Global analysis of IL-2 target genes: identification of chromosomal clusters of expressed genes

Panu E. Kovanen1,5, Lynn Young3, Amin Al-Shami1, Valentina Rovella1, Cynthia A. Pise-Masison2, Michael F. Radonovich2, John Powell4, Jacqueline Fu1, John N. Brady2, Peter J. Munson3 and Warren J. Leonard1

1 Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892-1674, USA
2 Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
3 Analytical Biostatistics Section, Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5626, USA
4 Bioinformatics and Molecular Analysis Section, Computational Bioscience and Engineering Laboratory, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5626, USA
5 Present address: Haartman Institute, Department of Pathology, University of Helsinki, Haartmaninkatu 3, University of Helsinki, PO Box 21, Finland

Correspondence to: W. J. Leonard; E-mail: wjl{at}helix.nih.gov


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
T lymphocytes play a central role in controlling adaptive immune responses. IL-2 critically regulates both T cell growth and death and is involved in maintaining peripheral tolerance, but the molecules involved in these and other IL-2 actions are only partially known. We now provide a comprehensive compendium of the genes expressed in T cells and of those regulated by IL-2 based on a combination of DNA microarrays and serial analysis of gene expression (SAGE). The newly identified IL-2 target genes include many genes previously linked to apoptosis in other cellular systems that may contribute to IL-2-dependent survival functions. We also studied the mRNA expression of known regulators of signaling pathways for their induction in response to IL-2 in order to identify potential novel positive and/or negative feedback regulators of IL-2 signaling. We show that IL-2 regulates only a limited number of these genes. These include suppressors of cytokine signaling (SOCS) 1, SOCS2, dual-specificity phosphatase (DUSP) 5, DUSP6 and non-receptor type phosphatase-7 (PTPN7). Additionally, we provide evidence that many genes expressed in T cells locate in chromosomal clusters, and that select IL-2-regulated genes are located in at least two clusters, one at 5q31, a known cytokine gene cluster, and the other at 6p21.3, a region that contains genes encoding the tumor necrosis factor (TNF) superfamily members TNF, LT-{alpha} and LT-ß.

Keywords: genomics, microarray, target gene, SAGE, interleukin-2


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
T lymphocytes play a central role in orchestrating adaptive immune responses to pathogens such as bacteria and viruses. T cell development, maturation and responsiveness are regulated in part by secreted proteins called cytokines. Cytokines are essential for the integrity of the immune system, and abnormal cytokine function can result in immunodeficiency or autoimmunity (1, 2). One cytokine that has an essential role in regulating immune responses is IL-2 (1), which is produced mainly by activated CD4+ T lymphocytes in response to antigen stimulation (1). The actions of IL-2 are mediated by signal transduction cascade(s) that are initiated by IL-2-induced oligomerization of IL-2R{alpha} chain, IL-2Rß and the common cytokine receptor {gamma} chain ({gamma}c) on activated T cells (2, 3). This juxtaposes cytoplasmic Janus family tyrosine kinases Jak1 and Jak3 that associate with IL-2Rß and {gamma}c, respectively (24). Activated Jak kinases phosphorylate specific tyrosine residues in IL-2Rß that serve as docking sites for SH2 domain-containing signaling molecules, such as Stat5a, Stat5b and Shc (2). Eventually, activation of multiple signaling pathways, including phosphatidylinositol 3kinase (PI3-K), Jak-STAT and Ras-MAP kinase (MAPK)-coupled pathways leads to the transcription of target genes that contribute to IL-2-dependent biologic actions (2, 3).

IL-2 has multiple roles in regulating T cell function (1). During T cell clonal expansion after initial antigen encounter, IL-2 functions as a growth and survival factor. Additionally, IL-2 can sensitize proliferating T cells to death triggered by secondary TCR stimulation in a process known as activation-induced cell death (AICD) (4). IL-2 is also necessary for the development and maintenance of regulatory T cells, which are a population of CD4+ T cells that suppress other T cells (5, 6). Loss of IL-2 function results in defective AICD and in a decrease of regulatory T cells, resulting in the expansion of autoreactive T lymphocytes and autoimmunity (610). Thus, an essential role for IL-2 is to serve as a key guardian of peripheral tolerance.

Although many functions of IL-2 are known and the early signaling events induced by IL-2 are well established, the genes that mediate IL-2-dependent biological actions remain poorly characterized. IL-2 is known to regulate the function of transcription factors such as Stat5a, and Stat5b and Stat3, and these are important for IL-2-dependent functions (1114). For example, T cells from mice deficient either in Stat5a (13) or Stat5b (14) show decreased IL-2-dependent proliferation, and mice deficient in both Stat5a and Stat5b show a more severe defect in IL-2-dependent proliferation (15). Thus, IL-2-activated Stat5-regulated genes such as cyclin D2, Pim-1, Fas ligand (FasL), CIS and suppressors of cytokine signaling (SOCS) 1 are implicated in mediating the biologic actions of IL-2 (3). IL-2 presumably also regulates gene expression through other factors, as it also activates Ras-MAPK and PI3-K/Akt-signaling pathways but the genes regulated by these pathways are less well characterized (3, 15). In order to understand how IL-2-regulated signaling pathways converge to mediate biologic actions, we sought to identify the genes regulated by those pathways.

The identification of genes regulated by IL-2 was long hindered by lack of suitable methodologies. However, the development of technologies such as microarrays and serial analysis of gene expression (SAGE) has allowed the comprehensive analysis of genes involved in different cellular processes (16, 17). When combined with large-scale transcript mapping, novel insights into disease pathogenesis as well as gene regulation have emerged from these methodologies in other systems (18, 19). Recently, microarrays were used to identify ~200 putative IL-2 target genes (20, 21). We now extend this analysis and use both DNA microarrays and SAGE to generate a broader compendium of genes expressed in T cells and in particular those regulated by IL-2. We have studied the genomic organization of these genes and present evidence that many genes expressed in T cells are organized into chromosomal clusters, with some IL-2-regulated genes locating in clusters at chromosomes 5q31 and 6p21 as well. The relevance of these findings from the viewpoint of gene regulation is discussed.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Cell cultures
Peripheral blood mononuclear cells (PBMC) were isolated from healthy volunteers at the National Institutes of Health Blood Bank. PBMC were isolated by centrifugation on Ficoll-Paque density gradient (Pharmacia, Uppsala, Sweden), suspended in RPMI 1640 medium (GIBCO, Grand Island, NY, USA) containing 10% heat-inactivated fetal bovine serum (U.S. Biotechnologies, Inc., Parkerford, PA, USA), 100 µg ml–1 streptomycin, 100 U ml–1 penicillin and 2 mM glutamine (all from Cellgro, VA, USA), and stimulated for 18 h with 2 µg ml–1 PHA (Boehringer Mannheim). T lymphocytes were selectively expanded for 10 days in complete RPMI medium supplemented with 500 ng ml–1 PHA and 50 U ml–1 IL-2 (TecinTM, Roche, Nutley, NJ, USA). The cells were then washed twice, resuspended in complete RPMI medium and rested for 3 days. T cells (>95% CD3+, IL-2R{alpha}+) were stimulated or not stimulated with 100 U ml–1 of IL-2 for 4 h to induce gene expression, and then pelleted and stored at –80°C. In some experiments CD4+ or CD8+ T cells were isolated prior to culture using positive selection and magnetic beads (Miltenyi Biotec, GmBH, Germany). For TCR stimulation experiments, T cells were purified using magnetic beads (Pan-T cell isolation kit, Miltenyi Biotec) and stimulated for 4 h on plates that had been coated for 1 h with anti-CD3 (5 µg ml–1) and anti-CD28 (1 µg ml–1) (both from PharMingen, San Diego, CA, USA), after which the cells were pelleted and stored at –80°C. Four replicate experiments were performed.

