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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Immunol. Author manuscript; available in PMC 2009 June 15.
Published in final edited form as:
PMCID: PMC2505276

Role of the RNA-binding Protein Tristetraprolin in Glucocorticoid-mediated Gene Regulation1


Glucocorticoids (GCs) are the mainstay of anti-inflammatory therapy. Modulation of post-transcriptional regulation (PTR) of gene expression by GCs is a relevant yet poorly characterized mechanism of their action. The RNA-binding protein tristetraprolin (TTP) plays a central role in PTR by binding to AU-rich elements in the 3’untranslated region of proinflammatory transcripts and accelerating their decay. We found that GCs induce TTP expression in primary and immortalized human bronchial epithelial cells. To investigate the importance of PTR and the role of TTP in GC function, we compared the effect of GC treatment on genome-wide gene expression using mouse embryonic fibroblasts (MEFs) obtained from wild-type and TTP−/− mice. We confirmed that GCs induce TTP in MEFs and observed in TTP−/− MEFs a striking loss of up to 85% of GC-mediated gene expression. Gene regulation by TNF-α was similarly affected, as was the antagonistic effect of GC on TNF-α-induced response. Inflammatory genes, including cytokines and chemokines, were among the genes whose sensitivity to GCs was affected by lack of TTP. Silencing of TTP in WT MEFs by siRNAconfirmed loss of GC response in selected targets. Immunoprecipitation of ribonucleoprotein complexes revealed binding of TTP to several validated transcripts. Changes in the rate of transcript degradation studied by Actinomycin D were documented for only a subset of transcripts bound to TTP. These results reveal a strong and previously unrecognized contribution of PTR to the anti-inflammatory action of GCs and point at TTP as a key factor mediating this process through a complex mechanism of action.

Keywords: Inflammation, glucocorticoids, posttranscriptional gene regulation, chemokines


Glucocorticoids are potent anti-inflammatory steroids that are used in treatment of the majority of inflammatory diseases (1, 2). The mechanism of their action is complex, and involves interference with many regulatory components of gene expression, from early actions on signal transduction pathways to late effects on post-translational modifications (3, 4). GCs are well known inhibitors of key pro-inflammatory genes such as TNF-α, granulocyte/macrophage colony stimulating factor (GM-CSF), many interleukins and chemokines (4). Besides the powerful inhibition of the adaptive immune response, GCs also promote the innate arm of immunity by inducing genes functioning as anti- inflammatory factors, or preserving the expression of mediators of the innate immune responses (5, 6).

The regulation of gene transcription by GCs is a key feature of their anti-inflammatory action. Binding of GCs to the cytosolic GC receptor (GR) results in translocation of the ligand-activated GR to the nucleus, where it dimerizes and acts as a transcription factor, exerting effects on transcription through both DNA-dependent and DNA-independent mechanisms (7, 8).

Mounting evidence indicates that GCs can regulate gene expression also altering posttranscriptional events (3). Transcriptional control is in fact highly integrated with the multiple steps of posttranscriptional regulation, which modulates the rates of mRNA transport, decay and translation and crucially influences the timing and magnitude of cellular responses (9, 10). Phosphorylation-dependent stabilization of early response gene mRNAs, chiefly mediated by the mitogen-activated protein kinases (MAPK) p38, extracellular regulated kinase (ERK,) and the stress-activated protein kinase/c-Jun N-terminal kinase (SAPK/JNK) allows the achievement of a rapid rise in steady-state levels and translation of cytokines and other key mediators in response to changes in cellular environment. This action is counterbalanced by pathways promoting mRNA decay in order to limit the amplitude and the duration of the response (11, 12). Regulation of mRNA turnover is well-recognized as a central means of controlling the inflammatory response (13, 14). Changes of mRNA stability can lead to rapid degradation of mRNA species and cessation of an inflammatory signal, or conversely, to the continued production of inflammatory genes through stabilization of their transcript and subsequent persistence of its inflammatory activity.

The adenylate/uridylate-rich elements (AREs) present within the 3′-UTR of mRNAs represent the most characterized and well conserved group of sequences functionally associated with the regulation of mRNA stability and translation (15, 16). Several in vivo and in vitro evidence indicate that the posttranscriptional control of inflammatory transcripts is strongly dependent on ARE-mediated mechanisms (9, 11, 17), highlighting the patophysiological relevance to this pathway (14, 17-19).

Numerous ARE-binding proteins have been cloned and characterized as regulatory factors of mRNA decay and translation (20). The product of the ZFP-36 gene Tristetraprolin (TTP), also known as TIS11, Nup475, and GOS24, is an RNA binding protein (RBP) and member of a family of CCCH zinc finger proteins that include TTP, Butyrate-response factor (BRF)-1 and BRF-2 (21). Tristetraprolin promotes mRNA decay through binding of its zinc finger domain to an ARE consisting in adjacent UUAU/UUAU half-sites (22-24). It is induced as an immediate early response gene by inflammatory mediators, phorbol esters, lipopolysaccharide and growth factors in a number of cell types, including T cells, macrophages and fibroblasts where it displays a predominantly cytoplasmic localization (25). TTP has been shown to limit inflammation through a negative feedback loop on TNF-α-mediated activity, as TNF-α induces TTP synthesis, which in turn leads to destabilization of TNF-α mRNA (26). Moreover, TTP also mediates the mRNA decay of GM-CSF, COX-2, IL-2, IL-3, and INOS (27-31). Additional transcripts whose decay is regulated by TTP have been identified in a recent genome-wide study of mouse embryonic fibroblasts (MEFs) isolated from TTP-knockout (TTP−/) mice (32), and in mouse macrophages in which TTP expression was silenced (33). The importance of TTP in limiting the inflammatory response has been convincingly demonstrated in TTP−/− mice, which develop severe inflammatory arthritis, autoimmune dysfunction and myeloid hyperplasia through the deregulated expression of TNF-α and GM-CSF (34).

