology (GO) terms identified (data not shown). Due to the absence of DEGs between population B and population D, there were also no GO terms identified. The top GO terms identified for the population C versus population A and population B versus population A comparisons were overlapping and largely associated with transcription and biosynthesis (Fig. 3D). In contrast, the population D versus population C comparison highlighted several GO terms associated with immune cell activation, proliferation, adhesion, development, and signaling. In light of these findings, we decided to focus on the population D versus population C comparison and the transcripts that were upregulated in population D. The GO terms associated with the genes upregulated in population D are consistent with an activated T cell, suggesting that the gene expression profile induced by strong tonic TCR signaling shares features with that of activated T cells.

FIGURE 3.

Increasing tonic TCR signal strength correlates with gene expression changes.

Bulk RNA-seq analysis was performed on CD4+CD44loFoxp3-IRES-RFP population A–D cells purified from the spleen and lymph nodes of three Nur77-GFP Foxp3-IRES-RFP mice. (A) Principal component analysis of 232 differentially expressed genes identified for the six pairwise comparisons between cell populations. Individual replicates corresponding to populations A–D are indicated by separate color-coded dots. Circles denote 99% confidence intervals. (B) Heatmap shows the relative expression levels of the 232 differentially expressed genes with a >1.5-fold change in expression. (C) Table shows the number of differentially expressed genes between each pairwise comparison. (D) Table shows the top Gene Ontology (GO) terms identified from pathway analysis of DEGs identified in the indicated pairwise comparisons. The top terms are shown with associated false discovery rate (FDR). n.s., not significant. (E) Volcano plots highlight subsets of differentially expressed genes within the indicated categories. Selected genes are labeled. (F and G) Contour plots show Nur77-GFP and Ly6C expression by total naive CD4+CD44loFoxp3-IRES-RFP cells from (F) Nur77-GFP Foxp3-IRES-RFP mice, or (G) AND TCR transgenic Nur77-GFP Foxp3-IRES-RFP mice. Histograms show expression of the indicated cell surface molecules for populations A–D. For (F) and (G), n = 3 independent experiments. (H) Graphs of gene set enrichment analysis (GSEA) comparing the genes upregulated in population D compared with population C, versus the indicated gene sets. NES, normalized enrichment score.

FIGURE 3.

Increasing tonic TCR signal strength correlates with gene expression changes.

Bulk RNA-seq analysis was performed on CD4+CD44loFoxp3-IRES-RFP population A–D cells purified from the spleen and lymph nodes of three Nur77-GFP Foxp3-IRES-RFP mice. (A) Principal component analysis of 232 differentially expressed genes identified for the six pairwise comparisons between cell populations. Individual replicates corresponding to populations A–D are indicated by separate color-coded dots. Circles denote 99% confidence intervals. (B) Heatmap shows the relative expression levels of the 232 differentially expressed genes with a >1.5-fold change in expression. (C) Table shows the number of differentially expressed genes between each pairwise comparison. (D) Table shows the top Gene Ontology (GO) terms identified from pathway analysis of DEGs identified in the indicated pairwise comparisons. The top terms are shown with associated false discovery rate (FDR). n.s., not significant. (E) Volcano plots highlight subsets of differentially expressed genes within the indicated categories. Selected genes are labeled. (F and G) Contour plots show Nur77-GFP and Ly6C expression by total naive CD4+CD44loFoxp3-IRES-RFP cells from (F) Nur77-GFP Foxp3-IRES-RFP mice, or (G) AND TCR transgenic Nur77-GFP Foxp3-IRES-RFP mice. Histograms show expression of the indicated cell surface molecules for populations A–D. For (F) and (G), n = 3 independent experiments. (H) Graphs of gene set enrichment analysis (GSEA) comparing the genes upregulated in population D compared with population C, versus the indicated gene sets. NES, normalized enrichment score.

Close modal

Transcripts upregulated in population D include Nr4a1, which was predicted by the high levels of Nur77-GFP expressed by this cell population, and Cd5, which has also been characterized as a surrogate marker of tonic TCR signal strength or TCR reactivity to self-pMHC. Also upregulated were costimulatory receptors (Icos, Tnfrsf4, and Tnfrsf9) and inhibitory receptors (Pdcd1, Lag3, and Cd200). Several transcription factors were upregulated in population D, including Bcl6, Eomes, Foxp3, and Ikzf2. Despite excluding Foxp3-IRES-RFP–expressing cells from our analysis, Foxp3 was identified as a DEG in population D. This observation correlates with previous work, showing a higher propensity of population D cells to induce Foxp3-IRES-RFP expression when stimulated in culture with TGF-β and IL-2 under Treg polarizing conditions (28). We verified elevated expression of selected DEGs including CD200, Helios, Bcl6, CD5, PD1, FR4, and CD127 at the protein level in naive CD4+CD44loFoxp3-IRES-RFP T cells from non-TCR transgenic mice (Fig. 3F) or AND TCR transgenic mice (Fig. 3G). Previous work showed that AND TCR transgenic cells in H-2b background mice exhibit high basal expression of Nur77-GFP and are highly skewed toward population C and population D phenotypes (10). Elevated expression of Bcl6 at the transcript and protein level in population D suggests that these cells express a lineage-defining transcription factor associated with Tfh cells. Consistent with this concept, recent studies showed a correlation between strong tonic TCR signaling and Tfh lineage differentiation (29).

