Open Access

Expression profiling of Dexamethasone-treated primary chondrocytes identifies targets of glucocorticoid signalling in endochondral bone development

  • Claudine G James1,
  • Veronica Ulici1,
  • Jan Tuckermann2,
  • T Michael Underhill3 and
  • Frank Beier1Email author
BMC Genomics20078:205

DOI: 10.1186/1471-2164-8-205

Received: 26 February 2007

Accepted: 01 July 2007

Published: 01 July 2007

Abstract

Background

Glucocorticoids (GCs) are widely used anti-inflammatory drugs. While useful in clinical practice, patients taking GCs often suffer from skeletal side effects including growth retardation in children and adolescents, and decreased bone quality in adults. On a physiological level, GCs have been implicated in the regulation of chondrogenesis and osteoblast differentiation, as well as maintaining homeostasis in cartilage and bone. We identified the glucocorticoid receptor (GR) as a potential regulator of chondrocyte hypertrophy in a microarray screen of primary limb bud mesenchyme micromass cultures. Some targets of GC regulation in chondrogenesis are known, but the global effects of pharmacological GC doses on chondrocyte gene expression have not been comprehensively evaluated.

Results

This study systematically identifies a spectrum of GC target genes in embryonic growth plate chondrocytes treated with a synthetic GR agonist, dexamethasone (DEX), at 6 and 24 hrs. Conventional analysis of this data set and gene set enrichment analysis (GSEA) was performed. Transcripts associated with metabolism were enriched in the DEX condition along with extracellular matrix genes. In contrast, a subset of growth factors and cytokines were negatively correlated with DEX treatment. Comparing DEX-induced gene expression data to developmental changes in gene expression in micromass cultures revealed an additional layer of complexity in which DEX maintains the expression of certain chondrocyte marker genes while inhibiting factors that promote vascularization and ultimately ossification of the cartilaginous template.

Conclusion

Together, these results provide insight into the mechanisms and major molecular classes functioning downstream of DEX in primary chondrocytes. In addition, comparison of our data with microarray studies of DEX treatment in other cell types demonstrated that the majority of DEX effects are tissue-specific. This study provides novel insights into the effects of pharmacological GC on chondrocyte gene transcription and establishes the foundation for subsequent functional studies.

Background

Cartilage provides a scaffold for the deposition of osteoblast precursors and ultimately the development of long bones. This process, termed endochondral ossification, describes a coordinated developmental series that involves commitment of mesenchymal precursor cells to the chondrogenic lineage and subsequent alternating phases of proliferation and differentiation, which culminate in the replacement of the cartilage by bone tissue [14]. In the first phase of this process, multipotent mesenchymal progenitors condense and initiate expression of the pro-chondrogenic Sox family members 9, 5 and 6 [5, 6]. A subset of cells at the center of these aggregates differentiates into chondrocytes. Newly formed chondrocytes secrete an extracellular matrix rich in type II collagen (Col2a1), proliferate and ultimately terminally differentiate into hypertrophic chondrocytes [7]. Chondrocyte hypertrophy precedes the end of the chondrocyte life cycle by apoptosis and is accompanied by vascularization of the hypertrophic template and mineralization of the cartilaginous extracellular matrix [812]. Concomitantly, osteoclasts degrade the calcified cartilage extracellular matrix, making way for the invasion and deposition of an osteoprogenitor population that form the primary ossification center [13].

These events take place in a region called the growth plate that illustrates the organization of different phases of cartilage development into distinct zones. The resting zone delineates newly differentiated chondrocytes with low mitotic activity and the cellular reserve for subsequent stages of chondrocyte differentiation. Proliferative zone chondrocytes exhibit higher mitotic activity resulting in distinct columns containing cells reminiscent of stacked coins. The hypertrophic zone demarcates terminally differentiated chondrocytes which are identified by high cytoplasm to nuclear ratio and the expression of type X collagen (Col10a1) [1416]. Terminally differentiated chondrocytes are fated for programmed cell death after which primary ossification occurs by way of vascularization of the remaining cartilaginous matrix and the deposition of osteoprogenitor cells [1719].

Glucocorticoids (GC) are among various endocrine molecules including growth hormone (GH) and thyroid hormone (TH) known to regulate linear growth [2023]. Regulation of linear growth follows the paradigm in which steroid hormones affect target tissue through both local and systemic mechanisms [2427]. Indirect effects occur through modulation of other endocrine systems such as the GH/IGF-I axis. Generally, GC decrease IGF-I, GH receptor and IGF receptor 1 expression and also abrogate the release of GH from the pituitary [20, 28, 29]. Direct regulation of growth occurs through GC receptor (GR)-mediated gene transcription in chondrocytes [24, 30, 31].

GC functions are primarily mediated by the glucocorticoid receptor (GR) that is encoded by the Nr3c1 gene. The GR is ubiquitously expressed in mammalian tissues, including the growth plate, and is essential for life [3136]. Many studies have examined GC regulation of the skeleton and have led to various theories on potential modes of GC function in cartilage [3740]. The specific function of the receptor in terms of its transcriptional regulation in cartilage, however, remains enigmatic.

While endogenous GCs have been shown to promote the differentiation of both chondrocytes and osteoblasts, exogenous GCs in pharmacological doses which are also widely used in clinical practice to treat inflammatory disorders [4146]. Their have different effects. Indeed, their utility in treating various diseases is, however, limited by numerous side effects such as growth failure and decreased bone quality [47]. GC-target genes including C-type natriuretic peptide and VEGF have been identified in chondrocytes [28, 48, 49]; however, the cartilage-specific transcriptional consequences of high-GC-doses in the growth plate have not been studied comprehensively.

Work in our laboratory identified GR amongst factors that were up-regulated during chondrocyte maturation [50] Thus, to comprehensively understand the transcriptional effects of pharmacological GC doses in growth plate, we completed a genomic screen of gene expression changes in chondrocytes derived from E15.5 day old mouse embryos. Primary monolayer chondrocytes were treated with a synthetic GC, dexamethasone (DEX), and RNA was isolated for microarray analysis. We complemented traditional microarray analysis methods with the gene set enrichment algorithm to correlate the behaviour of specific molecular classes with DEX treatment [51, 52].

Results and Discussion

Microarray screen of dexamethasone-treated primary chondrocyte monolayers

We identified the GR as a candidate for the regulation of chondrocyte hypertrophy in a previous expression profiling screen using primary micromass cultures [50]. The Nr3c1 probe set which encodes the GR was up-regulated 4-fold from day 3 to day 15 of micromass culture (Figure 1A, top panel). Confirmation of the GR expression profile with qRT-PCR showed an approximately 8-fold increase over the same time course (Figure 1A, bottom panel). Studies in our laboratory and others have implicated GCs in chondrocyte differentiation and growth plate function [25, 26, 47, 48, 53, 54]. In addition, our cell counting experiments revealed that DEX consistently decreases cell numbers after 24 hrs (Figure 1B), in agreement with other studies that show increased apoptosis [38, 55] and reduced proliferation [56] in response to GCs. We therefore aimed at extending this analysis to examine pharmacological effects of GCs on growth plate chondrocytes by systematically identifying downstream effector genes of DEX. Primary chondrocytes derived from the long bones of 15.5 day old embryonic mice were treated with DEX or the vehicle control, and total RNA was isolated after 6 and 24 hrs of culture, respectively.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-205/MediaObjects/12864_2007_Article_918_Fig1_HTML.jpg
Figure 1

Gene expression changes in DEX-treated primary chondrocytes. Microarray and quantitative RT-PCR expression profiles of the Glucocorticoid receptor (Nr3c1) in primary mesenchymal micromass cultures (A). Primary chondrocytes are plated in high density monolayers and treated with DEX or vehicle for 24 hrs and counted with a hemocytometer (B). Ordered list of global microarray data set derived from the hybridization of RNA isolated from primary chondrocytes treated with 10-7 M DEX and the vehicle (v) control (C, left panel). One-Way ANOVA testing for significantly expressed probe sets between DEX-treated samples and the vehicle control resulted in a list of 1158 transcripts. Mean normalized signal intensities for all 1158 probe sets are shown (C, right panel). Fold change filtering of these transcripts reveal that the majority of probe sets vary in the range of 1 to 2-fold (D).

Gene expression was evaluated using Affymetrix MOE 430 2.0 mouse genome chips using three independent cell isolations. We first analyzed gene expression using conventional analysis functions in GeneSpring GX*. After pre-processing the data set using the GC-RMA algorithm and eliminating probe sets showing expression levels close to background, 22 091 probe sets remained, reducing the data set by 48% (Table 1). Significance testing with one-Way ANOVA analysis identified probe sets differentially expressed between DEX and vehicle-treated cultures over the entire time course (Figure 1C, left panel). The resulting list contained 1158 probe sets, which is 2% of the data set's original size. Approximately 70% of significantly changed probe sets exhibited upregulation in response to DEX treatment. This data set was further subdivided by using 1.5-, 5- and 10- fold change filters which generated lists of 162, 21 and 7 probe sets for the 6 hr time point and 399, 53 and 19 probe sets for the 24 hr time point, respectively (Table 1). Examination of the overall differences between the mean normalized signal intensities associated with each condition showed minimal changes in gene expression (Figure 1C, right panel), indicating that GC treatment affects the expression of only a small subset of all expressed genes in this system. A distribution of fold differences between 6 and 24 hrs showed that the majority of gene expression changes did not exceed 2-fold (Figure 1D). In each case, both time points exhibited the same overall trends in gene expression, but, as expected, the 24 hr time point consistently showed a higher proportion of probe sets altered by DEX treatment.
Table 1

Microarray analysis of DEX-treated primary chondrocyte monolayers.

Specifications

Probe sets at 6 hrs

Probe sets at 24 hrs

Total number of probe sets

45101

45101

Significantly expressed

22091

22091

Differentially expressed

1158

1158

1.5-fold changed

162

399

5-fold changed

21

53

10-fold changed

7

33

1.5-fold up-regulated

141

342

5-fold up-regulated

20

50

10-fold up-regulated

7

19

1.5-fold down-regulated

21

57

5-fold down-regulated

1

3

10-fold down-regulated

0

0

Probe set validation

To confirm the accuracy of the microarrays in identifying biologically significant differences, we selected a variety of expressed transcripts for qRT-PCR analysis (Figure 2A). Transcripts that either belonged to a functional class implicated in cartilage development or exhibited marked changes with DEX treatment were chosen. Markers exhibiting marginal changes in gene expression were also selected for control purposes. Specifically, we evaluated the expression patterns of Indian hedgehog (Ihh), Tissue inhibitor of matrix metalloproteinase 4 (Timp4), Cyclin-dependent kinase inhibitor 1C (Cdkn1c), which contains a GC response element in its promoter [57], Integrin beta like 1 protein (Itgbl1), GC receptor (Nr3c1), Integrin beta 1 (Itgb1) and Kruppel-like factor 15 (Klf15) over 0, 6, 12, and 24 hrs of culture with or without DEX treatment. Transcripts for Klf15 were up-regulated from 0 to 6 hrs while Ihh, Timp4, Cdkn1c and Itgbl1 all increased after the 6 hr time point. Nr3c1, which encodes the GR, was not affected by DEX-treatment at both 6 and 24 hrs, but does contain a putative GRE [58]. Transcripts such as Itgb1 that exhibited less than 1.5-fold change in our arrays were also confirmed with qRT-PCR, providing further evidence that the microarray data represented authentic gene expression data. Interestingly, the fold change difference varied according to the experimental method. In cases such as Timp4 and to a lesser extent Cdkn1c, qtPCR data showed higher fold change increases with the DEX treatment than in microarrays. In contrast, the expression pattern for Klf15 exhibits a higher fold-change difference in the microarrays compared to the control. While data normalization using the RMA algorithm provides excellent estimates of reliable signal intensities, other methods such as the M.A.S. 5.0 algorithm are known to outperform RMA in its ability to accurately estimate fold change differences in transcript levels [59].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-205/MediaObjects/12864_2007_Article_918_Fig2_HTML.jpg
Figure 2

Identification of significantly expressed probe sets and subsequent validation with real-time RT-PCR. Expression profiles for selected transcripts in vehicle- or DEX-treated chondrocytes are confirmed with real-time RT-PCR at 0, 6, 12 and 24 hr time points. Indian hedgehog (Ihh), tissue inhibitor of matrix metalloproteinase 4 (Timp4), cyclin-dependent kinase inhibitor 1C (Cdkn1c, p57), integrin beta like 1 protein (Itgbl1), glucocorticoid receptor (Nr3c1), integrin beta 1 (Itgb1) and kruppel-like factor 15 (Klf15) microarray data are shown on the left at the 6 and 24 hr time points and corresponding real-time expression values are shown on the right. P-values less than 0.01 are deemed significant. Specifically, Ihh, Timp4, Itgbl1 and Klf15 exhibit significant differences between the 6 and 24 hr time point and between treatments. Dotted lines indicate the control and solid lines denote DEX treatment.

