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The Journal of Immunology, 2007, 178: 6931-6940.
Copyright © 2007 by The American Association of Immunologists, Inc.
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Human Resting CD4+ T Cells Are Constitutively Inhibited by TGFbeta under Steady-State Conditions1,2

Sabine Classen3,*, Thomas Zander3,*, Daniela Eggle*, Jens M. Chemnitz*, Benedikt Brors{dagger}, Ingrid Büchmann*, Alexey Popov*, Marc Beyer*, Roland Eils{dagger}, Svenja Debey* and Joachim L. Schultze4,*

* Department of Internal Medicine I, Molecular Tumor Biology and Tumor Immunology, University of Cologne, Cologne, Germany; and {dagger} Division for Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
Based on studies in knockout mice, several inhibitory factors such as TGFbeta, IL-10, or CTLA-4 have been implicated as gate keepers of adaptive immune responses. Lack of these inhibitory molecules leads to massive inflammatory responses mainly mediated by activated T cells. In humans, the integration of these inhibitory signals for keeping T cells at a resting state is less well understood. To elucidate this regulatory network, we assessed early genome-wide transcriptional changes during serum deprivation in human mature CD4+ T cells. The most striking observation was a "TGFbeta loss signature" defined by down-regulation of many known TGFbeta target genes. Moreover, numerous novel TGFbeta target genes were identified that are under the suppressive control of TGFbeta. Expression of these genes was up-regulated once TGFbeta signaling was lost during serum deprivation and again suppressed upon TGFbeta reconstitution. Constitutive TGFbeta signaling was corroborated by demonstrating phosphorylated SMAD2/3 in resting human CD4+ T cells in situ, which were dephosphorylated during serum deprivation and rephosphorylated by minute amounts of TGFbeta. Loss of TGFbeta signaling was particularly important for T cell proliferation induced by low-level TCR and costimulatory signals. We suggest TGFbeta to be the most prominent factor actively keeping human CD4+ T cells at a resting state.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
Although much is known about activation, differentiation, and expansion of lymphocytes involved in Ag responses, the maintenance of lymphocyte homeostasis before and after an immune response is less well understood (1, 2, 3, 4, 5). As suggested by Grossman and colleagues (3), T cell homeostasis requires temporary T cell activation followed by either self-renewal or differentiation. This "balance of growth" of activated T cells was proposed to be regulated by feedback loops most likely on the level of APCs. The mechanisms involved are supposed to function in each differentiation compartment from the naive to the memory cell differentiation stage. To initiate differentiation or self-renewal, T cells must first undergo a transition between the resting and an activated state.

Using genetic approaches in murine model systems, it became apparent that internal stimuli, in particular self-peptide-MHC ligands for the TCR (6, 7, 8) and IL-7R interactions (9, 10, 11, 12, 13), are essential for T cell survival and homeostasis. The necessity of MHC-TCR interaction for T cell maintenance was further supported by TCR {alpha}-chain depletion in mature T cells resulting in a decay of T cells over time (14). In addition to IL-7 other common {gamma}-chain cytokines such as IL-4 or IL-15 were shown to support survival of naive T cells in vitro but IL-7 seems to be the dominant cytokine in vivo (12, 13). IL-15 can augment the homeostatic proliferation of naive T cells, but important components of the IL-15R are not expressed until naive T cells begin homeostatic proliferation in response to IL-7 (1). Nevertheless, genetic disruption of the common {gamma}-chain receptor (present in the IL-2, IL-4, IL-7, IL-9, and IL-15 receptors) or components of its signaling pathway results in impaired homeostasis of CD4+ T cells (15, 16). Similarly, homeostatic regulation is also disturbed in genetically altered mice lacking the IL-2 gene (17).

Because the above-mentioned genes are also necessary for T cell activation during immune responses, mechanisms must have evolved that allow T cells to choose between survival and homeostatic proliferation rather than activation and immune response-associated proliferation. Several explanations have been proposed so far, including inhibition by physical T cell-T cell interaction, competition for limiting resources, e.g., of IL-7 (13, 18, 19), or involvement of inhibitory factors. In fact, genetic disruption of several genes encoding inhibitory factors, such as TGFbeta, Fas, Fas ligand, or CTLA-4, leads to significant disturbances of T cell biology resulting in severe lymphoproliferative diseases (20, 21, 22, 23, 24, 25, 26). CTLA-4–/– mice are characterized by a spontaneous lethal lymphoproliferative disease with infiltrates of highly activated T cells in many tissues (25). The role of CTLA-4 during thymic development and the requirement of T cell activation before cell surface expression suggests that CTLA-4 plays a role during T cell activation rather than being directly involved in regulation of peripheral T cell survival and homeostasis (5, 27). In comparison to CTLA-4, a different phenotype has been shown in IL-10- deficient mice with less acute inflammatory diseases mainly manifesting in the intestine (28). This bowel disease may be due to uncontrolled chronic inflammation induced by enteropathogens.

In mice there is clear evidence that TGFbeta is an important factor restraining the size of the T cell compartment (20, 22, 29, 30, 31). Mice defective in TGFbeta1 develop symptoms of a lymphoproliferative disease 2–3 wk after birth. Due to the pleiotropic effects of TGFbeta1, studying the role of TGFbeta1 on T cell homeostasis in TGFbeta1–/– mice is hampered by unrelated effects also responsible for the observed phenotype (22, 29). To circumvent such limitations, transgenic mice with a dominant-negative TGFbeta-RII specifically expressed in T cells were previously introduced by Gorelik and Flavell (20, 29). Mice expressing such dominant-negative TGFbeta-RII within the T cell compartment show disruption of homeostasis, a strong inflammatory response, and significant signs of autoimmunity (20, 22). These mice are characterized by a reduced number of naive T cells with a concomitant increase of memory T cells, of which a significant fraction demonstrates an activated phenotype. Furthermore, these cells show spontaneous differentiation in vitro in response to T cell activation. Taken together, numerous inhibitory mechanisms responsible for preventing T cells from being activated have been revealed in murine models. But even in the murine system their integration into homeostatic circuits is not fully understood.

