Skip Navigation


Rheumatology Advance Access first published online on April 3, 2008
This version published online on April 14, 2008
Rheumatology, doi:10.1093/rheumatology/ken083
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
47/6/780    most recent
ken083v2
ken083v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Sekiguchi, N.
Right arrow Articles by Takeuchi, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Sekiguchi, N.
Right arrow Articles by Takeuchi, T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Messenger ribonucleic acid expression profile in peripheral blood cells from RA patients following treatment with an anti-TNF-{alpha} monoclonal antibody, infliximab

N. Sekiguchi1, S. Kawauchi2, T. Furuya2, N. Inaba2, K. Matsuda2, S. Ando2, M. Ogasawara2, H. Aburatani3, H. Kameda1, K. Amano1, T. Abe1, S. Ito2 and T. Takeuchi1

1Division of Rheumatology/Clinical Immunology, Department of Internal Medicine, Saitama Medical Center, Saitama Medical University, Saitama, 2Japan Genome Solutions, Inc., Tokyo and 3Genome Science Division, Research Center for Advanced Science and Technologies, The University of Tokyo, Tokyo, Japan.

Correspondence to: T. Takeuchi, Division of Rheumatology/Clinical Immunology, Department of Internal Medicine, Saitama Medical Center, Saitama Medical University, 1981 Tsujido-machi, Kamoda, Kawagoe-shi, Saitama 350-8550, Japan. E-mail: tsutake{at}saitama-med.ac.jp


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
 Acknowledgements
 References
 
Objectives. We monitored the mRNA expression profiles of peripheral blood cells during treatment with a TNF-{alpha} inhibitor, infliximab, in patients with RA. Using a DNA microarray analysis, we demonstrated a unique set of genes, with distinct baseline and post-treatment changes in expression between responders and non-responders to infliximab treatment.

Methods. Using a customized low-density cDNA microarray with 747 genes and a reliable data collection system, we monitored the mRNA expression profiles of whole blood cells from 18 RA patients before and after the infusion of infliximab for up to 22 weeks. The clinical response to treatment with infliximab was determined using the ACR response criteria, the disease activity score of 28 joints (DAS28), and individual clinical parameters. The patients were classified as responders or non-responders based on their ACR50% response at 22 weeks.

Results. Approximately 15% of the total genes were found to exhibit a >1.5-fold change, compared with their reference values, at one or more time points during the 22 weeks of infliximab therapy. The expression of inflammatory genes, such as IFN-related genes, was strongly correlated with the serum level of CRP and the DAS28. The increased expression of inflammatory genes in responders was normalized within 2 weeks and then remained at a normal level during the treatment period. In contrast, in the non-responders, the elevated expression at baseline, although it was significantly decreased at 2 weeks, returned to the baseline level after 14 weeks. In addition to inflammatory genes, we identified several groups of genes with distinct differences in expression between the responders and non-responders.

Conclusions. Our results suggest that a customized low-density microarray is useful for monitoring mRNA expression profiles in peripheral blood cells, enabling us to identify a unique set of genes with differentially regulated expressions in responders and non-responders to a TNF inhibitor among patients with RA.

KEY WORDS: Rheumatoid arthritis, Oligonucleotide array sequence analysis, Biological products, Infliximab, Tumour necrosis factor, Interferons, Messenger ribonucleic acid, Reverse transcriptase-polymerase chain reaction


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 Supplementary data
 Acknowledgements
 References
 
Biological agents that inhibit the action of TNF-{alpha}, such as infliximab, etanercept and adalimumab, have shown excellent clinical efficacy and a striking ability to prevent structural damage in patients with RA [1–4]. This clinical evidence supports the hypothesis that TNF-{alpha} lies upstream of the pro-inflammatory cytokine network and plays a pivotal role in the pathogenesis of RA [5]. Nevertheless, a satisfactory clinical response is not achieved in all patients treated with anti-TNF biologics. For example, the response to anti-TNF biologics in MTX-resistant RA patients is typically around 50–70% in terms of the ACR20% response criteria at 6 months [1–4]. To maximize the clinical response to these agents, the mechanism underlying the variable response in individual patients must be understood; furthermore, a strategy for predicting responders and non-responders is needed. Although clinical parameters, autoantibody profiles and biomarkers like serum MMP-3 have been studied [6–9], reliable prediction markers have not been identified to date. A pharmacogenomic analysis has also been performed [10]. Some studies have shown that single nucleotide polymorphisms (SNPs) in genes encoding TNF-{alpha}, TNF-{alpha} receptors, other cytokines and the MHC are significantly associated with a favourable response to ant-TNF biologics, while others conclude that such SNPs are irrelevant to predicting response [10–12]. In particular, a recent study in a large RA cohort demonstrated that a shared epitope was a marker of disease severity but was not a predictor of infliximab response [13]. Using a comprehensive analysis of mRNA expression profiles [14], synovial cells and peripheral blood cells from RA patients have been examined [15–17]. Based on the results of a transcriptome analysis, possible predictors of drug response are now being explored using not only synovial biopsy specimens, but also peripheral blood samples [18–20]. While baseline profiling is important for predicting a favourable response to a given drug, information regarding differences in the mRNA profiles of responders and non-responders is indispensable to understanding the molecular basis of drug response. To monitor the changes in mRNA expression following the administration of anti-TNF biologics, we attempted to analyse the mRNA expression profiles in peripheral blood samples from RA patients taken at multiple time points during treatment with a chimeric anti-TNF-{alpha} monoclonal antibody, infliximab. In this report, we show that several sets of genes were closely correlated with inflammatory response markers, like CRP, while other gene groups with unique kinetics were also identified. Interestingly, the kinetics of the expression patterns of several genes was clearly discordant between responders and non-responders to infliximab. This information may help to personalize therapeutic strategies using anti-TNF biologics.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 Supplementary data
 Acknowledgements
 References
 
Patients
To fulfil the ACR criteria for RA [21, 22], patients had to be at least 18 yrs of age, have an ACR functional class of I–III and should have been receiving MTX ≥6 mg/week for a minimum of 3 months and a stable dose for at least 6 weeks at the time of study enrolment. Patients meeting these criteria were recruited at the Division of Rheumatology/Clinical Immunology, Saitama Medical Center, Saitama Medical University, Japan. All patients must have had inadequate control of RA symptoms, as defined by a combination of ≥6 swollen joints, ≥6 painful joints and an ESR ≥28 mm/h or a CRP level of 20 mg/l, while receiving a stable MTX dose that at its maximum did not exceed the 2.5 x upper normal limit of a liver enzyme test. The exclusion criteria for infliximab treatment were based on the Japanese guidelines for the use of infliximab [23]. In addition to these guidelines, RA patients with other collagen-vascular disease complications were excluded from the study, with the exception of patients with secondary SS. The diagnosis of SS was made according to the American–European criteria [24]. Informed consent was obtained from all patients, in accordance with the Helsinki protocol.