GSEA to identify the effects of dexamethasone on gene expression in chondrocytes

Traditional microarray analysis methods are useful for the identification of probe sets exhibiting transcriptional responses to DEX-treatment, but are limited in certain capacities. Alternate statistical methods such as ANOVA testing produced transcript lists that, while effectively reducing the dimensionality or sample size of the data set, increased the rate of false negative data thus hampering our ability to generate meaningful hypotheses from the data (Figure 1). Also, the overall effect of DEX treatment on gene expression was modest, which may have reduced the significance of biologically relevant genes because their signal intensities were close to background levels. Accordingly, we did not have a clear concept of the central pathways and biological categories affected by DEX treatment. Similarly, Gene Ontology annotations were not sufficiently robust to detect differences in the representation of specific molecular categories (data not shown). We therefore implemented GSEA [52], an algorithm that is designed to effectively evaluate the effect of a specific experimental condition on known biological pathways and functional categories. These analyses show whether a given treatment (e.g. DEX stimulation) results in enrichment of genes sets involved in the regulation of a specific phenotype (see materials and methods for details).

We created a gene set consisting of 77 gene lists representing different tissue types, functional categories and pathways derived from other microarray studies in the literature (Table 2). We drew conclusions from the top gene sets that had a false discovery rate (FDR) less than 25% and a p-value less than 0.001, both of which are acceptable cut-offs for the identification of biologically relevant probe sets. This cut-off, although relatively high, was optimized to reduce the occurrence of false negative data in data sets interrogating a small number of gene sets. Additionally, the FDR compensates for the inherent lack of coherence microarray data sets exhibit between gene expression and specific experimental conditions [52]. Enriched gene sets were identified in both DEX and vehicle data (Table 3). Specifically, the highest statistical confidence and correlation with the DEX phenotype was assigned to metabolism and extracellular matrix, which contained 196 and 228 genes, respectively (Figure 3, left panels, Table 4 and 5). In each case, the expression of genes positively correlated with the DEX phenotype at the 24 hr time point exceeded the number of genes at the 6 hr time point (Figure 3, right panels). Metabolic genes included aldehyde and alcohol dehydrogenases (Table 4), among others, and were identified in accordance with previously documented roles for GC in various metabolic processes and tissues [60, 61]. Closer examination of the genes contributing to the enrichment scores for the ECM gene set revealed that Dentin matrix protein 1 (Dmp1) was the top ranking gene (Table 5). DMP1 belongs to the SIBLING family of matrix molecules and has been linked to chondrocyte differentiation. Dmp1 knockout mice display disordered postnatal chondrogenesis, among other skeletal abnormalities [62]. Interestingly, integrin binding sialoprotein (Ibsp) [6366]), another SIBLING family member, and osteocalcin (Bglap2) both contain putative GRE sequences, but did not contribute to the enrichment score for this category [63, 66]. They did, however, belong to the core group of genes that were enriched when a micromass culture gene set was used to interrogate the DEX data (Figure 4).
Table 2

Gene sets used in GSEA.

Category name

Number of genes

Category name

Number of genes

Adipose

70

Nucleus_3

510

Apoptosis

39

Fkbp

33

Bone

116

3vs15_1.5x_1

497

Cartilage

28

3vs15_1.5x_2

497

Catalytic

245

3vs15_1.5x_3

497

Chaperone

81

3vs15_1.5x_4

497

Chemokine

31

3vs15_1.5x_5

76

Chromatin/Hdacs

24

Igf

48

Cyclin

225

Cart_2

299

Cytokine

127

Cart_3

352

1_Dnabind

500

Liver_1

260

2_Dnabind

448

Liver_2

260

Ecm

228

Blood

111

Electron_Transp

40

Protease_1

269

Gf Receptor

327

Protease_2

269

Gluconeogen

31

Phosphatase

473

Growth Factor

106

Dusp

20

Gtpase Activator

46

Kinase_1

499

Gtpase Activ

73

Kinase_2

499

Heparin Bind

37

Kinase_3

227

Hormone

75

Integrin_Rel

173

Muscle

198

Brain_Rel

379

Neg_Apoptosis

50

Hepatocyte

19

Oncogene

154

Obl_Oclast

16

Pos_Apoptosis

79

Interleukinrelated

175

Related_Apoptosis

311

Rgs_Related

44

Structure

151

Caspase_Related

47

Sugar_Bind

104

Creb_Atf3

32

Tf_Activ

56

Nuclear Receptor

138

Tf_Repress

55

Nuc_Hormone_Receptor

55

Tgfb

45

Mapkrelated

267

Tnf_Receptor

69

Membrane

260

Tumor Suppressor

48

Metabolism

196

Wnt

53

Nucleus_1

494

Actin_Cytoskel

38

Nucleus_2

494

Angiogen

57

Pzhorton.Farnum

413

Bmprelated

62

Hzhorton.Farnum

407

Cytoplasm

411

  

Erk_Related

40

  

Fgf_Related

64

  
Table 3

GSEA of DEX-treated primary chondrocytes.

Gene set name

Size

ES

NES

NOM p-val

FDR q-val

Metabolism

196

0.471

1.935

<0.001

0.016

Extracellualr Matrix

228

0.451

1.878

<0.001

0.016

Fkbp

33

0.559

1.696

0.011

0.054

Integrin_Related

173

0.407

1.643

<0.001

0.001

Angiogenesis

57

0.479

1.610

0.012

0.065

Kinase_1

499

0.343

1.549

<0.001

0.092

Tumor Suppressor

48

0.457

1.492

0.037

0.126

Catalytic

245

0.337

1.420

0.008

0.172

Hepatocyte

19

0.529

1.406

0.104

0.161

D3 Vs D15_2

497

0.304

1.368

0.004

0.194

Igf

48

0.412

1.348

0.093

0.208

Cyclin

224

0.322

1.344

0.028

0.199

Actin_Cytoskel

38

0.426

1.325

0.124

0.213

Structure

151

0.332

1.312

0.053

0.219

Cytoplasm

411

0.292

1.300

0.023

0.224

Adipose

70

0.368

1.285

0.116

0.232

Gtpase Activity

73

0.363

1.280

0.113

0.230

Cartilage

28

0.432

1.262

0.169

0.246

Chemokine

31

-0.779

-2.40

<0.001

0

Cytokine

127

-0.579

-2.31

<0.001

0

Growth Factor

106

-0.517

-2.01

<0.001

7.698E-04

Interleukinrelated

175

-0.469

-1.98

<0.001

9.475E-04

Bone

16

-0.577

-1.51

0.051

8.945E-02

Creb_Atf3

30

-0.469

-1.43

0.065

1.300E-01

Dusp

20

-0.508

-1.40

0.102

1.418E-01

Blood

111

-0.351

-1.37

0.037

1.425E-01

3vs15_1.5x_3

496

-0.288

-1.35

0.002

1.518E-01

Protease_2

268

-0.306

-1.35

0.015

1.411E-01

Nuc_Hormone_Receptor

55

-0.381

-1.32

0.086

1.570E-01

Tf_Repress

55

-0.380

-1.32

0.091

1.498E-01

3vs15_1.5x_4

497

-0.272

-1.28

0.011

1.817E-01

Erk_Related

40

-0.385

-1.25

0.157

2.169E-01

ES, enrichment score

NES, normalized enrichment score

FDR q-val, false discovery rate and multiple testing corrections (q-value)

NOM p-val; the uncorrected p-value

Table 4

Metabolic transcripts enriched in DEX-treated chondrocytes. I.

HUGO symbol

Rank

RMS*

RES**

HUGO symbol

Rank

RMS*

RES**

Aldh1a1

26

0.417

0.053

Slc27a4

1616

0.058

0.426

Eya2

40

0.355

0.099

Ltbp2

1721

0.056

0.428

Vcl

106

0.228

0.125

Hsd17b1

1783

0.055

0.432

Adhfe1

116

0.222

0.154

P4ha2

1783

0.055

0.432

Ids

123

0.212

0.181

Mut

1850

0.053

0.443

Cbr3

133

0.204

0.207

Pde3a

2195

0.048

0.432

Aldh6a1

202

0.165

0.225

Sulf2

2200

0.048

0.438

Bcat2

224

0.157

0.245

Prep

2316

0.046

0.438

Pmm1

278

0.145

0.261

Plod3

2387

0.045

0.441

Pcx

553

0.105

0.261

1110013G13RIK

2510

0.043

0.440

Fthfd

554

0.105

0.275

Pld1

2669

0.041

0.437

Atp1a1

560

0.104

0.288

Au041707

2721

0.040

0.440

Gstm1

619

0.099

0.298

Decr1

2837

0.039

0.439

Gstm2

742

0.088

0.303

Gstm5

2872

0.038

0.443

1700061G19RIK

787

0.086

0.312

Bckdha

2932

0.038

0.445

Slc38a4

833

0.084

0.321

Atp11a

2951

0.038

0.449

Pyp

847

0.083

0.331

Gstp1

2967

0.037

0.453

Aacs

901

0.080

0.339

Dhrs7

3014

0.037

0.455

Plod1

934

0.079

0.348

Cbr2

3147

0.035

0.453

Acas2

983

0.077

0.355

Echdc3

3152

0.035

0.458

Auh

1068

0.074

0.361

Acy3

3254

0.035

0.457

Gcat

1109

0.072

0.368

Dhrs1

3483

0.032

0.450

Dhrs8

1184

0.070

0.373

Itgb1

3527

0.032

0.452

Egln3

1232

0.068

0.380

4933406E20RIK

3553

0.031

0.454

Mthfs

1268

0.067

0.387

Plod2

3574

0.031

0.458

Mvk

1298

0.066

0.394

Pmm2

3582

0.031

 

Aup1

1325

0.065

0.401

Ugp2

3583

0.031

 

Spr

1456

0.062

0.403

Gnpat

3633

0.031

 

Sc5dl

1462

0.062

0.411

1110003P22RIK

3636

0.031

 

1300018J18RIK

1516

0.061

0.416

Dbt

3710

0.030

 

Agpat3

1524

0.061

0.423

    

Rank = position of genes in the context of the ranked list of array genes

RMS = the ranked metric score

RES = the running enrichment score

Table 5

ECM-related transcripts enriched in DEX-treated chondrocytes.

HUGO symbol

Rank

RMS

RES

HUGO symbol

Rank

RMS*

RES**

Dmp1

18

0.470

0.036

Matn4

882

0.081

0.420

Omd

27

0.409

0.068

Lama3

886

0.081

0.427

Itga5

38

0.358

0.095

Nyx

992

0.077

0.427

Adamts1

57

0.305

0.118

Lamb2

1082

0.073

0.429

Timp4

61

0.296

0.141

Bsg

1100

0.072

0.433

Col4a1

86

0.268

0.161

Fbn2

1242

0.068

0.432

Col4a2

98

0.247

0.180

Ntn4

1245

0.068

0.437

Adam12

112

0.225

0.197

5730577E14RIK

1381

0.064

0.435

Prelp

139

0.200

0.211

Col6a2

1405

0.064

0.439

Postn

142

0.195

0.227

Ntn3

1415

0.063

0.443

Chad

176

0.174

0.239

Tgfb2

1531

0.060

0.442

Mgp

195

0.168

0.251

Mia1

1575

0.059

0.445

Col1a1

232

0.154

0.261

Mmp14

1803

0.054

0.438

Mfap5

233

0.153

0.273

Col15a1

1845

0.053

0.440

Col10a1

266

0.146

0.283

Ctgf

1882

0.052

0.442

Smoc2

279

0.145

0.294

Col6a1

1942

0.052

0.443

Aspn

294

0.141

0.304

Gpld1

1946

0.051

0.447

Col4a5

367

0.128

0.310

Emid2

2043

0.050

0.446

Adamts15

385

0.126

0.319

Col7a1

2047

0.050

0.450

Tgfb1

394

0.125

0.329

Adam10

2107

0.049

0.451

Sparcl1

440

0.119

0.336

Col9a2

605

0.100

0.370

Adam17

483

0.112

0.343

Matn3

610

0.099

0.377

Lama5

508

0.110

0.350

Col11a2

636

0.097

0.384

Lamc1

517

0.109

0.358

Hapln1

650

0.096

0.391

Spock2

581

0.102

0.363

Lama2

685

0.092

0.396

Lama1

688

0.092

0.403

Gpc3

796

0.086

0.412

Ltbp4

704

0.091

0.410

Lama4

827

0.084

0.417

*RMS = the ranked metric score

**RES = the running enrichment score

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-205/MediaObjects/12864_2007_Article_918_Fig3_HTML.jpg
Figure 3

