Gene profiling reveals unknown enhancing and ... - CiteSeerX

Gene profiling reveals unknown enhancing and suppressive actions of glucocorticoids on immune cells ˆ ME GALON,*,‡,1,2 DENIS FRANCHIMONT,†,§,1 NAOKI HIROI,†,‡‡ GREGORY FREY,† JE´RO ANTJE BOETTNER,†† MONIKA EHRHART-BORNSTEIN,†,‡‡ JOHN J. O’SHEA,* GEORGE P. CHROUSOS,† AND STEFAN R. BORNSTEIN†,‡‡ *Lymphocyte Cell Biology Section, NIAMS, and †Pediatric and Reproductive Endocrinology Branch, NICHD, National Institutes of Health, Bethesda, Maryland 20892, USA; ‡INSERM U255, Curie Institute, Paris, France; §Department of Gastroenterology, Erasme Hospital, Brussels, Belgium; and ††Department of Pediatrics, University of Leipzig, 04103 Leipzig, and ‡‡Department of Endocrinology, University of Du¨sseldorf, 40225 Du¨sseldorf, Germany Glucocorticoids continue to be the major immunomodulatory agents used in clinical medicine today. However, their actions as anti-inflammatory and immunosuppressive drugs are both beneficial and deleterious. We analyzed the effect of glucocorticoids on the gene expression profile of peripheral blood mononuclear cells from healthy donors. DNA microarray analysis combined with quantitative TaqMan PCR and flow cytometry revealed that glucocorticoids induced the expression of chemokine, cytokine, and complement family members as well as of newly discovered innate immune-related genes, including scavenger and Toll-like receptors. In contrast, glucocorticoids repressed the expression of adaptive immune-related genes. Simultaneous inhibitory and stimulatory effects of glucocorticoids were found on inflammatory T helper subsets and apoptosis-related gene clusters. In cells activated by T cell receptor cross-linking, glucocorticoids down-regulated the expression of specific genes that were previously up-regulated in resting cells, suggesting a potential new mechanism by which they exert positive and negative effects. Considering the broad and continuously renewed interest in glucocorticoid therapy, the profiles we describe here will be useful in designing more specific and efficient treatment strategies.—Galon, J., Franchimont, D., Hiroi, N., Frey, G., Boettner, A., Ehrhart-Bornstein, M., O’Shea, J. J., Chrousos, G. P., Bornstein, S. R. Gene profiling reveals unknown enhancing and suppressive actions of glucocorticoids on immune cells. FASEB J. 16, 61–71 (2002)

ABSTRACT

Key Words: DNA microarray 䡠 clinical medicine 䡠 immune system

Inflammation is the physiological homeostatic response elicited to shield our tissues from injurious agents. The regulation of the immense array of cellular and molecular mediators involved in inflammation is harmonized by the immune and neuroendocrine sys0892-6638/02/0016-0061 © FASEB

tems (1, 2). Adrenal-derived glucocorticoids (GC) assist the innate and adaptive immunity to eliminate toxins, tumor cells, and foreign pathogens to limit tissue damage (1, 3, 4). The broad and triumphant clinical use of synthetic GC has improved the life of millions of patients with inflammatory/autoimmune diseases and graft rejection during the past 50 years (5). Renewed interest is surfacing within the field of GC therapy for patients with severe sepsis, septic shock, and the acute respiratory distress syndrome (ARDS), based on evidence from several clinical trials where prolonged GC treatment resulted in significant improvement and a decreased mortality (6 –9). However, GC may also worsen the course of such diseases and prime deleterious inflammatory cascades, resulting in death (10). A great deal of effort has been focused on trying to understand the cellular and molecular mechanisms of action of GC (5, 11). In vivo and in vitro studies have suggested both enhancing and suppressive effects of GC on the immune and inflammatory response, and even more discrepancies have emerged (3, 12, 13). A comprehensive analysis of gene regulation by GC has not yet been performed. Therefore, we analyzed the effect of GC on the gene expression profile of human peripheral blood mononuclear cells (PBMC) from healthy donors treated with dexamethasone (Dex). To accomplish this, we used the novel technique of DNA microarray chip analysis in combination with online PCR and flow cytometry. First, our study showed that GC regulated numerous newly discovered genes that play critical roles in innate and adaptive immune responses. Second, the data revealed a role for GC not only as immunosuppressants, but also as major immunopermissive and immuno-enhancing agents. Finally, we showed that GC could regulate some genes in 1