Study procedures and evaluations
Infliximab (Remicade; Tanabe Seiyaku, Osaka, Japan) was infused at a dose of 3 mg/kg at 0, 2, 6 and every other 8 weeks thereafter in combination with the ongoing administration of MTX at a dose >6 mg/week. The doses of MTX, other DMARDs, NSAIDs and steroids were fixed throughout the entire study period. IA steroid injections were not permitted. Blood samples were collected immediately before the first intravenous injection of infliximab and 2, 14 and 22 weeks after the first injection to measure the serum protein and autoantibody levels and for mRNA profiling.

The patients were examined at every hospital visit they made to receive an infusion. The ACR core set of variables, including the number of swollen joints, the number of painful joints, physician's global assessment on a visual analogue scale (VAS) of 0–100 mm, patient's global assessment of disease activity on a scale of VAS (0–100 mm), duration of morning stiffness (minutes) and pain on a scale of VAS (0–100 mm), was evaluated. Disease activity was assessed using the ACR criteria for 20% improvement (ACR20), the ACR50, the ACR70 and the disease activity score of 28 joints (DAS28) [25].

The serum CRP level was measured using an LPIA CRP kit (Mitsubishi Chemical Iatron, Tokyo, Japan), the MMP-3 level was measured using an MMP-3 ELISA kit (Daiichi Chemicals, Tokyo, Japan) and the RF level was measured using an N-Latex-RF kit (Dade Behring, Tokyo, Japan). ANA was measured using indirect immunofluorescence on Hep-2 (MBL, Nagoya, Japan) and was defined as positive if the observed titre was x80 or greater than x80. Anti-SS-A(Ro), anti-SS-B(La) and anti-U1RNP were measured using an ELISA (MBL) and were defined as positive if the observed units were greater than the cut-off units. Human anti-chimeric antibody (HACA; anti-infliximab antibody) was measured using an ELISA (Immunodiagnostik, Bensheim, Germany) and was determined as positive according to the manufacturer's instructions. Briefly, the diluted serum samples (x200) were added onto infliximab-coated microtitre plates and incubated overnight at 4°C After extensive washing with buffer, infliximab conjugated with horseradish peroxidase was added and the samples were incubated, followed by extensive washing. Finally, the substrates were added to the plates and the optical density at 450–620 nm was measured.

Hand and foot X-rays were obtained at baseline, and two expert readers scored the images according to the previously reported method of vdH-Sharp [26].

The design of this study was approved by the ethical committees of Saitama Medical University (No. 173), and all the patients provided their written informed consent at the time of enrolment in the study.

Preparation of RNA from blood
Samples (2.5 ml x 2) of whole blood were drawn into PAXgene RNA tubes (Qiagen, Hilden, Germany), and the total RNA was extracted and purified according to the tube manufacturer's instructions. The quantity of RNA obtained from the extraction step was assessed using a NanoDrop ND-1000 instrument (Nano Technologies, Wilmington, DE, USA). The quality of the extracted RNA was determined using a Bioanalyser 2100 (Agilent Technologies, Palo Alto, CA, USA); the ribosomal RNA 28S/18S ratio was verified to be >1.3 in all the experiments.

Preparation of the cDNA microarray
We designed and prepared a low-density cDNA microarray for mRNA expression profiling in whole blood. Genes for this microarray were selected from the public database of SAGE (serial analysis of gene expression) results (http://133.11.248.12/; homepage of the Department of Molecular Prevent Medicine, School of Medicine, The University of Tokyo) for activated blood cells, such as T cells, dendritic cells, monocytes and macrophages [27–29]. We also incorporated findings from a high-density oligo-chip assay (U-95 GeneChip; Affymetrix, Santa Clara, CA, USA) that were obtained using peripheral blood mononuclear cells (PBMCs) isolated before and after infliximab treatment. A total of 747 genes were spotted onto SuperAmine (Telechem International, Sunnyvale, CA, USA) in quadruplicate along with positive and negative control genes, as described previously [29]. For most of the genes, each cDNA was designed to be ~500–600 bp and to be within ~1 kb from the 3'-poly A tail. All cDNAs for the microarray probe were cloned into the pGEM vector (Promega, Madison, WI, USA). All clones for the capture probe were sequenced and validated by comparison with the GenBank sequence.

To confirm the sensitivity and reproducibility of this customized DNA microarray, we used PBMCs stimulated in vitro with lipopolysaccharide (LPS) at a concentration of 5 µg/ml and monitored the mRNA expression for up to 16 h using this microarray. At the same time, an ELISA was employed to measure the TNF-{alpha} produced in the culture supernatant. A significant increase in TNF-{alpha} in the LPS-stimulated culture supernatants was confirmed, indicating that this system worked well (data not shown). While we did not detect any significant change in TNF-{alpha} mRNA before stimulation, the amount of transcript sharply increased with LPS stimulation, as detected using the microarray system (a 25-fold increase, compared with an unstimulated control, at 2 h), followed by a gradual decrease to the basal level. Following LPS stimulation, other transcripts, such as plasminogen activator inhibitor-II (PAI-II) and IFN-induced cellular resistance mediator protein A (MxA), showed a biphasic response, increasing initially and then returning to a lower level. Other transcripts, such as orsomucoid-1 and IL-10, showed a continuous increase for at least 16 h. Similar results were obtained in five repeated experiments, confirming the sensitivity and reproducibility of this system.