Given the importance of post-transcriptional regulation in modulating the inflammatory response and the central role of GCs as anti-inflammatory agents, the question of whether TTP is involved in GC function arises. Glucocorticoids have been shown to accelerate the mRNA decay of a number of cytokines, chemokines and other pro-inflammatory molecules, but the molecular mechanisms by which GCs act on post-transcriptional events are still poorly understood (3). Smoak and Cidlowski have recently demonstrated that GCs induce the production of TTP in vivo in several organs in mice, as well as in vitro in the airway A549 human epithelial cell line (35). Induction of TTP was transcriptionally regulated by GCs and was essential for GC-mediated inhibition of TNF-α through its ARE-bearing 3’UTR.

Airway epithelium is a central player in the pathogenesis of airway allergic diseases such as asthma and a key target of inhaled glucocorticoids, the main therapeutic tool for inflammatory diseases (36). Several mediators of GC actions are induced in these cells, such as GILZ (37) and MKP-1(38). In the present study we show that GCs induce TTP expression in human primary bronchial epithelial cells (PBEC) and in the bronchial epithelial cell line BEAS-2B. To investigate the importance of TTP in GC function, we then examined by gene array analysis the global gene expression profile in wild-type and TTP−/− mouse embryonic fibroblasts (MEFs) treated with the potent GC budesonide. We confirmed that GCs induce TTP expression also in our experimental system and report that GC-mediated gene expression is severely blunted in the absence of TTP. Antagonism of TNF-α induced gene expression by GCs is also significantly diminished in TTP−/− MEFs. The loss of GC-induced gene repression was reproduced for selected validated genes in WT MEFs in which TTP expression was silenced with specific siRNA treatment. Investigation into the mechanism of TTP action showed the association of TTP with a number of endogenous, ARE-bearing transcripts. However, lack of association of TTP with other validated genes points at the existence of concomitant complex, indirect effects of TTP in GC-mediated action. Furthermore, the acceleration of mRNA decay of TTP-bound mRNA in response to GCs was reverted in TTP−/− cells only for a subset of transcripts, suggesting a regulatory role for TTP not limited to mRNA turnover.

Overall, our data implicate TTP as an important protein in the function of GCs, supporting its regulatory function on gene expression through complex mechanisms of action, and clearly indicate that post-transcriptional regulation is a major mechanism of the anti-inflammatory action of GCs.


Cell culture and experimental protocols

Primary bronchial epithelial cells (PBECs) were isolated by pronase digestion from bronchi of cadaveric lungs, as described (39). PBECs were cultured on collagen-coated flasks and maintained in serum-free LHC-9 medium (Biofluids, Rockville, MD). The BEAS-2B cell line, derived from human tracheal epithelium transformed by an adenovirus 12-SV40 hybrid virus (40), was generously supplied by Dr. Curtis Harris (NIH, Bethesda, MD) and was maintained in F12/DMEM (Gibco/Invitrogen, Frederick, MD), containing 5% heat-inactivated fetal calf serum, 2 mM L-glutamine, penicillin (100 U/ml) and streptomycin (100 mg/ml) (Invitrogen, Carlsbad, CA). Littermate wild type (WT) and TTP−/− E14.5 embryos were used to generate MEF cell lines 67+/+ and 66−/−, respectively (kindly provided by Dr. P.J. Blackshear, NIEHS, Research Triangle Park, NC). Cells were grown as a monolayer in DMEM (Invitrogen) containing 10% FBS, 2 mM L-glutamine (Invitrogen), 100 U/mL each of penicillin and streptomycin (hereafter referred to as complete DMEM). TTP−/− cells were supplemented with 0.3 g/L of geneticin (selection antibiotic, Invitrogen) every 5 passages as described (32). Once grown at 70% confluence, cells were cultured for 24 hr prior to treatment in the same medium, but supplemented instead with 10% of a charcoal-stripped FBS (steroid-depleted, Hyclone, Logan, UT).

Budesonide was dissolved in the diluent dimethylsulfoxide (DMSO, Sigma) to make a 0.1 M stock solution. To study the effect of budesonide on TTP expression, cell cultures (n=3) were treated for 24 h with an equal volume of DMSO, budesonide (10−7 M) or TNF-α (10 ng/mL) for 1 h, 2 h, 6 h, or 24 h. TTP−/− MEFs treated with 10−7 M budesonide for 2 h served as a negative control. Cells were harvested by trypsinization and whole cell lysates were isolated for protein analysis by Western blot as described (41).

For the gene expression study using Illumina arrays, cells (n=3) were treated with an equal volume of DMSO or budesonide (10−7 M) for 3 h. DMSO- and budesonide-treated cells were left unstimulated or challenged with TNF-α for 1 additional hour prior the end of the incubation.

To study the rate of decay for selected targets, WT or TTP−/− MEFs were grown to 80% confluence and stimulated with budesonide (10−7 M) or DMSO for 3 h. Cells were then either harvested as control (Time 0) or further cultured in the presence of the transcriptional inhibitor actinomycin D (Act D) (3μg/ml). Total RNA was then isolated at various time points and the amount of remaining mRNA over control was quantified using real time PCR, as described below.

RNA isolation and analysis of gene expression

Total RNA was extracted using the Trizol Reagent method (Invitrogen) and further purified using RNAeasy columns (Qiagen, Valencia, CA). The quality of total RNA samples was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA).