T cells from wild-type, Stat5a–/–, and Stat5b–/– mice were activated with anti-CD3 (5 µg ml–1) and anti-CD28 (1 µg ml–1) mAbs and cultured in the presence of IL-2 (100 U ml–1) for 7 days. T cells from Stat5a/b–/– double-knockout mice were activated for only 18 h, because the cells do not proliferate in response to anti-CD3 and IL-2 (22). The generation of Stat5a–/–, Stat5b–/– and Stat5a/b double-deficient mice has been described (2325). After activation, cells were washed twice with PBS, and rested for 18 h in RPMI complete medium without IL-2, then stimulated with IL-2 (100 U ml–1) for 4 h, lysed with Trizol (GIBCO) and the lysates were stored at –20°C.

mRNA preparation and GeneChip hybridization
The mRNA was extracted from frozen cells using either RNeasy (Qiagen, Valencia, CA, USA) or FastTrac 2.0 (Invitrogen, Carlsbad, CA, USA) RNA-extraction kits.

Identification of constitutively expressed and differentially expressed genes by GeneChip microarrays
RNA was processed to cRNA probes for GeneChip analysis (Affymetrix Inc., Santa Clara, CA, USA). The probes were hybridized to U95A GeneChips (Affymetrix), washed and scanned (Hewlett Packard, Gene Array scanner G2500A) according to procedures outlined by the manufacturer (Affymetrix). Scanned images were processed using the Microarray SuiteTM version 4 (Affymetrix) which yielded for each probe set average difference values, as well as ‘absent’ or ‘present’ calls. Four similar experiments were performed, each of which consisted of one control and one IL-2-treated sample. A transcript was considered to be ‘present’ when the corresponding probe set gave a ‘present call’ in at least three of four experiments for either control or IL-2-treated T cells. Data were further analyzed for differential expression using methods and programs developed by some of us (P.J.M.), and these are available from our website, http://abs.cit.nih.gov/main/CIT_Bioinformatics_Cooper.htm or http://affylims.cit.nih.gov, as scripts for the JMP-software's (SAS Inc, Cary, NC, USA) statistics package. Average difference values were normalized, as described previously (2628). The statistical significance of change in expression was determined using a consistency test for each gene (29). This statistical test is similar to a paired Student's t-test, but has greater power with small replication numbers. A set of differentially expressed genes was chosen to have an estimated false discovery rate (FDR) of <5% (i.e. at most 5% of the set is likely due only to chance) (30, 31). The analysis of TCR-regulated genes was performed as described above, using U133A GenechipsTM (Affymetrix).

Genomic localization of expressed genes
Affymetrix target nucleotide sequences (~400 bases in length) for 12 453 of the 12 625 probe sets were compared to the public human genome sequence (NCBI build, December 22, 2001 release) using BLAT (3234). A target sequence was considered to be located on the genome if its alignment score (number of matched bases minus number of mismatches minus gap penalties) (33) was ≥100 and its percent identity to the genomic locus was ≥60%. A total of 11 115 target sequences were so located. Of these, 36 target sequences mapped to more than one genomic locus and were excluded, leaving 11 079 probe sets with unique genome locations. When multiple probe sets represented a single gene (i.e. same UniGene cluster or same GenBank identifier), we chose the probe set of the highest apparent quality (lowest calculated FDR for differential expression). In this fashion, we obtained expression measurements for 8582 distinct genes.

Clusters of differentially or constitutively expressed genes
We sought to determine if the distribution of expressed genes along the chromosome was random or whether the expressed genes were clustered. A set of genes was considered to form a cluster if the size of the genomic region they occupied was less than would be expected by chance, or alternatively a region represented a cluster if it contained more genes than it should by chance. More specifically, we looked for gene clusters by observing the length of chromosome segments occupied by each set of k consecutive differentially (or constitutively) expressed genes. We define k genes as clustered if the length of their occupied segment (k-span) is shorter than would be expected by chance. To determine the statistical significance for an observed k-span, we generated numerous copies of the genome with the actual gene locations, but with the differentially expressed genes marked at random, keeping the number of marked genes per chromosome constant. For each k, and each randomly marked copy of the genome, the smallest k-span was computed, forming a distribution of values for the minimal k-span under the null hypothesis that the differentially expressed genes are distributed randomly among the genes in each chromosome. The statistical significance of a cluster of k genes under consideration was then evaluated by comparing its span to this distribution. Since the number of genes, k, was kept fixed in this simulation, we call the significance calculated as above, the ‘k-specific’ P value. In fact, we did not know, a priori, what size clusters to look for, and therefore scanned over all values of k from 2 to 10 genes (i.e. we searched for clusters comprising up to 10 genes). A correction on the significance was therefore required and was obtained by retaining the minimum k-specific P value for each iteration, over the range 2 ≤ k ≤ 10. This second distribution allows for correction for the fact that k is, in general, unspecified. We refer to the significance calculated in this fashion as the k-unspecified P value’. No restrictions were placed on k for clusters of constitutively expressed genes. Careful simulations done in this manner protect from finding false clusters solely due to the non-uniform spacing of genes in the genome, or due to the fact that neither the size nor the length of clusters being sought could be specified ahead of time.

