How to manage rheumatoid arthritis according to classic biomarkers and polymorphisms?

Abstract

OBJECTIVES: Single nucleotide polymorphisms (SNPs), genetic background, and epigenetics play important roles in rheumatoid arthritis (RA). These factors can be useful in RA diagnosis, prognosis, and treatment response evaluation, particularly with the growing trends in personalized medicine. Therefore, categorizing classic genes and SNPs in RA can present an appropriate guideline for RA management.

DISCUSSION: Prognostic and diagnostic biomarkers play important roles in RA diagnosis and treatment. Categorizing SNPs is not an easy process yet, but selecting classic SNPs can be useful worldwide, according to basic similarities that exist in genomes. In this review, we compiled some of these RA-associated SNPs and biomarkers in a table, according to newly identified factors. The role of epigenetics in RA is undeniable; using epigenetic biomarkers like histone deacetylase (HDACs) can be useful in RA diagnosis and treatment. miRs such as miR-146a, miR-155, and miR-222 are useful in diagnosis and can be used in treatment by interfering with other factors’ functions. Interleukins (ILs) seem to be good prognostic and diagnostic markers and can be targeted in RA treatment.

CONCLUSION: Using multiple types of biomarkers, such as genes, SNPs, and epigenetic biomarkers like HDACs can be useful in RA management and treatment. PTPN22, HLA-DR polymorphisms, miRs, and HDACs are considerable in RA susceptibility; hence, they can be valuable biomarkers in future studies. This article gathered separate information from approximately 100 articles to present useful biomarkers and polymorphisms in one review.

Keywords

polymorphism / rheumatoid arthritis / miRs / HLA-DR / epigenetics

Introduction

Rheumatoid arthritis (RA) is a multifactorial disease affecting approximately 1% of people worldwide (Guo et al., 2014). Still, the exact mechanism of RA pathogenesis is unknown (Chimenti et al., 2015). On the other hand, early diagnosis and treatment are effective for controlling disease severity (Zengin et al., 2016). There are several diagnostic and prognostic biomarkers that are useful in RA management including instant Rheumatoid Factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) (Gavrilă et al., 2016). In addition, inflammatory cytokines such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) can reflect the inflammatory status in the body (Chimenti et al., 2015; Lavric et al., 2016). Biomarkers such as Cartilage Oligomeric Matrix Protein (COMP) are useful for predicting cartilage destruction (Deane and El-Gabalawy, 2014). Genetic biomarkers have important roles in RA development and severity, and affect therapeutic response as well (Ospelt, 2016). Epigenetics seems to also be responsible for RA susceptibility and inflammatory response (2e).

This review presents two tables representing diagnostic, prognostic, and therapeutic response biomarkers, as well as golden SNPs associated with RA severity and therapeutic response. These tables can be useful in RA management because this review gathered considerable information from studies conducted worldwide to offer an impactful insight regarding classic and genetic biomarkers to rheumatologists.

Overview of rheumatoid arthritis

RA is known as a multifactorial disease; the environment, genetic background, job, gender, smoking, and many other factors can affect RA susceptibility (Anaya et al., 2016; Arleevskaya et al., 2016; Chung et al., 2016) (Fig. 1).

Early diagnosis and prognosis are important elements in RA management. A current challenge in many diseases is personalizing therapeutic approaches by using genetic biomarkers and SNPs. However, the application of genetic biomarkers and SNPs in RA is limited because RA development depends on the presence of SNPs in the majority of people, not an exact mutation in the genome (Tedesco et al., 2009; Zengin et al., 2016). HLA subtypes may just increase the risk of change in DNA methylation, which can lead to disease development and relate to RA risk (Viatte et al., 2013; Glant et al., 2014). Environmental factors can cause genetic predispositions to RA and SNPs can be early predictors of RA development.