Gene set enrichment analysis suggested that there was a high degree of overlap between the genes upregulated in population D and genes upregulated in CD4+ cells following acute TCR stimulation (28) or CD4+ cells overexpressing Nr4a1 (Fig. 3H) (30). Recent evidence indicates that Nr4a1 expression is required for the maintenance of tolerance (28, 3032). Taken together, these observations are consistent with a model whereby strong tonic TCR signaling induces increased expression of Nr4a1 by population D cells. These findings are also consistent with a gene expression profile associated with CD4+ T cell dysfunction induced by chronic stimulation of CD4+ cells with cognate Ag (33), as well as the gene expression program in naturally occurring CD73+FR4+ anergic T cells (Fig. 3H).

Considering that there were differences at the transcript level, we decided to investigate whether there were differences in chromatin accessibility associated with changes in tonic TCR signal strength. We performed ATAC-seq analysis of purified CD4+CD44loFoxp3-IRES-RFP population A–D cells from three individual Nur77-GFP Foxp3-IRES-RFP mice. A total of 3234 differentially accessible chromatin regions (DARs) with ≥2-fold difference in accessibility were detected. Most DARs can generally be clustered into two groups: one in which accessibility is highest in population A with a graded downward trend from population A to population D, and a larger group of DARs with progressive increases in accessibility from population A to population D (Fig. 4A). This trend is also apparent in the PCA analysis of all DARs, which shows a progression along PC1 that parallels the increases in tonic TCR signal strength (Fig. 4B). Most DARs (3101) were differentially accessible between populations A and D and the least accessible (33) between populations A and B (Fig. 4C). There were 490 known Nur77 binding sites within DARs in this dataset. The mean accessibility score of these DARs increased progressively from population A to D, implying a positive correlation between Nur77-GFP expression and Nur77-dependent activity (Fig. 4D). Pathway analysis of the DARs in the population D versus population C comparison identified GO terms including cell activation, biological adhesion, and cytokine production, which are suggestive of a program of T cell activation (Fig. 4E). These processes were consistent with the top GO terms identified in the RNA-seq analysis of transcripts upregulated in population D, further supporting the concept that population D cells have experienced the strongest tonic TCR signals. The top GO terms identified for the population D versus population C DARs were also highlighted by analyses of DARs in the comparisons between population C versus population A, population C versus population B, and population D versus population C (data not shown), which suggests that these comparisons have many overlapping DARs.

FIGURE 4.

Increasing tonic TCR signal strength correlates with differences in chromatin accessibility.

ATAC-seq analysis was performed on CD4+CD44loFoxp3-IRES-RFP population A–D cells purified from the spleen and lymph nodes of three Nur77-GFP Foxp3-IRES-RFP mice. (A) Heatmap shows normalized chromatin accessibility at 3234 chromatin regions that are differentially accessible identified for the six pairwise comparisons between cell populations. (B) PCA of individual replicates from populations A–D. Circles denote 99% confidence intervals. Individual replicates corresponding to populations A–D are indicated by separate color-coded dots. (C) Table shows the number of differentially accessible regions (DARs) for the six pairwise comparisons between cell populations. (D) Box-and-whisker graph shows the accessibility of 490 DARs containing Nur77 binding motifs. Box indicates interquartile range and error bars represent 1.5 times the interquartile range. rppm, reads per peak per million. (E) Table shows the top Gene Ontology terms identified in pathway analysis of the genes containing chromatin regions that are differentially accessible in the population D versus population C comparison. (F) Box-and-whisker graphs show the z-scores for accessibility of DARs associated with the indicated categories of genes. Box indicates interquartile range and error bars represent 1.5 times the interquartile range. To the right side of each graph is a partial list of genes with DARs in that group.

FIGURE 4.

Increasing tonic TCR signal strength correlates with differences in chromatin accessibility.

