Kinetic assessment of general gene expression changes during human naive CD4+ T cell activation

Krista Hess1, Yinhua Yang1, Susanne Golech1, Alexei Sharov2, Kevin G. Becker3 and Nan-ping Weng1

1 Laboratory of Immunology, 2 Laboratory of Genetics and 3 DNA Array Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA

Correspondence to: N.-p. Weng; E-mail: wengn{at}grc.nia.nih.gov


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
The consequence of naive CD4+ T cell activation is the differentiation and generation of effector cells. How the engagement of T cell receptors and co-stimulatory receptors leads to profound differential changes is not fully understood. To assess the transcription changes during T cell activation, we developed human T cell specific cDNA microarray gene filters and examined the gene expression profiles in human naive CD4+ T cells for 10 continuous time points during the first 24 h after anti-CD3 plus anti-CD28 (anti-CD3/CD28) stimulation. We report here a global and kinetic analysis of gene expression changes during naive CD4+ T cell activation and identify 196 genes having expression levels that significantly changed after activation. Based on the temporal change, there are 15 genes that changed between 0–1 h (early), 25 genes between 2–8 h (middle) and 156 genes between 16–24 h (late) after stimulation. Further analyses of the functions of those genes indicate their roles in maintenance of resting status, activation, adhesion/migration, cell cycle progression and cytokine production. However, a significant majority of these genes are novel to T cells and their functions in T cell activation require further study. Together, these results present a kinetic view of the gene expression changes of naive CD4+ T cells in response to T cell receptor-mediated activation for the first time, and provide a basis in understanding how the complex network of gene expression regulation is programmed during CD4+ T cell activation.

Keywords: activation, cytokine, cDNA microarray, resting


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Upon completion of development within the thymus, naive T cells enter the peripheral blood and circulate to secondary lymphoid tissues (1,2). Naive T cells are characterized by expressing unique cell surface markers, pursuing specific lymphocyte extravasation patterns, and persisting in a non-proliferating state. However, after interaction with antigen presenting cells under optimal conditions, naive T cells alter all of these characteristics and undergo a series of steps of differentiation to become effector T cells. The activation and differentiation of naive T cells involves multiple steps of cellular changes and plays a key role in the adaptive immune response (3,4). However, the mechanisms underlying the differentiation process from naive to effector T cells are only partially understood.

Increasing evidence suggests that the resting status of circulating naive CD4+ T cells is vigorously maintained via expression of genes involved in anti-proliferation and anti-apoptosis (58). Activation of naive T cells is initiated at the surface via engagement of T cell receptors (TCR) and co-stimulatory receptors (CD28 and others), developed through signal transduction pathways, and controlled by a complex network of transcriptional changes of many genes. Such changes include: (i) down-regulation of genes associated with maintenance of resting status and (ii) up-regulation of genes interrelated with the cell cycle, cytokines and receptors, as well as apoptosis (79). These activation-induced changes are the consequences of highly coordinated signaling and transcriptional processes that determine the outcome of naive CD4+ T cell response. Although enormous progress has been made in the past years defining the roles of individual genes during naive T cell activation, it is unclear how many genes are involved, and how the actions of individual genes are temporally coordinated during such complex cellular changes throughout naive CD4+ T cell activation.

With the recent development of the cDNA microarray technique, it becomes feasible to address global gene expression changes of lymphocytes during activation. Several recent reports, including ours, have compared gene expression changes in resting and activated human and mouse T cells, and have identified differentially expressed genes (7,914). Although an increasing volume of data were generated from the initial analyses with microarray technology, it also revealed some weaknesses, e.g. (i) partial collection of immune system-related genes in the general array chips/filters and (ii) lack of temporal and kinetic assessment of gene expression changes in naive T cells. To overcome these shortcomings, we have developed human T cell specific cDNA microarray gene filters that were applied to analyze kinetic changes in gene expression profiles in human naive CD4+ T cells after in vitro stimulation. We report here the identification of 196 genes that are differentially expressed in human naive CD4+ T cells during the first 24 h after anti-CD3/CD28 stimulation. We present the temporal expression patterns of these genes at mRNA and protein levels in resting and activated naive CD4+ T cells. This kinetic analysis not only identifies novel genes in naive CD4+ T cell activation, but also provides temporal details of coordinate multiple, diverse functions at rest and after activation of naive CD4+ T cells.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Isolation of naive CD4+ T cells from peripheral blood
Human naive CD4+ T cells were isolated from peripheral blood by immunomagnetic separation as previously described (15). In brief, blood was obtained from normal donors (age ranging from 25 to 46 years) of the National Institute on Aging (NIA) Clinical Core Facility under an approved protocol (MRI2003-054, ‘Cytophoresis of Volunteer Donors’). Blood mononuclear cells (PBMC) were isolated by Ficoll gradient centrifugation (ICN Biomedicals Inc., Aurora, USA), and then were incubated with a panel of mouse monoclonal antibodies against CD8 (B9.8), CD19 (FMC63), CD11b (NIH11b-1), CD14 (63D3), CD16 (3G8), MHC class II (IVA12), erythrocytes (glycophorin, 10F7), platelets (37F9-E7) and CD45R0 (UCHL-1). Antibody-bound cells were subsequently removed by incubation with anti-mouse IgG-conjugated magnetic beads (Qiagen, Valencia, USA). The purity of isolated naive CD4+ T cells was generally >92% as determined by FACS analysis. The 8% non-CD4 cells were neither T cells (CD3+ or CD8+) or B cells (CD19+) or NK cells (CD16+), or monocytes (CD14+) tested by flow cytometry analysis (data not shown).

Stimulation of naive CD4+ T cells in vitro
The procedure for stimulation of naive CD4+ T cells was previously described (16). Naive CD4+ T cells were resuspended at 2–5 x 106 cells/ml in RPMI1640 medium (InVitrogen, Carlsbad, USA) supplemented with 10% fetal bovine serum (Gemini BioProducts, Calabasas, USA) and 1x penicillin–streptomycin (InVitrogen), mixed with anti-CD3 (OKT3) plus anti-CD28 (9.3) mAb conjugated magnetic beads (Drs Bruce Levine and Carl June, University of Pennsylvania) at a 1:1 cell/bead ratio, and incubated for variable times over 24 h.

