Gene-gene interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in systemic lupus erythematosus
Objective Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene–gene interactions need to be further investigated. Rec...
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Published in | Arthritis & rheumatology (Hoboken, N.J.) Vol. 64; no. 1; pp. 222 - 231 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.01.2012
Wiley Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
ISSN | 0004-3591 2326-5191 1529-0131 1529-0131 2326-5205 |
DOI | 10.1002/art.33318 |
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Abstract | Objective
Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene–gene interactions need to be further investigated. Recent genome‐wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF‐κB signaling pathway. The aim of this study was to search for possible gene–gene interactions based on identified single‐nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways.
Methods
The SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2 × 2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation.
Results
Single‐marker analysis validated the association of BLK rs2736340 (P = 4.25 × 10−6) as well as TNFSF4 rs2205960 (P = 2.82 × 10−5) and TNFAIP3 rs5029939 (P = 1.92 × 10−3) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P = 6.57 × 10−4). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P = 2.18 × 10−3) and Caucasians (P = 2.86 × 10−4). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased.
Conclusion
The results of this study provide evidence of the possible gene–gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF‐κB pathway in determining immunologic aberration. |
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AbstractList | Objective Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene-gene interactions need to be further investigated. Recent genome-wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF-[kappa]B signaling pathway. The aim of this study was to search for possible gene-gene interactions based on identified single-nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways. Methods The SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2 × 2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation. Results Single-marker analysis validated the association of BLK rs2736340 (P = 4.25 × 10-6) as well as TNFSF4 rs2205960 (P = 2.82 × 10-5) and TNFAIP3 rs5029939 (P = 1.92 × 10-3) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P = 6.57 × 10-4). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P = 2.18 × 10-3) and Caucasians (P = 2.86 × 10-4). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased. Conclusion The results of this study provide evidence of the possible gene-gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF-[kappa]B pathway in determining immunologic aberration. [PUBLICATION ABSTRACT] Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene-gene interactions need to be further investigated. Recent genome-wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF-κB signaling pathway. The aim of this study was to search for possible gene-gene interactions based on identified single-nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways.OBJECTIVEAlthough the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene-gene interactions need to be further investigated. Recent genome-wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF-κB signaling pathway. The aim of this study was to search for possible gene-gene interactions based on identified single-nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways.The SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2×2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation.METHODSThe SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2×2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation.Single-marker analysis validated the association of BLK rs2736340 (P=4.25×10(-6)) as well as TNFSF4 rs2205960 (P=2.82×10(-5)) and TNFAIP3 rs5029939 (P=1.92×10(-3)) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P=6.57×10(-4)). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P=2.18×10(-3)) and Caucasians (P=2.86×10(-4)). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased.RESULTSSingle-marker analysis validated the association of BLK rs2736340 (P=4.25×10(-6)) as well as TNFSF4 rs2205960 (P=2.82×10(-5)) and TNFAIP3 rs5029939 (P=1.92×10(-3)) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P=6.57×10(-4)). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P=2.18×10(-3)) and Caucasians (P=2.86×10(-4)). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased.The results of this study provide evidence of the possible gene-gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF-κB pathway in determining immunologic aberration.CONCLUSIONThe results of this study provide evidence of the possible gene-gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF-κB pathway in determining immunologic aberration. Objective Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene–gene interactions need to be further investigated. Recent genome‐wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF‐κB signaling pathway. The aim of this study was to search for possible gene–gene interactions based on identified single‐nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways. Methods The SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2 × 2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation. Results Single‐marker analysis validated the association of BLK rs2736340 (P = 4.25 × 10−6) as well as TNFSF4 rs2205960 (P = 2.82 × 10−5) and TNFAIP3 rs5029939 (P = 1.92 × 10−3) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P = 6.57 × 10−4). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P = 2.18 × 10−3) and Caucasians (P = 2.86 × 10−4). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased. Conclusion The results of this study provide evidence of the possible gene–gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF‐κB pathway in determining immunologic aberration. Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene-gene interactions need to be further investigated. Recent genome-wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF-κB signaling pathway. The aim of this study was to search for possible gene-gene interactions based on identified single-nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways. The SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2×2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation. Single-marker analysis validated the association of BLK rs2736340 (P=4.25×10(-6)) as well as TNFSF4 rs2205960 (P=2.82×10(-5)) and TNFAIP3 rs5029939 (P=1.92×10(-3)) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P=6.57×10(-4)). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P=2.18×10(-3)) and Caucasians (P=2.86×10(-4)). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased. The results of this study provide evidence of the possible gene-gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF-κB pathway in determining immunologic aberration. |
Author | Su, Yin Lv, Ji-cheng Zhu, Sai-nan Nath, Swapan K. Li, Zhan-guo Qin, Lian-xiang Lu, Xiao-lan Zhang, Hong Zhou, Xu-jie Shen, Nan Zhao, Ming-hui Yang, Hai-zhen |
AuthorAffiliation | 4 Peking University First Hospital, Beijing, China 3 Oklahoma Medical Research Foundation, Oklahoma City 5 Joint Molecular Rheumatology Laboratory of the Institute of Health Sciences and Shanghai Renji Hospital, Shanghai JiaoTong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China 1 Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China 2 Peking University People's Hospital, Beijing, China |
AuthorAffiliation_xml | – name: 1 Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China – name: 2 Peking University People's Hospital, Beijing, China – name: 4 Peking University First Hospital, Beijing, China – name: 5 Joint Molecular Rheumatology Laboratory of the Institute of Health Sciences and Shanghai Renji Hospital, Shanghai JiaoTong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China – name: 3 Oklahoma Medical Research Foundation, Oklahoma City |
Author_xml | – sequence: 1 givenname: Xu-jie surname: Zhou fullname: Zhou, Xu-jie organization: Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China – sequence: 2 givenname: Xiao-lan surname: Lu fullname: Lu, Xiao-lan organization: Peking University People's Hospital, Beijing, China – sequence: 3 givenname: Swapan K. surname: Nath fullname: Nath, Swapan K. organization: Oklahoma Medical Research Foundation, Oklahoma City – sequence: 4 givenname: Ji-cheng surname: Lv fullname: Lv, Ji-cheng organization: Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China – sequence: 5 givenname: Sai-nan surname: Zhu fullname: Zhu, Sai-nan organization: Peking University First Hospital, Beijing, China – sequence: 6 givenname: Hai-zhen surname: Yang fullname: Yang, Hai-zhen organization: Peking University First Hospital, Beijing, China – sequence: 7 givenname: Lian-xiang surname: Qin fullname: Qin, Lian-xiang organization: Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China – sequence: 8 givenname: Ming-hui surname: Zhao fullname: Zhao, Ming-hui organization: Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China – sequence: 9 givenname: Yin surname: Su fullname: Su, Yin organization: Peking University People's Hospital, Beijing, China – sequence: 10 givenname: Nan surname: Shen fullname: Shen, Nan organization: Joint Molecular Rheumatology Laboratory of the Institute of Health Sciences and Shanghai Renji Hospital, Shanghai JiaoTong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China – sequence: 11 givenname: Zhan-guo surname: Li fullname: Li, Zhan-guo organization: Peking University People's Hospital, Beijing, China – sequence: 12 givenname: Hong surname: Zhang fullname: Zhang, Hong email: hongzh@bjmu.edu.cn organization: Peking University First Hospital, Peking University Institute of Nephrology, and Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China |
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Contributor | Criswell, Lindsey A Vyse, Timothy J Langefeld, Carl D Tsao, Betty P Harley, John B Alarcón-Riquelme, Marta E Jacob, Chaim O Kimberly, Robert P Moser, Kathy L |
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Copyright | Copyright © 2012 by the American College of Rheumatology 2015 INIST-CNRS Copyright © 2012 by the American College of Rheumatology. 2012, American College of Rheumatology 2012 |
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CorporateAuthor | International Consortium on the Genetics of Systemic Lupus Erythematosus |
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Keywords | Immunopathology Connective tissue disease Skin disease Systemic lupus erythematosus Systemic disease Rheumatology Autoimmune disease |
Language | English |