In humans even less is known about the major signals involved in T cell homeostasis, which is mainly due to the inability of in vivo manipulation of single genes. It is tempting to speculate that the same factors involved in murine T cell homeostasis are also involved in human T cell homeostasis, especially because many of these factors have similar roles during induction of immunity. However, so far no experimental evidence exists supporting such a postulate.

Because T cell homeostasis seems to be mainly regulated by exogenous stimuli, we hypothesized that deprivation of resting human T cells of any exogenous signals should reverse intracellular signaling cascades actively keeping T cells at a resting state. We further postulated that such changes should certainly be recognizable on the genomic level. To this end, we interrogated genome-wide transcriptional changes of human mature CD4+ T cells in response to deprivation of exogenous signals. Using this unbiased approach, we demonstrate TGFbeta to be the most prominent inhibitory factor detected that constitutively acts on mature human resting CD4+ T cells keeping them at a "resting phenotype."


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Disclosures
 References
 
Peripheral blood samples

Following approval by the institutional review board, blood samples from healthy blood donors were collected after written informed consent had been obtained.

Isolation of CD4+ T cells, CD8+ T cells, B cells, and monocytes

CD4+ T cells were isolated from blood samples by using a RosetteSep CD4+ enrichment kit (StemCell Technologies); purity was >90% as determined by flow cytometry. For comparing SMAD2 and SMAD3 phosphorylation in CD4+ T cells, CD8+ T cells, B cells, and monocytes, cells were isolated by positive selection using CD4, CD8, CD19, or CD14 microbeads (Miltenyi Biotec).

Serum deprivation and T cell treatment

CD4+ T cells were cultured in serum-free medium (AIM V (Invitrogen Life Technologies) plus ExCell 640 1:1 (JRH Bioscience)) for 2, 8, 12, or 18 h (serum deprivation). TGFbeta1 (R&D Systems) was added after serum deprivation for 1, 2, or 8 h. In other experiments, 50% freshly isolated human serum, in combination with different concentrations of the ALK5 inhibitor SB431542 (Tocris Bioscience), was added for 2 h to CD4+ T cells after serum deprivation.

Immunofluorescence for SMAD2 and SMAD3

T cells and B cells were isolated from venipuncture blood samples by using RosetteSep T cell enrichment and B cell enrichment kits (StemCell Technologies). Cells were centrifuged on glass coverslips, fixed with 4% paraformaldehyde (Sigma-Aldrich), and permeabilized in 0.2% Triton X-100 (Invitrogen Life Technologies) before blocking for 0.5 h (1% fish skin gelatin (Sigma-Aldrich), 10% goat serum (DakoCytomation) in PBS). Slides were incubated with anti-phospho-SMAD3 (Merck) followed by secondary Ab (Alexa Fluor 568 goat anti-rabbit IgG (H + L); Invitrogen Life Technologies). Subsequently, cells were incubated with mAbs against CD4 (Novocastra), CD8 (DakoCytomation), or CD19 (Serotec) followed by secondary Ab (Alexa Fluor 488 goat anti-mouse IgG (H + L); Invitrogen Life Technologies). Afterward, cells were incubated with 4',6'-diamidino-2-phenylindole dihydrochloride (Invitrogen Life Technologies) for nuclear staining. Photographs were taken with a Zeiss Axioplan microscope (x63 magnification) and Zeiss AxioVision Rel 4.5 software.

For control stainings, freshly isolated CD4+ T cells were cultured for 24 h under serum-free conditions and either analyzed directly or treated with 30 ng/ml TGFbeta1 for 1 h.

Cell lysis and Western blot

Cells were lysed (5 ml of 1% Triton X-100 (Promega), 750 µl of 150 mM NaCl (Roth), 250 µl of 50 mM Tris-HCl (Invitrogen Life Technologies), 50 µl of Phosphatase Inhibitor Cocktail 1 (Sigma-Aldrich), 50 µl of Phosphatase Inhibitor Cocktail 2 (Sigma-Aldrich), protease inhibitor (Roche, Complet Mini), 10 µl of 1 M PMSF), lysates resolved by SDS-PAGE, proteins were transferred to nitrocellulose, and blots were probed with appropriate primary and secondary Ab combinations. The following Abs were used: anti-phospho-SMAD3/SMAD1 Ab (Cell Signaling), anti-phospho-SMAD2 Ab (Cell Signaling), anti- SMAD2/3 (BD Biosciences), anti-beta-actin Ab (Chemicon International), anti-mouse IgG-HRP (DakoCytomation), anti-rabbit IgG-HRP (DakoCytomation).

Microarray procedure

When using the Affymetrix platform RNA, isolation, quantification, target preparation, and hybridization were performed as described previously (32). Biotin-labeled cRNA preparation for the Illumina platform was performed using the Ambion Illumina RNA amplification kit (Ambion Europe). Biotin-labeled cRNA (1.5 µg) was hybridized to Sentrix whole 6 x 2 genome bead chips (Illumina) and scanned on the Illumina BeadStation 500x (33). Table I represents a summary of all microarray experiments performed within this study.


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  		Table I. Summary of all microarray experiments performeda

 
Data analysis and software

For data collection, assessment and statistical analysis we used Affymetrix Microarray Analysis Suite 5.0, Affymetrix Data Mining Tool 3.0, Illumina BeadStudio, and R language (Bioconductor project). In R language, the vsn method and the quantile method were used for data normalization of Affymetrix Illumina microarrays, respectively. Unpaired t tests were calculated as appropriate. For visualization and gene ontology assessment, we used GenMAPP and MAPPfinder (both from Gladstone Institutes, University of California, San Francisco) (34). All heat maps were visualized using MAYDAY (PAS-group, University Tübingen) (35). For further gene ontology analysis, the R platform was used.

