Open Access

Gene expression profiles in the rat streptococcal cell wall-induced arthritis model identified using microarray analysis

  • Inmaculada Rioja1Email author,
  • Chris L Clayton2,
  • Simon J Graham2,
  • Paul F Life1 and
  • Marion C Dickson1
Arthritis Res Ther20047:R101

DOI: 10.1186/ar1458

Received: 3 July 2004

Accepted: 9 October 2004

Published: 19 November 2004

Abstract

Experimental arthritis models are considered valuable tools for delineating mechanisms of inflammation and autoimmune phenomena. Use of microarray-based methods represents a new and challenging approach that allows molecular dissection of complex autoimmune diseases such as arthritis. In order to characterize the temporal gene expression profile in joints from the reactivation model of streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rats, total RNA was extracted from ankle joints from naïve, SCW injected, or phosphate buffered saline injected animals (time course study) and gene expression was analyzed using Affymetrix oligonucleotide microarray technology (RAE230A). After normalization and statistical analysis of data, 631 differentially expressed genes were sorted into clusters based on their levels and kinetics of expression using Spotfire® profile search and K-mean cluster analysis. Microarray-based data for a subset of genes were validated using real-time PCR TaqMan® analysis. Analysis of the microarray data identified 631 genes (441 upregulated and 190 downregulated) that were differentially expressed (Delta > 1.8, P < 0.01), showing specific levels and patterns of gene expression. The genes exhibiting the highest fold increase in expression on days -13.8, -13, or 3 were involved in chemotaxis, inflammatory response, cell adhesion and extracellular matrix remodelling. Transcriptome analysis identified 10 upregulated genes (Delta > 5), which have not previously been associated with arthritis pathology and are located in genomic regions associated with autoimmune disease. The majority of the downregulated genes were associated with metabolism, transport and regulation of muscle development. In conclusion, the present study describes the temporal expression of multiple disease-associated genes with potential pathophysiological roles in the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat. These findings improve our understanding of the molecular events that underlie the pathology in this animal model, which is potentially a valuable comparator to human rheumatoid arthritis (RA).

Keywords

arthritis differential gene expression microarray rat SCW induced arthritis

Introduction

Rheumatoid arthritis (RA) is an autoimmune chronic inflammatory disease of unknown aetiology that is characterized by infiltration of monocytes, T cells and polymorphonuclear cells into the synovial joints. The pathogenesis of this disease is still poorly understood, and fundamental questions regarding the precise molecular nature and biological significance of the inflammatory changes remain to be answered [1, 2]. A powerful way to gain insight into the molecular complexity and pathogenesis of arthritis has arisen from oligonucleotide-based microarray technology [3], because this platform provides an opportunity to analyze simultaneously the expression of a large number of genes in disease tissues.

The earliest preclinical stages of human RA are not easily accessible to investigation, but a diverse range of experimental arthritis models are considered valuable tools for delineating mechanisms of inflammation and autoimmune phenomena. An animal model that shares some of the hallmarks of human RA is the reactivation model of streptococcal cell wall (SCW)-induced arthritis in rats. In this model, a synovitis with maximal swelling at 24 hours is induced by local injection of SCW antigen directly into an ankle joint. The initial response is reactivated by systemic (intravenous) challenge with SCW, which produces a more prolonged and severe inflammation confined to the joint previously injected with SCW. In contrast to some other animal models, in which the arthritic response develops gradually and unpredictably, in this model the flare response develops synchronously, allowing precise analysis of pathophysiological mechanisms [4, 5].

Some pathological changes observed in SCW-induced arthritis that are of relevance to human RA include infiltration of polymorphonuclear cells, CD4+ T cells and macrophages, hyperplasia of the synovial lining layer, pannus formation and moderate erosion of cartilage and bone [4]. Previous reports have shown the dependency of this model on tumour necrosis factor (TNF)-α, IL-1α, IL-4, P-selectin, vascular cell adhesion molecule-1, macrophage inflammatory protein (MIP)-2, MIP-1α and monocyte chemoattractant protein (MCP)-1 [6, 7]. Although the involvement of nitric oxide synthase (NOS) [8] and cyclo-oxygenase [9] in the development of SCW-induced arthritis has also been noted, a global analysis of coordinated gene expression during the time course of disease in this experimental arthritis model has not been investigated.

Arthritis involves many cell types from tissues adjacent to the synovium. Therefore, as shown in previous studies [10, 11], analysis of gene expression profiles by processing whole homogenized joints can provide useful information about dysregulated genes, not only in synoviocytes but also in other, neighbouring cells (myocytes, osteocytes and chondrocytes) that may also contribute to disease pathology.

In the present study, whole homogenized rat ankle joints from naïve, SCW-injected and phosphate-buffered saline (PBS; vehicle)-injected animals, included in a time-course study, were analyzed for differential gene expression using the RAE230A Affymetrix GeneChip® microarray (Affymetrix Inc., Santa Clara, CA, USA). In order to identify different patterns of gene expression during the course of SCW-induced arthritis, a selected set of genes whose expression was statistically significantly different between arthritic and control animals on days -13.8, -13 and 3 were analyzed using agglomerative hierarchical clustering, Spotfire® (Spotfire Inc., Cambridge, MA, USA) profile search and K-means cluster analysis. Validation of microarray data for a subset of genes was performed by real-time RT-PCR TaqMan® (Applied Biosystems, Foster City, CA, USA) analysis, which provides a highly accurate method for quantifying mRNA expression levels for any particular differentially expressed gene. To further investigate the possible association of 20 selected upregulated genes with arthritis pathogenesis, their chromosomal locations and the chromosomal locations of their corresponding human orthologue were compared with the locations of previously reported quantitative trait loci (QTLs) for inflammation, arthritis and other autoimmune diseases. Our findings show, for the first time, the gene expression profiles and kinetics of expression of hundreds of genes that are differentially expressed in arthritic joints from the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat, thereby improving our understanding of the biological pathways that contribute to the pathogenesis of arthritis in this animal model and providing a valuable comparator to human RA.

Methods

Reagents

The peptidoglycan–polysaccharide (PG-PS) 100p fraction of SCW was purchased from Lee Laboratories (Grayson, GA, USA). RAE230A Affymetrix GeneChip® were purchased from Affymetrix Inc. All reagents required for RT-PCR were from PE Applied Biosystems (Warrington, UK). Forward and reverse primers were purchased from Invitrogen™ Life Technologies (Invitrogen Ltd, Paisley, UK). TaqMan® probes were synthesized by PE Applied Biosystems. RiboGreen, used to quantify RNA, was obtained from Molecular Probes Inc. (Leiden, The Netherlands) and RNA 6000 Nano LabChip Kit®, used to assess RNA integrity, was from Agilent Technologies Inc. (Stockport, UK).

Animals

All in vivo studies were undertaken in certified, dedicated in vivo experimental laboratories at the GlaxoSmithKline Medicines Research Centre (Stevenage, UK). The studies complied with national legislation and with local policies on the care and use of animals, and with related codes of practice. Male Lewis (LEW/N) rats obtained from Harlan UK Ltd (Oxon, UK), at age 6–7 weeks, were housed under standard conditions and received food and water ad libitum. Animals were habituated to the holding room for a minimum of 1 week before the experimental procedures.

Induction and assessment of SCW-induced arthritis

SCW arthritis was induced in 6- to 8-week-old male Lewis (LEW/N) rats (weight 125–150 g) following a method similar to that previously described by Esser and coworkers [4]. A SCW preparation (PG-PS, 100p fraction) was suspended in PBS and 10 μl of the suspension containing 5 μg PG-PS from Streptococcus pyogenes was injected into the right ankle joint (day -14). Animals from control groups were injected similarly with 10 μl PBS. A group of noninjected rats was also included in our study to assess gene expression profiles in joints from naïve animals. Reactivation of the arthritic inflammation was induced 14 days after intra-articular injection (designated day 0) by intravenous injection of 200 μg PG-PS. This resulted in monoarticular arthritis involving the joint originally injected with PG-PS [7]. Ankle swelling at different time points was measured using a caliper. The inflammatory response is expressed as the change in ankle diameter relative to the starting diameter. Five animals injected with PG-PS or PBS were killed at different time points (4 hours after intra-articular injection [day -13.8], day -13, day -10, day 0, 6 hours after intravenous challenge [day 0.4], day 1, day 3 and day 7) and ankle joints were dissected, snap frozen in liquid nitrogen and stored at -80°C for subsequent analysis.

Total RNA isolation from rat joints

Frozen ankle joints were pulverized in liquid nitrogen using a mortar and pestle, and total RNA was isolated from individual homogenized joints (four or five animals/group) using RNeasy® Mini-kits (Qiagen Ltd, Crawley, UK), following the manufacturer's instructions. In our experimental design, a nonpooling strategy for total RNA samples was used (a total of 75 samples from different animals were analyzed). In order to ensure that no contamination with genomic DNA occurred, samples were treated for 15 min with 10 units of RNase-free DNase (Qiagen Ltd) at room temperature. RiboGreen® RNA Quantitation Kit (Molecular Probes Inc.) with optical densities at 260 nm and 280 nm was used to determine the total RNA concentration of the samples. The quality of the RNA was assessed based on demonstration of distinct intact 28S and 18S ribosomal RNA bands using RNA 6000 Nano LabChip Kit® (Agilent 2100 Bioanalyser; Agilent Technologies UK Ltd, Stockport, UK). Five of the 75 total RNA samples exhibited evidence of RNA degradation and were excluded from subsequent analyses.