These authors contributed equally to the work. Correspondence: Laboratoire d’Immunlogie Cellulaire et Clinique, Unite´ INSERM U255, Centre de Recherches Biome´dicales des Cordeliers, 15 Rue de l’Ecole de Me´decine, 75270 Paris Cedex 06 France. E-mail: [email protected] 2

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opposite directions, depending on the state of activation of the target cells. This study supports a major and highly complex regulatory role of GC in human immune functions.

were isolated by Ficoll-Paque gradient centrifugation (Pharmacia, Piscataway, NJ), activated or not with coated anti-CD3/ anti-CD28 antibodies at 10 ␮g/ml, and stimulated with dexamethasone (10⫺7 M) for 18 h in RPMI 1640 containing 1% FCS. TaqMan primers are described in the online Table 1 supplemental.

MATERIALS AND METHODS

Microarrays

Antibodies and cells

Peripheral blood mononuclear cells were prepared as described. RNA extraction was performed (RNAgents, Promega, Madison, WI) and mRNA was purified with oligoTex mRNA isolation columns (Qiagen, Valencia, CA). The cDNA made from 600 ng of purified mRNA from each sample was labeled with the fluorescent dyes Cy5 and Cy3. The two cDNA probes were mixed and simultaneously hybridized to a microarray containing 9182 human expressed genes (GEM microarray;

Monoclonal mouse anti-human CD3 and anti-CD28, FITC- or PE-conjugated monoclonal anti-CD68, anti-TNFR1, antiCD36, anti-CD163, anti-CD27, anti-CD49d, anti-CD39, antiHLA-DR, anti-CD74, anti-TCR-␥␦, anti-TCR-␣␤, anti-CD127, and isotype-matched IgG controls were purchased from PharMingen (San Diego, CA). PBMC from healthy donors

TABLE 1 Supplemental. Primer sequences, size and Genebank accession numbera Name

Primer sequences (5⬘-3⬘)

Indoramine-pyrrole TLR4 CD44 CD163 Integrine alphal MARCO THBS1 STAT1 IRF4 Diubiquitin PAI2 CD127 IL10 CCR2

S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM S AS TM

AGTCCGTGAGTTTGTCCTTTCAA TTTCACACAGGCGTCATAAGCT CCCGCAGGCCAGCATCACCT AGAGTTTCCTGCAATGGATCAAG TTATCTGAAGGTGTTGCACATTCC TTCGTTCAACTTCCACCAAGAGCTGCCT AAAAATGGTCGCTACAGCATCTC GGTGCTATTGAAAGCCTTGCA AGGTCAGCGGCCTCCGTCCG GGTCGCTCATCCCGTCAGT CGAAAATGGCCAACAGAACA CCCAAGGATCCCGACTGCAATAAAGGAT TCTGGTTTTAAGTAGCAGCAATCAA GCAGCATTAACAGCAACAATCC TGCCCGGTAGCCCATCTTTGGATATTT GACAGCCCGTCCTTCTCCTT GACTTGCAGCCTCGATGCA AGCACACCCTGGAGAACACCTGGCT CATCCGCAAAGTGACTGAAGAG GTACTGAACTCCGTTGTGATAGCATAG CCAATGAGCTGAGGCGGCCTCC GTGGAAAGACAGCCCTGCAT ACTGGACCCCTGTCTTCAAGAC CAACGCACCCTCAGAGGCCGC GCCCAGGTTCACAACTACATGAT TTTCCGGGTGTGGCTGAT ACCGAAGCTGGAGGGACTACGTCCC ATGGCTCCCAATGCTTCCT TGGCATCAAAGGTCATTAAATCC CCTCTGTGTGCATGTCCGTTCCGA GCATGGAGGACGCCTTCA ACAGGTCATTCCTCTCCGACAT CAAGGGACGGGCCAATTTCTCAGG GCATGTGACGCCCCTATTCT GGCCCATTCTTGCCACTCT TCCTCTTCCAGGTCCCTAGACTGCAGG ACGGCGCTGTCATCGATT TGGAGCTTATTAAAGGCATTCTTCA TGTGAAAACAAGAGCAAGGCCGTGGA TGAGTAACTGTGAAAGCACCAGTCA GCAGTGAGTCATCCCAAGAGTCT CTGGACCAAGCCACGCAGGTGAC