Role of epigenetics in RA

RA onset seems to be a result of complex interactions between genetic susceptibility and epigenetics (Angiolilli et al., 2017). Epigenetics covers DNA methylation, various histone modifications, and non-coding RNAs (Angiolilli et al., 2014). Epigenetics works as a backup of cellular reprogramming; when reprogramming fails, epigenetics can be transmitted to the next generation. Epigenetic mutations, whether they are natural or deleterious, frequently involve transposable elements and are inherited adaptively or even in response to environmental changes (Heard and Martienssen, 2014). Recent studies revealed that errors in epigenetic mechanisms can lead to inflammatory disorders (Glant et al., 2014). Several studies suggest that epigenetic changes can cause permanent gene expression changes and can also be heritable (Glant et al., 2014). Histone acetylation and deacetylation changes by histone acetyl transferase (HAT) and histone deacetylase (HDAC), respectively, are epigenetic changes that can affect inflammatory cytokine production. In other words, the balance between HATs and HDACs can be useful in controlling inflammation by interfering with inflammatory signaling pathways in a number of diseases like (Toussirot et al., 2013). HDAC4 is a distinguishing member of the HDACs family because HDAC4 may play a protective role by preserving cartilage integrity (Angiolilli et al., 2017). HDAC7 may be able to suppress Runx2 activity and osteoblast differentiation (Angiolilli et al., 2017). HDAC5 is associated with disease severity in RA; it can be suppressed by tumor necrosis factor (TNF). HDAC9 can suppress FOXP3 in Treg cells (Parra, 2015). HDACs may play a specific role in normal and pathological circumstances, and HDAC inhibitor (HDACi) usage may emerge as a beneficial therapy in RA treatment, and acetyl code aberrations can be used as prognostic markers in RA (Parra, 2015).

Biomarkers in RA diagnosis

RF, anti-CCP, CRP, and ESR

Rheumatoid Factor (RF) and anti-CCP are RA-related biomarkers that are associated with disease (Sharma and Bhar, 2017). Interestingly, anti-CCP can be detectable in the serum of patients with RA, years before pathological symptoms of disease emerge (Mc Ardle et al., 2015). CRP and ESR are not RA-specific and they remain normal while RA progresses; hence, CRP and ESR are not sensitive predictors. On the other hand, acute phase serum amylase-A (A-SAA) levels can reflect local and systemic inflammation in RA (Connolly et al., 2012), and A-SAA can be a good biomarker for therapeutic response to disease-modifying antirheumatic drugs (DAMARs) (Hwang et al., 2016). RF is disease-specific and it is useful in early diagnosis but not for estimating disease progress, while anti-CCP is predictive in early and progressive stages of the disease (Nell et al., 2005; Syversen et al., 2008). RF is an Immunoglobulin M (IgM) but anti-CCP is an Immunoglobulin G (IgG); RF affects the complementary system stronger and leads to a stronger inflammatory response (Aletaha et al., 2015).

Biomarkers for joint damage and cartilage destruction

Finding a reliable predictive biomarker for joint damage would be revolutionary. Some inflammatory biomarkers such as IL-6 and IL-33 are associated with joint damage (Chimenti et al., 2015). Additionally, products of collagen damage such as C-terminal telopeptides of collagen CTX \ and CTX II are diagnostic and prognostic (Siebuhr et al., 2012; Henrotin et al., 2015). Enzyme mediators of destruction, such as matrix metalloproteinases (MMPs) like trapeze, might be involved in pathogenesis and increased cartilage damage (Kim et al., 2011). COMP is a valuable biomarker when evaluating cartilage damage (Gavrilă et al., 2016). S100A8/9 is associated with joint damage in RA, and may represent a good prognostic biomarker (Kang et al., 2014). Matrix metalloproteinase-3 (MMP-3) is one of the most important biomarkers in bone destruction and cartilage damage (Zhai et al., 2016).

miRs as novel inflammatory biomarkers

Micro-RNAs (miRs) are tissue- and disease-specific, “inflammaMiRs” like miR-155 and miR-146a showed differential expression in RA but sufficient expression level sensitivity and specificity have not been determined yet (Elmesmari et al., 2016; Ospelt, 2016). miR-146a expression can be a negative regulator of interferon (INF) pathways (Picascia et al., 2015). miR-146a and miR-155 are highly expressed in synovial fibroblasts of patients with RA (Picascia et al., 2015). miR-146a can be used as a biomarker for disease severity (Castro-Santos et al., 2015). miR-16 and miR-223 also can be used as biomarkers in joint destruction diagnosis (Castro-Santos et al., 2015). Several studies suggest that miR-19 may be involved in RA pathology by inducing inflammatory cytokine release (Li et al., 2016). For example, miR-23a directly targets IKKa and decreases IL-17 mediators’ expression (Hu et al., 2017). The miR-17-92 cluster is a useful factor in osteoblastogenesis, and it can be used as a biomarker to evaluate bone formation (Ibrahim et al., 2016). miR-22 significantly suppresses NF-κB activity and miR-340-5p can be a potentiated biomarker for osteoclastogenesis (Ma et al., 2016). Another recent use of miRs, like using miR-222 and miR-29a, as HDACi have been suggested as an RA therapy (Desiderio et al., 2014; Song et al., 2014). Therefore, miRs have gained significant value in the recent century.