Enrichment plots for statistically significant gene sets identified by GSEA. User-defined gene sets enriched with the DEX or vehicle conditions are depicted. Black bars illustrate the position of probe sets belonging to metabolic, extracellular matrix (A), cytokine and growth factor (B) gene sets in the context of all probes on the DEX array. The running enrichment score (RES) plotted as a function of the position within the ranked list of array probes is shown in green. The ranked list metric shown in gray illustrates the correlation between the signal to noise values of all individually ranked genes according and the class labels (experimental conditions). Metabolic and ECM genes are overrepresented in the left side of the enrichment plot indicating correlation to differential expression in DEX-treated chondrocytes. In contrast, cytokines and growth factor genes are enriched in the right side of the plots and correspond to the vehicle control. Significantly enriched data sets are defined according to GSEA default settings i.e., a p < 0.001 and a false discovery rate (FDR) < 0.25. Individual expression profiles for probe sets contributing to the normalized enrichment score are shown in the right panel. R.L.M = ranked list metric, E.S. = enrichment score.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-205/MediaObjects/12864_2007_Article_918_Fig4_HTML.jpg
Figure 4

Comparison of DEX-treated primary chondrocytes to a time course of chondrocyte differentiation in micromass culture. The Venn diagram depicts probe sets that are common between the list of 2119 probe sets differentially expressed between days 3 and 15 of micromass culture and the list of 22 091 significantly expressed probe sets in primary chondrocyte monolayer cultures (A). The matrix of 77 user-defined gene sets are used to interrogate microarray data from days 15 and day 3 of micromass culture. Normalized enrichment scores (NES) generated from this analysis are then compared to NES scores derived from the DEX study to evaluate similarities in the regulation of different groups of genes in chondrocytes (B). Positive enrichment scores (ES) indicate gene sets that are enriched and up-regulated in DEX-treated chondrocytes or d15 of micromass culture. Negative ES indicate gene set enrichment and down-regulation in the DEX-treatment or up-regulation in the day 3 samples of the micromass (MM) culture data set.

Osteomodulin, an additional matrix molecule shown to be structurally similar to IBSP [67], ranked second in the list of enriched ECM genes. Additional ECM molecules expressed in terminally differentiated chondrocytes such as collagen 10 (Col10a1) and osteonectin (Spock1) were identified, suggesting that this molecular classification is important for transmitting GC signaling in the growth plate.

Interestingly, the normalized enrichment scores for factors down-regulated by DEX treatment were higher than those positively correlated with DEX, but contained fewer probe sets contributing to the scores. Gene sets composed of 127 and 106 genes associated with cytokine and growth factor activity, respectively, were negatively correlated with DEX treatment (Figure 4, Table 6, 7). In other studies, cytokines such as Il-8 and GROα were found to promote the hypertrophy of osteoarthritic cartilage, and excess interleukins 1β(IL-1β), interleukin 6 (IL-6) and Tumor Necrosis Factor alpha (TNF-α) cause growth failure in children [6870]. Our studies identified three members of the GP-130 family of cytokines, namely interleukins -11,-6 (Il11, Il6) and leukemia inhibitory factor (Lif), as part of the core enrichment group for cytokines (Table 6). Transgenic mice overexpressing Il-6 exhibit growth retardation, and LIF is thought to regulate the rate at which terminally differentiated cartilage is calcified and vascularized [71, 72].
Table 6

Cytokine transcripts enriched in vehicle-treated chondrocytes. I.

HUGO gene symbol

Rank

RMS

RES

Cklfsf2b

16971

-0.0424

-0.574

Il7

16981

-0.0425

-0.569

Il1f9

17007

-0.0427

-0.566

Grn

17130

-0.0439

-0.567

Il1f6

17153

-0.0442

-0.563

Ifna2

17418

-0.0468

-0.571

Tslp

17503

-0.0477

-0.570

Il17

17568

-0.0483

-0.568

A730028g07rik

17606

-0.0486

-0.564

Cxcl11

17634

-0.0490

-0.560

Ctf1

17857

-0.0519

-0.565

Lta

17864

-0.0519

-0.559

Il1a

18018

-0.0539

-0.561

Ccl20

18038

-0.0542

-0.556

Ccl17

18334

-0.0584

-0.564

Ccl12

18384

-0.0592

-0.560

Cklf

18618

-0.0639

-0.564

Ifna11

18855

-0.0688

-0.568

Cklfsf6

18874

-0.0693

-0.561

Il15

18955

-0.0719

-0.557

Ltb

19146

-0.0779

-0.558

Ccl3

19220

-0.0814

-0.552

Tnfsf9

19228

-0.0816

-0.543

Cx3cl1

19523

-0.0975

-0.547

Gdf15

19660

-0.1100

-0.541

Bmp5

19775

-0.1238

-0.533

Cxcl14

19798

-0.1289

-0.519

Cxcl1

19928

-0.1698

-0.507

Cxcl10

19951

-0.1807

-0.487

Ccl7

19956

-0.1849

-0.466

Gdf5

19973

-0.2066

-0.444

Cxcl12

19978

-0.2104

-0.420

Areg

19983

-0.2189

-0.395

Cxcl2

19996

-0.2421

-0.369

Ppbp

20014

-0.2944

-0.336

Lif

20024

-0.3296

-0.299

Ccl2

20030

-0.3589

-0.258

Il11

20035

-0.4036

-0.213

Cxcl5

20039

-0.5406

-0.152

Tnfsf11

20041

-0.5835

-0.085

Il6

20043

-0.7529

 

Rank = position of genes in the context of the ranked list of array genes

RMS = the ranked metric score

RES = the running enrichment score

Table 7

Growth factor transcripts vehicle in DEX-treated chondrocytes.

HUGO gene symbol

Rank

RMS

RES

Fgf21

18968

-0.073

-0.508

Nrg3

19132

-0.077

-0.506

Fgf5

19190

-0.080

-0.499

Ereg

19507

-0.096

-0.502

Fgf7

19581

-0.102

-0.493

Gdf15

19660

-0.110

-0.483

Igf1

19679

-0.111

-0.469

Bmp5

19775

-0.124

-0.458

Nov

19848

-0.144

-0.443

Vegf

19877

-0.150

-0.425

Ptn

19885

-0.153

-0.406

Cxcl1

19928

-0.170

-0.386

Bdnf

19939

-0.176

-0.364

Inhba

19971

-0.204

-0.340

Gdf5

19973

-0.207

-0.313

Cxcl12

19978

-0.210

-0.287

Areg

19983

-0.219

-0.259

Hbegf

20006

-0.264

-0.226

Ngfb

20013

-0.287

-0.189

Lif

20024

-0.330

-0.148

Il11

20035

-0.404

-0.096

Il6

20043

-0.753

0.000

Rank = position of genes in the context of the ranked list of array genes

RMS = the ranked metric score

RES = the running enrichment score

This group also contained the gene encoding Tumor necrosis factor (ligand) superfamily, member 11 (Tnfsf11, RANKL), which has been localized to mature chondrocytes and is thought to promote degradation of the calcified cartilage ECM and ultimately endochondral ossification through activation of osteoclasts [7375]. It is important to note that several independent gene sets connected to inflammation such as cytokines, chemokines and interleukins exhibit some overlap and showed similar enrichment patterns, which provides additional confirmation that DEX is indeed downregulating inflammatory molecules in chondrocytes. GC have been previously reported to down-regulate the expression of VEGF, one of the central growth factors involved in vascularization of calcified cartilage matrix [49], in agreement with our data (Table 7). Since some of these factors, such as RANKL, VEGF and LIF, promote normal tissue remodeling processes during endochondral ossification, our data suggest that DEX prevents the replacement of hypertrophic cartilage by bone. GC have been shown to delay chondrocyte maturation while retaining their capacity to re-engage in their developmental program [21]. This could account for upregulation of genes typically associated with the chondrocyte phenotype, such as ECM genes and the coordinated downregulation of factors that promote the transition from cartilage into bone.

Identification of cartilage-specific dexamethasone-effects

Identification of cartilage-specific gene sets affected by DEX treatment provided further insight into the complex nature of GC functions in cartilage. We knew from other studies that DEX effects on chondrogenic differentiation are dependent on cell source, experimental system and DEX concentration [40, 42, 7678]. We aimed to systematically characterize the effects of DEX on growth plate chondrocytes. To ensure that our DEX data set was expressing bona fide cartilage markers, we compared the DEX data to our previously generated micromass culture data set [50]. We compared all expressed probe sets in the DEX array to probe sets exhibiting a minimum 1.5-fold change in expression between days 3 and 15 of micromass cultures that encompass the various stages of the chondrocyte life cycle. Day 3 of micromass culture likely coincides with the onset of the cartilage developmental program and early chondrogenesis. After 15 days of culture, the cell population is comprised primarily of terminally differentiated chondrocytes and thus corresponds mostly to the hypertrophic zone of the growth plate [50, 79], although small numbers of other cells are present at all stages. Out of the 2119 probe sets displaying at least 1.5-fold changes in expression in the micromass culture data set (a probe set list generated from the pair-wise comparison of day 3 versus day 15 of micromass culture), 1730 were also expressed in the DEX array. This shows that our primary chondrocyte monolayers do exhibit prototypical chondrocyte gene expression patterns in both the presence and absence of DEX treatment.

To complete more robust classification of the data in which we could correlate chondrocyte gene expression to the DEX phenotype, we created a gene set from this list of 2119 probe sets (Table 8, 9). The micromass derived gene list was enriched in this study; however, the list was found to correlate both positively and negatively with different aspects of the DEX phenotype. We therefore proceeded to evaluate both the micromass (MM) data set and the DEX data set using GSEA analysis and the previously created gene sets. If both the micromass time course and the DEX data sets show the same enrichment pattern, we would have evidence to suggest that pharmacological DEX doses promote chondrocyte differentiation. Normalized enrichment scores for gene sets common to both culture methods were therefore compared to identify differences and similarities between DEX-treated chondrocytes and the chondrocyte phenotype (Figure 4B).
Table 8

Micromass culture-derived gene sets are enriched in DEX-treated primary chondrocytes (d3 vs d15_2). I.