Reference RNA
Reference RNA was established from a mixture of whole blood (drawn into PAXgene tubes) RNA samples from healthy volunteers. The extracted total RNA, which was certified to be of sufficient quality using the Agilent RNA chip, was amplified using the MessageAmp aRNA kit (Ambion, Austin, TX, USA) to generate amplified RNA (aRNA). An external non-human artificial RNA (a Caenorhabditis elegans Y49G5B fragment) was spiked into the reference aRNA to distinguish it from the sample aRNA.

Preparation of sample RNA, labelling, hybridization and scanning
Total RNA (1 µg) from the patients was transcribed and amplified into aRNA using the MessageAmp aRNA kit (Ambion, Austin, Texas), according to the manufacturer's instructions. Next, an external control RNA mixture [lambda DNA (LD), a baculovirus glycoprotein gene (GP) and a Renilla luciferase gene (RL); 9 µg each] were added to both the sample and the reference aRNA. The sample and reference aRNAs were then labelled with Cy5-dUTP and Cy3-dUTP (PerkinElmer, Boston, MA, USA), respectively, using a SuperScript II kit (Invitrogen, Carlsbad, CA, USA) and random hexamers (TaKaRa, Kyoto, Japan). Competitive hybridization of Cy3-labelled reference and Cy5-labelled sample cDNA on the microarray was performed using a chamber system (Agilent Technologies, Palo Alto, CA, USA), according to the method described by Khodursky et al. [30]. The slides were scanned five times with five different power ranges using a ScanArray 5000 (PerkinElmer, Boston, MA, USA). For further statistical analysis, the data were converted from TIFF image data to signals using ImaGene software (BioDiscovery, El Segundo, CA, USA). The data files for the five scans were merged to establish a single representative data set for each gene (patent pending, PCT/JP03/06677). The Cy5 (patient sample)/Cy3 (reference sample) ratio for each mRNA signal was calculated after global Lowess normalization [31].

Real-time PCR
The primer and probe sets for IFN-inducible double-stranded RNA-activated protein kinase (PKR) (forward, CCTGTCCTCTGGTTCTTTTG; reverse, TGTCAGGAAGGTCAAATCTG; probe, CTACGTGTGAGTCCCAAAGCAAC) and IFN-inducible transmembrane 9–27 (forward, CCGTGCCCGACCATGT; reverse, CCCAGACAGCACCAGTTCAA; probe, TGGTCCCTGTTCAACACCCTCT) were prepared according to TaqMan Gene Expression Assays (a pre-formulated assay; Applied BioSystems, Foster City, California, USA). The probes were fluorescently labelled with 5-carboxyfluorescein (FAM; reporter) and tetramethyl-rhodamine (TAMRA; quencher) dye systems (Applied BioSystems, Foster, CA, USA). To determine the relative amount of RNA, standard curves were generated for each primer-probe set using the same reference aRNA as the microarray. TaqMan fluorescence-based quantitative real-time PCR was performed in 384-well plates on an ABI Prism7900HT sequence detection system, according to the manufacturer's protocol. According to the manufacturer's instructions, reverse transcription and amplification were accomplished in a single step using the TaqMan EZ RT-PCR core reagent (Applied BioSystems), 10 ng of patient aRNA, 100 nM of each primer and 200 nM of TaqMan probe labelled at the 5'-end with FAM and at the 3'-end with TAMRA. The reaction was performed using the following sequence: 2 min at 50°C, 30 min at 60°C, 5 min at 95°C and 50 cycles of 20 s at 95°C followed by 1 min at 62°C.

Statistics
Statistical analyses were performed using JMP software version 6.0 (SAS Institute, Tokyo, Japan), unless otherwise specified. The Wilcoxon/Kruskal–Wallis test was used for non-parametric comparisons between subgroups. Chronological changes in the same items, such as microarray data (Cy5/Cy3 ratio), the serum CRP level and the DAS28 score in responders and non-responders, were initially analysed using an equal variance f-test. If the P-value from the f-test was <0.05, the Welch t-test was used for comparisons. Differences were considered significant when the P-value was <0.05.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 Supplementary data
 Acknowledgements
 References
 
Patient characteristics and changes in clinical parameters
The baseline characteristics and the response to infliximab at 22 weeks in the 18 RA patients enrolled in this study are shown in Table 1. Four of the 18 patients had early stages of RA, with durations of <2 yrs. No significant differences in the clinical parameters were observed between the responders and the non-responders, although the CRP and MMP-3 levels were higher and the RF titres and vdH-Sharp scores were lower in the responders than in the non-responders in this patient population. During the infliximab treatment, significant changes in the populations of various blood cell types, including white blood cells, neutrophils, eosinophils, basophils, monocytes and lymphocytes, were not observed (data not shown). To assess the clinical response to infliximab treatment, we calculated the ACR response rate and the DAS28 based on the clinical data obtained at each visit. The 18 RA patients were then categorized as responders (n = 8) or non-responders (n = 10) based on their ACR50 response criteria at week 22, as shown in Table 1. The ACR50 response rate to infliximab was 8/18 (44.4%), which was higher than that observed in a Japanese clinical trial in a 3 mg/kg infliximab group at 14 weeks (30.2%) [32]. The reductions in the serum CRP levels and the DAS28 scores after the first infusion of infliximab were similar between the responders and the non-responders. However, these values increased with subsequent infusions in non-responders, while they remained at low levels in responders (Fig. 1). HACA directed against infliximab was detected in 3 of the 10 non-responders but was not detected in the 8 responders. HACA positivity in patients with ACR0, ACR20, ACR50 and ACR70 was 14.3, 30.0, 0 and 0%, respectively.