Changes in cytokine and other protein profiles in RA

Imbalanced cytokine production plays an important role in RA pathogenesis. Tumor necrosis factor a (TNFa) is responsible for inflammation in RA and IL-1 is mostly responsible for cartilage damage and bone destruction. Pro-inflammatory cytokines induce gene expression of inflammatory mediators (Brzustewicz and Bryl, 2015). Cytokines can increase synovial membrane inflammation and cartilage damage; IL-12 is associated with disease activity and IL-23 stimulates RANK/RANKL to induce osteoclastogenesis (Niu and Chen, 2014). IL-23 increases Th17 proliferation and inflammation. Therefore, IL-17 and IL-23 play roles in disease severity, and IL-23 can be an especially good target for knockout strategies as a new emerging therapy (Guo et al., 2016). Anti-CCP, TNFa, and IL-6 act as triggers for RA and increase inflammation; IL-6 and TNFa also cause bone erosion and osteoclastogenesis (Wei et al., 2015). TNFa inhibits Treg function too; hence, TNFa, IL-6 and IL-17 are potential biomarkers for inflammation (Zhang et al., 2013). IL-33 signals to the IL-1RL1/STs receptor, which activates MAPK pathways and finally NF-κb as well (Martin NT and Martin MU, 2016). IL-33 induces Auto-antibody Induced Arthritis (AIA); therefore, blocking IL-33 can reduce the effects of RA (Palmer et al., 2009) (Table 1).

Using SNPs as biomarkers

Genetics affects RA severity and therapeutic response. Genes and SNPs can be good prognostic biomarkers for RA management.

6q23 is one of the important loci associated with RA, after HLA-DRB1 and PTPN22 (McGovern et al., 2016). Genes PADI4 and CTLA-4 are RA susceptibility factors (61). Several studies revealed that the rs247660 SNP in PTPN22 is a loss-of-function allele that destabilizes the gene and is associated with RA susceptibility (Viatte et al., 2013). STAT3 overexpression increases IL-6 production in anti-CCP-negative RA in undifferentiated Arthritis (UA), and can be a prognostic biomarker in Early Arthritis (EA) (Anderson et al., 2016). RA patients that are anti-CCP-negative have different signals in HLA-B products, and this difference depends on the gene DRB1 that encodes serine11, and HLA-B8 which produces aspartate9 (Yamamoto et al., 2015). RA patients with HLA-DRB1, that shares an epitope with RANK, RANK Ligand (RANKL), Osteoprotegrin (OPG), and IL-17 have been associated with the age of RA onset and radiographic progression in Japanese patients (Paradowska-Gorycka et al., 2010). Current studies suggest that the G2677A/T SNP was significantly associated with a glucocorticoid-resistant response (Cuppen et al., 2017). According to new findings, the VAV1 gene plays a role in T cell signaling and is associated with RA. Vav1 SNPs such as rs682626, rs2546133, rs2617822, and rs12979659 are associated with anti-CCP-negative RA (Guerreiro-Cacais et al., 2017).

Conclusions and future perspectives

RA is a multifactorial autoimmune disease associated with genetic and epigenetic factors. Genes and epigenetic factors such as HDACs and miRs can be used as prognostic markers in early RA diagnosis, and can be targeted in future therapeutic approaches.

RA SNPs and classic biomarkers can work synergistically for early RA diagnosis, to provide significant insights into RA management. HLA-DR, PTPN22, genes in the 6q23 region and Xp11 region, respectively, are relevant for RA susceptibility. Other SNPs categorized in the tables presented herein possess good potential for developing RA guidelines. SNPs are touted as a new type of RA biomarker but their effects are not well-understood by rheumatologists yet. Classic biomarkers work stronger beside SNPs and can offer a meaningful overview in prognosis and early diagnosis of RA. Additionally, SNPs can be useful for predicting a therapeutic response. Finally, choosing good prognostic and diagnostic biomarkers can be helpful in early treatment and disease management and can decrease the treatment costs involved.

References

[1] Aletaha D, Alasti F, Smolen J S (2015). Rheumatoid factor, not antibodies against citrullinated proteins, is associated with baseline disease activity in rheumatoid arthritis clinical trials. Arthritis Res Ther, 17(1): 229

[2] Anaya J M, Ramirez-Santana C, Alzate M A, Molano-Gonzalez N, Rojas-Villarraga A (2016). The autoimmune ecology. Front Immunol, 7: 139

[3] Anderson A E, Pratt A G, Sedhom M A, Doran J P, Routledge C, Hargreaves B, Brown P M, Lê Cao K A, Isaacs J D, Thomas R (2016). IL-6-driven STAT signalling in circulating CD4⁺ lymphocytes is a marker for early anticitrullinated peptide antibody-negative rheumatoid arthritis. Ann Rheum Dis, 75(2): 466–473

