Skip to main content

Molecular signature of cell cycle exit induced in human T lymphoblasts by IL-2 withdrawal



The molecular mechanisms of cell cycle exit are poorly understood. Studies on lymphocytes at cell cycle exit after growth factor deprivation have predominantly focused on the initiation of apoptosis. We aimed to study gene expression profile of primary and immortalised IL-2-dependent human T cells forced to exit the cell cycle by growth factor withdrawal, before apoptosis could be evidenced.


By the Affymetrix microarrays HG-U133 2.0 Plus, 53 genes were distinguished as differentially expressed before and soon after IL-2 deprivation. Among those, PIM1, BCL2, IL-8, HBEGF, DUSP6, OSM, CISH, SOCS2, SOCS3, LIF and IL13 were down-regulated and RPS24, SQSTM1, TMEM1, LRRC8D, ECOP, YY1AP1, C1orf63, ASAH1, SLC25A46 and MIA3 were up-regulated. Genes linked to transcription, cell cycle, cell growth, proliferation and differentiation, cell adhesion, and immune functions were found to be overrepresented within the set of the differentially expressed genes.


Cell cycle exit of the growth factor-deprived T lymphocytes is characterised by a signature of differentially expressed genes. A coordinate repression of a set of genes known to be induced during T cell activation is observed. However, growth arrest following exit from the cell cycle is actively controlled by several up-regulated genes that enforce the non-dividing state. The identification of genes involved in cell cycle exit and quiescence provides new hints for further studies on the molecular mechanisms regulating the non-dividing state of a cell, the mechanisms closely related to cancer development and to many biological processes.


Cell growth, proliferation and arrest mechanisms are crucial for development and homeostasis. Extensive studies have examined the mechanisms of cell activation and cell cycle progression, which involve sequential effects of growth factors and their receptors and activation of intracellular signal transduction pathways, transcription factors, cyclins and cyclin-dependent kinases [1]. Studies on lymphocytes withdrawn from the cell cycle have focused on the initiation of apoptosis following growth factor deprivation [28] or on the cell-to-cell contact mechanisms that prevent cells from apopotosis [911]. Still, the molecular mechanisms of cell cycle exit remain poorly understood. With the lack of mitogenic signals, and in response to antiproliferative signals such as mitogen withdrawal, growth factor starvation, contact inhibition or DNA damage, a proliferating cell arrests to become quiescent (a reversible, nondividing state) or senescent, or to undergo apoptotic death. Most somatic cells in an adult body remain in a postmitotic G0 phase. However, during tissue regeneration and repair, wound healing and immune response, cells re-enter the cell cycle and subsequently withdraw from proliferation.

Studies in lymphocytes and fibroblasts have shown that resting cells are not in a passive state resulting simply from the lack of stimulation. Pajalunga et al. [12] summarized cell cycle exit as "a shift in the balance between positive and negative regulators of proliferation in favour of the latter". Indeed, resting cells were found to express sets of up-regulated and down-regulated genes that maintained an active state of non-division [1, 12, 13, 15, 16]. A group of genes required for cell cycle exit and the maintenance of cell quiescence in human fibroblasts following serum deprivation has been recently identified [17]. Comparative studies of transcriptional profiles of resting and stimulated lymphocytes [1, 18] have shown that T lymphocyte quiescence is actively maintained by products of a set of genes, including TOB and KLF (LKLF, GKLF and BKLF) and FOXO families of transcription factors. Other candidate genes involved in lymphocyte quiescence, including TSC-22 and Dyrk1, have been identified in a mouse model among genes highly expressed in resting lymphocytes and down-regulated after T or B cell activation [1].

The present study on a human T cell model may provide new insights into the molecular changes involved in cell proliferation control, cell cycle exit and cellular quiescence. Cellular quiescence represents an important safeguard against tumorigenesis; therefore the identification of quiescence-controlling genes and a better understanding of the relevant molecular pathways may open new possibilities in cancer diagnosis and treatment. Thus, in our study, proliferating interleukin (IL)-2-dependent human T cells were forced to exit cell cycle by growth factor withdrawal, and their gene expression profiles were examined.

Results and discussion

To gain new insights into cell cycle exit of T lymphocytes, we performed gene expression profiling in human IL-2-dependent T lymphoblasts deprived of the growth factor. Following IL-2 withdrawal, IL-2-dependent, proliferating T lymphoblasts cease to divide and undergo apoptosis [19]. Therefore, transcription profiles of T cells were analysed soon after IL-2 withdrawal (8 hours), before apoptotic changes could be observed. In addition, to minimize effects associated with the heterogeneity of primary T cell populations, we also studied clonal populations of immortalised IL-2-dependent T lymphoblasts and looked for the common changes in the two cellular models. We believe that this approach also limited the "non-specific" gene expression changes in the primary IL-2-deprived T lymphoblasts, i.e. not related to IL-2 withdrawal, but rather to the possible paracrine signalling in polyclonal populations by other cytokines characterised by a redundant activity.

Five cell lines were subjected to IL-2 deprivation and microarray analysis, three primary, IL-2-dependent T lymphoblast lines derived from three different donors (denoted "6", "43" and "j") and two immortalised T cell lines (denoted line 5 and S9), examined in three biological replicates each. Altogether, 18 microarray hybridisations were performed.

In the first step, unsupervised Principal Component Analysis was performed to assess the reliability of the experiments. Biological replicates were shown to cluster together. The gene expression profile of all the analysed cell lines was shown to be affected by IL-2 withdrawal, but the major variability of the gene expression profile in the analysed dataset was found between primary and immortalised cell lines (unpublished data). Differences between primary and immortalised cells were much greater than those resulting from IL-2 withdrawal. Therefore, our analysis was carried out in a block design, and changes in gene expression following IL-2 withdrawal were assessed separately in primary and immortalised cell lines. By means of the parametric non-corrected t-test we selected 158 differentially expressed genes at p < 0.001 (see Additional File 1: Table A1 for genes differentiating between samples before and after IL-2 withdrawal, selected by a non-paired analysis). The probability of a random selection of such a number of genes was low (0.03 in a global test) which confirmed the reliability of the observed differences. A false discovery rate for the selected genes ranged from 0.003 to 0.12. The hierarchical clustering of the 158 differentially expressed probe-sets obtained in this analysis resulted in an ideal discrimination between the samples before and after IL-2 withdrawal, with a rather uniform pattern of expression (Figure 1).