Primer size

967–989 1033–1012 991–1010 1927–1949 2008–1985 1956–1983 140–162 205–185 164–183 3213–3231 3282–3263 3233–3260 3261–3285 3391–3370 3305–3331 268–287 345–327 297–321 1002–1023 1086–1060 1037–1058 1151–1170 1217–1196 1173–1193 612–634 690–673 646–670 19–37 91–69 39–62 1055–1072 1120–1099 1074–1097 1148–1167 1215–1197 1169–1195 399–416 479–455 424–499 901–925 974–952 927–949

Genebank no.

NM_002164 NM_003266 NM_000610 NM_004244 X68742 NM_006770 NM_003246 NM_007315 NM_002460 NM_006398 NM_002575 XM_004013 M57627 NM_000648

a S: sense primer; AS: antisense primer; TM: TaqMan probe; Primer size: Base pairs; TLR4: Toll-like receptor 4; MARCO: macrophage receptor with collagenouse structure; THBS1: thrombospondin 1; STAT1: signal transducer and activator of transcription 1; IRF4: interferon regulatory factor 4; PAI2: plasminogen activator inhibitor type2; CD127: interleukin 7 receptor; IL10: interleukin 10; CCR2: chemokine receptor 2.

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Incyte, St. Louis, MO). The microarray was scanned and the intensity of the fluorescence at each array element was proportional to the expression level of that gene in the sample. The ratio of the two fluorescence intensities provided a quantitative measurement of the relative gene expression level in the two cell samples. The detectable dynamic range of individual mRNA in a sample range from 2 to 2000 pg and reproducibility was assessed on a validation study (www.incyte. com/incyte science/technology/gem/reproducibility. shtml). Using specially designed DNA control elements and a known RNA set added to each probe labeling reaction, all GEM microarray hybridizations are assessed for labeling, hybridization efficiency, and sensitivity. A 96-well plate is arrayed on each GEM microarray that contains a variety of control elements arrayed in quadruplicate. The elements are composed of DNA fragments derived from inter-ORF regions in Saccharomyces cerevisiae. These fragments are all ⬃1000 bp in length and have minimal homology to any known gene, to ensure that no cross-hybridization occurs with experimental samples. A control set of 14 RNA molecules is generated from these fragments using PCR with composite primers that include a T7 promoter and a 30-base oligo-dT sequence. A simple transcription reaction then generates artificial mRNA fragments that hybridize specifically to individual control elements. These fragments are added to the two probelabeling reactions in known amounts, together with the experimental samples. A set of four fragments is added in increasing concentrations in both the Cy3 and Cy5 reactions to control for signal sensitivity. Six additional are added in known ratios in the two fluorescent labeling reactions to control for expected ratios. Four RNA fragments previously converted to fluorescent DNA are either arrayed onto the control plate or added to the experimental sample to control for overall scanning and hybridization signal, independent of either labeling reaction. Additional elements composed of either housekeeping genes or mixed cDNA libraries are arrayed in the last two rows of the control plate. These elements are hybridized by probe from the experimental sample, and their signal intensity is used to measure experimental RNA quality. All signal intensities in the control plate as well as total GEM microarray hybridization signals are collected. The values are then compared with a set of minimum intensity criteria for each particular control. Because the controls differentiate between processes in the labeling and hybridization reaction, the quality of experimental RNA can be measured independent of labeling or hybridization efficiency. An absolute balanced differential expression ⬎ 1.4 was considered significant based on three parameters: reproducibility of differential expression based on a statistical study encompassing 70 differential hybridization, known genes regulated by GC under these conditions, and TaqMan PCR confirmation. Differential expression accuracy and precision were determined by hybridizing samples against themselves in replicates of 10. The expected differential expression ratio for each gene being 1, experimental differential expression (calculated from 100,000 data points) ranged from 0.99977 to 0.99998. Known and unknown GCinducible genes were confirmed for their up- or downregulation by GC using quantitative online PCR or flow cytometry. GemTools software (version 2.5.0) was used to analyze the gene distribution, and genes were clustered based on GemTools clustering software and published literature. Online TaqMan PCR RT-PCR experiments were carried out according to the Thermoscript RT-PCR system kit for RT-PCR instructions provided by the manufacturer (Life Technologies, Gaithersburg, MD). Total RNA (1 ␮g) of human lymphocyte were