Generation of SAGE libraries
SAGE libraries were generated from 10 µg mRNA from IL-2-stimulated or unstimulated T lymphocytes. We used the protocol available at www.SAGENET.org/sage_protocol.htm, except that streptavidin-coated tubes (Boehringer Mannheim) were used instead of streptavidin-coated magnetic beads, and the elution step was done using an Elutrap chamber (S&S). Clones containing different numbers of tags were screened by PCR, and those with an insert size of ≥500 bp were sequenced by the National Institutes of Health Intramural Sequencing core. SAGE tag analysis was done using eSAGE (http://genome.nhgri.nih.gov/eSAGE/) and the SAGE2000 (www.SAGENET.org/sage_protocol.htm) software. Replicate ditags as well as tags corresponding to the linker sequence were excluded. The tags were identified by linking them with UniGene clusters (build #146, ftp://ncbi.nlm.nih.gov), and tag counts were summed for each cluster. UniGene identifiers linked by one or more SAGE tags were mapped to the UCSC build of the genome (UCSC, December 22, 2001), using the UCSC Human Genome Project annotation database for UniGene identifiers and the same criteria as for the microarray data. Relative positions from the different genome builds were used to compare UniGene clusters identified from SAGE data with those from Affymetrix data. Of the entire UniGene database of 52 783 identifiers that could be mapped, our SAGE tags corresponded to 8883 of these (http://genome-archive.cse.ucsc.edu/goldenPath/22dec2001/database/uniGene.txt.gz).

Real-time PCR
Real-time PCR was performed using an ABI Prism Instrument (Applied Biosystems). Total RNA was reverse transcribed using the RNA PCR gold kit (Applied Biosystems). Ten nanograms of cDNA was amplified for 40 cycles with primers for Bcl-2, Bcl-XL, nuclear factor (NF)-{kappa}B1, STK17A, Caspase 3, FasL, TRAIL or tumor necrosis factor (TNF) and normalized against mRNA for 18S rRNA, a housekeeping gene. All primers were ordered ready-made from the ‘Gene expression assays’ system (Applied Biosystems). In the case of genes encoding for IL-2R{alpha}, LT-{alpha}, LT-ß or TNF, total RNA was reverse transcribed (Superscript II, Invitrogen) and 10 ng of cDNA was amplified for 40 cycles with primers from Applied Biosystems. A standard curve was prepared by amplifying different amounts of cDNA prepared from IL-2-stimulated murine T cells. Results obtained with specific primers were normalized against hypoxanthine guanine phosphoribosyl transferase, a housekeeping gene.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Identification of IL-2-regulated genes using GeneChip microarrays
Recently, we reported a limited comparative study of genes regulated by IL-2, IL-4, IL-7 and IL-15 (21). We sought to identify a more comprehensive set of IL-2-regulated genes, and we used U95A GeneChip microarrays, which correspond to >9000 genes or transcripts (12 625 probe sets), as well as SAGE analysis. For the microarray analysis, we analyzed mRNAs from resting and IL-2-stimulated mRNA samples from four separate experiments. The transcript profiles were compared at 4 h, a time at which we found that most known IL-2-regulated genes are induced or repressed (21). We defined a set of IL-2-regulated genes using a false discovery rate (FDR) of ≤0.05. The 12 625 probe sets on the GeneChip corresponded to 8582 UniGene clusters. Of these, 460 were up-regulated and 419 down-regulated. Among these 879 differentially expressed genes, we identified many previously reported IL-2 target genes (e.g. IL-2R{alpha}, Pim-1, Bcl-2, Bcl-XL and granzyme B), but most of them had not been previously reported as IL-2-regulated genes. The complete list of IL-2-regulated genes is available at dir.nhlbi.nih.gov/labs/supplements/ (Supplementary Tables 1 and 2, available at International Immunology Online).

Functional annotation of IL-2-regulated genes identified by expression profiling
IL-2 has pleiotropic effects on human T cells. To identify factors possibly related to known IL-2 functions, we linked Affymetrix probe set IDs to the corresponding ‘functional’ Gene Ontology annotations using the websites http://netaffx.com and http://www.geneontology.org. We thus identified, for example, the list of IL-2-regulated genes corresponding to term ‘apoptosis’ (Fig. 1). Some of these genes, such as the anti-apoptotic molecules Bcl-2 and Bcl-XL (35), and pro-apoptotic cytokines FasL and TNF have known functions, whereas most have not been functionally evaluated in T cells. We further studied the kinetics of expression of select anti- and pro-apoptotic genes in purified CD4+ and CD8+ T cells; these included Bcl-2, Bcl-XL, NF-{kappa}B1, STK17A, Caspase 3, FasL, TNF and TRAIL (Fig. 2). The kinetics of induction of both anti- and pro-apototic genes was similar with the peak of induction after 4 or 8 h of IL-2 treatment. The genes were also similarly regulated in both CD4+ and CD8+ cells. Thus, neither the kinetics of induction nor the specific pattern of expression in CD4+ versus CD8+ T cells readily explains the survival versus death (AICD)-inducing functions of IL-2.



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Fig. 1. Shown are microarray expression data of four T cell samples not stimulated or stimulated with IL-2. Shown are data for 23 IL-2-regulated genes that are annotated as being involved in ‘apoptosis’ at the gene ontology database (www.geneontology.org). Green squares correspond to genes with relatively low-level expression and red squares to genes with relatively high expression as compared to the average expression of all genes on the microarray (see scale at bottom of page).

 


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Fig. 2. Shown are real-time PCR data demonstrating the kinetics of IL-2 induction of three anti-apoptotic (Bcl-2, Bcl-XL and NF-{kappa}B1) genes and suppression of one pro-apoptotic gene (STK17A), as well as the kinetics of induction of four known pro-apoptotic genes (CASP3, FasL, TNF and TRAIL) in purified CD4+ and CD8+ T cells. Data represent average expression of genes in CD4+ and CD8+ T cells isolated from six different donors and cultured as in Fig. 1. The asterisk marks statistically significant difference in expression when compared to control (time 0) using the Student's t-test.

 
IL-2 regulation of genes possibly involved in feedback regulation of IL-2 signaling
To identify potential novel regulators of IL-2 signaling, we collected microarray data for members of gene families involved in the regulation of signaling pathways, including dual-specificity phosphatases (DUSPs) (Fig. 3A), SOCS and protein inhibitor of activated Stats (PIAS) gene family members (Fig. 3B) and other phosphatases (Fig. 3C); these are listed in Fig. 3 according to the level of IL-2 inducibility. We also collected corresponding microarray data from T cells stimulated with anti-CD3 plus anti-CD28 (Figs 3A, B and C, left panel; Supplementary Table 3, available at International Immunology Online). DUSPs are phosphatases that dephosphorylate both tyrosine and serine/threonine residues and are known to negatively regulate MAPK (36, 37). Of 12 DUSPs represented on the microarrays, 2 of these, DUSP5 and DUSP6, were induced by IL-2 whereas DUSP2 was repressed by IL-2. DUSP11 mRNA was highly expressed in both resting and IL-2-stimulated T cells. In naive T cells, TCR stimulation strongly induced the expression of DUSP5 and DUSP2, whereas DUSP6 was not induced (Fig. 3A). SOCS1 was strongly induced, whereas SOCS2 was only weakly induced by both stimuli (Fig. 3B). The other SOCS mRNAs and the PIAS mRNAs were not regulated in T cells by IL-2 nor were they highly expressed. From the many phosphatases represented on the microarrays, IL-2 regulated nine as defined by the FDR, albeit weakly in most cases. Only protein tyrosine phoshatase non-receptor type 7 was induced by IL-2, but not by TCR stimulation.