Figure 1
figure 1

Hierarchical clustering analysis based on the expression values of 158 probesets differentiating samples before and after IL-2 withdrawal in the non-paired analysis. Samples before IL-2 withdrawal are in the light-blue columns, samples after IL-2 withdrawal in the navy-blue columns. Primary samples are in the yellow column, immortalised samples are in the green one. The names of the genes of the "T lymphocyte cell cycle exit signature" (Table 2) are marked.

Having confirmed the statistical significance of differences related to IL-2 withdrawal, we have selected the most consistently changed genes in all five different cell lines analysed, by employing a paired analysis (of the corresponding cells before and after IL-2 withdrawal). The univariate non-corrected permutation test revealed 166 probe-sets changed significantly at P < 0.001 (while in a global test, a borderline significance of P = 0.0625 was obtained). A hundred and eight of these genes were repressed after growth factor withdrawal, and the remaining 58 were up-regulated (see Additional File 2: Table A2 for genes differentiating between samples before and after IL-2 withdrawal, selected by a paired sample analysis). Out of those, DUSP6, OSM, CISH, SOCS2, LIF and IL13 were found to be the most prominently down-regulated (more than 20-fold), and the expression of EMB, SOX4, RPS24, WNT5B, SLC1A4, SQSTM1, SUMF1, TMEM1, STK38, LAMP2 and C17orf42 was found to be the most increased (2-fold to 3.5-fold change).

We carried out an external validation of the results obtained in the microarray analysis by a qRT-PCR. For validation we used the same cell lines and we chose 8 genes from the list of 158 most significantly changed transcripts, and a further 4 genes, known for their interesting function, with lower significance of differences. Most of the qRT-PCR results confirmed the microarray findings (Table 1): 7 of 8 top genes and 3 of 4 less significant transcripts were confirmed in a qRT-PCR experiment, usually with a similar fold-change.

Table 1 Validation of the selected microarray data by the qRT-PCR.

To select transcripts consistently changed upon IL-2 withdrawal, we combined the two lists of 166 and 158 significant genes, obtained in the paired and non-paired analyses, respectively. We identified a set of 53 genes comprised of 13 up-regulated and 40 down-regulated genes, which we designated a "T lymphocyte cell cycle exit signature" (Table 2).

Table 2 Gene expression signature of T lymphocyte cell cycle exit.

The up-regulated genes identified after IL-2 deprivation in both primary and immortalised cells included SQSTM1, ECOP, YY1AP1, RPS24, TMEM1, LRRC8D, C1orf63, ASAH1, SLC25A46 and MIA3, some of which have been associated with cellular growth control.

The SQSTM1 protein, previously known as p62 protein, interacts with the ubiquitinated proteins to mediate their clearance and is an important scaffold molecule in the RANK-NF-kappaB signalling pathway [20]. In addition, while the p62 protein is also a ligand for Lck kinase of the Src protein-tyrosine kinase family that is involved in T-cell receptor-dependent activation [21], Köller et al. [22] stress the role of Lck in transient T cell unresponsiveness, mediated by a selective IL-2 deficiency. Thus, SQSTM1 may play an important role in cell growth, especially because it may facilitate cell survival through signalling cascades for example those that result in Akt activation, by interaction with protein kinase C, as shown in human neuronal cells [23].

Interestingly, the product of the ECOP gene, which was found here to be up-regulated, has also been previously shown to be involved in NF-kappa B-related regulation of cellular growth. ECOP (EGFR-Co-amplified and over-expressed protein) has been implicated as a key regulator in the NF-kappaB signalling, and it has been postulated that high-level, amplification-mediated ECOP expression, such as that occurring in tumours with amplified EGFR, could contribute to resistance to apoptosis [24].

The identified genes may as well be associated with other cell proliferation pathways. For example, a product of the YYAP1 gene, called YY1-associated protein, a co-activator of the YY1 (Yin Yang 1) transcription factor (YYAP1), had been initially identified as a long splice variant of HCC (hepatocellular carcinoma)-specific protein encoded by the HCCA2 gene (HCC-associated gene 2). An over-expression of HCCA2 has been found to lead to a cell cycle arrest at G0/G1 phase and to an inhibition of cell proliferation [25].

For the other up-regulated genes distinguished in this study, we could not identify links to the known mechanisms of cell cycle arrest, but some of the genes have been previously associated with different cancers. An over-expression of ASAH1 has been found in different malignant tumours, including prostate cancer, head and neck squamous cell cancers and T-cell large granular lymphocyte (LGL) leukaemia [2628]. An inhibition of acid ceramidase, the product of ASAH1, the over-expression of which in cancer cells has been implicated in drug resistance, is suggested as an efficient and promising novel treatment strategy [29, 30]. The ribosomal protein gene RPS24 has been associated with hepatocellular carcinoma [14]. The MIA3 (TANGO) protein has been identified as a tumour suppressor in malignant melanoma and in colon and hepatocellular carcinomas [31, 32].