View this table:
[in this window]
[in a new window]

  		TABLE 1. Characteristics of enrolled patients at baseline

 

Figure 1
View larger version (9K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 1. Time course of CRP and DAS28 in RA patients during infliximab treatment. Serum CRP level (A) and the DAS28 score (B) in the responder (n = 8) and non-responder groups (n = 10). *P < 0.05 and **P < 0.01 between responders (filled circle) and non-responders (open circle).

 
cDNA microarray analysis of peripheral blood cells in RA patients treated with infliximab
We first selected genes that were differentially expressed by >1.5-fold between the patient and the reference control samples for at least one sampling point (see Supplementary Fig. 1, available as supplementary data at Rheumatology Online). Next, we searched for genes with statistically significant differences in expression between responders and non-responders to infliximab.

Approximately 15% of the total genes were found to have a >1.5-fold change, compared with the reference value, at one or more time points during 22 weeks of infliximab therapy. These genes included those for ribosomal proteins, proteins related to degradation or apoptosis (e.g. caspase and proteasome components) and proteins related to metabolism (e.g. folate receptor). As shown in Table 2, 18 genes that were differentially expressed between responders and non-responders during the monitoring period were successfully identified. Interestingly, the top ten genes in Table 2 were IFN-related. Prior to treatment, IFN-related genes were up-regulated in >50% of the RA patients; regulation was normalized in some of the patients, as shown in Fig. 2A. When the patients were classified as responders or non-responders, distinct patterns were observed at 14 weeks and similar patterns, but consisting of three clusters, at 22 weeks, but this was not obvious at 2 weeks. It is interesting to note that one of the two responders in the non-responder cluster at 14 weeks (marked as violet) was moved to the responder cluster at 22 weeks, but another responder (marked as pink) remained at the non-responder cluster at 22 weeks. Also, one non-responder in the responder cluster at 14 weeks (marked as yellow) remained in the responder cluster at 22 weeks. The responders were characterized by continued suppression to the normal level, while expression in the non-responders returned to the baseline level (Table 2). The expressions of IFN-related genes, including IFN-inducible gene family (transmembrane 2), IFN-induced cellular resistance mediator protein B (MxB), IFN-{alpha}-inducible peptide (6–16) gene, PKR and IFN-inducible transmembrane protein 9–27, showed similar patterns (Table 2 and Fig. 2B), suggesting that these genes may be regulated by a common key molecule. Indeed, some of these genes, including PKR, 2',5'-AS (OAS1), ISG-56 and IFI-6-16, have an IFN-stimulated response element (ISRE) in their promoter regions [33, 34].


View this table:
[in this window]
[in a new window]

  		TABLE 2. Genes differentially expressed between the responders and non-responders

 

Figure 2
Figure 2
View larger version (111K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 2. (A) Cluster analysis of gene expression profile at 0, 2, 14 and 22 weeks after infliximab treatment. Responders are shown in green and non-responders are shown in red in the tree-cluster. The gene set used here was as follows (JGS No., symbol, GenBank ID): 105: CDC42 (NM_001791), 148: PKR (NM_002759), 174: ISGF3G (NM_006084), 237: IL2RB (NM_000878), 243: STMN1 (NM_005563), 265: IFIT1 (NM_001548), 299: CD68 (NM_001251), 1028: IFITM1 (NM_003641), 1081: BST2 (NM_004335), 1264: MxB (NM_002463), 1269: EIF3S10 (NM_003750), 1299: IFIT3 (NM_001549), 1328: SELP (NM_003005), 1380: GZMA (NM_006144), 1381: GZMB (NM_004131), 1386: CX3CR1 (NM_001337), 1514: IFITM2 (NM_006435), 1520: CCL4 (NM_002984), 1554: PTMA (NM_002823). *P < 0.05 and **P < 0.01 between responders (filled circle) and non-responders (open circle). (B) Changes in the expression of IFN-related genes in RA patients during infliximab treatment as determined by microarray analysis and correlation between the expression and DAS28 score. In the left panel, the typical time courses for the expression of INF-related genes are shown, including IFN-inducible gene family (transmembrane protein 2), MxB and INF-{alpha}-inducible peptide (6–16) gene in responders (filled circle) (n = 8) and non-responders (open circle) (n = 10). The data represent the mean values ± S.E. In the right panel, the correlations between the level of individual gene expressions and the DAS28 score are shown. The Y-axis represents the microarray fold-change, and the X-axis represents the DAS score. *P < 0.05 and **P < 0.01 between responders (filled circle) and non-responders (open circle).

 
To confirm the data obtained using the customized cDNA microarray, we also analysed mRNA expression using real-time PCR. Figure 3 shows the results of real-time PCR data for IFN-inducible transmembrane protein 9–27 in responders and non-responders. The microarray data and the real-time PCR data in Fig. 3 are comparable, confirming the results. Expression profiling showed that the marked up-regulation of genes related to inflammation was suppressed to the reference level after treatment with infliximab. As shown in the left half of the Fig. 2B, a significant decrease in the up-regulated genes at baseline was obtained at 2 weeks after infliximab treatment in most of the patients, regardless of whether they were responders (closed circles) or non-responders (open circles). The decrease in the up-regulated genes in the responders was maintained throughout the treatment period after 2 weeks. Interestingly, the up-regulation of IFN-related genes re-appeared in parallel with the flare in DAS28 and serum CRP levels after 2 weeks in the non-responders (Fig. 1). Changes in the DAS28 level were strongly correlated with the expression of IFN-related genes, as shown in the right half of Fig. 2B.


Figure 3
View larger version (9K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 3. Changes in the expression of IFN-inducible transmembrane protein 9–27 mRNA in RA patients during infliximab treatment, as determined by microarray and semi-quantitative real-time PCR. The expression of IFN-inducible transmembrane protein 9–27 (IFITM1) mRNA was determined by microarray (A) and by real-time PCR (B). The results are depicted by the symbols for responders (filled circle) and non-responders (open circle). *P < 0.05 between responders (filled circle) and non-responders (open circle).