To better understand changes in transcriptional regulation, an algorithm was developed that integrates gene ontology information provided by the international gene ontology (GO)5 consortium, a quantitative distance analysis between different experimental groups (here time points), a calculation of the statistical power of the method based on a permutation approach and a visualization of the data. The algorithm will be described in more detail elsewhere (D. Eggle, T. Zander, and J. L. Schultze, manuscript in preparation).

Briefly, in a first step, so-called "gene spaces" are determined which are based on GO classifications. A gene space is composed of genes within a specific GO identification (ID). GO IDs defining a gene space are restricted to one of the specified categories: biological process, molecular function, or cellular component. Only those GO IDs which are represented with at least five probe sets on the array in use are used for further analysis. In the second step, pairwise centroid distances using the Euclidean distance are calculated between the different experimental groups. The third step is a significance analysis of the calculated distances. In this study, group assignments of the samples are randomly permuted followed by recalculation of distances between centroids. This is done 1000 times. Corresponding p values are determined as the fraction of iterations where the centroid distance obtained from the permuted groups is greater than the centroid distance in the original data. In the last step, a network of contributing genes is being constructed and visualized using Cytoscape. Therefore, gene spaces meeting a specified significance criterion are determined and genes included in the respective gene space (contributing genes) are extracted. The network of contributing genes is constructed by drawing edges between genes belonging to the same GO IDs and is subsequently visualized in Cytoscape.

All microarray data can be accessed using the National Center for Biotechnology Information (GEO accession number).

Real-time-PCR

Five hundred nanograms of RNA were reverse transcribed using the Transcriptor First Strand cDNA Synthesis kit (Roche Diagnostics). RT-PCR was performed with a LightCycler TaqMan master kit and a Universal Probe Library Assay on a LightCycler 1.3 instrument (Roche Diagnostics). Analysis was performed with LightCycler3 and RelQuant software (Roche Diagnostics) using a calibrator normalized relative quantification based on the beta2-microglobulin expression. Primers used are listed in Table II.


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  		Table II. RT-PCR primer

 
Functional T cell assays

CD4+ T cells were cultured in serum-free medium and TGFbeta1 was supplemented as described (1 ng/ml). The cells were stimulated 16 h after serum deprivation with artificial APCs (aAPC) comprised of magnetic beads (Dynal Biotech) coated with the following Abs: 5% anti-CD3 (OKT3; Janssen-Cilag), 14% anti-CD28 (9.3, a gift from Drs. C. June and J. Riley, Abaramson Cancer Research Center, University of Pennsylvania, Philadelphia, PA),and 81% anti-MHC class I (W6/32) as previously described (36). Before stimulation with aAPCs, CD4+ T cells were labeled with CFSE (Invitrogen Life Technologies). After four days of culture, T cell proliferation was assessed by flow cytometry.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Disclosures
 References
 
Significant transcriptional changes in human CD4+ T cells after short-term serum deprivation

To assess factors keeping T cells at a resting state, we exposed purified human CD4+ T cells to an environment depleted of blood-derived soluble factors present in serum. Early genome-wide transcriptional changes were assessed using Affymetrix microarrays (Fig. 1A). Filtering based on fold changes (FC) and significance (variable probe sets, FC >1.5 or FC < –1.5 and p < 0.05) revealed a high number of genes (878 genes, 443 up- and 435 down-regulated) with altered transcription after 2 h of serum deprivation in highly purified CD4+ T cells. Changes of transcription even further increased at a later time point (910 genes at 8 h; 593 up- and 317 down-regulated) (Fig. 1A). When hierarchical clustering using all variable probe sets was performed, time of serum withdrawal was the major factor separating the sample groups (Fig. 1B).


Figure 1
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  		FIGURE 1. The TGFbeta pathway is significantly changed by serum deprivation in CD4+ T cells: A, To visualize significant changes in gene expression, Vulcano plots were used. The FC (log2 FC) in gene expression was plotted against the negative p value (log10). All CD4+ T cell samples assessed on the Affymetrix platform are included. Plotted are genes changed between t = 0 (n = 10) and t = 2 h (n = 3) as well as t = 0 and t = 8 h (n = 3). Genes significantly changed are defined by FC < –1.5 or FC >1.5 and a p < 0.05 (see respective lines). B, Hierarchical clustering of all T cell experiments on the Affymetrix platform. Before clustering, genes were filtered using all variable probe sets (0.5 < SD/mean <10). C, All GO IDs (all) were filtered first on the category biological process (BP), next filtered on the presence on the HGU133A array (HGU133A/BP), and finally on those represented with at least five probe sets (>5 PS/ID). D, GO analysis revealed several significantly changed GO IDs (number of GO IDs) in CD4+ T cells after 8 h of incubation in serum-free medium. The number of cell cycle-related GO IDs is given; highlighted on the right site are offsprings of the overall GO term cell cycle; *, TGFbeta targets is a set of known TGFbeta target genes. Significance levels are presented as error rates (in percent).

 
Quantitative analysis reveals the TGFbeta signaling pathway as a major target after short-term serum deprivation

Next, we were interested in determining which biological systems mainly contribute to these changes of gene expression. Therefore, we applied a systems biology approach based on GO. In short, a set of gene spaces was defined as the group of genes given by one GO ID in the category biological process (www.geneontology.org). Only those GO IDs were used that are represented with at least five probe sets on the HGU133A Affymetrix array (Fig. 1C). Of the 18,455 currently known GO IDs, 9,805 comprise biological processes, 2,616 are present on the HGU133A array, but only 1,336 of them include at least 5 probe sets (Fig. 1C). Within these 1,336 predefined gene spaces, Euclidean distances were calculated between the different sample groups (time points t = 0, 2, and 8 h). Significance of the obtained distances was validated by permutation analysis. As demonstrated in Fig. 1D, 384 GO IDs were identified to be affected on a significance level below 0.1%, 180 GO IDs between 0.1 and 1%, and 230 GO IDs between 1 and 5% after 8 h of serum deprivation.