reverse transcribed to complementary DNA (cDNA) by a reaction containing 2 mM deoxynucleotide mix, 100 mM DTT, 40 units RNase inhibitor, 50 ng random primer, and 15 units Thermoscript reverse transcriptase. The reaction was run 25°C for 10 min and 50°C for 50 min, heated at 85°C for 5 min, then cooled to 4°C. To quantify expression of IDO, TLR-4, CD44, CD163, integrin alpha1, MARCO, TSP-1, STAT1, PLA2, IRF-4, CD127, IL-10, CCR2, and diubiquitin, we applied the TaqMan PCR using the 7700 Sequence Detector (Perkin Elmer Applied Biosystems, Foster, CA). Reactions contained 1⫻ TaqMan Universal PCR Master Mix, 900 nM of forward and reverse primers and 200 nM for the TaqMan-probes (Table 1, supplemental). 18 S primers and probes were added at 50 nM. Thermal cycling proceeded with 50 cycles of 95°C for 15 s and 60° C for 1 min. Input RNA amounts were calculated with relative standard curves for both the mRNAs of interest and 18S. Statistical analysis Microarrays The genes were classified into three categories: not regulated, up-regulated, and down-regulated. The statistical significance of the correlation between the two DNA microarray experiments was determined using correlation matrix algorithm, Kendall correlation algorithm, or Spearman correlation algorithm. Similar results were found with all algorithms. When no fluorescent intensities were detected in one or both experiments, the corresponding genes were removed from the statistical analysis. Correlation analyses were performed by plotting differential gene expression of one experimentagainst the value from the other experiment. The correlation coefficient (r) and the statistical significance (P) were determined. Taqman PCR Input RNA amounts were calculated with relative standard curves for all mRNA of interest and 18S RNA. Division by the amount of 18S RNA in each sample corrected the amounts of input mRNAs. Specific mRNA transcript levels after Dex stimulation were expressed as percentage of increase vs. unstimulated cells. Data are presented as mean ⫾ se and analyzed by ANOVA T test for each gene from individual healthy donors (n⫽6). The statistical significance (P) was determined and is represented for each histogram. Flow cytometry Flow cytometry (FACS) was previously described (14). Cells were washed and incubated with conjugated antibodies and isotype-matched IgG-PE/and FITC-IgG as controls for 30 min at 4°C. Cells were washed three times with phosphate-buffered saline containing 0.5% bovine serum albumin and analyzed by flow cytometry on a FACSCalibur flow cytometer (Becton Dickinson, San Jose, CA).

RESULTS Global analysis of gene expression profile We analyzed PBMC gene expression changes mediated through GC (Fig. 1). In a DNA microarray containing

GENE EXPRESSION PROFILES OF GLUCOCORTICOIDS ON IMMUNE CELLS

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Figure 1. Global gene expression analysis. Untreated and Dex-treated PBMC were analyzed for gene expression using DNA microarray. a) Clustering analysis. Total number of genes up- or down-regulated among 6 major clusters. b) Subclustering analysis. The immune cluster was divided into subclusters and subcategories; percentage of GC-regulated genes within the immune cluster is presented. c) Correlation analysis was performed by plotting differential gene expression of one experiment against the value from the other experiment. Dot plot represents the correlation between data points from the two experiments and histograms represent the frequency of differential expression. Correlation coefficient (r⫽0.85) between experiments (P⬍0.0001).

