Molecular Remission at T cell level in Patients with Rheumatoid Arthritis

doi:10.21203/rs.3.rs-51665/v2

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Molecular Remission at T cell level in Patients with Rheumatoid Arthritis

https://doi.org/10.21203/rs.3.rs-51665/v2

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Background. While numerous disease-modifying anti-rheumatic drugs (DMARDs) has brought about a dramatic paradigm shift in the management of rheumatoid arthritis (RA), unmet needs remain such as small proportion of achievement drug-free patients. The aim of this study is to explore key molecules for remission at T cell level, which is known to involve the pathogenesis of RA deeply, and investigate disease course of patients who achieved molecular remission (MR).

Methods. We enrolled a total of 46 patients with RA and 10 healthy controls (HCs). We performed gene expression profiling and selected remission signature genes in CD4⁺ T cells and CD8⁺ T cells of patients with RA using machine learning methods. In addition, we investigated the benefit of achieving MR on disease control thereafter.

Results. We identified 9 and 23 genes that were associated with clinical remission in CD4⁺ and CD8⁺ T cells. Principal component analysis (PCA) demonstrated expression profiling of them was similar with HCs. As to remission signature genes in CD4⁺ T cells, PCA result was reproduced using validation cohort, indicating their robustness. There was a trend towards better disease control during 12 months follow-up in patients treated with tocilizumab in deep MR than those in non-deep MR, although it was not significant.

Conclusion. We identified robust genes which represented remission status in CD4⁺ T cells using machine learning techniques. The current study will promote our understandings about molecular mechanism to achieve deep remission in the management of RA.

Trial registration: Not required.

Rheumatology

Rheumatoid arthritis

T cells

molecular remission

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the synovial tissue [1]. The advent of disease-modifying anti-rheumatic drugs (DMARDs) has brought about a dramatic paradigm shift in the management of RA. Currently, the goal for RA treatment is to achieve clinical remission (CR), which has been facilitated by the development of various types of biological agents. However, unmet needs in the management of RA remain in not a small proportion of patients with remission. More than half of patients with CR experience a flare following DMARD cessation [2,3]. Moreover, the existence of patients with difficult-to-treat RA [4], who ultimately become resistant to multi-types of DMARDs, raises requirement of understanding about molecular status of remission.

Thus, molecular remission (MR) has been proposed for distinguishing patients with “deep” remission from others [5,6]. Proteome study demonstrated that a low multi-biomarker disease activity score, which they developed, was associated with limited radiographic progression over the following 12 months [5]. We recently investigated molecular signatures that were associated with deep remission at multi-omics level [6]. In the previous report, drug treatments altered the molecular profile closer to that of healthy controls (HCs) at the transcriptome, serum proteome, and immunophenotype level. In addition, a longitudinal monitoring suggests that the achievement of MR by DMARDs was associated with long-term stable CR. However, as to transcriptome, how each remission signature molecule links with clinical trait was unclear because we used whole blood specimen and expression profile was various according to cell subsets, indicating necessity of further study using each cell subpopulation [6].

It is well known that T cells contribute to the pathogenesis of RA. Susceptible genes for RA outside the MHC locus are highly expressed in CD4⁺ T cells [7,8]. The evidence that subset of CD8⁺ T cells is also critical contributor for development of RA is accumulating. CD8⁺ T cells are required for development of ectopic germinal center in synovium, which is considered to be the home of core immune response in RA [9]. Recently, to investigate clarify the comprehensive characteristics of T cells in RA, we conducted multi-dimensional, immunophenotyping analysis according to developmental stage: CD4⁺ T cells were classified into four subsets (naïve (TN), stem cell memory (TSCM), central memory (TCM) and effector memory (TEM)), whereas CD8⁺ T cells were classified into five stages (TN, TSCM, TCM, TEM and CD45RA-positive effector memory (TEMRA)). The study demonstrated that CD8⁺ TEMRA increased in patients with RA compared with HCs, and TEM-follicular helper (Tfh) cells and TEM-T helper 17(Th17) cells were correlated with disease activity, suggesting T cells in patients with remission may represent MR state of RA [10].

Here, we report key molecules for remission at T cell level, and investigate disease course of patients who achieved MR.