ATAC-seq analysis was performed on CD4+CD44loFoxp3-IRES-RFP population A–D cells purified from the spleen and lymph nodes of three Nur77-GFP Foxp3-IRES-RFP mice. (A) Heatmap shows normalized chromatin accessibility at 3234 chromatin regions that are differentially accessible identified for the six pairwise comparisons between cell populations. (B) PCA of individual replicates from populations A–D. Circles denote 99% confidence intervals. Individual replicates corresponding to populations A–D are indicated by separate color-coded dots. (C) Table shows the number of differentially accessible regions (DARs) for the six pairwise comparisons between cell populations. (D) Box-and-whisker graph shows the accessibility of 490 DARs containing Nur77 binding motifs. Box indicates interquartile range and error bars represent 1.5 times the interquartile range. rppm, reads per peak per million. (E) Table shows the top Gene Ontology terms identified in pathway analysis of the genes containing chromatin regions that are differentially accessible in the population D versus population C comparison. (F) Box-and-whisker graphs show the z-scores for accessibility of DARs associated with the indicated categories of genes. Box indicates interquartile range and error bars represent 1.5 times the interquartile range. To the right side of each graph is a partial list of genes with DARs in that group.

Close modal

Examples of DARs that exhibit decreasing accessibility from population A to population D include regions in Il7r and Ly6c1 (Fig. 4F, left). These were also validated to be differentially expressed at the protein level (Fig. 3F, 3G). In contrast, most DARs increase in accessibility from population A to population D. We next analyzed DARs that are within genes encoding cell surface receptors, intracellular signaling molecules, transcription factors, and function in cell adhesion or migration. The average accessibility score of DARs within these groups of genes increased from population A and peaked in population D (Fig. 4F). Notably, several DARs were within or near genes encoding costimulatory (Tnfrsf4 and Tnfrsf9) and coinhibitory receptors (Ctla4, Havcr2, Lag3, and Pdcd1) (Fig. 4F). These data suggest that strong tonic signaling promotes greater accessibility of genes associated with positive and inhibitory roles in T cell activation.

To determine whether there was a correlation between gene expression and chromatin accessibility for the population D versus population C comparison, we analyzed the fold change in gene expression and the fold change in chromatin accessibility. Out of 232 DEGs, 161 had associated DARs. This subset of 161 DEGs had a total of 929 associated DARs. For this comparison, there was a positive correlation between the change in gene expression versus the change in chromatin accessibility (Fig. 5A). Examples of genes with a positive correlation between chromatin accessibility and gene expression include Arhgap20, Bcl6, Lag3, and Pdcd1 (Fig. 5B). DARs in Arhgap20 and Lag3 were near the first exon and accessibility of both DARs was highest in population D cells (Fig. 5C, 5D). There was an additional DAR in an intron in the Lag3 locus that also was most accessible in population D (Fig. 5D). DARs associated with Bcl6 and Pdcd1 were upstream of the first exon and were most accessible in population D (Fig. 5E, 5F).

FIGURE 5.

Positive correlation between gene expression and chromatin accessibility.

(A) Graph plots the log2 fold change in gene expression versus the log2 fold change in chromatin accessibility for 161 out of 232 total DEGs that had associated DARs. Dots represent DARs associated with DEGs. Red dots represent DARs within genes that are expressed >1.5-fold higher in population D compared with population C; blue dots represent DARs within genes expressed >1.5-fold lower in population D compared with population C; black dots represent DARs associated with genes that are not differentially expressed in the population D versus population C. The line represents the correlation with a 95% confidence interval and the p value is indicated. (B) Graph is similar to the one in (A), except the highlighted dots represent DARs associated with four DEGs, Arhgap20, Lag3, Pdcd1, and Bcl6, as indicated. (CF) Genome plots represent chromatin accessibility score (y-axis) as a function of chromosome location (x-axis) for the indicated genes, as determined by ATAC-seq. Each trace includes a schematic of the coding region of each gene at the top. Label at the bottom shows the chromosome coordinates for each region. DARs with >2-fold differential accessibility between any of the six pairwise comparisons between populations are enclosed by boxes. (G) Taiji analysis of transcriptional targets. Bar graph shows the transcription factors with the highest fold change in PageRank scores between population D versus population C.

FIGURE 5.

Positive correlation between gene expression and chromatin accessibility.

(A) Graph plots the log2 fold change in gene expression versus the log2 fold change in chromatin accessibility for 161 out of 232 total DEGs that had associated DARs. Dots represent DARs associated with DEGs. Red dots represent DARs within genes that are expressed >1.5-fold higher in population D compared with population C; blue dots represent DARs within genes expressed >1.5-fold lower in population D compared with population C; black dots represent DARs associated with genes that are not differentially expressed in the population D versus population C. The line represents the correlation with a 95% confidence interval and the p value is indicated. (B) Graph is similar to the one in (A), except the highlighted dots represent DARs associated with four DEGs, Arhgap20, Lag3, Pdcd1, and Bcl6, as indicated. (CF) Genome plots represent chromatin accessibility score (y-axis) as a function of chromosome location (x-axis) for the indicated genes, as determined by ATAC-seq. Each trace includes a schematic of the coding region of each gene at the top. Label at the bottom shows the chromosome coordinates for each region. DARs with >2-fold differential accessibility between any of the six pairwise comparisons between populations are enclosed by boxes. (G) Taiji analysis of transcriptional targets. Bar graph shows the transcription factors with the highest fold change in PageRank scores between population D versus population C.

