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Song S, Jia C, Li C, Ma Y. The causal association between thyroid disease and gout: A Mendelian randomization study. Medicine (Baltimore) 2023; 102:e35817. [PMID: 37932979 PMCID: PMC10627627 DOI: 10.1097/md.0000000000035817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
Observational studies have reported some associations between thyroid disease and gout, but the causal relationship between the 2 is not clear. We used Mendelian randomization (MR) Analysis to investigate the causal association between some thyroid diseases (autoimmune hypothyroidism, autoimmune hyperthyroidism, thyroid nodules, and thyroid cancer) and gout. GWAS data were used for analysis. The exposure factors were autoimmune hypothyroidism, autoimmune hyperthyroidism, thyroid nodules and thyroid cancer, and the outcome variables were gout. IVW, MR-Egger, Weighted median and Weighted mode were used for MR analysis. Cochran Q test MR-PRESSO and MR-Egger intercept analysis were used to detect heterogeneity and multi directivity. Autoimmune hypothyroidism has a causal effect on gout, IVW results show (OR = 1.13, 95% CI = 1.03-1.21, PFDR = 0.0336); Autoimmune hyperthyroidism has a causal effect on gout, IVW results show (OR = 1.07, 95% CI = 1.01-1.12, PFDR = 0.0314); Thyroid cancer has no causal effect on gout, IVW results show (OR = 1.03, 95% CI = 0.98-1.09, PFDR = 0.297); Thyroid nodules has no causal effect on gout, IVW results show (OR = 1.03, 95% CI = 0.98-1.08, PFDR = 0.225); Reverse MR Studies show that gout have no causal effect on the above thyroid diseases. Autoimmune hypothyroidism and autoimmune hyperthyroidism increase the risk of gout.
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Affiliation(s)
- Shuai Song
- Department of Traditional Chinese Medicine External Treatment Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Congcong Jia
- Department of Nephrology, Shandong First Medical University Affiliated Occupational Disease Hospital, Jinan, Shandong, China
| | - ChunJing Li
- College of Acupuncture and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Yuxia Ma
- College of Acupuncture and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
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de Lima JD, de Paula AGP, Yuasa BS, de Souza Smanioto CC, da Cruz Silva MC, Dos Santos PI, Prado KB, Winter Boldt AB, Braga TT. Genetic and Epigenetic Regulation of the Innate Immune Response to Gout. Immunol Invest 2023; 52:364-397. [PMID: 36745138 DOI: 10.1080/08820139.2023.2168554] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Gout is a disease caused by uric acid (UA) accumulation in the joints, causing inflammation. Two UA forms - monosodium urate (MSU) and soluble uric acid (sUA) have been shown to interact physically with inflammasomes, especially with the nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3), albeit the role of the immune response to UA is poorly understood, given that asymptomatic hyperuricemia does also exist. Macrophage phagocytosis of UA activate NLRP3, lead to cytokines release, and ultimately, lead to chemoattract neutrophils and lymphocytes to the gout flare joint spot. Genetic variants of inflammasome genes and of genes encoding their molecular partners may influence hyperuricemia and gout susceptibility, while also influencing other comorbidities such as metabolic syndrome and cardiovascular diseases. In this review, we summarize the inflammatory responses in acute and chronic gout, specifically focusing on innate immune cell mechanisms and genetic and epigenetic characteristics