Oligonucleotide microarray analysis

The rat RAE230A GeneChip® oligonucleotide microarray (Affymetrix Inc.), containing about 16,000 probe sets, representing 4699 well annotated full-length genes, 10,467 expressed sequence tags (ESTs) and 700 non-ESTs (excluding full lengths), was used to analyze gene expression profiles in joints from SCW-injected or PBS-injected animals during the course of disease. Isolated total RNA (10 μg/chip) was used to generate biotin-labelled cRNA. Aliquots of each sample (n = 70) were then hybridized to RAE230A Affymetrix GeneChip® arrays at 45°C for 16 hours, followed by washing and staining, in accordance with the standard protocol described in the Affymetrix GeneChip® Expression Analysis Technical Manual [12]. The GeneChip®s were scanned using the Affymetrix 3000 Scanner™ and the expression levels were calculated for all 16,000 probe sets (about 12,000 genes) with Affymetrix® MicroArraySuite software (MAS 5.0).

Statistical analysis of microarray data

The Affymetrix GeneChip® data were processed, normalized and statistically analyzed (analysis of variance [ANOVA]) using Rosetta Resolver® v3.2 software (Rosetta Biosoftware, Kirkland, WA, USA). Genes with P < 0.01 (ANOVA) were considered to be differentially expressed. Fold changes in gene expression were calculated by dividing the mean intensity signal from all the individual SCW-injected rats included in each group by the mean intensity signal from the corresponding PBS control group. The level of statistical significance was determined by ANOVA. Subsequent data analysis involved two-dimensional data visualization, principal component analysis (PCA) using SIMCA-P v10.2 Statistical Analysis Software (Umetrics, Windsor, UK) [13] and agglomerative hierarchical clustering analysis [14]. For identification of different temporal patterns and levels of gene expression, Spotfire® profile search analysis and K-means clustering analysis [15] were performed using the Spotfire® DecisionSite for Functional Genomics programme. In this analysis the mean signal intensity of gene expression in each group included in the study (four to five samples/group) was used. Identification of the ontology, accession number and chromosomal location of the genes of interest was performed combining information from GlaxoSmithKline Bioinformatics Databases and other existing public databases http://www.ncbi.nlm.nih.gov. The mapping of the differentially expressed genes to QTLs for arthritis was investigated using Rat and Human Genome browsers from Ensembl http://www.ensembl.org/, Rat Genome Database http://rgd.mcw.edu and the ARB Rat Genetic Database http://www.niams.nih.gov/rtbc/ratgbase/.

Quantitative real-time PCR (TaqMan®)

Expression levels of selected genes found to be upregulated by gene array analysis were validated by real-time RT-PCR TaqMan® analysis using the ABI Prism 7900 Sequence Detector System® (PE Applied Biosystems, Foster City, CA, USA), as previously described [16]. For cDNA synthesis 600 ng total RNA (from the same samples analysed by RAE230A GeneChip® microarray) were reverse transcribed using TaqMan® RT reagents (PE Applied Biosystems) in a MJ Research PTC-200 PCR Peltier Thermal Cycler (MJ Research, Rayne Brauntree, Essex, UK).

TaqMan® probes and primers for the genes of interest were designed using primer design software Primer Express™ (PE Applied Biosystems) and optimized for use. The forward primers, reverse primers and probes used are summarized in Table 1. The final optimized concentrations of forward primer, reverse primer and probe for all of the target genes were 900 nmol/l, 900 nmol/l and 100 nmol/l, respectively, except for CD14, for which the concentrations were 300 nmol/l, 300 nmol/l and 100 nmol/l. Standard curves for each individual target amplicon were constructed using sheared rat genomic DNA (BD Biosciences, Cowley, Oxford, UK). All PCR assays were performed in duplicate, and results are represented by the mean values of copy no./50 ng cDNA. Ubiquitin [17] was used as a housekeeping gene against which all samples were normalized.
Table 1

TaqMan® probes and primers for the genes of interest

Gene of interest

Forward primer

Reverse primer

Probe

IL-1β

5'-CACCTCTCAAGCAGAGCACAG

5'-GGGTTCCATGGTGAAGTCAAC

5'-6-FAM-TGTCCCGACCATTGCTGTTTCCTAGG-TAMRA

IL-6

5'-CAAGACCATCCAACTCATCTTG

5'-CACAGTGAGGAATGTCCACAAAC

5'-6-FAM-TCGGCAAACCTAGTGTGCTATGCCTAAGCA-TAMRA

TNF-α

5'-CCAGGTTCTCTTCAAGGGACAA

5'-CTCCTGGTATGAAATGGCAAATC

5'-6-FAM-CCCGACTATGTGCTCCTCACCCACA-TAMRA

GRO1

5'-TGTGTTGAAGCTTCCCTTGGA

5'-TGAGACGAGAAGGAGCATTGGT

5'-6-FAM-TGTCTAGTTTGTAGGGCACAATGCCCT-TAMRA

CD14

5'-GGACGAGGAAAGTGTCCGCT

5'-AGGTACTCCAGGCTGCGACC

5'-6-FAM-TTCTATGCGCGGGGGCGGAA-TAMRA

CD3

5'-GGATGGAGTTCGCCAGTCAA

5'-GGTTTCCTTGGAGACGGCTG

5'-6-FAM-ACAGGTCTACCAGCCCCTCAAGGACCG-TAMRA

Ubiquitin

5'-CGAGAACGTGAAGGCCAAGA

5'-GGAGGACAAGGTGCAGGGTT

5'-6-FAM-CCCCTGACCAGCAGAGGCTCATCTTTG-TAMRA

IL, interleukin; TNF, tumour necrosis factor.

Data presentation

The data included in Table 2 show the mean fold change (Delta) increase or decrease in gene expression in joints from SWC-injected rats compared with the expression in the corresponding PBS control group, along with the P value. As selection criteria to present the most relevant genes, a cutoff of 1.8-fold increased/decreased expression and P < 0.01 were arbitrarily chosen. Gene expression profile plots (Fig. 6) represent data from Affymetrix Rat Genome RAE230A GeneChip® and real-time RT-PCR TaqMan® as the mean of signal intensity or the mean of normalized copy no./50 ng cDNA for all the samples from the same group (four to five), respectively.
Table 2

Genes upregulated in ankle joints from SCW-induced arthritis in Lewis (LEW/N) rats

Accession no.

Gene name

Day -13.8

Day -13

Day 3

C

I

  

Delta

P

Delta

P

Delta

P

  