RNA isolation and cDNA probe preparation
Total RNA was extracted from naive CD4+ T cells using STAT-60 RNA isolation solution (Tel-Test Inc., Friendswood, USA). In general, total RNA (10 µg) was mixed with 50 µM oligo-dT18 (InVitrogen), incubated at 65°C for 5 min and at 4°C for 10 min, and lyophilized to dryness. The dried total RNA and oligo-dT mixtures were dissolved in reserve transcription solution containing 12 µl of DEPC-treated water, 4 µl of 5x First-Strand Buffer, 1 µl of 10 mM dNTP mix of 10 mM dATP, 10 mM dGTP and 10 mM dTTP, 40 µCi [{alpha}-33P] dCTP (2000 Ci/mmol, 10 µCi; Perkin Elmer, Boston, USA), 2 µl 0.1M DTT and 1 µl (200 units) of SUPERSCRIPT II reverse transcriptase (InVitrogen), and incubated at 42°C for 50 min, and followed by incubation at 70°C for 15 min to inactivate the reverse transcriptase. To degrade RNA, 113 µl of water and 36 µl of 1 N NaOH were added to the reaction mixture and incubated at 37°C for 10 min, and then neutralized by adding 36 µl of 1 M Tris–HCl (pH 8.0) and 28.5 µl of 1 N HCl. The unincorporated nucleotides were removed from the probe by centrifugation with Micro Bio-Spin 30 Chromatography Columns (BioRad, Hercules, USA). The labeled cDNA probes were heated at 100°C for 3 min, quickly cooled on ice and used immediately.

Custom-made cDNA microarray filters and cDNA microarray experiments
The construction of the custom-made cDNA microarray filters was previously described (7). A total of 5760 cDNA clones were selected for constructing the custom-made cDNA microarray. cDNA clones were either purchased from Incyte Genomics Inc. (Palo Alto, USA), Research Genetics Inc. (Huntsville, USA), or were cloned in this laboratory. Filters were prehybridized in 8 ml of Microhyb hybridization solution (InVitrogen) at 43°C for 2 h in roller bottles and hybridized in the presence of sheared salmon sperm DNA (115 µg/ml) (InVitrogen), Cot1 DNA (0.1 µg/ml) (InVitrogen) and cDNA probes at 43°C for 20 h. Filters were washed twice with 50 ml of 2x SSC/0.1% SDS at 55°C for 15 min and twice with 50 ml of 0.5x SSC/0.1% SDS at 60°C for 20 min. The filters were then exposed to PhosphorImager screens (Amersham Bioscience, Piscataway, USA) for 24 h and the images were collected by a PhosphorImager scanner (Storm 860, Amersham Bioscience).

Analysis of cDNA microarray results by ArrayPro
Image files were collected from the PhosphorImager and were processed using the ArrayPro analysis software (Media Cybernetics, L.P., Silver Spring, USA). Briefly, the hybridization spots on the image of the microarray were located, and the spot intensity was determined. These numerical intensities were normalized of spot intensity by dividing the median spot intensity of the entire image. Three repeat experiments with a total of 6–12 individual measurements for each individual clone were performed, and the median value of the intensity was assigned as 1. Normalized spot intensity was compared with a modified ANOVA. To reduce the number of false positives, we calculated F-statistics using either the actual variance for the gene or the averaged error variance for genes with similar spot intensity (whichever was larger). This is a more conservative approach than the Bayesian method proposed by Baldi and Long (17). Because P-values are not relevant for simultaneous assessment of multiple hypotheses, we determined statistical significance of differentially expressed genes using the False Discovery Rate (FDR) method (18). Two additional criteria were used for the selection: (i) a 2-fold intensity difference between two conditions for at least two consecutive time points, except for the 24 h time point, was applied for those statistically significant clones; (ii) the intensity of the highly expressed clone must be greater than 1 to ensure that expression was visible. Resulting lists of significant clones were then verified by re-sequencing. Only those sequence-confirmed gene/EST clones are presented.

RNase protection assay
Probe synthesis, RNA preparation and hybridization, and RNase treatments were followed using the manufacturer's instructions (BD Pharmingen, Franklin Lakes, USA). RNase-protected probes were electrophoresed within a 6% TBE-Urea pre-cast acrylamide gel (InVitrogen). The gels were then exposed to PhosphorImager screens for 3 h and the images were collected by a PhosphorImager scanner (Storm 860, Amersham Bioscience).

Real time RT–PCR
Ten micrograms of total RNA isolated from naive CD4+ T cells were used for first-strand cDNA synthesis via reverse transcription (Super Script II, RNase H reverse transcriptase, InVitrogen). Real-time PCR primers were designed with ABI PRISM Primer Express 2.0 (Applied Biosystems, Foster City, USA) and made by Integrated DNA Technologies (Coralville, USA). The names of the genes and sequences of primers, which were used for RT–PCR analysis, are included in Table 1. The real time PCR reaction was performed in an ABI PRISM 7700 sequence detector (Applied Biosystems) using SYBR green PCR mix (Applied Biosystems). The threshold cycle (Ct) values obtained from each reaction were normalized with the Ct value of the ribosomal protein L32 (RPL32) at each correlating time point. The specificity of the PCR reaction was confirmed by performing control reactions such as PCR reaction with templates processed without the addition of reverse transcriptase and PCR reaction without the addition of PCR template. After the PCR reaction, the PCR products were run on 2% agarose gel to confirm the size of the PCR products. Each RT–PCR reaction was repeated twice with essentially the same results generated.