 
In addition to the IFN-related genes, several genes, such as the AP-1-associated adaptor complex subunit ({gamma}-adaptin) and some chemokines, showed unique kinetics patterns. As shown in Fig. 4D, the gene expression of the AP-1-associated adaptor complex subunit ({gamma}-adaptin) was increasingly up-regulated during the late phase of infliximab treatment in responders, in contrast to the pattern observed in the IFN-related genes. Interestingly, significant differences in the expression of CX3CR1, IL2RB and chemokine ligand 4 genes (Fig. 4A–C) as well as TNF-related genes (data not shown) were observed between responders and non-responders at baseline.


Figure 4
View larger version (13K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 4. Changes in the expression of cytokines, chemokines, and AP-1 mRNA in RA patients during infliximab treatment, as determined by microarray. X-axis shows treatment period. The expressions of CX3C chemokine receptor-1 (A), chemokine (C–C motif) ligand 4 (B), IL-2 receptor β-chain (C) and AP-1 clathrin adaptor complex, sigma 1B subunit (D) mRNA were determined by microarray in responders (filled circle) and non-responders (open circle). The data are represented as the mean ± S.E. *P < 0.05 and **P < 0.01 between responders (filled circle) and non-responders (open circle).

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
Supplementary data
 Acknowledgements
 References
 
In this report, we utilized a customized microarray to monitor the kinetics of mRNA expression profiles in peripheral blood samples from RA patients during treatment with infliximab, a chimeric monoclonal antibody against TNF-{alpha}. Although synovial biopsy samples reflect the consequences of inflammation at a specific joint and the therapeutic response to anti-TNF biologics more directly [19, 35], obtaining such samples, particularly from multiple sites and at multiple time points, is difficult. Thus, we focused on the use of peripheral blood samples.

Variability in microarray data arises from many sources, including sampling, RNA amplification and labelling, and the hybridization conditions. In particular, reliable procedures for blood sampling and the separation of PBMCs are indispensable for minimizing ex vivo changes in mRNA expression [36, 37]. Since the PAXgene RNA tube blood-drawing system can reduce or eliminate problems arising during handling or storage [38–40], we used this system and obtained an excellent quality and quantity of mRNA in whole blood cells from RA patients. Our initial experiments using LPS-activated PBMCs indicated that our customized microarray detected an increase in the expression of TNF-{alpha} mRNA prior to that of IFN-related genes, including MxA and 2',5'-AS (OAS1). Since the corresponding protein levels measured by ELISA also changed in a fashion similar to the changes in mRNA expression detected using the customized microarray, this array seems to be useful for detecting biologically meaningful changes in mRNA levels.

Open-labelled studies tend to exhibit a bias in the interpretations of responses towards a favourable outcome, particularly when the responders are judged according to ACR20 criteria. Instead, we used the ACR50 response to measure the response rate in this study because this measure is thought to better reflect the patients’ levels of satisfaction [41, 42]. Indeed, when we categorized the responders according to the ACR20 criteria, we did not observe a clear difference in the CRP level or the DAS28 score between the responders and non-responders. In turn, when we used ACR50 as the definition of response, significant differences in the CRP level and the DAS28 score were observed between the two groups. Thus, the patients were grouped as responders or non-responders based on their ACR50 response, similar to the protocol previously used to group therapeutic responses to MYX and etanercept [11]. Recently, accumulating evidence has demonstrated that the combination of anti-TNF biologics and MTX protects against joint destruction in all RA patients, irrespective of their clinical response, for up to 2 yrs of observation [43–45]; this finding raises the question as to whether a non-responder phenotype really exists and whether this phenotype is stable, particularly in terms of the response to joint damage. In these studies, the mean change in the joint score of the RA patients treated with anti-TNF plus MTX was almost zero. However, several groups of patients were examined, including those with the progression of radiographic joint damage, those with no significant change and those with an improvement. In this regard, it may be interesting to apply this microarray analysis to the prediction of patients with variable radiographic changes.

We observed a clear, statistically significant difference in the kinetics of IFN-related genes during infliximab treatment between the responders and the non-responders. One may wonder whether this difference is a consequence of the successful sustained inhibition of TNF-{alpha} production in responders, but not in non-responders. The sustained inhibition of TNF-{alpha} production depends, in part, on the trough level of the serum infliximab concentration, which has been reported to be correlated with the efficacy of infliximab [46–48]. The serum trough level of infliximab can be determined by several factors, such as the dose of infliximab, immunoglobulin clearance and HACA [49] in individual patients. Although the necessary trough level of infliximab should be determined in the future, HACA was positive at 22 weeks for 3 out of 10 non-responders, while it was not detected in 8 responders, suggesting that HACA and the subsequent promotion of infliximab clearance may lead to the inefficient inhibition of TNF-{alpha} production. Nevertheless, it is important to note that TNF-{alpha} itself does not differ significantly between responders and non-responders to infliximab at 22 weeks, indicating that other mechanisms may also be involved in this process.

Interestingly, the IFN signature was originally reported in the expression profiles of PBMCs from patients with SLE, but not from patients with RA [50, 51]. In this study, the IFN signature was also observed in a subset of RA patients, and TNF-{alpha} blockade caused an immediate and significant reduction in IFN-related gene expression, although the level of the increase in IFN-related genes in RA patients was 2–3 times that in healthy individuals, while the increase in patients with SLE was much higher. Recently, TNF-{alpha} has been shown to regulate type 1 IFN. Juvenile idiopathic arthritis patients treated with anti-TNF exhibited the IFN signature, which is responsible for the seroconversion to anti-dsDNA antibody, and lupus-like clinical manifestations developed in RA patients receiving anti-TNF therapy [52]. While these studies were performed in a limited number of juvenile patients, other reports have identified patients with the IFN signature in a subset of SLE patients with renal disease and anti-RNA-binding proteins like anti-RNP/Sm and anti-Ro/La, but not with anti-dsDNA antibodies. Furthermore, a significant association between high IFN-{alpha} scores and the absolute counts of lymphocytes in PBMC samples was observed, partly because of lymphocytopenia [53]. In this regard, SS complications may confer an IFN signature as a result of lymphocytopenia and anti-Ro/La positivity. Among our 18 RA patients, 4 patients had been diagnosed as having secondary SS. However, the IFN signature was not limited to the RA patients with secondary SS, ruling out this possibility. An elevated level of serum IFN-{alpha} may contribute to the IFN signature seen in patients with SLE [54]. Finally, it has been reported that the serum IFN-{alpha} level is elevated in patients with RA [55] and that IFN-{alpha} treatment for hepatitis C complications in RA patients induced or aggravated RA [56]. Again, it should be noted that quantitative RT-PCR showed no significant increases in IFN-{gamma} or IFN-{alpha}/β genes in peripheral blood samples, as demonstrated in this study. The mechanism responsible for the sustained inhibition of the IFN signature in responders and its reappearance in non-responders remains to be clarified, and such knowledge will likely identify new therapeutic targets for RA.