For the array analysis, RNA samples (n=3) were labeled according to the protocols recommended by the Illumina chip manufacturers. In brief, 0.5 μg of total RNA from each sample was labeled by using the Illumina TotalPrep RNA Amplification Kit (Ambion). Single stranded RNA (cRNA) was generated and labeled by incorporating biotin-16-UTP (Roche Diagnostics GmbH, Mannheim, Germany, cat. no. 11388908910); 0.85 ug of biotin-labeled cRNA was hybridized (for 16 h) to Illumina's Sentrix MouseRef-8 Expression BeadChips (Illumina, San Diego, CA 92121−1975, BD-26−201). The hybridized biotinylated cRNA was detected with streptavidin-Cy3 and quantitated using Illumina's BeadStation 500GX Genetic Analysis Systems scanner. Multivariate correlations comparing each data set for each condition showed correlations with an r2= 0.99 or above, with the only exception of one DMSO-treated data set in WT cells, which was excluded from the analysis due to a r2= 0.93 in correlation with the other 2 sets for this condition.

Target validation for genes identified by array and by TTP-specific IP (see below) was performed by real-time PCR using Taqman probe and primers sets [Applied Biosystems (ABI), Rockville, MD]. All samples were run in triplicate for real-time PCR fluorimetric determination in an ABI 7700 sequence detector (Perkin Elmer) and quantified using the comparative cycle threshold (CT) method (42, 43).

Analysis of protein levels

Protein concentration of whole cell lysates was determined using a micro BCA assay kit (Pierce, Rockford, IL). Protein samples (40 μg total protein/sample) were subjected to SDS-PAGE separation and Western blot analysis as described (44, 45). A rabbit polyclonal anti-TTP antibody (CARM3) was used at a 1:5000 dilution (starting concentration of 0.9 mg/mL) to detect TTP, while expression of the glucocorticoid receptor (GR) was tested using rabbit polyclonal anti-GR antibody (Santa Cruz Biotechnology, Santa Cruz, CA) at a 1:500 dilution. Anti-rabbit IgG linked to horseradish peroxidase (Amersham Biosciences, Buckinghamshire, UK) was used as a secondary antibody at a 1:5000 dilution. Following chemiluminescent development, the membranes were then probed with a 1:1000 dilution of β-tubulin (Santa Cruz Biotechnology, Santa Cruz, CA) as a loading control. Densitometry of immunoreactive bands was performed on a Bio-Rad ChemiDoc XRS instrument. Bands were quantified as adjusted optical density units per square mm and normalized to the β-tubulin signal.

Analysis of Array Data

Initial analysis of the scanned data was performed using Illumina BeadStudio software. The primary Illumina data is returned from the scanner in the form of an “.idat” file which contains single intensity data values/gene following the computation of a trimmed mean average for each probe type as measured by a variable number of bead probes/gene on the array. The Bead Studio software returns information on the number and standard deviation of all the bead measurements per probe/gene as well as a detection call based on a comparison between the measured intensity calculated for a single probe/gene and the intensities measured for a large number of negative control beads built-in to the BeadChip arrays (D = %above negative/100, 1 = perfect, i.e. the intensity value of a gene is greater than all the intensities for every negative control tested). Any gene consistently below D=.98 for all samples was eliminated from further analysis.

Raw intensity data for each experiment is log10 transformed and then used for the calculation of Z scores (46). Z scores are calculated by subtracting the overall average gene intensity (within a single experiment) from the raw intensity data for each gene, and dividing that result by the standard deviation of all the measured intensities, according to the formula:


where G is any gene on the microarray and G1...Gn represents the aggregate measure of all the genes.

Calculation of significant changes in gene expression, which maximizes the power of replicates and takes into account variation between replicates on a gene by gene basis, is the two-sample-for-means Z test (47). The formula for this statistical test is as follows:


where (G1) represents the average Z score for any particular gene being tested under multiple experimental conditions (in this case, experimental versus control). The mean difference is corrected by the standard error for the difference between means where σ2 is the standard deviation of repeated hybridization intensity measurements (expressed as Z scores) for either condition 1 or condition 2, and, n equals the number of repeated measurements for either condition 1 or condition 2. P-values can be assigned to the calculated Z test value by consulting the critical Z value for a two-tailed test in a standard normal distribution table. The lists of significant genes described (Figure 3, Tables I, II and III, Supplemental files S1 to S3) were calculated by selecting genes which satisfied a significance threshold criteria of Z test p≤ 0.001, a false discovery rate less than or equal to 0.1 (46), and a fold change ± 2.0 or greater.

Gene Ontology (GO) Analysis

Characterization of several observed gene clusters (Table IV) was performed using the PANTHER software tool ( This classification system groups genes based on function, using published scientific evidence and evolutionary relationships (47).

Immunoprecipitation of TTP-bound mRNAs

Immunoprecipitation of RNP complexes (RNP-IP) was performed as described previously (41, 48). Briefly, 100 μL of pre-swelled protein A sepharose beads (Sigma) were washed with 1 mL NT-2 buffer (50 mM Tris, pH 7.4, 150 mM NaCl, 1mmM MgCl2, 0.5 % Nonidet P-40) and mixed with 10 μg of anti-TTP antibody or 10 μg of rabbit IgG (isotype control, BD Pharmingen, San Diego, CA). MEFs were cultured and as described above. Equal number of viable MEFs (10×106) treated with budesonide for 3 h were lysed in polysome lysis buffer [100 mM KCl, 5 mM MgCl2, 10 mM Hepes, pH 7.0, 0.5% Nonidet P-40, 1 mM DTT, 100 Units/ml RNaseOUT (Invitrogen), 0.2% vanadyl-ribonucleoside complex (Invitrogen), 0.2 mM PMSF, 1 mg/ml pepstatin A, 5 mg/ml bestatin, and 20 mg/ml leupeptin]. The TTP- and control antibody-coated beads were then resuspended in 900 μl of the above buffer supplemented with 100 Units/ml RNaseOUT, 0.2% vanadyl-ribonucleoside complex, 1 mM DTT, and 20 mM EDTA, then incubated (2 hr at RT) with 100 μl of the mRNP cell lysate (30 mg/ml protein content). Washed beads were then incubated (30 min at 55°C) in buffer supplemented with 0.1% SDS and 30 μg proteinase K. An aliquot of the immunoprecipitant (10 μg) was analyzed by Western Blot, and RNA was extracted from the remaining sample using phenol/chloroform (Ambion, Austin, TX).