of participating molecules. Unprecedently, a novel UA binding protein - the neuronal apoptosis inhibitor protein (NAIP) - is suggested as responsible for the asymptomatic hyperuricemia paradox.Abbreviation: β2-integrins: leukocyte-specific adhesion molecules; ABCG2: ATP-binding cassete family/breast cancer-resistant protein; ACR: American college of rheumatology; AIM2: absent in melanoma 2, type of pattern recognition receptor; ALPK1: alpha-protein kinase 1; ANGPTL2: angiopoietin-like protein 2; ASC: apoptosis-associated speck-like protein; BIR: baculovirus inhibitor of apoptosis protein repeat; BIRC1: baculovirus IAP repeat-containing protein 1; BIRC2: baculoviral IAP repeat-containing protein 2; C5a: complement anaphylatoxin; cAMP: cyclic adenosine monophosphate; CARD: caspase activation and recruitment domains; CARD8: caspase recruitment domain-containing protein 8; CASP1: caspase 1; CCL3: chemokine (C-C motif) ligand 3; CD14: cluster of differentiation 14; CD44: cluster of differentiation 44; Cg05102552: DNA-methylation site, usually cytosine followed by guanine nucleotides; contains arbitrary identification code; CIDEC: cell death-inducing DNA fragmentation factor-like effector family; CKD: chronic kidney disease; CNV: copy number variation; CPT1A: carnitine palmitoyl transferase - type 1a; CXCL1: chemokine (CXC motif) ligand 1; DAMPs: damage associated molecular patterns; DC: dendritic cells; DNMT(1): maintenance DNA methyltransferase; eQTL: expression quantitative trait loci; ERK1: extracellular signal-regulated kinase 1; ERK2: extracellular signal-regulated kinase 2; EULAR: European league against rheumatism; GMCSF: granulocyte-macrophage colony-stimulating factor; GWAS: global wide association studies; H3K27me3: tri-methylation at the 27th lysine residue of the histone h3 protein; H3K4me1: mono-methylation at the 4th lysine residue of the histone h3 protein; H3K4me3: tri-methylation at the 4th lysine residue of the histone h3 protein; HOTAIR: human gene located between hoxc11 and hoxc12 on chromosome 12; IκBα: cytoplasmatic protein/Nf-κb transcription inhibitor; IAP: inhibitory apoptosis protein; IFNγ: interferon gamma; IL-1β: interleukin 1 beta; IL-12: interleukin 12; IL-17: interleukin 17; IL18: interleukin 18; IL1R1: interleukin-1 receptor; IL-1Ra: interleukin-1 receptor antagonist; IL-22: interleukin 22; IL-23: interleukin 23; IL23R: interleukin 23 receptor; IL-33: interleukin 33; IL-6: interleukin 6; IMP: inosine monophosphate; INSIG1: insulin-induced gene 1; JNK1: c-jun n-terminal kinase 1; lncRNA: long non-coding ribonucleic acid; LRR: leucine-rich repeats; miR: mature non-coding microRNAs measuring from 20 to 24 nucleotides, animal origin; miR-1: miR followed by arbitrary identification code; miR-145: miR followed by arbitrary identification code; miR-146a: miR followed by arbitrary identification code, "a" stands for mir family; "a" family presents similar mir sequence to "b" family, but different precursors; miR-20b: miR followed by arbitrary identification code; "b" stands for mir family; "b" family presents similar mir sequence to "a" family, but different precursors; miR-221: miR - followed by arbitrary identification code; miR-221-5p: miR followed by arbitrary identification code; "5p" indicates different mature miRNAs generated from the 5' arm of the pre-miRNA hairpin; miR-223: miR followed by arbitrary identification code; miR-223-3p: mir followed by arbitrary identification code; "3p" indicates different mature miRNAs generated from the 3' arm of the pre-miRNA hairpin; miR-22-3p: miR followed by arbitrary identification