9182 human expressed genes, 9% were considered down-regulated (845⫾41 genes) and 12% up-regulated (1125⫾135 genes) (Fig. 1a). The sensitivity and reproducibility of DNA microarrays are highlighted by the fact that appropriate changes of known GC-regulated genes were detected in separate DNA microarray experiments. Statistical significance of the correlation between the two DNA microarray experiments was determined. When no fluorescent intensities were detected in one or both experiments, the corresponding genes were removed from the statistical analysis. One experiment showed a greater difference among gene 64

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expression values. This is represented by the histograms (Fig. 1c). However, even with a broader differential expression, the genes were coregulated in the same manner in both experiments. Correlation analyses were performed by plotting differential gene expression of one experiment against the value from the other experiment. As shown in Fig. 1c, on average, we found a correlation between data points from the two experiments. The correlation coefficient (r⫽0.85) confirmed the good correlation between experiments and the correlation appears to be statistically significant (P⬍0.0001). Using independent experimental samples (n⫽6), online PCR results and/or FACS analysis confirmed in all cases the direction and magnitude of the gene expression changes observed (panels a and b in Tables 1–3; data not shown). Global gene expression analysis, based on GemTools clustering software and published literature, enabled us to quantitate the relative contribution of GC to changes in gene cluster expression. Of six major clusters analyzed, immune response-related genes and unknown genes (ESTs) were mostly regulated. The immune cluster was divided into subclusters and subcategories (Fig. 1b). The first group of immune genes regulated by GC included key anti-inflammatory and proinflammatory factors (Table 1). Global analysis revealed symmetrical up- or down-regulation within the inflammation subcluster. In contrast, the subclustering analysis revealed a shift toward innate compared with adaptive recognition (Fig. 1b). Within the immune system, a second group involved genes related to the innate immune response that constitutes the first line of defense during an inflammatory reaction (Table 2). Finally, we identified a third clusters of genes involved the adaptive immune response (Table 3). This group of genes specifically directs the appropriate type of cellular or humoral immune reaction according to the nature of the injurious agent. GC as anti-inflammatory agents Inflammation is an important homeostatic mechanism that limits the effects of injurious agents, but has the potential for inducing host tissue damage and must be controlled by the organism. GC suppress inflammation by down-regulating the expression of proinflammatory cytokines such as IL-1␤, lymphotoxin-␤, IL-1␣, IL-8, IFN-␣, and IFN-␤ (11). Indeed, the expression of these cytokines was decreased by GC (Table 1, panel a). A balance between the effects of pro- and anti-inflammatory cytokines may determine the outcome of the inflammatory response. Cytokines such as transforming growth factor (TGF)␤1–3 and IL-10 suppress the production of proinflammatory mediators. We found that the expression of TGF-␤3, IL-10, and IL-10R was up-regulated by GC, supporting their anti-inflammatory action (Table 1). An important group of proinflammatory genes is that of chemokines, small peptides that facilitate chemo-

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TABLE 1.

taxis of leukocytes from the circulation to the tissues. The prototypical chemoattractant IL-8 causes neutrophils to degranulate and cause tissue damage. The expression of many chemokines, including IL-8, Mip1␤, Mip-3␤, Mcp-2, Mcp-3, Mcp-4, TARC, and eotaxin, was down-regulated by dexamethasone treat-

ment (15, 16). In contrast, we found that I-309, IP-10, and fractalkine were up-regulated by GC (Table 2). Finally, IL-1RII, a decoy receptor limiting the deleterious effects of IL-1, was among the genes most strongly up-regulated by GC (17). Thus, many proinflammatory ligands were down-


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TABLE 2.

