RNA-seq analysis of synovial fibroblasts brings new insights into rheumatoid arthritis

  • Daniel P Heruth1Email author,

    Affiliated with

    • Margaret Gibson1,

      Affiliated with

      • Dmitry N Grigoryev1,

        Affiliated with

        • Li Qin Zhang1 and

          Affiliated with

          • Shui Qing Ye1, 2

            Affiliated with

            Cell & Bioscience20122:43

            DOI: 10.1186/2045-3701-2-43

            Received: 12 October 2012

            Accepted: 23 November 2012

            Published: 21 December 2012

            Abstract

            Background

            Rheumatoid arthritis (RA) is a chronic autoimmune-disease of unknown origin that primarily affects the joints and ultimately leads to their destruction. Growing evidence suggests that synvovial fibroblasts play important roles in the initiation and the perpetuation of RA but underlying molecular mechanisms are not understood fully. In the present study, Illumina RNA sequencing was used to profile two human normal control and two rheumatoid arthritis synvovial fibroblasts (RASFs) transcriptomes to gain insights into the roles of synvovial fibroblasts in RA.

            Results

            We found that besides known inflammatory and immune responses, other novel dysregulated networks and pathways such as Cell Morphology, Cell-To-Cell Signaling and Interaction, Cellular Movement, Cellular Growth and Proliferation, and Cellular Development, may all contribute to the pathogenesis of RA. Our study identified several new genes and isoforms not previously associated with rheumatoid arthritis. 122 genes were up-regulated and 155 genes were down-regulated by at least two-fold in RASFs compared to controls. Of note, 343 known isoforms and 561 novel isoforms were up-regulated and 262 known isoforms and 520 novel isoforms were down-regulated by at least two-fold. The magnitude of difference and the number of differentially expressed known and novel gene isoforms were not detected previously by DNA microarray.

            Conclusions

            Since the activation and proliferation of RASFs has been implicated in the pathogenesis of rheumatoid arthritis, further in-depth follow-up analysis of the transcriptional regulation reported in this study may shed light on molecular pathogenic mechanisms underlying synovial fibroblasts in arthritis and provide new leads of potential therapeutic targets.

            Keywords

            RNA-seq Next generation sequencing Rheumatoid arthritis Synovial fibroblasts Transcriptional regulation

            Background

            Rheumatoid arthritis [RA] is a chronic, systemic autoimmune disorder associated with both genetic and environmental factors. RA affects 1% of the world’s population, develops most commonly in adults between 40 – 70 years old, and occurs more frequently in women than in men [14]. xAlthough the etiology of the disease has not been elucidated fully, the pathogenesis of RA is characterized by the influx of cells from both the innate and the adaptive immune systems [5]. These cells induce increased pro-inflammatory cytokine production, decreased synthesis of anti-inflammatory cytokines, and the subsequent activation and proliferation of synovial fibroblasts (SFs) [3, 4]. Rheumatoid arthritis synovial fibroblasts (RASFs) produce additional cytokines, chemokines and matrix-degrading enzymes which ultimately leads to the thickening and progressive destruction of joint membrane, cartilage and bone [57]. Characterization of the cytokine signaling pathways involved in RA has provided a significant opportunity for identifying pro-inflammatory cytokines which can be targeted for novel therapeutic intervention. The development of biological response modifiers (BRMs), particularly the TNF, IL-1, and IL-6 antagonists, have led to major advances in RA therapy [3, 7]. However, these agents are not effective in all patients, underscoring the genetic heterogeneity of the disease and the need for the development of additional BRMs [8]. RASFs are intricately involved in the pathogenesis of RA and provide a source for the identification of new genes and pathways that can be targeted for therapeutic intervention.

            With the advent of next generation DNA sequencing technologies [9], such as RNA sequencing (RNA-seq), a more comprehensive and accurate transcriptome analysis has become feasible and affordable. In RNA-seq, short fragments of complementary DNA (cDNA) are sequenced (reads) and then mapped onto the reference genome. RNA-seq enables not only the identification of differentially expressed genes, but also the precise quantitative determination of exon and isoform (alternative splicing) expression, along with the characterization of transcription initiation sites (TSSs) and new splicing variants [10]. In the present study, we performed a comprehensive transcriptome analysis of RNA from RASFs from two adult female RA patients and the SF RNA from two healthy female donors, using the RNA-seq technique. We found significant differences in the expression levels of both genes and gene isoforms between normal SFs and RASFs RNA samples. These data provide broader and deeper insights, particularly with respect to isoform expression, into the effect of RA on the transcriptional regulation of synovial fibroblasts and a rich resource for further experimentation into the pathogenesis of the disease.

            Results

            RNA sequencing

            Human SFs RNAs from two healthy control donors and two patients with RA were purchased from Cell Applications, Inc. (San Diego, CA). Diseased samples were age and sex-matched with normal controls (Additional file 1). Paired-end cDNA libraries for each RNA sample were prepared and sequenced using the Illumina TruSeq RNA Sample Preparation Kit, as outlined previously [11, 12].

            Quality analysis of RNA-seq data

            Real-time analysis of the sequencing run was performed by the Illumina HiSeq Control Software. Clusters of identical sequences were generated on the Illumina cBot and the number of those clusters was reported, along with the percentage of those clusters passing an internal quality filter. Across the 4 samples, between 433,000 and 482,000 raw clusters were detected, with a median of 446,000 clusters per lane. Between 90.9% and 95.0% of those clusters passed the filter, with a median of 93.2% of the clusters passing the filter. Each lane was aligned in real-time with the phiX genome and between 0.80% and 0.84% of the clusters aligned, with a median of 0.81% aligned. Our control lane of phiX produced 290,000 clusters with 97.9% passing the filter and 99.08% aligning to the phiX genome. All these values were within the recommended limits established by Illumina.

            Post-run quality analysis of RNA-seq data was carried out as described by Twine et al. [13]. The total number of reads produced from each sample was between 80,782,262 and 89,757,726, with a mean across all samples of 84,177,268 (Table 1). The difference in the number of reads between the control samples and the RA samples was not statistically significant (Student’s t-test, p=0.27). To assess the quality of the reads, data was pulled from the TopHat log files as well as the output files. Between 0.10% and 0.15% of the reads were removed due to low quality before mapping to the reference genome began. Between 82.8% and 89.1% of the total reads mapped to the human genome. To ensure the uniform coverage across the genome, the data was visualized using a local copy of the Integrative Genomics Viewer. An example of the reads for both normal and RA patient samples mapped against chromosome 1 is shown in Figure 1. The average alignment was computed across the genome and those alignment scores were log-transformed (base 2) to better visualize the full range of the data. As expected, no reads mapped to the centromere or areas of the chromosome without genes.
            Table 1

            RNA-seq sequence reads mapping to UCSC Human genome build 19 by TopHat v1.3.0/Bowtie v0.12.7

             

            WT

             

            RA

             
             

            1

            2

            Average

            1

            2

            Average

            Total reads

            80,782,262

            82,738,536

            81,760,399

            89,757,726

            83,430,548

            86,594,137

            Reads removed

            0.10%

            0.12%

            0.11%

            0.15%

            0.12%

            0.13%

            Read aligned toreference genome

            82.8%

            84.6%

            83.7%

            89.1%

            87.6%

            88.4%

            Total reads and the percentage of those reads removed due to low quality and aligned to hg19 by TopHat. TopHat allows two mismatches when aligning to a reference genome.

            http://static-content.springer.com/image/art%3A10.1186%2F2045-3701-2-43/MediaObjects/13578_2012_Article_86_Fig1_HTML.jpg
            Figure 1

            A transcription profile of RNA from control synovial fibroblasts and rheumatoid arthritis fibroblasts for chromosome 1. The RNA-seq read density plotted along chromosome 1 is shown. Average alignment was computed by igvtools. Each bar represents the log2 frequency of reads along the chromosome which range from 0 to 3000 for both control synovial fibroblasts and rheumatoid arthritis fibroblasts.

            Differentially expressed genes and isoforms

            After mapping the sequencing reads to the reference genome with TopHat, transcripts were assembled and their relative expression levels were calculated with Cufflinks in Fragments Per Kilobase of exon per Million fragments mapped (FPKM). The sub-program, Cuffdiff was then used to calculate the differential expression on the gene and transcript level, as well as the calculation of alternative promoter usage and alternative splicing. Cufflinks calculates the differential gene expression with the ratio of the RA group to the control group for every gene and transcript along with the statistical significance of the values. Two categories of differential gene/isoform expression were identified. The first category consists of genes/isoforms expressed only in control SFs or only in RASFs. The second category consists of genes/isoforms in which expression of both samples in each group was up-regulated or down-regulated two-fold or greater between control SFs and RASFs.

            Overall, there are 12,977 expressed genes in the control SFs and 13,445 expressed genes in the RASFs, which were aligned to the reference genome (Table 2). There are 214 genes, whose expressions were only detected in the normal SFs, while 682 genes whose expressions were only detected in RASFs. There are 122 up-regulated and 155 down-regulated genes in RASFs with at least two-fold change compared to the SFs (Table 2). As for known isoforms, there are 20,647 in the normal SFs and 21,102 in RASFs. Among them, there are 526 known isoforms, whose expressions were detected only in the normal SFs, while 981 known isoforms whose expressions were detected only in RASFs. There are 343 up-regulated and 262 down-regulated known isoforms in RASFs by at least two-fold change compared to the SFs (Table 2). For novel isoforms whose annotations are not known in the current reference gene or transcript database, there are 42,124 expressed in the normal SFs and 42,171 expressed in RASFs. Among them, there are 105 novel isoforms whose expressions were only detected in the normal SFs, while 152 novel isoforms were only detected in RASFs. There are 561 up-regulated and 520 down-regulated novel isoforms in RASFs by at least two-fold change compared to the SFs (Table 2).
            Table 2

            Gene/isoform expression summary

             

            Genes

             

            Control

            RA Patients

            Total Genes Expressed

            12,977

            13,445

            Control Only

            214

             

            RA Patients Only

             

            682

            Up-regulated (2-fold or greater difference)

             

            122

            Down-regulated (2-fold or greater difference)

             

            155

             

            Known Isoforms

             

            Control

            RA Patients

            Total Known Isoforms Expressed

            20,647

            21,102

            Control Only

            526

             

            RA Patients Only

             

            981

            Up-regulated (2-fold or greater difference)

             

            343

            Down-regulated (2-fold or greater difference)

             

            262

             

            Novel Isoforms

             

            Control

            RA Patients

            Total Novel Isoforms Expressed

            42,124

            42,171

            Control Only

            105

             

            RA Patients Only

             

            152

            Up-regulated (2-fold or greater difference)

             

            561

            Down-regulated (2-fold or greater difference)

             

            520

            Genes, known isoforms and novel isoforms expressed in control synovial fibroblasts and synovial fibroblasts from patients with rheumatoid arthritis. Expression determined by Cufflinks, after normalization to a panel of housekeeping genes. The fold change is the ratio of RA FPKM to WT FPKM.