Angiogenesis

NM_030985

AGTR1

_

_

_

_

4.0

2.3E-05

7

L

AI639162

ANGPT1

_

_

_

_

1.8

8.5E-08

7

L

NM_031012

ANPEP

_

_

_

_

2.2

2.9E-18

7

M

AI101782

COL18A1

_

_

_

_

2.9

6.3E-26

7

L

AI170324

FIGF

_

_

1.6

9.4E-04

2.4

4.6E-11

6

L

NM_012620

SERPINE1

6.6

2.3E-06

_

_

27.6

0.0E+00

4

L

Cell adhesion

NM_012830

CD2

_

_

2.1

2.9E-04

_

_

3

L

NM_054001

CD36L2

_

_

_

_

2.6

5.4E-05

7

L

AF065147

CD44

2.1

2.1E-08

1.6

1.1E-03

_

_

2

M

BE108345

COL12A1

_

_

_

_

2.5

4.6E-43

7

M

AI172281

COL5A2

_

_

_

_

2.0

9.3E-06

7

H

NM_021760

COL5A3

_

_

_

_

3.1

6.9E-27

7

M

AF084544

CSPG2

2.3

2.4E-03

2.1

5.8E-04

8.9

7.2E-37

5

L

NM_053719

EMB

_

_

_

_

3.5

1.7E-19

7

L

NM_053634

FCNB

8.7

3.0E-28

8.0

1.1E-17

29.0

0.0E+00

5

M

AI236745

GALNT1

_

_

_

_

2.9

0.0E+00

7

L

NM_133298

GPNMB

_

_

2.2

3.6E-12

2.5

0.0E+00

6

H

NM_012967

ICAM1

8.9

0.0E+00

4.4

7.2E-09

4.0

2.2E-11

5

L

AF268593

ITGAM

2.0

2.6E-03

3.8

1.7E-15

6.4

3.3E-18

5

L

BI296880

ITGB3

1.5

7.0E-03

_

_

2.1

2.3E-03

4

L

AF003598

ITGB7

2.0

8.9E-14

1.6

1.2E-03

1.9

1.1E-06

5

L

U56936

KLRB1B

_

_

_

_

3.0

6.4E-03

7

L

NM_022393

MGL

_

_

2.1

7.3E-08

2.1

2.3E-11

6

L

U72660

NINJ1

_

_

_

_

1.8

6.6E-28

7

M

BE097805

PCDHGC3

_

_

1.8

3.0E-03

_

_

3

L

AJ299017

RET

_

_

_

_

2.8

1.6E-08

7

L

AF071495

SCARB1

_

_

_

_

1.8

6.9E-03

7

L

L25527

SELE

_

_

3.1

3.6E-04

_

_

3

L

D10831

SELL

1.6

9.3E-05

_

_

1.8

6.9E-06

4

L

BI296054

SELP

1.8

7.1E-08

1.9

2.6E-07

2.2

4.2E-13

5

L

AI176034

TNC

_

_

_

_

2.6

3.7E-26

7

M

AF159103

TNFIP6

2.2

6.2E-04

2.2

3.1E-05

5.0

1.3E-21

5

L

NM_031590

WISP2

_

_

_

_

2.6

0.0E+00

7

M

Chemotaxis

NM_053619

C5AR1

1.6

5.0E-06

2.4

9.6E-21

2.7

2.8E-32

5

M

NM_019205

CCL11

3.9

2.3E-03

3.7

2.3E-07

_

_

2

L

NM_057151

CCL17

2.2

1.1E-05

_

_

_

_

1

L

AF053312

CCL20

8.3

7.3E-19

10.2

2.8E-10

15.5

3.5E-32

5

L

U22414

CCL3

15.3

1.7E-19

3.2

3.8E-05

2.1

1.1E-08

5

L

U06434

CCL4

6.0

1.5E-17

_

_

_

_

1

L

NM_031116

CCL5

_

_

2.6

5.9E-11

2.0

1.1E-03

6

L

NM_020542

CCR1

5.2

1.4E-15

2.1

3.0E-05

2.1

3.2E-03

5

L

NM_021866

CCR2

5.1

2.7E-07

3.3

2.9E-05

6.9

2.7E-13

5

L

NM_053960

CCR5

6.2

4.3E-19

6.0

1.8E-09

6.0

1.4E-10

5

L

D87927

CINC2

3.5

7.0E-03

_

_

_

_

1

L

AF253065

CKLF1

3.3

6.3E-09

3.0

2.7E-07

8.2

8.6E-08

5

L

NM_022218

CMKLR1

_

_

2.5

3.4E-03

_

_

3

L

U22520

CXCL10

3.2

4.4E-09

2.5

9.0E-03

1.4

1.3E-03

5

L

NM_053647

CXCL2

38.7

1.6E-07

2.3

9.1E-03

2.6

1.0E-03

5

L

NM_022214

CXCL6

2.2

2.3E-04

_

_

7.5

3.2E-06

4

L

NM_017183

CXCR2

10.6

1.5E-07

3.6

1.3E-03

_

_

2

L

NM_053415

CXCR3_V1

_

_

_

_

1.9

9.5E-04

7

L

AA945737

CXCR4

1.6

1.7E-03

1.7

3.9E-04

3.4

2.7E-15

5

L

NM_030845.1

GRO

17.1

0.0E+00

23.0

2.4E-04

19.8

1.8E-12

5

L

NM_053321

PTAFR

_

_

2.5

2.0E-03

_

_

3

L

NM_031530

SCYA2

3.4

6.0E-26

3.2

1.8E-16

6.0

0.0E+00

5

M

Complement activation

D88250

C1S

_

_

1.6

4.4E-03

1.8

7.5E-22

6

M

_

C2

6.9

9.20E-42

3.5

1.28E-11

16.8

0.0E+00

5

L

NM_016994.1

C3

2.7

2.0E-10

3.0

5.4E-12

10.4

0.0E+00

5

L

AI169829

MASP1

_

_

_

_

2.4

8.5E-08

7

L

Immune response/inflammatory response

XM_215303

RT1.S3

_

_

2.0

0.0012

1.6

1.5E-03

6

L

AF307302

BTNL2

_

_

2.1

1.0E-15

3.2

0.0E+00

6

M

NM_021744

CD14

2.8

7.8E-18

2.0

4.4E-06

1.7

7.3E-05

5

M

NM_012705

CD4

_

_

_

_

1.8

1.3E-07

7

L

NM_013069

CD74

_

_

2.2

3.5E-18

2.7

1.1E-31

6

H

NM_031538

CD8a

_

_

9.5

2.7E-03

10.9

6.2E-07

6

L

BI282755

EDG3

_

_

_

_

2.1

5.9E-03

7

L

X73371

FCGR2

3.1

1.4E-20

3.8

2.4E-08

6.5

0.0E+00

5

L

NM_053843

FCGR3

2.2

3.3E-15

2.0

2.8E-12

2.6

0.0E+00

5

M

NM_133624

GBP2

3.4

6.2E-35

_

_

1.5

3.8E-06

4

L

AF176534

HFE

_

_

_

_

1.8

6.6E-03

7

L

XM_215347

HLA-DMA

_

_

2.0

1.22E-14

2.7

0.0E+00

6

L

_

HLA-DMB

_

_

2.0

5.42E-15

3.2

1.1E-13

6

M

NM_022605

HPSE

_

_

_

_

1.9

3.0E-09

7

L

NM_133533

IGB

_

_

_

_

2.9

8.4E-09

7

L

NM_053374

IGIFBP

_

_

2.2

7.2E-03

_

_

3

L

AJ245643

IL1a

2.7

1.2E-03

_

_

_

_

1

L

NM_031512

IL1b

22.0

1.1E-30

9.5

4.5E-15

5.7

1.6E-35

5

L

NM_053953

IL1R2

2.5

1.9E-15

_

_

_

_

1

L

NM_022194

IL1RN

7.4

2.8E-03

_

_

_

_

1

L

NM_012589

IL6

10.0

7.3E-17

20.7

7.9E-04

21.4

5.5E-17

5

L

NM_013110

IL7

_

_

_

_

2.8

2.4E-04

7

L

NM_012591

IRF1

2.9

4.7E-13

2.6

2.8E-08

3.3

2.0E-13

5

L

NM_130741

LCN2

2.4

1.7E-09

3.4

9.8E-12

13.2

0.0E+00

5

M

BF282471

LCP2

2.6

2.2E-04

3.3

2.4E-04

6.2

4.4E-06

5

L

NM_022634

LST1

4.9

6.2E-14

6.3

6.4E-14

16.7

2.0E-36

5

L

NM_031634

MEFV

2.7

1.9E-07

_

_

1.9

7.4E-05

4

L

X52711

MX1

_

_

2.8

4.9E-07

1.9

2.1E-15

6

L

NM_134350

MX2

_

_

2.9

5.9E-04

_

_

3

L

NM_053734

NCF1

_

_

2.0

9.4E-16

2.0

3.7E-06

6

L

AA858801

NFKB1

2.1

1.1E-12

_

_

_

_

1

M

AW672589

NFKBIA

2.5

5.2E-36

_

_

_

_

1

M

L12562

NOS2A

6.0

1.9E-05

_

_

_

_

1

L

Z18877

OAS1

1.6

8.4E-06

2.4

3.8E-06

1.8

3.7E-06

5

L

NM_053288

ORM1

_

_

2.0

7.0E-04

3.1

9.8E-19

6

L

NM_031713

PIRB

2.4

3.9E-06

2.5

2.6E-06

3.3

7.6E-10

5

L

AF349115

PPBP

_

_

_

_

3.2

8.5E-03

7

L

NM_080767

PSMB8

1.5

3.0E-03

2.3

2.0E-09

3.3

0.0E+00

5

L

AI599350

PSMB9

2.0

2.0E-07

2.1

7.4E-09

3.7

2.3E-25

5

L

AB048730

PTGES

8.2

8.1E-40

3.9

1.0E-04

2.4

6.7E-04

5

L

NM_012645

RT1Aw2

_

_

3.3

0.000334

5.4

2.7E-10

6

L

X57523.1

TAP1

1.6

2.8E-04

1.6

9.8E-03

2.4

5.6E-07

5

L

NM_021578

TGFB1

_

_

2.1

8.4E-06

2.6

1.7E-10

6

L

AA819227

TNF

11.1

1.3E-27

2.5

1.9E-04

_

_

2

L

BM390522

TNFRSF1b

14.3

8.2E-19

3.7

4.2E-06

8.0

3.5E-06

5

L

NM_012759

VAV1

4.6

7.1E-05

7.6

1.2E-07

10.8

1.2E-12

5

L

Proteolysis and peptidolysis

NM_024400

ADAMTS1

3.1

9.2E-16

2.1

7.0E-04

3.5

1.3E-16

5

L

AA849399

CTSZ

1.6

6.4E-08

1.5

8.9E-12

3.4

1.6E-33

5

M

NM_012582

HP

2.1

4.8E-20

_

_

1.7

5.5E-05

4

L

NM_031670

KDAP

18.8

8.7E-23

6.6

5.0E-07

48.2

2.3E-37

5

L

AF154349

LGMN

_

_

2.1

1.8E-06

2.8

0.0E+00

6

M

NM_053963

MMP12

_

_

4.1

8.6E-05

7.7

8.2E-13

6

L

M60616.1

MMP13

_

_

_

_

2.0

4.7E-08

7

M

X83537

MMP14

_

_

_

_

1.8

2.1E-17

7

H

NM_053606

MMP23A

_

_

_

_

2.1

1.6E-11

7

L

NM_133523

MMP3

2.9

5.7E-29

2.7

1.4E-12

9.3

0.0E+00

5

H

AI102069

NSF

_

_

1.7

8.1E-03

1.8

3.9E-04

6

L

BF549923

PCSK5

_

_

1.8

1.5E-03

3.4

7.5E-21

6

L

X63434

PLAU

_

_

_

_

1.8

2.9E-14

7

M

AF007789

PLAUR

6.2

4.6E-04

_

_

4.9

7.2E-03

4

L

NM_053372

SLPI

2.6

8.3E-09

2.6

5.4E-22

7.0

0.0E+00

5

M

NM_053819

TIMP1

2.2

0.0E+00

1.8

5.9E-09

6.4

0.0E+00

5

H

NM_053299

UBD

_

_

_

_

4.7

9.1E-04

7

L

Signal transduction

NM_019285

ADCY4

_

_

_

_

2.3

3.5E-05

7

L

BF285345

ARRB2

_

_

1.8

2.5E-05

2.5

6.5E-20

6

L

NM_057196

BAIAP2

_

_

4.4

5.5E-03

_

_

3

L

NM_012766

CCND3

_

_

_

_

2.1

6.0E-08

7

L

NM_013169

CD3d

_

_

_

_

3.9

3.7E-07

7

L

AF065161

CISH

2.5

1.1E-03

_

_

_

_

1

L

NM_031352

DBNL

_

_

1.7

6.6E-11

1.8

1.2E-04

6

L

BI278868

EPIM

_

_

_

_

2.1

6.7E-03