regulated whereas anti-inflammatory soluble mediators were mostly up-regulated, by GC, highlighting their immunosuppressive and protective role against inflammation. GC induce proinflammatory mediators A different pattern of regulation was observed for immune-related membrane receptors and intracellular mediators. Among genes most strongly up-regulated by GC were some proinflammatory receptors IL-1RI, the gp130 subunit of the IL-6 type receptors, IL-8R, IFN␣R, CSFRI, CFSRII, IFN␥RI, IFN␥RII, and TNFR family members (Table 1). Some of the intracellular mediators from the IL-1/Toll signaling pathway, such as IRAK, were up-regulated by GC. In contrast, the antiinflammatory mediator, IL-1Ra, a soluble receptor antagonist released during inflammation, was among the genes most strongly down-regulated by GC (18). An66

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other hallmark of inflammation is the influx of immune cells, mediated by chemokines and chemotactic factors of the complement system (19). Chemokine receptors or associated molecules, including Dez, an orphan G receptor-coupled protein, CCR1, CCR2, and CCR2-like, were up-regulated by GC. The activation of complement forms the first line of defense and causes the release of potent chemotactic complement factors C3a, C4a, and C5a. The proinflammatory components of the classical pathway (C1q, C3, C5) and receptors C3aR1, CR2, and C5aR1 were up-regulated. Our analysis also revealed that C8, a terminal complement component involved in the lytic activity of complement, was up-regulated by GC. In contrast, the two inhibitors tightly controlling the complement cascade, C1-INH and C4bp, were down-regulated. The release of matrix metalloproteinases (MMPs) allows leukocytes to extravasate and penetrate tissues, a key event in inflammatory disease (20). They also act as

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TABLE 3.

regulatory molecules by functioning in enzyme cascades and processing matrix proteins such as cytokines, growth factors, and adhesion molecules to generate fragments with biological effects. With the exception of MMP-9, metalloproteinases were up-regulated by GC (Table 2). Other molecules promoting chemotaxis of leukocytes to inflammatory sites, such as the thrombospondins (TSPs) and their APC receptor (CD36), are abundantly expressed in chronically inflamed tissues. Thrombospondins are structurally related to MMPs and

regulate their functions (21, 22). The expression of TSP-1, 2, 4 and CD36 was strongly up-regulated by GC (Table 1, panels a, b). In contrast, TSPs membrane receptor on T cells (CD47) was down-regulated by GC (Table 2). Therefore, proinflammatory mediators, such as complement, or cytokine and chemokine receptors were up-regulated by GC whereas soluble inhibitors of these pathways were down-regulated, supporting the positive, enhancing role of GC in the inflammatory response.


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GC support innate immunity and regulate specific genes in opposite directions depending on the activation state of the cells A key aspect of the innate immune response is the ability to discriminate between self and non-self molecules. This is achieved through pattern recognition receptors, which directly recognize molecular epitopes expressed by microbes. Scavenger receptors (SRs) are also involved in the innate immune response for the removal of damaged tissue and debris, and recognize a wide variety of pathogens. CD163 expression is downregulated by proinflammatory and up-regulated by anti-inflammatory mediators and GC (23, 24). Here we show that CD163, as well as eight other scavenger receptors, were strongly up-regulated by GC (Table 2, panels a, b). Finally, the IL-1R/Toll-like receptor (TLR) superfamily has emerged as an expanding family of receptors whose function is to respond rapidly to infection and injury (25). TLR-4 and TLR-2 mediate the host response to gram-negative and gram-positive bacteria, respectively, and the finding that TLR-4 is important for responses to LPS may allow for novel means to intervene therapeutically during sepsis (25, 26). Three members of the Toll family were regulated by GC; TLR-4 and TLR-2 were up-regulated, and TLR-3 downregulated (Table 2). That GC regulate this superfamily could be critical for many aspects of inflammation and host defense, since TLRs control innate immune responses in vivo. Besides the positive and negative role of GC on immune gene clusters, we analyzed the potential differential action of GC at the single gene level. The regulation of genes implicated in innate and adaptive immune response in cells at different activation stages was of particular interest. To mimic an antigenic response, we analyzed the action of GC on human immune cells activated by T cell receptor cross-linking with anti-CD3 and anti-CD28 antibodies. Among 20 genes tested by online PCR under these different conditions, 25% revealed a bidirectional action of GC. For instance, GC induced TLR-4 expression in the resting condition, yet after T cell receptor activation, GC decreased TLR-4 expression (n⫽7) (Fig. 2a, b). Indoleamine 2,3-dioxygenase (IDO), which participates in innate and adaptive immune responses, is a novel antioxidant defense molecule. It prevents oxidative tissue damage and suppresses T cell-mediated immune responses during inflammation (27, 28). The expression of IDO was oppositely regulated by GC in resting and activated conditions (n⫽7) (Fig. 2c, d). These results illustrate the complex bi-directional regulation of a single gene by GC, depending on the state of activation of the cells. This study reveals a major positive role of GC on the innate immune system, and supports differential actions of GC on the innate and adaptive immune response during an antigen-mediated immune reaction. 68

