Open Access

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

  • Daniel P Heruth1Email author,
  • Margaret Gibson1,
  • Dmitry N Grigoryev1,
  • Li Qin Zhang1 and
  • Shui Qing Ye1, 2
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.

https://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

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