Close modal

To analyze which transcription factor networks may be most active in population D cells, we performed Taiji analysis, which integrates RNA-seq and ATAC-seq data (25). Analysis of the fold change in PageRank scores between population D and population C datasets suggested that Nr4a3 (Nor1), NFATc1, and NFATc3, transcription factors activated by TCR signaling, were among the differentially activated factors (Fig. 5G). Also differentially active in population D cells was Eomes, which is expressed in Th1 effector cells but also expressed in dysfunctional CD4+ cells that have been chronically stimulated (33).

We next investigated selected individual DARs detected near exon 4 of Havcr2 (Tim3) and in introns 1 and 3 of Ctla4, both of which were more accessible in population D (Fig. 6A, 6B). These DARs are similar in location to DARs that have been reported to occur selectively in Ag-experienced cells, including effector, memory, and effector CD8+ cells, but not naive T cells (34, 35). It has also been reported that DARs in intron 1 of Cd200r1 and near exon 1 of Cxcr5 are selectively detected in exhausted cells, but not in naive, effector, or memory CD8+ cells (36). Strikingly, we detected DARs in population D cells that were in similar regions of Cd200r1 and Cxcr5 (Fig. 6C, 6D). Moreover, multiple DARs throughout the Tox locus have been selectively detected in exhausted CD8+ T cells (37). In our analysis, two DARs were detected in intron 1 and intron 3 of the Tox locus in population D cells, the locations of which were similar to DARs detected in exhausted CD8+ cells (Fig. 6E) (37). These data suggest that naive CD4+ T cells that experience the strongest tonic TCR signals share some epigenetic features with Ag-experienced T cells. Furthermore, constitutive strong tonic TCR signaling may induce some chromatin accessibility patterns that are shared with exhausted T cells. These data also support a model where naive T cells adapt to intense tonic TCR signals to induce hyporesponsiveness.

FIGURE 6.

Some differentially accessible regions of chromatin in population D are also associated with Ag-experienced cells.

(AE) Genome plots for (A) Havcr2 (encoding Tim3), (B) Ctla4, (C) Cd200r1, (D) Cxcr5, and (E) Tox represent chromatin accessibility score as a function of chromosome location, as determined by ATAC-seq analysis. A schematic of the coding region of each gene is located above each trace. Labels below each trace display the chromosome coordinates for each region shown. DARs are enclosed by boxes. Asterisks indicate DARs that have been previously reported to occur in effector, memory, or exhausted CD8+ cells. For the Tox locus (E), the DARs highlighted in yellow boxes are enlarged below to more clearly show the DARs within the selected regions.

FIGURE 6.

Some differentially accessible regions of chromatin in population D are also associated with Ag-experienced cells.

(AE) Genome plots for (A) Havcr2 (encoding Tim3), (B) Ctla4, (C) Cd200r1, (D) Cxcr5, and (E) Tox represent chromatin accessibility score as a function of chromosome location, as determined by ATAC-seq analysis. A schematic of the coding region of each gene is located above each trace. Labels below each trace display the chromosome coordinates for each region shown. DARs are enclosed by boxes. Asterisks indicate DARs that have been previously reported to occur in effector, memory, or exhausted CD8+ cells. For the Tox locus (E), the DARs highlighted in yellow boxes are enlarged below to more clearly show the DARs within the selected regions.

Close modal

The strength of tonic TCR signaling can vary broadly between individual naive CD4+ cells. Our data support a model whereby a subpopulation of CD4+ T cells adapts to sustained, strong tonic TCR signaling, as a result of relatively high affinity to self-pMHC. Furthermore, such T cells adapt to the strength of tonic TCR signaling in part through changes in gene expression and chromatin accessibility, and ultimately in reduced responsiveness to subsequent TCR stimulation.

In previous two-dimensional micropipette studies, the relative affinities of OT-II and SMARTA TCR transgenic cells to their respective cognate pMHC Ags were measured across multiple individual cells (38). The relative affinities differed between OT-II and SMARTA T cells; however, there was also heterogeneity within each TCR transgenic population. The relative affinities for individual OT-II T cells spanned an ∼10-fold range. These studies revealed heterogeneity of individual T cell affinities, even in populations that expressed identical transgenic TCRs. In this study, we show that the hierarchy of relative affinities within this 10-fold range correlates with the intensity of basal Nur77-GFP expression. Importantly, the OT-II T cells used in these studies were from mice that were also homozygous for the null allele of the endogenous TCR α-chain, which prevents expression of endogenously rearranged TCRs. Previous work shows that in polyclonal T cell populations, clones with relatively low affinities to a given Ag can comprise a substantial proportion of a T cell response (39). This is consistent with our finding that population A cells are most responsive to stimulation with varying concentrations of peptide, with a less potent peptide, or under conditions where ZAP70 catalytic activity is partially inhibited.