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Figure 2. Quantitation of TLR-4 (a, b) and IDO (c, d) mRNA expression by TaqMan PCR in resting (a, c) and CD3/CD28activated (b, d) PBMC stimulated or not with the indicated concentrations of Dex. Data represent the mean ⫾ se (n⫽7). *P ⬍ 0.05; **P ⬍ 0.01; ***P ⬍ 0.001.

GC inhibit adaptive immunity Innate immunity is the first line of defense and functions to protect the host from infection while slower adaptive (acquired) responses are developing. Costimulatory molecules such as CD40-ligand (CD40L)/ CD40 are central regulators of the adaptive immune response, and we found that GC down-regulated CD40 (Table 3). CD40 regulates immune responses through its ability to enhance the expression of B-7 costimulatory molecules and through the increased production of IL-12, a major Th1 cytokine, by monocytes and dendritic cells. Thus, GC appeared to have an important role in down-regulating CD4⫹ T cell-dependent Th1 immune responses (29, 30). Furthermore, interferon regulatory factors (IRF) -1, -3, and -7 induced by Th1 cytokines were down-regulated by GC. Conversely, the expression of c-maf, the transcriptional regulatory protein involved in Th2 differentiation and IL-4 transcription, was up-regulated by GC. Thus, corticosteroids appear to bias the immune response toward Th2 (31– 33). Another T cell type that produces only transforming growth factor-␤ (TGF-␤), but not IL-4 or IFN-␥, has been termed a Th3 cell. This type of cell has been suggested to be important in mediating immune suppression in autoimmune disease. Six members of the TGF family [TGF-␣, TGF-␤3, TGF-␤-induced (68 kDa) protein, TRAP-1, TGF-␤RI, TGF-␤RII, and TGF-␤RIII] were up-regulated by GC (Table 1). Glucocorticoids are potent immunosuppressants and induce apoptosis. At the same time, they up-regulate ⬃50% of antiapoptoticand proliferation-related molecules (Table 3 and data not shown).

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Given the essential function of MHC-II molecules in T cell activation, the molecular mechanisms regulating their complex expression profile obviously represent an important parameter in the normal physiological control of the immune response (34). Transcription of MHC-II, CD74 (the invariant chain of the class II MHC molecules, Ii), and MHC-II-DM genes is up-regulated by the major CIITA complex, which is induced by intracellular signals (such as STAT1) generated by IFN-␥. Products of the MHC-II-DM genes are essential for the peptide loading step and are modulated by MHC-II-DO, which enhances stringent MHC/peptide association through antigen receptor recognition (35). Among the adaptive immunity clusters, 17 genes encoding MHC molecules, Ii, MHC-II-DM and -DO, TCR-␤, TCR-␦, granzyme A and B, CD86, and STAT1 and STAT5a were down-regulated by GC (Table 3, see panels a, b). A remarkably consistent and coherent picture emerged, where GC down-regulated positive acting elements of the MHC-II expression and all functional units necessary for optimal antigen recognition. Therefore, these data support a role of GC in inhibiting adaptive immunity.