7

L

NM_024147

EVL

_

_

_

_

3.6

8.0E-09

7

L

L02530

FZD2

_

_

_

_

3.3

6.3E-07

7

L

NM_030829.1

GPRK5

_

_

_

_

2.0

1.0E-04

7

L

U87863.1

HGS

_

_

1.9

3.6E-03

_

_

3

L

AY044251

IL13RA1

_

_

_

_

3.2

3.0E-08

7

L

AI178808

IL2RG

2.4

8.1E-14

2.5

10.0E-23

5.6

0.0E+00

5

L

NM_133380

IL4R

_

_

6.5

4.8E-05

7.5

3.4E-18

6

L

NM_017020

IL6R

_

_

1.8

2.4E-10

1.8

1.6E-12

6

L

NM_019311

INPP5D

_

_

_

_

1.9

7.9E-20

7

L

NM_012798

MAL

2.1

1.9E-04

_

_

_

_

1

L

AW533194

MAPK10

2.4

5.7E-03

_

_

_

_

1

L

AF411318

MT1A

2.6

2.4E-27

2.4

2.2E-04

3.4

6.0E-34

5

M

NM_012613

NPR1

_

_

_

_

3.3

1.9E-03

7

L

U32497

P2RX4

_

_

1.6

4.2E-15

1.9

2.5E-20

6

L

AF202733

PDE4B

2.4

1.1E-07

2.5

8.1E-04

2.3

2.5E-03

5

L

BE099769

PLAA

_

_

2.5

8.7E-03

_

_

3

L

X04440

PRKCB1

_

_

_

_

1.8

3.3E-08

7

L

AF254800

RAB0

_

_

_

_

1.9

7.8E-04

7

L

NM_019250

RALGDS

1.9

8.1E-05

_

_

_

_

1

L

NM_021661

RGS19

_

_

_

_

1.8

1.8E-05

7

L

AF321837

RGS2

_

_

_

_

2.3

2.4E-09

7

L

NM_053338

RRAD

7.0

5.2E-05

4.8

1.6E-05

4.0

6.5E-03

5

L

BE117558

SFRP1

_

_

_

_

1.8

2.4E-08

7

M

BF389682

SOCS3

3.8

0.0E+00

2.0

1.2E-05

3.6

3.2E-33

5

L

NM_022230

STC2

_

_

3.1

2.2E-03

_

_

3

L

BG668493

STMN2

_

_

2.3

2.6E-06

14.0

7.2E-42

6

L

U21683

SYK

_

_

_

_

1.8

2.1E-05

7

L

Genes upregulated (Delta > 1.8 and P < 0.01) on days -13.8 (4 hours after intra-articular injection of streptococcal cell wall [SCW]), -13 and 3 are grouped by their general ontology and clustered based on their similarity in terms of pattern of expression (C) and expression level (I). Data are expressed as the mean fold increase in gene expression (Delta) in SCW-injected animals as compared with expression in the corresponding phosphate-buffered saline (PBS) control group (four to five animals/group), along with the P value. C, number of clusters to which the gene corresponds (trend plots are given in Fig. 6); I, intensity of gene expression (L = low intensity [0–500], M = medium intensity [500–1500], H = high intensity [1500–4000]). A line (_) in the Delta or P cell indicates that the gene was not found to be differentially expressed at that particular time point.

Results

Time course of inflammation in the SCW-induced arthritis model

Intra-articular injection of SCW resulted in increased ankle swelling that peaked 24 hours after injection (day -13), followed by a gradual reduction by day 0 (Fig. 1). At this time point intravenous challenge with SCW led to reactivation of the inflammatory response, which peaked 72 hours thereafter (day 3). Animals injected intra-articularly with PBS (vehicle in which the SCW was suspended) were used as control groups at each specific time point. Another group of naïve animals (noninjected rats) was used to assess a possible inflammatory response due to the intra-articular injection alone.
https://static-content.springer.com/image/art%3A10.1186%2Far1458/MediaObjects/13075_2004_Article_1339_Fig1_HTML.jpg
Figure 1

Schematic representation of the experimental design for the time course study in the reactivation model of streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rats. The inflammatory response is represented as the change in ankle diameter (mm) relative to the starting diameter. Data are expressed as means ± standard error (four to five animals/group). Intra-articular (i.a.) injection of SCW resulted in increased ankle swelling that peaked 24 hours after injection (day -13) followed by a gradual reduction by day 0. At this time point, intravenous (i.v.) challenge with SCW led to reactivation of the inflammatory response, which peaked 72 hours thereafter (day 3). Animals injected with a suspension of SCW (continuous line) in PBS or with PBS alone (dashed line; five animals/group) were killed on the days indicated, and joints taken and processed for gene expression profiling analysis and mRNA quantification by GeneChip® microarray and real-time RT-PCR TaqMan®, respectively. A group of naïve noninjected animals (n = 4) was also included in the study to assess basal expression levels of the analyzed genes.

Gene expression profiling in SCW-induced arthritis

Analysis of RAE230A GeneChip® microarray data identified about 9000 probes (5479 upregulated and 3898 downregulated) that were differentially expressed to a highly significant degree (P < 0.01) in arthritic rat joints from the time course study. After applying selection criteria (Delta > 1.8 and P < 0.01), 631 of the dysregulated probes had well characterized full-length sequences in databases (441 upregulated and 190 downregulated) and 697 were unknown (ESTs; 444 upregulated and 253 downregulated). These genes are too numerous to describe in detail, and therefore we present a selected list of upregulated genes in Table 2 and Fig. 2, and a selection of downregulated genes based on the ontologies that reflect the major changes occurring in arthritic animals (Fig. 3). ESTs were excluded from Table 2 and from subsequent clustering analysis. See Additional file 1, which contains all genes that were upregulated and downregulated.
https://static-content.springer.com/image/art%3A10.1186%2Far1458/MediaObjects/13075_2004_Article_1339_Fig2_HTML.jpg
Figure 2

Representative graph of genes that were upregulated (Delta > 1.8 and P < 0.01) in arthritic joints from streptococcal cell wall (SCW)-induced arthritis model on day -13.8 (4 hours after systemic challenge), day -13 and day 3. The graphs represent the fold increase in gene expression (Delta) and the name of the genes associated with the following ontologies: apoptosis (A; red bars), regulation of cell cycle and cell proliferation (B; blue bars), transport (C; green bars) and regulation of transcription, DNA-dependent (D; yellow bars).

https://static-content.springer.com/image/art%3A10.1186%2Far1458/MediaObjects/13075_2004_Article_1339_Fig3_HTML.jpg
Figure 3

Downregulated genes (Delta < -1.8 and P < 0.01) in arthritic joints from streptococcal cell wall (SCW)-induced arthritis model on day 3 after systemic challenge. This graph shows the fold decrease in gene expression (Delta) on day 3 and the name of the downregulated genes associated with the following ontologies: metabolism (E; red bars), regulation of muscle development (F; blue bars) and transport (G; green bars).

Principal component analysis and hierarchical clustering

An overview of the experimental RAE230A GeneChip® data was obtained using PCA (graphs not shown) [13] and agglomerative hierarchical clustering [14]. Both two-dimensional analyses identified day -13.8 (4 hours after intra-articular injection of SCW), day -13 and day 3 as the time points at which the greatest changes in gene expression in arthritic joints occurred in comparison with corresponding PBS control groups. The results from the hierarchical clustering are shown for visual inspection as a coloured heat map in Fig. 4. As shown on the x-axis (panel at the top of Fig. 4), the majority of the PBS samples clustered together, except the PBS samples from day -13.8, which clustered close to the SCW-injected animals from day 3. This observation indicated the presence of a mild inflammatory response in joints from rats killed 4 hours after the initial intra-articular injection of PBS, when compared with expression levels in joints from naïve animals or the PBS samples from later time points.