RNA Interference Assay

Wild-type MEFs were grown to 40% confluence. Complete DMEM was replaced with serum and antibiotics-free DMEM and cells were transiently transfected with TTP Stealth Select RNAi, MSS238871 (SiRNA1) or MSS238872 (SiRNA2, Invitrogen), or with negative control (Sc, Stealth RNAi Negative Control Med GC, Invitrogen) at a final concentration of 24 nM using Lipofectamine RNAiMAX reagent (Invitrogen) per the manufacturer's instructions. After 6 h, the media was replaced with complete DMEM, and the cells were brought to 80% confluence by a 36 h-incubation. Cells were then stimulated with budesonide or DMSO, and protein and RNA were isolated as described (n=4).

Statistical analysis

Statistical analysis for experiments besides the arrays (Figure 1, 4 and 5-7) was performed using the Microsoft Excel software. Significance values (p ≤ 0.05) were determined by a two-tailed Student's t-Test of treatment condition relative to control. Error bars on graphs represent the standard error of the mean (SEM).


Glucocorticoids induce the synthesis of TTP in human airway epithelial cells and MEFs

Airway epithelial cells, either PBEC or BEAS-2B, were treated with budesonide (10−7 M) at different time points or with TNF-α (10 ng/mL) for 2 h as a positive control, or with the GC diluent DMSO as unstimulated control at the beginning and at the end of the kinetic experiments. Western blot analysis showed significant upregulation of TTP following GC treatment. On average, TTP levels increased between 2.5 to 3.5 over the control in airway epithelial cells (Figure 1A). In MEFs, TTP levels increased approximately 3-fold upon stimulation with budesonide from a low baseline signal (Figure 1B), which remained unchanged between the two datapoints assessed (data not shown). Densitometric analysis of the Western blots revealed that expression of TTP was statistically significant between 1 and 6 h for both budesonide and TNF-α treated cells, with TTP levels remaining significantly elevated at 24 h only in the BEAS-2B cell line. Immunoreactive TTP appeared as multiple bands and/or as broad band, secondary to phosphorylation as observed previously (49). As expected, TNF-α stimulated cells induced TTP in epithelial cells and in WT MEFs, while TTP was undetectable in the TTP−/− MEFs (Figure 1C). To assess the reliability of the response to GCs in our experimental mouse model, we confirmed that the levels of the GC receptor were comparable in the two cell types (Figure 1D).

Figure 1
Induction of TTP by budesonide in human airway epithelial cells and MEFs

Glucocorticoid-mediated gene regulation is highly dependent on TTP in MEFs

To investigate the role of TTP in the mechanism of action of GCs, we set up a genome-wide analysis of GC-mediated gene expression in MEFs cell lines developed from TTP KO mice in comparison to MEFs of wild-type littermates. Both cell types were incubated with 10−7 M budesonide or DMSO for 3 h and then treated in the absence or presence of TNF-α (10 ng/mL) for 1 h.

Data were first analyzed with a principal components analysis (PCA), a statistical method for displaying the global relationship of the array data among experimental condition (Figure 2). Clustering of microarray-derived gene patterns on a PCA diagram reflects the inherent kinship, or otherwise, of their genetic profiles, by measuring true differences in datasets (50). In WT cells, the four treatments separate out as distinct clusters on the PCA diagram, indicating that a distinct genotypic signature for each experimental condition is recognizable at the microarray level. The cluster of budesonide+TNF-α falls in between the two clusters of budesonide and TNF-α, showing a distinctly graded effect of TNF-α. In the TTP−/− dataset, however, the cluster of budesonide+TNF-α is closer to TNF-α, implying that budesonide has lesser effect than in WT cells. Also the distinct separation between the four groups visible in the WT datasets is diminished. These findings provide a first indication of the role of TTP in determining the changes in gene expression that separate these experimental conditions.

Figure 2
Patterns of differential regulation of gene expression in WT and TTP−/− MEFs

Profound global changes in gene expression in the TTP−/−, both at baseline and in response to treatment, can be further appreciated from the heat map of the array data by comparison with the data from the WT. The heat map in Figure 2B clearly shows the pleiotropic phenotypic effect of this single gene deletion. Baseline gene expression is altered in TTP−/− cells, as well as the response to treatment with either budesonide, TNF-α or their combination. The latter effects are highlighted in the heatmap shown in panel C, Figure 2, which illustrates the changes in global gene expression in WT and TTP−/− MEFs in response to budesonide and TNF-α treatment.

In the analysis of the array data, we compared budesonide-treated samples with DMSO-treated samples in the absence of TNF-α (B-D) to assess the effect of GCs in the absence of an inflammatory stimulus. We then analyzed the changes in gene expression induced by TNF-α over the DMSO-treated, unstimulated cells (T-D), so that we could compare the effect of BUD treatment in TNF-α-stimulated cells versus TNF-α alone (BT-T), in order to identify TTP-dependent changes in the antagonistic effects of GCs on TNF-α-driven gene expression. As shown in Figure 2C, stimulus-induced changes leading to either upregulation of downregulation of gene expression in WT cells are lost for a large number of genes in the TTP−/− MEFs. However, WT and TTP−/− MEFs importantly retain for a subset of genes a common response to TNF-α, demonstrating a conservation of gene expression despite the loss of TTP.

Importantly, the global changes in gene expression observed following GC treatment in WT MEFs, as well as the identity of the genes affected, were consistent with the known functional profile of GC action, which produces concomitant suppression of proinflammatory factors and induction of genes involved in homeostatic, innate immune and metabolic functions. These data, together with the highly distinct clustering on the PCA, confirms the reliability of this cell line as a model of GC action.