Out of genes encoding for the suggested crucial regulators of T lymphocyte quiescence, such as TOB and members of the KLF and FOXO transcription factors' families, as well as TSC-22 and Dyrk1, which had previously been found to be overexpressed in resting T cells [1, 18, 3335], we only found the FOXO3 gene to be slightly, but significantly up-regulated after IL-2 withdrawal. This discrepancy might be explained by differences between the different cell models employed (mouse vs. human) and by different experimental settings. On the other hand, the activity of these transcription factors may be regulated posttranscriptionally, so that their mRNA levels may not necessarily reflect their activity. Thus, the expression of some of the known FOXO target genes was examined and found to be affected following IL-2 withdrawal. IL7R, the FOXO1 target gene was up to 4-fold overexpressed in the primary T lymphocytes, but not in immortalised T cells. CDKN1B (p27), another FOXO target in human, mouse and C. elegans models [1, 3437], encoding a cell-cycle regulatory protein, presented slightly, but significantly increased expression. Similarly, Pink1, a recently identified FOXO target, the product of which helps to protect lymphocytes from apoptosis after growth factor deprivation [38], was among genes of significantly elevated expression in our model of cell-cycle exit. Studies of Murphy [36] on C. elegans model pointed at metallothionein genes as possible FOXO targets. In the present study, several metallothionein genes (MT1H, MT1X, MT1G, MT2A, MT1M, MT1E, MT1F) were overexpressed (2.6 to 3.8 times) following IL-2 withdrawal. In addition, we show here, that Cyclin D expression, reported to be downregulated by FOXO and KLF [1, 35], decreased after IL-2 withdrawal. However, some genes reported by other research groups as human FOXO targets, such as p130 gene, Cyclin G2 [37], or IGF1R, BCL-2BP, or Sestrin 1 found to be FOXO3a-inducible genes in mouse T cells [34], as well as most of the KLF targets summarised by Tsachanis [16] and Yusuf [33], were unaffected by IL-2 deprivation in our experimental model.

A set of genes identified as significantly down-regulated 8 hours following IL-2 withdrawal included PIM1, BCL2, IL-8, HBEGF, DUSP6, OSM, CISH, SOCS2, LIF and IL13. It is notable that some of the identified genes, namely PIM1, BCL2, DUSP6, CISH and SOCS2 were previously demonstrated to be the IL-2 target genes or the regulators of IL-2 signalling [3941]. In addition, it is clear that PIM1, CISH and suppressors of cytokine signalling (SOCS) are implicated in the biological actions of IL-2 [42], and that IL-2 was found to regulate mRNA levels of SOCS2, CISH, DUSP5 and DUSP6 [41]. It has also been demonstrated that OSM can quickly up-regulate CISH [43]. The coordinated repression of OSM and CISH that we showed in T cells following IL-2 withdrawal is consistent with these observations.

To perform a functional analysis of the up- and down-regulated genes, the 158 genes selected in the non-paired comparison between IL-2-deprived and non-deprived lymphocytes were first analysed by Gene Ontology (GO) Categories (see Additional File 3. Table A3 for gene ontology analysis of genes selected in a non-paired sample comparison between IL-2-deprived and non-deprived lymphocytes). The number of genes changed in each category was compared with the expected quantity of genes and the greatest enrichment was found in two molecular function (MF) classes, GO:0016868, intramolecular transferase activity, phosphotransferases; (observed/expected ratio = 43.9) and GO:0005126, hematopoietin/interferon-class (D200-domain) cytokine receptor binding, (observed/expected ratio = 26.3) and two biological process (BP) classes, GO:0007259, JAK-STAT cascade (observed/expected ratio = 16.7) and GO:0009968, negative regulation of signal transduction, (observed/expected ratio = 6.8). Secondly, a gene set analysis was performed by comparing the overall significance of gene groups defined by GO categories. Out of the 2348 GO classes 317 were found to be significantly affected by IL-2 withdrawal, as shown by at least one of the three tests used to assess the significance of the differences. The selected GO categories are shown in Table 3. The LS/KS permutation tests, which find gene sets that have more differentially expressed genes among the classes than expected by chance, identified 206 significant gene-sets. Efron-Tibshirani's test, which uses 'maxmean' statistics to identify differentially expressed gene-sets, found 231 significant gene-sets (under 200 permutations). Eleven out of the 215 cellular component categories, 69 of 545 molecular function (MF) categories and 237 of 1588 biological process (BP) categories were significant. Thirty-nine GO categories with p < 0.005 in all of the three applied gene group significance tests were identified (see Additional File 4: Table A4 for overall significance of expression changes following IL-2 deprivation in gene groups defined by GO categories).

Table 3 Selected Gene Ontology GO categories significantly affected by genes deregulated following IL-2 deprivation.

The same analytical procedure applied to the database of biological pathways (Biocarta, human database) resulted in 54 significantly up-regulated gene sets. Selected pathways affected by IL-2 withdrawal are shown in Table 4. Both analyses, GO and Biocarta, confirmed the consistent up-regulation of the genes belonging to the JAK-STAT pathway.

Table 4 A selection of significantly impacted pathways (BioCarta) in T lymphocytes following IL-2 deprivation.

Our data demonstrate for the first time that IL-2 withdrawal induces a coordinate repression of the same set of genes that have been found to be induced during T cell activation as IL-2 targets [44, 45]. A similar "symmetry" has been previously shown in human fibroblasts, where approximately half of the genes of the early response to serum stimulation were correspondingly repressed at the cell cycle exit after mitogen deprivation [17].

A few previous studies have shown that some mRNAs that are expressed in resting T and B lymphocytes become repressed following cell activation, and it was suggested that cell quiescence is under an active transcriptional control [1]. Similarly, as shown recently, exit from the cell cycle of human fibroblasts is under control of a "quiescence program" dependent upon a set of genes that enforce the non-dividing state, and ensure the reversibility of the cell cycle arrest [15, 17]. Our data support the idea of cell cycle arrest as an active state, controlled by some up-regulated genes.

While in vitro T lymphocytes deprived of a growth factor would eventually undergo apoptosis, in vivo a small proportion of antigen-activated cells would exit to G0 and become quiescent. Cellular quiescence is thought to be an indispensable state for the maintenance of lymphocyte homeostasis following immune response, and therefore it is an important barrier against tumorigenesis [4648]. Some autoimmune and chronic inflammatory disorders involving an excessive lymphocyte proliferation, were found to be associated with increased risks of lymphoma [44, 45]. Thus, it is very likely that a deregulated expression of genes responsible for cellular quiescence may contribute to the development of some lymphoid malignancies.