            Genes expressed only in control SFs or only in RASFs

            The top 10 up- and down-regulated genes expressed only in control SFs or only in RASFs are presented in Table 3. An expanded list of the top 50 genes expressed only in either control SFs or in RASFs is presented in Additional file 2. Analysis of the genes expressed only in RASF reveals that nine of the top ten genes, including the major histocompatibility complex (MHC) genes HLA-A. –B, -C, and –E, are located on chromosome 6 (Table 3). Remarkably, 36 of the top 50 genes (Additional file 2) expressed only in RASFs are located on chromosome 6. The MHC, particularly the HLA-DRB1 alleles are strongly associated with RA [1416]. A recent study by Plenge et al. has also identified associations of alleles lying outside the MHC on chromsome 6 with RA [17]. Our observation that the CLIC1 gene (chloride intracellular protein) is expressed in RASFs correlates with the finding that CLIC1(-/-) mice were protected from development of serum transfer induced K/BxN arthritis [18]. Two genes, the high mobility group box 1 (HMGA1) and the latent transforming growth factor beta binding protein 1 (LTBP1) have been reported to be elevated in RA [19, 20] however they are not expressed in the RASFs examined in this study (Table 3). Interestingly, HMGA1 is the only gene on chromosome 6 in the list of top 50 genes expressed in normal SFs but not expressed in RASFs (Additional file 2). The CD59 complement regulatory protein (CD59) is not expressed in RASFs in this study. This observation supports the finding that CD59 is protective as CD59 (-/-) knockout mice present with more severe symptoms in the murine antigen-induced arthritis model [21]. An automated literature search using PubMatrix [22] reveals that eleven of the twenty genes listed in Table 3 have not yet been identified to be associated with RA (Additional file 3). These genes, which include chromosome 6 open reading frame 48 (C6orf48), the scavenger receptor class A, member 5 (SCARA5), CutA divalent cation tolerance homolog (CUTA), Leucine rich repeat containing 59 (LRRC59), and the protein phosphatase 1, regulatory (inhibitor) subunit 14A (PPP1R14A), may provide additional therapeutic targets. These potential targets include characterized genes, like the iron receptor SCARA5 [23] and genes, such as C6orf48, that have not yet been well-studied. CutA, which is up-regulated in RASFs, interacts with BACE1 to regulate B-cleavage of the B-amyloid protein (APP) [24]. CutA may play a role in the pathogenesis of Alzheimer’s, however, its role in rheumatoid arthritis remains to be elucidated. LRRC59 is required for the nuclear transport of the fibroblast growth factor 1 (FGF1) [25]. The affect on FGF1 function resulting from decreased LRRC59 expression in RASFs warrants further investigation. PPP1R14A, which inhibits protein phosphatase 1 activity, is not expressed in RASFs compared to normal SFs, suggesting that PP1 activity will increase dramatically in RASFs. PP1 controls the Akt signal transduction pathway to regulate cell growth, cell survival, and cell differentiation [26].
            Table 3

            Top ten up- and down- regulated genes expressed only in normal synovial RNA or only in rheumatoid arthritis synovial RNA

            Gene

            Description

            Chr

            RA FPKM

            RA2 FPKM

            WT1 FPKM

            WT2 FPKM

            Avg.RA

            Avg.WT

            Ensembl gene ID

            HLA-B

            Major histocompatibility complex, class 1, B

            chr6

            704.3

            728.3

            --

            --

            716.3

            --

            ENSG00000228964

            HLA-A

            Major histocompatibility complex, class 1, A

            chr6

            778.2

            585.6

            --

            --

            681.9

            --

            ENSG00000223980

            HLA-C

            Major histocompatibility complex, class 1, C

            chr6

            534.8

            452.1

            --

            --

            493.5

            --

            ENSG00000206435

            TUBB

            Tubulin, beta class I

            chr6

            405.3

            416.7

            --

            --

            411

            --

            ENSG00000232421

            CLIC1

            Chloride intracellular channel 1

            chr6

            350.5

            369.6

            --

            --

            360

            --

            ENSG00000223639

            RPS18

            Ribosomal Protein S18

            chr6

            260.4

            269.1

            --

            --

            264.8

            --

            ENSG00000227794

            HLA-E

            Major histocompatibility complex, class 1, E

            chr6

            243.5

            260.2

            --

            --

            251.9

            --

            ENSG00000230254

            C6orf48

            Chromosome 6 open reading frame 48

            chr6

            119.5

            225.8

            --

            --

            172.6

            --

            ENSG00000206380

            SCARA5

            Scavenger receptor class A, member 5

            chr8

            11.36

            316

            --

            --

            163.7

            --

            ENSG00000168079

            CUTA

            CutA divalent cation tolerance homolog

            chr6

            168.7

            133.7

            --

            --

            151.2

            --

            ENSG00000226492

            ACTG2

            Actin, gamma 2, smooth muscle, enteric

            chr2

            --

            --

            1087.76

            2.58

            --

            545.17

            ENSG00000163017

            RPS24

            Ribosomal Protein S24

            chr10

            --

            --

            407.72

            429.08

            --

            418.40

            ENSG00000138326

            PSAP

            Prosaposin

            chr10

            --

            --

            236.86

            519.83

            --

            378.35

            ENSG00000197746

            HMGA1

            High mobility group box 1

            chr6:

            --

            --

            139.35

            265.81

            --

            202.58

            ENSG00000189403

            CD59

            CD59 molecule, complement regulatory protein

            chr11

            --

            --

            111.47

            149.28

            --

            130.38

            ENSG00000085063

            LRRC59

            Leucine rich repeat containing 59

            chr17

            --

            --

            116.95

            88.57

            --

            102.76

            ENSG00000108829

            PPP1R14A

            Protein phosphatase 1, regulatory (inhibitor) subunit 14A

            chr19

            --

            --

            142.49

            1.10

            --

            71.79

            ENSG00000167641

            LTBP1

            Latent transforming growth factor beta binding protein 1

            chr2

            --

            --

            54.38

            66.08

            --

            60.23

            ENSG00000049323

            SNHG6

            Small nucleolar RNA host gene 6

            chr8

            --

            --

            46.68

            65.62

            --

            56.15

            ENSG00000245910

            HNRNPC

            Heterogeneous nuclear ribonucleoprotein C (C1/C2)

            chr14

            --

            --

            52.08

            58.46

            --

            55.27

            ENSG00000092199

            Genes which were differentially expressed as determined by Cufflinks, after normalization to a panel of housekeeping genes.

            The genes were ranked by FPKM and the 10 with the highest or lowest values are listed here.

            Genes differentially expressed two-fold or greater between control SFs and RASFs

            The top 10 up- and down-regulated genes, along with the expanded top 50 list, in which expression of both samples in each group was up-regulated or down-regulated two-fold or greater between control SFs and RASFs are presented in Table 4 and in Additional file 4, respectively. Three genes in the top 10 up-regulated list have been associated with rheumatoid arthritis (Additional file 3). Interleukin 26 (IL26) is up-regulated (80.8-fold) in RASFs compared to SFs. Corvaisier et al. has demonstrated that IL26 is over-expressed in arthritis and induces inflammatory cytokine production [27]. The v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) (MAFB) gene is up-regulated (16.2-fold) in RASFs. Liu et al. identified polymorphisms in the MAFB gene associated with altered response to anti-TNF treatment in patients with RA [28]. Expression of the adrenergic, alpha-2A-, receptor (ADRA2A) increased 14.4-fold in RASFs. The adrenergic, alpha-2A-, receptor may play a critical role in the proliferation and differentiation of synoviocytes [29]. Although thrombospondin 4 (THSB4) has not yet been associated with arthritis (Additional file 3), thrombospondin 1 (THBS1) is over-expressed in RA tissue [30]. Thrombospondin 1 and 4 are extracellular matrix remodeling proteins that have been associated with increased inflammation in coronary artery disease (CAD) [30, 31], and thus may provide a link between RA and CAD. Like THBS4, the remaining six genes up-regulated in RASFs (Table 4) have not yet been associated with RA but provide potential for further investigation. The solute carrier family members, SLC2A5, SLC14A1, and SLC12A8 are over-expressed in RASFs suggesting alterations in cellular metabolism. Complement Factor 1 (CF1) may represent a new target as the complement system plays a major role in the pathogenesis of rheumatoid disease [32]. Expression of the plasminogen activator inhibitor gene, serpin peptidase inhibitor, clade B (ovalbumin), member 2 (SERPINB2) is decreased (-79.1-fold) in RASFs compared to control SFs (Table 4). The plasminogen activation pathway is dysregulated in arthritis [33]. Aquoporin 1 (AQP1) expression has been shown to be up-regulated in the synovium from RA patients [34], but is down-regulated (-44.3-fold) in our samples. The coagulation factor X (F10), which may contribute to tissue injury and remodeling [35], is down-regulated (-27.5 fold). The hedgehog interacting protein (HHIP) inhibits the sonic hedgehog (SSH) signaling pathway. Inactivation of SSH inhibitor smoothened (Smo) blocks sonic hedgehog signaling and prevents osteophyte formation in the murine serum transfer arthritis model [36]. Thus, the decrease (-26.6-fold) in HHIP expression observed in RASFs in this study may result in increased SSH activity resulting in advanced osteophyte formation.
            Table 4