Patients and control subjects

The current study utilized cohorts from our previous report [10]. The detailed information was described previously. Briefly, there were 2 cohorts in this study (cohort 6 in Supplementary Table S9 and cohort 7 in Supplementary Table S10 of the previous report): cross-sectional gene expression profiling was performed in derivation cohort, and longitudinal gene expression profiling was performed at pre-treatment and post-treatment in validation cohort (Figure 1). Patients in validation cohort were treated by tocilizumab (TCZ) for 6 months and achieved remission after treatment. Peripheral blood samples and synovial fluid (SF) samples were collected before treatment from patients with drug-naïve RA and during treatment from other patients, respectively. We separated each T cell subpopulation from specimens, and RNA was isolated. Then, we conducted RNA-sequencing and performed bioinformatic analysis. Read count normalization was performed using the transcripts per million (TPM) method [13]. Disease activity was assessed by standard composite indices, such as DAS28-ESR (disease activity score 28-ESR) and DAS28-CRP, and remission was defined as a DAS28-CRP < 2.4 [14].

Development of RA remission signature models

To select genes that had a strong relationship with the remission, we applied least absolute shrinkage and selection operator (lasso) to normalized gene expression data of patients with drug-naïve RA and treatment in derivation cohort. Lasso is one of machine learning methods, and suitable for detecting key variables without prior feature selection from multi-variate dataset that contains only a few covariates which are associated with the outcome, improving the prediction accuracy and interpretability of regression models [15]. To avoid overfitting, we first divided gene expression dataset of derivation cohort to training data and test data (with ratio = 7:3), and conducted lasso using training data with seven-fold cross-validation. Then, a partial least-squares regression (PLS-R) was utilized to weight values to selected genes and construct a model that was useful for separating remission and non-remission patients. Variable importance in projection (VIP) score obtained by PLS-R is a significant measurement of each predictor variable. Genes with VIP score with greater than 1 was considered to be related with remission [16]. Thus, we can evaluate the importance of each gene. Receiver operating characteristics (ROC) analysis was used to evaluate the prediction accuracy in test data by statistical model generated from training set. Then, the statistical model was applied to whole set data to enumerate “remission odds” for each sample. The caret and ropls R package were used for lasso and PLS-R modeling, respectively [17].

Principal component analysis and pathway mapping

Principal component analysis was performed using normalized data with the FactoMineR R package [18]. To gain a functional annotation of selected genes, Enrichr’s plugin [19] KEGG pathways [20] was used.

Statistics

Continuous data were presented as the median and interquartile range or as a number with a percentage value, as appropriate. The Wilcoxon rank sum test was used to examine the differences between continuous variables. Fisher’s exact test was used to compare proportions in categorical data between groups. All statistical analyses were performed with R (R Foundation for Statistical Computing, Vienna, Austria).

Remission signature genes in RA

The primary objective is to explore genes that are relevant to remission status in RA (so called, remission signature genes). Therefore, we first selected genes that were useful for separating patients with remission and those with non-remission using training dataset of derivation cohort, with dividing for CD4⁺and CD8⁺ T cells. By lasso, of whole 15304 transcripts, 17 and 46 genes were selected as important molecules in CD4⁺ and CD8⁺ T cells for classifying patients with remission or non-remission, respectively.

Then, we weighted genes selected by lasso and construct statistical model that separate remission and non-remission (so called, RA remission signature model) by applying PLS-R. As a result, ROC analysis by applying test set that wasn’t used for selection separated them with good accuracy (area under the curve [AUC], 0.947 and 0.929 for CD4⁺ and CD8⁺ T cells, respectively) (Figure 2A). This result indicated that the combination of lasso and PLS-R captured genes that were informative in our data. In addition, 9 (e.g., MST1, ASB2, SULT2B1 and SOCS3) and 23 (e.g., CRLF2, NIM1 and ID1) genes were passed through criteria (VIP > 1) in CD4⁺ and CD8⁺ T cells, respectively (Figure 2B and 2C and Supplementary Table S1). Hereafter, we refer to these genes as remission signature genes. To understand the function of remission signature genes, pathway analysis was performed (Figure 2B and 2C). In CD4⁺ T cells, molecules involved in various metabolic pathways (Vitamin B6 metabolism and Glycine, serine and threonine metabolism), endocrine pathways (steroid hormone biosynthesis, adipocytokine signaling pathway, prolactin signaling pathway and insulin resistance) and TNF signaling pathway were enriched. As to CD8⁺ T cells, as well, molecules involved in metabolic pathways (taurine and hypotaurine metabolism and fatty acid degradation) and JAK-STAT signaling pathway were enriched.