In light of their increased apparent affinities, we propose that mature naive Nur77-GFPhi T cells may arise from immature T cells expressing TCRs that are near the affinity threshold of negative selection. This concept is consistent with previous work implying that subpopulations of peripheral T cells are derived from precursors that survived incomplete negative selection (4043). As a result of relatively high affinity for self-pMHC, Nur77-GFPhi cells and population D cells in particular likely experience strong tonic TCR signaling chronically. Recent work described the functional and gene expression changes associated with chronic stimulation of naive CD4+ cells in the absence of inflammation (33). In these studies, chronic stimulation resulted in a hypofunctional state characterized by attenuated effector cytokine potential, and a gene expression pattern associated with anergy or T cell exhaustion (Cblb, Tox, Nr4a1, Eomes, Lag3, Nt5e, Izumo1r, and Pdcd1). We found these genes were also upregulated in population D, suggesting that chronic TCR stimulation in response to self-antigens may promote a gene expression program with common features. In addition to adaptations at the transcriptional level, we also detected differential chromatin accessibility patterns in population D cells that share some features with exhausted CD8+ T cells. Among the genes with differentially accessible chromatin regions in exhausted CD8+ cells and population D cells were Cd200r1, Cxcr5, and Tox. Although it is currently unknown precisely which signals induce differential accessibility at these loci, we propose that the chronicity of TCR signaling experienced by both population D cells and exhausted CD8+ T cells may have a role. It is notable that the transcriptional signatures of population B and D cells were similar, yet there were multiple differences in chromatin accessibility between these populations. We propose that RNA-seq provides a transcriptional profile of cells at the time they were lysed, whereas ATAC-seq provides information on chromatin accessibility, which can reflect the cumulative experience of population A–D cells from previous time points up to the time when the cells were lysed. Thus, the differences between population B and D detected by ATAC-seq could reflect previously experienced signals that continue to influence chromatin accessibility.

Among the differentially expressed genes that were upregulated in population D were transcription factors Foxp3 and Bcl6, which are associated with Tregs and Tfh cells, respectively. The cells that were sorted for RNA-seq analysis were sorted to exclude expression of a Foxp3-RFP reporter gene. Therefore, we conclude that population D cells are not Tregs but may exhibit a bias toward peripheral Treg differentiation. Previous studies have indicated that strong tonic TCR signaling, as marked by high expression of basal Nur77-GFP and CD5, or absence of Ly6C expression, correlates with a higher propensity for inducing Foxp3 expression under Treg polarizing conditions in vitro (10, 44, 45). Similarly, the presence of elevated Bcl6 transcript levels in population D is suggestive of a correlation between strong tonic TCR signaling and Tfh lineage differentiation, which is consistent with recent studies (29). These findings add support to the concept that the strength of tonic TCR signaling influences the effector differentiation of naive CD4+ T cells.

We thank Julie Zikherman, Haopeng Wang, and Wan-Lin Lo for critical reading of the manuscript and Jeremy Boss for technical advice. Flow cytometry and cell sorting were performed in the Emory Department of Pediatrics/Winship Flow Cytometry Core.

RNA-seq and ATAC-seq data presented in this article have been submitted to the Gene Expression Omnibus under accession number GSE206074.

This work was supported by National Institutes of Health Grant K01 AR06548 (to B.B.A.-Y.)

Abbreviations used in this article:

     
  • ATAC-seq

    assay for transposase-accessible chromatin with sequencing

  •  
  • CD4SP

    CD4 single-positive

  •  
  • CD8SP

    CD8 single-positive

  •  
  • DAR

    differentially accessible chromatin region

  •  
  • DEG

    differentially expressed gene

  •  
  • FDR

    false discovery rate

  •  
  • GO

    Gene Ontology

  •  
  • MHC-I

    MHC class I

  •  
  • MHC-II

    MHC class II

  •  
  • PC

    principal component 1

  •  
  • PCA

    principal component analysis

  •  
  • RNA-seq

    RNA sequencing

  •  
  • self-pMHC

    self-peptides presented by MHC

  •  
  • Treg

    regulatory T cell

  •  
  • WT

    wild-type

  •  
  • ZAP70AS

    ZAP70 analog-sensitive

1.
Hogquist
K. A.
,
S. C.
Jameson
.
2014
.
The self-obsession of T cells: how TCR signaling thresholds affect fate “decisions” and effector function.
Nat. Immunol.
15
:
815
823
.
2.
Myers
D. R.
,
J.
Zikherman
,
J. P.
Roose
.
2017
.
Tonic signals: why do lymphocytes bother?
Trends Immunol.
38
:
844
857
.
3.
Stefanová
I.
,
J. R.
Dorfman
,
R. N.
Germain
.
2002
.
Self-recognition promotes the foreign antigen sensitivity of naive T lymphocytes.
Nature
420
:
429
434
.
4.
van Oers
N. S.
,
N.
Killeen
,
A.
Weiss
.
1994
.
ZAP-70 is constitutively associated with tyrosine-phosphorylated TCR ζ in murine thymocytes and lymph node T cells.
Immunity
1
:
675
685
.
5.
Matson
C. A.
,
N. J.
Singh
.
2020
.
Manipulating the TCR signaling network for cellular immunotherapy: challenges & opportunities.
Mol. Immunol.
123
:
64
73
.
6.
Tubo
N. J.
,
M. K.
Jenkins
.
2014
.
TCR signal quantity and quality in CD4+ T cell differentiation.
Trends Immunol.
35
:
591
596
.
7.
Moran
A. E.
,
K. L.
Holzapfel
,
Y.
Xing
,
N. R.
Cunningham
,
J. S.
Maltzman
,
J.
Punt
,
K. A.
Hogquist
.
2011
.
T cell receptor signal strength in Treg and iNKT cell development demonstrated by a novel fluorescent reporter mouse.
J. Exp. Med.
208
:
1279
1289
.
8.
Zikherman
J.
,
R.
Parameswaran
,
A.
Weiss
.
2012
.
Endogenous antigen tunes the responsiveness of naive B cells but not T cells.
Nature
489
:
160
164
.
9.
Au-Yeung
B. B.
,
H. J.
Melichar
,
J. O.
Ross
,
D. A.
Cheng
,
J.
Zikherman
,
K. M.
Shokat
,
E. A.
Robey
,
A.
Weiss
.
2014
.
Quantitative and temporal requirements revealed for Zap70 catalytic activity during T cell development. [Published erratum appears in 2015 Nat. Immunol. 16: 214.]
Nat. Immunol.
15
:
687
694
.
10.
Zinzow-Kramer
W. M.
,
A.
Weiss
,
B. B.
Au-Yeung
.
2019
.
Adaptation by naïve CD4+ T cells to self-antigen-dependent TCR signaling induces functional heterogeneity and tolerance.
Proc. Natl. Acad. Sci. USA
116
:
15160
15169
.
11.
Au-Yeung
B. B.
,
J.
Zikherman
,
J. L.
Mueller
,
J. F.
Ashouri
,
M.
Matloubian
,
D. A.
Cheng
,
Y.
Chen
,
K. M.
Shokat
,
A.
Weiss
.
2014
.
A sharp T-cell antigen receptor signaling threshold for T-cell proliferation.
Proc. Natl. Acad. Sci. USA
111
:
E3679
E3688
.
12.
Au-Yeung
B. B.
,
G. A.
Smith
,
J. L.
Mueller
,
C. S.
Heyn
,
R. G.
Jaszczak
,
A.
Weiss
,
J.
Zikherman
.
2017
.
IL-2 modulates the TCR signaling threshold for CD8 but not CD4 T cell proliferation on a single-cell level.
J. Immunol.
198
:
2445
2456
.
13.
Martin
B.
,
C.
Auffray
,
A.
Delpoux
,
A.
Pommier
,
A.
Durand
,
C.
Charvet
,
P.
Yakonowsky
,
H.
de Boysson
,
N.
Bonilla
,
A.
Audemard
, et al
2013
.
Highly self-reactive naive CD4 T cells are prone to differentiate into regulatory T cells.
Nat. Commun.
4
:
2209
.
14.
Anderson
K. G.
,
K.
Mayer-Barber
,
H.
Sung
,
L.
Beura
,
B. R.
James
,
J. J.
Taylor
,
L.
Qunaj
,
T. S.
Griffith
,
V.
Vezys
,
D. L.
Barber