DISCUSSION The ubiquitous distribution of the glucocorticoid receptor renders cell differentiation crucial in discriminating the effects of GC on different physiological systems. Our analysis was carried out on human PBMC and represents a large panel of a specific set of genes that participate in the immune and inflammatory response. This panoramic study of gene regulation reveals that GC regulate numerous newly discovered genes and control the expression of gene families, such as SRs, TLRs, TSPs, complement, chemokine, TNF, TGF-␤, and MHC families. It also elucidates new mechanisms of GC action by unveiling unexpected regulated genes or gene (super) families and shows how GC may facilitate one biological signal while counteracting its inhibitor. This is well documented with the selective modulation of the complement cascade activation and inhibition and the regulation of cytokines/chemokines and their receptors. Finally, it is noteworthy that within the same gene family, some genes are stimulated and others repressed, increasing the complexity of GC action. Potential sources of discordance between RNA and protein levels include translational control and altered protein stability, and protein analysis may uncover post-translational modifications that are not predictable at the RNA level. Therefore, we analyzed protein expression by FACS analysis; for all membrane receptors tested, a perfect correlation between RNA and protein expression was found. Thus, these results illustrate the broad and coherent regulation of the immune system by GC. Glucocorticoids clearly have immunosuppressive actions. In fact, anti-inflammatory genes such as IL-10, TGF-␤, and IL-1Ra, whose deletion in mice results in

increased susceptibility to inflammatory disease (36, 37), are strongly increased by GC. However, a more comprehensive genetic dissection of the inflammatory and immune response shows a more selective and complex picture of the actions of GC. First, at the gene cluster level, our study shows a bidirectional action of GC, which are both immunostimulatory and immunosuppressive at the same time even for the inflammation cluster (12). They seemed to prime and enhance the innate immune response while repressing part of the adaptive immune response in a resting state. This suggests that GC help clear antigens by stimulating cell trafficking, scavenger systems, and matrix metalloproteinases while they halt cellular immune responses by inhibiting antigen presentation and T cell activation. Second, at the single gene level, we found an opposite regulation of molecules implicated in the inflammatory and immune responses by GC, depending on the state of activation of the cells. This effect may underline that the signaling through the glucocorticoid receptor system may be mediated by a different set of transcription factors, coactivators, and corepressors, allowing a switch in promoter activity depending on the state of cellular activation (38). Therefore, GC may drive the inflammatory and immune response in a context-dependent fashion, which could be related to the state of disease (13, 39). Indeed, the complete understanding of the complex GC-mediated gene regulation may shed light on the variable responses to GC, in patients with GC-sensitive or -resistant inflammatory and autoimmune diseases (33, 40). Glucocorticoids increase susceptibility to intracellular and opportunistic infections, which appears to result from the profound immune suppression of antigen cell presentation and adaptive immune response induced by these hormones. At the same time, GC appear highly beneficial in the presence of an overwhelming systemic inflammation when administered in a sustained fashion throughout the course of the disease. This is observed in sepsis, septic shock, ARDS, and, less frequently, in meningococcal meningitis (6, 9, 41, 42). However, their delayed and time-limited use can be devoid of beneficial effects and even disastrous (10). This is clearly documented by the enhanced TLR-4 expression by GC, which may be progressively lost in the later phase of the disease, reflecting the clear permissive action of GC early on the innate immune response. Depending on the state of activation of the cells, the differential GC actions underscore that the timing of GC administration is crucial and may explain some of the harmful effects of GC. Therefore, clinical experience, together with a global molecular approach of gene regulation, may reveal the efficient timing of GC treatment and will allow a better selection of patient groups that are likely to produce the most benefit. Although it is possible that peripheral blood mononuclear cells from patients with different diseases may respond differently from healthy donors, microarray screening techniques in combination with quantitative RNA and protein assays will enable testing


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the profile of new steroids in the search for discriminating between desired and adverse effects. Thus, synthetic steroids could be designed to act more specifically on certain components of the immune response, selectively enhancing or suppressing such a response.

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Received for publication May 1, 2001. Revised for publication August 15, 2001.

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