Remission odds of T cell subpopulation

To investigate the effects of different DMARDs and difference of T cell subpopulation (TN, TCM, TEM and TEMRA) on remission signature genes, we compared remission odds according to each subgroup. Using RA remission signature model generated by the combination of lasso and PLS-R, remission odds of each subject were produced: if patients were in remission status, remission odds toward 1 (>0.5), and if patients were far apart from remission, remission odds toward 0 (<0.5). Since T cells in SF are considered to reflect pathological status, we calculated remission odds of T cell subpopulation in SF from some patients.

In CD4⁺ T cells, remission odds of patients with DMARDs, all of whom were in remission except one patient (Figure 1), were significantly higher compared with drug-naïve patients (Figure 3A). Of note, although there was no significance due to limited samples, SF samples had trend toward low remission odds as well as drug-naïve samples from peripheral blood, suggesting remission signature genes might represent pathogenic status of RA. Conversely, in comparison with HCs, they were similar values without difference of type of drugs, indicating all drugs pushed pathogenic gene expression profile of remission signature genes back to healthy state. Correspondingly, principal component analysis (PCA) using remission signature genes in CD4⁺ T cells demonstrated only drug-naïve samples made different cluster from others except naïve subset (Figure 3B). To validate classification ability of remission signature genes, PCA analysis was conducted using expression data in validation cohort. Along with earlier results, samples from patients in remission created cluster apart from those in non-remission, supporting robustness of remission signature genes of CD4⁺ T cells (Figure 3C).

Regarding to CD8⁺ T cell subpopulations, like CD4⁺ T cells, all of remission odds of patients with DMARDs were significantly higher compared with those of drug-naïve (Figure 4A). However, remission odds of some samples in groups of DMARDs were also significantly higher than those of HCs, suggesting selected genes in CD8⁺ T cells might not represent healthy state correctly. In addition, PCA demonstrated all clusters overlapped except TEM subpopulation (Figure 4B). Further, PCA using validation cohort also couldn’t show validity of selected genes, indicating vulnerable ability of remission signature genes in CD8⁺ T cells (Figure 4C).

Relations between molecular remission and following disease activity

To elucidate the benefits of MR, we next addressed whether there were any differences between patients in “deep” MR and non-deep MR. To achieve this goal, we conducted a follow-up study of consecutive 29 patients (MTX, n=10; IFX, n=10; TCZ, n=9) treated with DMARDs in derivation cohort for up to 12 months after the time point at measurement of gene expression. We defined MR of each cell subset as remission odds greater than average value of remission odds in each cell subset, and deep MR of each patient as the number of cell subsets in MR greater than 4 (maximum 7). Of the 29 patients treated with DMARDs, 12 and 17 patients were classified as deep MR and non-deep MR. Disease activity did not have a statistically significant difference at any time point (Figure 5A). Cumulative DAS28-ESR (described as AUC), however, of patients treated with TCZ in deep MR had a trend lower than those with TCZ in non-deep MR (12.48 [11.25-13.82] vs 18.26 [17.07-18.36], p = 0.19) (Figure 5B). In comparison among drugs, the difference was significant between patients treated with TCZ and those with MTX in deep MR. Although we conducted sensitivity analysis by changing outcome (e.g., DAS28-CRP and each component of DAS28) and definition of deep MR (e.g., the cut-off number of cell subpopulation, limited to CD4⁺ T cell subpopulations), we couldn’t find significant benefit of deep MR in our data (data not shown).

In the current study, we identified remission signature genes of RA in CD4⁺ and CD8⁺ T cells. Although those in CD8⁺ T cells were vulnerable when applying to other cohort, those in CD4⁺ T cells had robust ability to classify remission and non-remission patients in both cross-sectional and longitudinal cohorts. Further, remission odds calculated by RA remission model of CD4⁺ T cells showed similar value among patients in remission and HCs, suggesting selected 9 genes represent whether CD4⁺ T cells of RA is pushed back to healthy state or not. In addition, deep MR by TCZ had the potential to be associated with better disease control thereafter.

As shown in previous reports, obviously T cells were involved in the development and chronicity of RA [7-10]. Compared with CD4⁺ T cells, evidence of a role for CD8⁺ T cells in RA are emerging [9]. Clonal expansion was observed for CD8+ T cells but not for CD4+ T cells in newly diagnosed patients with RA, indicating requirement of them for initial phase of RA [21]. However, RA risk alleles were preferentially expressed in CD4⁺ T cells, not CD8⁺ T cells [7,8], and proportion of only CD4⁺ T cell subpopulations were positively associated with disease activity [10], suggesting CD4⁺T cells more deeply contribute to the activity state of RA than CD8⁺ T cells.