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Fig. 3. Shown are microarray expression data from naive (TCR–) or anti-CD3 plus anti-CD28 (TCR+)-stimulated T cells (left), or T cells stimulated (+) or not stimulated (–) with IL-2 (right). Shown are data of expression for DUSP (A), SOCS (B) and PIAS (B) gene family members and IL-2-regulated phosphatases (C). Green squares correspond to genes with relatively low expression and red squares to genes with relatively high expression as in Fig. 1.

 
SAGE analysis of IL-2-regulated genes
The microarray analysis covered ~30% of the predicted number of human genes (38, 39). To obtain a more comprehensive view of IL-2-regulated transcripts, we also constructed SAGE libraries from mRNA of resting and IL-2-activated T cells and sequenced ~20 000 SAGE tags from each library. Only ~4000 of the 40 000 sequenced tags were observed more than once (data not shown), consistent with most mammalian transcripts being expressed at low copy number. The most abundant tags in both SAGE libraries are at dir.nhlbi.nih.gov/labs/supplements/ (Supplementary Table 4, available at International Immunology Online). As expected, the most highly expressed tags correspond to housekeeping genes. After eliminating tags derived from linkers and duplicate tags, ~20 000 unique tags remained out of the 40 000 initially sequenced tags. We further removed those tags that did not map to any UniGene cluster, and when different tags mapped to the same UniGene cluster, the expression counts were combined. This resulted in 6349 different UniGene clusters for the SAGE data of which 100 were differentially expressed (P < 0.05), with 52 being induced by IL-2 and 48 being repressed (see Supplementary Table 5 at dir.nhlbi.nih.gov/labs/supplements/). When SAGE data were compared to the microarray analysis, 22 genes were found in common and similarly regulated by IL-2, indicating statistically significant concordance between the two data sets (P < 0.0014 by Fisher's exact test). An even better concordance would likely be achieved by sequencing more SAGE tags. Table 1 lists the 10 most induced (top 10 genes) and the 10 most repressed genes (bottom 10 genes) corresponding to SAGE tags. In the SAGE analysis, we also identified eight SAGE tags corresponding to IL-2-up-regulated transcripts and 13 SAGE tags corresponding to down-regulated transcripts that were not identified by microarrays (i.e. no oligonucleotides corresponding to these transcripts were on the microarray) (Table 2), demonstrating the advantage of using complementary approaches to identify differentially regulated genes.


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Table 1. Ten most highly IL-2-induced and repressed transcripts identified by SAGE

 

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Table 2. IL-2-regulated genes identified by SAGE, that were not represented on microarrays (U95A GeneChips)

 
Genomic localization of genes expressed in T cells, and identification of gene clusters
We scanned the human genome sequence for evidence of an uneven, possibly clustered distribution of IL-2-regulated genes. As noted in Methods, 8582 distinct genes including 460 IL-2-induced and 419 repressed genes represented on the Affymetrix U95A chip could be located in the genome. Potential clusters were identified by cataloging the spans of all possible clusters and calculating statistical significance (see Methods). We identified non-random clusters in both differentially expressed and constitutively expressed genes (Table 3; Fig. 4).


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Table 3. Clusters of expressed genes identified from U95A GeneChip microarray expression data

 


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Fig. 4. Locations of induced and repressed genes on chromosomes of the human genome. The density of locations of the genes mapped from the Affymetrix U95A array is represented by a gray scale on the chromosome plot. The locations of the induced and repressed genes are represented by the peaks extending to the left of each chromosome. The height of each peak corresponds to the number of such differentially expressed genes found within a 1-Mb window along the chromosome. The locations of the ‘present’ genes are represented by peaks extending to the right of each chromosome using the same window size. The length of each chromosome is based on estimates from the International Human Genome Sequencing Consortium (38). The green arrow heads represent non-random clusters of IL-2-regulated genes and red arrow heads clusters of genes (less than 1 megabase) expressed in T cells.

 
Clusters of IL-2-regulated genes and the role for Stat5a and Stat5b proteins in the regulation of LTA and TNF genes in 6p21.3 cluster
For the IL-2-regulated genes, two clusters were identified [Tables 3, A, and 4; Fig. 4, green arrow heads]. The 5q31 cluster spans 635 kb and overlaps the 5q31 cytokine gene cluster including those encoding IL-3, IL-4 and granulocyte macrophage colony-stimulating factor (GM-CSF). The cluster at 6p21.3 spans 7 kb and consists of three TNF superfamily members (TNF, LT-{alpha} and LT-ß). Stat5a and Stat5b transcription factors regulate the transcription of several IL-2 target genes, including IL-2R{alpha}, perforin, CIS and cyclin D2 (3). To investigate if Stat5 proteins are involved in the regulation of TNF, LT-{alpha} and LT-ß in the 6p21.3 cluster, we studied the expression of these genes in normal and in mouse T cells deficient in expression of Stat5a and/or Stat5b (Fig. 5). Although LT-ß was IL-2-regulated in human cells, it was constitutively expressed and not regulated by IL-2 in mouse T cells (data not shown). However, TNF and LT-{alpha} mRNAs were potently induced by IL-2 in wild-type murine lymphocytes, but were less well induced in T cells derived from Stat5a- or Stat5b-deficient animals, and not induced at all in T cells lacking both Stat5a and Stat5b (Fig. 5). This indicates that IL-2-dependent Stat5 activation is critical for the IL-2-mediated regulation of TNF and LT-{alpha} in the 6p21.3 cluster.


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Table 4. Clusters of IL-2-regulated genes identified from U95A GeneChip microarray data

 


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Fig. 5. IL-2 induction of TNF and LT-{alpha} mRNA is dependent on Stat5a and Stat5b. Mouse T cells derived from wild-type mice or mice lacking Stat5a or Stat5b or both were stimulated with IL-2 for 4 h. Expression of IL-2R{alpha}, TNF and LT-{alpha} mRNA was analyzed by real-time PCR, and normalized relative to the expression of the hypoxanthine guanine phosphoribosyl transferase, a housekeeping gene.

 
Clusters of expressed genes in T lymphocytes
We also identified chromosomal clusters of genes expressed in T cells but not necessarily regulated by IL-2 in T cells [Tables 3, B, and 5; Fig. 4, red arrow heads]. Some of these clusters were large, but the methodology also identified clusters with smaller spans on chromosomes 3 (7 Mb), 6 (16 Mb), 9 (9 Mb), 21 (3 Mb) and clusters with spans <1 Mb on chromosomes 11 and X [Tables 3, B, and 5]. The cluster on chromosome 6 overlaps the 6p21.3 cluster of IL-2-regulated genes. We performed a similar analysis using SAGE data and identified 22 non-random clusters of expressed genes (see Methods for details, Table 6). Clusters from the analysis of SAGE data overlapped with those from microarray data, except that on chromosome 11, generally validating our analysis. Identification of more clusters by SAGE than by microarrays is consistent with the SAGE data set being considerably larger.