The AP-1-associated adaptor complex subunit is a member of the vesicle budding protein family and plays a role in protein transport between membrane compartments in receptor-mediated endocytosis [57]. This gene was up-regulated in the responder groups during the later phase of treatment. We suspect that TNF receptor recycling is activated in patients during the later stages of infliximab treatment, but the biological significance of this process is currently unclear. The expression of CD28 (data not shown) and ribosomal proteins also exhibited the same pattern as the AP-1-associated adaptor complex subunit.

We found that HLA-DQA1 was up-regulated at baseline, and its expression did not change significantly during the course of treatment. Although SNP analyses indicated a correlation between responsiveness to infliximab and the expression of HLA-B-associated transcript 2 and some other antigens [12], recent studies have shown that the shared epitope is correlated with disease severity, but not with the response to anti-TNF biologics [13]. On the contrary, the expression levels of HLA-DRB1 alleles and some other loci have been reported to be associated with the response to etanercept [11], supporting our results.

A recent microarray analysis of mononuclear cells separated from the peripheral blood of RA patients identified 20 genes that were significantly correlated with a favourable response to infliximab [20]. Interestingly, most of the genes listed were not related to the pathophysiological condition and were not TNF-{alpha} targets, but two genes, PTPN12 and MUSTN1, are regulated by the TNF-{alpha}/nuclear factor-{kappa}B pathway [20]. The 20 genes that were listed are not the same as those identified in this report, with the exception of HLA class II. This discrepancy may be due to differences in the samples (mononuclear cells vs whole blood cells), array systems, statistical analysis methods, patient ethnic and treatment backgrounds, criteria for responders and so on. Consistent with their results, we did not observe a clear change in TNF-related gene expression in peripheral blood cells after infliximab treatment. Our microarray may not have been sufficiently sensitive, although an in vitro experiment demonstrated a definite ability of the system to detect changes in TNF-{alpha} expression. Since the decay of the TNF-{alpha} gene transcript was rapid and occurred after <12 h in the in vitro experiment, the sampling timing may be critical for detecting significant changes in vivo.

In conclusion, we showed that a customized microarray could be used to monitor mRNA expression in peripheral blood cells from RA patients treated with an anti-TNF biologic, infliximab. Our results support the strategy of using a downsized, customized microarray and whole blood cell samples to identify potential responders to TNF inhibitors and to identify new molecular targets by analysing the expression profiles of non-responders.

Formula


   Supplementary data
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
Acknowledgements
 References
 
Supplementary data are available at Rheumatology Online.


   Acknowledgements
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
 Acknowledgements
References
 
We acknowledge Prof. Koji Matshishima at the University of Tokyo for his helpful discussions and suggestions. We also thank Dr Garcia-De La Torre for providing samples from patients in the late phases of treatment during our preliminary studies.

Funding: This study was supported by a grant from the Ministry of Health, Labor and Welfare of Japan. Disclosure statement: The authors have declared no conflicts of interest.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
 Acknowledgements
 References
 