To assess whether T cells adopt quiescent, memory cell phenotype upon IL-2 withdrawal in our model, we compared the gene expression patterns obtained with those described by other researchers as characterising memory vs. naïve vs. effector T cells. Two molecules known to facilitate T cell homing to lymphoid tissues, SELL (CD62L) and CCR7 are expressed predominantly by naïve and memory T cells [4952], but CCR7 expression seems not to be affected by growth factor withdrawal [52]. In our model of cell cycle exit the SELL gene was up-regulated in primary T cell lines, while CCR7, was unchanged in primary T cells and slightly down-regulated in immortalised T cells after IL-2 deprivation. Out of other genes identified by Holmes et al. [49] as up-regulated in memory T cells, T cells deprived of IL-2 overexpressed SMAD3, TRIM66 and NAP1L3. There was also an increased expression of IL7R, thought to be expressed in memory [53] and naïve [50, 52] T cells, but this was found not significant by the qRT-PCR (Table 1) when analysed for all T cells, possibly because this was truth only in primary T cells. The CD58 (LFA3) gene, encoding an adhesion molecule known to be expressed on activated and memory T cells [54], was indeed highly expressed in actively growing cells and consistently up-regulated after IL-2 withdrawal in our experimental system.

Holmes et al. [49] reported Granzyme B and KLRD1 expression to be induced in activated T cells. Our experiments showed that growth factor-deprived T cells presented decreased Granzyme B expression, while KLRD1 expression was slightly upregulated, but only in primary T cells. A hierarchical clustering of some genes outlined by Holmes et al. [49] is shown in an Additional file 5: Figure A1.

Haining et al. [55] demonstrated a gene expression profile common for CD8 and CD4 memory T cells. We showed that some of the genes of this profile were significantly changed at cell cycle exit; there was an increased expression of S100A4, S100A11, ANXA1, ANXA2, CRIP1 and CASP1, which, according to Haining et al. [55], are up-regulated in memory T cells, and a reduced expression of Granzyme A, downregulated in memory cells [55].

In summary, T cells withdrawn from cell cycle gained some crucial characteristics of memory T cells. Nevertheless, the cell cycle exit signature that we describe here is different from memory T cell gene expression signatures published so far.

Further studies will reveal whether there is a common mechanism of quiescence for different cell types, such as the "common quiescence program" in fibroblasts stimulated to exit to G0 by different stimuli [15]. Interestingly, it has been found recently that a group of genes required for cell cycle exit in human fibroblasts following serum deprivation, were coordinately repressed in many types of human cancers, and repression of these genes predicted an increased risk of cancer progression and death in breast cancer patients [17]. Our studies on a model of human primary and immortalised IL-2-dependent T lymphoblasts extended these observations and brought new insights into the understanding of molecular events important in cell cycle exit, deregulation of which may relate to cancer development.


Cell cycle exit of growth factor-deprived primary and immortalised T lymphocytes is characterised by a gene expression signature, comprising 13 up-regulated and 40 down-regulated genes. Some of these genes are implicated in the mechanisms of cell growth control and show deregulated expression in different tumours. The gene set identified in the non-paired analysis was enriched in transcription, cell cycle, cell growth, proliferation and differentiation, cell adhesion, and immune function genes. Cells at cell cycle exit seem to be under an active transcriptional control. Upon IL-2 withdrawal, some genes become repressed, including those known to be induced during T-cell activation, while others become up-regulated. The identification of genes involved in cell cycle exit and quiescence provides new hints for further studies on the molecular mechanisms regulating the non-dividing state of a cell, the mechanisms closely related to cancer development and to many biological processes.


Cell sources and cell sample preparation

Primary, IL-2-dependent T lymphoblast cell lines ("6", "43" and "j") were generated and propagated as previously described [56]. Briefly, peripheral blood mononuclear cells (PBMC) derived from three healthy donors ("6", "43" and "j") were suspended in a standard medium (RPMI 1640, 10–12% FCS, 50 μg/ml gentamycin [Sigma]), activated for 24 h with 20 μg/ml of wheat germ agglutinin (Pharmacia) and subsequently cultured in the standard medium supplemented with 20 U/ml of recombinant (r) IL-2 (R&D). Flow cytometry cell surface analysis showed that the three primary T lymphoblast cell populations were composed of T cells exclusively, as they expressed all the pan-T markers, and a subset of the cells also expressed CD4 or CD8 surface molecules (unpublished data).

Viable cells were assessed every 3–7 days by the trypan blue exclusion test and resuspended to the concentration of 0.5–2.0 × 105 cells/ml in a fresh standard medium supplemented with rIL-2. The number of population doublings (PD) was calculated according to the formula:

The cells "6", "43" and "j" were collected after 4–5, 7–8 and 6–7 PD, respectively. The samples of the three primary cell lines were analysed as biological replicates.

Spontaneously immortalised IL-2-dependent T cell lines were derived from normal spleen (line 5) and from PBMC derived from a Nijmegen Breakage Syndrome patient (line S9), as previously described [56, 57]. Samples of the immortalised cell lines were collected in three biological replicates each.

The differential gene expression analysis was performed in cell samples collected directly from the IL-2-containing cultures (samples: 6/0, 43/0, j/0, line 5/0 and S9/0) and after 8-hrs incubation of the lymphoblasts in a standard medium without IL-2, following triple thorough washing of the cells in a standard medium (samples: 6/8, 43/8, j/8, line 5/8 and S9/8). In the cultures deprived of IL-2 for 8 hours, no apoptosis was observed in the primary T lymphoblast lines, and in the immortalised cell lines a negligible proportion of cells had the characteristics of early apoptotic cells, as detected by flow cytometry following Annexin-V staining with the use of the Annexin-V-FLUOS Staining Kit of ROCHE (Table 5). But S-phase depletion and G1/S block were observed, as evaluated by flow cytometry with the use of the BD Cycletest Plus (Becton Dickinson) (unpublished data).

Table 5 Percentages of apoptotic and necrotic cells in cultures of IL-2-dependent primary (j, 43) and immortalised (line 5, S9) T lymphoblasts (0 h) and at different time points after IL-2 withdrawal (2 h, 8 h, 24 h), analysed by flow cytometry following Annexin-V staining.

Microarray procedures

Total RNA was isolated using Nucleospin RNA II kit (Macherey-Nagel, Germany). RNA quality was assessed using the Agilent 2100 Bioanalyzer and RNA 6000 Nano Chip Kit (Agilent Technologies).

The Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays, which measure the expression level of over 47,000 transcripts and variants, including 38,500 well-characterised human genes, were used. cDNA synthesis was carried out from 5 μg of RNA (One-Cycle cDNA Synthesis Kit, Affymetrix). After purification (GeneChip Sample Cleanup Module), 7 μl of double-stranded cDNA were used for biotinylated cRNA synthesis (IVT Labeling Kit, Affymetrix). Labeled cRNA was purified using a GeneChip Sample Cleanup Module, fragmented and hybridised with genome array. Washing, staining with streptavidin-phycoerythrin conjugate and scanning of the arrays in Affymetrx GeneChip 3000 scanner were performed as recommended by the Affymetrix Gene Expression Analysis Technical Manual.

Data analysis

All 18 arrays were normalised by the GC-RMA algorithm using a Bioconductor gcrma package. We filtered out all genes with small amplitude of differences (less than 10% of samples with at least 1.5-fold difference in either direction from the median gene expression among all samples). The filtered dataset comprised of 18770 probe-sets. The full dataset has been deposited in the Gene Expression Omnibus repository (accession no GSE13909).

Differentially expressed genes were identified by a random-variance t-test, with the statistical significance threshold set at p < 0.001. A stringent significance cut-off was used to limit the number of false positive genes obtained (at this threshold no more than 19 false positives are expected in each analysis). A global test of the differences in expression profiles between the classes was also performed by sample permutation. Simultaneously, we assessed a False Discovery Rate for each probeset using the Benjamini-Hochberg method.

To examine the effects of IL-2 withdrawal, samples were compared either according to the type of the cell line (primary/immortalised) in a block design or in a paired sample t-test analysis (for this analysis the replicates of the same sample were averaged).

For cluster analysis, genes identified in the non-paired analysis were used to obtain a heatmap plot with dendrograms, using Ward's hierarchical cluster algorithm (Bioconductor).

Gene Ontology classes for the resulting lists of genes were compared to the number of genes expected, considering the number of genes on the microarray in each category. GO classes and parent classes with at least 5 observations in the selected subset and with an observed vs expected ratio of at least 2 were analysed.

The statistical significance of global gene expression changes in gene ontology categories and BioCarta pathways was assessed by LS/KS permutation tests and the Efron-Tibshirani's GSA maxmean test. All data analyses were performed using the BRB-ArrayTools (developed by Dr. Richard Simon and BRB Array Tools development team) or by Bioconductor packages.

Real time RT-PCR validation of gene expression

Based on the microarray results, the following genes were selected for validation by the real-time RT-PCR technique: DUSP6, ASAH1, HBEGF, MT1F, MT1X, IL7R, IL8, CLK4, C3orf59, SOCS2, OSM and CISH. Specific primers were designed using Primer Express v.2.0 software (Applied Biosystems). Detailed primer sequences are available on request.

The cDNA was synthesised with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) using 1 μg of total RNA as a template, and Oligo-dT-Primers (Applied Biosystems), according to the manufacturer's instructions. The real-time (= quantitative, q) RT-PCR reactions were performed with Power SYBR Green PCR Master Mix (Applied Biosystems) using the 7000 Real-Time PCR system (Applied Biosystems). Reaction mixtures (25 μl) were subjected to a two-step program: 95°C for 10 minutes followed by 40 cycles of 95°C for 15 s and 60°C for 1 minute. The data were analysed using ABI PRISM 7000 SDS Software. The relative fold difference in gene expression was calculated by the ΔΔCt method using the ACTB gene as a normalisation control. The statistical significance of the fold change was assessed by the ANOVA test (Statistica package).



biological process


gene ontology




molecular function


peripheral blood mononuclear cells




  1. Teague TK, Hildeman D, Kedl RM, Mitchell T, Rees W, Schaefer BC, Bender J, Kappler J, Marrack P: Activation changes the spectrum but not the diversity of genes expressed by T cells. Proc Natl Acad Sci USA. 1999, 96: 12691-12696. 10.1073/pnas.96.22.12691.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Ropke C, Gladstone P, Nielsen M, Borregaard N, Ledbetter JA, Svejgaard A, Odum N: Apoptosis following interleukin-2 withdrawal from T cells: evidence for a regulatory role of CD18 (beta 2-integrin) molecules. Tissue Antigens. 1996, 48: 127-135. 10.1111/j.1399-0039.1996.tb02617.x.

    Article  CAS  PubMed  Google Scholar 

  3. Akbar AN, Borthwick NJ, Wickremasinghe RG, Panayoitidis P, Pilling D, Bofill M, Krajewski S, Reed JC, Salmon M: Interleukin-2 receptor common gamma-chain signaling cytokines regulate activated T cell apoptosis in response to growth factor withdrawal: selective induction of anti-apoptotic (bcl-2, bcl-xL) but not pro-apoptotic (bax, bcl-xS) gene expression. Eur J Immunol. 1996, 26: 294-299. 10.1002/eji.1830260204.

    Article  CAS  PubMed  Google Scholar 

  4. Flores I, Martinez-A C, Hannun YA, Merida I: Dual role of ceramide in the control of apoptosis following IL-2 withdrawal. J Immunol. 1998, 60: 3528-3533.

    Google Scholar 

  5. Hieronymus T, Blank N, Gruenke M, Winkler S, Haas JP, Kalden JR, Lorenz HM: CD 95-independent mechanisms of IL-2 deprivation-induced apoptosis in activated human lymphocytes. Cell Death Differ. 2000, 7: 538-547. 10.1038/sj.cdd.4400684.

    Article  CAS  PubMed  Google Scholar 

  6. Devireddy LR, Green MR: Transcriptional program of apoptosis induction following interleukin 2 deprivation: identification of RC3, a calcium/calmodulin binding protein, as a novel proapoptotic factor. Mol Cell Biol. 2003, 23: 4532-4541. 10.1128/MCB.23.13.4532-4541.2003.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Gabler C, Blank N, Hieronymus T, Schiller M, Berden JH, Kalden JR, Lorenz HM: Extranuclear detection of histones and nucleosomes in activated human lymphoblasts as an early event in apoptosis. Ann Rheum Dis. 2004, 63: 1135-1144. 10.1136/ard.2003.011452.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Asselin-Labat ML, David M, Biola-Vidamment A, Lecoeuche D, Zennaro MC, Bertoglio J, Pallardy M: GILZ, a new target for the transcription factor FoxO3, protects T lymphocytes from interleukin-2 withdrawal-induced apoptosis. Blood. 2004, 104: 215-223. 10.1182/blood-2003-12-4295.