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Table 1. Real time RT–PCR primers

 
Western blot analysis
Cells were lysed in CHAPS lysis buffer (10 mM Tris–HCL, pH 7.5, 1 mM MgCl2, 1 mM EGTA, 5 µM ß-mercaptoethanol, 0.5% CHAPS, 10% glycerol and DEPC-treated water). Twenty microgams of total cell protein were loaded to each well and separated by SDS–PAGE. Proteins were transferred to Immobilon-P membranes (Millipore, Bedford, USA). The membranes were probed with anti-cyclin D2 Ab, anti-cyclin D3 Ab, anti-cyclin F or anti-p27 Ab (BD Pharmingen, Franklin Lakes, USA), washed three times, and incubated with anti-mouse Ig HRP-linked whole antibody (Amersham Biosciences). Signals were detected using the ECLPlus detection system (Amersham Biosciences) according to the manufacturer's instructions. The membranes were stripped and probed again with anti-Zap70 antibody (a gift from Ron Wange, National Institutes on Aging, National Institutes of Health, Baltimore, MD).

Cytokine assays
Supernatants were collected from freshly isolated and anti-CD3/CD28-stimulated naive CD4+ T cells. Concentrations of IL-2 and LT-{alpha} were ascertained using ELISA immunoassay kits (BioSource International, Camarillo, USA) as suggested by the manufacturer's instructions.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Experimental design
To assess the general changes in gene expression of T cells, we isolated human naive CD4+ T cells from the peripheral blood of normal donors by utilizing a negative immunomagnetic separation procedure as previously described (7), and we developed human T cell specific cDNA microarray gene filters to maximize the assessment of the number of genes expressed in T cells. Our custom made cDNA microarray gene filters consist of 5016 cDNA clones which were selected from: (i) expressed clones in resting and activated human T cells after an initial screening of >40 000 human unique cDNA clones and (ii) genes of immunological interest that were not included in the initial screening gene pool. The custom-made gene filters were used for the analysis of gene expression changes in human naive CD4+ T cells after in vitro crosslinking of T cell receptors (TCR) and co-stimulatory receptors, CD28, (anti-CD3/CD28). The kinetic analysis includes naive CD4+ T cells of freshly isolated and stimulated after 15, 30, 45, 60 min, 2, 4, 8, 16 and 24 h for gene expression and 0, 8, 16, 24, 48 and 72 h for protein expression. We carried out three independent microarray experiments using total RNA from different pools of donors, and each of the clones was measured at least six times independently. The differentially expressed genes between non-stimulated and stimulated naive cells were selected using the following criteria: (i) statistical analysis using the False Discovery Rate (FDR < 0.05); (ii) the intensity difference between two conditions must be >2 and such difference maintains for at least two consecutive time points (except for the last time point at 24 h); (iii) the intensity of the highly expressed gene must be >1 to ensure that expression was visible. The identities of the selected clones were subsequently sequence confirmed, and the expression profiles of selected clones were further confirmed by independent analyses, such as by RNase Protection Assays (RPA) and by real time RT–PCR.

Overall temporal changes in gene expression profiles of naive CD4+ T cells after in vitro stimulation
Based on the selection criteria described above, we have identified a total of 196 cDNA clones whose expression levels were significantly altered after anti-CD3/CD28 stimulation over a 24 h period. Clustering analysis was utilized to present the selected down-regulated genes and the up-regulated genes (Fig. 1A and B). Based upon when the first significant change in gene expression was observed, these selected genes were categorized into ‘early’ (15–60 min), ‘mid’ (2–8 h) and ‘late’ (16–24 h of stimulation) groups (Fig. 1C). The number of genes with altered expression levels increased as the time of activation increased, i.e. early (7%), mid (14%) and late (79%), reflecting a gradual increment of the complexity of transcription programs (the complete set of data can be found in Supplementary table 1, available at International Immunology Online). To ensure that gene expression changes detected at the earliest time point were specific to anti-CD3/CD28 stimulation and not due to in vitro condition changes, such as temperature, medium, etc., we compared gene expression levels between freshly isolated naive cells and cultured naive cells with and without stimulation for 15 min at 37°C, and found that culture for a short period of time without stimulation did not induce significant changes (data not shown). In addition, we have analyzed the expression of over a dozen selected genes between stimulated and non-stimulated naive CD4+ T cells at several parallel early time points (15 min to 24 h) and the results were also agreeable with the comparison to freshly isolated cells (data not shown), thus confirming that the cell culture conditions did not significantly impact gene expression levels at these time points.



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Fig. 1. Differentially expressed down- and up-regulated genes in naive CD4+ T cells after in vitro stimulation with anti-CD3/CD28. (A) Cluster presentation of down- (A) and up- (B) regulated genes. Results of three independent hybridizations from each time point are presented within the colored squares. The scale of the intensity ratio is 5-fold changes for either direction with green color representing decrease and red color representing increase in expression intensity. All genes presented here have been verified by DNA sequencing. (C) Temporal presentations of down- and up-regulated genes in naive CD4+ T cells after in vitro stimulation with anti-CD3/CD28. The data of the representative genes were derived from three independent experiments and presented as the mean gene expression ratio between stimulated and unstimulated naive CD4+ T cells against time. Both down- and up-regulated genes were present at three phases: early (a and b), middle (c and d) and late (e and f).

 
Out of 196 selected genes, 112 genes (57%) were down-regulated post anti-CD3/CD28 stimulation when compared to the freshly isolated naive T cells. Representative down-regulated genes within the ‘early’, the ‘mid’ and the ‘late’ time points after stimulation are shown in Fig. 1(Ca, c and e). In contrast, a total of 84 genes (43%) were up-regulated post anti-CD3/CD28 stimulation. Also contained within Fig. 1(C) are graphs representing identified genes that were up-regulated during the early, the mid, or the late time points (Fig. 1Cb, d and f). To further analyze those significantly changed genes, we categorized them into functional groups (Fig. 2, and Supplementary table 2).



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Fig. 2. Categorization of selected genes into functional groups. Cluster presentation of 48 out of the 196 selected genes placed into cellular functional groups. Each colored square represents the mean of three independent hybridizations for each time point. The scale of the intensity ratio is –5 to 5 (5-fold changes for both directions) with green color representing decrease and red color representing increase in expression intensity. The Unigene number and gene names are indicated at the right.