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Table 5. Chromosome 11 and X clusters of genes constitutively expressed in T lymphocytes identified from U95 microarray data

 

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Table 6. Clusters of expressed genes in T cells identified by SAGE data

 
To determine if the clusters we identified were unique for T cells, we also analyzed the organization of expressed genes derived from human heart and brain [publicly available from www.ncbi.nlm.nih.gov/geo as data sets GSM2829, GSM2856, GSM2828 and GSM2837 or from expression.gnf.org (40)]. We identified seven clusters of expressed genes from human heart (Table 3, C), and one from brain tissue (Table 3, D). Of the seven clusters found in heart tissue, only the clusters on chromosomes 6 and X overlapped with those identified from T cells. These results indicate that some expressed genes appear in chromosomal clusters, but the genomic distribution of clusters shows tissue specificity.


    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Recent advances in genomics have provided new approaches for studying biological systems. High-throughput methods for mRNA expression profiling such as DNA microarrays and SAGE have enabled quantitative analysis of gene expression at the genomic scale. Recently, combining large-scale expression profiling and genomic mapping has been used to define candidate genes for pathological conditions such as retinal diseases and schizophrenia (18, 41). Moreover, an understanding of genes expressed in a particular cell type and regulated within a biological pathway can facilitate studies aimed at identifying novel molecules that are important to these pathways (16, 4244). We have used DNA microarrays and SAGE to identify genes expressed in T cells as well as those regulated by IL-2. The resulting compendium of genes can be utilized for a systematic study of their role in IL-2 responses and T cell biology and as a reference database for candidate gene searches for T cell-associated diseases.

Using microarrays and SAGE, we identified close to 900 genes whose expression is regulated by IL-2, extending earlier studies aimed at identifying IL-2-regulated genes (20, 21). The complete list of IL-2-regulated genes is provided at dir.nhlbi.nih.gov/labs/supplements/. This website also includes the genomic locations of these genes as well as their functional annotations as defined in the gene ontology database (http://www.geneontology.org). Combining expression data with functional annotations allowed us to define subsets of IL-2-regulated genes and to identify, among others, a subset of 23 IL-2-regulated genes that are involved in apoptosis in different cellular systems. Some of these (Bcl-2 and Bcl-XL) have known pro- or anti-apoptotic functions in T cells, whereas others are poorly studied in T lymphocytes. For example, TNF-dependent NF-{kappa}B activation supports survival in a variety of cell types (45, 46), but the possible functional relevance of IL-2 induction of TNF and NF-{kappa}B1 is not yet known. Similarly, the significance of the induction by IL-2 of molecules such as aryl hydrocarbon receptor and cytochrome c, as well as the inhibition by IL-2 of a pro-apoptotic serine/threonine kinase 17a (STK17A) remains to be determined.

IL-2 is required for the maintenance of peripheral tolerance (4, 47, 48). This likely involves IL-2-dependent generation of CD4+CD25+ regulatory T cells, and IL-2-mediated sensitization of proliferating T cells to TCR-dependent apoptotic stimuli (AICD) (4, 6). The molecules involved in these processes are largely not known. TCR-mediated induction of FasL is critical for AICD, and mice deficient in Fas or FasL show defective AICD, uncontrolled lymphocyte proliferation and autoimmunity (49, 50). In combination with TCR signaling, IL-2 stimulation potently induces FasL and TNF, and this may partly explain the IL-2-dependent sensitization of T cells to AICD (51, 52). We also found that IL-2 induces a number of other pro-apoptotic genes as well, including those encoding TRAIL (TNF gene family member, (45, 46), Caspase 3 [an essential intracellular mediator of apoptosis (53)], death-associated protein (DAP), and serine/threonine kinase 17b (STK17B). DAP and STK17B have been shown to mediate IFN-dependent apoptotic signals (54). TOSO is a potent inhibitor of Fas-mediated apoptosis (55), and TOSO mRNA expression was repressed by IL-2. Thus, we found that IL-2 modulates the expression of a set of apoptosis-related molecules that have not previously been linked to the survival or cell death-inducing functions of IL-2.

Recently, several gene families have been identified that regulate signaling through cytokine receptors and modulate immune responses. The best-characterized negative regulators of cytokine signaling include SOCS proteins, PIAS proteins (protein inhibitors of activated STATs) and phosphatases. The SOCS family consists of eight members (CIS and SOCS1–7) (56). They are induced by cytokines and regulate cytokine signaling by at least two distinct mechanisms; SOCS1 and SOCS3 directly inhibit cytokine receptor-associated Jak kinases and provide general inhibitory signals, whereas CIS and SOCS2 more specifically inhibit cytokine-induced STAT activation, likely by preventing their association with cytokine receptors (56). We identified SOCS1 as strongly induced by IL-2 as previously noted (57, 58), while SOCS2 was moderately induced, which is a novel finding that requires further evaluation. PIAS proteins regulate cytokine signaling by interacting with and inhibiting the activation of STATs (59). We did not observe IL-2 regulation of PIAS1, PIASx (PIAS2), PIAS3 or PIASy by microarray or SAGE analysis.

Protein phosphatases are essential regulators of cellular functions, but their role in IL-2 signaling is largely unknown. We recently characterized DUSP5 as a negative regulator of IL-2-induced MAPK activity and identified DUSP6 as another strongly IL-2-induced potential regulator of MAPK activity (21). The DUSP gene family consists of 61 genes, 11 of which are designated as typical MAPK phosphatases and 19 as atypical MAPK phosphatases that lack MAPK-targeting motifs (60). DUSP5 and DUSP6 are specific for Erk-1/2, the primary MAPKs regulated by IL-2 in primary T cells. Of the many known protein phosphatases, only the mRNA of non-receptor protein tyrosine phosphatase 7 (PTPN7, also known as LCPTP or HePTP) was strongly induced by IL-2 in microarray data. Interestingly, PTPN7 has also been shown to be a specific Erk-2 phosphatase (61, 62).

Taken together, IL-2 appears to regulate the mRNA levels of relatively few known feedback regulators of signaling; SOCS1, SOCS2, CIS, DUSP5, DUSP6 and PTPN7. SOCS1 regulates the activity of Jaks and provides general inhibitory signals, whereas CIS and SOCS2 likely regulate the activity of Stat5. IL-2 mediated Erk-1/2 signaling is presumably controlled by DUSP5, DUSP6 and PTPN7.