When analyzing the most significant GO IDs, it became apparent that genes belonging to the biological terms cell cycle, cell growth, and transcription regulation were major contributors to differences in gene expression after serum deprivation (Fig. 1D). Surprisingly, 31 of 56 cell cycle-related GO IDs were affected on a significance level below 0.1% and only 5 cell cycle-related GO IDs did not reach the 0.05 significance level (error rate >5%). To identify signals that might account for these changes in T cells after serum deprivation, we next searched for potential extrinsic signals upstream of cell cycle, cell growth, and transcription regulation. This analysis identified the TGFbeta pathway to be the most significantly changed exogenous signaling cascade (error rate <1%). To corroborate the GO-based approach, a set of genes containing previously described TGFbeta1 target genes (37, 38) was subjected to GO analysis. We postulated that these TGFbeta target genes should again reveal significant changes in gene expression associated with serum deprivation. Indeed, this set of genes was even more significantly changed in human primary CD4+ T cells (error rate <0.1%) (Fig. 1D).

To further evaluate the specificity of our results, GO IDs containing genes associated with immune regulation were studied. Strikingly, none of these GO IDs reached a level of significance exceeding 1% (three GO IDs with an error rate between 1 and 5%, and eight GO IDs with an error rate >5%). Taken together, the genome-wide screen for transcriptional changes and a quantitative bioinformatics approach revealed that changes in TGFbeta-related genes are major contributors to the overall transcriptional changes observed after serum deprivation in human CD4+ T cells.

Serum-deprived human CD4+ T cells present a TGFbeta loss signature

To visualize the impact of serum deprivation on TGFbeta-related genes, we adapted the TGFbeta signaling map provided by GenMAPP adding target genes significantly changed in gene expression (FC >1.5 or FC < –1.5, p < 0.05 at t = 8 h) after serum deprivation (Fig. 2A). After 2 h, inhibitors of TGFbeta signaling (SMURF1 and 2, SMAD7, TGIF, and SKI) were significantly up-regulated while at the same time many genes known to be induced by TGFbeta including JUN, JUNB, GADD45B, ZFP36L2, ID2, BHLB2, and KLF11 were down-regulated. This was further pronounced after 8 h: 13 known target genes of TGFbeta were significantly reduced in expression, while three genes usually repressed by TGFbeta (ID3, MYB, and phosphatidylinositol glycan class F (PIGF)) were induced (Fig. 2A). To further support these findings, we performed an additional experiment extending the time of serum deprivation to 12 and 18 h (Fig. 2B, plotted are only genes differentially expressed after 8 h). For those TGFbeta target genes, we observed a similar expression pattern at the later time points, further supporting that the transcriptional control of these genes by TGFbeta is lost after serum deprivation.


Figure 2
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  		FIGURE 2. Changes of TGFbeta target genes following serum deprivation. A, Changes in gene expression within the TGFbeta signal transduction pathway and exemplified target genes after 2 and 8 h of culturing CD4+ T cells were analyzed using the GenMAPP software. Genes with increased expression (FC ≥1.5, p < 0.05) are highlighted in red, those with a decreased expression (FC ≤ –1.5, p < 0.05) in blue. The mean expression value was used for each time point (0 h: n = 10; 2 h: n = 3; 8 h: n = 3). B, Heat map visualizing FCs of known TGFbeta target genes at the 2-, 8-, 12-, and 18-h time points. Only genes differentially expressed after 8 h are depicted. All FCs were computed in comparison to the 0-h time point and color coded. Genes with increased expression (FC ≥1.5, p < 0.05) are shown in red, those with a decreased expression in blue (FC ≤ –1.5, p < 0.05). Changes in gene expression were assessed using two different microarray platforms (Affymetrix and Illumina). Before visualization, data were normalized and cross-annotated.

 
Identification of novel TGFbeta target genes in resting CD4+ T cells

To investigate which genes are main targets of TGFbeta in resting CD4+ T cells, human CD4+ T cells were first serum-deprived for 18 h followed by a single pulse of TGFbeta1 (10 ng/ml). TGFbeta target genes were defined as genes showing transcriptional changes after serum deprivation (FC >1.5 or FC < –1.5, p < 0.05 at t = 8 h after serum deprivation) and counterregulation after addition of TGFbeta1 (FC >1.25 or FC < –1.25, p < 0.05 at t = 2 h following addition of TGFbeta1). As demonstrated in Fig. 3A, most of the known TGFbeta target genes identified as significantly regulated during serum deprivation (see Fig. 2B) were indeed counterregulated after addition of TGFbeta1, with only a few exceptions.


Figure 3
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  		FIGURE 3. Counterregulation of TGFbeta target genes by stimulation with recombinant TGFbeta1 following serum deprivation. CD4+ T cells were cultured under serum-free conditions for up to 18 h before 10 ng/ml TGFbeta1 was added, and the transcriptional changes were assessed 1, 2, or 8 h after addition of TGFbeta1. In control cultures, no TGFbeta1 was added. A, Heat map displaying FCs of known TGFbeta target genes. Genes were selected based on differential expression (FC >1.5 or FC < –1.5, p < 0.05) at 8 h after serum deprivation compared with the 0-h time point. FCs for the 8, 12, and 18 h after serum deprivation were computed in comparison to the 0-h time point. FCs for the 1, 2, and 8 h after addition of TGFbeta1 were computed in comparison to the 18-h time point of serum deprivation. FCs are color coded showing genes being up- and down-regulated in red and blue, respectively. B, Identification of novel TGFbeta target genes. Genes were selected based on differential expression after serum deprivation (0- vs 8-h time point; FC >1.5 or FC < –1.5, p < 0.05) and counterregulation following addition of TGFbeta1 (FC >1.25 or FC < –1.25, p < 0.05). FCs were computed and are color coded in the same way as in A. Genes marked with an asterisk are known TGFbeta target genes. C, Regulation of CXCR4, KLF10, and SLAMF1 mRNA assessed by RT-PCR. The relative expression levels compared with B2-microglobulin are plotted. Data represent mean ± SD of three independent experiments. {square}, T cells before culture; {blacksquare}, T cells under serum-free conditions; Figure 3, T cells after addition of TGFbeta1 (10 ng/ml). Statistically significant differences (paired t test, p < 0.05) are marked with an asterisk.