PCA and hierarchical clustering analysis allowed us to identify two outliers corresponding to arthritic animals from day 3, which did not show any sign of measurable inflammation after intravenous challenge. Both samples were excluded from subsequent mean or Delta calculations.
https://static-content.springer.com/image/art%3A10.1186%2Far1458/MediaObjects/13075_2004_Article_1339_Fig4_HTML.jpg
Figure 4

Heat map diagram of differential gene expression in joints from the time course study in the streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rat. Gene expression data were obtained using Affymetrix Rat Genome RAE230A GeneChip®. The cluster diagram represents 631 differentially expressed probes with P < 0.01 and Delta > 1.8. Each column represents a single joint tissue and each row represents a single gene. Expression levels are coloured green for low intensities and red for high intensities (see scale at the top left corner). At the top of the cluster diagram is an enlarged panel including the names and hierarchical clustering order of the individual samples analyzed. Red names are joint tissues from SCW-injected animals, indicating the corresponding time point of sample collection, and blue names are the samples from the phosphate-buffered saline (PBS) control groups. As shown, the major changes in gene expression occurred in samples corresponding to arthritic animals from days -13.8 (4 hours after intra-articular injection of SCW), -13 and 3. N, naïve animals.

Identification of different patterns of gene expression

The selected 631 dysregulated genes (P < 0.01 and Delta > 1.8) were analyzed using Spotfire® profile search analysis and K-means clustering [15], allowing the identification of different patterns and levels of gene expression throughout the time course of disease. As shown in Fig. 5, the upregulated genes were grouped into seven clusters (C-1 to C-7) according to their kinetics of expression. Thus, all genes exhibiting similar patterns of expression at the analyzed time points were grouped into the same cluster (e.g. C-1 for those genes whose expression reached a peak on day -13.8). These genes were also sorted into three K-means clusters according to their level of expression (low, medium and high). The cluster number to which each gene belongs is summarized in Table2.
https://static-content.springer.com/image/art%3A10.1186%2Far1458/MediaObjects/13075_2004_Article_1339_Fig5_HTML.jpg
Figure 5

Temporal gene expression profiles in the reactivation model of streptococcal cell wall (SCW)-induced arthritis in rat identified using Spotfire® profile search analysis. The seven different clusters identified are termed C-1 to C-7. Each graph shows the characteristic pattern of expression throughout the time course of disease for a representative gene from the defined cluster. Results are expressed as the mean of the signal intensity of gene expression for each group (four to five samples/group). The number of the cluster to which each gene belongs is included in Table 2. The time course of inflammation, expressed as change in ankle diameter (mm) relative to the starting diameter, is shown in the upper left panel. N, naïve; PBS, phosphate-buffered saline.

Interestingly, the expressions of different markers for cell types associated with the pathogenesis of RA were found to be upregulated throughout the time course of SCW-induced arthritis. These markers were grouped into different clusters as follows: C-2 = CD44 (leucocytes, erythrocytes); C-3 = CD2 (T cell, natural killer [NK] cells), E-selectin (SELE; activated endothelial cells); C-4 = L-selectin (SELL; lymphocytes, monocytes and NK cells); C-5 = CD14 (monocytes), ICAM1 (endothelial cells), α M integrin (ITGAM or CD11b; granulocytes, monocytes, NK cells), P-selectin (SELP; endothelial cells, activated platelets), lipocalin 2 (LCN2; neutrophils); C-6 = CD74 (B cells, monocytes), CD38 (activated T cells, plasma cells), CD8a (cytotoxic/suppressor T cells, NK cells); and C-7 = CD3d (T cells), CD4 (helper–inducer T cells). The different temporal expression of these markers highlights that expression levels for CD3d and CD4 were significantly upregulated only at day 3 after challenge, in contrast to CD2 and E-selectin, whose expression was found to be upregulated only at day -13. The rest of the markers exhibited significant fold changes in gene expression at both phases of disease (4 hours after intra-articular injection of SCW, day -13 and day 3 after challenge), except CD8a, CD74 and CD38, which were found to be upregulated at a later time point in the pre-reactivation phase (day -13). Only CD44 was not found to be upregulated on day 3 after challenge. Lipocalin 2, αM integrin and CD8a exhibited the greatest fold changes in gene expression.
https://static-content.springer.com/image/art%3A10.1186%2Far1458/MediaObjects/13075_2004_Article_1339_Fig6_HTML.jpg
Figure 6

Confirmation of the expression levels of six of the highly differentially expressed genes highlighted in Table 2 by real-time RT-PCR TaqMan® analysis. The graphs compare the gene expression profiles for IL-1β, tumour necrosis factor (TNF)-α, IL-6, GRO1, CD14 and CD3 obtained using two different methods: Affymetrix Rat Genome RAE230A GeneChip® (filled squares) and real-time RT-PCR TaqMan® analysis (open squares). Data are expressed as the mean of signal intensity or the mean of copy no./50 ng cDNA normalized against the housekeeping gene ubiquitin, for all of the samples from the same group (four to five). The Pearson product moment correlation coefficient (r) for each comparison is given. PBS, phosphate-buffered saline; SCW, streptococcal cell wall.

Functional grouping of dysregulated genes

In order to establish functional annotations for the selected dysregulated genes, the biological processes and molecular functions of the genes were investigated using different databases. This search identified 19 ontologies for the upregulated genes, allowing us to organize them according to their major functions (Table 2 and Fig. 2). Because of space limitations in the manuscript, we could not include all of the upregulated genes in Table 2 and Fig. 2. The genes not included were involved in blood coagulation, catabolism, defence response, G-protein-coupled receptor protein signalling pathways, metabolism and protein modification, or were genes with unknown functions (for more information, please see Additional file 1). A hallmark of RA is infiltration of leucocytes into synovial tissue mediated by a complex network of cytokines, adhesion molecules and chemoattractants [18].

Interestingly, most of the genes exhibiting the greatest fold increase in gene expression (Delta > 5) on days -13.8, -13 or 3 were involved in chemotaxis. These included several CC chemokine ligands (CCLs; CCL20, CCL2 [also called SCYA2 or MCP-1]), CXC chemokine ligands (CXCLs; CXCL2, CXCL6 and GRO1), CC chemokine receptors (CCRs; CCR1, CCR2, CCR5), CXC chemokine receptors (CXCRs; CXCR2) and a recently characterized cytokine called chemokine-like factor 1 [19].

Our results also showed marked upregulation (Delta > 5) for numerous genes that are involved in the immune and/or inflammatory response, such as IL-1β, IL-6, TNF-α, TNFRSF1b, IL-1Rn, NOS2, CD8a, VAV1, LST1 (leukocyte specific transcript 1), LCP2 (lymphocyte cytosolic protein 2), FCGR2 (Fc receptor, IgG, low affinity Iib), PTGES (microsomal prostaglandin E synthase-1) and the major histocompatibility complex (MHC) class Ib gene (RTAW2). Other components of the MHC such as MHC class II (HLA-DMA and HLA-DMB) and MHC class Ib RT1.S3 genes were also found to be upregulated in this model. Genes participating in cell adhesion such as TNFIP6, FCNB (ficolin B), CSPG2 (versican), ICAM1 and αM integrin (ITGAM) also exhibited a significant fold increase in gene expression (Delta > 5). Among other genes, some mediators controlling extracellular matrix (ECM) turnover and breakdown under normal and disease conditions, including five matrix metalloproteinases (MMPs; MMP-3, -12, -13, -14 and -23a), the aggrecanase ADAMTS-1, tissue inhibitor of metalloproteinases (TIMP)1, and the secretory leucocyte protease inhibitor (SLPI) were also found to be significantly upregulated in arthritic joints. The majority of the downregulated genes were associated with regulation of metabolism, myogenesis, or regulation of muscle development and transport (Fig. 3).

Differentially expressed genes: QTL association

From the 441 selected genes that were upregulated during SCW-induced arthritis, we selected a list of 20 genes that exhibited a greater than fivefold change in gene expression and that had not previously been linked to autoimmune arthritis. To further investigate the possibility that these genes play a role in arthritis pathogenesis, their rat chromosomal locations and the locations of their human orthologues were identified and compared with those of rat and human QTLs for autoimmune diseases. Interestingly, 10 of these genes were found to be located in chromosomal regions that mapped to rat and/or human QTLs previously reported to be associated with inflammation, arthritis, or autoimmune diseases, such as systemic lupus erythematosus, multiple sclerosis, allergic rhinitis and asthma (Table 3).
Table 3

Upregulated genes (Delta > 5, P < 0.01) not previously reported to be associated with arthritis

Accession no.