Remission signature genes identified in CD4⁺ T cells were downregulated in patients with remission. MST1 promotes migration of T cells via activating LFA-1 [22,23]. In addition, MST1 deficient T cells prone to apoptosis [24]. ASB2 is known to promote NF-κB activation, leading suppression of apoptosis of T cells [25]. SULT2B1 is involved in cholesterol homeostasis and expressed in activated T cells, prompting proliferation via inhibition of LXR signaling [26]. As to SOCS3, since it inhibits STAT3 that is downstream molecule of JAK-STAT pathway which is inhibited by TNFα- and IL-6- inhibitor [27], downregulation of SOCS3 may be assumed to lead to activation of inflammatory pathway [28]. However, indeed, loss of SOCS3 in CD4⁺ T cells promotes anti-inflammatory cytokines, such as interleukin 10 and transforming growth factor-beta 1, and suppresses inflammatory responses [29], suggesting decrease in expression of SOCS3 itself has benefit to control RA. Regarding to other 5 genes identified as remission signature genes in CD4⁺ T cells (ABHD11, KCNK1, PSAT1, CYB5D2 and CABP4), function in T cells is unknown and further functional study is needed to clarify its significance in RA.

However, we couldn’t show the significant benefit of achieving deep MR defined by remission signature genes on clinical course, while those treated with TCZ in deep MR had a favorable trend. To date, although several studied tried to predict following change of disease activity using only transcriptome data, they failed to show robust predictability [6,30]. In our data, most patients even in non-deep MR we could follow were under good control during follow-up as shown in Figure 5A, which might make hard to detect statistical significance. In addition, definition of “deep” MR in the current study lacks of evidence. Therefore, to explore impact of MR on clinical trait and association between MR and type of drugs, we need to plan prospective study with larger sample size.

This study suffers from several limitations. First, we didn’t examine association between functional subpopulations and MR. We reported previously that Tfh and Th17 cells in TEM were correlated with disease activity, suggesting various extent of contribution of T cells to disease status according to their function [10]. Second, another benefit of MR is not considered in the current study, such as lower risk of flare. Because concept of MR in RA is emerging recently, we need to validity of definition and effect of MR on real-world patients in future research.

We identified robust remission signature genes in CD4⁺ T cells. The current study will highlight the utility of transcriptome in CD4⁺ T cells for classifying remission and non-remission in RA, and promote development of novel therapeutic target against RA.

Ethics approval and consent to participate

Ethics approval was obtained from the Institutional Review Board of Keio University School of Medicine. 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

Transcriptome data are available at the GEO database. The accession codes are GSE113156 and GSE118829. All custom computer codes in the generation or processing of the described data are available upon reasonable request.

Competing interests

YO, KK, YK, MT and RK are employees of 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, MT, YK and RM declare no potential conflict of interest.

Funding

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

Authors’ Contributions

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

Acknowledgements

We thank Ms. Harumi Kondo, Ms. Mayumi Ota, Ms. Yoshiko Yogiashi, Mr. Yuki Otomo, Mr. Fumitsugu Yamane and Ms. Miku Shimizu for helping with the experiments.

area under the curve (AUC); CD45RA-positive effector memory T cells (TEMRA); central memory T cells (TCM) ; clinical remission (CR); disease activity score-28 ESR (DAS28-CRP); disease activity score-28 ESR (DAS28-ESR); disease-modifying anti-rheumatic drugs (DMARDs); effector memory T cells (TEM); healthy controls (HCs); infliximab (IFX); least absolute shrinkage and selection operator (lasso); methotrexate (MTX); molecular remission (MR); naïve T cells (TN); partial least-squares regression (PLS-R) ; principal component analysis (PCA); Receiver operating characteristics (ROC); rheumatoid arthritis (RA); stem cell memory T cells (TSCM); synovial fluid (SF) ; T helper 17 cell (Th17); T-follicular helper cells (Tfh) ; tocilizumab (TCZ) ; transcripts per million (TPM) ; Variable importance in projection (VIP).

McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365:2205-19.
Baker KF, Skelton AJ, Lendrem DW, et al. Predicting drug-free remission in rheumatoid arthritis: A prospective interventional cohort study. J Autoimmun. 2019;105:102298.
Kuijper TM, Lamers-Karnebeek FBG, Jacobs JWG , et al. Flare rate in patients with rheumatoid arthritis in low disease activity or remission when tapering or stopping synthetic or biologic DMARD: a systematic review. J Rheumatol 2015;42:2012-22.
Roodenrijs NMT, de Hair MJH, van der Goes MC, et al. Characteristics of difficult-to-treat rheumatoid arthritis: results of an international survey. Ann Rheum Dis. 2018;77:1705-1709.
van der Helm-van Mil AH, Knevel R, Cavet G, et al. An evaluation of molecular and clinical remission in rheumatoid arthritis by assessing radiographic progression. Rheumatology (Oxford). 2013;52:839-46.
Tasaki S, Suzuki K, Kassai Y, et al. Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission. Nat Commun. 2018 ;9:2755.
Farh KK-H, Marson A, Zhu J, et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 2015;518:337–43.
Hu X, Kim H, Stahl E, et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. The American Journal of Human Genetics 2011;89:496–506.
Kang YM, Zhang X, Wagner UG, et al. CD8 T cells are required for the formation of ectopic germinal centers in rheumatoid synovitis. J Exp Med 2002;195:1325–36.
Takeshita M, Suzuki K, Kondo Y, et al. Multi-dimensional analysis identified rheumatoid arthritis-driving pathway in human T cell. Ann Rheum Dis. 2019;78:1346-1356.
Aletaha D, Neogi T, Silman A J, et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis. 2010;69:1580-1588.
Saag K G, Teng G G, Patkar N M, et al. American College of Rheumatology 2008 recommendations for the use of nonbiologic and biologic disease-modifying antirheumatic drugs in rheumatoid arthritis. Arthritis Rheum 2008;59:762-784.
Li B, Ruotti V, Stewart RM, et al. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26:493-500
Fleischmann R, van der Heijde D, Koenig AS, et al. How much does Disease Activity Score in 28 joints ESR and CRP calculations underestimate disease activity compared with the Simplified Disease Activity Index? Ann Rheum Dis 2015;74:1132-7.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16:385-95.
Noppamas A, Seree C, Kidakan S. Cutoff threshold of variable importance in projection for variable selection. Int J Pure Appl Math. 2014;94:307-322.
Thévenot EA, Roux A, Xu Y, et al. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J Proteome Res. 2015;14:3322-35.
Lê, S., Josse, J., Husson, F. FactoMineR: An R Package for Multivariate Analysis. J Stat Softw. 2008;25:1-18.
Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;128(14).
Kanehisa M, Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28:27-30.
Savola P, Kelkka T, Rajala HL, et al. Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis. Nat Comms 2017;8.
Katagiri K, Imamura M, Kinashi T. Spatiotemporal regulation of the kinase Mst1 by binding protein RAPL is critical for lymphocyte polarity and adhesion. Nat Immunol. 2006;7:919-28.
Ueda Y, Katagiri K, Tomiyama T, et al. Mst1 regulates integrin-dependent thymocyte trafficking and antigen recognition in the thymus. Nat Commun. 2012;3:1098.
Choi J, Oh S, Lee D, et al. Mst1-FoxO signaling protects Naïve T lymphocytes from cellular oxidative stress in mice. PLoS One. 2009;4:e8011.
Wu W1, Nie L, Zhang L, et al. The notch pathway promotes NF-κB activation through Asb2 in T cell acute lymphoblastic leukemia cells. Cell Mol Biol Lett. 2018;23:37.
Bensinger SJ, Bradley MN, Joseph SB, et al. LXR signaling couples sterol metabolism to proliferation in the acquired immune response. Cell. 2008;134:97-111.
Schwartz DM, Kanno Y, Villarino A, et al. JAK inhibition as a therapeutic strategy for immune and inflammatory diseases. Nat Rev Drug Discov. 2017;16:843-862.
Shouda T, Yoshida T, Hanada T, et al. Induction of the cytokine signal regulator SOCS3/CIS3 as a therapeutic strategy for treating inflammatory arthritis. J Clin Invest. 2001;108:1781-8.
Kinjyo I, Inoue H, Hamano S, et al. Loss of SOCS3 in T helper cells resulted in reduced immune responses and hyperproduction of interleukin 10 and transforming growth factor-beta 1. J Exp Med. 2006;203:1021-31.
Lewis MJ, Barnes MR, Blighe K, et al. Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes. Cell Rep. 2019;28:2455-2470.e5.

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Molecular Remission at T cell level in Patients with Rheumatoid Arthritis

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