[4] Angiolilli C, Baeten D L, Radstake T R, Reedquist K A ( 2017). The acetyl code in rheumatoid arthritis and other rheumatic diseases. Epigenomics, 9(4):447–461

[5] Angiolilli C, Grabiec A, Ferguson B, Fernandez BM, Tak P, McKinsey T, Baeten D L, Reedquist K A (2014). HDAC5 regulates CXCL chemokine expression in RA FLS via the transcription factor IRF1. Ann Rheum Dis,73(Suppl 1):A8 

[6] Arleevskaya M I, Kravtsova O A, Lemerle J, Renaudineau Y, Tsibulkin A P (2016). How rheumatoid arthritis can result from provocation of the immune system by microorganisms and viruses. Front Microbiol, 7: 1296

[7] Barbi J, Pardoll D, Pan F (2013). Metabolic control of the Treg/Th17 axis. Immunol Rev, 252(1): 52–77

[8] Brzustewicz E, Bryl E (2015). The role of cytokines in the pathogenesis of rheumatoid arthritis--Practical and potential application of cytokines as biomarkers and targets of personalized therapy. Cytokine, 76(2): 527–536

[9] Castro-Santos P, Laborde C M, Díaz-Peña R (2015). Genomics, proteomics and metabolomics: their emerging roles in the discovery and validation of rheumatoid arthritis biomarkers. Clin Exp Rheumatol, 33(2): 279–286

[10] Chimenti M S, Triggianese P, Conigliaro P, Candi E, Melino G, Perricone R (2015). The interplay between inflammation and metabolism in rheumatoid arthritis. Cell Death Dis, 6(9): e1887

[11] Choe J Y, Kim S K (2015). Association between serum uric acid and inflammation in rheumatoid arthritis: perspective on lowering serum uric acid of leflunomide. Clin Chim Acta, 438: 29–34

[12] Chung I M, Ketharnathan S, Thiruvengadam M, Rajakumar G (2016). Rheumatoid Arthritis: The Stride from Research to Clinical Practice. Int J Mol Sci, 17(6): E900160;

[13] Connolly M, Mullan R H, McCormick J, Matthews C, Sullivan O, Kennedy A, FitzGerald O, Poole A R, Bresnihan B, Veale D J, Fearon U (2012). Acute-phase serum amyloid A regulates tumor necrosis factor α and matrix turnover and predicts disease progression in patients with inflammatory arthritis before and after biologic therapy. Arthritis Rheum, 64(4): 1035–1045

[14] Cuppen B V, Pardali K, Kraan M C, Marijnissen A C, Yrlid L, Olsson M, Bijlsma J W, Lafeber F P, Fritsch-Stork R D (2017). Polymorphisms in the multidrug-resistance 1 gene related to glucocorticoid response in rheumatoid arthritis treatment. Rheumatol Int, 37(4): 531–536

[15] Deane K D, El-Gabalawy H (2014). Pathogenesis and prevention of rheumatic disease: focus on preclinical RA and SLE. Nat Rev Rheumatol, 10(4): 212–228

[16] Desiderio V, Tirino V, Papaccio G, Paino F (2014). Bone defects: molecular and cellular therapeutic targets. Int J Biochem Cell Biol, 51: 75–78

[17] Elmesmari A, Fraser A R, Wood C, Gilchrist D, Vaughan D, Stewart L, McSharry C, McInnes I B, Kurowska-Stolarska M (2016). MicroRNA-155 regulates monocyte chemokine and chemokine receptor expression in Rheumatoid Arthritis. Rheumatology (Oxford), 55(11): 2056–2065

[18] Gall B J, Wilson A, Schroer A B, Gross J D, Stoilov P, Setola V, Watkins C M, Siderovski D P (2016). Genetic variations in GPSM3 associated with protection from rheumatoid arthritis affect its transcript abundance. Genes Immun, 17(2): 139–147

[19] Gavrilă B I, Ciofu C, Stoica V (2016). Biomarkers in rheumatoid arthritis, what is new? J Med Life, 9(2): 144–148

[20] Glant T T, Mikecz K, Rauch T A (2014). Epigenetics in the pathogenesis of rheumatoid arthritis. BMC Med, 12(1): 35

[21] Guerreiro-Cacais A O, Norin U, Gyllenberg A, Berglund R, Beyeen A D, Petit-Teixeira E, Cornélis F, Saoudi A, Fournié G J, Holmdahl R, Alfredsson L, Klareskog L, Jagodic M, Olsson T, Kockum I, Padyukov L, and the Rheumatoid Arthritis Consortium International (RACI) (2017). VAV1 regulates experimental autoimmune arthritis and is associated with anti-CCP negative rheumatoid arthritis. Genes Immun, 18(1): 48–56