            Top ten up- and down- regulated genes expressed in rheumatoid arthritis synovial RNA

            Gene

            Description

            Chr

            RAFPKM

            RA2FPKM

            WT2FPKM

            WT2FPKM

            Avg.RA

            Avg.WT

            Foldchange

            Ensembl gene ID

            IL26

            interleukin 26 solute carrier family 2 (facilitated

            chr12

            17.913

            1.927

            0.101

            0.144

            9.920

            0.123

            80.83

            ENSG00000111536

            SLC2A5

            glucose/fructose transporter), member 5

            chr1

            65.268

            21.844

            0.340

            3.851

            43.556

            2.096

            20.79

            ENSG00000142583

            PLXDC2

            plexin domain containing 2 v-maf musculoaponeurotic

            chr10

            9.222

            2.316

            0.098

            0.573

            5.769

            0.335

            17.21

            ENSG00000120594

            MAFB

            fibrosarcoma oncogene homolog B (avian) solute carrier family 14 (urea

            chr20

            37.177

            6.679

            0.605

            2.096

            21.928

            1.351

            16.24

            ENSG00000204103

            SLC14A1

            transporter), member 1 (Kidd blood group

            chr18

            6.188

            2.096

            0.360

            0.177

            4.142

            0.269

            15.41

            ENSG00000141469

            ADRA2A

            adrenergic, alpha-2A-, receptor

            chr10

            4.373

            10.666

            0.899

            0.145

            7.519

            0.522

            14.42

            ENSG00000150594

            MAN1C1

            mannosidase, alpha, class 1C, member 1

            chr1

            16.774

            24.3346

            0.65654

            2.7991

            20.554

            1.728

            11.90

            ENSG00000117643

            CFI

            complement factor I solute carrier family 12

            chr4

            21.803

            28.7589

            0.10437

            4.3263

            25.281

            2.215

            11.41

            ENSG00000205403

            SLC12A8

            (potassium/chloride transporters), member 8

            chr3

            7.442

            15.2539

            0.62851

            1.4395

            11.348

            1.034

            10.97

            ENSG00000221955

            THBS4

            thrombospondin 4

            chr5

            5.8622

            7.09226

            0.05843

            1.2202

            6.477

            0.639

            10.13

            ENSG00000113296

            SERPINB2

            serpin peptidase inhibitor, clade B (ovalbumin), member 2

            chr18

            0.236

            0.095

            16.207

            10.030

            0.166

            13.118

            −79.11

            ENSG00000197632

            AQP1

            aquaporin 1 (Colton blood group)

            chr7

            5.675

            3.860

            396.183

            25.802

            4.768

            210.992

            −44.26

            ENSG00000240583

            APOBEC3B

            apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3B

            chr22

            0.0775

            0.23398

            2.59902

            7.4102

            0.156

            5.005

            −32.13

            ENSG00000179750

            NEFM

            neurofilament, medium polypeptide

            chr8

            0.0592

            0.07122

            3.69528

            0.2014

            0.065

            1.948

            −29.87

            ENSG00000104722

            CCDC3

            coiled-coil domain containing 3

            chr10

            0.1241

            0.09255

            4.48673

            1.7074

            0.108

            3.097

            −28.59

            ENSG00000151468

            F10

            coagulation factor X

            chr13

            0.197

            0.21727

            8.44739

            2.9581

            0.207

            5.703

            −27.53

            ENSG00000126218

            HHIP

            hedgehog interacting protein

            chr4

            0.073

            0.26532

            7.88577

            1.1075

            0.169

            4.497

            −26.58

            ENSG00000164161

            ARL2-SNX15

            -

            chr11

            0.361

            0.39062

            8.29552

            11.168

            0.376

            9.732

            −25.90

            -

            HES4

            hairy and enhancer of split 4

            chr1

            0.3015

            0.46829

            16.2396

            1.0894

            0.385

            8.664

            −22.51

            ENSG00000188290

            GPAT2

            glycerol-3-phosphate acyltransferase 2, mitochondrial

            chr2

            0.5547

            0.36585

            17.6005

            3.0486

            0.460

            10.325

            −22.43

            ENSG00000186281

            Genes which were differentially expressed as determined by Cufflinks, after normalization to a panel of housekeeping genes. The fold change is the ratio of RASF FPKM to control FPKM. Genes with a fold change of 1.2-fold or greater were defined as significant. The genes were ranked on their fold change and the 10 with the highest or lowest fold changes are listed here.

            Known isoforms expressed only in control SFs or only in RASFs

            The top 10 isoforms expressed only in control SFs or only in RASFs are presented in Table 5. An expanded list of the top 50 up- and down-regulated known isoforms expressed only in either control SFs or in RASFs is presented in Additional file 5. The known isoforms identified in Table 5 correlate with the genes expressed only in control SFs or only in RASFs (Table 3 and Additional file 2). Single isoforms were detected for SCARA5, PLA2G2A, SPCS1, CITED2, IL13RA2, SLP1, FAM20A, NUMA1, PSAP, LRRC59, PPP1R14A, and SNHG6. Two isoforms were identified for PRG4, ACTG2, and CD59, while five and six isoforms exist for RPS24 and HMG1A, respectively (Table 5 and Additional file 5).
            Table 5

            Top ten up- and down- regulated isoforms expressed only in normal synovial RNA or only in rheumatoid arthritis synovial RNA

            Gene

            Description

            Locus

            Length

            RA1FPKM

            RA2FPKM

            WT1FPKM

            WT2FPKM

            Avg. RA

            Avg. WT

            Ensembl gene ID

            SCARA5

            Scavenger receptor class A, member 5

            chr8

            4151

            11.36

            353.51

            --

            --

            182.43

            --

            ENSG00000168079

            PLA2G2A

            Phopholipase A2, group IIA

            chr1

            969

            3.81

            264.84

            --

            --

            134.33

            --

            ENSG00000188257

            SPCS1

            Signal peptidase complex subunit 1 homolog

            chr3

            1084

            81.32

            112.54

            --

            --

            96.93

            --

            ENSG00000114902

            CITED2

            Cbp/p300-interacting transactivator, 2

            chr6

            1929

            69.03

            119.73

            --

            --

            94.38

            --

            ENSG00000164442

            IL13RA2

            Interleukin 13 receptor, alpha 2

            chrX

            1373

            9.94

            90.27

            --

            --

            50.10

            --

            ENSG00000123496

            SLPI

            Secretory leukocyte peptidase inhibitor

            chr20

            598

            1.36

            98.75

            --

            --

            50.06

            --

            ENSG00000124107

            KYNU

            Kynureninase

            chr2

            1672

            16.46

            79.31

            --

            --

            47.88

            --

            ENSG00000115919

            FAM20A

            Family with sequence similarity 20, member A

            chr17

            4275

            28.98

            60.54

            --

            --

            44.76

            --

            ENSG00000108950

            NUMA1

            Nuclear mitotic apparatus protein 1

            chr11

            7182

            40.51

            45.42

            --

            --

            42.96

            --

            ENSG00000137497

            PRG4

            Proteoglycan 4

            chr1

            4765

            1.27

            81.59

            --

            --

            41.43

            --

            ENSG00000116690

            ACTG2

            Actin, gamma 2, smooth muscle, enteric

            chr2

            1331

            --

            --

            1046.45

            2.16

            --

            524.30

            ENSG00000163017

            PSAP

            Prosaposin

            chr10

            2822

            --

            --

            234.56

            510.86

            --

            372.71

            ENSG00000197746

            RPS24

            Ribosomal Protein S24

            chr10

            655

            --

            --

            149.70

            298.29

            --

            224.00

            ENSG00000138326

            LRRC59

            Leucine rich repeat containing 59

            chr17

            2915

            --

            --

            116.95

            88.57

            --

            102.76

            ENSG00000108829

            HMGA1

            High mobility group box 1

            chr6

            1846

            --

            --

            44.78

            99.59

            --

            72.19

            ENSG00000189403

            CD59

            CD59 molecule, complement regulatory protein

            chr11

            7619

            --

            --

            68.61

            75.31

            --

            71.96

            ENSG00000085063

            PPP1R14A

            Protein phosphatase 1, regulatory (inhibitor) subunit 14A

            chr19

            718

            --

            --

            142.49

            1.10

            --

            71.79

            ENSG00000167641

            HMGA1

            High mobility group box 1

            chr6

            1993

            --

            --

            43.81

            93.26

            --

            68.54

            ENSG00000189403

            RPS24

            Ribosomal Protein S24

            chr10

            633

            --

            --

            80.53

            47.31

            --

            63.92

            ENSG00000138326

            SNHG6

            Small nucleolar RNA host gene 6

            chr8

            472

            --

            --

            46.68

            65.62

            --

            56.15

            ENSG00000245910

            Isoforms which were differentially expressed as determined by Cufflinks, after normalization to a panel of housekeeping genes.

            The isoforms were ranked by FPKM and the 10 with the highest or lowest values are listed here.