 
In addition to known TGFbeta target genes we identified 42 novel genes that so far have not been recognized as TGFbeta target genes in other cellular systems (Fig. 3B). Although most of the known TGFbeta target genes were down-regulated during serum deprivation and restored following TGFbeta1 pulse (Fig. 3A), the majority of the new target genes were found to be suppressed by TGFbeta1 (Fig. 3B). This might be explained by previous strategies that mainly identified TGFbeta target genes solely by exposing cells to increased concentrations of TGFbeta1 (37). Applying a GO approach, four major groups of genes were identified: genes encoding for membrane-associated proteins (transport and signaling), proteins with nuclear localization (transcriptional regulation), proteins involved in cell cycle regulation, and genes of unknown function. Six genes (RDH11, SLC35A1, VDP, PIGF, B3GALT2, and GNPAT) associated with intracellular membranes, especially of the Golgi apparatus and the endoplasmic reticulum, are repressed by TGFbeta. Except for PIGF, a key enzyme involved in GPI anchor biosynthesis, the function of the other genes in T cells is still elusive. Expression of three extracellular membrane proteins, CXCR4, FLT3LG, and SLC7A5, is significantly reduced upon serum deprivation, while ICAM-2, an adhesion molecule, and SLAMF1 (CD150) a costimulatory molecule, are suppressed by TGFbeta. SLAMF1 has been shown to be constitutively expressed in T cells and increased expression of SLAMF1 is clearly associated with T cell activation (39). Our data further suggest that the level of constitutive SLAMF1 expression is under the control of TGFbeta with increased expression in the absence of TGFbeta.

The second group of genes codes for proteins with nuclear localization. In addition to regulators of transcription known to be TGFbeta target genes (KLF10, JUN, and MYB), expression of seven novel genes involved in transcriptional regulation (HDAC2, SF1, ZFP36, RNPC1, RACGAP1, YWHAE, and IFI16) was shown to be altered by TGFbeta in T cells. ZFP36, which is decreased upon serum deprivation, can bind to AU-rich elements in mRNAs coding for inflammatory cytokines such as TNF-{alpha} or CSF2, thereby increasing the liability of these mRNAs (40).

In addition to known TGFbeta target genes involved in cell cycle regulation, such genes were identified among the novel TGFbeta targets, namely, CDC7, CUL2, SMC4L1, PLK3, and RACGAP1. Several newly identified TGFbeta target genes were genes with yet unknown function, particularly in T cells (C12orf11, F25965, FLJ20125, MGC17330, M11S1, NOC3L, and SLC35A5).

To verify these findings by a second technique, we performed real-time PCR for exemplary target genes (Fig. 3C). CD4+ T cells were cultured under the same culture conditions. Similar to the microarray data, mRNA for CXCR4 and KLF10 was significantly down-regulated following serum deprivation and stayed low over the whole culture period (up to 26 h). In contrast, after a single pulse of TGFbeta1 (10 ng/ml) at 18 h, the expression levels of both genes were counterregulated, almost restoring the levels of CXCR4 expression to baseline and significantly exceeding baseline levels for KLF10. Exemplary for a gene suppressed by TGFbeta1 RT-PCR results obtained for SLAMF1 are shown. mRNA for SLAMF1 was significantly up-regulated following serum deprivation and was highly transcribed over the whole culture period (up to 26 h). Addition of TGFbeta1 (10 ng/ml) after 18 h of serum deprivation reduced the expression to baseline levels.

Ablation of TGFbeta signaling is associated with reduced SMAD phosphorylation

The genome-wide transcription analysis suggested that constitutive TGFbeta signaling controls resting T cells on the transcriptional level. Phosphorylation of receptor-regulated SMAD2 and SMAD3 is an early event following binding of TGFbeta to its cognate receptor complex. Loss of transcriptional control by TGFbeta should therefore be accompanied by loss of SMAD phosphorylation. To study SMAD3 phosphorylation on the single cell level, immunofluorescence analysis was performed on human lymphocytes from venipuncture blood immediately after isolation. As depicted in Fig. 4, all freshly isolated CD4+ T cells contained significant amounts of phosphorylated nuclear SMAD3 (Fig. 4B). CD4+ T cells cultured under serum-free conditions for 24 h completely lost phosphorylation of SMAD3 (Fig. 4A), whereas addition of TGFbeta1 restored SMAD3 phosphorylation. In contrast, SMAD3 was not significantly phosphorylated in CD8+ T cells while CD19+ B cells showed only moderate amounts of phosphorylated SMAD3 (Fig. 4B). SMAD2 and SMAD3 phosphorylation was also assessed by Western blot analysis of highly purified CD4+ T cells, CD8+ T cells, CD14+ monocytes, and CD19+ B cells (Fig. 5A). Although both SMAD molecules are phosphorylated in CD4+ T cells, only SMAD2 phosphorylation was detectable in CD8+ T cells. In contrast, monocytes only expressed background levels of phosphorylated SMAD2 and SMAD3. In accordance with the immunofluorescence analysis, B cells showed only weak SMAD3 phosphorylation. To assess the variability of SMAD phosphorylation in human T cells, CD4+ T cells derived from five healthy individuals were analyzed by Western blot analysis. SMAD2 and SMAD3 were constitutively phosphorylated in T cells of all donors analyzed ex vivo (Fig. 5B). The TGFbeta signal intensity among different individuals was heterogeneous (Fig. 5C), which is not due to donor-specific differences in the amounts of total SMAD protein, as protein levels are comparable in all tested donors (Fig. 5B).