,
D.
Masopust
.
2014
.
Intravascular staining for discrimination of vascular and tissue leukocytes.
Nat. Protoc.
9
:
209
222
.
15.
Huang
J.
,
V. I.
Zarnitsyna
,
B.
Liu
,
L. J.
Edwards
,
N.
Jiang
,
B. D.
Evavold
,
C.
Zhu
.
2010
.
The kinetics of two-dimensional TCR and pMHC interactions determine T-cell responsiveness.
Nature
464
:
932
936
.
16.
Kolawole
E. M.
,
R.
Andargachew
,
B.
Liu
,
J. R.
Jacobs
,
B. D.
Evavold
.
2018
.
2D kinetic analysis of TCR and CD8 coreceptor for LCMV GP33 epitopes.
Front. Immunol.
9
:
2348
.
17.
Evans
E.
,
A.
Leung
,
V.
Heinrich
,
C.
Zhu
.
2004
.
Mechanical switching and coupling between two dissociation pathways in a P-selectin adhesion bond.
Proc. Natl. Acad. Sci. USA
101
:
11281
11286
.
18.
Patterson
D. G.
,
A. K.
Kania
,
M. J.
Price
,
J. R.
Rose
,
C. D.
Scharer
,
J. M.
Boss
.
2021
.
An IRF4-MYC-mTORC1 integrated pathway controls cell growth and the proliferative capacity of activated B cells during B cell differentiation in vivo.
J. Immunol.
207
:
1798
1811
.
19.
Dobin
A.
,
C. A.
Davis
,
F.
Schlesinger
,
J.
Drenkow
,
C.
Zaleski
,
S.
Jha
,
P.
Batut
,
M.
Chaisson
,
T. R.
Gingeras
.
2013
.
STAR: ultrafast universal RNA-seq aligner.
Bioinformatics
29
:
15
21
.
20.
Lawrence
M.
,
W.
Huber
,
H.
Pagès
,
P.
Aboyoun
,
M.
Carlson
,
R.
Gentleman
,
M. T.
Morgan
,
V. J.
Carey
.
2013
.
Software for computing and annotating genomic ranges.
PLOS Comput. Biol.
9
:
e1003118
.
21.
Robinson
M. D.
,
D. J.
McCarthy
,
G. K.
Smyth
.
2010
.
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
Bioinformatics
26
:
139
140
.
22.
Langmead
B.
,
S. L.
Salzberg
.
2012
.
Fast gapped-read alignment with Bowtie 2.
Nat. Methods
9
:
357
359
.
23.
Zhang
Y.
,
T.
Liu
,
C. A.
Meyer
,
J.
Eeckhoute
,
D. S.
Johnson
,
B. E.
Bernstein
,
C.
Nusbaum
,
R. M.
Myers
,
M.
Brown
,
W.
Li
,
X. S.
Liu
.
2008
.
Model-based analysis of ChIP-Seq (MACS).
Genome Biol.
9
:
R137
.
24.
Heinz
S.
,
C.
Benner
,
N.
Spann
,
E.
Bertolino
,
Y. C.
Lin
,
P.
Laslo
,
J. X.
Cheng
,
C.
Murre
,
H.
Singh
,
C. K.
Glass
.
2010
.
Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.
Mol. Cell
38
:
576
589
.
25.
Zhang
K.
,
M.
Wang
,
Y.
Zhao
,
W.
Wang
.
2019
.
Taiji: system-level identification of key transcription factors reveals transcriptional waves in mouse embryonic development.
Sci. Adv.
5
:
eaav3262
.
26.
Hogquist
K. A.
,
Y.
Xing
,
F. C.
Hsu
,
V. S.
Shapiro
.
2015
.
T cell adolescence: maturation events beyond positive selection.
J. Immunol.
195
:
1351
1357
.
27.
Robertson
J. M.
,
P. E.
Jensen
,
B. D.
Evavold
.
2000
.
DO11.10 and OT-II T cells recognize a C-terminal ovalbumin 323–339 epitope.
J. Immunol.
164
:
4706
4712
.
28.
Hiwa
R.
,
H. V.
Nielsen
,
J. L.
Mueller
,
R.
Mandla
,
J.
Zikherman
.
2021
.
NR4A family members regulate T cell tolerance to preserve immune homeostasis and suppress autoimmunity.
JCI Insight
6
:
e151005
.
29.
Rogers
D.
,
A.
Sood
,
H.
Wang
,
J. J. P.
van Beek
,
T. J.
Rademaker
,
P.
Artusa
,
C.
Schneider
,
C.
Shen
,
D. C.
Wong
,
A.
Bhagrath
, et al
2021
.
Pre-existing chromatin accessibility and gene expression differences among naive CD4+ T cells influence effector potential.
Cell Rep.
37
:
110064
.
30.
Liu
X.
,
Y.
Wang
,
H.
Lu
,
J.
Li
,
X.
Yan
,
M.
Xiao
,
J.
Hao
,
A.
Alekseev
,
H.
Khong
,
T.
Chen
, et al
2019
.
Genome-wide analysis identifies NR4A1 as a key mediator of T cell dysfunction.
Nature
567
:
525
529
.
31.
Kalekar
L. A.
,
S. E.
Schmiel
,
S. L.
Nandiwada
,
W. Y.
Lam
,
L. O.
Barsness
,
N.
Zhang
,
G. L.
Stritesky
,
D.
Malhotra
,
K. E.
Pauken
,
J. L.
Linehan
, et al
2016
.
CD4+ T cell anergy prevents autoimmunity and generates regulatory T cell precursors.