code, "3p" indicates different mature miRNAs generated from the 3' arm of the pre-miRNA hairpin; MLKL: mixed lineage kinase domain-like pseudo kinase; MM2P: inductor of m2-macrophage polarization; MSU: monosodium urate; mTOR: mammalian target of rapamycin; MyD88: myeloid differentiation primary response 88; n-3-PUFAs: n-3-polyunsaturated fatty-acids; NACHT: acronym for NAIP (neuronal apoptosis inhibitor protein), C2TA (MHC class 2 transcription activator), HET-E (incompatibility locus protein from podospora anserina) and TP1 (telomerase-associated protein); NAIP: neuronal apoptosis inhibitory protein (human); Naip1: neuronal apoptosis inhibitory protein type 1 (murine); Naip5: neuronal apoptosis inhibitory protein type 5 (murine); Naip6: neuronal apoptosis inhibitory protein type 6 (murine); NBD: nucleotide-binding domain; Nek7: smallest NIMA-related kinase; NET: neutrophil extracellular traps; Nf-κB: nuclear factor kappa-light-chain-enhancer of activated b cells; NFIL3: nuclear-factor, interleukin 3 regulated protein; NIIMA: network of immunity in infection, malignancy, and autoimmunity; NLR: nod-like receptor; NLRA: nod-like receptor NLRA containing acidic domain; NLRB: nod-like receptor NLRA containing BIR domain; NLRC: nod-like receptor NLRA containing CARD domain; NLRC4: nod-like receptor family CARD domain containing 4; NLRP: nod-like receptor NLRA containing PYD domain; NLRP1: nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 1; NLRP12: nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 12; NLRP3: nod-like receptor family pyrin domain containing 3; NOD2: nucleotide-binding oligomerization domain; NRBP1: nuclear receptor-binding protein; Nrf2: nuclear factor erythroid 2-related factor 2; OR: odds ratio; P2X: group of membrane ion channels activated by the binding of extracellular; P2X7: p2x purinoceptor 7 gene; p38: member of the mitogen-activated protein kinase family; PAMPs: pathogen associated molecular patters; PBMC: peripheral blood mononuclear cells; PGGT1B: geranylgeranyl transferase type-1 subunit beta; PHGDH: phosphoglycerate dehydrogenase; PI3-K: phospho-inositol; PPARγ: peroxisome proliferator-activated receptor gamma; PPARGC1B: peroxisome proliferative activated receptor, gamma, coactivator 1 beta; PR3: proteinase 3 antigen; Pro-CASP1: inactive precursor of caspase 1; Pro-IL1β: inactive precursor of interleukin 1 beta; PRR: pattern recognition receptors; PYD: pyrin domain; RAPTOR: regulatory associated protein of mTOR complex 1; RAS: renin-angiotensin system; REDD1: regulated in DNA damage and development 1; ROS: reactive oxygen species; rs000*G: single nuclear polymorphism, "*G" is related to snp where replaced nucleotide is guanine, usually preceded by an id number; SLC2A9: solute carrier family 2, member 9; SLC7A11: solute carrier family 7, member 11; SMA: smooth muscular atrophy; Smac: second mitochondrial-derived activator of caspases; SNP: single nuclear polymorphism; Sp3: specificity protein 3; ST2: serum stimulation-2; STK11: serine/threonine kinase 11; sUA: soluble uric acid; Syk: spleen tyrosine kinase; TAK1: transforming growth factor beta activated kinase; Th1: type 1 helper T cells; Th17: type 17 helper T cells; Th2: type 2 helper T cells; Th22: type 22 helper T cells; TLR: tool-like receptor; TLR2: toll-like receptor 2; TLR4: toll-like receptor 4; TNFα: tumor necrosis factor alpha; TNFR1: tumor necrosis factor receptor 1; TNFR2: tumor necrosis factor receptor 2; UA: uric acid; UBAP1: ubiquitin associated protein; ULT: urate-lowering therapy; URAT1: urate transporter 1; VDAC1: voltage-dependent anion-selective channel 1.