The total number of genes significantly modified in WT cells by the indicated treatments (shown in the horizontal bar graphs in Figure 3) was dramatically reduced in TTP−/− cells, with the greatest loss in GC sensitivity for genes whose expression was downregulated in WT by GCs. Using Venn diagrams to dissect the relationship between the two datasets, only 5 out of the 145 genes downregulated by budesonide in WT were still comparably repressed in TTP−/− cells (Figure 3A), accounting for a 97% loss of response to GCs. A small subset of genes (n=11) showed a significant increase in GC-mediated inhibition of expression in TTP−/− cells compared to WT. Interestingly, also the induction of gene expression by GCs in WT was blunted in TTP−/− cells by 89%, with only 19 out of 172 GC-induced genes showing changes comparable to the WT. Besides this significant loss in GC-induced response, 34 genes either became upregulated after GC treatment or increased their GC-mediated induction only in TTP−/− cells. Also TNF-α-dependent responses in the absence of budesonide were globally affected, with a loss of 94% of the downregulated and 75% of the upregulated genes (Figure 3B). Given the strong influence of TTP in TNF-α-induced gene expression, we focused the analysis of GC antagonism on TNF-α-regulated genes on those for which significant expression by TNF-α was present in both WT and TTP−/−. Genes with a Z ratio difference of three or more, which represents a change of three-standard deviations, between cells treated with TNF-α alone and cells treated with TNF-α plus budesonide were considered antagonized by GCs. There were eight genes for which, fulfilling these criteria, the antagonistic effect of budesonide was lost in TTP−/− (Figure 3C). As expected, even in such a small sample the genes affected were mostly related to inflammation, including chemokines (CCL2, CCL7, CXC3CL1), the adhesion molecule VCAM-1 and TLR2. These data indicate an unprecedented, central role of TTP in mediating GC-and, to a lesser extent, TNF-α-mediated effects.

Figure 3
Loss of GC sensitivity and of TNF-α response in TTP−/− MEFs

A complete list of the gene profiles shown in Figure 2, ranked according to the value of Z ratios after treatment in WT cells and showing the corresponding Z ratio in TTP−/− cells is provided in the online Supplementary Material (Files S1 to S3). The most significant changes in gene expression in budesonide-treated WT and TTP−/− MEFs are shown in Tables I and andII,II, while those occurring in TNF-α-treated cells are shown in Table III.

Table I
Loss of GC- induced gene downregulation in TTP−/− MEFs compared to WT cells.
Table II
Loss of GC- induced gene upregulation in TTP−/− MEFs compared to WT cells.
Table III
Loss of TNF-α induced responses in TTP−/− MEFs compared to WT cells

Nine genes were selected for single gene validation by real time PCR, based on highly significant Z ratios and large magnitude of change in expression displayed in TTP−/− cells (Figure 4). For six important inflammatory genes (CCL7, CXCL1, CCL2, CXCL5, CXCL7, MMP-9) we confirmed the significant downregulation by budesonide in WT, but not TTP−/− MEFs (Figure 4A). Similarly, Serpina3n mRNA was induced by GCs in WT but not in TTP−/− cells, consistent with results of the array. We also validated induction of CCL5 and IL-6 by TNF-α in WT but not in TTP−/− cells (Figure 4B).

Figure 4
Validation of array-based changes in gene expression by real-time PCR

Functional classification of GC-sensitive, TTP-dependent genes

Genes for whom response to GC treatment was affected in TTP−/− cells (Figure 3A) were subjected to GO classification (Table IV). A significant percentage of these genes are involved in immunity and signal transduction, biological processes that are chiefly targeted by GCs for their anti-inflammatory action. Involvement of TTP in the effect of GCs on genes important for metabolism and cell development indicate that TTP is likely to mediate other homeostatic and non-immune functions driven by GCs.

Table IV
Gene Ontology of GC-regulated, TTP dependent genes.

Association of GC-sensitive transcripts with TTP and role of TTP on mRNA decay

We then tested if budesonide-induced TTP, in order to convey GC action, would associate with endogenous mRNAs, and whether it would exert its effects by modulating transcript stability. To test this hypothesis, wild-type MEFs were treated with 10−7 M budesonide for 3h, and RNP complexes were immunoprecipitated from cytoplasmic lysates using a rabbit polyclonal anti-TTP antibody or an isotype-matched antibody as control. Western blot showed that TTP was selectively immunoprecipitated using the anti-TTP antibody, whereas no immunoreactive TTP band is observed using the isotype control antibody (Figure 5A). Subsequently, mRNA isolated from the IP was used to quantify by real-time PCR the enrichment of selected mRNAs (Figure 5B). We selected for this analysis the genes that were validated by PCR, since their expression showed both highly significant GC-induced modulation and strong TTP dependence, and were thus reasonable candidates for TTP binding. For the quantitation of the enrichment of specific mRNAs after mRNP-IP, the difference of cycles in the CT value between the TTP and the control IP (ΔCT) indicated a 2-(-ΔCT) = fold enrichment in target mRNA in the TTP IP (43). Of the eleven transcripts analyzed, six showed a significant enrichment in the IP done with the anti-TTP over that performed with the IgG control, indicating association of these endogenous mRNAs with TTP. The chemokine CXCL1 showed the greatest fold enrichment, followed by CCL2, CCL7, IL-6, CXCL5 and MMP9 (Figure 5C). Importantly, five of the six transcripts enriched in the TTP IP display multiple AREs in their 3’UTR (Table S1 in the online Supplementary Material). The remaining five transcripts showed very small or no difference between the two IP conditions, in a range similar to that displayed by β-actin, GAPDH and 18s mRNA, which we used as negative control for the relative lack of specific enrichment despite the more abundant message, indicated by the lower CT number (Figure 4B, 18s mRNA).