    Article  CAS  PubMed  Google Scholar 

  9. Scott S, Pandolfi F, Kurnick JT: Fibroblasts mediate T cell survival: a proposed mechanism for retention of primed T cells. J Exp Med. 1990, 172: 1873-1876. 10.1084/jem.172.6.1873.

    Article  CAS  PubMed  Google Scholar 

  10. Gombert W, Borthwick NJ, Wallace DL, Hyde H, Bofill M, Pilling D, Beverley PC, Janossy G, Salmon M, Akbar AN: Fibroblasts prevent apoptosis of IL-2-deprived T cells without inducing proliferation: a selective effect on Bcl-XL expression. Immunology. 1996, 89: 397-404. 10.1046/j.1365-2567.1996.d01-759.x.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Pilling D, Akbar AN, Girdlestone J, Orteu CH, Borthwick NJ, Amft N, Scheel-Toellner D, Buckley CD, Salmon M: Interferon-beta mediates stromal cell rescue of T cells from apoptosis. Eur J Immunol. 1999, 29: 1041-1050. 10.1002/(SICI)1521-4141(199903)29:03<1041::AID-IMMU1041>3.0.CO;2-#.

    Article  CAS  PubMed  Google Scholar 

  12. Pajalunga D, Mazzola A, Puggioni E, Crescenzi M: Non-proliferation as an active state: conceptual and practical implications. Cell Cycle. 2007, 6: 1415-1418.

    Article  CAS  PubMed  Google Scholar 

  13. Coppock DL, Kopman C, Scandalis S, Gilleran S: Preferential gene expression in quiescent human lung fibroblasts. Cell Growth Differ. 1993, 4: 483-493.

    CAS  PubMed  Google Scholar 

  14. Liu Y, Zhu X, Zhu J, Liao S, Tang Q, Liu K, Guan X, Zhang J, Feng Z: Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray. Oncol Rep. 2007, 18: 943-951.

    PubMed  Google Scholar 

  15. Coller HA, Sang L, Roberts JM: A new description of cellular quiescence. PLoS Biol. 2006, 4: e83-10.1371/journal.pbio.0040083.

    Article  PubMed Central  PubMed  Google Scholar 

  16. Tzachanis D, Lafuente EM, Li L, Boussiotis VA: Intrinsic and extrinsic regulation of T lymphocyte quiescence. Leuk Lymphoma. 2004, 45: 1959-1967. 10.1080/1042819042000219494.

    Article  CAS  PubMed  Google Scholar 

  17. Liu H, Adler AS, Segal E, Chang HY: A transcriptional program mediating entry into cellular quiescence. PLoS Genet. 2007, 3: e91-10.1371/journal.pgen.0030091.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Glynne R, Ghandour G, Rayner J, Mack DH, Goodnow CC: B-lymphocyte quiescence, tolerance and activation as viewed by global gene expression profiling on microarrays. Immunol Rev. 2000, 176: 216-246. 10.1034/j.1600-065X.2000.00614.x.

    Article  CAS  PubMed  Google Scholar 

  19. Duke RC, Cohen JJ: IL-2 addiction: withdrawal of growth factor activates a suicide program in dependent T cells. Lymphokine Res. 1986, 5: 289-299.

    CAS  PubMed  Google Scholar 

  20. Layfield R, Searle MS: Disruption of ubiquitin-mediated processes in diseases of the brain and bone. Biochem Soc Trans. 2008, 36: 469-471. 10.1042/BST0360469.

    Article  CAS  PubMed  Google Scholar 

  21. Némorin JG, Duplay P: Evidence that Lck-mediated phosphorylation of p56dok and p62dok may play a role in CD2 signaling. J Biol Chem. 2000, 275: 14590-14597. 10.1074/jbc.275.19.14590.

    Article  PubMed  Google Scholar 

  22. Köller MD, Kiener HP, Aringer M, Graninger WB, Meuer S, Samstag Y, Smolen JS: Functional and molecular aspects of transient T cell unresponsiveness: role of selective interleukin-2 deficiency. Clin Exp Immunol. 2003, 132: 225-231. 10.1046/j.1365-2249.2003.02150.x.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Joung I, Kim HJ, Kwon YK: p62 modulates Akt activity via association with PKCzeta in neuronal survival and differentiation. Biochem Biophys Res Commun. 2005, 334: 654-660. 10.1016/j.bbrc.2005.06.138.

    Article  CAS  PubMed  Google Scholar 

  24. Park S, James CD: ECop (EGFR-coamplified and overexpressed protein), a novel protein, regulates NF-kappaB transcriptional activity and associated apoptotic response in an IkappaBalpha-dependent manner. Oncogene. 2005, 24: 2495-2502. 10.1038/sj.onc.1208496.

    Article  CAS  PubMed  Google Scholar 

  25. Li L, Shi Y, Wu H, Wan B, Li P, Zhou L, Shi H, Huo K: Hepatocellular carcinoma-associated gene 2 interacts with MAD2L2. Mol Cell Biochem. 2007, 304: 297-304. 10.1007/s11010-007-9512-8.

    Article  CAS  PubMed  Google Scholar 

  26. Saad AF, Meacham WD, Bai A, Anelli V, Elojeimy S, Mahdy AE, Turner LS, Cheng J, Bielawska A, Bielawski J, Keane TE, Obeid LM, Hannun YA, Norris JS, Liu X: The functional effects of acid ceramidase overexpression in prostate cancer progression and resistance to chemotherapy. Cancer Biol Ther. 2007, 6: 1455-1460.