 
Maintenance of resting status prior to naive T cell activation
The resting state of naive T cells is actively maintained by expressing specific genes (8). Lung Kruppel-like factor (LKLF) is one gene known to be highly expressed in resting T cells that inhibit proliferation (5). Some cytokines and cell division inhibitors are also capable of repressing proliferation as well (7,9). Here, we observed that naive CD4+ T cells express high levels of additional genes with known anti-proliferative functions, e.g. B-cell translocation gene1 (BTG1) and Rho GDP dissociation inhibitor (GDI) (Fig. 2). Both of these genes were highly expressed in the freshly isolated resting naive CD4+ T cells and were significantly down-regulated after anti-CD3/CD28 activation. BTG1 can inhibit cellular proliferation in NIH3T3 cells (19), while GDI has been shown to prohibit cardiomyocyte proliferation by inhibiting Rho family GTPases (20). It is thus conceivable that BTG1 and GDI may play similar roles in the maintenance of resting status of naive CD4+ T cells.

Cyclin-dependent kinase inhibitors (CKI), such as p27Kip1, inhibit the activity of cyclin/Cdk complexes and thereby block cell cycle progression (21), and are known to regulate T cell cycle arrest (22). In our studies, we found that p27Kip1 was highly expressed in freshly isolated naive CD4+ T cells and was down-regulated after anti-CD3/CD28 activation from our cDNA microarray analysis (Fig. 3A), confirmed by RNase protection assay (RPA) (Fig. 3A), and by protein level (Fig. 3B and C). This is interesting because p27Kip1 was considered to be post-translationally regulated in a previous report (23). Here we show that the levels of p27Kip1 mRNA as well as protein are regulated in naive CD4+ T cells. Furthermore, other cell cycle-related genes, such as cyclin-dependent kinase 6 (Cdk6) and S-phase response (SPHAR), were dramatically down-regulated upon activation (Fig. 2C). Taken together, these results suggest that the maintenance of resting status by naive CD4+ T cells involves many genes, including those identified here.



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Fig. 3. Expression of cell cycle regulators during naive CD4+ T cell activation. RNase protection assay and western blot analysis were used to confirm the microarray results. Cyclin kinase inhibitor p27Kip1 mRNA expression levels in naive CD4+ T cells post anti-CD3/CD28 stimulation were determined by cDNA microarray analysis (A) and by RPA (A) normalized with L32 level. p27 (B), cyclin D2 (E) and cyclin D3 (E) protein levels were determined by western blot, normalized with ZAP-70 level (47) (B and E), and relative intensity values were calculated (C and F). Cyclin D2 and D3 mRNA expression levels in naive CD4+ T cells post anti-CD3/CD28 stimulation were determined by cDNA microarray (D) and by RPA (D).

 
Cell cycle progression
A hallmark of T cell activation is the exit from resting status which is then accompanied by the entrance into the cell cycle. We detected 15 cell cycle-related genes that showed significant changes in expression following activation. The down-regulation of CKIs p27Kip1 was accompanied by the up-regulation of genes pertinent to cell division and proliferation, such as cyclin D2 and cyclin D3 (24,25). Cyclin D2 was up-regulated significantly around 4 h and cyclin D3 was up-regulated at 16 h after stimulation as observed by cDNA microarray analysis (Fig. 3D) and subsequently confirmed by RPA (Fig. 3D). We also determined the protein levels of both cyclins by western blot. Dramatic increases of cyclin D2 protein could be detected as early as 8 h of stimulation, while increased protein levels of cyclin D3 were found at 16 h of stimulation (Fig. 3E and F). These findings are in good agreement with previous studies demonstrating that cyclin D2 and D3 expression controls T cell progression through the G1/S phase of the cell cycle (2426). In addition, we have also identified other cell cycle regulators such as myc (MYC) (Fig. 2C), which was up-regulated, and protein phosphatase 6 (PPP6C) (Fig. 2A), which was down-regulated after activation. Together, these results provide a temporal picture of the changes in expression of multiple cell cycle regulators during CD4+ T cell activation and suggest that cell cycle status from resting to cycling in naive CD4+ T cells is regulated by multiple genes.

Genes regulating signal transduction events
Upon activation, naive CD4+ T cells undergo a series of specific changes resulting from coordinated TCR and co-stimulatory receptor signal transduction events. Several genes involving signal transduction, such as Janus kinase 1 (JAK1), protein kinase C, theta, (PRKCQ), annexin A1 (ANXA1), CREB binding protein (CREBBP), interferon regulatory factor 2 (IRF2), POU domain, class 6, transcription factor 1 (POU6F1), prefoldin 5 and ring finger protein 4 (RNF4) (Fig. 2A, B and G), were down-regulated upon anti-CD3/CD28 stimulation. As PRKCQ has been described to provide a survival signal against apoptosis (27), its down-regulation could be correlated with activation-induced cell death, a consequence of T cell activation. Meanwhile, the message levels of several genes involved in activation signaling pathways were increased post stimulation. It is interesting to note that early growth response 1 (EGR1) was up-regulated during the ‘early’ time points, whereas proliferating cell nuclear antigen (PCNA), CD48, histone 2 (H2aa) and lymphocyte cytosolic protein 1 (L-plastin) were up-regulated in the latter portion of the time course (Fig. 2A, B and G). In addition, NF{kappa}B1, a transcription factor which can deliver signal from the cytoplasm to the nucleus and influence target gene transcription upon CD28 costimulation in T cells (28), was significantly up-regulated during the ‘late’ time points as well. Finally, Eph receptor tyrosine kinases and their ligands (ephrins) have recently been shown to modulate T cell behavior (29,30). Interestingly, we observed that EphA1 was down-regulated while EphB1 and ephrin B3 were up-regulated (Fig. 2A). This coordinated change of different members of Eph receptors during naive CD4+ T cell activation provides insight into the diverse functions of these receptors.

Activation-, adhesion- and migration-related genes
Previous studies have shown activation-induced changes transpiring on the surface of naive CD4+ T cells (31). We observed the up-regulation of CD69, CD25 (IL-2 receptor-chain) and CD44 (hyaluronan receptor) in naive CD4+ T cells in a temporal fashion by both flow cytometric analysis and RT–PCR (data not shown). Moreover, major histocompatibility complex (MHC) molecules, such as MHC class II, DO alpha and MHC class II and DR beta 3, were up-regulated in the ‘late’ time points (Fig. 2G).