[22] Guo W, Yu D, Wang X, Luo C, Chen Y, Lei W, Wang C, Ge Y, Xue W, Tian Q, Gao X, Yao W (2016). Anti-inflammatory effects of interleukin-23 receptor cytokine-binding homology region rebalance T cell distribution in rodent collagen-induced arthritis. Oncotarget, 7(22): 31800–31813

[23] Guo Y, Wu Q, Ni B, Mou Z, Jiang Q, Cao Y, Dong H, Wu Y (2014). Tryptase is a candidate autoantigen in rheumatoid arthritis. Immunology, 142(1): 67–77

[24] Hashemi M, Sandoughi M, Fazeli SA, Bahari G, Rezaei M, Zakeri Z( 2016). Evaluation of HLA-G 14 bp Ins/Del and+3142G>C polymorphism with susceptibility and early disease activity in rheumatoid arthritis. Adv Med, 2016:4985745

[25] Heard E, Martienssen R A (2014). Transgenerational epigenetic inheritance: myths and mechanisms. Cell, 157(1): 95–109

[26] Henrotin Y, Sanchez C, Cornet A, Van de Put J, Douette P, Gharbi M (2015). Soluble biomarkers development in osteoarthritis: from discovery to personalized medicine. Biomarkers, 20(8): 540–546

[27] Honne K, Hallgrímsdóttir I, Wu C, Sebro R, Jewell N P, Sakurai T, Iwamoto M, Minota S, Jawaheer D (2016). A longitudinal genome-wide association study of anti-tumor necrosis factor response among Japanese patients with rheumatoid arthritis. Arthritis Res Ther, 18(1): 12

[28] Hu J, Zhai C, Hu J, Li Z, Fei H, Wang Z, Fan W (2017). MiR-23a inhibited IL-17-mediated proinflammatory mediators expression via targeting IKKα in articular chondrocytes. Int Immunopharmacol, 43: 1–6

[29] Hwang Y G, Balasubramani G K, Metes I D, Levesque M C, Bridges S L Jr, Moreland L W (2016). Differential response of serum amyloid A to different therapies in early rheumatoid arthritis and its potential value as a disease activity biomarker. Arthritis Res Ther, 18(1): 108

[30] Ibrahim M, Mohan S, Xing M J, Kesavan C (2016). Conditional knockout of the microRNA 17-92 cluster in type-I collagen-expressing cells decreases alveolar bone size and incisor tooth mechanical properties. Folia Biol (Praha), 62(4): 175–179

[31] Jiang X, Askling J, Saevarsdottir S, Padyukov L, Alfredsson L, Viatte S, Frisell T (2016). A genetic risk score composed of rheumatoid arthritis risk alleles, HLA-DRB1 haplotypes, and response to TNFi therapy- results from a Swedish cohort study. Arthritis Res Ther, 18(1): 288

[32] Kang K Y, Woo J W, Park S H (2014). S100A8/A9 as a biomarker for synovial inflammation and joint damage in patients with rheumatoid arthritis. Korean J Intern Med, 29(1): 12–19

[33] Khalifa O, Pers Y M, Ferreira R, Sénéchal A, Jorgensen C, Apparailly F, Duroux-Richard I (2016). X-linked  miRNAs associated with gender differences in Rheumatoid arthritis. Int J Mol Sci, 17(11): E1852

[34] Kim K S, Choi H M, Lee Y A, Choi I A, Lee S H, Hong S J, Yang H I, Yoo M C (2011). Expression levels and association of gelatinases MMP-2 and MMP-9 and collagenases MMP-1 and MMP-13 with VEGF in synovial fluid of patients with arthritis. Rheumatol Int, 31(4): 543–547

[35] Lavric M, Miranda-García M A, Holzinger D, Foell D, Wittkowski H (2016). Alarmins firing arthritis: Helpful diagnostic tools and promising therapeutic targets. Joint Bone Spine: S1297-319X(16)30127-0

[36] Li Z, Cai J, Cao X (2016). MiR-19 suppresses fibroblast-like synoviocytes cytokine release by targeting toll like receptor 2 in rheumatoid arthritis. Am J Transl Res, 8(12): 5512–5518

[37] Liu F, Wang X, Zhang X, Ren C, Xin J (2016). Role of serum cartilage oligomeric matrix protein (COMP) in the diagnosis of rheumatoid arthritis (RA): A case-control study. J Int Med Res, 44(4): 940–949

[38] Ma Y, Shan Z, Ma J, Wang Q, Chu J, Xu P, Qin A, Fan S (2016). Validation of downregulated microRNAs during osteoclast formation and osteoporosis progression. Mol Med Rep, 13(3): 2273–2280