Gene

Delta

Rat CL

Rat QTLs

Human CL

Human QTLs

NM_178144

AMIGO3

Nd/Nd/5.9

8q32

Cia6

3p21.31

Asthma

NM_130411

CORO1A

3.1/2.7/6.6

1q36

Pia11

16p12.1

Blau syndrome, asthma

NM_024381

GYK

6.7/Nd/Nd

Xq22

Cia19

Xp21.3

Allergic rhinitis

NM_031670

KDAP

18.8/6.6/48.2

1q22

_

19q13.3

Asthma, SLE, MS, SD

NM_569105

LCP2

2.6/3.3/6.2

10q12

Cia16, Pia15

5q33.1

RA, PDB, asthma, IBD, psoriasis, ATD

NM_021586

LTBP2

Nd/Nd/6.5

6q31

Pia3, Pia24

14q24

SLE, MODY3

NM_198746

Ly-49.9

Nd/2.0/5.6

4q42

Cia13, Cia24, Pia7, Pia23, Oia2, Oia7, Oia8, Ciaa4

12p13-p12

RA, allergic rhinitis, hypophosphataemic rickets

NM_022667

MATR1

1.7/1.9/5.7

8q32

Cia6

3q21

Atopic dermatitis, asthma, psoriasis

NM_133306

OLR1

8.3/2.8/3.7

4q42

Cia13, Cia24, Pia7, Pia23, Oia2, Oia7, Oia8, Ciaa4

12p13.2-p12.3

RA, hypophosphataemic rickets, allergic rhinitis

NM_053687

SLFN4

5.8/4.6/4.8

10q26

Cia16, Cia21, Cia22, Cia23, Oia4, Ciaa2

17q11.2-q21.1

SLE, MS

The rat chromosomal location and the chromosomal locations of the corresponding human orthologue were identified and mapped to quantitative trait loci (QTLs) previously associated with inflammation, arthritis and/or other autoimmune diseases. Delta values are given for the following time points: day -13.8/day -13/day 3. ATD, autoimmune thyroid disease; CIA, type II collagen-induced arthritis; Ciaa, CIA autoantibody; CL, chromosome location; IBD, inflammatory bowel disease; MOYD 3, maturity-onset diabetes of the young 3; MS, multiple sclerosis; Nd, not differentially expressed; Oia, oil-induced arthritis; PDB, Paget's disease of bone; PIA, pristane-induced arthritis; RA, rheumatoid arthritis; SD, spondylocostal dysostosis; SLE, systemic lupus erythematosus.

Analysis of expression profiles of specific transcripts

In order to validate microarray data, mRNA expression levels for a subset of genes were quantified by real-time RT-PCR TaqMan® analysis. As shown in Fig. 6, there was a significant correlation (Pearson product moment correlation coefficient r > 0.9 and P < 0.01) between the gene expression profiles for the proinflammatory cytokines IL-1β, TNF-α and IL-6, the chemokine GRO1 and the cell markers CD14 and CD3, when microarray data were compared with RT-PCR TaqMan® data. Although the fold changes in gene expression calculated using data from both methods were not exactly the same (probably due to differences in the sensitivities of the assays), the quantitative real-time RT-PCR TaqMan® method verified the results of the gene array analysis.

Discussion

The temporal expression of multiple disease-associated genes with potential pathophysiological roles in the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat has not previously been fully addressed. The present study analyzed gene expression profiles in rat joints with SCW-induced arthritis using RAE230A GeneChip® oligonucleotide microarray (Affymetrix Inc.). We chose to profile gene expression in whole ankle joint tissues, which comprises heterogeneous cell types, with the aim of gaining a global insight into the molecular changes associated with arthritis pathology in this model. Analysis of the time course data generated by microarray identified 631 genes (441 upregulated and 190 downregulated) with full-length sequences in databases that were significantly differentially expressed (Delta > 1.8 and P < 0.01). Our experimental design (time course study) and use of K-means cluster analysis allowed us to identify specific patterns of gene expression for the different dysregulated genes, highlighting the importance of performing kinetic studies to identify the time point at which a particular gene is maximally expressed. Thus, these gene expression data indicate optimal times for measuring potential disease biomarkers in rat joints, and our approach offers a useful tool with which to investigate the clinical efficacy and mechanism of action of novel therapeutic agents in rat SCW-induced arthritis.

Changes in gene expression may reflect regulation at the mRNA level or changes in the number of cells (proliferation or infiltration) that synthesize these mRNAs. Thus, optimally, microarray analysis should be conducted in isolated populations of cells so that differential gene expression may be directly correlated with transcription of the genes. However, complex diseases such as RA involve extensive tissue injury, and not all of the cell types that contribute to RA pathogenesis have been identified. Hence, analysis of the damaged tissue, rather than analysis of an isolated cell type, increases the probability that differential gene expression will be examined in those cells that are important in RA pathogenesis. In the present study we conducted a global analysis of coordinated gene expression in injured tissue. Further bioinformatic analysis of the data to examine cell markers, and genes whose expression may correlate with them, in combination with analysis of the cell populations present in the arthritic joint using immunohistochemistry or fluorescence activated cell sorting techniques, would be required to corroborate the differential gene expression of a particular gene of interest. Previous studies have already shown that cell-specific gene expression patterns can indicate the presence of immune cells [20]. RAE230A GeneChip® oligonucleotide microarray analysis identified the expression of different markers for cell types associated with the pathogenesis of RA. Based on the level of gene expression and Delta values detected for the different markers, our results suggest that the main cell types present in arthritic joints in this model are T cells, neutrophils, monocytes/macrophages and B cells, confirming previous descriptions of the joint cell composition in this model [6, 21].

Gene expression profiling of arthritic rat joints revealed a spectrum of genes exhibiting extensive inflammatory activity, infiltration of activated cells, angiogenesis, regulation of apoptosis and ECM remodelling activities. Most of the genes found to be upregulated in SCW-induced arthritic joints have also been reported to be highly expressed in human RA synovial tissue [22, 23] or in joints from other rodent experimental arthritis models [10, 11, 24, 25]. The upregulated expression of TNF-α, IL-1α, IL-1β, IL-4R, P-selectin, MIP-1α (CCL3), MCP-1 (CCL2), NOS2 and NOS3 [68] demonstrated in the present study is in agreement with previous observations of the dependency of the rat SCW-induced arthritis model on these mediators. The SLPI has previously been reported to be upregulated in arthritic joints and to mediate tissue destruction and inflammation in a rat model of arthritis induced by intraperitoneal injection of SCW [26]. Similar results were found in our study, because significant upregulation of SLPI gene expression was observed during both phases of the disease. Additionally, previous studies have shown that nuclear factor-κB (NF-κB) is activated in the synovium of rats with SCW-induced arthritis and that inhibition of the activity of this transcription factor enhances synovial apoptosis, which is consistent with the potential involvement of NF-κB in synovial hyperplasia [27]. In accord with these observations, the microarray data showed early upregulation of genes involved in the NF-κB signalling pathway, such as NF-κB1 (p50 or p105), NFKBIA (IκBα), TNF-α, TNFRSF1a and TNFRSF1b, suggesting a possible regulatory role of NF-κB in the transcription of genes that mediate disease progression in SCW-induced arthritis.

Histopathological studies in arthritic rat joints from the reactivation SCW-induced arthritis model have shown that only moderate histological changes in articular cartilage, with few erosive effects on bone, occur at early stages in the flare reaction (day 3), whereas evident cartilage degradation is observed at later time points (20 days after intravenous challenge with SCW) [4]. The microarray data suggest that tissue remodelling is an active process in this model because abundant expression of collagen-related genes (Col5A2, Col5A3, Col12A1 and Col18A1), enzymes that degrade matrix molecules such as MMPs and the aggrecanase ADAMTS-1 (a disintegrin-like and metalloproteinase with thrombospondin type 1 motif, which is capable of cleaving versican), together with other genes that control ECM turnover and breakdown (TIMP1, PLAU [plasminogen activator, urokinase], PLAU receptor [PLAUR]), were found to be upregulated in arthritic joints. MMP-3 (stromelysin) appears to be pivotal in the activation of collagenases, whereas MMP-13 is crucial in collagen breakdown [28]. The PLAU/PLAUR system plays a critical role in cartilage degradation during osteoarthris by regulating pericellular proteolysis mediated by serine proteases [22, 29]. The complement system has also been reported to participate in tissue injury during inflammatory and autoimmune diseases [30], and ficolins can initiate the lectin pathway of complement activation through attached serine proteases (Mannan-binding lectin serine proteases [MASPs]) [31]. Interestingly, the microarray data revealed significant upregulation of the first complement component C1, which exerts collagenolytic activity in addition to the role it plays in the classic cascade [29]. In addition, upregulation of the expression of C2, C3, ficolin B (FCNB) and MASP1 was also noted, supporting the concept that activation of the complement system, together with the imbalance between MMPs, TIMPs and other related molecules, could mediate cartilage destruction in this experimental model of RA.