One of the consequences of activation is the alteration of migration behavior of naive T cells which is dependent upon the expression of specific adhesion molecules. We identified five genes involved in the adhesion/migration process that had dramatic changes at the transcriptional level upon activation, including CD11b, CD48, CD49c, CD84 and selectin L (Fig. 2A and H). In addition to surface adhesion molecules, lymphocyte migration is also dependent upon changes in cytoskeletal structures and metabolic states. Cytoskeletal proteins are essential for circulating lymphocytes undergoing deformation upon transendothelial migration. Several cytoskeletal proteins were identified in our analysis. Vimentin proteins allow for a regulated collapse and an overall change in cell shape (32); oscillating levels of vimentin were observed during the 24 h of activation, implying a subtle regulation of vimentin expression in activated T cells (Fig. 2D).

Changes in cytokine production upon naive CD4+ T cell activation
A key function of CD4+ T cells is the production of an array of cytokines that determine the effectiveness of an immune response. Cytokines are essential for efficient functions of cytotoxic T cells and B cells, as well as the growth of activated CD4+ T cells in an autocrine fashion. We detected 12 cytokine/cytokine receptor genes, some of which were up-regulated upon stimulation, such as interleukin 2 (IL-2), lymphotoxin-alpha (LT-{alpha}), hepatoma-derived growth factor (HDGF) and fibroblast growth factor receptor 1 (FGFR1) (Fig. 2F). Other cytokine-related genes, such as interleukin 10 receptor alpha (IL10RA), tumor necrosis factor superfamily member 10 (TNFSF10) and interleukin 13 receptor alpha 1 (IL13RA) were down-regulated upon stimulation (Fig. 2F). IL-2, produced by activated T cells, can function in either an autocrine or paracrine manner for T cell growth (33). The up-regulation of IL-2 observed in our studies resulted in naive CD4+ T cell proliferation and can also enhance the cytotoxic effects of CD8+ T cells and NK cells (34). LT-{alpha} plays a crucial role in host defense and inflammatory processes and is involved in cell-mediated immune protection (35). Both IL-2 and LT-{alpha} transcripts were detected at the 4 h time point after activation by cDNA microarray analysis (Fig. 4A) and by real time RT–PCR (Fig. 4B). Elevated protein levels of IL-2 and LT-{alpha} were detected after 24 and 48 h of stimulation (Fig. 4C). Signal transduction events leading to IL-2 production via TCR-activation utilize the MAP kinase pathway and calcineurin pathway (36). We found a number of genes involved in both pathways to be up-regulated in our microarray studies, but did not reach a 2-fold change in expression intensity, which included a ras-related GTP-binding protein, G-proteins RAB-2 and RhoC, calcium/calmodulin-dependent protein kinase II and FK506-binding protein 2 (data not shown).



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Fig. 4. Cytokine (IL-2 and LT-{alpha}) expression during naive CD4+ T cell activation. The mRNA levels of IL-2 and LT-{alpha} were determined by cDNA microarray analysis (A) and confirmed by real time RT–PCR (B). The secreted IL-2 and LT (C) concentrations were measured from cell culture supernatants by ELISA.

 
The potential roles of novel genes in naive CD4+ T cell activation
Among the 196 genes reported here, ~25% possess functions that have been previously described, and the roles of the majority of the differentially expressed genes have not been directly linked to naive CD4+ T cell activation. The reason behind the expression changes of these novel genes during activation remains to be revealed, but correlations to their functions in other cells can assist in the understanding of the potential roles they may perform in activated naive CD4+ T cells. To analyze their potential functions, we categorized these novel genes based on their known cellular location.

Cell surface expressed genes.
We identified altered expression of genes that encode products which appear to be expressed on the cell surface of naive CD4+ T cells. Laminin receptor 1 (LAMR1) is involved in cell adhesion (37), and was increasingly up-regulated at 16 and 24 h (Fig. 5). In addition, dystroglycan 1 (DAG1), a cell-surface receptor which assists in the assembly of basement membranes by binding and redistributing soluble laminin (38), was also up-regulated in the ‘late’ time points (Fig. 2D). Certainly, further studies are warranted to elucidate their function in naive CD4+ T cells.



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Fig. 5. Expression of a few novel genes during naive CD4+ T cell activation. The expression levels of novel genes were determined by microarray and real time RT–PCR. The value of microarray data is the average of six independent measurements, and the value of real time RT–PCR is the average of two independent experiments. LAMR1 = laminin receptor 1; TIMP3 = tissue inhibitor of metalloproteinase 3; ARHGAP8 = Rho GTPase activating protein 8; ARHGDIB = Rho GDP dissociation inhibitor (GDI) beta; KPNB1 = karyopherin (importin) beta 1; SSBP1 = single-stranded DNA binding protein 1; TARDBP = TAR DNA binding protein; VBP1 = von Hippel-Lindau binding protein 1.

 
Secreted protein.
One gene with expression patterns altered by activation encode secreted protein: tissue inhibitor of metalloproteinase 3 (TIMP3). TIMP3 functions in extra-cellular matrix degradation (39) and was highly expressed during resting status, but dramatically decreased upon stimulation (Figs 2F and 5). TIMP3 may also be required for adhesion and migration of naive CD4+ T cells.

Diverse functions of cytosolic proteins.
Approximately 40% of the novel genes identified in this study appear to be located in the cytosol and serve diverse functions in numerous types of cells. First, four proteasome subunit genes (proteasome 26S, {alpha}2, 3 and 5) were up-regulated after activation (Fig. 2E). Proteasome subunits are the main provider of peptide ligands for major histocompatibility complex class I molecules and thereby influence the cytotoxic immune response (40). Whether the increased expression of proteasome subunits plays similar roles in CD4+ T cells requires further study.