Figure 5
Immunoprecipitation of endogenous mRNA-TTP complexes

Given the association of these GC-sensitive transcripts with TTP, which is functionally known as an mRNA decay-promoting RBP, we investigated whether GC would accelerate the mRNA decay of the TTP-associated targets and whether this process was impaired in TTP−/− cells. Experiments with Act D (Figure 6) showed that in WT MEFs, GC induced acceleration of CCL7 mRNA. In fact, the transcript's half life (t½), calculated for each condition as the time (h) required for the transcript to decrease to 50% of its initial abundance, was significantly decreased in cells treated with budesonide as compared to DMSO (0.9 ± 0.04 h versus 1.4 ± 0.2 h, respectively, p = 0.02). This effect was abolished in TTP−/− MEFs, in which the half-life of CCL7 mRNA in GC-treated cells was similar to that detected in DMSO-treated cells. Consequently, the half lives in WT and TTP−/− cells treated with budesonide were significantly different (0.9 ± 0.04 h vs. 1.7 ± 0.2 h, respectively, p = 0.04), demonstrating that TTP is needed for GC-induced acceleration of CCL7 mRNA decay. Similarly, TTP appears to be necessary to convey enhanced degradation of CCL2 mRNA under GC treatment (half-life of 1.0 ± 0.1 h in WT, 1.5 ± 0.2 h in TTP−/−, p=0.02).

Figure 6
Determination of mRNA decay of selected transcripts by Real-time PCR

The decay of CXCL5 RNA was affected differently by the lack of TTP. Significant differences in RNA levels between WT and TTP−/− cells were noticeable already in DMSO-treated cells at 3h and 5h after transcriptional shut-off (at 5 hr, 55 ± 14% of control remained in WT vs 9 ± 3% in TTP−/− cells, p=0.04), resulting in a much shorter mRNA half life in DMSO-treated TTP−/− cells versus WT MEFs (1.3 ± 0.2 h versus 9.1 ± 3.2 h, respectively). Therefore it appears that loss of TTP affects GC response as part of a more global defect.

Interestingly, the rate of IL-6 mRNA decay was not changed by GC treatment in WT MEFs, nor it was affected in budesonide-treated TTP−/− MEFs despite the strong association with TTP of the IL-6 mRNA. Half-lives for each of the conditions were therefore almost superimposable (1.2 ± 0.1 h versus 1.2 ± 0.12 h in DMSO-treated WT and TTP−/−MEFs, respectively; 1.1 ± 0.2 h versus 1.3 ± 0.3 h in budesonide-treated WT and TTP−/− MEFs, p=NS). Similar results were obtained for CXCL1 (data not shown).

Analysis of CXCL7, CCL5 and serpina3n mRNA turnover could not be pursued with this experimental system, as treatment with Act D resulted in artifactual stabilization of the transcripts (100% of the RNAs present after 5h treatment) (data not shown). Despite a low but consistent enrichment after RNP-IP of the housekeeping β-actin and GAPDH mRNA, their steady-state levels and decay rates were unchanged by Act D treatment and between cell types (data not shown).

Effect of Silencing of TTP in WT MEFs on GC response

To verify whether the different GC sensitivity of TTP-bound targets observed between the WT and TTP−/− cells could be reproduced in a model of acute suppression of TTP, we inhibited TTP expression in WT MEFs using an RNA interference assay. Wild-type MEFs were transiently transfected with two TTP-specific siRNA (SiRNA1 and 2), and the effect of the TTP silencing on expression of CCL7,CCL2, CXCL5, and IL-6 after GC treatment was compared to the effect of GC treatment on the expression of these genes in cells transfected with a scrambled siRNA. We consistently observed by Western Blot a loss of TTP expression in cells treated with the two TTP siRNA. Inhibition of TTP was paralleled by a partial but consistent loss of the GC inhibition of the four transcripts present in scrambled-transfected cells, which became significant in cells transfected with the siRNA inducing maximal TTP suppression (SiRNA1, 16.5 ± 2.9 % of budesonide-induced TTP in cells transfected with scrambled siRNA) (Figure 7A).

Figure 7
RNA interference of TTP and the effects on selected transcripts


Mounting evidence indicate that GCs modulate gene expression far beyond transcriptional regulation, but the molecular signature of GC effect on posttranscriptional regulation has been only partially revealed. In particular, little is known on the effect of GCs on the complex biology of the ARE-binding proteins, which act downstream the kinase signaling cascades and ultimately convey the stimulus-driven changes in mRNA turnover, by recruiting or inhibiting the enzymatic complexes responsible for mRNA deadenylation and decay.

In the present study we report that the ARE-binding protein TTP is a critical mediator of GC action, as global gene regulation exerted by GCs was severely blunted in MEFs cells isolated from TTP−/− mice while conserved in the WT littermates. Similar effects on the expression of select genes were observed by targeted disruption of TTP by siRNA in WT MEFs.

We found TTP to be inducible by GCs in primary and immortalized airway epithelium, providing evidence for a more direct control of posttranscriptional gene regulation by GC in a relevant therapeutic cell target. Induction of TTP was reproducible in MEFs, in line with data showing upregulation of TTP in mice tissues after GC administration in vivo (35). This allowed us to use MEFs from TTP−/− mice to characterize the relevance of TTP in the global changes in gene expression mediated by GCs. It is interesting to note that TTP is inducible by both proinflammatory stimuli like TNF-α as well as by anti-inflammatory signals like GCs, likely due to its function as a central regulator in the expression of genes involved in inflammation.

Comparison of the gene expression profile elicited by GCs in WT and TTP−/− MEFs revealed for the first time a striking dependence of the GCs response on TTP, indicating that the impact of posttranscriptional regulation in the mechanism of action of GCs is larger than ever appreciated, and supporting the relevance of the GC-driven induction of anti-inflammatory genes (6, 51).

Inhibition of chemokine expression is considered a major factor in the suppressive action of GCs on adaptive immune responses (1). The strong antagonistic effect of GCs on chemokine expression, which was detected in GC-treated WT MEFs both in TNFα-stimulated and at baseline, was one of the most affected by the lack of TTP. Loss of significant GC inhibition of adhesion molecules, like VCAM-1, further points at TTP as an essential molecular component of one of the main mechanism of anti-inflammatory action of GCs, the inhibition of inflammatory cell recruitment (1).