Figure 4
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  		FIGURE 4. SMAD3 phosphorylation in human lymphocytes. Immunofluorescence analysis of venipuncture blood samples; cells were stained for either CD4, CD8, or CD19 (green, Alexa 488) and p-SMAD3 (red, Alexa 568). Nuclear staining with 4',6'-diamidino-2-phenylindole (blue). The overlay is depicted in the top row. A, CD4+ T cells analyzed after 24 h of serum deprivation (–TGFbeta1) and after an additional incubation period of 1 h with 30 ng/ml TGFbeta1 (+TGFbeta1). B, Freshly isolated cells were either stained with Abs against CD4, CD8, or CD19 as well as p-SMAD3 mAb. One representative experiment of four is shown.

 

Figure 5
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  		FIGURE 5. SMAD2 and SMAD3 are phosphorylated in primary human CD4+ T cells. A, In four individuals, p-SMAD2 and p-SMAD3 were analyzed in highly purified CD4+, CD8+, CD14+, and CD19+ cells by Western blotting using Abs specifically detecting phosphorylated SMAD2 (p-SMAD2) and SMAD3 (p-SMAD3). The relative amount of p-SMAD2 and p-SMAD3 in comparison to beta-actin was measured by densitometry. Shown here are mean ± SD of the data after normalization to CD4+ cells. B, p-SMAD2, p-SMAD3, and total SMAD2/3 were analyzed in highly purified CD4+ T cells derived from peripheral blood of five healthy individuals by Western blotting. –, The negative control (T cells after serum deprivation); +, the positive control (T cells stimulated with 10 ng/ml TGFbeta1). C, The relative amount of p-SMAD2 and p-SMAD3 in comparison to beta-actin (see B) was measured by densitometry.

 
We further purified T cell subpopulations and assessed SMAD phosphorylation to address whether all CD4+ T cell subsets are influenced by TGFbeta. CD4+CD45RA+ naive T cells, CD4+CD45RA– memory T cells, CD4+CD25– conventional T cells, as well as CD4+CD25high regulatory T cells showed phosphorylated SMAD2 and SMAD3 molecules (data not shown).

Ablation of TGFbeta signaling is associated with reduced SMAD phosphorylation

Next, we determined whether serum deprivation would be accompanied by loss of phosphorylation of SMAD molecules. Indeed, SMAD2 and SMAD3 phosphorylation were significantly reduced after 2 h and basically undetectable after 8 h (Fig. 6, A and B) while the amount of total SMAD protein remained constant during serum deprivation, ruling out that loss of total SMAD protein is responsible for the decrease of SMAD phosphorylation. Therefore, signaling events distal of the TGFbeta receptor become inactive in human resting CD4+ T cells shortly after removal of TGFbeta. If removal of TGFbeta leads to loss of SMAD phosphorylation, reconstitution with TGFbeta should restore SMAD phosphorylation in serum-deprived resting CD4+ T cells. Therefore, CD4+ T cells were serum deprived for 18 h to reduce SMAD phosphorylation to undetectable levels and afterward stimulated with increasing concentrations of TGFbeta1. As shown in Fig. 6C, as little as 0.001 ng/ml TGFbeta1 induced significant phosphorylation of SMAD3 and at 0.01 ng/ml TGFbeta1 SMAD2 phosphorylation was clearly detectable 2 h after TGFbeta1 addition. As we have shown constitutive phosphorylation of SMAD molecules in resting CD4+ T cells from peripheral blood, we postulated that freshly isolated human serum, containing active TGFbeta, should also restore SMAD phosphorylation following serum deprivation. Again, T cells were cultured under serum-free conditions for up to 24 h (Fig. 6D) with subsequent addition of serum to the culture for 2 h. At this time point, SMAD phosphorylation was significantly increased and exceeded baseline levels. To demonstrate that this effect was due to signaling via the TGFbeta-RII, we used the TGFbeta receptor kinase inhibitor SB431542. As shown in Fig. 6D, serum-induced SMAD phosphorylation was decreased by this inhibitor in a concentration-dependent manner, further supporting that SMAD phosphorylation in human resting T cells is controlled by TGFbeta. The observed signaling events downstream of TGFbeta are independent of other signals such as TCR stimulation or costimulation, because the effects were observed in the absence of further T cell stimulation.


Figure 6
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  		FIGURE 6. Kinetics of dephosphorylation and phosphorylation of SMAD2 and SMAD3. A, CD4+ T cells were serum deprived for up to 8 h. p-SMAD2, p-SMAD3, and total SMAD2/3 were analyzed by Western blotting using Abs specifically detecting phosphorylated SMAD2 (p-SMAD2), SMAD3 (p-SMAD3), and total SMAD2/3. Shown is one representative experiment of three. B, The relative amount of p-SMAD2 and p-SMAD3 in comparison to beta-actin was measured by densitometry (n = 3). C, CD4+ T cells were deprived of serum for 16 h followed by incubation with increasing concentrations of TGFbeta1. Cells were lysed 2 h after addition of TGFbeta1 and SMAD2/SMAD3 phosphorylation was measured by Western blotting. Data are representative of three independent experiments. D, CD4+ T cells were serum deprived for 24 h followed by incubation with freshly isolated human serum and increasing concentrations of SB431542. Cells were lysed 2 h later and SMAD2 phosphorylation was measured by Western blotting. Data represent mean ± SD of three independent experiments.