[39] Martin N T, Martin M U (2016). Interleukin 33 is a guardian of barriers and a local alarmin. Nat Immunol, 17(2): 122–131

[40] Mc Ardle A, Flatley B, Pennington S R, FitzGerald O (2015). Early biomarkers of joint damage in rheumatoid and psoriatic arthritis. Arthritis Res Ther, 17(1): 141

[41] McGovern A, Schoenfelder S, Martin P, Massey J, Duffus K, Plant D, Yarwood A, Pratt A G, Anderson A E, Isaacs J D, Diboll J, Thalayasingam N, Ospelt C, Barton A, Worthington J, Fraser P, Eyre S, Orozco G (2016). Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23. Genome Biol, 17(1): 212

[42] Nell V P, Machold K P, Stamm T A, Eberl G, Heinzl H, Uffmann M, Smolen J S, Steiner G (2005). Autoantibody profiling as early diagnostic and prognostic tool for rheumatoid arthritis. Ann Rheum Dis, 64(12): 1731–1736

[43] Niki Y, Takeuchi T, Nakayama M, Nagasawa H, Kurasawa T, Yamada H, Toyama Y, Miyamoto T (2012). Clinical significance of cartilage biomarkers for monitoring structural joint damage in rheumatoid arthritis patients treated with anti-TNF therapy. PLoS One, 7(5): e37447

[44] Niu X, Chen G( 2014). Clinical biomarkers and pathogenic-related cytokines in rheumatoid arthritis. J Immunol Res, 2014:698192

[45] Ombrello M J, Remmers E F, Tachmazidou I, Grom A, Foell D, Haas J P, Martini A, Gattorno M, Özen S, Prahalad S, Zeft A S, Bohnsack J F, Mellins E D, Ilowite N T, Russo R, Len C, Hilario M O, Oliveira S, Yeung R S, Rosenberg A, Wedderburn L R, Anton J, Schwarz T, Hinks A, Bilginer Y, Park J, Cobb J, Satorius C L, Han B, Baskin E, Signa S, Duerr R H, Achkar J P, Kamboh M I, Kaufman K M, Kottyan L C, Pinto D, Scherer S W, Alarcón-Riquelme M E, Docampo E, Estivill X, Gül A, de Bakker P I, Raychaudhuri S, Langefeld C D, Thompson S, Zeggini E, Thomson W, Kastner D L, Woo P, and the British Society of Pediatric and Adolescent Rheumatology (BSPAR) Study Group, and the Childhood Arthritis Prospective Study (CAPS) Group, and the Randomized Placebo Phase Study of Rilonacept in sJIA (RAPPORT) Investigators, and the Sparks-Childhood Arthritis Response to Medication Study (CHARMS) Group, and the Biologically Based Outcome Predictors in JIA (BBOP) Group, and the International Childhood Arthritis Genetics (INCHARGE) Consortium (2015). HLA-DRB1*11 and variants of the MHC class II locus are strong risk factors for systemic juvenile idiopathic arthritis. Proc Natl Acad Sci USA, 112(52): 15970–15975

[46] Ortea I, Roschitzki B, López-Rodríguez R, Tomero E G, Ovalles J G, López-Longo J, de la Torre I, González-Alvaro I, Gómez-Reino J J, González A (2016). Independent Candidate Serum Protein Biomarkers of Response to Adalimumab and to Infliximab in Rheumatoid Arthritis: An Exploratory Study. PLoS One, 11(4): e0153140

[47] Ospelt C (2016). Epigenetic biomarkers in rheumatology–the future? Swiss Med Wkly, 146: w14312

[48] Palmer G, Talabot-Ayer D, Lamacchia C, Toy D, Seemayer C A, Viatte S, Finckh A, Smith D E, Gabay C (2009). Inhibition of interleukin-33 signaling attenuates the severity of experimental arthritis. Arthritis Rheum, 60(3): 738–749

[49] Paradowska-Gorycka A, Wojtecka-Lukasik E, Trefler J, Wojciechowska B, Lacki J K, Maslinski S (2010). Association between IL-17F gene polymorphisms and susceptibility to and severity of rheumatoid arthritis (RA). Scand J Immunol, 72(2): 134–141

[50] Parra M (2015). Class IIa HDACs- new insights into their functions in physiology and pathology. FEBS J, 282(9): 1736–1744

[51] Pawlik A, Kotrych D, Malinowski D, Dziedziejko V, Czerewaty M, Safranow K (2016). IL17A and IL17F gene polymorphisms in patients with rheumatoid arthritis. BMC Musculoskelet Disord, 17(1): 208

[52] Picascia A, Grimaldi V, Pignalosa O, De Pascale M R, Schiano C, Napoli C (2015). Epigenetic control of autoimmune diseases: from bench to bedside. Clin Immunol, 157(1): 1–15