The completion of human and other genome projects together with the availability of data from genome-wide gene expression studies have provided new insights into regulation of gene expression. Transcribed genes are not always randomly distributed in the genome, but are clustered in chromosomes. Such clusters have been detected among highly transcribed genes, including housekeeping genes in the human genome as well as in Drosophila (6366). Tissue-specific genes also appear to be organized in part in chromosomal clusters (6772), and functionally related genes sometimes form chromosomal clusters of co-regulated genes, somewhat analogous to the clustering of operons in Caenorhabditis elegans (7377). We identified two significant clusters of IL-2-regulated genes as well as several chromosomal clusters of T cell-expressed genes. The 5q31 cluster of IL-2-regulated genes overlaps with the cytokine gene cluster consisting of cytokines IL-3, GM-CSF, IL-5, IL-13 and IL-4 (78), and the 6q21 cluster consists of TNF, LT-{alpha} and LT-ß genes. We found that Stat5 is important for the regulation of TNF and LT-ß in the 6q21 cluster of IL-2-regulated genes, suggesting a partial mechanism for their co-regulation. Such clustering of transcription factor recognition sequences has been detected in the Drosophila genome (7981). We also observed that genes expressed in T cells (constitutively expressed, but not necessarily IL-2 regulated) formed clusters. Interestingly, these appear similar to and co-localize with previously observed domains of highly expressed genes (63, 64). Although the significance of these domains remains unclear, they have been hypothesized to be segments of chromatin that are particularly accessible for transcriptional activation (63, 64, 82).

In summary, we have used microarrays and SAGE analysis and obtained a genome-wide view of genes expressed in T cells of those induced by IL-2. Our detailed mapping data may facilitate candidate gene searches for T cell-associated diseases. Moreover, we have identified a number of novel target genes for IL-2, including genes potentially involved in IL-2-dependent survival functions and in AICD. The characterization of these molecules should allow a better understanding of and provide novel target molecules for the modulation of T cell immune responses.


    Supplementary data
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 
Supplementary data are available at International Immunology Online.


    Acknowledgements
 
We thank Hyoung-Pyo Kim for advice in regard to real-time PCR and Jian-Xin Lin for critical reading of the manuscript. P.E.K. was funded in part with grants from the Academy of Finland, Emil Aaltonen Foundation and Finnish Cultural Foundation.


    Abbreviations
 
AICD   Activation-induced cell death
DAP   Death-associated protein
DUSP   Dual-specificity phosphatase
FasL   Fas ligand
FDR   False discovery rate
GM-CSF   Granulocyte macrophage colony-stimulating factor
MAPK   MAP kinase
PBMC   Peripheral blood mononuclear cells
PI 3-K   Phosphatidylinositol-3-kinase
PIAS   Protein inhibitor of activated Stats
SAGE   Serial analysis of gene expression
SOCS   Suppressor of cytokine signaling
STAT   Signal transducer and activator of transcription
TNF   Tumor necrosis factor

    Notes
 
Transmitting editor: T. Hirano

Received 7 February 2005, accepted 11 May 2005.


    References
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Supplementary data
 References
 