HUGO gene symbol

Rank

RMS

RES

Itgbl1

32

0.391

0.015

Adrb2

54

0.308

0.026

Bst1

80

0.271

0.036

Gpx3

83

0.269

0.047

Myocd

90

0.259

0.058

Grk5

105

0.229

0.066

Ids

123

0.212

0.074

Ms4a6b

140

0.200

0.082

1810057c19rik

146

0.193

0.090

Igfbp2

149

0.190

0.097

Zfp36

218

0.159

0.100

Serpina3n

222

0.158

0.107

P2ry6

225

0.157

0.113

Adm

228

0.156

0.120

Crym

277

0.145

0.123

Ppap2a

303

0.139

0.128

Pycard

307

0.138

0.133

Kcns1

320

0.134

0.138

Cd80

321

0.134

0.144

Trim24

330

0.133

0.149

C1qtnf6

339

0.131

0.154

A330049m08rik

377

0.127

0.157

Adamts15

385

0.126

0.162

Elovl4

398

0.124

0.167

C1qa

402

0.124

0.172

Sox9

434

0.119

0.175

Htra3

455

0.116

0.179

Adam17

483

0.112

0.182

Mgll

493

0.112

0.186

Ibsp

507

0.110

0.190

C1qb

511

0.109

0.194

Bambi

516

0.109

0.199

Anxa4

551

0.105

0.201

Cd109

555

0.105

0.206

Nrk

559

0.104

0.210

Gstm1

619

0.099

0.211

Asb4

634

0.097

0.214

Pygl

654

0.095

0.217

Rasl11b

655

0.095

0.221

Cdc42ep4

674

0.093

0.224

Slc9a3r2

683

0.092

0.227

Lama1

688

0.092

0.231

Bb146404

707

0.091

0.234

Ai194308

724

0.090

0.237

Smn1

752

0.088

0.239

Alcam

772

0.087

0.242

Cst3

790

0.086

0.244

Pyp

847

0.083

0.245

2700017m01rik

870

0.082

0.247

Fgfr3

884

0.081

0.250

Mrpl34

912

0.080

0.252

C9orf46

972

0.077

0.252

Maf

981

0.077

0.255

8430420c20rik

1028

0.075

0.255

Gfm2

1030

0.075

0.259

Anxa6

1041

0.075

0.261

Isg20

1064

0.074

0.263

Auh

1068

0.074

0.266

Bsg

1100

0.072

0.267

Peg3

1179

0.070

0.266

Adam23

1208

0.069

0.268

Ezh1

1213

0.069

0.270

2810022l02rik

1214

0.069

0.273

0610011i04rik

1248

0.068

0.274

Pbx2

1257

0.067

0.277

Jup

1291

0.066

0.278

Zcwcc2

1301

0.066

0.280

Whsc2

1317

0.066

0.282

2410004l22rik

1344

0.065

0.283

Lmnb2

1388

0.064

0.284

Fndc1

1435

0.063

0.284

Rarres2

1460

0.062

0.285

Tap2

1512

0.061

0.285

Ctbs

1559

0.060

0.285

Jdp2

1574

0.059

0.287

Hck

1712

0.056

0.282

5031400m07rik

1792

0.054

0.281

Pkn1

1839

0.053

0.280

Dag1

1929

0.052

0.278

Fth1

1976

0.051

0.278

1110001e17rik

1979

0.051

0.280

Rbp4

1984

0.051

0.282

Pdcd6ip

2044

0.050

0.281

Siat7d

2050

0.050

0.283

Kcnd2

2074

0.050

0.284

2310004k06rik

2076

0.050

0.286

D19ertd678e

2106

0.049

0.286

Npdc1

2114

0.049

0.288

Fts

2116

0.049

0.290

Prickle1

2123

0.049

0.291

1110037f02rik

2171

0.048

0.291

Cdc42se1

2246

0.047

0.289

Chpt1

2261

0.047

0.290

Wwp2

2341

0.045

0.288

Dact1

2363

0.045

0.289

Rragd

2380

0.045

0.290

Irf5

2406

0.044

0.291

Nrbf2

2414

0.044

0.292

Cox4i2

2436

0.044

0.293

Bmp7

2456

0.044

0.294

1810008a18rik

2517

0.043

0.292

Asph

2533

0.043

0.293

Stat2

2550

0.042

0.294

Hoxa11

2560

0.042

0.296

Bax

2599

0.042

0.295

Sspn

2611

0.042

0.297

Ifngr2

2612

0.042

0.298

Glrx1

2672

0.041

0.297

Gba

2739

0.040

0.295

Fzd2

2759

0.040

0.296

Crtap

2772

0.040

0.297

Slc1a5

2786

0.040

0.298

Slco3a1

2831

0.039

0.297

3110040n11rik

2833

0.039

0.299

Tep1

2845

0.039

0.300

Fastk

2860

0.039

0.301

Tmed3

2869

0.038

0.302

Ephb4

2876

0.038

0.303

Asah2

2908

0.038

0.303

Pold4

2989

0.037

0.301

1110001a07rik

2995

0.037

0.302

Pcp4

3010

0.037

0.303

Mab21l2

3025

0.037

0.304

Rank = position of genes in the context of the ranked list of array genes

RMS = the ranked metric score

RES = the running enrichment score

Table 9

Micromass culture-derived transcripts enriched in vehicle-treated primary chondrocytes (d3 vs d15_3/4). I.

HUGO gene symbol

Rank

RMS

RES

Rabggtb

16734

-0.040

-0.271

Ube2e2

16759

-0.041

-0.270

Cd68

16769

-0.041

-0.269

H2-T23

16830

-0.041

-0.270

Derl1

16834

-0.041

-0.268

Smarcc1

16853

-0.041

-0.267

Srxn1

16856

-0.041

-0.266

Klf10

16868

-0.042

-0.264

Zfhx1b

16879

-0.042

-0.263

H2afy3

16929

-0.042

-0.264

Wisp2

16973

-0.042

-0.264

Tbl1xr1

16976

-0.042

-0.262

Ppp1r3c

16979

-0.042

-0.260

D11lgp2e

17036

-0.043

-0.261

Smpdl3b

17079

-0.043

-0.262

Dock2

17125

-0.044

-0.262

Purb

17127

-0.044

-0.260

Grn

17130

-0.044

-0.258

1110035l05rik

17139

-0.044

-0.257

Kiaa1008

17185

-0.045

-0.257

E430025l02rik

17195

-0.045

-0.256

Timm8a

17293

-0.046

-0.259

C130006e23

17307

-0.046

-0.257

Rbm10

17319

-0.046

-0.256

A230103n10rik

17347

-0.046

-0.255

Cd151

17401

-0.047

-0.256

Srf

17409

-0.047

-0.254

Cacna1s

17507

-0.048

-0.257

Ythdf1

17529

-0.048

-0.256

Ppp2r1b

17539

-0.048

-0.254

Tead2

17545

-0.048

-0.252

Igsf7

17590

-0.049

-0.252

Per3

17604

-0.049

-0.251

G1p2

17739

-0.050

-0.256

Slco2a1

17786

-0.051

-0.256

Coq7

17918

-0.053

-0.260

Rarb

17940

-0.053

-0.259

Lcp1

17954

-0.053

-0.257

Dnaja1

17987

-0.053

-0.256

Thoc3

17993

-0.054

-0.254

Cd44

18041

-0.054

-0.254

Slc41a1

18171

-0.056

-0.258

Kif11

18232

-0.057

-0.259

Hspa5bp1

18235

-0.057

-0.257

Ncf4

18290

-0.058

-0.257

Bub1b

18292

-0.058

-0.254

Cap2

18295

-0.058

-0.252

Aig1

18340

-0.059

-0.251

Rfc3

18361

-0.059

-0.250

Stmn1

18396

-0.060

-0.249

9130213b05rik

18408

-0.060

-0.247

Tyms-Ps

18432

-0.060

-0.245

Timp3

18513

-0.062

-0.247

Tiparp

18564

-0.063

-0.247

Thbs4

18627

-0.064

-0.247

Wasf1

18652

-0.064

-0.245

Nupr1

18686

-0.065

-0.244

Ezh2

18706

-0.066

-0.242

Fbxl14

18709

-0.066

-0.239

Prim1

18780

-0.067

-0.240

Insig2

18805

-0.068

-0.238

B3gnt5

18858

-0.069

-0.238

Fam60a

18963

-0.072

-0.240

H2-M3

18972

-0.073

-0.237

Gja7

18974

-0.073

-0.234

Bex2

18987

-0.073

-0.231

Tk1

19043

-0.074

-0.231

1200015n20rik

19109

-0.076

-0.231

Clecsf5

19114

-0.077

-0.228

Ms4a7

19141

-0.078

-0.226

Cdca5

19163

-0.079

-0.223

C730042f17rik

19180

-0.079

-0.220

Trim25

19194

-0.080

-0.218

Efnb2

19207

-0.081

-0.215

Apex1

19236

-0.082

-0.212

Ddah2

19243

-0.082

-0.209

Bub1

19262

-0.083

-0.206

Nup43

19263

-0.083

-0.203

Rdh10

19270

-0.083

-0.199

2610201a13rik

19330

-0.086

-0.199

Rp2h

19406

-0.089

-0.198

Tnni1

19407

-0.089

-0.195

Myog

19423

-0.091

-0.191

Osmr

19486

-0.095

-0.190

Mmp9

19524

-0.097

-0.188

Tnnt1

19525

-0.098

-0.184

Fhod3

19528

-0.098

-0.179

D930038m13rik

19537

-0.099

-0.175

Nes

19567

-0.101

-0.172

Sbk1

19571

-0.102

-0.168

Dusp9

19594

-0.103

-0.165

Akr1b8

19622

-0.106

-0.161

Pdgfrb

19663

-0.110

-0.158

Tfrc

19667

-0.111

-0.154

Moxd1

19670

-0.111

-0.149

1810008k03rik

19681

-0.112

-0.145

Cpeb1

19710

-0.115

-0.141

6720475j19rik

19716

-0.116

-0.136

Ripk4

19718

-0.116

-0.131

Itga6

19756

-0.121

-0.127

Bmp5

19775

-0.124

-0.123

Lhx9

19776

-0.124

-0.117

Pkp2

19797

-0.129

-0.113

Chrna1

19808

-0.131

-0.108

Bhlhb2

19837

-0.142

-0.103

Gp49a

19847

-0.144

-0.097

Clecsf10

19893

-0.155

-0.092

Gch1

19902

-0.159

-0.086

D0h4s114

19908

-0.161

-0.079

Cxcl1

19928

-0.170

-0.072

Ch25h

19946

-0.178

-0.065

Mkrn3

19988

-0.228

-0.057

Ptprc

20016

-0.297

-0.046

Car6

20017

-0.298

-0.032

Nr1d2

20031

-0.368

-0.017

Evi2a

20033

-0.393

0.001

Plxnc1

18075

-0.055

-0.286

Cilp2

18106

-0.055

-0.285

Brca1

18148

-0.056

-0.285

Litaf

18149

-0.056

-0.283

Bc027246

18154

-0.056

-0.281

6820424l24rik

18268

-0.057

-0.285

Hrb

18272

-0.057

-0.283

Nnat

18303

-0.058

-0.282

P2ry12

18329

-0.058

-0.282

Cdca4

18343

-0.059

-0.280

6030404e16rik

18367

-0.059

-0.279

Tfec

18429

-0.060

-0.280

Nfe2l2

18440

-0.060

-0.278

Gtf2h2

18467

-0.061

-0.277

4930469p12rik

18504

-0.062

-0.277

Cul4b

18535

-0.062

-0.276

H2afy2

18547

-0.063

-0.274

1190002n15rik

18582

-0.063

-0.274

B430218l07rik

18591

-0.063

-0.272

Rgs18

18607

-0.064

-0.270

Frk

18631

-0.064

-0.269

Slc6a9

18633

-0.064

-0.267

Tgfbr2

18687

-0.065

-0.267

Tia1

18802

-0.068

-0.270

Lgr5

18844

-0.068

-0.270

Sgpp1

18909

-0.071

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Matn2

18924

-0.071

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Sox11

18931

-0.071

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Hus1

18980

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D930015e06rik

19028

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Apob48r

19032

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Av344025

19045

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Eno2

19047

-0.074

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2610024e20rik

19053

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Chd1l

19093

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Emr1

19145

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Rgs4

19200

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D030028o16rik

19211

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Kif2c

19216

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Ccl3

19220

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Trim30

19232

-0.082

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Qrsl1

19242

-0.082

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Nr3c1

19281

-0.083

-0.240

Trip13

19282

-0.084

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Dna2l

19317

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Tcf8

19335

-0.086

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Clecsf8

19341

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Lyzs

19422

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Palmd

19475

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Tjp2

19487

-0.095

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D430019h16rik

19493

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Sesn3

19501

-0.096

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Ereg

19507

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-0.218

Cx3cl1

19523

-0.097

-0.215

Fzd6

19529

-0.098

-0.211

Sod3

19564

-0.101

-0.209

Tnnt2

19580

-0.102

-0.206

Satb1

19599

-0.104

-0.203

Cd14

19606

-0.104

-0.200

Gbp2

19607

-0.104

-0.196

Tgfbi

19609

-0.105

-0.192

Chek1

19652

-0.109

-0.190

Tm4sf1

19653

-0.109

-0.186

Igf1

19679

-0.111

-0.183

Enpp1

19695

-0.113

-0.180

Slc15a3

19704

-0.114

-0.176

Pdpn

19725

-0.117

-0.173

Dkk1

19747

-0.119

-0.169

Slk

19759

-0.121

-0.166

Ankrd1

19794

-0.128

-0.163

Trp53bp1

19801

-0.129

-0.158

C79407

19804

-0.130

-0.153

2210010l05rik

19809

-0.131

-0.149

Eps8

19815

-0.133

-0.144

Dkk2

19862

-0.147

-0.141

Arhgap18

19863

-0.147

-0.136

Twist2

19878

-0.151

-0.131

Pcdha8

19915

-0.164

-0.126

Il4r

19926

-0.169

-0.121

Mdm1

19931

-0.172

-0.115

Phlda1

19957

-0.188

-0.109

Bhlhb5

19960

-0.192

-0.102

C130076o07rik

19964

-0.196

-0.095

5830411e10rik

19974

-0.207

-0.088

Ptpre

19989

-0.228

-0.080

Trib3

19990

-0.235

-0.071

9230117n10rik

19994

-0.241

-0.062

Pcdhb7

19998

-0.249

-0.053

Mmp3

20001

-0.252

-0.044

Cd34

20009

-0.274

-0.034

Thbd

20022

-0.310

-0.023

A830016g23rik

20023

-0.323

-0.011

Ahr

20028

-0.336

0.001

Rank = position of genes in the context of the ranked list of array genes

RMS = the ranked metric score

RES = the running enrichment score

Four different patterns were observed when comparing DEX treatment and micromass differentiation data sets for gene enrichments scores (Figure 4B). First, similar gene sets were indeed enriched in both day 15 micromass and DEX-treated monolayer cultures, and core genes contributing to the normalized enrichment scores were similarly overlapping between the two data sets in results with low FDR. For example, ECM genes were enriched with both DEX treatment and the day 15 micromass phenotype. Other gene sets following this enrichment pattern included genes involved in integrin function, angiogenesis, catalytic activity, IGF related, adipocyte and cartilage, all of which have a precedent for being involved in chondrocyte maturation [28, 49, 80, 81]. The enrichment of angiogenic transcripts with DEX treatment was unexpected since DEX was shown to have anti-angiogenic roles in cartilage; however, upon closer examination of the genes contributing to the enrichment score, Vegf, which is thought to be a central angiogenic factor in endochondral ossification [82], was excluded from the core enrichment genes and had the lowest correlation with the DEX phenotype in that gene set. In contrast, Vegf was enriched in the growth factor data set which positively correlated with the vehicle control and not DEX treatment (Table 7).