[53] Plant D, Webster A, Nair N, Oliver J, Smith S L, Eyre S, Hyrich K L, Wilson A G, Morgan A W, Isaacs J D, Worthington J, Barton A (2016). Differential methylation as a biomarker of response to etanercept in patients with rheumatoid arthritis. Arthritis Rheumatol, 68(6): 1353–1360

[54] Robinson W H, Lindstrom T M, Cheung R K, Sokolove J (2013). Mechanistic biomarkers for clinical decision making in rheumatic diseases. Nat Rev Rheumatol, 9(5): 267–276

[55] Ruyssen-Witrand A, Degboé Y, Cantagrel A, Nigon D, Lukas C, Scaramuzzino S, Allanore Y, Vittecoq O, Schaeverbeke T, Morel J, Sibilia J, Cambon-Thomsen A, Dieudé P, Constantin A (2016). Association between RANK, RANKL and OPG polymorphisms with ACPA and erosions in rheumatoid arthritis: results from a meta-analysis involving three French cohorts. RMD Open, 2(2): e000226

[56] Sellam J, Rivière E, Courties A, Rouzaire P O, Tolusso B, Vital E M, Emery P, Ferraciolli G, Soubrier M, Ly B, Hendel Chavez H, Taoufik Y, Dougados M, Mariette X (2016). Serum IL-33, a new marker predicting response to rituximab in rheumatoid arthritis. Arthritis Res Ther, 18(1): 294

[57] Sharma J, Bhar S (2017). C SD. A review on Interleukins: the key manipulators in Rheumatoid Arthritis. Mod Rheumatol, doi:10.1080/14397595.2016.1266071

[58] Siebuhr A S, Wang J, Karsdal M, Bay-Jensen A C, Jin Y, Zheng Q (2012). Matrix metalloproteinase-dependent turnover of cartilage, synovial membrane, and connective tissue is elevated in rats with collagen induced arthritis. J Transl Med, 10(1): 195

[59] Smolenska Z, Smolenski R T, Zdrojewski Z (2016). Plasma concentrations of amino acid and nicotinamide metabolites in rheumatoid arthritis--potential biomarkers of disease activity and drug treatment. Biomarkers, 21(3): 218–224

[60] Snir O, Gomez-Cabrero D, Montes A, Perez-Pampin E, Gómez-Reino J J, Seddighzadeh M, Klich K U, Israelsson L, Ding B, Catrina A I, Holmdahl R, Alfredsson L, Klareskog L, Tegnér J, Gonzalez A, Malmström V, Padyukov L (2014). Non-HLA genes PTPN22, CDK6 and PADI4 are associated with specific autoantibodies in HLA-defined subgroups of rheumatoid arthritis. Arthritis Res Ther, 16(4): 414

[61] Sode J, Vogel U, Bank S, Andersen PS, Hetland ML, Locht H, Heegaard NH, Andersen V (2016). Confirmation of an IRAK3 polymorphism as a genetic marker predicting response to anti-TNF treatment in rheumatoid arthritis. Pharmacogenomics J. 

[62] Song J, Jin E H, Kim D, Kim K Y, Chun C H, Jin E J (2014). MicroRNA-222 regulates MMP-13 via targeting HDAC-4 during osteoarthritis pathogenesis. BBA Clin, 3: 79–89

[63] Syversen S W, Gaarder P I, Goll G L, Ødegård S, Haavardsholm E A, Mowinckel P, van der Heijde D, Landewé R, Kvien T K (2008). High anti-cyclic citrullinated peptide levels and an algorithm of four variables predict radiographic progression in patients with rheumatoid arthritis: results from a 10-year longitudinal study. Ann Rheum Dis, 67(2): 212–217

[64] Tedesco A, D’Agostino D, Soriente I, Amato P, Piccoli R, Sabatini P (2009). A new strategy for the early diagnosis of rheumatoid arthritis: a combined approach. Autoimmun Rev, 8(3): 233–237

[65] Tejasvi T, Stuart P E, Chandran V, Voorhees J J, Gladman D D, Rahman P, Elder J T, Nair R P (2012). TNFAIP3 gene polymorphisms are associated with response to TNF blockade in psoriasis. J Invest Dermatol, 132(3 Pt 1): 593–600

[66] Torices S, Julia A, Muñoz P, Varela I, Balsa A, Marsal S, Fernández-Nebro A, Blanco F, López-Hoyos M, Martinez-Taboada V, Fernández-Luna J L (2016). A functional  variant  of TLR10 modifies the  activity  of  NFkB  and  may help  predict  a  worse  prognosis  in patients with  rheumatoid  arthritis.  Arthritis  Res  Ther, 18(1):  221