Finally, several genes involved in various signaling pathways were identified. For example, Von Hippel-Lindau binding protein 1 (VBP1) and karyopherin beta 1 (KPNB1) (Figs 2I and 5) were significantly up-regulated during the late stages of stimulation and are involved in cytosolic and nuclear functions. Von Hippel-Lindau (VHL) binding protein has been shown to redistribute from the cytoplasm to the nucleus after complexing with a tumor suppressor VHL protein, while KPNB1 is capable of assisting the nuclear entry of the high-risk human papillomavirus type 16 at the cytoplasmic/nuclear pore complex (41). It is conceivable that these genes provide new information on the importance of active cytosol to nuclear transportation during naive CD4+ T cell activation, but further characterization is required.

Genes expressed in the nucleus.
Numerous genes are acknowledged as influential participants regulating events of transcription, mRNA processing and transportation within the nucleus and were identified in this study. Among 43 genes, 28 were down-regulated and 15 were up-regulated in naive CD4+ T cells after activation. Matrin 3, which is involved in mRNA editing and transporting (42), was one of the ‘early’ down-regulated novel genes (Fig. 2B). Interestingly, nuclear up-regulated genes were detected only in the ‘mid’ or ‘late’ time points after stimulation in naive CD4+ T cells, implying that time is required for signaling events to travel from the cell surface to the nucleus. Such genes include single-stranded DNA-binding protein 1 (SSBP1) (Figs 2C and 5), TAR DNA binding protein (TARDBP) (Figs 2B and 5) and KH-type splicing regulatory (KHSRP) (Fig. 2B). The complexity of regulation is evident given the number of factors identified in this study. The challenge ahead is to identify the targets that are controlled by these factors, and to understand this highly coordinated function during naive CD4+ T cell activation.


    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
T cell activation is a complex and highly coordinated cellular process. Once the threshold of activation is reached, the process of differentiation and proliferation of T cells proceeds as programmed. To decipher this process at the gene level, we have developed T cell specific microarray gene filters, and analyzed general gene expression in naive CD4+ T cells after anti-CD3/CD28 stimulation in the first 24 h. We show here: (i) identification of 196 genes (the majority are novel genes in T cells) whose expression significantly changed after anti-CD3/CD28 stimulation, (ii) temporal characteristics with altered expression of those genes over the first 24 h post stimulation and (iii) extension of mRNA analysis to proteins of selected genes. Although the roles of those novel genes in naive CD4+ T cell activation are not known, this identification provides a basis for further analysis which will reveal new insights of naive CD4+ T cell activation.

Previous reports including ours have utilized cDNA microarray to examine the changes of gene expression in T cells. From these studies, gene expression changes in resting and activated human and mouse T cells were analyzed and differentially expressed genes were identified (12,1518). More recently, kinetic analyses were applied to determine the temporal changes of gene expression after activation in human T cells (13,14). Diehn et al. analyzed expression of 2926 genes over seven time points (0, 1, 2, 6, 12, 24 48 h) post anti-CD3/CD28 stimulation in total blood T cells (CD4+ and CD8+) using a custom-made general gene chip (14). Riley et al. analyzed human CD4+ T cells (containing both naive and memory cells) in response to anti-CD3/CD28 stimulation at 2, 8 and 24 h with a gene chip containing >13 000 known genes (13). Compared to these two kinetic studies, this study has three distinct features: (i) naive CD4+ T cells rather than total CD4+ or total T cell populations which consist of different functional subsets such as naive, memory and regulatory cells were used in this distinct study, (ii) a custom-made gene filter enriched with T cell expressed genes (selected from screening >40 000 unique cDNA clones) over a general gene chip was also used in this study and (iii) we focused on the early changes in gene expression within the first 24 h (five time points within the first 2 h) and performed extended analysis to measurement of protein levels of selected gene products. Among the 196 genes identified here, ~17 % of them (33 genes/EST) were not found in the Riley report and expression patterns of 9% (18 genes/EST) did not agree with the changes reported by Riley et al. at the 24 h time point. Such differences could reflect the differences in cell subsets and microarray chips used in each of these studies.

The activation-induced cellular changes in CD4+ T cells are well documented. Our findings provide a combined change in gene expression which can identify several critical cellular processes in naive CD4+ T cells. The coordinated molecular changes during activation of naive CD4+ T cells are summarized (Fig. 6). Circulating naive CD4+ T cells maintain a resting state by actively expressing genes which inhibit proliferation (BTG1 and GDI) and prevent cell cycle entrance (p27). Ly-GDI, a homologous counterpart to Rho GDP dissociation inhibitor (GDI) that is predominantly expressed in T and B cells and is implicated in activation pathways (43), could potentially serve as a hematopoietic-specific regulator that assists in maintaining the resting status of naive CD4+ T cells. Upon engagement of the TCR and co-stimulatory receptors, naive CD4+ T cells undergo a series of ordered differentiation processes initiated with the down-regulation of genes that maintain the resting states, such as down-regulation of the cell cycle inhibitor, p27, and up-regulation of genes that promote cell cycle entrance (cyclin D2 and D3), production of cytokines (IL-2, LT-{alpha} and HDGF), acquisition of adhesion and migration patterns (CD11b, selectin L, CD49c and LAMR1) and alterations in structural and metabolic status (VIM and DAG1) (Fig. 6).



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Fig. 6. A kinetic model for the cellular changes during naive CD4+ T cell activation. Upon stimulation with anti-CD3/CD28, naive CD4+ T cells undergo a series of cellular changes to become effector cells. Based on gene expression changes identified in this study, we grouped them into five functional categories. (i) Maintenance of resting status against proliferation, (ii) alteration of adhesion and migration pattern, (iii) entry of cell cycle, (iv) production of cytokines and (v) induction of apoptosis machinery.

 
In summary, the kinetic assessment of the global gene expression profiles in resting naive CD4+ T cells as they undergo activation provides a wealth of information about gene expression in a coordinated and temporal fashion. At this time, we have presented the temporal details of known genes controlling the resting status as well as the activation of naive CD4+ T cells. Furthermore, we have identified many novel genes whose functions in naive CD4+ T cells require additional studies. Further characterization of those novel genes will provide new insights on how the resting and activation states are regulated in naive CD4+ T cells. A better understanding of naive CD4+ T cell activation has clear implications in the assessment of immune response to pathogens and in the evaluation of vaccines and therapeutic interventions.