Gene sets associated with the actin cytoskeleton, tumour suppressors, structure, cytoplasmic genes, hepatocyte markers and dual specificity phosphatases (DUSPs) were enriched in the DEX data set and the phenotype positively correlated with day 3 of micromass culture. The identification of DUSPs was particularly interesting since DEX has been shown to induce genes encoding for these proteins [77, 83, 84]. DUSPS counteract the activation of MAP kinase pathways, known regulators of chondrocyte differentiation [85], and are thought to mediate DEX's anti-inflammatory functions and to influence hepatic gluconeogenesis [83, 86, 87].

Additional comparisons identified genes that show enrichment in day 15 micromass cultures and downregulation with DEX treatment. These include the previously identified chemokines, cytokines and interleukins. A final trend in similarly enriched gene sets identified lists that were negatively correlated both with the DEX phenotype and day 15 of micromass cultures. Only transcriptional repressors and molecules involved in the extracellular signal-regulated kinase (ERK) pathway were identified. This pattern is consistent with DEX's anti-proliferative functions, as another study showed that DEX decreases ERK phosphorylation and thus cell cycle progression in a pre-osteoblast cell line [77]. Altogether this analysis shows that DEX regulation of growth plate chondrocyte differentiation is multifaceted. The patterns identified here are in agreement with a dual role of DEX in maintenance of the cartilage phenotype and delay in the cartilage-to-bone transition, as we suggested above.

We also wanted to determine whether DEX target genes identified in the current study were similar to DEX-responsive genes identified in alternate studies, in different cell types [88]. Out of a total of twelve microarray studies evaluating the transcriptional effects of DEX treatment on a specific tissue or cell type, only ten genes were common to at least three of the chosen DEX studies. Specifically, bone morphogenetic protein 2 (Bmp2), delta sleep inducing factor 1 (Dsip1), beta-2 microglobulin (B2m), neuroepithelial cell transforming gene 1 (Net1), TNFAIP3 interacting protein 1 (Tnip1), bone marrow stromal cell antigen 2 (Bst2), B-cell leukemia/lymphoma 6 (Bcl6), nuclear factor of kappa light chain gene enhancer in B-cells inhibitor, alpha (Nfkbia), FK506 binding protein 5 (Fkbp5) and B-cell translocation gene 1, anti-proliferative (Btg1) were identified. It therefore appears that while DEX affects similar functional categories across various species, tissue types and experimental conditions, the individual genes that respond to DEX treatment are variable. These results also reinforce the paradigm that GC regulation is inextricably linked to its physiological context [8899].

Analyses of GC response elements in dexamethasone target genes in chondrocytes

Classical genomic GC action is thought to be mediated by a cytoplasmic GR that modifies transcription upon binding its cognate ligand and translocating to the nucleus. In the nucleus, the GR binds a GRE sequence. GR can both activate and repress transcription, depending on the GRE variant present in the regulatory regions of GC target genes [100]. Binding to composite GREs involves homodimerization of the GR to bind a non-palindromic consensus sequence comprised of two GR binding sites and is generally associated with transcriptional activation. In some instances, however, GR can function to block access or activity of transcription factors within promoter regions of certain genes, thus impeding transcription [101]. GR are also able to bind a modified GRE consisting of composite GRE half-sites, termed negative GREs, since they have documented roles in transcriptional repression. Variations on the genomic functions of GC include transcriptional regulation at the level of protein-protein interactions between the GR and other transcription factors, co-activators or co-repressors. In addition to the GRE-dependent roles, the GR is capable of interacting with other co-activators and repressors to influence transcription indirectly [102, 103].

The 100 most highly expressed probe sets with greatest enrichment in the DEX or vehicle-treated chondrocytes are shown in Figure 5. Probe sets identified in this analysis included both known cartilage markers and established DEX target genes such as Vegf, Ibsp, Bglap2 and Fkbp5 [49, 6366, 104, 105]. We examined the proximal promoter regions of three separate gene lists, the top 100 DEX-responsive transcripts generated by GSEA analysis (Figure 5), the 22 091 probe sets deemed expressed in primary chondrocyte cultures and the 1158 transcripts deemed differentially expressed between DEX and vehicle treated cultures by one-Way ANOVA. Specifically, we searched the 9990 base pairs upstream regulatory regions in this list for the composite GRE consensus sequence. We identified putative GRE sequences in many genes, including Fkbp5, pyruvate dehydrogenasekinase (Pdk4), RANKL (Tnfsf11), Interleukin 6 (Il6) and prostaglandin I2 synthase (Ptgis) (bold in Figure 5). However, the majority of DEX-regulated probe sets such as prostaglandin-endoperoxide synthase 2 (Ptgs2), phosphodiesterase 4A (Pde4), Vegf, Period homolog 1 (Per1) and Krüppel like factor 15 (Klf15) do not appear to contain a GRE in the first 10 kilobases and may by regulated by DEX via a GRE-independent mechanism, through a GRE that deviates from the consensus GRE sequence or through GREs at other locations in the gene.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-8-205/MediaObjects/12864_2007_Article_918_Fig5_HTML.jpg
Figure 5

Heat map of top 100 probe sets determined by GSEA analysis. GSEA-derived heat maps of the top 100 differentially expressed probe sets enriched with DEX or the vehicle control are shown (B). Expression profiles for all experimental replicates are shown for each time point. Genes containing a putative GRE are shown in bold, and examples of genes that do not contain GREs but have been documented as targets of DEX regulation are depicted by bold gray lettering. Signal intensities are illustrated by varying shades of red (up-regulation) and blue (down-regulation).

Examination of all lists generated similar results in that approximately 16–20% of all probes contained the consensus GRE. Consequently, we cannot exclude the presence of less conventional GRE loci in the transcripts, or the presence of GREs that deviate from the consensus sequence or are located outside the queried sequence. Since many of the genes affected at the 6 hr time point encode transcription factors, it is likely that a large proportion of the genes that only change after 24 hrs are regulated indirectly by DEX, through altered expression of these transcription factors and other regulatory proteins (e.g. phosphatases and cytokines, as discussed above).

Functional analysis is required to unequivocally evaluate the contribution of GRE-dependent mechanisms to GC regulation in chondrocytes. In addition to the genomic functions of GC, non-genomic modes of GC regulation have been documented. Non-genomic mechanisms are thought to occur through specific and non-specific mechanisms. Specific non-genomic GC regulation occurs through the classical GR and its cytoplasmic heteroprotein complex or non-classical GRs such as membrane GR [106109]. Conversely, non-specific non-genomic mechanisms rely on the physiochemical properties of GC and the phospholipid bilayer (Buttgereit and Scheffold, 2002). Further, studies in which candidate molecules are selected and characterized in depth are imperative to discern the specific regulatory mechanisms occurring in chondrocytes.

Conclusion

This study elucidates the downstream transcriptional impact of pharmacological GC exposure on developing chondrocytes. We have identified a small subset of transcripts containing putative GREs in cartilage, but it appears that GRE-independent or indirect mechanisms of GC regulation also contribute to GC regulation in primary chondrocyte monolayer cultures. In addition, traditional microarray analysis methods and gene class testing point to a dual role for pharmacological GC doses in chondrocytes. DEX acts in a gene class-specific manner in cartilage in which it promotes the expression of ECM and metabolic transcripts necessary for maintaining the chondrocyte phenotype while simultaneously downregulating cytokines and growth factors which stimulate the cartilage to bone transition. Understanding the implications of gene expression changes and integrating them into the network of molecules controlling cartilage development continues to be challenging, but robust analytical methods will prove to be useful in constructing the networks of gene interactions and understanding the complex nature of GC signaling in the skeleton. The ultimate objective of this study will be to translate these findings into more efficacious therapeutic GCs.

Methods

Animals and Materials

Timed-pregnant CD1 mice were purchased from Charles River Laboratories at embryonic day E15.5 mice (E15.5). Dexamethasone was obtained from Calbiochem and reconstituted in Dimethyl sulfoxide (DMSO, vehicle) according to the manufacturer's instructions. Cell culture materials and general chemicals were obtained from Invitrogen, Sigma or VWR unless otherwise stated.

Primary cell culture and dexamethasone-treatment

Tibiae, femurs and humeri were isolated from E15.5 mouse embryos and placed in α-MEM media (Invitrogen) containing 0.2% Bovine Serum Albumin (BSA), 1 mM β-glycerophosphate, 0.05 mg/ml ascorbic acid and penicillin/streptomycin and incubated at 37°C in a humidified 5% CO2 incubator overnight. The following morning media was removed and the bones placed in 4 ml of 0.25% trypsin-EDTA (Invitrogen) for 15 min at 37°C. Trypsin was subsequently replaced with 1 mg/ml collagenase P (Roche) in DMEM/10% fetal bovine serum (Invitrogen), and cells were incubated at 37°C with rotation at 100 rpm for 90 min. Following digestion, the cell suspension was centrifuged for 5 min at 1000 rpm, and the collagenase containing supernatant was decanted. Chondrocytes were resuspended in media containing 2:3 DMEM:F12, 10% fetal bovine serum, 0.5 mM L-glutamine, and penicillin/streptomycin (25 units/ml). Cells were seeded in 6-well NUNC plates at a density of 2.5 × 104 cells per ml and incubated overnight. Primary monolayer chondrocytes were treated with 10-7 M dexamethasone (DEX) or the DMSO control (vehicle) diluted in fresh media supplemented with 0.25 mM ascorbic acid (Sigma) and 1 mM β-glycerophosphate (Sigma) and incubated for up to 24 hrs. Micromass cultures were completed as previously described [50].

Cell counting studies

Chondrocytes were isolated and seeded in 24-well NUNC plates (Nunc Inc.) at a density of 16 000 cells/cm2. Cells were cultured, treated and enzymatically digested as described with some modifications. Collagenase digestion occurred for 5 minutes followed by mechanical digestion to liberate cells from the ECM. Cells were counted with a hemocytometer in triplicate with a minimum of 3 individual wells per treatment and three independent cell isolations.

RNA isolations and quantitative real-time PCR

All RNA protocols were completed as previously outlined [50]. Total RNA was isolated at 6 hrs and 24 hrs after treatment using the RNeasy mini extraction kit (Qiagen) according to the manufacturer's instructions. RNA quantity and integrity was assessed using the Bioanalyzer 2000 system (Agilent). Quantitative real-time polymerase chain reaction (qRT-PCR) amplification was completed using the ABI Prism 7900 Sequence Detection System (Applied Biosystems). Triplicate reactions were executed for each sample of each of three independent trials. The TaqMan one-step master mix kit (Applied Biosystems) with gene-specific target primers and probes were used for amplification. The collagen X (Col10a1) probe and primer set (forward primer 5'-ACGCCTACGATGTACACGTATGA-3', reverse primer 5'-ACTCCCTGAAGCCTGATCCA-3', 6-FAM-5'-AGTACAGCAAAGGCTAC-MGBNFQ) was designed with PrimerDesigner 2.0 software (Applied Biosystems) [79]. TaqMan GAPDH control reagents for house-keeping gene glyceraldehyde-3-phosphate dehydrogenase (Gapdh, forward primer 5'-GAAGGTGAAGGTCGGAGTC; reverse primer 5'-GAAGATGGTGATGGGATTTC; probe JOE-CAAGCTTCCCGTTCTCAGCC-TAMRA) was used as an internal amplification control. Probes for Indian hedgehog (Ihh), Tissue inhibitor of matrix metalloproteinase 4 (Timp4), Cyclin-dependent kinase inhibitor 1C (Cdkn1c, p57), Integrin beta like 1 protein (Itgbl1), GC receptor (Nr3c1), Integrin beta 1 (Itgb1) and Kruppel-like factor 15 (Klf15) were assayed using the TaqMan® gene expression assays in accordance with the manufacturers directions. Amplified transcripts were quantified using the standard curve method, and the relative transcript abundance was determined by calculating the quotient of the gene of interest and equivalent Gapdh values.