Nat. Immunol.
17
:
304
314
.
32.
Liebmann
M.
,
S.
Hucke
,
K.
Koch
,
M.
Eschborn
,
J.
Ghelman
,
A. I.
Chasan
,
S.
Glander
,
M.
Schädlich
,
M.
Kuhlencord
,
N. M.
Daber
, et al
2018
.
Nur77 serves as a molecular brake of the metabolic switch during T cell activation to restrict autoimmunity.
Proc. Natl. Acad. Sci. USA
115
:
E8017
E8026
.
33.
Trefzer
A.
,
P.
Kadam
,
S.-H.
Wang
,
S.
Pennavaria
,
B.
Lober
,
B.
Akçabozan
,
J.
Kranich
,
T.
Brocker
,
N.
Nakano
,
M.
Irmler
, et al
2021
.
Dynamic adoption of anergy by antigen-exhausted CD4+ T cells.
Cell Rep.
34
:
108748
.
34.
Li
J.
,
Y.
He
,
J.
Hao
,
L.
Ni
,
C.
Dong
.
2018
.
High levels of Eomes promote exhaustion of anti-tumor CD8+ T cells.
Front. Immunol.
9
:
2981
.
35.
Li
J.
,
Y.
Lee
,
Y.
Li
,
Y.
Jiang
,
H.
Lu
,
W.
Zang
,
X.
Zhao
,
L.
Liu
,
Y.
Chen
,
H.
Tan
, et al
2018
.
Co-inhibitory molecule B7 superfamily member 1 expressed by tumor-infiltrating myeloid cells induces dysfunction of anti-tumor CD8+ T cells.
Immunity
48
:
773
786.e5
.
36.
Pauken
K. E.
,
M. A.
Sammons
,
P. M.
Odorizzi
,
S.
Manne
,
J.
Godec
,
O.
Khan
,
A. M.
Drake
,
Z.
Chen
,
D. R.
Sen
,
M.
Kurachi
, et al
2016
.
Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade.
Science
354
:
1160
1165
.
37.
Khan
O.
,
J. R.
Giles
,
S.
McDonald
,
S.
Manne
,
S. F.
Ngiow
,
K. P.
Patel
,
M. T.
Werner
,
A. C.
Huang
,
K. A.
Alexander
,
J. E.
Wu
, et al
2019
.
TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion.
Nature
571
:
211
218
.
38.
DiToro
D.
,
C. J.
Winstead
,
D.
Pham
,
S.
Witte
,
R.
Andargachew
,
J. R.
Singer
,
C. G.
Wilson
,
C. L.
Zindl
,
R. J.
Luther
,
D. J.
Silberger
, et al
2018
.
Differential IL-2 expression defines developmental fates of follicular versus nonfollicular helper T cells.
Science
361
:
eaao2933
.
39.
Martinez
R. J.
,
R.
Andargachew
,
H. A.
Martinez
,
B. D.
Evavold
.
2016
.
Low-affinity CD4+ T cells are major responders in the primary immune response.
Nat. Commun.
7
:
13848
.
40.
Malhotra
D.
,
J. L.
Linehan
,
T.
Dileepan
,
Y. J.
Lee
,
W. E.
Purtha
,
J. V.
Lu
,
R. W.
Nelson
,
B. T.
Fife
,
H. T.
Orr
,
M. S.
Anderson
, et al
2016
.
Tolerance is established in polyclonal CD4+ T cells by distinct mechanisms, according to self-peptide expression patterns.
Nat. Immunol.
17
:
187
195
.
41.
Truckenbrod
E. N.
,
K. S.
Burrack
,
T. P.
Knutson
,
H.
Borges da Silva
,
K. E.
Block
,
S. D.
O’Flanagan
,
K. R.
Stagliano
,
A. A.
Hurwitz
,
R. B.
Fulton
,
K. R.
Renkema
,
S. C.
Jameson
.
2021
.
CD8+ T cell self-tolerance permits responsiveness but limits tissue damage.
eLife
10
:
e65615
.
42.
Yu
W.
,
N.
Jiang
,
P. J.
Ebert
,
B. A.
Kidd
,
S.
Müller
,
P. J.
Lund
,
J.
Juang
,
K.
Adachi
,
T.
Tse
,
M. E.
Birnbaum
, et al
2015
.
Clonal deletion prunes but does not eliminate self-specific αβ CD8+ T lymphocytes.
Immunity
42
:
929
941
.
43.
Daley
S. R.
,
D. Y.
Hu
,
C. C.
Goodnow
.
2013
.
Helios marks strongly autoreactive CD4+ T cells in two major waves of thymic deletion distinguished by induction of PD-1 or NF-κB.
J. Exp. Med.
210
:
269
285
.
44.
Henderson
J. G.
,
A.
Opejin
,
A.
Jones
,
C.
Gross
,
D.
Hawiger
.
2015
.
CD5 instructs extrathymic regulatory T cell development in response to self and tolerizing antigens.
Immunity
42
:
471
483
.
45.
Guichard
V.
,
N.
Bonilla
,
A.
Durand
,
A.
Audemard-Verger
,
T.
Guilbert
,
B.
Martin
,
B.
Lucas
,
C.
Auffray
.
2017
.
Calcium-mediated shaping of naive CD4 T-cell phenotype and function.
eLife
6
:
e27215
.

The authors have no financial conflicts of interest.

This article is distributed under the terms of the CC BY 4.0 Unported license.