            Known isoforms differentially expressed two-fold or greater between control SFs and RASFs

            The top 10 up- and down-regulated known isoforms, along with the expanded top 50 list, in which expression of both samples in each group was up-regulated or down-regulated two-fold or greater between control SFs and RASFs are presented in Table 6 and in Additional file 6, respectively. Thirteen of the known isoforms identified in Table 6 can be found in the top 50 up-regulated and down-regulated genes presented in Table 4 and Additional file 4. A single isoform of IL26 is expressed 80.8-fold and correlates with the expression (80.8-fold) of the IL26 gene in RASFs. Seven known isoforms (ILI27, DHPS, BLCAP, LYNX1, C5orf13, APLP2, and CSRP1) are not represented in the top 50 regulated genes. One reason for this observation is differential isoform expression, as demonstrated by the two isoforms of Interferon, alpha-inducible protein 27 (ILI27). One ILI27 isoform is up-regulated 35.8-fold and one is down-regulated 216.8-fold. Two known isoforms were also identified for GCNT1, SLC2A5 and C5orf13 in the top 50 list.
            Table 6

            Top ten up- and down- regulated known isoforms expressed in rheumatoid arthritis synovial RNA

            Gene

            Description

            Locus

            Length

            RA1FPKM

            RA2FPKM

            WT1FPKM

            WT2FPKM

            Avg.RA

            Avg.WT

            Fold change

            Ensembl geneID

            IL26

            interleukin 26

            chr12

            1047.00

            17.91

            1.93

            0.10

            0.14

            9.92

            0.12

            80.83

            ENSG00000111536

            GCNT1

            glucosaminyl (N-acetyl) transferase 1, core 2

            chr9

            5478.00

            3.99

            3.35

            0.08

            0.05

            3.67

            0.07

            55.82

            ENSG00000187210

            IFI27

            interferon, alpha-inducible protein 27

            chr14

            652.00

            272.04

            223.93

            5.09

            8.77

            247.99

            6.93

            35.79

            ENSG00000165949

            GCNT1

            glucosaminyl (N-acetyl) transferase 1, core 2

            chr9

            5596.00

            3.81

            2.20

            0.06

            0.13

            3.01

            0.09

            32.54

            ENSG00000187210

            IGFBP3

            insulin-like growth factor binding protein 3

            chr7

            2631.00

            123.09

            213.34

            2.30

            8.88

            168.22

            5.59

            30.09

            ENSG00000146674

            DHPS

            deoxyhypusine synthase

            chr19

            1184.00

            12.04

            2.92

            0.40

            0.14

            7.48

            0.27

            27.42

            ENSG00000095059

            BLCAP

            bladder cancer associated protein

            chr20

            2073.00

            9.25

            2.16

            0.20

            0.32

            5.70

            0.26

            22.14

            ENSG00000166619

            SLC2A5

            solute carrier family 2 (facilitated glucose/fructose transporter), member 5

            chr1

            2438.00

            62.93

            20.78

            0.21

            3.63

            41.85

            1.92

            21.79

            ENSG00000142583

            SLC12A8

            solute carrier family 12 (potassium/chloride transporters), member 8

            chr3

            3447.00

            6.34

            16.64

            0.32

            0.73

            11.49

            0.52

            22.01

            ENSG00000221955

            LYNX1

            Ly6/neurotoxin 1

            chr8

            1290.00

            6.07

            3.23

            0.39

            0.06

            4.65

            0.23

            20.48

            ENSG00000180155

            C5orf13

            chromosome 5 open reading frame 13

            chr5

            1996.00

            0.32

            0.71

            303.46

            6.35

            0.52

            154.91

            −300.00

            ENSG00000134986

            IFI27

            interferon, alpha-inducible protein 27

            chr14

            648.00

            0.40

            1.10

            5.62

            318.85

            0.75

            162.23

            −216.80

            ENSG00000165949

            C5orf13

            chromosome 5 open reading frame 13

            chr5

            2068.00

            0.14

            0.19

            40.42

            1.50

            0.16

            20.96

            −127.90

            ENSG00000134986

            APLP2

            amyloid beta (A4) precursor-like protein 2

            chr11

            3274.00

            0.06

            0.23

            5.27

            15.27

            0.14

            10.27

            −71.07

            ENSG00000084234

            CSRP1

            cysteine and glycine-rich protein 1

            chr1

            1938.00

            0.20

            0.41

            34.33

            3.74

            0.31

            19.03

            −61.54

            ENSG00000159176

            AQP1

            aquaporin 1

            chr7

            2807.00

            4.71

            2.55

            390.29

            23.23

            3.63

            206.76

            −56.95

            ENSG00000240583

            PARP2

            poly (ADP-ribose) polymerase 2

            chr14

            1887.00

            0.10

            0.19

            2.43

            6.85

            0.14

            4.64

            −32.85

            ENSG00000129484

            APOBEC3B

            apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3B

            chr22

            1536.00

            0.08

            0.26

            2.60

            7.41

            0.17

            5.00

            −29.50

            ENSG00000179750

            CCDC3

            coiled-coil domain containing 3

            chr10

            2738.00

            0.12

            0.10

            4.49

            1.71

            0.11

            3.10

            −27.21

            ENSG00000151468

            MTIF3

            mitochondrial translational initiation factor 3

            chr13

            1098.00

            0.14

            0.22

            3.98

            5.73

            0.18

            4.85

            −26.92

            ENSG00000122033

            Isoforms which were differentially expressed as determined by Cufflinks, after normalization to a panel of housekeeping genes. The fold change is the ratio of RASF FPKM to control FPKM. Isoforms with a fold change of 1.2-fold or greater were defined as significant. The isoforms were ranked on their fold change and the 10 with the highest or lowest fold changes are listed here.

            Novel isoforms expressed only in control SFs or only in RASFs

            The top 10 up- and down-regulated novel isoforms expressed only in control SFs or only in RASFs are presented in Table 7. An expanded list of the top 50 up- and down-regulated known isoforms expressed only in either control SFs or in RASFs is presented in Additional file 7. The list of the top 10 up-regulated novel isoforms includes transcripts for four unannotated genomic regions. The top 50 novel isoforms contains 21 transcripts from unannotated genomic regions. The list of top 10 down-regulated novel isoforms is divided into nine isoforms from annotated genes, including a novel transcript for HHIP, and one down-regulated novel isoform. There are transcripts for fourteen unannotated genomic regions in the top 50 down-regulated novel isoforms.
            Table 7

            Top ten up- and down- regulated novel isoforms expressed only in normal synovial RNA or rheumatoid arthritis synovial RNA

            Gene

            Description

            Coordinates

            Length

            FPKM Wildtype

            FPKM RA

            Ensembl gene ID

            GIPC1

            GIPC PDZ domain containing family. Member 1

            chr19:14588570-14606944

            1650

            --

            8.09135

            ENSG00000123159

            MPPE1

            Metallophosphoesterase 1

            chr18:11883385-11908455

            1973

            --

            5.66007

            ENSG00000154889

            -

            NA

            chr11:69066649-69184402

            410

            --

            5.19725

            NA

            EPB41L2

            Erythrocyte membrane protein band 4.1-like 2

            chr6:131160487-131384462

            3393

            --

            4.45046

            ENSG00000079819

            MRPL14

            Mitochondrial ribosomal protein L14

            chr6:44072507-44123256

            658

            --

            4.29538

            ENSG00000180992

            PPIEL

            Peptidylprolyl isomerase E-like pseudogene

            chr1:39987953-40025316

            509

            --

            4.21536

            ENSG00000243970

            -

            NA

            chr6:166822859-167041186

            3525

            --

            3.83632

            NA

            -

            NA

            chr21:39607975-39679370

            1369

            --

            3.3631

            NA

            FAM101A

            Family with sequence similarity 101. member A

            chr12:124774147-124800566

            2242

            --

            3.16322

            ENSG00000178882

            -

            NA

            chr4:39454172-39460535

            666

            --

            3.12642

            NA

            -

            NA

            chr20:30432079-30433458

            1379

            18.1953

            --

            NA

            GPAT2

            Glycerol-3-phosphate acyltransferase 2, mitochondrial

            chr2:96687342-96700658

            2732

            6.12103

            --

            ENSG00000186281

            PCDHGC5

            Protocadherin gamma subfamily C, 5

            chr5:140746308-140914003

            4930

            6.08509

            --

            ENSG00000240764

            RSAD2

            Radical S-adenosyl methionine domain containing 2

            chr2:6988770-7038095

            5210

            4.87066

            --

            ENSG00000134321

            HEYL

            Hairy/enhancer of split related with YRPW motif-like

            chr1:40089102-40105348

            3872

            4.34476

            --

            ENSG00000163909

            GPR107

            G protein-coupled receptor 107

            chr9:132815745-132902440

            3463

            4.15184

            --

            ENSG00000148358

            GOLGA2

            Golgin A2

            chr9:131018105-131038268

            3014

            2.8846

            --

            ENSG00000167110

            HHIP

            Hedgehog interacting protein

            chr4:145567142-145660251

            2628

            2.57248

            --

            ENSG00000164161

            ITIH3

            Inter-alpha-trypsin inhibitor heavy chain 3

            chr3:52828743-52838029

            1944

            2.20862

            --

            ENSG00000162267

            HEATR5A

            HEAT repeat containing 5A

            chr14:31757730-31889797

            6427

            2.17675

            --

            ENSG00000129493

            Novel isoforms which were differentially expressed as determined by CuffDiff after Benjamini-Hochberg correction. The isoforms were ranked by FPKM and the 10 with the highest or lowest fold changes are listed here.