    Article  CAS  PubMed  Google Scholar 

  27. Elojeimy S, Liu X, McKillop JC, El-Zawahry AM, Holman DH, Cheng JY, Meacham WD, Mahdy AE, Saad AF, Turner LS, Cheng J, Day TA, Dong JY, Bielawska A, Hannun YA, Norris JS: Role of acid ceramidase in resistance to FasL: therapeutic approaches based on acid ceramidase inhibitors and FasL gene therapy. Mol Ther. 2007, 15: 1259-1263. 10.1038/

    Article  CAS  PubMed  Google Scholar 

  28. Shah MV, Zhang R, Irby R, Kothapalli R, Liu X, Arrington T, Frank B, Lee NH, Loughran TP: Molecular profiling of LGL leukemia reveals role of sphingolipid signaling in survival of cytotoxic lymphocytes. Blood. 2008, 112: 770-781. 10.1182/blood-2007-11-121871.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Huwiler A, Zangemeister-Wittke U: Targeting the conversion of ceramide to sphingosine 1-phosphate as a novel strategy for cancer therapy. Crit Rev Oncol Hematol. 2007, 63: 150-159. 10.1016/j.critrevonc.2007.04.010.

    Article  PubMed  Google Scholar 

  30. Liu X, Elojeimy S, Turner LS, Mahdy AE, Zeidan YH, Bielawska A, Bielawski J, Dong JY, El-Zawahry AM, Guo GW, Hannun YA, Holman DH, Rubinchik S, Szulc Z, Keane TE, Tavassoli M, Norris JS: Acid ceramidase inhibition: a novel target for cancer therapy. Front Biosci. 2008, 13: 2293-2298. 10.2741/2843.

    Article  CAS  PubMed  Google Scholar 

  31. Arndt S, Bosserhoff AK: TANGO is a tumor suppressor of malignant melanoma. Int J Cancer. 2006, 119: 2812-2820. 10.1002/ijc.22242.

    Article  CAS  PubMed  Google Scholar 

  32. Arndt S, Bosserhoff AK: Reduced expression of TANGO in colon and hepatocellular carcinomas. Oncol Rep. 2007, 18: 885-891.

    CAS  PubMed  Google Scholar 

  33. Yusuf I, Fruman DA: Regulation of quiescence in lymphocytes. Trends Immunol. 2003, 24: 380-386. 10.1016/S1471-4906(03)00141-8.

    Article  CAS  PubMed  Google Scholar 

  34. You H, Pellegrini M, Tsuchihara K, Yamamoto K, Hacker G, Erlacher M, Villunger A, Mak TW: FOXO3a-dependent regulation of Puma in response to cytokine/growth factor withdrawal. J Exp Med. 2006, 203: 1657-1663. 10.1084/jem.20060353.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Greer EL, Brunet A: FOXO transcription factors at the interface between longevity and tumor suppression. Oncogene. 2005, 24: 7410-7425. 10.1038/sj.onc.1209086.

    Article  CAS  PubMed  Google Scholar 

  36. Murphy CT: The search for DAF-16/FOXO transcriptional targets: approaches and discoveries. Exp Gerontol. 2006, 41: 910-921. 10.1016/j.exger.2006.06.040.

    Article  CAS  PubMed  Google Scholar 

  37. Weidinger C, Krause K, Klagge A, Karger S, Fuhrer D: Forkhead box-O transcription factor: critical conductors of cancer's fate. Endocr Relat Cancer. 2008, 15: 917-929. 10.1677/ERC-08-0153.

    Article  CAS  PubMed  Google Scholar 

  38. Mei Y, Zhang Y, Yamamoto K, Xie W, Mak TW, You H: FOXO3a-dependent regulation of Pink1 (Park6) mediates survival signaling in response to cytokine deprivation. Proc Natl Acad Sci USA. 2009, 106: 5153-5158. 10.1073/pnas.0901104106.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Beadling C, Smith KA: DNA array analysis of interleukin-2-regulated immediate/early genes. Med Immunol. 2002, 1: 2-10.1186/1476-9433-1-2.

    Article  PubMed Central  PubMed  Google Scholar 

  40. Kovanen PE, Rosenwald A, Fu J, Hurt EM, Lam LT, Giltnane JM, Wright G, Staudt LM, Leonard WJ: Analysis of gamma c-family cytokine target genes. Identification of dual specificity phosphatase 5 (DUSP5) as a regulator of mitogen activated protein kinase activity in interleukin-2 signaling. J Biol Chem. 2003, 278: 5205-5213. 10.1074/jbc.M209015200.

    Article  CAS  PubMed  Google Scholar 

  41. Kovanen PE, Leonard WJ: Cytokines and immunodeficiency diseases: critical roles of the gamma(c)-dependent cytokines interleukins 2, 4, 7, 9, 15, and 21, and their signaling pathways. Immunol Rev. 2004, 202: 67-83. 10.1111/j.0105-2896.2004.00203.x.

    Article  CAS  PubMed  Google Scholar 

  42. Kovanen PE, Young L, Al-Shami A, Rovella V, Pise-Masison CA, Radonovich MF, Powell J, Fu J, Brady JN, Munson PJ, Leonard WJ: Global analysis of IL-2 target genes: identification of chromosomal clusters of expressed genes. Int Immunol. 2005, 17: 1009-1021. 10.1093/intimm/dxh283.

    Article  CAS  PubMed  Google Scholar 

  43. Heinrich PC, Behrmann I, Haan S, Hermanns HM, Muller-Newen G, Schaper F: Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem J. 2003, 374: 1-20. 10.1042/BJ20030407.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Chiu BC, Weisenburger DD: An update of the epidemiology of non-Hodgkin's lymphoma. Clin Lymphoma. 2003, 4: 161-168. 10.3816/CLM.2003.n.025.

    Article  PubMed  Google Scholar 

  45. Ekström-Smedby K: Epidemiology and etiology of non-Hodgkin lymphoma–a review. Acta Oncol. 2006, 45: 258-271. 10.1080/02841860500531682.

    Article  PubMed  Google Scholar 

  46. Malumbres M, Barbacid M: To cycle or not to cycle: a critical decision in cancer. Nat Rev Cancer. 2001, 1: 222-231. 10.1038/35106065.

    Article  CAS  PubMed  Google Scholar 

  47. Khaled AR, Durum SK: Lymphocide: cytokines and the control of lymphoid homeostasis. Nat Rev Immunol. 2002, 2: 817-830. 10.1038/nri931.