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References_xml | – reference: Mahdi H, Fisher BA, Kallberg H, Plant D, Malmstrom V, Ronnelid J, et al. Specific interaction between genotype, smoking and autoimmunity to citrullinated α-enolase in the etiology of rheumatoid arthritis. Nat Genet 2009; 41: 1319-24. – reference: Iles MM. The impact of incomplete linkage disequilibrium and genetic model choice on the analysis and interpretation of genome-wide association studies. Ann Hum Genet 2010; 74: 375-9. – reference: Lewis CM. Genetic association studies: design, analysis and interpretation. Brief Bioinform 2002; 3: 146-53. – reference: Thu YM, Richmond A. NF-κB inducing kinase: a key regulator in the immune system and in cancer. Cytokine Growth Factor Rev 2010; 21: 213-26. – reference: Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009; 461: 747-53. – reference: Hom G, Graham RR, Modrek B, Taylor KE, Ortmann W, Garnier S, et al. Association of systemic lupus erythematosus with C8orf13-BLK and ITGAM-ITGAX. N Engl J Med 2008; 358: 900-9. – reference: Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL. The human disease network. Proc Natl Acad Sci U S A 2007; 104: 8685-90. – reference: Yang W, Shen N, Ye DQ, Liu Q, Zhang Y, Qian XX, et al. Genome-wide association study in Asian populations identifies variants in ETS1 and WDFY4 associated with systemic lupus erythematosus. PLoS Genet 2010; 6: e1000841. – reference: Hochberg MC, for the Diagnostic and Therapeutic Criteria Committee of the American College of Rheumatology. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus [letter]. Arthritis Rheum 1997; 40: 1725. – reference: Tan EM, Cohen AS, Fries JF, Masi AT, McShane DJ, Rothfield NF, et al. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1982; 25: 1271-7. – reference: Graham RR, Hom G, Ortmann W, Behrens TW. Review of recent genome-wide association scans in lupus. J Intern Med 2009; 265: 680-8. – reference: Wang YH, Liu YJ. OX40-OX40L interactions: a promising therapeutic target for allergic diseases? J Clin Invest 2007; 117: 3655-7. – reference: Saijo K, Schmedt C, Su IH, Karasuyama H, Lowell CA, Reth M, et al. Essential role of Src-family protein tyrosine kinases in NF-κB activation during B cell development. Nat Immunol 2003; 4: 274-9. – reference: Bates JS, Lessard CJ, Leon JM, Nguyen T, Battiest LJ, Rodgers J, et al. Meta-analysis and imputation identifies a 109 kb risk haplotype spanning TNFAIP3 associated with lupus nephritis and hematologic manifestations. Genes Immun 2009; 10: 470-7. – reference: Eyre S, Hinks A, Flynn E, Martin P, Wilson AG, Maxwell JR, et al. Confirmation of association of the REL locus with rheumatoid arthritis susceptibility in the UK population. Ann Rheum Dis 2010; 69: 1572-3. – reference: Crow MK. Collaboration, genetic associations, and lupus erythematosus. N Engl J Med 2008; 358: 956-61. – reference: Gateva V, Sandling JK, Hom G, Taylor KE, Chung SA, Sun X, et al. A large-scale replication study identifies TNIP1, PRDM1, JAZF1, UHRF1BP1 and IL10 as risk loci for systemic lupus erythematosus. Nat Genet 2009; 41: 1228-33. – reference: Frazer KA, Murray SS, Schork NJ, Topol EJ. Human genetic variation and its contribution to complex traits. Nat Rev Genet 2009; 10: 241-51. – reference: Kurreeman FA, Goulielmos GN, Alizadeh BZ, Rueda B, Houwing-Duistermaat J, Sanchez E, et al. The TRAF1-C5 region on chromosome 9q33 is associated with multiple autoimmune diseases. Ann Rheum Dis 2010; 69: 696-9. – reference: Graham RR, Cotsapas C, Davies L, Hackett R, Lessard CJ, Leon JM, et al. Genetic variants near TNFAIP3 on 6q23 are associated with systemic lupus erythematosus. Nat Genet 2008; 40: 1059-61. – reference: Borchers AT, Naguwa SM, Shoenfeld Y, Gershwin ME. The geoepidemiology of systemic lupus erythematosus. Autoimmun Rev 2010; 9: A277-87. – reference: Criswell LA. Gene discovery in rheumatoid arthritis highlights the CD40/NF-κB signaling pathway in disease pathogenesis. Immunol Rev 2010; 233: 55-61. – reference: Burgos P, Metz C, Bull P, Pincheira R, Massardo L, Errazuriz C, et al. Increased expression of c-rel, from the NF-κB/Rel family, in T cells from patients with systemic lupus erythematosus. J Rheumatol 2000; 27: 116-27. – reference: Perdigones N, Vigo AG, Lamas JR, Martinez A, Balsa A, Pascual-Salcedo D, et al. Evidence of epistasis between TNFRSF14 and TNFRSF6B polymorphisms in patients with rheumatoid arthritis. Arthritis Rheum 2010; 62: 705-10. – reference: Plenge RM, Seielstad M, Padyukov L, Lee AT, Remmers EF, Ding B, et al. TRAF1-C5 as a risk locus for rheumatoid arthritis: a genomewide study. 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Snippet | Objective
Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few... Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years,... Objective Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few... |
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SubjectTerms | Adult Asian Continental Ancestry Group - genetics Biological and medical sciences Diseases of the osteoarticular system DNA-Binding Proteins Epistasis, Genetic - physiology Female Genetic Predisposition to Disease - genetics Genome-Wide Association Study Genotype Humans Intracellular Signaling Peptides and Proteins - genetics Lupus Erythematosus, Systemic - genetics Male Medical sciences Nuclear Proteins - genetics OX40 Ligand - genetics Polymorphism, Single Nucleotide Proto-Oncogene Proteins c-rel - genetics Sarcoidosis. Granulomatous diseases of unproved etiology. Connective tissue diseases. Elastic tissue diseases. Vasculitis Signal Transduction src-Family Kinases - genetics TNF Receptor-Associated Factor 1 - genetics Tumor Necrosis Factor alpha-Induced Protein 3 |
Title | Gene-gene interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in systemic lupus erythematosus |
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