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Affiliation(s)
- Jordana Dinorá de Lima
- Microbiology, Parasitology and Pathology Program, Universidade Federal do Parana (UFPR), Curitiba, Brazil
| | | | - Bruna Sadae Yuasa
- Microbiology, Parasitology and Pathology Program, Universidade Federal do Parana (UFPR), Curitiba, Brazil
| | | | - Maria Clara da Cruz Silva
- Microbiology, Parasitology and Pathology Program, Universidade Federal do Parana (UFPR), Curitiba, Brazil
| | | | - Karin Braun Prado
- Genetics Program, Universidade Federal do Parana (UFPR), Curitiba, Brazil
| | - Angelica Beate Winter Boldt
- Program of Internal Medicine, Universidade Federal do Parana (UFPR), Curitiba, Brazil
- Genetics Program, Universidade Federal do Parana (UFPR), Curitiba, Brazil
| | - Tárcio Teodoro Braga
- Microbiology, Parasitology and Pathology Program, Universidade Federal do Parana (UFPR), Curitiba, Brazil
- Biosciences and Biotechnology Program, Instituto Carlos Chagas (ICC), Fiocruz-Parana, Brazil
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Wu YL, Lin ZJ, Li CC, Lin X, Shan SK, Guo B, Zheng MH, Li F, Yuan LQ, Li ZH. Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study. Signal Transduct Target Ther 2023; 8:98. [PMID: 36864020 PMCID: PMC9981733 DOI: 10.1038/s41392-023-01333-7] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/02/2023] [Accepted: 01/18/2023] [Indexed: 03/04/2023] Open
Abstract
Epigenetics regulates gene expression and has been confirmed to play a critical role in a variety of metabolic diseases, such as diabetes, obesity, non-alcoholic fatty liver disease (NAFLD), osteoporosis, gout, hyperthyroidism, hypothyroidism and others. The term 'epigenetics' was firstly proposed in 1942 and with the development of technologies, the exploration of epigenetics has made great progresses. There are four main epigenetic mechanisms, including DNA methylation, histone modification, chromatin remodelling, and noncoding RNA (ncRNA), which exert different effects on metabolic diseases. Genetic and non-genetic factors, including ageing, diet, and exercise, interact with epigenetics and jointly affect the formation of a phenotype. Understanding epigenetics could be applied to diagnosing and treating metabolic diseases in the clinic, including epigenetic biomarkers, epigenetic drugs, and epigenetic editing. In this review, we introduce the brief history of epigenetics as well as the milestone events since the proposal of the term 'epigenetics'. Moreover, we summarise the research methods of epigenetics and introduce four main general mechanisms of epigenetic modulation. Furthermore, we summarise epigenetic mechanisms in metabolic diseases and introduce the interaction between epigenetics and genetic or non-genetic factors. Finally, we introduce the clinical trials and applications of epigenetics in metabolic diseases.
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Affiliation(s)
- Yan-Lin Wu
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Zheng-Jun Lin
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.,Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Chang-Chun Li
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Xiao Lin
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Su-Kang Shan
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Bei Guo
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Ming-Hui Zheng
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Fuxingzi Li
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Ling-Qing Yuan
- National Clinical Research Center for Metabolic Disease, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
| | - Zhi-Hong Li
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. .,Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
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4
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Georgel PT, Georgel P. Where Epigenetics Meets Food Intake: Their Interaction in the Development/Severity of Gout and Therapeutic Perspectives. Front Immunol 2021; 12:752359. [PMID: 34603340 PMCID: PMC8484966 DOI: 10.3389/fimmu.2021.752359] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/31/2021] [Indexed: 01/02/2023] Open
Abstract
Gout is the most frequent form of inflammatory arthritis in the world. Its prevalence is particularly elevated in specific geographical areas such as in the Oceania/Pacific region and is rising in the US, Europe, and Asia. Gout is a severe and painful disease, in which co-morbidities are responsible for a significant reduction in life expectancy. However, gout patients remain ostracized because the disease is still considered "self-inflicted", as a result of unhealthy lifestyle and excessive food and alcohol intake. While the etiology of gout flares is clearly associated with the presence of monosodium urate (MSU) crystal deposits, several major questions remain unanswered, such as the relationships between diet, hyperuricemia and gout flares or the mechanisms by which urate induces inflammation. Recent advances have identified gene variants associated with gout incidence. Nevertheless, genetic origins of gout combined to diet-related possible uric acid overproduction account for the symptoms in only a minor portion of patients. Hence, additional factors must be at play. Here, we review the impact of epigenetic mechanisms in which nutrients (such as ω-3 polyunsaturated fatty acids) and/or dietary-derived metabolites (like urate) trigger anti/pro-inflammatory responses that may participate in gout pathogenesis and severity. We propose that simple dietary regimens may be beneficial to complement therapeutic management or contribute to the prevention of flares in gout patients.