Microarray analysis

Total RNA was extracted from control and DEX-treated cultures at 6 hr and 24 hr following treatment, in three independent experiments. RNA integrity and quantity was assessed using the Agilent 2000 Bioanalyzer system, and RNA samples were subsequently hybridized to the MOE 430 2.0 mouse chip from Affymetrix© containing 45 101 probe sets as described [50]. Bioanalysis, microarray hybridization, scanning and preliminary MAS 5.0 normalizations were completed at the London Regional Genomics Facility. Data were deposited in the GEO database (NCBI; accession number GSE7683).

Data normalization

Microarray data were pre-processed using the GC-RMA algorithm in Genespring GX*. Expression values were further filtered by retaining only those probe sets with expression values of at least 50 in at least 25% of all conditions, thus generating a list of 22 091 probe sets. To assess differential gene expression between treatments at both the 6 and 24 hr time points, a Welch ANOVA test with a p-value cut-off of 0.01 and a 5% false discovery rate (FDR) reduced the data to 1158 probe sets. Subsequent 1.5-, 5- and 10-fold change filters produced lists of 162, 21 and 7 probe sets for the 6 hr time point and 399, 53 and 19 probe sets for the 24 hr time point, respectively.

The same data set was normalized in parallel using Robust Multichip Analysis using RMAEXPRESS software v.0.4.1 developed by B. Bolstad, University of California, Berkeley [110]. Background adjustment and quantile normalization parameters were selected for data processing. Logarithmically transformed expression values were used to implement Gene Set Enrichment Analysis (GSEA).

Gene set enrichment analysis (GSEA)

The GSEA algorithm was implemented with GSEA v2.0 software [51, 52]. Ranked expression lists were derived from RMAEXPRESS and GeneSpring GX® 7.3.1. Briefly, the GSEA algorithm ranks all array genes according to their expression under each experimental condition. The resulting ranked metric score (RMS) is therefore a function of the correlation between a gene's signal intensity, the experimental conditions in question and all other genes in the data set. An enrichment score (ES) is then calculated for an a priori gene list or gene set that is associated with a particular molecular classification. In our analysis, gene sets were created from different functional groupings, molecular classifications, tissues, and other microarray screens. A Ranked enrichment score (RES) which determines the extent to which a given gene from a gene set is represented at the extremes of the ranked gene list is then calculated. Specifically, this value is obtained by walking along the ranked list using a cumulative sum statistic which increases when a member of a particular gene set is found in the ranked gene list and is coordinately penalized when it does not appear in the gene set. A null distribution of ES is subsequently generated by permutation filtering to evaluate the statistical significance of the observed RES values. Permutation filtering randomly assigns the experimental conditions or class labels (i.e., DEX versus vehicle) to the different microarray samples. After this procedure has been repeated for each gene set, the ES are normalized (NES) to account for differences in gene set size. The false discovery rate (FDR) is then calculated relative to the NES values to determine the false-positive rate. Significant FDR and p-values were less than 25% and 0.001, respectively in accordance with GSEA recommendations.

Gene set creation

Gene sets were generated using the probe set search tool and the molecular function class of Gene Ontology annotations in GeneSpring GX. Additional gene sets were created using lists from pairwise comparisons between day 3 and 15 of a previously generated micromass data set (James et al., 2005), and publications that identified DEX target genes in other cartilage array screens, other tissue types and experimental systems. A total of 2119 probe sets showing a minimum 1.5-fold change in gene expression were used in the analysis. Probe set redundancy was eliminated in all gene sets using the CollapseDataset function in the GSEA program. All probe set identifiers were assimilated to the Human Genome Organization (HUGO) annotations. Probe sets lacking corresponding HUGO annotations were excluded from the analysis. Default parameters were used to execute the analysis and median values taken to represent the range of duplicated probe sets for a given gene. A total of 77 user-defined gene sets were generated from GeneSpring derived Gene Ontology annotations for various molecular classifications and probe sets of differentially expressed genes between days 3 and 15 of micromass culture (James et al., 2005).

Glucocorticoid response element (GRE) analysis

Putative GRE were identified with the GenespringGX mouse genome9999 application which allows sequences up to 9999 bp upstream of the transcriptional start sites of all annotated MOE4302.0 transcripts to be interrogated for transcription factor binding sites. The GR consensus sequence GGTACAnnntgttCT [111] was queried from 10 bp to 10 000 bp upstream of the transcriptional start sites of available probe sets. The GRE consensus sequence was screened against 10 748 probe sets derived from the list of 22 091 reliably expressed probe sets exhibiting homology to upstream regulatory regions annotated in the program. Only exact matches were retained for subsequent analyses out a total of 1,073,741,824 tests.

Abbreviations

DEX: 

Dexamethasone

GSEA: 

gene set enrichment analysis

RES: 

ranked enrichment score

RMS: 

ranked metric score, ES: enrichment scores

NES: 

normalized enrichment score, SOM: self-organizing maps

FDR: 

false discovery rate

GR: 

glucocorticoid receptor

Declarations

Acknowledgements

CGJ is supported by a doctoral award from the Canadian Institutes of Health Research (CIHR) and previously by an Ontario Graduate Scholarship in Science and Technology. V.U. is the recipient of a graduate scholarship from the Canadian Arthritis Network. FB is the recipient of a Canada Research Chair. Operating funds for these studies were provided by the CIHR, the Canadian Arthritis Network and the Hospital for Sick Children Foundation to FB.

Authors’ Affiliations

(1)
CIHR Group in Skeletal Development and Remodelling, Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario
(2)
Group of Tissue Specific Hormone Action, Leibniz Institute for Age Research -Fritz Lipmann Institute
(3)
Department of Cellular & Physiological Sciences, University of British Columbia