    Article  CAS  PubMed  Google Scholar 

  48. Schluns KS, Lefrancois L: Cytokine control of memory T-cell development and survival. Nat Rev Immunol. 2003, 3: 269-279. 10.1038/nri1052.

    Article  CAS  PubMed  Google Scholar 

  49. Holmes S, He M, Xu T, Lee PP: Memory T cells have gene expression patterns intermediate between naive and effector. PNAS. 2005, 102: 5519-5523. 10.1073/pnas.0501437102.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Willinger T, Freeman T, Hasegawa H, McMichael AJ, Callan MF: Molecular signatures distinguish human central memory from effector memory CD8 T cell subsets. J Immunol. 2005, 175: 5895-5903.

    Article  CAS  PubMed  Google Scholar 

  51. Chtanova T, Newton R, Liu SM, Weininger L, Young TR, Silva DG, Bertoni F, Rinaldi A, Chappaz S, Sallusto F, Rolph MS, Mackay CR: Identyfication of T cell-restricted genes, and signatures for different T cell responses, using a comprehensive collection of mircroarray datasets. J Immunol. 2005, 175: 7837-7847.

    Article  CAS  PubMed  Google Scholar 

  52. Kerdiles YM, Beisner DR, Tinoco R, Dejean AS, Castrillon DH, DePinho RA, Hedrick SM: Foxo1 links homing and survival of naive T cells by regulating L-selectin, CCR7 and interleukin 7 receptor. Nat Immunol. 2009, 10: 176-184. 10.1038/ni.1689.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Kaech SM, Tan JT, Wherry EJ, Konieczny BT, Surh CD, Ahmed R: Selective expression of the interleukin 7 receptor identifies effector CD8 T cells that give rise to long-lived memory cells. Nat Imm. 2003, 4: 1191-1198. 10.1038/ni1009.

    Article  CAS  Google Scholar 

  54. Kaech SM, Hemby S, Kersh E, Ahmed R: Molecular and functional profiling of memory CD8 T cell differentiation. Cell. 2002, 111: 837-851. 10.1016/S0092-8674(02)01139-X.

    Article  CAS  PubMed  Google Scholar 

  55. Haining WN, Ebert BL, Subrmanian A, Wherry EJ, Eichbaum Q, Evans JW, Mak R, Rivoli S, Pretz J, Angelosanto J, Smutko JS, Walker BD, Kaech SM, Ahmed R, Nadler LM, Golub TR: Identification of an evolutionarily conserved transcriptional signature of CD8 memory differentiation that is shared by T and B cells. J Immunol. 2008, 181: 1859-1868.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Siwicki JK, Rymkiewicz G, Blachnio K, Rygier J, Kuzniar P, Ploski R, Janusz A, Skurzak H, Chrzanowska K, Steffen J: Spontaneously immortalized T lymphocytes from Nijmegen Breakage Syndrome patients display phenotypes typical for lymphoma cells. Leuk Res. 2008, 32: 569-577. 10.1016/j.leukres.2007.08.011.

    Article  CAS  PubMed  Google Scholar 

  57. Siwicki JK, Hedberg Y, Nowak R, Loden M, Zhao J, Landberg G, Roos G: Long-term cultured IL-2-dependent T cell lines demonstrate p16 overexpression, normal pRb/p53, and upregulation of cyclins E or D2. Exp Gerontol. 2000, 35: 375-388. 10.1016/S0531-5565(00)00088-7.

    Article  CAS  PubMed  Google Scholar 

Download references


We thank Prof. Leendert Looijenga, PhD, for the critical reading of the manuscript. The study was supported by the grant no 2 P05A 150 29 (to JKS, MCh, MG, and JS) and in part by the grant 2 P05A 022 30 (to BJ) of the Polish Ministry of Science and Higher Education.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Magdalena Chechlinska.

Additional information

Authors' contributions

MCh conceived of the study and participated in its design and coordination, carried out RNA isolation, analysed data and interpreted the study, drafted, wrote and edited the manuscript. JKS conceived of the study and participated in its design and coordination, carried out cell cultures, analysed data and interpreted the study, drafted and wrote the manuscript. MG carried out real-time PCR verification of gene expression, participated in drafting the manuscript. MO-W performed microarray gene expression profiling. MJ performed the biostatistical analyses, participated in drafting the manuscript. AP participated in the biostatistical analyses. BJ participated in the design of the study. JS conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript.

Electronic supplementary material


Additional File 1: Table A1. Genes differentiating between samples before and after IL-2 withdrawal identified by a non-paired sample T-test. Genes selected at the nominal univariate comparison level of 0.001. (PDF 30 KB)


Additional File 2: Table A2. Genes differentiating between samples before and after IL-2 withdrawal identified by a paired sample T-test. Genes selected at the nominal univariate comparison level of 0.001. (PDF 35 KB)


Additional File 3: Table A3. Gene Ontology (GO) analysis of genes selected in a non-paired sample comparison between IL-2-deprived and non-deprived lymphocytes. The number of genes changed in each category was compared with the number of expected occurrences. Only GO classes and parent classes with at least 5 observations in the selected subset and with an "observed vs. expected" ratio of at least 2 were shown. (PDF 11 KB)


Additional File 4: Table A4. Assessment of overall significance of expression changes following IL-2 deprivation in gene groups defined by Gene Ontology categories. Three independent tests: LS, KS and Efron-Tibshirani GSA were applied to select significantly affected gene classes. (PDF 15 KB)


Additional File 5: Figure A1. Hierarchical clustering analysis of selected memory T cell genes. The analysis based on the expression values of some of the genes identified by Holmes et. al [49] to characterise memory T cells. Samples before IL-2 withdrawal are in the light-blue columns, samples after IL-2 withdrawal in the navy-blue columns. Primary samples are in the yellow column, immortalised samples are in the green one. (PDF 25 KB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Chechlinska, M., Siwicki, J.K., Gos, M. et al. Molecular signature of cell cycle exit induced in human T lymphoblasts by IL-2 withdrawal. BMC Genomics 10, 261 (2009).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: