Identification of novel genes associated with dysregulation of B cells in patients with primary Sjögren's syndrome

doi:10.21203/rs.3.rs-26390/v3

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Research article

Identification of novel genes associated with dysregulation of B cells in patients with primary Sjögren's syndrome

https://doi.org/10.21203/rs.3.rs-26390/v3

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published 22 Jun, 2020

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Background. The aim of this study was to identify the molecular mechanism of dysregulation of B cell subpopulations of primary Sjögren's syndrome (pSS) at the transcriptome level.

Methods. We enrolled patients with pSS (n=6) and healthy controls (HC) (n=6) in the discovery cohort using microarray and pSS (n=14) and HC (n=12) in the validation cohort using quantitative PCR (qPCR). Peripheral B cells acquired from these subjects were separated by cell sorting into four subsets: CD38^-IgD⁺(Bm1), CD38⁺IgD⁺ (naïve B cells), CD38^highIgD⁺(pre-germinal centre B cells) and CD38^±IgD^- (memory B cells). We performed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA).

Results. Expression of the long non-coding RNA LINC00487 was significantly upregulated in all B cell subsets, as was that of HLA and interferon (IFN) signature genes. Moreover, the normalized intensity value of LINC00487 significantly correlated with the disease activity score of all pSS B cell subsets. Studies of human B cell lines revealed that the expression of LINC00487 was strongly induced by IFNα. WGCNA revealed six gene clusters associated with the B cell subpopulation of pSS. Further, SOX4 was identified as an inter-module hub gene.

Conclusion. Our transcriptome analysis revealed key genes involved in the dysregulation of B cell subpopulations associated with pSS.

Rheumatology

primary Sjögren's syndrome

B cells

long non-coding RNA

transcriptome

gene co-expression network

Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease characterized by exocrine gland dysfunction, which leads to dryness of the eyes and mouth [1]. Upregulation of the interferon (IFN) pathway [2] and dysregulation of B cells play a critical role in the pathogenesis of pSS. First, the loss of B cell tolerance can lead to overproduction of anti-Sjögren’s syndrome-related antigen A (anti-SSA) autoantibodies. Autoreactive B cells are important in the development of clinical disease, because serum autoantibodies may precede the onset of dryness. Second, IFN-stimulated genes (ISGs) are upregulated in B cells in peripheral blood as well as in the salivary gland lesions of patients with pSS [3,4]. Third, IFNα promotes loss of tolerance and development of autoreactive B cells [5]. Genome-wide association studies identified single nucleotide polymorphisms of ISGs that are associated with the risk of pSS [6,7]. Further, viral infection, which enhances the IFN pathway, may contribute to the pathogenesis of pSS [8].

Non-coding RNAs, which are emerging as critical regulators of signal transduction pathways, may be involved in the immune system. Non-protein coding microRNAs are involved in the dysregulation in B cells [9] and in the upregulation of the IFN pathway in patients with pSS [10]. Long non-coding RNAs (lncRNAs) are involved in diverse regulatory functions, including the modulation of chromatin structure and post-transcriptional regulation affecting the stability of mRNAs and proteins [11]. Recent evidence indicates that lncRNAs, which have become the focus of studies on autoimmune diseases, may contribute to the pathogenesis of pSS through multiple signal transduction pathways [12]. For example, differentially expressed lncRNAs in the labial salivary glands of pSS patients were identified using microarray analysis [13]. In the multi-omics study of whole blood specimen, the ratio of B cell subpopulation within white blood cells was associated with the expression of pSS-associated signature genes, raising the necessity of in-depth investigation including lncRNAs about the link between dysregulated B cells and transcriptome [14].

Considering that gene expression pattern is tissue-specific [15], it is better to focus on the target cell subpopulation to avoid noise from other cells in transcriptome analysis. However, we are unaware of studies that focus on the effects of dysregulation of transcriptomes, including lncRNAs, of B cells on the pathogenesis of pSS. Serum level of B cell activating factor was associated with disease activity of pSS [16-18]. In addition, the distribution of peripheral B cell subsets is profoundly altered in patients with pSS [19]. Staining for IgD/CD38 is helpful for studying circulating B cell subsets, separating developmental stages from naive to memory B cells (Bm1 to Bm5) [20]. Using this gating strategy, we previously reported that the proportion of pre-GC B cells (IgD⁺/CD38^high) was higher in patients with pSS compared with healthy controls and associated with clinical traits, such as disease activity [19], indicating the importance of evaluating the involvement of each B cell subset in the pathogenesis of pSS.

To determine the role of the B cell subset in the pathogenesis of pSS, we investigated potentially significant genes according to their differential levels of expression and connectivity with other genes. Thus, the current study consists of two sections: differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). First, we describe the upregulation of the interferon signalling pathway and the differential expression of genes encoding human leukocyte antigen (HLA) molecules and LINC00487 in B cell subpopulations of patients with pSS compared with healthy controls (HC). The expression levels of LINC00487 correlated with the disease activity of pSS and IFN-signature genes; and LINC00487 was induced by IFNα. Second, using WGCNA, we identified genes of co-expression networks specific to a B cell subset of patients with pSS, suggesting that aberrant molecular interactions in B cells contribute to the aetiology of pSS.

Patients and controls

The study protocol is shown in Figure 1A. We enrolled patients with pSS (n=6) and HC (n=6) matched for age and sex (Supplementary Table 1). The patients with pSS fulfilled the 2002 American-European criteria for SS [21] and the 2012 American College of Rheumatology classification criteria for SS [22]. All patients were female and had symptom of dryness, anti-SSA antibodies and biopsy-proven sialadenitis. The patients had not received immunosuppressive therapy, and the HCs did not have immunological disorders. Clinical and serological information of patients with pSS were collected from their medical records. The disease activity of pSS was assessed according to the guidelines of the European League against Rheumatism and the Primary Sjögren's syndrome disease activity index (ESSDAI) [23, 24]. The distribution of ESSDAI domain and clinical information of individual patients were described in Supplementary Table 17 and Supplementary Table 18.

This study was performed in accordance with relevant guidelines and regulations. The Ethics Committee of Keio University School of Medicine approved this study (IRB No. 20110258), and written informed consent was obtained from each subject before blood collection.

Cell sorting

Peripheral blood mononuclear cells from patients with pSS and HC were separated using gradient centrifugation with Lymphoprep (Axis-Shield; Oslo, Norway). Gating strategy was shown in Supplementary Figure 1. Peripheral CD19⁺ B cells were prepared with anti-CD19 antibody-coated microbeads (Miltenyi Biotec). As previously reported [19], the peripheral CD19⁺ B cells were incubated with anti-IgD and CD38 antibodies for fluorescence-activated cell sorting (FACS) analysis (FACSAria III flow cytometer, BD Biosciences). We defined subsets of B cells as follows: Bm1 cells; CD38^-IgD⁺, naïve B cells; CD38⁺IgD⁺, pre-germinal centre (pre-GC) B cells; CD38^highIgD⁺ and memory B cells; CD38^±IgD^-.

DEGs analysis

Total RNA was extracted from B cell subsets and transcribed into cDNA using NucleoSpin RNA (Macherey Nagel) and ReverTra Ace qPCR RT Master Mix (Toyobo). Gene expression was measured using the Human Genome U133 Plus 2.0 Array (Affymetrix). We applied percentile shift normalization to the raw signal data acquired from a microarray and annotated each probe with its gene symbol using GeneSpring software (Agilent Technologies). Probes with interquartile ranges in the lowest 20% were excluded. We next selected probes with >2.0-changes for pSS vs HC in any one B cell subset to identify DEGs. We controlled for the false discovery rate using the Bonferroni multiple testing-corrected p value <0.05. To functionally characterize DEGs identified in each B cell subpopulation from microarray analysis, we performed pathway analysis using Enrichr’s plugin [25] BioPlanet [26]. BioPlanet database incorporates more than 1500 human pathways sourced from publicly available, manually curated sources. In pathway analysis, p-value was adjusted using the Benjamini-Hochberg method for correction for multiple hypotheses testing.

WGCNA

To explore novel gene co-expression networks and common hub genes, we created another gene set. In order to select genes that are steadily expressed in each B cell subpopulation of pSS and HC (totally, 8 subpopulations; Bm1, naïve, pre-GC and memory B cells of pSS, Bm1, naïve, pre-GC and memory B cells of HC ), respectively, we removed genes with coefficients of variation greater than 0.3 according to each subpopulation after intensity filtering and applied them to the WGCNA R package [27] following the package’s tutorial. Briefly, we applied step-by-step construction of the gene network and identification of modules to fileted gene expression dataset. First, we chose the proper soft-thresholding power based on the criterion of approximate scale-free topology. After calculating the adjacencies with selected soft-thresholding power, we transformed the adjacency into Topological Overlap Matrix, and calculate the corresponding dissimilarity. Then, we used hierarchical clustering to produce a hierarchical clustering tree of genes. Module identification amounts to the identification of individual branches; we cut the branches off the dendrogram with setting minimum module size 30. As a result, 6 and 5 modules were generated in B cells of patients with pSS and HC, respectively. Further, to annotate and estimate the regulatory mechanism of co-expressed gene network, we investigated pathways, upstream regulators and functions associated with modules by using Ingenuity Pathway Analysis (IPA).

Cell culture and real-time quantitative PCR (qPCR)

B cell lines (Ramos, CCRF, U266B1) (each 1.0 × 10⁵ /mL) in RPMI 1640 medium (American Type Culture Collection) supplemented with 10% fetal calf serum were treated with different concentrations of IFNα, IFNγ and TNFα for 48 h. We next isolated total RNA that was transcribed into cDNAs using NucleoSpin RNA (Macherey Nagel) and ReverTra Ace qPCR RT Master Mix (Toyobo). qPCR was performed using PowerUp SYBR Green Master Mix (Thermo Fisher Scientific). The levels of total cellular RNA were similar among the cell lines. At least two biological repeats were performed for each experiment. The levels of GAPDH mRNA were used to adjust the values of target transcripts determined using qPCR analyses. We validated gene expression levels in CD19⁺ B cells of patients with pSS (n=14) and HC (n=12) (Supplementary Table 2) using quantitative PCR (qPCR) analysis. Sequence of primers for qPCR analysis was described in Supplementary Table 3.

Statistics

Continuous values are shown as the median and range of values. Differences between the groups were analysed using the Mann–Whitney test for continuous variables. Correlations were analysed using Spearman’s correlation coefficient, unless otherwise noted. P <0.05 indicates a significant difference. Statistical tests were conducted using R software version 3.5.2.

Identification of gene signatures in B cell subsets of pSS

First, we visualized the gene expression profile after intensity filtration. Using principal component and hierarchical clustering analyses, we found that clinical status, namely pSS or HC, had less influence on phenotype than the type of B cell subset (Figure 1B and C). Memory B cells, in particular, had a distinct expression pattern compared with those of other cell types.

Expression of LINC00487 correlates with IFN signature genes and disease activity score of pSS

Using validation cohort, we tried to conduct qPCR analysis using each B cell subset. However, because LINC00487 expressed at very low level in HCs, we couldn’t sort enough each B cell subset from them to detect expression of LINC00487. Therefore, considering that upregulation of LINC00487 was common in all B cell subsets in microarray cohort, we used a bulk of CD19⁺ B cells in validation cohort. As a result, the expression of LINC00487 in CD19⁺ B cells of patients with pSS was significantly upregulated compared with HC (p = 0.035) (Figure 3D). As the same as Figure 3A and B, expression of LINC00487 was correlated with IFI44L with lower p-value in patients with pSS (p < 0.001) (Figure 3E) compared with that of HC (p=0.04) (Figure 3F). To confirm the induction of LINC00487 in response to IFNα, we used three types of B cell lines and measured LINC00487 expression using qPCR. As with IFI44L, LINC00487 was strongly induced by treatment with IFNα (Figure 3E and F), unlike TNFα and IFNγ (Supplementary Figure 3A), suggesting that IFNα is an upstream regulator of LINC00487. Compared with clinical features, the expression of LINC00487 tended to be higher in patients with higher serum IgG levels and lower lymphocyte counts which were characteristic for pSS (Supplementary Figure 3B).

Co-expression gene networks constructed for each B cell subset

To identify transcriptional networks in the B cells of patients with pSS, we performed WGCNA. WGCNA can identify novel gene interactions which might be missed in the DEGs analysis, because WGCNA is based on correlations of gene expression [25].

After selection, we identified a new set comprising 3634 genes (Supplementary Table 6-7). In principal component analysis using these gene sets, Bm1 and naive B cells in pSS tended to be closer to the expression patterns of a more highly differentiated subset, respectively, compared to those in HC; i.e., Bm1 to naive and naive to pre-GC (Figure 4A). WGCNA identified 6 modules in pSS and an association between cell subsets and modules (Figure 4B). For example, the yellow and brown modules are associated with the pre-GC B cell subset, whereas the turquoise module is associated with the memory B cell subset. The grey module of pSS was enriched in Bm1/naïve B cell subsets with various ranges of eigengenes. In the analysis to detect an association between modules and clinical traits, the grey module significantly correlated with the clinical disease activity score (Figure 4C). Similarly, another five modules were identified in HC (Supplementary Figure 4A). The module pairs with the greatest number of genes in each pSS module overlapping with either HC modules were blue (pSS) – turquoise (HC), brown (pSS) – brown (HC), grey (pSS) – blue (HC), green (pSS) – turquoise (HC), yellow (pSS) – blue (HC), and turquoise (pSS) – turquoise (HC), respectively (Supplementary Figure 5). All of the above pairs overlapped more than half of the gene list, suggesting that the overall picture of the B cell modules in pSS and the B cell modules in HC were similar.

To extract the characteristics of pSS modules, we performed functional analysis using Ingenuity Pathway Analysis (IPA). The cluster of enriched pathway, upstream regulator and disease/biological functions in modules of pSS were distinguished from those of HC (Figure 5A and Supplementary Tables 8–13). The top five most significantly enriched pathways, regulators and disease/biological functions in modules of pSS are shown in Figure 5B. In upstream regulator analysis, transcriptional regulators such as Sry-related high mobility group box (Sox) family such as SOX4 were identified in the yellow and blue modules of pSS. Focusing on specificity (Supplementary Figure 6 and Supplementary Table 14-16), mir-21-5p was identified as the top significant regulator in the yellow module of pSS. To search for hub genes, we described a co-expressed gene network using top highly interconnected genes defined by topological overlap in each module (Figure 5C). Several genes bridged inter-modules, namely hub genes, such as SOX4. SOX4 was not identified in the co-expression network of HC (Supplementary Figure 4B), suggesting its involvement in the dysregulation of B cells in pSS.

In the present study, we conducted transcriptome analysis of B cell subpopulations. First, we found that expression of LINC00487 was upregulated in B cell subsets derived from patients with pSS. Further, LINC00487 expression significantly correlated with disease activity and was induced by IFNα stimulation. Second, using WGCNA, we identified several key networks and hub genes in the B cells of patients with pSS.

IFN signalling is a central component of the pathogenesis of pSS [28]. Type 1 IFN signalling plays a crucial role in the development of autoreactive B cells in a mouse model of autoimmune disease [5]. Principal component analysis using gene sets for WGCNA demonstrated that Bm1/naïve B cells of pSS were clearly distinguished from those of HC and showed a trend of expression patterns closer to the more highly differentiated subset. These results suggest that molecular dysfunction, such as disruption of peripheral tolerance, might start at an early stage in the maturation of B cells. Indeed, the frequencies of naïve B cells expressing autoreactive antibodies are significantly increased in patients with pSS [29].

Interestingly, expression of the HLA class II gene was upregulated. GWAS studies of pSS found associations of HLA-DQA1 and HLA-DQB1 loci [6,7]. Further, IFNα induces the expression of HLA-DQA1 [30]. In patients with pSS, HLA-DQA1 and HLA-DQB1 alleles are associated with higher concentrations of anti-SSA and SSB antibodies [31]. These findings suggest that aberrant interactions among IFN signalling and HLA class II genes may trigger the breakdown of B cell tolerance, leading to the development of pSS.

LINC00487 was upregulated in all B cell subsets of pSS and its expression significantly correlated with the disease activity scores of pSS and ISGs, although ESSDAI score of our patients skewed toward the low end. Moreover, we found that IFNα was an upstream regulator of LINC00487 in B cells. The sequence of LINC00487, which belongs to the class of long intergenic non-coding RNAs and resides on human chromosome 2, is atypically long (> 40,000 bases). Although LINC00487 is one of the hub genes in the normal development of human B cells [32], many of its properties, including its function, are unexplained. Further, there is no ortholog or paralog of this gene in species other than humans that may provide clues to its function in human cells. However, according to AceView, one of three transcriptional variants derived from LINC00487 has potential to encode a protein in silico prediction [33].

Four genomic locations are considered candidate enhancers of LINC00487 transcription. The targets of the regulators of LINC00487 overlap with those of other ISGs [34]. Moreover, referring to the public transcriptome database of microarray analyses of healthy humans, expression of LINC00487 is higher specifically in centroblasts and centrocytes of the germinal centre [35]. IFNα promotes the autoreactivity of B cells via germinal centre pathways [5]. Further, LINC00487 expression is upregulated in the subgroup of diffuse large B-cell lymphoma with molecular characteristics of germinal centre B cells, compared with other subgroups, and is associated with the efficacy of B cell depletion therapy [36]. Therefore, our study suggests that upregulation of LINC00487 expression in all B cell subsets may reflect or regulate the enrichment of a germinal centre-like reaction by IFNα from an early stage of B cell development, leading to B cell autoreactivity in patients with pSS.

Gene co-expression analysis revealed an aberrant network in B cell subpopulations. The top significant upstream regulator of the grey module of pSS, which was associated with clinical disease activity score and enriched in the early stage of B cell development in pSS, was the gene encoding T-cell acute lymphocytic leukemia protein 1 (TAL1). B cell development is stringently controlled by stage-specific transcription factors. The transcription factor TAL1 regulates genes such as IKAROS family zinc finger 3 (IKZF3), which is one of hub genes in the grey module of pSS (Figure 5C). IKZF3 is a lineage-specific transcription factor that is important in the regulation of B cell proliferation and development [37].

In the pre-GC B cells-associated module of pSS, SOX4 was identified as an upstream regulator and a hub gene. In mice, SOX4 regulates the differentiation of early-stage B cells by activating the expression of Rag1 and Rag2 [38]. Further, SOX4 contributes to the formation of ectopic lymphoid-like structures via promoting CXCL13, which is ligand of CXCR5 on naïve B cells and critical for migration into light zone of germinal centre undergoing somatic hypermutation, production from PD-1^hiCXCR5^–CD4⁺T cells [39]. Additionally, in proteome analysis, CXCL13 positively correlates with the disease activity score and serum IgG levels of patients with pSS [19]. Further study is needed to explore the role of SOX4 in mature B cells.

Further, we identified miR-21 as an upstream regulator of the yellow module of pSS. Although miR-21 is upregulated in peripheral blood mononuclear cells of pSS [40], the present study is the first to propose its involvement in pSS via B cell dysregulation. Interestingly, miR-21 regulates the immune response of memory T cells via induction of transcription networks, such as SOX4 [41], implying that the interaction between miR-21 and SOX4 may also affect function of B cells.

pSS is enormously heterogeneous disease from the molecular point of view. In WGCNA, despite filtering out genes with high variation in each cell subpopulation, module expression showed the strong variation among samples in the same group, in particular Bm1 subset (Figure 4B). One of the reasons for the high variability seen in Bm1 subset may be related to the fact that some cells with the CD38^-IgD⁺ express CD27 [20]. Namely, the CD38^-IgD⁺ B cell subpopulation includes both naive Bm1 cells and IgD⁺ memory B cells. However, the finding in the current study suggested that there might be different regulatory mechanisms within each subgroup, although it was information that couldn’t be analyzed due to the limited sample size.

The current study suffers from several limitations. First, patients in our cohort have low disease activity/severity. Because there were few patients with high disease activity before immunosuppressive treatment, it was difficult to include such patients in this study. Therefore, correlation between expression of LINC00487 and disease activity score is needed to be validated in other cohort including patients with high ESSDAI scores. Second, healthy controls were younger than the pSS patients, and this could be a confounding variable. Third, sample size was limited. To overcome limitation about small sample size, we validated by qPCR using another cohort, supporting results derived from microarray analysis. However, regarding WGCNA, we couldn’t include subjects enough to replicate by qPCR.

Our focus on B cell subpopulation using a multi-level approachemploying analysis of DEGs and WGCNA identified significant genesand networks as novel players in the pathogenesis of pSS. Toconfirm our results, further study is needed.

Ethics approval and consent to participate

Ethics approval was obtained from the Institutional Review Board of Keio University School of Medicine (IRB No. 20110258). Consent to participate was obtained from all subjects in the current study before blood specimen was collected.

Consent for publication

Not applicable.

Availability of data and materials

The transcriptome data are available at the GEO database. The accession codes are GSE135809. All custom computer codes in the generation or processing of the described data are available upon reasonable request. Supplementary Table 4-18 and R script for WGCNA are deposited in figshare (https://figshare.com/articles/Supplementary_Table_4-14/11683959).

Competing interests

YK, YO, MTaki and RK are employed by Takeda Pharmaceutical Company Limited. S.T. was employed by Takeda Pharmaceutical Company Limited. KS has received research grants from Eisai, Bristol-Myers Squibb, Kissei Pharmaceutical, and Daiichi Sankyo, and speaking fees from Abbie Japan, Astellas Pharma, Bristol-Myers Squibb, Chugai Pharmaceutical, Eisai, Fuji Film Limited, Janssen Pharmaceutical, Kissei Pharmaceutical, Mitsubishi Tanabe Pharmaceutical, Pfizer Japan, Shionogi, Takeda Pharmaceutical, and UCB Japan, consulting fees from Abbie, and Pfizer Japan. AY has received speaking fees from Chugai Pharmaceutical, Mitsubishi Tanabe Pharmaceutical, Pfizer Japan, Ono Pharmaceutical, Maruho, and Novartis, and consulting fees from GSK Japan. TT has received research grants from Astellas Pharma Inc, Bristol-Myers KK, Chugai Pharmaceutical Co. Ltd., Daiichi Sankyo Co. Ltd, Takeda Pharmaceutical Co. Ltd, Teijin Pharma Ltd, AbbVie GK, Asahikasei Pharma Corp, Mitsubishi Tanabe Pharma Co, Pfizer Japan Inc, and Taisho Toyama Pharmaceutical Co. Ltd, Eisai Co. Ltd, AYUMI Pharmaceutical Corporation, and Nipponkayaku Co. Ltd, and speaking fees from AbbVie GK, Bristol-Myers KK, Chugai Pharmaceutical Co. Ltd, Mitsubishi Tanabe Pharma Co, Pfizer Japan Inc, and Astellas Pharma Inc, and Diaichi Sankyo Co. Ltd, and consultant fees from Astra Zeneca KK, Eli Lilly Japan KK, Novartis Pharma KK, Mitsubishi Tanabe Pharma Co, Abbivie GK, Nipponkayaku Co. Ltd, Janssen Pharmaceutical KK, Astellas Pharma Inc, and Taiho Pharmaceutical Co. Ltd. JI and MT declare no potential conflict of interest.

Funding

This study was funded by Keio University (grunt number: 04-013-0188) and Takeda Pharmaceutical Company Limited (grunt number: 04-078-0067).

Authors’ Contributions

Conceptualization: JI, KS, MTake, YK, YO, MTaki, RK and ST. Funding acquisition: KS and TT. Data acquisition: YK, MTaki and RK. Formal analysis: JI, YO and ST. Supervision: KS. Writing and original draft preparation: JI. Writing review and editing: KS, AY and TT.

Acknowledgements

This work was supported by Takeda Pharmaceutical Company Limited, Kanagawa, Japan (grant number 04-078-0067) and Keio University (grant number 04-013-0188).

We thank Ms. Harumi Kondo, Ms. Mayumi Ota, Ms. Yoshiko Yogiashi, Ms. Yuki Otomo, Mr. Fumitsugu Yamane for helping with the experiments, and Digital Medical Communications Corp. for proofreading sentences for English proofreading.

ESSDAI, EULAR Sjögren's Syndrome Disease Activity Index: FACS, fluorescence-activated cell sorting: GC-B, germinal centre B cell: HC, healthy control: HLA, human leukocyte antigen: IFN, interferon: IKZF3, IKAROS family zinc finger 3: ISGs, interferon-stimulated genes: lncRNA, long non-coding RNA: pSS, primary Sjögren's syndrome: TAL1, T-cell acute lymphocytic leukemia protein 1: WGNCA, weighted gene co-expression network analysis.

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Identification of novel genes associated with dysregulation of B cells in patients with primary Sjögren's syndrome

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