 
Removal of constitutive TGFbeta signaling leads to increased T cell proliferation

The transcriptional analysis revealed significant changes of gene expression associated with cell cycle regulation (Fig. 1D). Does this have a functional consequence for CD4+ T cells? Ablating TGFbeta signaling in resting CD4+ T cells should lead to a more pronounced activation and proliferation because entry of the cells into the cell cycle should be enhanced in the absence of TGFbeta-dependent regulators.

To address the removal of TGFbeta before activation of resting human CD4+ T cells, the cells were deprived from serum for 16 h and stimulated with increasing concentrations of aAPC (ratios aAPC:T cell from 1:10 to 2:1) comprised of magnetic beads coated with suboptimal concentrations of anti-CD3 and anti-CD28 mAbs (36) in the presence or absence of TGFbeta1 (1 ng/ml). When only low concentrations of aAPC were present (1:10 ratio), approximately one-half of the T cells underwent cell division (46.5%) in the absence of TGFbeta1, while addition of TGFbeta1 decreased the percentage of proliferating cells to 25% (Fig. 7A). Even more important, whereas a significant number of cells reached four or five cell divisions in response to low Ag (1:10 ratio) in the absence of TGFbeta1, virtually no cells had divided five times and only 4% divided four times in presence of TGFbeta1 (Fig. 7B).


Figure 7
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  		FIGURE 7. CD4+ T cell proliferation is increased after TGFbeta withdrawal. Freshly isolated CD4+ T cells were cultured in serum-free medium for 16 h and then stimulated with the indicated ratios of CD3/CD28/MHC-I aAPC in the presence or absence of 1 ng/ml TGFbeta1. A, CD4+ T cell proliferation was assessed by CFSE labeling. The percentage of cells dividing at least once is indicated inside the respective histogram plot. B, Displayed is the percentage of cells dividing at least for the indicated numbers of cycles (e.g., 1+, all dividing cells; 2+, cells that divided two or more times; 5+, cells that divided five or more times) depending on the presence (•) or absence ({circ}) of TGFbeta1 for the indicated ratios of aAPC:T cells.

 
Albeit CD4+ T cell proliferation was always lower in the presence of TGFbeta1, this difference was less pronounced at higher concentrations of aAPC, suggesting that TGFbeta1 is particularly able to inhibit T cell proliferation at low levels of Ag and/or costimulation. When increasing the amount of aAPC to very high concentrations, aAPC were not able to activate TGFbeta1-treated CD4+ T cells to the same extent as T cells in the absence of TGFbeta1. Taken together, the lack of TGFbeta1 signaling, as a consequence of serum deprivation, leads to an enhanced capacity of human resting T cells to respond to low Ag concentrations.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Disclosures
 References
 
From elegant studies using knockout mice, it became apparent that T cell homeostasis is tidily regulated by extrinsic factors. Although positive signals via the TCR derived from MHC-peptide complexes on APCs (6, 7, 8) and IL-7 signaling (9, 10, 11, 12, 13) have been well established (1, 2, 3), negative regulators are less well integrated in current models of T cell homeostasis (3). In mice lacking inhibitory molecules such as TGFbeta, CTLA-4, or IL-10, profound pathophysiology with severe lymphoproliferative disease has been demonstrated (20, 21, 22, 23, 24, 25, 26). A role of TGFbeta during T cell differentiation and proliferation has been described in dominant-negative TGFbeta-RII mice (20). In human T cells, the role of inhibitory factors for the balance between the resting and the active state of human T cells is far less understood. To address this important issue, we applied an unbiased genomics approach. We hypothesized that potentially negatively regulating factors constitutively acting on T cells in vivo can be removed by depriving highly enriched CD4+ T cells from their natural environment and that this would be accompanied by specific transcriptional changes. In this study, we demonstrate that a strictly quantitative assessment of genome-wide gene expression changes combined with a search for GO-defined biological processes reveals the TGFbeta pathway to be the major exogenous inhibitory signaling pathway constitutively repressing human CD4+ T cells in vivo. Several of the TGFbeta target genes induced upon TGFbeta stimulation were shown to be under the permanent control of TGFbeta in resting T cells. Moreover, this approach led to the identification of numerous novel TGFbeta target genes, which have not yet been recognized as such in other cell types.

Constitutive TGFbeta signaling in resting CD4+ T cells was additionally demonstrated by constitutively phosphorylated SMAD2 and SMAD3, two early events after receptor ligation by TGFbeta. This phosphorylation was completely lost during serum deprivation and quickly restored after addition of either exogenous TGFbeta or freshly isolated human serum. By further isolating CD4+ T cell subsets (naive, memory, conventional, and regulatory cells), we demonstrated that both SMAD2 and SMAD3 are constitutively phosphorylated in all CD4+ T cells. In contrast, in CD8+ T cells, only SMAD2 phosphorylation occurred, whereas in B cells, only phosphorylated SMAD3 was detectable. No phosphorylation of SMADs was apparent in CD14+ monocytes. Either TGFbeta-induced cell signaling is not dependent on phosphorylation of both SMAD2 and SMAD3 in cells other than CD4+ T cells, or the effect of TGFbeta can be mediated by phosphorylation of only one SMAD molecule. It might also be possible that other pathways might be more important in CD8+ T cells, B cells, or monocytes to exert the TGFbeta inhibitory effect within these cells. Alternatively, these cells are not as dependent as CD4+ T cells on TGFbeta signaling to be kept in a resting state.

Because transcriptional changes of genes associated with cell cycle regulation indicated the potential for enhanced cell cycle entry, we analyzed whether decreased TGFbeta signaling would also have functional consequences for subsequent T cell activation. Indeed, the loss of TGFbeta signaling under serum deprivation significantly increased the capacity of resting T cells to proliferate in response to low-level TCR stimulation. TGFbeta seems to be the major negative regulator of T cell homeostasis in humans not only inhibiting T cell differentiation and proliferation, but also keeping T cells at the resting state.