[67] Toussirot E, Abbas W, Khan K A, Tissot M, Jeudy A, Baud L, Bertolini E, Wendling D, Herbein G (2013). Imbalance between HAT and HDAC activities in the PBMCs of patients with ankylosing spondylitis or rheumatoid arthritis and influence of HDAC inhibitors on TNF alpha production. PLoS One, 8(8): e70939

[68] Uemura Y, Hayashi H, Takahashi T, Saitho T, Umeda R, Ichise Y, Sendo S, Tsuji G, Kumagai S (2015). MMP-3 as a biomarker of disease activity of rheumatoid Arthritis Rinsho Byori, 63(12): 1357–1364 

[69] van Steenbergen H W, Raychaudhuri S, Rodríguez-Rodríguez L, Rantapää-Dahlqvist S, Berglin E, Toes R E, Huizinga T W, Fernández-Gutiérrez B, Gregersen P K, van der Helm-van Mil A H (2015). Association of valine and leucine at HLA-DRB1 position 11 with radiographic progression in rheumatoid arthritis, independent of the shared epitope alleles but not independent of anti-citrullinated protein antibodies. Arthritis Rheumatol, 67(4): 877–886

[70] Viatte S, Lee J C, Fu B, Espéli M, Lunt M, De Wolf J N, Wheeler L, Reynolds J A, Castelino M, Symmons D P, Lyons P A, Barton A, Smith K G (2016). Association between genetic variation in FOXO3 and reductions in inflammation and disease activity in inflammatory polyarthritis. Arthritis Rheumatol, 68(11): 2629–2636

[71] Viatte S, Plant D, Raychaudhuri S (2013). Genetics and epigenetics of rheumatoid arthritis. Nat Rev Rheumatol, 9(3): 141–153

[72] Visvanathan S, Wagner C, Rojas J, Kay J, Dasgupta B, Matteson E L, Mack M, Baker D G, Rahman M U (2009). E-selectin, interleukin 18, serum amyloid a, and matrix metalloproteinase 9 are associated with clinical response to golimumab plus methotrexate in patients with active rheumatoid arthritis despite methotrexate therapy. J Rheumatol, 36(7): 1371–1379

[73] Walsh A M, Whitaker J W, Huang C C, Cherkas Y, Lamberth S L, Brodmerkel C, Curran M E, Dobrin R (2016). Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations. Genome Biol, 17(1): 79

[74] Watanabe T, Takahashi N, Hirabara S, Ishiguro N, Kojima T (2016). Hyaluronan inhibits Tlr-4-dependent RANKL expression in human rheumatoid arthritis synovial fibroblasts. PLoS One, 11(4): e0153142

[75] Wei S T, Sun Y H, Zong S H, Xiang Y B (2015). Serum levels of IL-6 and TNF-α may correlate with activity and severity of rheumatoid arthritis. Med Sci Monit, 21: 4030–4038

[76] Yamamoto K, Okada Y, Suzuki A, Kochi Y (2015). Genetic studies of rheumatoid arthritis. Proc Jpn Acad, Ser B, Phys Biol Sci, 91(8): 410–422

[77] Yang J, Du H, Lv J, Zhang L (2016). Association of rs1137101 polymorphism in LEPR and susceptibility to knee osteoarthritis in a Northwest Chinese Han population. BMC Musculoskelet Disord, 17(1): 311

[78] Yi J P, Wu Y Z, Yu N, Yu Z W, Xie F Y, Yuan Q (2016). VEGF gene polymorphisms affect serum protein levels and alter disease activity and synovial lesions in rheumatoid arthritis. Med Sci Monit, 22: 316–324

[79] Zengin O, Onder M E, Kalem A, Bilici M, Türkbeyler I H, Ozturk Z A, Kisacik B, Onat A M (2016). New inflammatory markers in early rheumatoid arthritis. Z Rheumatol,doi:10.10071S00393-016-0187-y

[80] Zhai T, Gao C, Huo R, Sheng H, Sun S, Xie J, He Y, Gao H, Li H, Zhang J, Li H, Sun Y, Lin J, Shen B, Xiao L, Li N (2016). Cyr61 participates in the pathogenesis of rheumatoid arthritis via promoting MMP-3 expression by fibroblast-like synoviocytes. Mod Rheumatol, 27(3): 466–475160;

[81] Zhang Y, Ren G, Guo M, Ye X, Zhao J, Xu L, Qi J, Kan F, Liu M, Li D (2013). Synergistic effects of interleukin-1β and interleukin-17A antibodies on collagen-induced arthritis mouse model. Int Immunopharmacol, 15(2): 199–205

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