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Supplementary data for this paper are available at International Immunology Online.


    Acknowledgements
 
We thank Drs Richard Hodes and Ron Wange for the critical reading of the manuscript. We thank Dr. Monchou Fann, Yu Li and Bob Pyle for technical assistance, William Wood for printing the cDNA microarray filters and Drs Carl June and Bruce Levine for providing the anti-CD3/CD28-conjugated magnetic beads. We also want to thank the NIA Clinical Core Facility for providing blood samples and the Flow Cytometry Core Lab for assistance in FACS analysis.


    Notes
 
Transmitting editor: S. Swain

Received 28 February 2004, accepted 14 September 2004.


    References
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 Abstract
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 Supplementary data
 References
 

  1. Fehling, H. J. and Von Boehmer, H. 1997. Early alpha beta T cell development in the thymus of normal and genetically altered mice. Curr. Opin. Immunol. 9:263.[CrossRef][ISI][Medline]
  2. Spits, H. 2002. Development of alphabeta T cells in the human thymus. Nat. Rev. Immunol. 2:760.[CrossRef][ISI][Medline]
  3. Tseng, S. Y. and Dustin, M. L. 2002. T-cell activation: a multidimensional signaling network. Curr. Opin. Cell Biol. 14:575.[CrossRef][ISI][Medline]
  4. Jenkins, M. K., Khoruts, A., Ingulli, E., Mueller, D. L., McSorley, S. J., Reinhardt, R. L., Itano, A. and Pape, K. A. 2001. In vivo activation of antigen-specific CD4 T cells. Annu. Rev. Immunol. 19:23.[CrossRef][ISI][Medline]
  5. Kuo, C. T., Veselits, M. L. and Leiden, J. M. 1997. LKLF: A transcriptional regulator of single-positive T cell quiescence and survival. Science 277:1986.[Abstract/Free Full Text]
  6. Marrack, P., Mitchell, T., Hildeman, D., Kedl, R., Teague, T. K., Bender, J., Rees, W., Schaefer, B. C. and Kappler, J. 2000. Genomic-scale analysis of gene expression in resting and activated T cells. Curr. Opin. Immunol. 12:206.[CrossRef][ISI][Medline]
  7. Liu, K., Li, Y., Prabhu, V., Young, L., Becker, K. G., Munson, P. J. and Weng, N. N. 2001. Augmentation in expression of activation-induced genes differentiates memory from naive CD4(+) T cells and is a molecular mechanism for enhanced cellular response of memory CD4(+) T cells. J. Immunol. 166:7335.[Abstract/Free Full Text]
  8. Yusuf, I. and Fruman, D. A. 2003. Regulation of quiescence in lymphocytes. Trends Immunol. 24:380.[CrossRef][ISI][Medline]
  9. Teague, T. K., Hildeman, D., Kedl, R. M., Mitchell, T., Rees, W., Schaefer, B. C., Bender, J., Kappler, J. and Marrack, P. 1999. Activation changes the spectrum but not the diversity of genes expressed by T cells. Proc. Natl Acad. Sci. USA 96:12691.[Abstract/Free Full Text]
  10. Lechner, O., Lauber, J., Franzke, A., Sarukhan, A., Von Boehmer, H. and Buer, J. 2001. Fingerprints of anergic T cells. Curr. Biol. 11:587.[CrossRef][ISI][Medline]
  11. Rogge, L., Bianchi, E., Biffi, M. et al. 2000. Transcript imaging of the development of human T helper cells using oligonucleotide arrays. Nat. Genet. 25:96.[CrossRef][ISI][Medline]
  12. Chtanova, T., Kemp, R. A., Sutherland, A. P., Ronchese, F. and Mackay, C. R. 2001. Gene microarrays reveal extensive differential gene expression in both CD4(+) and CD8(+) type 1 and type 2 T cells. J. Immunol. 167:3057.[Abstract/Free Full Text]
  13. Riley, J. L., Mao, M., Kobayashi, S., Biery, M., Burchard, J., Cavet, G., Gregson, B. P., June, C. H. and Linsley, P. S. 2002. Modulation of TCR-induced transcriptional profiles by ligation of CD28, ICOS and CTLA-4 receptors. Proc. Natl Acad. Sci. USA 99:11790.[Abstract/Free Full Text]
  14. Diehn, M., Alizadeh, A. A., Rando, O. J., Liu, C. L., Stankunas, K., Botstein, D., Crabtree, G. R. and Brown, P. O. 2002. Genomic expression programs and the integration of the CD28 costimulatory signal in T cell activation. Proc. Natl Acad. Sci. USA 99:11796.[Abstract/Free Full Text]
  15. Weng, N. P., Levine, B. L., June, C. H. and Hodes, R. J. 1995. Human naive and memory T lymphocytes differ in telomeric length and replicative potential. Proc. Natl Acad. Sci. USA 92:11091.[Abstract]
  16. Weng, N., Levine, B. L., June, C. H. and Hodes, R. J. 1996. Regulated expression of telomerase activity in human T lymphocyte development and activation. J. Exp. Med. 183:2471.[Abstract]
  17. Baldi, P. and Long, A. D. 2001. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17:509.[Abstract/Free Full Text]
  18. Benjamini, Y. a. H. Y. 1995. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57:289.[ISI]
  19. Rouault, J. P., Rimokh, R., Tessa, C., Paranhos, G., French, M., Duret, L., Garoccio, M., Germain, D., Samarut, J. and Magaud, J. P. 1992. BTG1, a member of a new family of antiproliferative genes. EMBO J. 11:1663.[Abstract]