  1. Maini R, St Clair EW, Breedveld F, et al. Infliximab (chimeric anti-tumour necrosis factor alpha monoclonal antibody) versus placebo in rheumatoid arthritis patients receiving concomitant methotrexate: a randomised phase III trial. ATTRACT Study Group. Lancet (1999) 354:1932–9.[CrossRef][Web of Science][Medline]
  2. Lipsky PE, van der Heijde DM, St Clair EW, et al. Infliximab and methotrexate in the treatment of rheumatoid arthritis. Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. N Engl J Med (2000) 343:1594–602.[Abstract/Free Full Text]
  3. Klareskog L, van der Heijde D, de Jager JP, et al. Therapeutic effect of the combination of etanercept and methotrexate compared with each treatment alone in patients with rheumatoid arthritis: double-blind randomised controlled trial. Lancet (2004) 363:675–81.[CrossRef][Web of Science][Medline]
  4. Breedveld FC, Weisman MH, Kavanaugh AF, et al. The PREMIER study: a multicenter, randomized, double-blind clinical trial of combination therapy with adalimumab plus methotrexate versus methotrexate alone or adalimumab alone in patients with early, aggressive rheumatoid arthritis who had not had previous methotrexate treatment. Arthritis Rheum (2006) 54:26–37.[CrossRef][Web of Science][Medline]
  5. Feldmann M, Maini RN. Anti-TNF alpha therapy of rheumatoid arthritis: what have we learned? Annu Rev Immunol (2001) 19:163–96.[CrossRef][Web of Science][Medline]
  6. Hyrich KL, Watson KD, Silman AJ, Symmons DP. Predictors of response to anti-TNF-alpha therapy among patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register. Rheumatology (2006) 45:1558–65.[Abstract/Free Full Text]
  7. Braun-Moscovici Y, Markovits D, Zinder O, et al. Anti-cyclic citrullinated protein antibodies as a predictor of response to anti-tumor necrosis factor-alpha therapy in patients with rheumatoid arthritis. J Rheumatol (2006) 33:497–500.[Abstract/Free Full Text]
  8. Lequerre T, Jouen F, Brazier M, et al. Autoantibodies, metalloproteinases and bone markers in rheumatoid arthritis patients are unable to predict their responses to infliximab. Rheumatology (2007) 46:446–53.[Abstract/Free Full Text]
  9. Yamanaka H, Tanaka Y, Sekiguchi N, et al. Retrospective clinical study on the notable efficacy and related factors of infliximab therapy in a rheumatoid arthritis management group in Japan (RECONFIRM). Mod Rheumatol (2007) 17:28–32.[CrossRef][Medline]
  10. Ranganathan P. Pharmacogenomics of tumor necrosis factor antagonists in rheumatoid arthritis. Pharmacogenomics (2005) 6:481–90.[CrossRef][Web of Science][Medline]
  11. Criswell LA, Lum RF, Turner KN, et al. The influence of genetic variation in the HLA-DRB1 and LTA-TNF regions on the response to treatment of early rheumatoid arthritis with methotrexate or etanercept. Arthritis Rheum (2004) 50:2750–6.[CrossRef][Web of Science][Medline]
  12. Martinez A, Salido M, Bonilla G, et al. Association of the major histocompatibility complex with response to infliximab therapy in rheumatoid arthritis patients. Arthritis Rheum (2004) 50:1077–82.[CrossRef][Web of Science][Medline]
  13. Marotte H, Pallot-Prades B, Grange L, et al. The shared epitope is a marker of severity associated with selection for, but not with response to, infliximab in a large rheumatoid arthritis population. Ann Rheum Dis (2006) 65:342–7.[Abstract/Free Full Text]
  14. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (1995) 270:467–70.[Abstract/Free Full Text]
  15. Gu J, Marker-Hermann E, Baeten D, et al. A 588-gene microarray analysis of the peripheral blood mononuclear cells of spondyloarthropathy patients. Rheumatology (2002) 41:759–66.[Abstract/Free Full Text]
  16. Olsen N, Sokka T, Seehorn CL, et al. A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells. Ann Rheum Dis (2004) 63:1387–92.[Abstract/Free Full Text]
  17. Neumann E, Kullmann F, Judex M, et al. Identification of differentially expressed genes in rheumatoid arthritis by a combination of complementary DNA array and RNA arbitrarily primed-polymerase chain reaction. Arthritis Rheum (2002) 46:52–63.[CrossRef][Web of Science][Medline]
  18. Ospelt C, Neidhart M, Gay RE, Gay S. Gene analysis for exploring the effects of drugs in rheumatoid arthritis. Arthritis Rheum (2005) 52:2248–56.[CrossRef][Web of Science][Medline]
  19. Lindberg J, af Klint E, Catrina AI, et al. Effect of infliximab on mRNA expression profiles in synovial tissue of rheumatoid arthritis patients. Arthritis Res Ther (2006) 8:R179.[CrossRef][Medline]
  20. Lequerre T, Gauthier-Jauneau AC, Bansard C, et al. Gene profiling in white blood cells predicts infliximab responsiveness in rheumatoid arthritis. Arthritis Res Ther (2006) 8:R105.[CrossRef][Medline]
  21. Hochberg MC, Chang RW, Dwosh I, Lindsey S, Pincus T, Wolfe F. The American College of Rheumatology 1991 revised criteria for the classification of global functional status in rheumatoid arthritis. Arthritis Rheum (1992) 35:498–502.[Web of Science][Medline]
  22. Felson DT, Anderson JJ, Boers M, et al. American College of Rheumatology. preliminary definition of improvement in rheumatoid arthritis. Arthritis Rheum (1995) 38:727–35.[Web of Science][Medline]
  23. Miyasaka N, Takeuchi T, Eguchi K. Official Japanese guidelines for the use of infliximab for rheumatoid arthritis. Mod Rheumatol (2005) 15:4–8.[CrossRef][Medline]
  24. Vitali C, Bombardieri S, Jonsson R, et al. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Ann Rheum Dis (2002) 61:554–8.[Abstract/Free Full Text]
  25. Prevoo ML, van't Hof MA, Kuper HH, van Leeuwen MA, van de Putte LB, van Riel PL. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum (1995) 38:44–8.[Web of Science][Medline]
  26. Kameda H, Sekiguchi N, Nagasawa H, et al. Development and validation of handy rheumatoid activity score with 38 joints (HRAS38) in rheumatoid arthritis patients receiving infliximab. Mod Rheumatol (2006) 16:381–8.[CrossRef][Medline]
  27. Hashimoto S, Suzuki T, Dong HY, Yamazaki N, Matsushima K. Serial analysis of gene expression in human monocytes and macrophages. Blood (1999) 94:837–44.[Abstract/Free Full Text]
  28. Hashimoto SI, Suzuki T, Nagai S, Yamashita T, Toyoda N, Matsushima K. Identification of genes specifically expressed in human activated and mature dendritic cells through serial analysis of gene expression. Blood (2000) 96:2206–14.[Abstract/Free Full Text]
  29. Nagai S, Hashimoto S, Yamashita T, et al. Comprehensive gene expression profile of human activated T(h)1- and T(h)2-polarized cells. Int Immunol (2001) 13:367–76.[Abstract/Free Full Text]
  30. Khodursky AB, Peter BJ, Cozzarelli NR, Botstein D, Brown PO, Yanofsky C. DNA microarray analysis of gene expression in response to physiological and genetic changes that affect tryptophan metabolism in Escherichia coli. Proc Natl Acad Sci USA (2000) 97:12170–5.[Abstract/Free Full Text]