In our analysis we also identified 10 genes that are differentially expressed in arthritic joints and that that map to genomic regions previously reported to be QTLs for autoimmune diseases. Although it is premature to suggest that the 10 genes are candidates for these QTLs, our observations suggest that expression of these genes may influence the onset, severity and/or susceptibility to arthritis in this animal model. Of particular interest is KDAP (napsin) because of the high fold increase in gene expression observed in arthritic joints from SCW-injected animals (D = 48.2 on day 3). This aspartic protease was shown to be expressed in kidney, lung and lymphoid organs of mice [32], and it has been suggested that it functions as a lysosomal protease involved in protein catabolism in renal proximal tubules [33]. However, little is known about the role of KDAP in other organs and tissues. Interestingly, human KDAP resides on chromosome 19q13.3–19q13.4, a region previously identified to be involved in susceptibility to autoimmune diseases, including systemic lupus erythematosus, multiple sclerosis and insulin-dependent diabetes mellitus [34, 35]. Our results show, for the first time, that KDAP gene expression is upregulated in experimental arthritis tissue, and suggest that further characterization is required to unravel the biological/pathological activities of this gene in RA.

The microarray data also revealed high upregulation in runt-related transcription factor 1 (RUNX1) and a group of transporter genes (SLC11A1, SLC13A3, SLC1A3, SLC21A2 [MATR1], SLC28A2, SLC29A3, SLC5A2 and SLC7A7), from which the prostaglandin transporter gene MATR1 exhibited the greatest upregulation on day 3 after intravenous challenge with SCW. The rat MATR1 gene maps to the type II collagen induced arthritis severity QTL6 (Cia6) [36], and its human orthologue is located within autoimmune disease QTLs for asthma, psoriasis and atopic dermatitis [3739]. Several authors reported linkage of SLC11A1 (also named NRAMP1) to human RA [4042]. The Z-DNA forming polymorphic repeat in the RUNX1-containing promoter region of human SLC11A1 may contribute to the differing allelic associations observed with infectious versus autoimmune disease susceptibility [43]. Recent studies reported that regulation of expression of organic cation transporter gene SLC22A4 by RUNX1 is associated with susceptibility to RA [44]. Other transporter genes (SLC12A8 and SLC9A3R1) have also been linked to susceptibility to other autoimmune diseases such as psoriasis [45]. These observations together suggest that RUNX1 and the transporter genes found to be differentially expressed in arthritic joints may contribute to arthritis susceptibility and to the inflammatory processes that mediate the pathology of this model.

Conclusion

The present study identified the temporal gene expression profiles of hundreds of genes, including cytokines, chemokines, adhesion molecules, transcription factors, apoptotic and angiogenesis mediators, whose expression is associated with onset and progression of arthritis pathology in rat joints from the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat. This transcript profiling offers not only the optimal kinetics of expression for different potential disease biomarkers, but it also improves our understanding of the molecular events that underlie the pathology in this animal model of RA. In addition, although the majority of genes found to be differentially expressed in this model were previously associated with human RA, further genes not previously linked to autoimmune diseases were identified, providing a resource for future research and for the development of new therapeutic targets.

Abbreviations

ANOVA: 

analysis of variance

CCL: 

CC chemokine ligand

CCR: 

CC chemokine receptor

CXCL: 

CXC chemokine ligand

CXCR: 

CXC chemokine receptor

ECM: 

extracellular matrix

EST: 

expressed sequence tag

IL: 

interleukin

MCP: 

monocyte chemoattractant protein

MHC: 

major histocompatibility complex

MIP: 

macrophage inflammatory protein

MMP: 

matrix metalloproteinase

NF-κB: 

nuclear factor-κB

NK: 

natural killer

NOS: 

nitric oxide synthase

PBS: 

phosphate-buffered saline

PCA: 

principal component analysis

PCR: 

polymerase chain reaction

PG-PS: 

peptidoglycan–polysaccharide

QTL: 

quantitative trait locus

RA: 

rheumatoid arthritis

RT: 

reverse transcription

SCW: 

streptococcal cell wall

SLPI: 

secretory leucocyte protease inhibitor

TIMP: 

tissue inhibitor of matrix metalloproteinase

TNF: 

tumour necrosis factor.

Declarations

Acknowledgements

The authors wish to acknowledge Jacqueline Buckton for sharing her expertise on the animal model experiments, and Alan Lewis and Ramu Elango for bioinformatics support. Dr Inmaculada Rioja is supported by an EU Postdoctoral Marie Curie Fellowship HPMI-CT-1999-00025.

Authors’ Affiliations

(1)
Rheumatoid Arthritis Disease Biology Department, GlaxoSmithKline, Medicines Research Centre
(2)
Transcriptome Analysis Department, GlaxoSmithKline, Medicines Research Centre