            Novel isoforms differentially expressed two-fold or greater between control SFs and RASFs

            The top 10 up- and down-regulated novel isoforms, along with the expanded top 50 list, in which expression of both samples in each group was up-regulated or down-regulated two-fold or greater between control SFs and RASFs are presented in Table 8 and in Additional file 8, respectively. A transcript of Fibrillin 1 (FBN1) is the top up-regulated novel isoform. Of note, a mutation in FBN1, which encodes an extracellular matrix glycoprotein, has been associated with the coexistence of Marfan’s Syndrome and ankylosing spondylitis [37]. Novel isoforms from three unannotated regions of the genome were identified in the top 10 up-regulated novel isoforms. A total of 13 novel isoforms identified within unannotated regions of the genome were up-regulated in RASFs compared to SFs (Additional file 8). The list of top 10 down-regulated novel isoforms is divided into nine isoforms from annotated genes and one down-regulated novel isoform. A total of 10 novel isoforms within unannotated regions of the genome were down-regulated in RASFs compared to SFs. Interestingly, there are two novel transcripts for both HLA-DRB1 and SLC2A5 identified in this study (Additional file 8).
            Table 8

            Top ten up- and down- regulated novel isoforms expressed in rheumatoid arthritis synovial RNA

            Gene

            Description

            Coordinates

            Length

            FPKM Wildtype

            FPKM RA

            Fold change

            Ensembl gene ID

            FBN1

            fibrillin 1

            chr15:48700502-48944261

            3642

            0.35665

            122.625

            343.82

            ENSG00000166147

            TNXB

            tenascin XB

            chr6:31913771-32077409

            10005

            0.0711364

            9.52612

            133.91

            ENSG00000168477

            VCAN

            versican

            chr5:82767225-82878111

            7388

            0.145287

            17.706

            121.87

            ENSG00000038427

            LRP1

            low density lipoprotein receptor-related protein 1

            chr12:57522228-57607140

            6609

            0.223758

            19.9154

            89.00

            ENSG00000123384

            DPYSL2

            dihydropyrimidinase-like 2

            chr8:26435420-26515693

            3416

            0.287829

            23.9348

            83.16

            ENSG00000092964

            -

            Genes nearby:FAM198B: family with sequence similarity 198, member B

            chr4:159045731-159093718

            1964

            0.064901

            5.20752

            80.24

            ENSG00000164125

            -

            Genes nearby:TGFBR3: transforming growth factor, beta receptor III

            chr1:92145899-92351836

            1323

            0.137404

            11.0015

            80.07

            ENSG00000069702

            ALDH1L2

            aldehyde dehydrogenase 1 family, member L2

            chr12:105413561-105478341

            4568

            0.0522639

            3.45172

            66.04

            ENSG00000136010

            -

            NA

            chr14:74964883-75079368

            2880

            0.114262

            6.82866

            59.76

            NA

             

            Genes nearby: ISCA2: iron-sulfur cluster assembly 2 homolog

                 

            ENSG00000165898

            SNED1

            LTBP2: latent transforming growth factor beta binding protein 2

            chr2:241936998-242041710

            8107

            0.15755

            9.34599

            59.32

            ENSG00000119681

            TINAGL1

            tubulointerstitial nephritis antigen-like 1

            chr1:32041807-32053290

            995

            129.883

            0.462813

            −280.64

            ENSG00000142910

            TPM2

            tropomyosin 2 (beta)

            chr9:35681989-35690053

            1083

            78.6638

            0.329924

            −238.43

            ENSG00000198467

            MT2A

            metallothionein 2A

            chr16:56642376-56692994

            248

            701.232

            5.35657

            −130.91

            ENSG00000125148

            FSTL1

            follistatin-like 1

            chr3:120113060-120169918

            1640

            10.7318

            0.0822263

            −130.52

            ENSG00000163430

            ITPRIP

            inositol 1,4,5-trisphosphate receptor interacting protein

            chr10:106069730-106098576

            6523

            18.5495

            0.146115

            −126.95

            ENSG00000148841

            -

            NA

            chr13:41958154-41958844

            690

            51.6499

            0.522452

            −98.86

            NA

            SPTBN1

            spectrin, beta, non-erythrocytic 1

            chr2:54683453-54898583

            7086

            12.396

            0.126624

            −97.90

            ENSG00000115306

            HLA-DRB1

            major histocompatibility complex, class II, DR beta 5

            chr6:32441211-32557589

            513

            12.6351

            0.129095

            −97.87

            ENSG00000198502

            SEMA3F

            sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3F

            chr3:50192454-50226507

            3394

            6.92733

            0.0764133

            −90.66

            ENSG00000001617

            CNN1

            calponin 1, basic, smooth muscle

            chr19:11649578-11661139

            659

            67.4653

            0.799449

            −84.39

            ENSG00000130176

            Novel isoforms which were differentially expressed as determined by CuffDiff after Benjamini-Hochberg correction. The fold change is the ratio of RASF FPKM to control FPKM. Novel isoforms with a fold change of 1.2-fold or greater were defined as significant. The isoforms were ranked on their fold change and the 10 with the highest or lowest fold changes are listed here.

            Network and pathway analyses of differentially expressed genes

            To identify network and pathway connectivity, the differentially expressed gene lists of a two-fold or greater change in RASFs compared to SFs were submitted to Ingenuity Pathway Analysis (IPA) v9.0-3211 (Ingenuity Systems, Inc., Redwood City, CA), as described in the Material and Methods section. The networks affected by up-regulated genes and isoforms in RASFs compared to normal SFs are listed in Table 9. Consistent with the knowledge that RA is an immune disorder, the top network predicted to be affected by the up-regulated genes was Inflammatory Response, Immunological Disease, Cell Death, while the top network predicted to be affected by the up-regulated isoforms was Inflammatory Response, Cellular Movement, Cell-To-Cell Signaling and Interaction. The pathways affected by up-regulated genes and/or isoforms correlated with the pathways predicted to be affected by down-regulated gene expression and changes in isoform expression (Table 10). The top networks affected by down-regulated genes and isoforms in RASFs compared to normal SFs are Cellular Movement, Cell Death, and Tissue Development and Cellular Growth and Proliferation, Cell Death, Cellular Movement, respectively.
            Table 9

            Top networks affected by up-regulated genes/isoforms in rheumatoid arthritis synovial RNA

             

            Up-regulated genes

            Top Functions

            Score

            Genes

            Inflammatory Response, Immunological Disease, Cell Death

            68

            58

            Cell Morphology, Tissue Development, Cell Death

            30

            36

            Cell-To-Cell Signaling and Interaction, Hematological System

              

            Development and Function, Immune Cell Trafficking

            25

            32

            Inflammatory Response, Infectious Disease, Immunological Disease

            23

            31

            Cellular Development, Cancer, Developmental Disorder

            22

            30

            Inflammatory Response, Cellular Development, Cell Death

            22

            30

            Cell Death, Hematological System Development and Function, Tissue Morphology

            22

            30

             

            Up-Regulated Isoforms

            Top Functions

            Score

            Genes

            Inflammatory Response, Cellular Movement, Cell-To-Cell Signaling and Interaction

            88

            70

            Cellular Development, Cell Death, Cellular Growth and Proliferation

            26

            36

            Inflammatory Response, Organismal Injury and Abnormalities, Cellular Movement

            24

            35

            Cellular Growth and Proliferation, Cellular Development, Cancer

            24

            35

            Cell-To-Cell Signaling and Interaction, Inflammatory Response, Hematological System Development and Function

            23

            34

            Cellular Movement, Hematological System Development and Function, Immune Cell Trafficking

            23

            34

            Networks significantly affected in RASFs compared to control SFs as determined by Ingenuity Pathway Analysis. The score is based on the p-value of the affected network. Networks with a score of 15 or greater were defined as significant.

            Table 10

            Top networks affected by down-regulated genes/isoforms in rheumatoid arthritis synovial RNA

             

            Down-regulated genes

            Top Functions

            Score

            Genes

            Cellular Movement, Cell Death, Tissue Development

            35

            32

            Cellular Growth and Proliferation, Cellular Development, Hematological System Development and Function

            29

            28

            Cell Cycle, Cellular Growth and Proliferation, Cell Death

            27

            27

            Cellular Growth and Proliferation, Cell Cycle, Tissue Development

            23

            24

            Hematological System Development and Function, Tissue Morphology, Tissue Development

            21

            23

             

            Down-Regulated Isoforms

            Top Functions

            Score

            Genes

            Cellular Growth and Proliferation, Cell Death, Cellular Movement

            45

            43

            DNA Replication, Recombination, and Repair, Cell Cycle, Hematological System Development and Function

            28

            32

            Cellular Development, Cell Morphology, Cellular Assembly and Organization

            25

            30

            Cellular Growth and Proliferation, Tissue Morphology, Hematological System Development and Function

            25

            30

            Cellular Growth and Proliferation, Cellular Movement, Embryonic Development

            24

            29

            Cell Death, Cellular Development, Hematological System Development and Function

            24

            29

            Networks significantly affected in RASFs compared to control SFs as determined by Ingenuity Pathway Analysis. The score is based on the p-value of the affected network. Networks with a score of 15 or greater were defined as significant.

            Canonical pathways analyses identified the pathways from the Ingenuity Pathways Analysis library of canonical pathways that were most significant to the data set. Genes with a two-fold or greater change in expression between SFs and RASFs and that were associated with a canonical pathway in Ingenuity’s Knowledge Base were considered for the analyses. The top canonical pathways affected by up-regulated genes and isoforms (Table 11) and the top canonical pathways affected by down-regulated genes and isoforms (Table 12) are in agreement with the networks (Tables 9 and 10) affected in RASFs. The top canonical pathways affected by up-regulated genes and isoforms (Table 11) are consistent with the knowledge that B cells, T cells, and macrophage cells play key roles in the inflammatory response and are involved in the activation and proliferation of RASFs [3, 4, 7]. These findings are further supported by the analysis of the pathways affected by the down-regulated genes and isoforms (Table 12). Dysregulation of the innate immune response and alterations in the number and types of cytokines and chemokines are well known features of RA [4, 7]. Altered cell cycle control of chromosomal replication and BRCA1 in DNA damage response, are in concordance with the hyperproliferation of synovial tissue and the corresponding decrease in apoptosis in RA [3, 38]. The identification of potential networks and pathways involved in arthritis may provide additional insights into the molecular and cellular mechanisms by which RASFs are involved in the pathogenesis of RA.
            Table 11

            Top canonical pathways affected by up-regulated genes/isoforms in rheumatoid arthritis synovial RNA

             

            Up-regulated genes

             

            Canonical Pathway

            p-value

            Ratio

            Antigen Presentation Pathway

            0.000

            0.455

            Graft-versus-Host Disease Signaling

            0.000

            0.275

            Communication between Innate and Adaptive Immune Cells

            0.000

            0.188

            Crosstalk between Dendritic Cells and Natural Killer Cells

            0.000

            0.159

            Autoimmune Thyroid Disease Signaling

            0.000

            0.214

             

            Up-regulated Isoforms

             

            Canonical Pathway

            p-value

            Ratio

            Atherosclerosis Signaling

            0.001

            0.133

            Hepatic Fibrosis / Hepatic Stellate Cell Activation

            0.001

            0.119

            Colorectal Cancer Metastasis Signaling

            0.010

            0.088

            Toll-like Receptor Signaling

            0.011

            0.143

            FXR/RXR Activation

            0.012

            0.114

            Top canonical pathways significantly affected in RASFs compared to SFs as determined by Ingenuity Pathway Analysis. Pathways with a p-value less than 0.05 defined as significant.

            Table 12

            Top canonical pathways affected by down-regulated genes/isoforms in rheumatoid arthritis synovial RNA

             

            Down-regulated genes

             

            Canonical Pathway

            p-value

            Ratio

            LXR/RXR Activation

            0.011

            0.057

            Atherosclerosis Signaling

            0.011

            0.057

            LPS/IL-1 Mediated Inhibition of RXR Function

            0.017

            0.044

            Inhibition of Angiogenesis by TSP1

            0.018

            0.083

            Phenylalanine Metabolism

            0.019

            0.086

             

            Down-regulated Isoforms

             

            Canonical Pathway

            p-value

            Ratio

            Role of BRCA1 in DNA Damage Response

            0.002

            0.125

            Mitotic Roles of Polo-Like Kinase

            0.002

            0.123

            Cardiac β-adrenergic Signaling

            0.004

            0.079

            Type I Diabetes Mellitus Signaling

            0.005

            0.086

            Graft-versus-Host Disease Signaling

            0.007

            0.125

            Top canonical pathways significantly affected in RASFs compared to SFs as determined by Ingenuity Pathway Analysis. Pathways with a p-value less than 0.05 defined as significant.

            Discussion

            In the present study, we performed a comprehensive transcriptome analysis of human SF RNA isolated from healthy controls and patients with RA using the Illumina RNA-seq technique. It has revealed a complete picture of differentially expressed genes and their isoforms in RASFs and provided a global transcriptional insight into the novel roles of synovial fibroblasts in the pathogenesis of rheumatoid arthritis.

            For RNA-seq, we used the Illumina HiScanSQ instrument to perform a 2 × 101 paired end run for all of our samples. The advantage of a paired end run is that both reads contain long range positional information, allowing for highly precise alignment of reads. We calculated the number of differentially expressed genes between RNA from two control SF and two RASF samples. We obtained a mean value of 84,177,268 reads per sample, which meets the criteria for sufficient sequence coverage for transcriptome profiling [39]. Our mean rate of 86% total reads that map to the reference genome met quality standards of the RNA-seq technique [40]. The breadth of the RNA sequencing reads covering chromosome 1 for both the RASFs and normal SFs indicates quality RNA-seq runs (Figure 1). Therefore, we are confident that our RNA-seq data provides an objective, high quality profile of the transcriptome in human RASFs and normal SFs.

            The aim of this study was to provide a global glean into the transcriptional regulation in RASFs, which may provide mechanistic insights into the pathogenesis of rheumatoid arthritis. The activation and subsequent proliferation of SFs by proinflammatory cytokines produced by cells from both the innate and the adaptive immune systems plays a critical role in the pathogenesis of RA [35]. The production of additional cytokines, chemokines and matrix-degrading enzymes by RASFs leads ultimately to the progressive destruction of the joint that is a hallmark feature of RA [57]. However, the complete repertoire of active molecules, networks and pathways of differentially expressed genes and their isoforms of RASFs in this process are not characterized fully. Our study is filling this gap of knowledge. With RNA-seq, we found that 214 genes were not expressed in RASFs while 682 genes were only expressed in RASFs (Table 2). There are 122 up-regulated genes and 155 down-regulated genes by at least two-fold in RASFs compared to those in normal SFs. The majority of differentially expressed genes identified in this study (Tables 3 and 4 and Additional files 2 and 4) have not been previously reported to be altered in RASFs compared to normal SFs. One notable prowess of RNA-seq is to identify and quantify the expression of different isoforms of a gene. Gene isoforms are generated by alternative splicing or alternative promoter usage. Regulation of different gene isoform expression is a central aspect of most normal and disease processes. In this study, we detected more than 20,000 expressed known isoforms and more than 40,000 expressed novel isoforms (Table 2). Among them, there are 526 known isoforms which were not expressed in RASFs while 981 known isoforms were only expressed in RASFs. There are 343 up-regulated known isoforms and 262 down-regulated known isoforms by at least two-fold in RASFs compared to those in normal SFs. There are 105 novel isoforms which were not expressed in RASFs, while 152 novel isoforms were expressed only in RASFs. There are 561 up-regulated novel isoforms and 520 down-regulated novel isoforms by at least two-fold in RASFs compared to those in normal SFs. Network and canonical pathway analyses of differentially expressed genes and their known isoforms revealed that inflammatory response and cell death are represented strongly. Although these pathways have been predicted previously to correlate with RA, our study provided a more complete list of genes and isoforms involved in the inflammatory response and cell death pathways. We also identified other relevant novel networks and pathways, such as Antigen Presentation Pathway, Atherosclerosis Signalling, LXR/RXR Activation, and Role of BRCA1 in DNA Damage Response, whose dysregulation may each in part underlie their implication in the pathogenesis of RA.

            Several microarray transcriptome analyses have been performed on RASFs [4153]. The heterogeneous nature of RA and the different types of tissues used in these microarray studies leads to variations between the studies. The results from the present RNA-seq study both correlated and differed from previous microarray studies. The SFs used in our study were first isolated from synovial tissue either from healthy control donors or from patients with RA and cultured for two passages prior to RNA isolation. It should be noted, that this passage number is lower than what has been reported previously for gene profiling in SFs that have been cultured prior to RNA isolation. Del Rey et al. [43] and Masuda et al. [47] cultured SFs for 4 and 6 passages, respectively, before isolating RNA, while Haupl et al. [48] used immortalized SFs. The matrix metalloproteinases 1 (MMP1) and 3 (MMP3) are key players in the pathogenesis of RA [50]. MMP1 and MMP3 were up-regulated 816.2- fold and 215.6-fold, respectively, in our study. Microarray analyses of RA synovial tissue in three separate studies detected increased MMP1 expression of 63.1-fold [51], 31.0-fold [52], and 36.6-fold [53]. MMP3 expression was also increased 23.2-fold [52] and 18.7-fold [53] in these studies. Interleukin 1 beta (IL1B) and Interleukin 8 were up-regulated 3.2 and 9.3 fold, respectively, in RASFs from patients treated with prednisolone [48]. In the present study, IL1B was decreased by 25.3-fold and IL8 was down-regulated 9.5-fold. Collagen, Type III, alpha 1 (COL3A1) was increased 1.76 fold in a microarray study [44] compared to a 1.3-fold decrease in the present study. Keratin 7 (KRT7) was down-regulated 0.49 by microarray analysis [44] and 14.6-fold by RNA-seq. The results presented in our study correlate well with what has been previously reported in the literature. Of the top 40 differentially expressed genes (Tables 3 and 4), 16 have been reported previously to be associated with RA (Additional file 3). Thus, we have identified 24 new potential gene targets among the genes listed in Tables 3 and 4 for further exploration. These findings are strengthened further by the ability of RNA-seq, as described above, to identify isoforms, both known and novel, that are expressed differentially in RA. With further improvements of next generation DNA sequencing techniques and further reductions of sequencing costs, it may be feasible to extend this study to analyze the transcriptomes of RASFs isolated from multiple patient samples at progressing stages of pathogenesis.

            Conclusion

            In summary, our first complete transcriptome analysis of synovial fibroblast RNA from patients with rheumatoid arthritis using RNA-seq has provided important insights into the transcriptional regulation of gene expression in RASFs. Further in-depth, follow-up analyses using large patient populations will be necessary to validate the alterations in transcriptional regulation reported in this study and to provide the resources necessary to elucidate the molecular mechanisms underlying the role of SFs in the pathogenesis of RA.

            Methods

            RNA sequencing

            Human SF RNA from 2 healthy female donors and 2 adult female RA patients (Additional file 1) was purchased from Cell Applications, Inc. (San Diego, CA). SFs were first isolated from synovial tissue either from healthy control donors or from patients with RA and cultured for two passages prior to RNA isolation. Paired-end cDNA libraries were prepared for each sample and sequenced using the Illumina TruSeq RNA Sample Preparation Kit, as described previously [11, 12]. Briefly, the cDNA libraries were quantified using a Biotek EPOCH spectrophotometer and checked for quality and size using a Bio-Rad Experion DNA 1K chip. The four cDNA libraries were each diluted to 6 pM and spiked with 1% phiX control to improve base calling while sequencing. A 6 pM dilution of phiX control sample was also prepared for analysis. Following the Illumina cBot and HiSeq protocols, the four libraries and the phiX control underwent cluster generation on a HiSeq PE flow cell v3 and were then sequenced using a HiScanSQ (Illumina). A paired-end (2×101) run was performed using the SBS Kit (Illumina). Real-time analysis and base calling were performed using the HiSeq Control Software Version 1.4.5 (Illumina). The resulting basecalling (.bcl) files were converted to. FASTQ files using Illumina’s CASAVA 1.8 software. The number of reads for each sample type was analyzed using the Student’s t-test in SigmaPlot version 11.0 (Systat Software Inc., San Jose, CA). A p-value of below 0.05 was considered significant. The sequence data have been submitted to the NCBI Short Read Archive with accession number SRA048057.1.

            Mapping of RNA-seq reads and transcript assembly and abundance estimation using Tuxedo Suite

            Paired-end fastq sequence reads for each sample were aligned to the UCSC Homo sapiens reference genome hg19 using TopHat v1.3.0 [54, 55] integrated with Bowtie v0.12.7 [56], as described previously [11, 12]. The resulting aligned reads were analyzed further by Cufflinks v1.0.3 [55, 57]. The aligned reads were assembled into transcripts, either with or without a reference genome, and the expression of those transcripts were reported in Fragments Per Kilobase of exon per Million fragments mapped (FPKM). Cuffdiff analysis was performed, with use of the reference genome, to determine differential expression of known isoforms between pooled RA patient samples and pooled control samples. To detect novel isoforms, Cufflinks was run without a reference genome. The RA and control transcript files were compared to the reference genome using Cuffcompare to filter out previously discovered transcripts. To test the differential expression of these novel isoforms, Cuffdiff analyses were performed using the combined transcript files as the reference genome. Cuffdiff analyses were performed two ways: comparing the RA patient transcripts to the control transcripts, using the RA patient transcripts as the reference genome; and comparing the RA patient transcripts to the control transcripts, using the control transcripts as the reference genome.

            Visualization of mapped reads

            Aligned reads were visualized using a local copy of the Integrative Genomics Viewer (http://​www.​broadinstitute.​org/​igv/​). The output files generated from TopHat were converted into files viewable in IGV by BEDTools [58] and then processed further by the “count” function in igvtools (included with the IGV software) to create an average alignment track viewable as a bar chart. The log2 of the frequency of the reads was plotted to better visualize the extensive range of the read coverage. Individual gene views were created by first merging the TopHat output files from the RA and control samples into two files using SAMTools [59]. These merged files were processed in the same way as above with the “count” function in igvtools. The raw frequency of the reads was visualized in this case.

            Automated literature search

            Multiplex literature mining analysis was conducted with PubMatrix, [22] as described previously [60]. We restricted our search to human symbols approved by HUGO Gene Nomenclature Committee (HGNC) for the top 10 genes and isoforms for each category. Terms “rheumatoid arthritis”, “osteoarthritis”, “arthritis” and “disease” were used for cross-referencing candidate genes.

            Functional analysis of differentially expressed gene lists using ingenuity pathway analysis

            The differentially expressed gene lists were submitted to Ingenuity Pathway Analysis (IPA) v9.0-3211 (Ingenuity Systems, Inc., Redwood City, CA). Genes with a two-fold or greater change in expression between the RA group and the control group were used. The settings for the core analysis were as follows: Ingenuity Knowledge Base; Endogenous Chemicals not included; Direct and Indirect relationships; molecules per pathway: 70; and networks per analysis: 25.

            Declarations

            Acknowledgements

            This work is in part supported by the start-up fund and William R. Brown Missouri Endowment of Children’s Mercy Hospitals and Clinics, University of Missouri at Kansas City (Ye, S.Q.).

            Authors’ Affiliations

            (1)
            Department of Pediatrics, Children's Mercy Hospitals and Clinics, University of Missouri School of Medicine
            (2)
            Department of Biomedical and Health Informatics, Children's Mercy Hospitals and Clinics, University of Missouri School of Medicine