References

  1. Cancedda R, Descalzi Cancedda F, Castagnola P: Chondrocyte differentiation. Int Rev Cytol. 1995, 159: 265-358.PubMed
  2. Karsenty G, Wagner EF: Reaching a genetic and molecular understanding of skeletal development. Dev Cell. 2002, 2 (4): 389-406. 10.1016/S1534-5807(02)00157-0.PubMed
  3. Eames BF, de la Fuente L, Helms JA: Molecular ontogeny of the skeleton. Birth Defects Res Part C Embryo Today. 2003, 69 (2): 93-101. 10.1002/bdrc.10016.
  4. Cancedda R, Castagnola P, Cancedda FD, Dozin B, Quarto R: Developmental control of chondrogenesis and osteogenesis. Int J Dev Biol. 2000, 44 (6): 707-714.PubMed
  5. Ikeda T, Kamekura S, Mabuchi A, Kou I, Seki S, Takato T, Nakamura K, Kawaguchi H, Ikegawa S, Chung UI: The combination of SOX5, SOX6, and SOX9 (the SOX trio) provides signals sufficient for induction of permanent cartilage. Arthritis Rheum. 2004, 50 (11): 3561-3573. 10.1002/art.20611.PubMed
  6. Lefebvre V, Behringer RR, de Crombrugghe B: L-Sox5, Sox6 and Sox9 control essential steps of the chondrocyte differentiation pathway. Osteoarthritis Cartilage. 2001, 9 (Suppl A): S69-75. 10.1053/joca.2001.0447.PubMed
  7. Lefebvre V, Huang W, Harley VR, Goodfellow PN, de Crombrugghe B: SOX9 is a potent activator of the chondrocyte-specific enhancer of the pro alpha1(II) collagen gene. Mol Cell Biol. 1997, 17 (4): 2336-2346.PubMed CentralPubMed
  8. Gustafsson E, Aszodi A, Ortega N, Hunziker EB, Denker HW, Werb Z, Fassler R: Role of collagen type II and perlecan in skeletal development. Ann N Y Acad Sci. 2003, 995: 140-150.PubMed
  9. Li SW, Prockop DJ, Helminen H, Fassler R, Lapvetelainen T, Kiraly K, Peltarri A, Arokoski J, Lui H, Arita M, et al: Transgenic mice with targeted inactivation of the Col2 alpha 1 gene for collagen II develop a skeleton with membranous and periosteal bone but no endochondral bone. Genes Dev. 1995, 9 (22): 2821-2830. 10.1101/gad.9.22.2821.PubMed
  10. O'Keefe RJ, Puzas JE, Loveys L, Hicks DG, Rosier RN: Analysis of type II and type X collagen synthesis in cultured growth plate chondrocytes by in situ hybridization: rapid induction of type X collagen in culture. J Bone Miner Res. 1994, 9 (11): 1713-1722.PubMed
  11. Bell DM, Leung KK, Wheatley SC, Ng LJ, Zhou S, Ling KW, Sham MH, Koopman P, Tam PP, Cheah KS: SOX9 directly regulates the type-II collagen gene. Nat Genet. 1997, 16 (2): 174-178. 10.1038/ng0697-174.PubMed
  12. Ng LJ, Wheatley S, Muscat GE, Conway-Campbell J, Bowles J, Wright E, Bell DM, Tam PP, Cheah KS, Koopman P: SOX9 binds DNA, activates transcription, and coexpresses with type II collagen during chondrogenesis in the mouse. Dev Biol. 1997, 183 (1): 108-121. 10.1006/dbio.1996.8487.PubMed
  13. Kronenberg HM: Developmental regulation of the growth plate. Nature. 2003, 423 (6937): 332-336. 10.1038/nature01657.PubMed
  14. Kirsch T, Ishikawa Y, Mwale F, Wuthier RE: Roles of the nucleational core complex and collagens (types II and X) in calcification of growth plate cartilage matrix vesicles. J Biol Chem. 1994, 269 (31): 20103-20109.PubMed
  15. Kirsch T, Wuthier RE: Stimulation of calcification of growth plate cartilage matrix vesicles by binding to type II and X collagens. J Biol Chem. 1994, 269 (15): 11462-11469.PubMed
  16. Apte SS, Olsen BR: Characterization of the mouse type X collagen gene. Matrix. 1993, 13 (2): 165-179.PubMed
  17. Ohyama K, Farquharson C, Whitehead CC, Shapiro IM: Further observations on programmed cell death in the epiphyseal growth plate: comparison of normal and dyschondroplastic epiphyses. J Bone Miner Res. 1997, 12 (10): 1647-1656. 10.1359/jbmr.1997.12.10.1647.PubMed
  18. Gibson G, Lin DL, Roque M: Apoptosis of terminally differentiated chondrocytes in culture. Exp Cell Res. 1997, 233 (2): 372-382. 10.1006/excr.1997.3576.PubMed
  19. Zenmyo M, Komiya S, Kawabata R, Sasaguri Y, Inoue A, Morimatsu M: Morphological and biochemical evidence for apoptosis in the terminal hypertrophic chondrocytes of the growth plate. J Pathol. 1996, 180 (4): 430-433. 10.1002/(SICI)1096-9896(199612)180:4<430::AID-PATH691>3.0.CO;2-H.PubMed
  20. Itagane Y, Inada H, Fujita K, Isshiki G: Interactions between steroid hormones and insulin-like growth factor-I in rabbit chondrocytes. Endocrinology. 1991, 128 (3): 1419-1424.PubMed
  21. Siebler T, Robson H, Shalet SM, Williams GR: Dexamethasone inhibits and thyroid hormone promotes differentiation of mouse chondrogenic ATDC5 cells. Bone. 2002, 31 (4): 457-464. 10.1016/S8756-3282(02)00855-4.PubMed
  22. Robson H, Siebler T, Shalet SM, Williams GR: Interactions between GH, IGF-I, glucocorticoids, and thyroid hormones during skeletal growth. Pediatr Res. 2002, 52 (2): 137-147.PubMed
  23. Wang J, Zhou J, Cheng CM, Kopchick JJ, Bondy CA: Evidence supporting dual, IGF-I-independent and IGF-I-dependent, roles for GH in promoting longitudinal bone growth. J Endocrinol. 2004, 180 (2): 247-255. 10.1677/joe.0.1800247.PubMed
  24. Baron J, Huang Z, Oerter KE, Bacher JD, Cutler GB: Dexamethasone acts locally to inhibit longitudinal bone growth in rabbits. Am J Physiol. 1992, 263 (3 Pt 1): E489-492.PubMed
  25. Jux C, Leiber K, Hugel U, Blum W, Ohlsson C, Klaus G, Mehls O: Dexamethasone impairs growth hormone (GH)-stimulated growth by suppression of local insulin-like growth factor (IGF)-I production and expression of GH- and IGF-I-receptor in cultured rat chondrocytes. Endocrinology. 1998, 139 (7): 3296-3305. 10.1210/en.139.7.3296.PubMed
  26. Hochberg Z: Mechanisms of steroid impairment of growth. Horm Res. 2002, 58 (Suppl 1): 33-38. 10.1159/000064764.PubMed
  27. Raisz LG, Kream BE: Hormonal control of skeletal growth. Annu Rev Physiol. 1981, 43: 225-238. 10.1146/annurev.ph.43.030181.001301.PubMed
  28. Smink JJ, Buchholz IM, Hamers N, van Tilburg CM, Christis C, Sakkers RJ, de Meer K, van Buul-Offers SC, Koedam JA: Short-term glucocorticoid treatment of piglets causes changes in growth plate morphology and angiogenesis. Osteoarthritis Cartilage. 2003, 11 (12): 864-871. 10.1016/S1063-4584(03)00187-0.PubMed
  29. Devesa J, Barros MG, Gondar M, Tresguerres JA, Arce V: Regulation of hypothalamic somatostatin by glucocorticoids. J Steroid Biochem Mol Biol. 1995, 53 (1–6): 277-282. 10.1016/0960-0760(95)00065-8.PubMed
  30. Silvestrini G, Ballanti P, Patacchioli FR, Mocetti P, Di Grezia R, Wedard BM, Angelucci L, Bonucci E: Evaluation of apoptosis and the glucocorticoid receptor in the cartilage growth plate and metaphyseal bone cells of rats after high-dose treatment with corticosterone. Bone. 2000, 26 (1): 33-42. 10.1016/S8756-3282(99)00245-8.PubMed
  31. van der Eerden BC, Karperien M, Wit JM: Systemic and local regulation of the growth plate. Endocr Rev. 2003, 24 (6): 782-801. 10.1210/er.2002-0033.PubMed
  32. Tronche F, Kellendonk C, Reichardt HM, Schutz G: Genetic dissection of glucocorticoid receptor function in mice. Curr Opin Genet Dev. 1998, 8 (5): 532-538. 10.1016/S0959-437X(98)80007-5.PubMed
  33. Finotto S, Krieglstein K, Schober A, Deimling F, Lindner K, Bruhl B, Beier K, Metz J, Garcia-Arraras JE, Roig-Lopez JL, et al: Analysis of mice carrying targeted mutations of the glucocorticoid receptor gene argues against an essential role of glucocorticoid signalling for generating adrenal chromaffin cells. Development. 1999, 126 (13): 2935-2944.PubMed
  34. Benoyahu D, Akavia UD, Socher R, Shur I: Gene expression in skeletal tissues: application of laser capture microdissection. J Microsc. 2005, 220 (Pt 1): 1-8. 10.1111/j.1365-2818.2005.01511.x.PubMed
  35. Speirs HJ, Seckl JR, Brown RW: Ontogeny of glucocorticoid receptor and 11beta-hydroxysteroid dehydrogenase type-1 gene expression identifies potential critical periods of glucocorticoid susceptibility during development. J Endocrinol. 2004, 181 (1): 105-116. 10.1677/joe.0.1810105.PubMed
  36. Cole TJ, Blendy JA, Monaghan AP, Krieglstein K, Schmid W, Aguzzi A, Fantuzzi G, Hummler E, Unsicker K, Schutz G: Targeted disruption of the glucocorticoid receptor gene blocks adrenergic chromaffin cell development and severely retards lung maturation. Genes Dev. 1995, 9 (13): 1608-1621. 10.1101/gad.9.13.1608.PubMed
  37. Grigoriadis AE, Aubin JE, Heersche JN: Effects of dexamethasone and vitamin D3 on cartilage differentiation in a clonal chondrogenic cell population. Endocrinology. 1989, 125 (4): 2103-2110.PubMed
  38. Mushtaq T, Farquharson C, Seawright E, Ahmed SF: Glucocorticoid effects on chondrogenesis, differentiation and apoptosis in the murine ATDC5 chondrocyte cell line. J Endocrinol. 2002, 175 (3): 705-713. 10.1677/joe.0.1750705.PubMed
  39. Nadra R, Menuelle P, Chevallier S, Berdal A: Regulation by glucocorticoids of cell differentiation and insulin-like growth factor binding protein production in cultured fetal rat nasal chondrocytes. J Cell Biochem. 2003, 88 (5): 911-922. 10.1002/jcb.10396.PubMed
  40. Abu EO, Horner A, Kusec V, Triffitt JT, Compston JE: The localization of the functional glucocorticoid receptor alpha in human bone. J Clin Endocrinol Metab. 2000, 85 (2): 883-889. 10.1210/jc.85.2.883.PubMed
  41. Shalhoub V, Conlon D, Tassinari M, Quinn C, Partridge N, Stein GS, Lian JB: Glucocorticoids promote development of the osteoblast phenotype by selectively modulating expression of cell growth and differentiation associated genes. J Cell Biochem. 1992, 50 (4): 425-440. 10.1002/jcb.240500411.PubMed
  42. Boden SD, Hair G, Titus L, Racine M, McCuaig K, Wozney JM, Nanes MS: Glucocorticoid-induced differentiation of fetal rat calvarial osteoblasts is mediated by bone morphogenetic protein-6. Endocrinology. 1997, 138 (7): 2820-2828. 10.1210/en.138.7.2820.PubMed
  43. Boden SD, McCuaig K, Hair G, Racine M, Titus L, Wozney JM, Nanes MS: Differential effects and glucocorticoid potentiation of bone morphogenetic protein action during rat osteoblast differentiation in vitro. Endocrinology. 1996, 137 (8): 3401-3407. 10.1210/en.137.8.3401.PubMed
  44. Kato Y, Gospodarowicz D: Stimulation by glucocorticoid of the synthesis of cartilage-matrix proteoglycans produced by rabbit costal chondrocytes in vitro. J Biol Chem. 1985, 260 (4): 2364-2373.PubMed
  45. Sekiya I, Koopman P, Tsuji K, Mertin S, Harley V, Yamada Y, Shinomiya K, Nifuji A, Noda M: Dexamethasone enhances SOX9 expression in chondrocytes. J Endocrinol. 2001, 169 (3): 573-579. 10.1677/joe.0.1690573.PubMed
  46. Bianchi ML: Glucorticoids and bone: some general remarks and some special observations in pediatric patients. Calcif Tissue Int. 2002, 70 (5): 384-390. 10.1007/s00223-001-0043-0.PubMed
  47. De Luca F: Impaired growth plate chondrogenesis in children with chronic illnesses. Pediatr Res. 2006, 59 (5): 625-629. 10.1203/01.pdr.0000214966.60416.1b.PubMed
  48. Agoston H, Baybayan L, Beier F: Dexamethasone stimulates expression of C-type Natriuretic Peptide in chondrocytes. BMC Musculoskelet Disord. 2006, 7: 87-10.1186/1471-2474-7-87.PubMed CentralPubMed
  49. Koedam JA, Smink JJ, van Buul-Offers SC: Glucocorticoids inhibit vascular endothelial growth factor expression in growth plate chondrocytes. Mol Cell Endocrinol. 2002, 197 (1–2): 35-44. 10.1016/S0303-7207(02)00276-9.PubMed
  50. James CG, Appleton CT, Ulici V, Underhill TM, Beier F: Microarray analyses of gene expression during chondrocyte differentiation identifies novel regulators of hypertrophy. Mol Biol Cell. 2005, 16 (11): 5316-5333. 10.1091/mbc.E05-01-0084.PubMed CentralPubMed
  51. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, et al: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273. 10.1038/ng1180.PubMed
  52. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralPubMed
  53. Derfoul A, Perkins GL, Hall DJ, Tuan RS: Glucocorticoids promote chondrogenic differentiation of adult human mesenchymal stem cells by enhancing expression of cartilage extracellular matrix genes. Stem Cells. 2006, 24 (6): 1487-1495. 10.1634/stemcells.2005-0415.PubMed
  54. Robson H, Anderson E, Eden OB, Isaksson O, Shalet S: Chemotherapeutic agents used in the treatment of childhood malignancies have direct effects on growth plate chondrocyte proliferation. J Endocrinol. 1998, 157 (2): 225-235. 10.1677/joe.0.1570225.PubMed
  55. Chrysis D, Zaman F, Chagin AS, M T, Savendahl L: Dexamethasone induces apoptosis in proliferative chondrocytes through activation of caspases and suppression of the Akt-(PI3K) signaling pathway. Endocrinology. 2004
  56. Klaus G, Jux C, Fernandez P, Rodriguez J, Himmele R, Mehls O: Suppression of growth plate chondrocyte proliferation by corticosteroids. Pediatr Nephrol. 2000, 14 (7): 612-615. 10.1007/s004670000344.PubMed
  57. Alheim K, Corness J, Samuelsson MK, Bladh LG, Murata T, Nilsson T, Okret S: Identification of a functional glucocorticoid response element in the promoter of the cyclin-dependent kinase inhibitor p57Kip2. J Mol Endocrinol. 2003, 30 (3): 359-368. 10.1677/jme.0.0300359.PubMed
  58. Presul E, Schmidt S, Kofler R, Helmberg A: Identification, tissue expression, and glucocorticoid responsiveness of alternative first exons of the human glucocorticoid receptor. J Mol Endocrinol. 2007, 38 (1–2): 79-90. 10.1677/jme.1.02183.PubMed