  20. Wei, L., Imanaka-Yoshida, K., Wang, L., Zhan, S., Schneider, M. D., DeMayo, F. J. and Schwartz, R. J. 2002. Inhibition of Rho family GTPases by Rho GDP dissociation inhibitor disrupts cardiac morphogenesis and inhibits cardiomyocyte proliferation. Development 129:1705.[Abstract/Free Full Text]
  21. Coqueret, O. 2003. New roles for p21 and p27 cell-cycle inhibitors: a function for each cell compartment? 1. Trends Cell Biol. 13:65.[CrossRef][ISI][Medline]
  22. Huleatt, J. W., Cresswell, J., Bottomly, K. and Crispe, I. N. 2003. P27kip1 regulates the cell cycle arrest and survival of activated T lymphocytes in response to interleukin-2 withdrawal. Immunology 108:493.[CrossRef][ISI][Medline]
  23. Hengst, L. and Reed, S. I. 1996. Translational control of p27Kip1 accumulation during the cell cycle. Science 271:1861.[Abstract]
  24. Martino, A., Holmes, J. H., Lord, J. D., Moon, J. J. and Nelson, B. H. 2001. Stat5 and Sp1 regulate transcription of the cyclin D2 gene in response to IL-2. J. Immunol. 166:1723.[Abstract/Free Full Text]
  25. Miyatake, S., Nakano, H., Park, S. Y., Yamazaki, T., Takase, K., Matsushime, H., Kato, A. and Saito, T. 1995. Induction of G1 arrest by down-regulation of cyclin D3 in T cell hybridomas. J. Exp. Med. 182:401.[Abstract]
  26. Boonen, G. J., van Oirschot, B. A., van Diepen, A., Mackus, W. J., Verdonck, L. F., Rijksen, G. and Medema, R. H. 1999. Cyclin D3 regulates proliferation and apoptosis of leukemic T cell lines. J. Biol. Chem. 274:34676.[Abstract/Free Full Text]
  27. Altman, A. and Villalba, M. 2003. Protein kinase C-theta (PKCtheta): it's all about location, location, location. Immunol. Rev. 192:53.[CrossRef][ISI][Medline]
  28. Bryan, R. G., Li, Y., Lai, J. H., Van, M., Rice, N. R., Rich, R. R. and Tan, T. H. 1994. Effect of CD28 signal transduction on c-Rel in human peripheral blood T cells. Mol. Cell Biol. 14:7933.[Abstract]
  29. Sharfe, N., Freywald, A., Toro, A. and Roifman, C. M. 2003. Ephrin-A1 induces c-Cbl phosphorylation and EphA receptor down-regulation in T cells. J. Immunol. 170:6024.[Abstract/Free Full Text]
  30. Freywald, A., Sharfe, N. and Roifman, C. M. 2002. The kinase-null EphB6 receptor undergoes transphosphorylation in a complex with EphB1. J. Biol. Chem. 277:3823.[Abstract/Free Full Text]
  31. Schrum, A. G. and Turka, L. A. 2002. The proliferative capacity of individual naive CD4(+) T cells is amplified by prolonged T cell antigen receptor triggering. J. Exp. Med. 196:793.[Abstract/Free Full Text]
  32. Brown, M. J., Hallam, J. A., Colucci-Guyon, E. and Shaw, S. 2001. Rigidity of circulating lymphocytes is primarily conferred by vimentin intermediate filaments. J. Immunol. 166:6640.[Abstract/Free Full Text]
  33. Kukita, T., Arima, N., Matsushita, K., Arimura, K., Ohtsubo, H., Sakaki, Y., Fujiwara, H., Ozaki, A., Matsumoto, T. and Tei, C. 2002. Autocrine and/or paracrine growth of adult T-cell leukaemia tumour cells by interleukin 15. Br. J. Haematol. 119:467.[CrossRef][ISI][Medline]
  34. Sule, N. S., Nerurkar, R. P. and Kamath, S. 2001. Interleukin-2 as a therapeutic agent. J. Assoc. Physicians India 49:897.[Medline]
  35. Spellberg, B. and Edwards, J. E. Jr 2001. Type 1/Type 2 immunity in infectious diseases. Clin. Infect. Dis. 32:76.[CrossRef][ISI][Medline]
  36. Dumont, F. J., Staruch, M. J., Fischer, P., DaSilva, C. and Camacho, R. 1998. Inhibition of T cell activation by pharmacologic disruption of the MEK1/ERK MAP kinase or calcineurin signaling pathways results in differential modulation of cytokine production. J. Immunol. 160:2579.[Abstract/Free Full Text]
  37. Chen, A., Ganor, Y., Rahimipour, S., Ben Aroya, N., Koch, Y. and Levite, M. 2002. The neuropeptides GnRH-II and GnRH-I are produced by human T cells and trigger laminin receptor gene expression, adhesion, chemotaxis and homing to specific organs. Nat. Med. 8:1421.[CrossRef][ISI][Medline]
  38. Henry, M. D. and Campbell, K. P. 1998. A role for dystroglycan in basement membrane assembly. Cell 95:859.[ISI][Medline]
  39. Furness, P. N. 1997. Basement membrane synthesis and degradation. J. Pathol. 183:1.[CrossRef][ISI][Medline]
  40. Groettrup, M., van den, B. M., Schwarz, K., Macagno, A., Khan, S., de Giuli, R. and Schmidtke, G. 2001. Structural plasticity of the proteasome and its function in antigen processing. Crit. Rev. Immunol. 21:339.[ISI][Medline]
  41. Le Roux, L. G. and Moroianu, J. 2003. Nuclear entry of high-risk human papillomavirus type 16 E6 oncoprotein occurs via several pathways. J. Virol. 77:2330.[Abstract/Free Full Text]
  42. Zhang, Z. and Carmichael, G. G. 2001. The fate of dsRNA in the nucleus: a p54(nrb)-containing complex mediates the nuclear retention of promiscuously A-to-I edited RNAs. Cell 106:465.[ISI][Medline]
  43. Scherle, P., Behrens, T. and Staudt, L. M. 1993. Ly-GDI, a GDP-dissociation inhibitor of the RhoA GTP-binding protein, is expressed preferentially in lymphocytes. Proc. Natl Acad. Sci. USA 90:7568.[Abstract/Free Full Text]




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