References

  1. Feldmann M: Pathogenesis of arthritis: recent research progress. Nat Immunol. 2001, 2: 771-773. 10.1038/ni0901-771.View ArticlePubMedGoogle Scholar
  2. Choy EH, Panayi GS: Cytokine pathways and joint inflammation in rheumatoid arthritis. N Engl J Med. 2001, 344: 907-916. 10.1056/NEJM200103223441207.View ArticlePubMedGoogle Scholar
  3. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, et al: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996, 14: 1675-1680. 10.1038/nbt1296-1675.View ArticlePubMedGoogle Scholar
  4. Esser RE, Stimpson SA, Cromartie WJ, Schwab JH: Reactivation of streptococcal cell wall-induced arthritis by homologous and heterologous cell wall polymers. Arthritis Rheum. 1985, 28: 1402-1411.View ArticlePubMedGoogle Scholar
  5. Schwab JH, Anderle SK, Brown RR, Dalldorf FG, Thompson RC: Pro- and anti-inflammatory roles of interleukin-1 in recurrence of bacterial cell wall-induced arthritis in rats. Infect Immun. 1991, 59: 4436-4442.PubMed CentralPubMedGoogle Scholar
  6. Schimmer RC, Schrier DJ, Flory CM, Dykens J, Tung DK, Jacobson PB, Friedl HP, Conroy MC, Schimmer BB, Ward PA: Streptococcal cell wall-induced arthritis. Requirements for neutrophils, P-selectin, intercellular adhesion molecule-1, and macrophage-inflammatory protein-2. J Immunol. 1997, 159: 4103-4108.PubMedGoogle Scholar
  7. Schrier DJ, Schimmer RC, Flory CM, Tung DK, Ward PA: Role of chemokines and cytokines in a reactivation model of arthritis in rats induced by injection with streptococcal cell walls. J Leukoc Biol. 1998, 63: 359-363.PubMedGoogle Scholar
  8. McCartney-Francis N, Allen JB, Mizel DE, Albina JE, Xie QW, Nathan CF, Wahl SM: Suppression of arthritis by an inhibitor of nitric oxide synthase. J Exp Med. 1993, 178: 749-754. 10.1084/jem.178.2.749.View ArticlePubMedGoogle Scholar
  9. Sano H, Hla T, Maier JA, Crofford LJ, Case JP, Maciag T, Wilder RL: In vivo cyclooxygenase expression in synovial tissues of patients with rheumatoid arthritis and osteoarthritis and rats with adjuvant and streptococcal cell wall arthritis. J Clin Invest. 1992, 89: 97-108.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Thornton S, Sowders D, Aronow B, Witte DP, Brunner HI, Giannini EH, Hirsch R: DNA microarray analysis reveals novel gene expression profiles in collagen-induced arthritis. Clin Immunol. 2002, 105: 155-168. 10.1006/clim.2002.5227.View ArticlePubMedGoogle Scholar
  11. Ibrahim SM, Koczan D, Thiesen HJ: Gene-expression profile of collagen-induced arthritis. J Autoimmun. 2002, 18: 159-167. 10.1006/jaut.2001.0580.View ArticlePubMedGoogle Scholar
  12. Affymetrix Inc: Affymetrix GeneChip® Expression Analysis Technical Manual. [http://www.affymetrix.com/support/technical/manual/expression_manual.affx]
  13. Peterson LE: Partitioning large-sample microarray-based gene expression profiles using principal components analysis. Comput Methods Programs Biomed. 2003, 70: 107-119. 10.1016/S0169-2607(02)00009-3.View ArticlePubMedGoogle Scholar
  14. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-14868. 10.1073/pnas.95.25.14863.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Varela JC, Goldstein MH, Baker HV, Schultz GS: Microarray analysis of gene expression patterns during healing of rat corneas after excimer laser photorefractive keratectomy. Invest Ophthalmol Vis Sci. 2002, 43: 1772-1782.PubMedGoogle Scholar
  16. Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative PCR. Genome Res. 1996, 6: 986-994.View ArticlePubMedGoogle Scholar
  17. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3: RESEARCH0034-10.1186/gb-2002-3-7-research0034.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Ruschpler P, Lorenz P, Eichler W, Koczan D, Hanel C, Scholz R, Melzer C, Thiesen HJ, Stiehl P: High CXCR3 expression in synovial mast cells associated with CXCL9 and CXCL10 expression in inflammatory synovial tissues of patients with rheumatoid arthritis. Arthritis Res Ther. 2003, 5: R241-R252. 10.1186/ar783.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Han W, Lou Y, Tang J, Zhang Y, Chen Y, Li Y, Gu W, Huang J, Gui L, Tang Y, et al: Molecular cloning and characterization of chemokine-like factor 1 (CKLF1), a novel human cytokine with unique structure and potential chemotactic activity. Biochem J. 2001, 357: 127-135. 10.1042/0264-6021:3570127.PubMed CentralView ArticlePubMedGoogle Scholar
  20. Wester L, Koczan D, Holmberg J, Olofsson P, Thiesen HJ, Holmdahl R, Ibrahim S: Differential gene expression in pristane-induced arthritis susceptible DA versus resistant E3 rats. Arthritis Res Ther. 2003, 5: R361-R372. 10.1186/ar993.PubMed CentralView ArticlePubMedGoogle Scholar
  21. van den Broek MF, de Heer E, van Bruggen MC, de Roo G, Kleiverda K, Eulderink F, van den Berg WB: Immunomodulation of streptococcal cell wall-induced arthritis. Identification of inflammatory cells and regulatory T cell subsets by mercuric chloride and in vivo CD8 depletion. Eur J Immunol. 1992, 22: 3091-3095.View ArticlePubMedGoogle Scholar
  22. van der Pouw Kraan TC, van Gaalen FA, Huizinga TW, Pieterman E, Breedveld FC, Verweij CL: Discovery of distinctive gene expression profiles in rheumatoid synovium using cDNA microarray technology: evidence for the existence of multiple pathways of tissue destruction and repair. Genes Immun. 2003, 4: 187-196. 10.1038/sj.gene.6363975.View ArticlePubMedGoogle Scholar
  23. van der Pouw Kraan TC, van Gaalen FA, Kasperkovitz PV, Verbeet NL, Smeets TJ, Kraan MC, Fero M, Tak PP, Huizinga TW, Pieterman E, et al: Rheumatoid arthritis is a heterogeneous disease: evidence for differences in the activation of the STAT-1 pathway between rheumatoid tissues. Arthritis Rheum. 2003, 48: 2132-2145. 10.1002/art.11096.View ArticlePubMedGoogle Scholar
  24. Wester L, Koczan D, Holmberg J, Olofsson P, Thiesen HJ, Holmdahl R, Ibrahim S: Differential gene expression in pristane-induced arthritis susceptible DA versus resistant E3 rats. Arthritis Res Ther. 2003, 5: R361-R372. 10.1186/ar993.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Shahrara S, Amin MA, Woods JM, Haines GK, Koch AE: Chemokine receptor expression and in vivo signaling pathways in the joints of rats with adjuvant-induced arthritis. Arthritis Rheum. 2003, 48: 3568-3583. 10.1002/art.11344.View ArticlePubMedGoogle Scholar
  26. Song X, Zeng L, Jin W, Thompson J, Mizel DE, Lei K, Billinghurst RC, Poole AR, Wahl SM: Secretory leukocyte protease inhibitor suppresses the inflammation and joint damage of bacterial cell wall-induced arthritis. J Exp Med. 1999, 190: 535-542. 10.1084/jem.190.4.535.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Miagkov AV, Kovalenko DV, Brown CE, Didsbury JR, Cogswell JP, Stimpson SA, Baldwin AS, Makarov SS: NF-kappaB activation provides the potential link between inflammation and hyperplasia in the arthritic joint. Proc Natl Acad Sci USA. 1998, 95: 13859-13864. 10.1073/pnas.95.23.13859.PubMed CentralView ArticlePubMedGoogle Scholar
  28. van den Berg WB: Anti-cytokine therapy in chronic destructive arthritis. Arthritis Res. 2001, 3: 18-26. 10.1186/ar136.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Walter H, Kawashima A, Nebelung W, Neumann W, Roessner A: Immunohistochemical analysis of several proteolytic enzymes as parameters of cartilage degradation. Pathol Res Pract. 1998, 194: 73-81.View ArticlePubMedGoogle Scholar
  30. Nakagawa K, Sakiyama H, Tsuchida T, Yamaguchi K, Toyoguchi T, Masuda R, Moriya H: Complement C1s activation in degenerating articular cartilage of rheumatoid arthritis patients: immunohistochemical studies with an active form specific antibody. Ann Rheum Dis. 1999, 58: 175-181.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Holers VM: The complement system as a therapeutic target in autoimmunity. Clin Immunol. 2003, 107: 140-151. 10.1016/S1521-6616(03)00034-2.View ArticlePubMedGoogle Scholar
  32. Mori K, Kon Y, Konno A, Iwanaga T: Cellular distribution of napsin (kidney-derived aspartic protease-like protein, KAP) mRNA in the kidney, lung and lymphatic organs of adult and developing mice. Arch Histol Cytol. 2001, 64: 319-327.View ArticlePubMedGoogle Scholar
  33. Mori K, Shimizu H, Konno A, Iwanaga T: Immunohistochemical localization of napsin and its potential role in protein catabolism in renal proximal tubules. Arch Histol Cytol. 2002, 65: 359-368.View ArticlePubMedGoogle Scholar
  34. Pericak-Vance MA, Rimmler JB, Martin ER, Haines JL, Garcia ME, Oksenberg JR, Barcellos LF, Lincoln R, Goodkin DE, Hauser SL: Linkage and association analysis of chromosome 19q13 in multiple sclerosis. Neurogenetics. 2001, 3: 195-201. 10.1007/s100480100119.View ArticlePubMedGoogle Scholar
  35. Moser KL, Neas BR, Salmon JE, Yu H, Gray-McGuire C, Asundi N, Bruner GR, Fox J, Kelly J, Henshall S, et al: Genome scan of human systemic lupus erythematosus: evidence for linkage on chromosome 1q in African-American pedigrees. Proc Natl Acad Sci USA. 1998, 95: 14869-14874. 10.1073/pnas.95.25.14869.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Furuya T, Salstrom JL, McCall-Vining S, Cannon GW, Joe B, Remmers EF, Griffiths MM, Wilder RL: Genetic dissection of a rat model for rheumatoid arthritis: significant gender influences on autosomal modifier loci. Hum Mol Genet. 2000, 9: 2241-2250.View ArticlePubMedGoogle Scholar
  37. Enlund F, Samuelsson L, Enerback C, Inerot A, Wahlstrom J, Yhr M, Torinsson A, Riley J, Swanbeck G, Martinsson T: Psoriasis susceptibility locus in chromosome region 3q21 identified in patients from southwest Sweden. Eur J Hum Genet. 1999, 7: 783-790. 10.1038/sj.ejhg.5200365.View ArticlePubMedGoogle Scholar
  38. Lee YA, Wahn U, Kehrt R, Tarani L, Businco L, Gustafsson D, Andersson F, Oranje AP, Wolkertstorfer A, v Berg A, et al: A major susceptibility locus for atopic dermatitis maps to chromosome 3q21. Nat Genet. 2000, 26: 470-473. 10.1038/82625.View ArticlePubMedGoogle Scholar
  39. Haagerup A, Bjerke T, Schiotz PO, Binderup HG, Dahl R, Kruse TA: Asthma and atopy: a total genome scan for susceptibility genes. Allergy. 2002, 57: 680-686. 10.1034/j.1398-9995.2002.23523.x.View ArticlePubMedGoogle Scholar
  40. Rodriguez MR, Gonzalez-Escribano MF, Aguilar F, Valenzuela A, Garcia A, Nunez-Roldan A: Association of NRAMP1 promoter gene polymorphism with the susceptibility and radiological severity of rheumatoid arthritis. Tissue Antigens. 2002, 59: 311-315. 10.1034/j.1399-0039.2002.590410.x.View ArticlePubMedGoogle Scholar
  41. Singal DP, Li J, Zhu Y, Zhang G: NRAMP1 gene polymorphisms in patients with rheumatoid arthritis. Tissue Antigens. 2000, 55: 44-47. 10.1034/j.1399-0039.2000.550107.x.View ArticlePubMedGoogle Scholar
  42. Sanjeevi CB, Miller EN, Dabadghao P, Rumba I, Shtauvere A, Denisova A, Clayton D, Blackwell JM: Polymorphism at NRAMP1 and D2S1471 loci associated with juvenile rheumatoid arthritis. Arthritis Rheum. 2000, 43: 1397-1404. 10.1002/1529-0131(200006)43:6<1397::AID-ANR25>3.0.CO;2-6.View ArticlePubMedGoogle Scholar
  43. Searle S, Blackwell JM: Evidence for a functional repeat polymorphism in the promoter of the human NRAMP1 gene that correlates with autoimmune versus infectious disease susceptibility. J Med Genet. 1999, 36: 295-299.PubMed CentralPubMedGoogle Scholar
  44. Tokuhiro S, Yamada R, Chang X, Suzuki A, Kochi Y, Sawada T, Suzuki M, Nagasaki M, Ohtsuki M, Ono M, et al: An intronic SNP in a RUNX1 binding site of SLC22A4, encoding an organic cation transporter, is associated with rheumatoid arthritis. Nat Genet. 2003, 35: 341-348. 10.1038/ng1267.View ArticlePubMedGoogle Scholar
  45. Helms C, Cao L, Krueger JG, Wijsman EM, Chamian F, Gordon D, Heffernan M, Daw JA, Robarge J, Ott J, et al: A putative RUNX1 binding site variant between SLC9A3R1 and NAT9 is associated with susceptibility to psoriasis. Nat Genet. 2003, 35: 349-356. 10.1038/ng1268.View ArticlePubMedGoogle Scholar

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© Rioja et al., licensee BioMed Central Ltd. 2004

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.

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