  59. Milo M, Fazeli A, Niranjan M, Lawrence ND: A probabilistic model for the extraction of expression levels from oligonucleotide arrays. Biochem Soc Trans. 2003, 31 (Pt 6): 1510-1512.PubMed
  60. Dong Y, Poellinger L, Okret S, Hoog JO, von Bahr-Lindstrom H, Jornvall H, Gustafsson JA: Regulation of gene expression of class I alcohol dehydrogenase by glucocorticoids. Proc Natl Acad Sci USA. 1988, 85 (3): 767-771. 10.1073/pnas.85.3.767.PubMed CentralPubMed
  61. Bookout AL, Jeong Y, Downes M, Yu RT, Evans RM, Mangelsdorf DJ: Anatomical profiling of nuclear receptor expression reveals a hierarchical transcriptional network. Cell. 2006, 126 (4): 789-799. 10.1016/j.cell.2006.06.049.PubMed
  62. Ye L, Mishina Y, Chen D, Huang H, Dallas SL, Dallas MR, Sivakumar P, Kunieda T, Tsutsui TW, Boskey A, et al: Dmp1-deficient mice display severe defects in cartilage formation responsible for a chondrodysplasia-like phenotype. J Biol Chem. 2005, 280 (7): 6197-6203. 10.1074/jbc.M412911200.PubMed CentralPubMed
  63. Stein GS, van Wijnen AJ, Stein JL, Lian JB: Nuclear structure–skeletal gene expression interrelationships. Front Biosci. 1998, 3: d849-864.PubMed
  64. Ogata Y, Yamauchi M, Kim RH, Li JJ, Freedman LP, Sodek J: Glucocorticoid regulation of bone sialoprotein (BSP) gene expression. Identification of a glucocorticoid response element in the bone sialoprotein gene promoter. Eur J Biochem. 1995, 230 (1): 183-192. 10.1111/j.1432-1033.1995.0183i.x.PubMed
  65. Sodek J, Kim RH, Ogata Y, Li J, Yamauchi M, Zhang Q, Freedman LP: Regulation of bone sialoprotein gene transcription by steroid hormones. Connect Tissue Res. 1995, 32 (1–4): 209-217.PubMed
  66. Fisher LW, Fedarko NS: Six genes expressed in bones and teeth encode the current members of the SIBLING family of proteins. Connect Tissue Res. 2003, 44 (Suppl 1): 33-40. 10.1080/713713644.PubMed
  67. Ramstad VE, Franzen A, Heinegard D, Wendel M, Reinholt FP: Ultrastructural distribution of osteoadherin in rat bone shows a pattern similar to that of bone sialoprotein. Calcif Tissue Int. 2003, 72 (1): 57-64. 10.1007/s00223-002-2047-9.PubMed
  68. Olivotto E, Vitellozzi R, Fernandez P, Falcieri E, Battistelli M, Burattini S, Facchini A, Flamigni F, Santi S, Facchini A, et al: Chondrocyte hypertrophy and apoptosis induced by GROalpha require three-dimensional interaction with the extracellular matrix and a co-receptor role of chondroitin sulfate and are associated with the mitochondrial splicing variant of cathepsin B. J Cell Physiol. 2007, 210 (2): 417-427. 10.1002/jcp.20864.PubMed
  69. Merz D, Liu R, Johnson K, Terkeltaub R: IL-8/CXCL8 and growth-related oncogene alpha/CXCL1 induce chondrocyte hypertrophic differentiation. J Immunol. 2003, 171 (8): 4406-4415.PubMed
  70. MacRae VE, Farquharson C, Ahmed SF: The restricted potential for recovery of growth plate chondrogenesis and longitudinal bone growth following exposure to pro-inflammatory cytokines. J Endocrinol. 2006, 189 (2): 319-328. 10.1677/joe.1.06609.PubMed
  71. Grimaud E, Blanchard F, Charrier C, Gouin F, Redini F, Heymann D: Leukaemia inhibitory factor (lif) is expressed in hypertrophic chondrocytes and vascular sprouts during osteogenesis. Cytokine. 2002, 20 (5): 224-230. 10.1006/cyto.2002.2002.PubMed
  72. De Benedetti F, Rucci N, Del Fattore A, Peruzzi B, Paro R, Longo M, Vivarelli M, Muratori F, Berni S, Ballanti P, et al: Impaired skeletal development in interleukin-6-transgenic mice: a model for the impact of chronic inflammation on the growing skeletal system. Arthritis Rheum. 2006, 54 (11): 3551-3563. 10.1002/art.22175.PubMed
  73. Carda C, Silvestrini G, Gomez de Ferraris ME, Peydro A, Bonucci E: Osteoprotegerin (OPG) and RANKL expression and distribution in developing human craniomandibular joint. Tissue Cell. 2005, 37 (3): 247-255. 10.1016/j.tice.2005.03.002.PubMed
  74. Sakakura Y, Tsuruga E, Irie K, Hosokawa Y, Nakamura H, Yajima T: Immunolocalization of receptor activator of nuclear factor-kappaB ligand (RANKL) and osteoprotegerin (OPG) in Meckel's cartilage compared with developing endochondral bones in mice. J Anat. 2005, 207 (4): 325-337. 10.1111/j.1469-7580.2005.00466.x.PubMed CentralPubMed
  75. Silvestrini G, Ballanti P, Patacchioli F, Leopizzi M, Gualtieri N, Monnazzi P, Tremante E, Sardella D, Bonucci E: Detection of osteoprotegerin (OPG) and its ligand (RANKL) mRNA and protein in femur and tibia of the rat. J Mol Histol. 2005, 36 (1–2): 59-67. 10.1007/s10735-004-3839-1.PubMed
  76. Zheng ZH, Zhu P, Wang YH, Fan CM, Ding J, Shang P: [In vitro induction of directional differentiation of bone marrow mesenchymal stem cells towards chondrocytes]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2005, 21 (1): 79-82.PubMed
  77. Engelbrecht Y, de Wet H, Horsch K, Langeveldt CR, Hough FS, Hulley PA: Glucocorticoids induce rapid up-regulation of mitogen-activated protein kinase phosphatase-1 and dephosphorylation of extracellular signal-regulated kinase and impair proliferation in human and mouse osteoblast cell lines. Endocrinology. 2003, 144 (2): 412-422. 10.1210/en.2002-220769.PubMed CentralPubMed
  78. O'Brien CA, Jia D, Plotkin LI, Bellido T, Powers CC, Stewart SA, Manolagas SC, Weinstein RS: Glucocorticoids act directly on osteoblasts and osteocytes to induce their apoptosis and reduce bone formation and strength. Endocrinology. 2004, 145 (4): 1835-1841. 10.1210/en.2003-0990.PubMed
  79. Stanton LA, Sabari S, Sampaio AV, Underhill TM, Beier F: p38 MAP kinase signalling is required for hypertrophic chondrocyte differentiation. Biochem J. 2004, 378 (Pt 1): 53-62. 10.1042/BJ20030874.PubMed CentralPubMed
  80. Grigoriadis A, Heersche J, Aubin J: Differentiation of muscle, fat, cartilage, and bone from progenitor cells present in a bone-derived clonal cell population: effect of dexamethasone. J Cell Biol. 1988, 106 (6): 2139-2151. 10.1083/jcb.106.6.2139.PubMed
  81. Wang J, Zhou J, Bondy CA: Igf1 promotes longitudinal bone growth by insulin-like actions augmenting chondrocyte hypertrophy. Faseb J. 1999, 13 (14): 1985-1990.PubMed
  82. Zelzer E, Mamluk R, Ferrara N, Johnson RS, Schipani E, Olsen BR: VEGFA is necessary for chondrocyte survival during bone development. Development. 2004, 131 (9): 2161-2171. 10.1242/dev.01053.PubMed
  83. Abraham SM, Lawrence T, Kleiman A, Warden P, Medghalchi M, Tuckermann J, Saklatvala J, Clark AR: Antiinflammatory effects of dexamethasone are partly dependent on induction of dual specificity phosphatase 1. J Exp Med. 2006, 203 (8): 1883-1889. 10.1084/jem.20060336.PubMed CentralPubMed
  84. Sakai A, Han J, Cato AC, Akira S, Li JD: Glucocorticoids synergize with IL-1beta to induce TLR2 expression via MAP Kinase Phosphatase-1-dependent dual Inhibition of MAPK JNK and p38 in epithelial cells. BMC Mol Biol. 2004, 5: 2-10.1186/1471-2199-5-2.PubMed CentralPubMed
  85. Stanton LA, Underhill TM, Beier F: MAP kinases in chondrocyte differentiation. Dev Biol. 2003, 263 (2): 165-175. 10.1016/S0012-1606(03)00321-X.PubMed
  86. Hulley PA, Gordon F, Hough FS: Inhibition of mitogen-activated protein kinase activity and proliferation of an early osteoblast cell line (MBA 15.4) by dexamethasone: role of protein phosphatases. Endocrinology. 1998, 139 (5): 2423-2431. 10.1210/en.139.5.2423.PubMed
  87. Xu H, Yang Q, Shen M, Huang X, Dembski M, Gimeno R, Tartaglia LA, Kapeller R, Wu Z: Dual specificity MAPK phosphatase 3 activates PEPCK gene transcription and increases gluconeogenesis in rat hepatoma cells. J Biol Chem. 2005, 280 (43): 36013-36018. 10.1074/jbc.M508027200.PubMed
  88. Rozsa FW, Reed DM, Scott KM, Pawar H, Moroi SE, Kijek TG, Krafchak CM, Othman MI, Vollrath D, Elner VM, et al: Gene expression profile of human trabecular meshwork cells in response to long-term dexamethasone exposure. Mol Vis. 2006, 12: 125-141.PubMed
  89. Grenier J, Tomkiewicz C, Trousson A, Rajkowski KM, Schumacher M, Massaad C: Identification by microarray analysis of aspartate aminotransferase and glutamine synthetase as glucocorticoid target genes in a mouse Schwann cell line. J Steroid Biochem Mol Biol. 2005, 97 (4): 342-352. 10.1016/j.jsbmb.2005.06.034.PubMed
  90. Kolbus A, Blazquez-Domingo M, Carotta S, Bakker W, Luedemann S, von Lindern M, Steinlein P, Beug H: Cooperative signaling between cytokine receptors and the glucocorticoid receptor in the expansion of erythroid progenitors: molecular analysis by expression profiling. Blood. 2003, 102 (9): 3136-3146. 10.1182/blood-2003-03-0923.PubMed
  91. Wang JC, Derynck MK, Nonaka DF, Khodabakhsh DB, Haqq C, Yamamoto KR: Chromatin immunoprecipitation (ChIP) scanning identifies primary glucocorticoid receptor target genes. Proc Natl Acad Sci USA. 2004, 101 (44): 15603-15608. 10.1073/pnas.0407008101.PubMed CentralPubMed
  92. Wu W, Chaudhuri S, Brickley DR, Pang D, Karrison T, Conzen SD: Microarray analysis reveals glucocorticoid-regulated survival genes that are associated with inhibition of apoptosis in breast epithelial cells. Cancer Res. 2004, 64 (5): 1757-1764. 10.1158/0008-5472.CAN-03-2546.PubMed
  93. Yoshida NL, Miyashita T, U M, Yamada M, Reed JC, Sugita Y, Oshida T: Analysis of gene expression patterns during glucocorticoid-induced apoptosis using oligonucleotide arrays. Biochem Biophys Res Commun. 2002, 293 (4): 1254-1261. 10.1016/S0006-291X(02)00361-3.PubMed
  94. Phuc Le P, Friedman JR, Schug J, Brestelli JE, Parker JB, Bochkis IM, Kaestner KH: Glucocorticoid receptor-dependent gene regulatory networks. PLoS Genet. 2005, 1 (2): e16-10.1371/journal.pgen.0010016.PubMed CentralPubMed
  95. Rogatsky I, Hittelman AB, Pearce D, Garabedian MJ: Distinct glucocorticoid receptor transcriptional regulatory surfaces mediate the cytotoxic and cytostatic effects of glucocorticoids. Mol Cell Biol. 1999, 19 (7): 5036-5049.PubMed CentralPubMed
  96. Agbemafle BM, Oesterreicher TJ, Shaw CA, Henning SJ: Immediate early genes of glucocorticoid action on the developing intestine. Am J Physiol Gastrointest Liver Physiol. 2005, 288 (5): G897-906. 10.1152/ajpgi.00454.2004.PubMed
  97. Weber PS, Madsen-Bouterse SA, Rosa GJ, Sipkovsky S, Ren X, Almeida PE, Kruska R, Halgren RG, Barrick JL, Burton JL: Analysis of the Bovine Neutrophil Transcriptome During Glucocorticoid Treatment. Physiol Genomics. 2006
  98. Gupta V, Galante A, Soteropoulos P, Guo S, Wagner BJ: Global gene profiling reveals novel glucocorticoid induced changes in gene expression of human lens epithelial cells. Mol Vis. 2005, 11: 1018-1040.PubMed
  99. Wang Y, Middleton F, Horton JA, Reichel L, Farnum CE, Damron TA: Microarray analysis of proliferative and hypertrophic growth plate zones identifies differentiation markers and signal pathways. Bone. 2004, 35 (6): 1273-1293. 10.1016/j.bone.2004.09.009.PubMed
  100. Schoneveld OJ, Gaemers IC, Lamers WH: Mechanisms of glucocorticoid signalling. Biochim Biophys Acta. 2004, 1680 (2): 114-128.PubMed
  101. Saatcioglu F, Claret FX, Karin M: Negative transcriptional regulation by nuclear receptors. Semin Cancer Biol. 1994, 5 (5): 347-359.PubMed
  102. Barnes PJ: Corticosteroid effects on cell signalling. Eur Respir J. 2006, 27 (2): 413-426. 10.1183/09031936.06.00125404.PubMed
  103. Almawi WY, Beyhum HN, Rahme AA, Rieder MJ: Regulation of cytokine and cytokine receptor expression by glucocorticoids. J Leukoc Biol. 1996, 60 (5): 563-572.PubMed
  104. U M, Shen L, Oshida T, Miyauchi J, Yamada M, Miyashita T: Identification of novel direct transcriptional targets of glucocorticoid receptor. Leukemia. 2004, 18 (11): 1850-1856. 10.1038/sj.leu.2403516.PubMed
  105. Nuber UA, Kriaucionis S, Roloff TC, Guy J, Selfridge J, Steinhoff C, Schulz R, Lipkowitz B, Ropers HH, Holmes MC, et al: Up-regulation of glucocorticoid-regulated genes in a mouse model of Rett syndrome. Hum Mol Genet. 2005, 14 (15): 2247-2256. 10.1093/hmg/ddi229.PubMed
  106. Croxtall JD, Choudhury Q, Flower RJ: Glucocorticoids act within minutes to inhibit recruitment of signalling factors to activated EGF receptors through a receptor-dependent, transcription-independent mechanism. Br J Pharmacol. 2000, 130 (2): 289-298. 10.1038/sj.bjp.0703272.PubMed CentralPubMed
  107. Tasker JG: Rapid glucocorticoid actions in the hypothalamus as a mechanism of homeostatic integration. Obesity (Silver Spring). 2006, 14 (Suppl 5): 259S-265S.
  108. Buttgereit F, Scheffold A: Rapid glucocorticoid effects on immune cells. Steroids. 2002, 67 (6): 529-534. 10.1016/S0039-128X(01)00171-4.PubMed
  109. Bartholome B, Spies CM, Gaber T, Schuchmann S, Berki T, Kunkel D, Bienert M, Radbruch A, Burmester GR, Lauster R, et al: Membrane glucocorticoid receptors (mGCR) are expressed in normal human peripheral blood mononuclear cells and up-regulated after in vitro stimulation and in patients with rheumatoid arthritis. Faseb J. 2004, 18 (1): 70-80. 10.1096/fj.03-0328com.PubMed
  110. Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193. 10.1093/bioinformatics/19.2.185.PubMed
  111. Beato M, Chalepakis G, Schauer M, Slater EP: DNA regulatory elements for steroid hormones. J Steroid Biochem. 1989, 32 (5): 737-747. 10.1016/0022-4731(89)90521-9.PubMed

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