  31. Yang YH, Dudoit S, Luu P, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res (2002) 30:e15.[Abstract/Free Full Text]
  32. Abe T, Takeuchi T, Miyasaka N, et al. A multicenter, double-blind, randomized, placebo controlled trial of infliximab combined with low dose methotrexate in Japanese patients with rheumatoid arthritis. J Rheumatol (2006) 33:37–44.[Web of Science][Medline]
  33. Kuhen KL, Vessey JW, Samuel CE. Mechanism of interferon action: identification of essential positions within the novel 15-base-pair KCS element required for transcriptional activation of the RNA-dependent protein kinase pkr gene. J Virol (1998) 72:9934–9.[Abstract/Free Full Text]
  34. Koike F, Satoh J, Miyake S, et al. Microarray analysis identifies interferon beta-regulated genes in multiple sclerosis. J Neuroimmunol (2003) 139:109–18.[CrossRef][Web of Science][Medline]
  35. 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) 4:187–96.[CrossRef][Web of Science][Medline]
  36. Hartel C, Bein G, Muller-Steinhardt M, Kluter H. Ex vivo induction of cytokine mRNA expression in human blood samples. J Immunol Methods (2001) 249:63–71.[CrossRef][Web of Science][Medline]
  37. Baechler EC, Batliwalla FM, Karypis G, et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun (2004) 5:347–53.[CrossRef][Web of Science][Medline]
  38. Tsui NB, Ng EK, Lo YM. Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clin Chem (2002) 48:1647–53.[Abstract/Free Full Text]
  39. Rainen L, Oelmueller U, Jurgensen S, et al. Stabilization of mRNA expression in whole blood samples. Clin Chem (2002) 48:1883–90.[Abstract/Free Full Text]
  40. Thach DC, Lin B, Walter E, et al. Assessment of two methods for handling blood in collection tubes with RNA stabilizing agent for surveillance of gene expression profiles with high density microarrays. J Immunol Methods (2003) 283:269–79.[CrossRef][Web of Science][Medline]
  41. Felson DT, Anderson JJ, Lange ML, Wells G, LaValley MP. Should improvement in rheumatoid arthritis clinical trials be defined as fifty percent or seventy percent improvement in core set measures, rather than twenty percent? Arthritis Rheum (1998) 41:1564–70.[Web of Science][Medline]
  42. Pincus T, Stein CM. ACR 20: clinical or statistical significance? Arthritis Rheum (1999) 42:1572–6.[CrossRef][Web of Science][Medline]
  43. Smolen JS, Han C, Bala M, et al. Evidence of radiographic benefit of treatment with infliximab plus methotrexate in rheumatoid arthritis patients who had no clinical improvement: a detailed subanalysis of data from the anti-tumor necrosis factor trial in rheumatoid arthritis with concomitant therapy study. Arthritis Rheum (2005) 52:1020–30.[CrossRef][Web of Science][Medline]
  44. Smolen JS, Van der Heijde DM, St Clair EW, et al. Predictors of joint damage in patients with early rheumatoid arthritis treated with high-dose methotrexate with or without concomitant infliximab: results from the ASPIRE trial. Arthritis Rheum (2006) 54:702–10.[CrossRef][Web of Science][Medline]
  45. Landewe R, Van der Heijde D, Klareskog L, van Vollenhoven R, Fatenejad S. Disconnect between inflammation and joint destruction after treatment with etanercept plus methotrexate: results from the trial of etanercept and methotrexate with radiographic and patient outcomes. Arthritis Rheum (2006) 54:3119–25.[CrossRef][Web of Science][Medline]
  46. St Clair EW, Wagner CL, Fasanmade AA, et al. The relationship of serum infliximab concentrations to clinical improvement in rheumatoid arthritis: results from ATTRACT, a multicenter, randomized, double-blind, placebo-controlled trial. Arthritis Rheum (2002) 46:1451–9.[CrossRef][Web of Science][Medline]
  47. Wolbink GJ, Voskuyl AE, Lems WF, et al. Relationship between serum trough infliximab levels, pretreatment C reactive protein levels, and clinical response to infliximab treatment in patients with rheumatoid arthritis. Ann Rheum Dis (2005) 64:704–7.[Abstract/Free Full Text]
  48. Edrees AF, Misra SN, Abdou NI. Anti-tumor necrosis factor (TNF) therapy in rheumatoid arthritis: correlation of TNF-alpha serum level with clinical response and benefit from changing dose or frequency of infliximab infusions. Clin Exp Rheumatol (2005) 23:469–74.[Web of Science][Medline]
  49. Maini RN, Breedveld FC, Kalden JR, et al. Therapeutic efficacy of multiple intravenous infusions of anti-tumor necrosis factor alpha monoclonal antibody combined with low-dose weekly methotrexate in rheumatoid arthritis. Arthritis Rheum (1998) 41:1552–63.[CrossRef][Web of Science][Medline]
  50. Baechler EC, Batliwalla FM, Karypis G, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci USA (2003) 100:2610–5.[Abstract/Free Full Text]
  51. Bennett L, Palucka AK, Arce E, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med (2003) 197:711–23.[Abstract/Free Full Text]
  52. Palucka AK, Blanck JP, Bennett L, Pascual V, Banchereau J. Cross-regulation of TNF and IFN-alpha in autoimmune diseases. Proc Natl Acad Sci USA (2005) 102:3372–7.[Abstract/Free Full Text]
  53. Kirou KA, Lee C, George S, Louca K, Peterson MG, Crow MK. Activation of the interferon-alpha pathway identifies a subgroup of systemic lupus erythematosus patients with distinct serologic features and active disease. Arthritis Rheum (2005) 52:1491–503.[CrossRef][Web of Science][Medline]
  54. Kirou KA, Salmon JE, Crow MK. Soluble mediators as therapeutic targets in systemic lupus erythematosus: cytokines, immunoglobulin receptors, and the complement system. Rheum Dis Clin North Am (2006) 32:103–19, ix.[CrossRef][Web of Science][Medline]
  55. Hasler F. Immunoreactive circulating alpha-interferon in rheumatoid arthritis. Br J Rheumatol (1990) 29:315–6.[Free Full Text]
  56. Ioannou Y, Isenberg DA. Current evidence for the induction of autoimmune rheumatic manifestations by cytokine therapy. Arthritis Rheum (2000) 43:1431–42.[CrossRef][Web of Science][Medline]
  57. Takatsu H, Sakurai M, Shin HW, Murakami K, Nakayama K. Identification and characterization of novel clathrin adaptor-related proteins. J Biol Chem (1998) 273:24693–700.[Abstract/Free Full Text]
Submitted 13 April 2007; revised version accepted 4 February 2008.
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?



This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
47/6/780    most recent
ken083v2
ken083v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Sekiguchi, N.
Right arrow Articles by Takeuchi, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Sekiguchi, N.
Right arrow Articles by Takeuchi, T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?