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Affiliation(s)
- Philippe T Georgel
- Department of Biological Sciences, Cell Differentiation and Development Center, Joan C. Edwards School of Medicine, Byrd Biotechnology Science Center, Marshall University, Huntington, WV, United States
| | - Philippe Georgel
- Laboratoire d'ImmunoRhumatologie Moléculaire, Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1109, Institut thématique interdisciplinaire (ITI) de Médecine de Précision de Strasbourg, Transplantex NG, Faculté de Médecine, Fédération Hospitalo-Universitaire OMICARE, Fédération de Médecine Translationnelle de Strasbourg (FMTS), Université de Strasbourg, Strasbourg, France.,Unité de Recherche et d'Expertise Immunity and Inflammation, Institut Pasteur in New Caledonia, Pasteur Network, Nouméa, New Caledonia
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DNA methyltransferase genes polymorphisms are associated with primary knee osteoarthritis: a matched case-control study. Rheumatol Int 2019; 40:573-581. [PMID: 31713648 DOI: 10.1007/s00296-019-04474-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/02/2019] [Indexed: 10/25/2022]
Abstract
DNA methylation is an epigenetic mechanism involved in the development of primary osteoarthritis (OA). The association between DNA methyltransferases (DNMTs) genes polymorphisms and diseases in which DNA methylation plays a role in their pathogenesis has been described (e.g., cancer); however, its relationship with OA has not been investigated. The aim of this study was to analyze the association between DNMT1, DNMT3A, and DNMT3B polymorphisms with radiologic primary knee OA in Mexican mestizo population. A matched case-control study was conducted (ratio, 1:1). Cases included 244 subjects with definite radiographic knee OA (grade ≥ 2). Controls were matched by age and gender and were subjects with no definite radiographic knee OA/normal (grade < 2). The DNMTs polymorphisms were genotyped by TaqMan allelic discrimination assays. Conditional logistic regression was carried out, and the genetic association was tested under co-dominant, dominant, and recessive inheritance models. Haplotypes for DNMT1 polymorphisms were constructed and their associations were also tested. The CC genotypes of rs2228611 and rs2228612 of DNMT1 were associated with a lower risk for primary knee OA under a co-dominant and a recessive model [OR (95% CI) 0.4 (0.2-0.8)/0.5 (0.3-0.8) and 0.3 (0.1-0.8)/0.3 (0.1-0.7), respectively]. The CT haplotype of DNMT1 polymorphisms was associated with a lower risk [OR (95% CI) 0.71 (0.51-0.97)]. The CC genotype of rs2424913 of DNMT3B was associated with an increased risk under a co-dominant and a dominant model [OR (95% CI) 3.0 (1.1-8.0), and 1.6 (1.1-2.4), respectively]. Our results show that DNMTs polymorphisms are associated with primary knee OA.
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Yazdanpanahi N, Etemadifar M, Shams E. DNMT3B-579G>T (rs1569686G>T) polymorphism and the risk of multiple sclerosis in a subset of Iranian population. IRANIAN JOURNAL OF NEUROLOGY 2019; 18:70-75. [PMID: 31565203 PMCID: PMC6755508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Background: Deoxyribonucleic acid (DNA) methyltransferase 3 beta (DNMT3B) gene encodes an MT enzyme involving in de novo methylation of DNA. The present investigation aimed to explore the association of DNMT3B-579G>T (rs1569686) polymorphism with multiple sclerosis (MS). Methods: 130 Iranian patients with MS and 130 controls were genotyped for the DNMT3B-579G>T using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results: There was no statistically significant association between DNMT3B-579G>T and susceptibility to MS. The alleles and genotypes of DNMT3B-579G>T did not have different risks of MS development under various models [T vs. G (P = 0.86); GTvs. GG (P = 0.48); TT vs. GG (P > 0.99); GT+TT vs. GG (P = 0.60), and TT vs. GG+GT (P = 0.87)]. Also, there was no statistically significant association between genotypes and clinical and demographic characteristics of patients (P > 0.05). Conclusion: The current findings suggest that DNMT3B-579G>T is probably not a crucial potential risk marker in molecular diagnostics of MS among Iranian. However, to the best of our knowledge, this is the first genetic association study about the DNMT3B polymorphisms and MS. Therefore, further surveys should be included to estimate the exact relevance of DNMT3B gene to the development of autoimmune disorders like MS.
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Affiliation(s)
- Nasrin Yazdanpanahi
- Department of Biology and Genetics, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
| | - Masoud Etemadifar
- Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran,Isfahan Multiple Sclerosis Research Center, Isfahan, Iran
| | - Elaheh Shams
- Young Researchers and Elite Club, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
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