  1. Lin, J. X. and Leonard, W. J. 2003. Interleukin-2. In Thomson, A. and Lotze, M. T., eds, The Cytokine Handbook, p. 167. Academic Press, New York.
  2. Leonard, W. J. and O'Shea, J. J. 1998. Jaks and STATs: biological implications. Annu. Rev. Immunol. 16:293.[CrossRef][ISI][Medline]
  3. Kovanen, P. E. and Leonard, W. J. 2004. 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. 202:67.[CrossRef][ISI][Medline]
  4. Lenardo, M., Chan, K. M., Hornung, F. et al. 1999. Mature T lymphocyte apoptosis—immune regulation in a dynamic and unpredictable antigenic environment. Annu. Rev. Immunol. 17:221.[CrossRef][ISI][Medline]
  5. McHugh, R. S. and Shevach, E. M. 2002. The role of suppressor T cells in regulation of immune responses. J. Allergy Clin. Immunol. 110:693.[CrossRef][ISI][Medline]
  6. Malek, T. R. 2003. The main function of IL-2 is to promote the development of T regulatory cells. J. Leukoc. Biol. 74:961.[Abstract/Free Full Text]
  7. Suzuki, H., Kundig, T. M., Furlonger, C. et al. 1995. Deregulated T cell activation and autoimmunity in mice lacking interleukin-2 receptor beta. Science 268:1472.[ISI][Medline]
  8. Willerford, D. M., Chen, J., Ferry, J. A., Davidson, L., Ma, A. and Alt, F. W. 1995. Interleukin-2 receptor alpha chain regulates the size and content of the peripheral lymphoid compartment. Immunity 3:521.[CrossRef][ISI][Medline]
  9. Sharfe, N., Dadi, H. K., Shahar, M. and Roifman, C. M. 1997. Human immune disorder arising from mutation of the alpha chain of the interleukin-2 receptor. Proc. Natl Acad. Sci. USA 94:3168.[Abstract/Free Full Text]
  10. Sakaguchi, S. 2000. Regulatory T cells: key controllers of immunologic self-tolerance. Cell 101:455.[CrossRef][ISI][Medline]
  11. Akira, S. 2000. Roles of STAT3 defined by tissue-specific gene targeting. Oncogene 19:2607.[CrossRef][ISI][Medline]
  12. Lin, J. X. and Leonard, W. J. 2003. Mechanisms and biological consequences of STAT signaling by cytokines that share the common cytokine receptor g chain, gc. In Sehgal, P. B., ed., Signal Transducers and Activators of Transcription (STATs), p. 450. Kluwer Academic Publishers, Norwell, MA.
  13. Nakajima, H., Liu, X. W., Wynshaw-Boris, A. et al. 1997. An indirect effect of Stat5a in IL-2-induced proliferation: a critical role for Stat5a in IL-2-mediated IL-2 receptor alpha chain induction. Immunity 7:691.[CrossRef][ISI][Medline]
  14. Imada, K., Bloom, E. T., Nakajima, H. et al. 1998. Stat5b is essential for natural killer cell-mediated proliferation and cytolytic activity. J. Exp. Med. 188:2067.[Abstract/Free Full Text]
  15. Xue, H. H., Kovanen, P. E., Pise-Masison, C. A. et al. 2002. IL-2 negatively regulates IL-7 receptor alpha chain expression in activated T lymphocytes. Proc. Natl Acad. Sci. USA 99:13759.[Abstract/Free Full Text]
  16. Brown, P. O. and Botstein, D. 1999. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21:33.[CrossRef][ISI][Medline]
  17. Velculescu, V. E., Vogelstein, B. and Kinzler, K. W. 2000. Analysing uncharted transcriptomes with SAGE. Trends Genet. 16:423.[CrossRef][ISI][Medline]
  18. Blackshaw, S., Fraioli, R. E., Furukawa, T. and Cepko, C. L. 2001. Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes. Cell 107:579.[CrossRef][ISI][Medline]
  19. Wayne, M. L. and McIntyre, L. M. 2002. Combining mapping and arraying: an approach to candidate gene identification. Proc. Natl Acad. Sci. USA 99:14903.[Abstract/Free Full Text]
  20. Beadling, C. and Smith, K. A. 2002. DNA array analysis of interleukin-2-regulated immediate/early genes. Med. Immunol. 1:2.[CrossRef][Medline]
  21. Kovanen, P. E., Rosenwald, A., Fu, J. et al. 2003. 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. 278:5205.[Abstract/Free Full Text]
  22. Moriggl, R., Topham, D. J., Teglund, S. et al. 1999. Stat5 is required for IL-2-induced cell cycle progression of peripheral T cells. Immunity 10:249.[CrossRef][ISI][Medline]
  23. Liu, X., Robinson, G. W., Wagner, K. U., Garrett, L., Wynshaw-Boris, A. and Hennighausen, L. 1997. Stat5a is mandatory for adult mammary gland development and lactogenesis. Genes Dev. 11:179.[Abstract]
  24. Udy, G. B., Towers, R. P., Snell, R. G. et al. 1997. Requirement of STAT5b for sexual dimorphism of body growth rates and liver gene expression. Proc. Natl Acad. Sci. USA 94:7239.[Abstract/Free Full Text]
  25. Teglund, S., McKay, C., Schuetz, E. et al. 1998. Stat5a and Stat5b proteins have essential and nonessential, or redundant, roles in cytokine responses. Cell 93:841.[CrossRef][ISI][Medline]
  26. Munson, P. J. 2001. A consistency test for determining the significance of gene expression changes on replicate samples and two convenient variance-stabilizing transformations. In Genelogic Workshop on Low Level Analysis of Affymetrix Genechip Data. http://stat-www.berkeley.edu/users/terry/zarray/affy/GL_workshop/genelogic2001.htlm.
  27. Durbin, B. P., Hardin, J. S., Hawkins, D. M. and Rocke, D. M. 2002. A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics 18 (Suppl. 1):S105.[Abstract/Free Full Text]
  28. Huber, W., Von Heydebreck, A., Sultmann, H., Poustka, A. and Vingron, M. 2002. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 (Suppl. 1):S96.[Abstract/Free Full Text]
  29. Daoud, S. S., Munson, P. J., Reinhold, W. et al. 2003. Impact of p53 knockout and topotecan treatment on gene expression profiles in human colon carcinoma cells: a pharmacogenomic study. Cancer Res. 63:2782.[Abstract/Free Full Text]
  30. Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B 57:289.[ISI]
  31. Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. and Golani, I. 2001. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125:279.[CrossRef][ISI][Medline]
  32. Kent, W. J., Sugnet, C. W., Furey, T. S. et al. 2002. The human genome browser at UCSC. Genome Res. 12:996.[Abstract/Free Full Text]
  33. Kent, W. J. 2002. BLAT—the BLAST-like alignment tool. Genome Res. 12:656.[Abstract/Free Full Text]
  34. Karolchik, D., Baertsch, R., Diekhans, M. et al. 2003. The UCSC genome browser database. Nucleic Acids Res. 31:51.[Abstract/Free Full Text]
  35. Nakajima, H., Noguchi, M. and Leonard, W. J. 2000. Role of the common cytokine receptor gamma chain (gammac) in thymocyte selection. Immunol. Today 21:88.[CrossRef][ISI][Medline]
  36. Camps, M., Nichols, A. and Arkinstall, S. 2000. Dual specificity phosphatases: a gene family for control of MAP kinase function. FASEB J. 14:6.[Abstract/Free Full Text]
  37. Theodosiou, A. and Ashworth, A. 2002. MAP kinase phosphatases. Genome Biol. 3:REVIEW S3009.
  38. Lander, E. S., Linton, L. M., Birren, B. et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:860.[CrossRef][ISI][Medline]
  39. Venter, J. C., Adams, M. D., Myers, E. W. et al. 2001. The sequence of the human genome. Science 291:1304.[Abstract/Free Full Text]
  40. Su, A. I., Cooke, M. P., Ching, K. A. et al. 2002. Large-scale analysis of the human and mouse transcriptomes. Proc. Natl Acad. Sci. USA 99:4465.[Abstract/Free Full Text]
  41. Mimmack, M. L., Ryan, M., Baba, H. et al. 2002. Gene expression analysis in schizophrenia: reproducible up-regulation of several members of the apolipoprotein L family located in a high-susceptibility locus for schizophrenia on chromosome 22. Proc. Natl Acad. Sci. USA 99:4680.[Abstract/Free Full Text]
  42. Duggan, D. J., Bittner, M., Chen, Y., Meltzer, P. and Trent, J. M. 1999. Expression profiling using cDNA microarrays. Nat. Genet. 21:10.[CrossRef][ISI][Medline]