Based on the hypothesis that TTP would mediate mRNA destabilization, it was somewhat expected that lack of TTP would impact, to some degree, the ability of GCs to inhibit gene expression. With regard of genes induced by GCs, according to this proposed mode of action, loss of TTP could be expected to increase their steady-state levels due to lack of mRNA destabilization. Instead, in TTP−/− MEFs we found a loss, or else a significant decrease, in the expression of genes induced by GCs in WT cells. Some of the genes for which GC-driven induction was lost in TTP−/− MEFs are involved in the response of the acute phase of inflammation, such as the serine protease inhibitors (serpins) and orosomucoid, or display potent anti-inflammatory activity like metallothionein-2, which acts as a potent cytoprotective and antioxidant agent and inhibits the expression of inflammatory chemokines and cytokines (52, 53). This target profile underlines how TTP participates also to the modulation of the innate immune response by GCs (5).

The mechanism by which TTP mediates the GC response is bound to be complex, involving both direct effects, likely mediated by binding of TTP to discrete AREs, as well as indirect effects. In probing such complex system, we found the association of TTP with just six out of eleven of the transcripts whose differential response to GCs in TTP−/− cells had been validated. These transcripts - CXCL1, CCL7, CCL2, IL-6, CXCL5, and MMP-9 – all contain AUUUA pentamers or UUAUUUAUU nonamers in the 3’ UTRs that are compatible with TTP binding (54), although in different number and sequence contexts (Table S1 in the online Supplementary Material). The transcripts showing no association with TTP by mRNP-IP, despite profound changes in GC sensitivity in TTP−/− cells, displayed instead only one or none of these sequences in the 3’UTRs, with the noticeable exception of EGR-1 mRNA, which bears two UAUUUAU heptamers in a highly A/U-rich milieu (Table S1). As RBPs mediate changes in mRNA turnover in a dynamic fashion, it is likely that association of TTP with its targets would change over time and therefore could be missed in a single-timepoint experiment. It is well established that the context of the 3’UTR sequence where AREs are embedded, the secondary structure of the entire transcript and the ionic milieu of the cell environment are key determinants of the specific binding and the on-off rate of regulatory proteins. Consideration of all these factors will be needed to proceed to more in-depth investigations to define the molecular interface between TTP and its targets and its role in mediating GC response.

Along the same lines, the results from our experiments with Act D support only in part, at least in the condition tested, the assumption that association with TTP would accelerate the decay of the targeted transcripts upon GC treatment, but rather implicate that TTP would participate to a GC-induced remodeling of the RNP complex leading to different functional outcomes. For CCL7 and CCL2 mRNA, the acceleration of mRNA decay induced by GC in WT MEFs was indeed lost in TTP−/− cells, in line with the original hypothesis; however, CXCL5 mRNA displayed in DMSO-treated WT MEFs a much longer half-life as compared to that in TTP−/− cells, indicating that in this condition, TTP participates to a turnover mechanism with a final outcome of relative stabilization. The inhibitory effect of GC on CXCL5 seems to be lost in TTP−/− cells as a consequence to the loss of this function. Yet another different outcome was observed for the turnover of IL-6 mRNA, which was not modified by either GC treatment or by lack of TTP despite significant enrichment in the RNP-IP assay and the presence of a 3’UTR extremely rich in AREs (Table S1). For the latter reason, as well as for the clear TTP dependence of the GC responsiveness of this transcript we deem unlikely that IL-6 mRNA would become associated with TTP only during the preparation of cell extracts for the RNP-IP analysis.

Such differences might be in part due to the limitation of the experimental model used. Actinomycin D arrests the global transcriptional activity of the cell, and inducible or labile regulatory factors, possibly transcript-specific, may be absent or degraded, altering the physiological mRNA turnover rates. Moreover, nuclear export of mRNA is coupled to ongoing gene transcription in mammalian cells (55), as it is the nuclear reaccumulation following nucleocytoplasmic shuttling of several important mRNA-binding proteins, including HuR (56-59). These factors may have critically affected the decay rate of CXCL7, CCL5 and serpina3n mRNAs following Actinomycin D treatment.

Besides the assay's limitations, however, our data clearly underscore complex interactions between GC treatment and the role of TTP in regulating gene expression. The results obtained for CXCL5 and IL-6 mRNA turnover, as well as the concomitant loss of GC-induced gene expression in TTP−/− cells raises clearly the possibility that TTP could have a more pleiotropic function than previously appreciated. Dynamic interplay of TTP with other RNA-binding factors, rather than direct binding to AREs, may ultimately be responsible for a role of TTP in increased transcript stabilization. In a recent study, TTP has been found unable to bind to the ARE-bearing INOS mRNA, and to interact instead with the KH-type splicing regulatory protein (KSRP), another ARE-binding protein that bound to INOS mRNA, mediating its decay. Under proinflammatory stimulation, such protein-protein interaction would therefore ‘dislodge’ KSRP and the associated exosome and allow binding for HuR, which mediates INOS mRNA stabilization (31). An RNA-independent, mRNA stabilizing function of TTP had been first observed in TTP mutants lacking RNA-binding function, which were found to increase the half-life of known TTP targets such as TNFα (60). Many of the transcripts found in this study to be upregulated by GCs in a TTP-dependent manner do display AREs and could be therefore suitable candidates for testing the hypothesis that TTP mediates GC action through a complex remodeling of ribonucleoprotein complexes. Along the same lines, the lack of AREs in other genes whose sensitivity to GCs was significantly affected in TTP−/− cells indicate the presence of yet undiscovered mechanisms of GC-driven gene regulation by TTP, either dependent from binding to cis-elements distinct from AREs or due to protein-protein regulatory interactions. Along these lines, it is important to underscore the participation of TTP to the formation of processing bodies, which are distinct cytoplasmic sites of ribonucleoprotein complexes where mRNAs targeted for decay are transiently sequestered from translation (61). In this case, TTP could promote mRNA sequestration and decay independently from direct contact with the mRNA.