            References

            1. Lee DM, Weinblatt ME: Rheumatoid arthritis. Lancet. 2001, 358: 903-911. 10.1016/S0140-6736(01)06075-5View ArticlePubMed
            2. Huber LC, Distler O, Tarner I, Gay RE, Gay S, Pap T: Synovial fibroblasts: key players in rheumatoid arthritis. Rheumatol (Oxford). 2006, 45: 669-675. 10.1093/rheumatology/kel065. 10.1093/rheumatology/kel065View Article
            3. Bartok B, Firestein GS: Fibroblast-like synoviocytes: key effector cells in rheumatoid arthritis. Immunol Rev. 2010, 233: 233-255. 10.1111/j.0105-2896.2009.00859.xPubMed CentralView ArticlePubMed
            4. Scott DL, Wolfe F, Huizinga TW: Rheumatoid arthritis. Lancet. 2010, 376: 1094-1108. 10.1016/S0140-6736(10)60826-4View ArticlePubMed
            5. Gierut A, Perlman H, Pope RM: Innate immunity and rheumatoid arthritis. Rheum Dis Clin North Am. 2010, 36: 271-296. 10.1016/j.rdc.2010.03.004PubMed CentralView ArticlePubMed
            6. Cooles FA, Isaacs JD: Pathophysiology of rheumatoid arthritis. Curr Opin Rheumatol. 2011, 23: 233-240. 10.1097/BOR.0b013e32834518a3View ArticlePubMed
            7. Firestein GS: Evolving concepts of rheumatoid arthritis. Nature. 2003, 423: 356-361. 10.1038/nature01661View ArticlePubMed
            8. Buch MH, Emery P: New therapies in the management of rheumatoid arthritis. Curr Opin Rheumatol. 2011, 23: 245-251. 10.1097/BOR.0b013e3283454124View ArticlePubMed
            9. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML: Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011, 12: 499-510. 10.1038/nrg3012View ArticlePubMed
            10. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008, 18: 1509-1517. 10.1101/gr.079558.108PubMed CentralView ArticlePubMed
            11. Zhang LQ, Cheranova D, Gibson M, Ding S, Heruth DP, Fang D, Ye SQ: RNA-seq reveals novel transcriptome of genes and their isoforms in human pulmonary microvascular endothelial cells treated with thrombin. PLoS One. 2012, 7: e31229. 10.1371/journal.pone.0031229PubMed CentralView ArticlePubMed
            12. Cheranova DGM, Chaudhary S, Zhang LQ, Heruth DP, Grigoryev DN, Ye SQ: RNA-seq analysis of transcriptomes in thrombin-treated and control human pulmonary microvascular endothelial cells. J Vis Exp. 2012, in press.
            13. Twine NA, Janitz K, Wilkins MR, Janitz M: Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer's disease. PLoS One. 2011, 6: e16266. 10.1371/journal.pone.0016266PubMed CentralView ArticlePubMed
            14. Newton JL, Harney SM, Wordsworth BP, Brown MA: A review of the MHC genetics of rheumatoid arthritis. Genes Immun. 2004, 5: 151-157. 10.1038/sj.gene.6364045View ArticlePubMed
            15. Raum D, Awdeh Z, Glass D, Kammer G, Khan MA, Coblyn JS, Weinblatt M, Holdsworth D, Strong L, Rossen RD: Extended haplotypes of chromosome 6 in adult rheumatoid arthritis. Arthritis Rheum. 1984, 27: 516-521. 10.1002/art.1780270506View ArticlePubMed
            16. Taylor KE, Criswell LA: Conditional analysis of the major histocompatibility complex in rheumatoid arthritis. BMC Proc. 2009, 7 (3): S36.View Article
            17. Plenge RM, Cotsapas C, Davies L, Price AL, de Bakker PI, Maller J, Pe'er I, Burtt NP, Blumenstiel B, DeFelice M: Two independent alleles at 6q23 associated with risk of rheumatoid arthritis. Nat Genet. 2007, 39: 1477-1482. 10.1038/ng.2007.27PubMed CentralView ArticlePubMed
            18. Jiang L, Salao K, Li H, Rybicka JM, Yates RM, Luo XW, Shi XX, Kuffner T, Tsai VW, Husaini Y: Intracellular chloride channel protein CLIC1 regulates macrophage functions via modulation of phagosomal acidification. J Cell Sci. 2012, in press.
            19. Uesugi H, Ozaki S, Sobajima J, Osakada F, Shirakawa H, Yoshida M, Nakao K: Prevalence and characterization of novel pANCA, antibodies to the high mobility group non-histone chromosomal proteins HMG1 and HMG2, in systemic rheumatic diseases. J Rheumatol. 1998, 25: 703-709.PubMed
            20. Pohlers D, Beyer A, Koczan D, Wilhelm T, Thiesen HJ, Kinne RW: Constitutive upregulation of the transforming growth factor-beta pathway in rheumatoid arthritis synovial fibroblasts. Arthritis Res Ther. 2007, 9: R59. 10.1186/ar2217PubMed CentralView ArticlePubMed
            21. Williams AS, Mizuno M, Richards PJ, Holt DS, Morgan BP: Deletion of the gene encoding CD59a in mice increases disease severity in a murine model of rheumatoid arthritis. Arthritis Rheum. 2004, 50: 3035-3044. 10.1002/art.20478View ArticlePubMed
            22. Becker KG, Hosack DA, Dennis G, Lempicki RA, Bright TJ, Cheadle C, Engel J: PubMatrix: a tool for multiplex literature mining. BMC Bioinforma. 2003, 4: 61-10.1186/1471-2105-4-61. 10.1186/1471-2105-4-61View Article
            23. Li JY, Paragas N, Ned RM, Qiu A, Viltard M, Leete T, Drexler IR, Chen X, Sanna-Cherchi S, Mohammed F: Scara5 is a ferritin receptor mediating non-transferrin iron delivery. Dev Cell. 2009, 16: 35-46. 10.1016/j.devcel.2008.12.002PubMed CentralView ArticlePubMed
            24. Zhao Y, Wang Y, Hu J, Zhang X, Zhang YW: CutA divalent cation tolerance homolog (Escherichia coli) (CUTA) regulates beta-cleavage of beta-amyloid precursor protein (APP) through interacting with beta-site APP cleaving protein 1 (BACE1). J Biol Chem. 2012, 287: 11141-11150. 10.1074/jbc.M111.330209PubMed CentralView ArticlePubMed
            25. Zhen Y, Sorensen V, Skjerpen CS, Haugsten EM, Jin Y, Walchli S, Olsnes S, Wiedlocha A: Nuclear import of exogenous FGF1 requires the ER-protein LRRC59 and the importins Kpnalpha1 and Kpnbeta1. Traffic (Copenhagen, Denmark). 2012, 13: 650-664.View Article
            26. Xiao L, Gong LL, Yuan D, Deng M, Zeng XM, Chen LL, Zhang L, Yan Q, Liu JP, Hu XH: Protein phosphatase-1 regulates Akt1 signal transduction pathway to control gene expression, cell survival and differentiation. Cell Death Differ. 2010, 17: 1448-1462. 10.1038/cdd.2010.16View ArticlePubMed
            27. Corvaisier M, Delneste Y, Jeanvoine H, Preisser L, Blanchard S, Garo E, Hoppe E, Barré B, Audran M, Bouvard B: IL-26 is overexpressed in rheumatoid arthritis and induces proinflammatory cytokine production and Th17 cell generation. PLoS Biol. 2012, 10: e1001395. 10.1371/journal.pbio.1001395PubMed CentralView ArticlePubMed
            28. Liu C, Batliwalla F, Li W, Lee A, Roubenoff R, Beckman E, Khalili H, Damle A, Kern M, Furie R: Genome-wide association scan identifies candidate polymorphisms associated with differential response to anti-TNF treatment in rheumatoid arthritis. Mol med (Cambridge, Mass). 2008, 14: 575-581.
            29. Mishima K, Otani H, Tanabe T, Kawasaki H, Oshiro A, Saito N, Ogawa R, Inagaki C: Molecular mechanisms for alpha2-adrenoceptor-mediated regulation of synoviocyte populations. Jpn J Pharmacol. 2001, 85: 214-226. 10.1254/jjp.85.214View ArticlePubMed
            30. Rico MC, Rough JJ, Del Carpio-Cano FE, Kunapuli SP, DeLa Cadena RA: The axis of thrombospondin-1, transforming growth factor beta and connective tissue growth factor: an emerging therapeutic target in rheumatoid arthritis. Curr Vasc Pharmacol. 2010, 8: 338-343. 10.2174/157016110791112296View ArticlePubMed
            31. Frolova EG, Sopko N, Blech L, Popovic ZB, Li J, Vasanji A, Drumm C, Krukovets I, Jain MK, Penn MS: Thrombospondin-4 regulates fibrosis and remodeling of the myocardium in response to pressure overload. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2012, 26: 2363-2373. 10.1096/fj.11-190728. 10.1096/fj.11-190728View Article
            32. Happonen KE, Heinegard D, Saxne T, Blom AM: Interactions of the complement system with molecules of extracellular matrix: relevance for joint diseases. Immunobiology. 2012, 217: 1088-1096. 10.1016/j.imbio.2012.07.013View ArticlePubMed
            33. Busso N, Peclat V, So A, Sappino AP: Plasminogen activation in synovial tissues: differences between normal, osteoarthritis, and rheumatoid arthritis joints. Ann Rheum Dis. 1997, 56: 550-557. 10.1136/ard.56.9.550PubMed CentralView ArticlePubMed
            34. Mobasheri A, Moskaluk CA, Marples D, Shakibaei M: Expression of aquaporin 1 (AQP1) in human synovitis. Ann Anat= Anatomischer Anzeiger: official organ of the Anatomische Gesellschaft. 2010, 192: 116-121. 10.1016/j.aanat.2010.01.001. 10.1016/j.aanat.2010.01.001View Article
            35. Krupiczojc MA, Scotton CJ, Chambers RC: Coagulation signalling following tissue injury: focus on the role of factor Xa. Int J Biochem Cell Biol. 2008, 40: 1228-1237. 10.1016/j.biocel.2008.02.026View ArticlePubMed
            36. Ruiz-Heiland G, Horn A, Zerr P, Hofstetter W, Baum W, Stock M, Distler JH, Nimmerjahn F, Schett G, Zwerina J: Blockade of the hedgehog pathway inhibits osteophyte formation in arthritis. Ann Rheum Dis. 2012, 71: 400-407. 10.1136/ard.2010.148262View ArticlePubMed
            37. Kiss C, Jonap I, Gergely P, Poor G: Coexistent Marfan's syndrome and ankylosing spondylitis. J Rheumatol. 2006, 33: 1199-1200.PubMed
            38. Maas K, Westfall M, Pietenpol J, Olsen NJ, Aune T: Reduced p53 in peripheral blood mononuclear cells from patients with rheumatoid arthritis is associated with loss of radiation-induced apoptosis. Arthritis Rheum. 2005, 52: 1047-1057. 10.1002/art.20931View ArticlePubMed
            39. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 2008, 321: 956-960. 10.1126/science.1160342View ArticlePubMed
            40. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5: 621-628. 10.1038/nmeth.1226View ArticlePubMed
            41. Galligan CL, Baig E, Bykerk V, Keystone EC, Fish EN: Distinctive gene expression signatures in rheumatoid arthritis synovial tissue fibroblast cells: correlates with disease activity. Genes Immun. 2007, 8: 480-491. 10.1038/sj.gene.6364400View ArticlePubMed
            42. Del Rey MJ, Usategui A, Izquierdo E, Canete JD, Blanco FJ, Criado G, Pablos JL: Transcriptome analysis reveals specific changes in osteoarthritis synovial fibroblasts. Ann Rheum Dis. 2012, 71: 275-280. 10.1136/annrheumdis-2011-200281View ArticlePubMed
            43. Del Rey MJ, Izquierdo E, Usategui A, Gonzalo E, Blanco FJ, Acquadro F, Pablos JL: The transcriptional response of normal and rheumatoid arthritis synovial fibroblasts to hypoxia. Arthritis Rheum. 2010, 62: 3584-3594. 10.1002/art.27750View ArticlePubMed
            44. Watanabe N, Ando K, Yoshida S, Inuzuka S, Kobayashi M, Matsui N, Okamoto T: Gene expression profile analysis of rheumatoid synovial fibroblast cultures revealing the overexpression of genes responsible for tumor-like growth of rheumatoid synovium. Biochem Biophys Res Commun. 2002, 294: 1121-1129. 10.1016/S0006-291X(02)00608-3View ArticlePubMed
            45. Devauchelle V, Marion S, Cagnard N, Mistou S, Falgarone G, Breban M, Letourneur F, Pitaval A, Alibert O, Lucchesi C: DNA microarray allows molecular profiling of rheumatoid arthritis and identification of pathophysiological targets. Genes Immun. 2004, 5: 597-608. 10.1038/sj.gene.6364132View ArticlePubMed
            46. 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.6363975View ArticlePubMed
            47. Masuda K, Masuda R, Neidhart M, Simmen BR, Michel BA, Muller-Ladner U, Gay RE, Gay S: Molecular profile of synovial fibroblasts in rheumatoid arthritis depends on the stage of proliferation. Arthritis Res. 2002, 4: R8. 10.1186/ar427PubMed CentralView ArticlePubMed
            48. Haupl T, Yahyawi M, Lubke C, Ringe J, Rohrlach T, Burmester GR, Sittinger M, Kaps C: Gene expression profiling of rheumatoid arthritis synovial cells treated with antirheumatic drugs. J Biomol Screen. 2007, 12: 328-340. 10.1177/1087057107299261View ArticlePubMed
            49. Lindberg J, Wijbrandts CA, van Baarsen LG, Nader G, Klareskog L, Catrina A, Thurlings R, Vervoordeldonk M, Lundeberg J, Tak PP: The gene expression profile in the synovium as a predictor of the clinical response to infliximab treatment in rheumatoid arthritis. PLoS One. 2010, 5: e11310. 10.1371/journal.pone.0011310PubMed CentralView ArticlePubMed
            50. Green MJ, Gough AK, Devlin J, Smith J, Astin P, Taylor D, Emery P: Serum MMP-3 and MMP-1 and progression of joint damage in early rheumatoid arthritis. Rheumatol (Oxford). 2003, 42: 83-88. 10.1093/rheumatology/keg037. 10.1093/rheumatology/keg037View Article
            51. Biswas S, Manikandan J, Pushparaj PN: Decoding the differential biomarkers of Rheumatoid arthritis and Osteoarthritis: a functional genomics paradigm to design disease specific therapeutics. Bioinformation. 2011, 6: 153-157. 10.6026/97320630006153PubMed CentralView ArticlePubMed
            52. Ungethuem U, Haeupl T, Witt H, Koczan D, Krenn V, Huber H, von Helversen TM, Drungowski M, Seyfert C, Zacher J: Molecular signatures and new candidates to target the pathogenesis of rheumatoid arthritis. Physiol Genomics. 2010, 42A: 267-282. 10.1152/physiolgenomics.00004.2010View ArticlePubMed
            53. Yarilina A, Park-Min KH, Antoniv T, Hu X, Ivashkiv LB: TNF activates an IRF1-dependent autocrine loop leading to sustained expression of chemokines and STAT1-dependent type I interferon-response genes. Nat Immunol. 2008, 9: 378-387.View ArticlePubMed
            54. Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120PubMed CentralView ArticlePubMed
            55. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012, 7: 562-578.PubMed CentralView ArticlePubMed
            56. Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25. 10.1186/gb-2009-10-3-r25PubMed CentralView ArticlePubMed
            57. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010, 28: 511-515. 10.1038/nbt.1621PubMed CentralView ArticlePubMed
            58. Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842. 10.1093/bioinformatics/btq033PubMed CentralView ArticlePubMed
            59. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352PubMed CentralView ArticlePubMed
            60. Grigoryev DN, Ma SF, Irizarry RA, Ye SQ, Quackenbush J, Garcia JG: Orthologous gene-expression profiling in multi-species models: search for candidate genes. Genome Biol. 2004, 5: R34. 10.1186/gb-2004-5-5-r34PubMed CentralView ArticlePubMed

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