  43. Young, R. A. 2000. Biomedical discovery with DNA arrays. Cell 102:9.[CrossRef][ISI][Medline]
  44. Polyak, K. and Riggins, G. J. 2001. Gene discovery using the serial analysis of gene expression technique: implications for cancer research. J. Clin. Oncol. 19:2948.[Abstract/Free Full Text]
  45. Locksley, R. M., Killeen, N. and Lenardo, M. J. 2001. The TNF and TNF receptor superfamilies: integrating mammalian biology. Cell 104:487.[CrossRef][ISI][Medline]
  46. Chan, K. F., Siegel, M. R. and Lenardo, J. M. 2000. Signaling by the TNF receptor superfamily and T cell homeostasis. Immunity 13:419.[CrossRef][ISI][Medline]
  47. Van Parijs, L. and Abbas, A. K. 1998. Homeostasis and self-tolerance in the immune system: turning lymphocytes off. Science 280:243.[Abstract/Free Full Text]
  48. Nelson, B. H. 2002. Interleukin-2 signaling and the maintenance of self-tolerance. Curr. Dir. Autoimmun. 5:92.[Medline]
  49. Ju, S. T., Panka, D. J., Cui, H. et al. 1995. Fas(CD95)/FasL interactions required for programmed cell death after T-cell activation. Nature 373:444.[CrossRef][ISI][Medline]
  50. Siegel, R. M., Chan, F. K., Chun, H. J. and Lenardo, M. J. 2000. The multifaceted role of Fas signaling in immune cell homeostasis and autoimmunity. Nat. Immunol. 1:469.[CrossRef][ISI][Medline]
  51. Zheng, L., Trageser, C. L., Willerford, D. M. and Lenardo, M. J. 1998. T cell growth cytokines cause the superinduction of molecules mediating antigen-induced T lymphocyte death. J. Immunol. 160:763.[Abstract/Free Full Text]
  52. Refaeli, Y., Van Parijs, L., London, C. A., Tschopp, J. and Abbas, A. K. 1998. Biochemical mechanisms of IL-2-regulated Fas-mediated T cell apoptosis. Immunity 8:615.[CrossRef][ISI][Medline]
  53. Woo, M., Hakem, R., Soengas, M. S. et al. 1998. Essential contribution of caspase 3/CPP32 to apoptosis and its associated nuclear changes. Genes Dev. 12:806.[Abstract/Free Full Text]
  54. Deiss, L. P., Feinstein, E., Berissi, H., Cohen, O. and Kimchi, A. 1995. Identification of a novel serine/threonine kinase and a novel 15-kD protein as potential mediators of the gamma interferon-induced cell death. Genes Dev. 9:15.[Abstract]
  55. Hitoshi, Y., Lorens, J., Kitada, S. I. et al. 1998. Toso, a cell surface, specific regulator of Fas-induced apoptosis in T cells. Immunity 8:461.[CrossRef][ISI][Medline]
  56. Kubo, M., Hanada, T. and Yoshimura, A. 2003. Suppressors of cytokine signaling and immunity. Nat. Immunol. 4:1169.[CrossRef][ISI][Medline]
  57. Matsumoto, A., Masuhara, M., Mitsui, K. et al. 1997. CIS, a cytokine inducible SH2 protein, is a target of the JAK-STAT5 pathway and modulates STAT5 activation. Blood 89:3148.[Abstract/Free Full Text]
  58. Sporri, B., Kovanen, P. E., Sasaki, A., Yoshimura, A. and Leonard, W. J. 2001. JAB/SOCS1/SSI-1 is an interleukin-2-induced inhibitor of IL-2 signaling. Blood 97:221.[Abstract/Free Full Text]
  59. Wormald, S. and Hilton, D. J. 2004. Inhibitors of cytokine signal transduction. J. Biol. Chem. 279:821.[Abstract/Free Full Text]
  60. Alonso, A., Sasin, J., Bottini, N. et al. 2004. Protein tyrosine phosphatases in the human genome. Cell 117:699.[CrossRef][ISI][Medline]
  61. Pettiford, S. M. and Herbst, R. 2000. The MAP-kinase ERK2 is a specific substrate of the protein tyrosine phosphatase HePTP. Oncogene 19:858.[CrossRef][ISI][Medline]
  62. Gronda, M., Arab, S., Iafrate, B., Suzuki, H. and Zanke, B. W. 2001. Hematopoietic protein tyrosine phosphatase suppresses extracellular stimulus-regulated kinase activation. Mol. Cell. Biol. 21:6851.[Abstract/Free Full Text]
  63. Caron, H., van Schaik, B., van der Mee, M. et al. 2001. The human transcriptome map: clustering of highly expressed genes in chromosomal domains. Science 291:1289.[Abstract/Free Full Text]
  64. Lercher, M. J., Urrutia, A. O. and Hurst, L. D. 2002. Clustering of housekeeping genes provides a unified model of gene order in the human genome. Nat. Genet.
  65. Spellman, P. T. and Rubin, G. M. 2002. Evidence for large domains of similarly expressed genes in the Drosophila genome. J. Biol. 1:5.[CrossRef][Medline]
  66. Versteeg, R., van Schaik, B. D., van Batenburg, M. F. et al. 2003. The human transcriptome map reveals extremes in gene density, intron length, GC content, and repeat pattern for domains of highly and weakly expressed genes. Genome Res. 13:1998.[Abstract/Free Full Text]
  67. Boutanaev, A. M., Kalmykova, A. I., Shevelyov, Y. Y. and Nurminsky, D. I. 2002. Large clusters of co-expressed genes in the Drosophila genome. Nature 420:666.[CrossRef][ISI][Medline]
  68. Williams, E. J. and Hurst, L. D. 2002. Clustering of tissue-specific genes underlies much of the similarity in rates of protein evolution of linked genes. J. Mol. Evol. 54:511.[CrossRef][ISI][Medline]
  69. Roy, P. J., Stuart, J. M., Lund, J. and Kim, S. K. 2002. Chromosomal clustering of muscle-expressed genes in Caenorhabditis elegans. Nature 418:975.[CrossRef][ISI][Medline]
  70. Yamashita, T., Honda, M., Takatori, H., Nishino, R., Hoshino, N. and Kaneko, S. 2004. Genome-wide transcriptome mapping analysis identifies organ-specific gene expression patterns along human chromosomes. Genomics 84:867.[CrossRef][ISI][Medline]
  71. Boon, W. M., Beissbarth, T., Hyde, L. et al. 2004. A comparative analysis of transcribed genes in the mouse hypothalamus and neocortex reveals chromosomal clustering. Proc. Natl Acad. Sci. USA 101:14972.[Abstract/Free Full Text]
  72. Gotter, J., Brors, B., Hergenhahn, M. and Kyewski, B. 2004. Medullary epithelial cells of the human thymus express a highly diverse selection of tissue-specific genes colocalized in chromosomal clusters. J. Exp. Med. 199:155.[Abstract/Free Full Text]
  73. Cohen, B. A., Mitra, R. D., Hughes, J. D. and Church, G. M. 2000. A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression. Nat. Genet. 26:183.[CrossRef][ISI][Medline]
  74. Blumenthal, T., Evans, D., Link, C. D. et al. 2002. A global analysis of Caenorhabditis elegans operons. Nature 417:851.[CrossRef][ISI][Medline]
  75. Snel, B., Bork, P. and Huynen, M. A. 2002. The identification of functional modules from the genomic association of genes. Proc. Natl Acad. Sci. USA 99:5890.[Abstract/Free Full Text]
  76. Lee, J. M. and Sonnhammer, E. L. 2003. Genomic gene clustering analysis of pathways in eukaryotes. Genome Res. 13:875.[Abstract/Free Full Text]
  77. Lercher, M. J., Blumenthal, T. and Hurst, L. D. 2003. Coexpression of neighboring genes in Caenorhabditis elegans is mostly due to operons and duplicate genes. Genome Res. 13:238.[Abstract/Free Full Text]
  78. Kaiser, P., Rothwell, L., Avery, S. and Balu, S. 2004. Evolution of the interleukins. Dev. Comp. Immunol. 28:375.[CrossRef][ISI][Medline]
  79. Berman, B. P., Nibu, Y., Pfeiffer, B. D. et al. 2002. Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome. Proc. Natl Acad. Sci. USA 99:757.[Abstract/Free Full Text]
  80. Markstein, M., Markstein, P., Markstein, V. and Levine, M. S. 2002. Genome-wide analysis of clustered Dorsal binding sites identifies putative target genes in the Drosophila embryo. Proc. Natl Acad. Sci. USA 99:763.[Abstract/Free Full Text]
  81. Berman, B. P., Pfeiffer, B. D., Laverty, T. R. et al. 2004. Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura. Genome Biol. 5:R61.[CrossRef][Medline]
  82. van Driel, R., Fransz, P. F. and Verschure, P. J. 2003. The eukaryotic genome: a system regulated at different hierarchical levels. J. Cell Sci. 116:4067.[Abstract/Free Full Text]