Regardless the mechanism, the TTP-mediated effect can also be vastly amplified indirectly, for example in case the affected gene is a regulatory protein - a transcription factor, a signaling molecule, etc.-, by the loss of its target's downstream function. Transcription factors are often expressed as early-response genes and display fast transcript decay rates (62). We found that GC inhibition of pro-inflammatory transcription factors, like the NFκB molecular species NFκB1 and Early growth response (Egr)-1 (63-65) was abolished in TTP−/− cells (Table I). The early response, ARE-bearing gene Egr-1 encodes for a transcription factor that mediates tissue inflammation and remodeling by promoting the expression of multiple genes involved in inflammation, apoptosis and matrix production (64, 65). Consistent with a potential regulatory role of TTP, inhibition of Egr-1 by dexamethasone was reported to occur posttranscriptionally in a myelomonocytic cell line (66). The role of TTP in mediating the inhibition of Egr-1 by GCs would indirectly determine the GC-mediated inhibition of multiple Egr-1-dependent pro-inflammatory genes, indicating how regulation of mRNA turnover can also indirectly affect transcriptional control of multiple genes. More studies will be needed to elucidate the pathways mediating such indirect TTP actions.

Despite the different effect of the lack of TTP on targets' mRNA turnover, the loss of GC-induced repression of those genes - CCL2, CCL7, CXCL5, and IL-6 - that demonstrated TTP binding was also reproducible, although by different degree, following silencing of TTP by siRNA in WT MEFs. Despite differences between this model and the TTP KO-derived cells, due different level of TTP repression and to the diverse phenotype of cells carrying chronic vs. acute factor depletion, these data indicate the relevance of TTP in GC action beyond the TTP−/− mouse model. In line with these results and in additional support of the potential relevance of this mechanism beyond the model we tested and in human biology, Smoak and Cidlowski report the partial loss of dexamethasone-induced downregulation of TNFα in human airway epithelial cell line A549 where TTP was suppressed by stable transfection of a TTP shRNA (35). Although induction of TTP after in vivo administration of GC has been documented in several mouse tissues (35), in vitro treatment of mouse macrophages with GC is reported to inhibit LPS-induced TTP expression(67), suggesting tissue-specificity of TTP regulation possibly linked to the protective effect of GC treatment on innate immune responses.

Along with studies on the mechanism of TTP-mediated GC response, it is pressing to identify TTP regulation in clinical settings where GCs are administered, and to investigate disregulation of GC-driven TTP expression or function as a possible mechanism of steroid resistance. It can be envisioned that mutations affecting the levels of TTP or its ARE-binding ability could greatly impair GC action. To this end, multiple single-nucleotide polymorphisms have been identified in the human TTP gene (68).

Our genome-wide approach provides convincing evidence that the role of posttranscriptional gene regulation in GC response is much larger than previously appreciated, and point at TTP as a key mediator in this process, hence having far-reaching implications in our understanding of the pathogenesis and treatment of inflammatory diseases.


We thank Drs. Perry J. Blackshear and Dr. Wi Lai (National Institutes of Environmental Health Sciences, Research Triangle Park, NC) for providing the wild-type and TTP knockout MEFs. We thank Dr. Vinayakumar V. Prabhu (Gene Expression and Genomics Unit, National Institute of Aging, NIH, Baltimore) for his skillful assistance in the data analysis of the array study.


Act D
Actinomycin D
Adenylate-uridylate rich region
ARE-binding protein
cyclooxygenase 2
Early growth response-1
glucocorticoid-induced leucine zipper
glucocorticoid receptor
gene set enrichment analysis
immediate early response 3
inducible nitric oxide synthase
mouse embryonic fibroblast
mitogen-activated protein kinase phosphatase-1
primary bronchial epithelial cells
principal components analysis
posttranscriptional regulation
Tumor necrosis factor-α
RAB32 member RAS oncogene family
RNA-binding protein
immunoprecipitation of ribonucleoprotein complexes
untranslated region
wild type


1This work was supported by NIH grant R01 AI060990-01A1to Dr. Stellato. Dr. Ishmael is the recipient of the 2007 Strategic Training in Allergy Research (ST*AR) Award from the American Academy of Allergy, Asthma and Immunology. Dr. Gorospe is supported by the National Institute on Aging - Intramural Research Program, National Institutes of Health.

Supplementary Material


File S1 Excel spreadsheets listing all genes whose expression was significantly altered by treatment with budesonide (file S1) or TNF-α (file S2). In each file, genes are divided in six worksheets according to their grouping in the Venn diagram (shown in Figure 3A and B). File S3 lists genes induced by TNF-α in WT and TTP−/− MEFs for which antagonism by budesonide was lost in TTP−/− cells (Figure 3C).


File S2 Excel spreadsheets listing all genes whose expression was significantly altered by treatment with budesonide (file S1) or TNF-α (file S2). In each file, genes are divided in six worksheets according to their grouping in the Venn diagram (shown in Figure 3A and B). File S3 lists genes induced by TNF-α in WT and TTP−/− MEFs for which antagonism by budesonide was lost in TTP−/− cells (Figure 3C).


File S3: Excel spreadsheets listing all genes whose expression was significantly altered by treatment with budesonide (file S1) or TNF-α (file S2). In each file, genes are divided in six worksheets according to their grouping in the Venn diagram (shown in Figure 3A and B). File S3 lists genes induced by TNF-α in WT and TTP−/− MEFs for which antagonism by budesonide was lost in TTP−/− cells (Figure 3C).

Table S1

Table S1. Alignment of mouse and human 3’UTR sequences of transcripts tested for association with TTP (shown in Figure 4, Panel C.)


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