The effect of TGFbeta signaling has been clearly demonstrated to be exclusively cell and context dependent (41). Although many aspects of TGFbeta signaling in epithelial cells and fibroblasts have been discovered, far less is known in T cells, particularly human T cells. TGFbeta, especially at high concentrations, has been clearly shown to be a major immunoinhibitory factor (42, 43, 44, 45, 46). Especially in cancer patients, elevated levels of TGFbeta have been associated with reduced T cell proliferation and function (47, 48, 49, 50). It needs to be stressed that former work has almost exclusively focused on elevated levels of TGFbeta and its effect on human immune cells. However, there is little evidence so far that TGFbeta plays a physiological role under steady-state conditions in human CD4+ T cells.

The chosen approach has also led to the identification of several novel TGFbeta target genes, especially genes that were suppressed in human CD4+ T cells in vivo. Many of these genes are currently of unknown function, especially in T cells. However, some biological processes or associations with cellular components seemed to be overrepresented among the new target genes, e.g., nuclear localization, membrane association, or cell cycle regulation, suggesting that TGFbeta exerts specific and rather focused effects on human resting CD4+ T cells. The serum deprivation experiments also revealed novel aspects about the regulation of genes such as CXCR4, previously shown to be up-regulated in the presence of elevated levels of TGFbeta (51). The significant decrease of CXCR4 mRNA in resting T cells after serum deprivation actually suggests, that CXCR4 expression is not only a result of elevated TGFbeta levels but rather a function of constitutive TGFbeta signaling under physiological conditions. This model of CXCR4 regulation would certainly fit the known function of CXCR4 for maintenance of recirculation of resting T cells in vivo (52).

The immediate loss of TGFbeta signaling and transcriptional regulation of its downstream targets following withdrawal of TGFbeta by serum deprivation might redefine our understanding of "resting T cells," at least in humans. Resting T cells not only seem to be controlled by APCs as suggested by Grossman et al. (3) but also seem to be constitutively inhibited by TGFbeta. Active inhibition of T cell activation therefore follows rules established in other biological systems that need to quickly respond to stimuli. In view of energy consumption, active repression of cell activation followed by proliferation and differentiation seems to be a rather ineffective approach. However, considering the enormous growth rates of viruses and other infectious agents, a prompt and sufficient activation and proliferation of specific immune cells by releasing an active blockade is most likely leading to an evolutionary advantage. We have embarked on this concept by studying the importance of TGFbeta in the setting of T cell activation where the relative abundance of inhibitory signals, including TGFbeta vs proinflammatory signals, decides whether T cells are activated or inhibited by modulating central molecular switches (T. Zander and J. L. Schultze, unpublished results). The functional outcome of removal of even low physiological concentrations of TGFbeta further supports the hypothesis that T cells are constantly repressed by TGFbeta.

The significant loss of SMAD2 and SMAD3 phosphorylation as an early response upon serum deprivation strongly suggests that the changes observed on the transcriptional level are mediated, at least in part, by the classical signaling cascade via TGFbeta receptor and SMAD signaling. It will be an interesting question to determine whether other non-SMAD-mediated signaling pathways such as the MAPK pathway or the RAS pathway, which have been shown in other cell types (41, 53), are also involved in keeping the "resting phenotype" of human T cells.

Taken together, we provide evidence for a major role of TGFbeta keeping human CD4+ T cells at a resting phenotype. We conclude that the activation of resting human T cells, particularly after a low level of Ag encounter, is actively and constitutively blocked by TGFbeta. This TGFbeta-mediated T cell inhibition, particularly at low levels of Ag, might also be an important mechanism in the prevention of autoimmune diseases.


   Acknowledgments
 
We thank our colleagues at the Division of Transfusion Medicine for their technical support and Dr. Claudia Wickenhauser for providing reagents. We thank Drs. Klaus Rajewsky, Federica Sallusto, Antonio Lanzavecchia, Christoph Huber, Martin Krönke, and Markus Neurath for their great suggestions and fruitful discussion. We thank Drs. Carl June and Jim Riley (both at the Abramson Cancer Research Center, University of Pennsylvania, Philadelphia, PA) for providing the 9.3 anti-CD28 Ab.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
References
 
The authors have no financial conflict of interest.


   Footnotes
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 This work was mainly supported by the Alexander von Humboldt Foundation via Sofja-Kovalevskaja Awards (to J.L.S.). T.Z. was supported by the Frauke-Weiskam-Christel Ruranski Foundation and J.L.S. was supported by Grant TV89 from the Center for Molecular Medicine (Cologne, Germany). J.C. was supported by a fellowship from the Mildred Scheel Foundation from the Deutsche Krebshilfe. J.L.S. is a member of the Nationales Genomforschungsnetz (N1KR-S24T27). Back

2 S.C., T.Z., J.M.C., and J.L.S. designed the research. S.C., T.Z., J.M.C., I.B., A.P., M.B., and S.D. performed the research. S.C., T.Z., D.E., J.M.C., B.B., I.B., A.P., M.B., R.E., S.D., and J.L.S. analyzed the data. S.C., T.Z., D.E., B.B., R.E., and S.D. contributed reagents, material, and analysis tools. S.C., T.Z., D.E., and J.L.S. wrote the article. Back

3 S.C. and T.Z. contributed equally to this work. Back

4 Address correspondence and reprint requests to Dr. Joachim L. Schultze, Department of Internal Medicine I, Molecular Tumor Biology and Tumor Immunology, University of Cologne, Joseph-Stelzmann-Strasse 9, 50931 Cologne, Germany. E-mail address: Joachim.Schultze{at}uk-koeln.de Back

5 Abbreviations used in this paper: GO, gene ontology; ID, identification; aAPC, artificial APC; FC, fold change; PIGF, phosphatidylinositol glycan class F. Back

Received for publication August 11, 2006. Accepted for publication March 15, 2007.


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