Öz
Amaç: TNF reseptör üst ailesi üyesi 13B (TNFSRF13B), B hücre olgunlaşması, plazma hücresi farklılaşması ve antikor yanıtı için kritik olan TNF üst ailesinin bir üyesidir. TNFRSF13B geninin bozulmuş ifadesi, yaygın değişken immün yetmezlik (YDİY), otoimmünite ve lenfoproliferasyon bozuklukları ile ilişkilendirilir. Bu genin hastalığa neden olan varyantlarının yanı sıra bazı farklı izoformlarınında B hücre yanıtını değiştirdiği gösterilmiştir.
Gereç ve Yöntemler: Bu çalışmada, 68 YDİY hastası yeni nesil dizileme yöntemi ile taranarak TNFRSF13B geninde 26 varyant (üç sinonim, beş yanlış anlamlı, on bir UTR ve yedi intronik varyant) saptanmıştır. Tespit edilen varyantlar etkilerine göre biyoinformatik araçlar ile modellenmiş, etkili olduğu gösterilen varyantların klinik bulgular ile ilişkisi araştırılmıştır.
Bulgular: Saptanan varyantların (15/26) %58’i, transkripsiyon faktörü ya da miRNA bağlama bölgeleri, kırpılma bölgeleri veya protein üzerinde termodinamik etkisi olabileceği gösterilen varyantlardır. Biyoinformatik olarak kırpılma bölgesini değiştirdiği düşünülen varyantlara sahip hastalarda, diğer hastalara göre anlamlı derecede düşük IgA düzeylerinin (p=0,009), otoimmünite varlığının (p=0,02) ve gastrointestinal bulgular (p=0,05) gibi YDİY fenotipinde görülen bulguların olduğunu gözlemledik. Ayrıca c.*79A>G 3-UTR varyantının düşük IgA ve IgE seviyeleri ile ilişkili olduğu bulunmuştur. Global veritabanlarına kıyasla on üç varyantın en az on kat artmış alel frekanslarına sahip olduğu bulundu. Bu fark potansiyel düzenleyici etkiye sahip TNFRSF13B varyantlarının YDİY hastalarında daha yaygın olduğunu göstermektedir.
Sonuçlar: Bu bulgular, TNFRSF13B’deki varyantların YDİY fenotipini açıklamasa da, kırpılma bölgesini değiştirme potansiyeli olan varyantların YDİY’in altında yatan genetik arka plandan bağımsız olarak hastaların patogenezine katkıda bulunabileceğini göstermiştir.
Anahtar Kelimeler
Proje Numarası
project no: TYO-2017-24271
Kaynakça
- Fried AJ, Bonilla FA. Pathogenesis, diagnosis, and management of primary antibody deficiencies and infections. Clin Microbiol Rev 2009;22(3):396-414. google scholar
- Karaca NE, Severcan EU, Guven B, Azarsiz E, Aksu G, Kutukculer N. TNFRSF13B/TACI Alterations in Turkish Patients with Common Variable Immunodeficiency and IgA Deficiency. Avicenna J Med Biotechnol 2018;10(3):192-5. google scholar
- Wu Y, Bressette D, Carrell JA, Kaufman T, Feng P, Taylor K, et al. Tumor necrosis factor (TNF) receptor superfamily member TACI is a high affinity receptor for TNF family members APRIL and BLyS. J Biol Chem 2000;275(45):35478-85. google scholar
- Salzer U, Jennings S, Grimbacher B. To switch or not to switch--the opposing roles of TACI in terminal B cell differentiation. Eur J Immunol 2007;37(1):17-20. google scholar
- Ou X, Xu S, Lam KP. Deficiency in TNFRSF13B (TACI) expands T-follicular helper and germinal center B cells via increased ICOS-ligand expression but impairs plasma cell survival. Proc Natl Acad Sci U S A 2012;109(38):15401-6. google scholar
- Figgett WA, Fairfax K, Vincent FB, Le Page MA, Katik I, Deliyanti D, et al. The TACI receptor regulates T-cell-independent marginal zone B cell responses through innate activation-induced cell death. Immunity 2013;39(3):573-83. google scholar
- Salzer U, Grimbacher B. TACI deficiency - a complex system out of balance. Curr Opin Immunol 2021;71:81-8. google scholar
- He B, Santamaria R, Xu W, Cols M, Chen K, Puga I, et al. The transmembrane activator TACI triggers immunoglobulin class switching by activating B cells through the adaptor MyD88. Nat Immunol 2010;11(9):836-45. google scholar
- Bogaert DJ, Dullaers M, Lambrecht BN, Vermaelen KY, De Baere E, Haerynck F. Genes associated with common variable immunodeficiency: one diagnosis to rule them all? J Med Genet 2016;53(9):575-90. google scholar
- Ameratunga R, Koopmans W, Woon ST, Leung E, Lehnert K, Slade CA, et al. Epistatic interactions between mutations of TACI (TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus. Clin Transl Immunology 2017;6(10):159-65. google scholar
- Platt JL, de Mattos Barbosa MG, Huynh D, Lefferts AR, Katta J, Kharas C, et al. TNFRSF13B polymorphisms counter microbial adaptation to enteric IgA. JCI Insight 2021;(6):14-7. google scholar
- Firtina S, Ng YY, Ng OH, Kiykim A, Ozek EY, Kara M, et al. Primary antibody deficiencies in Turkey: molecular and clinical aspects. Immunol Res 2022;70(1):44-55. google scholar
- Firtina S, Yin Ng Y, Hatirnaz Ng O, Kiykim A, Aydiner E, Nepesov S, et al. Mutational landscape of severe combined immunodeficiency patients from Turkey. Int J Immunogenet 2020;47(6):529-38. google scholar
- Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 2020;48(1):127-31. google scholar
- Kozomara A, Birgaoanu M, and Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res 2019;47(1):155-62. google scholar
- Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015;(4):1-3. google scholar
- Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 2009;37(9):67-70. google scholar
- Pertea M, Lin X, Salzberg SL. GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 2001;29(5):1185-90. google scholar
- Ittisoponpisan S, Islam SA, Khanna T, Alhuzimi E, David A, Sternberg MJE. Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated? J Mol Biol2019;431(11):2197-212. google scholar
- Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph 1996;14(1):27-33. google scholar
- Schwarz JM, Hombach D, Kohler S, Cooper DN, Schuelke M, Seelow D. RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants. Nucleic Acids Res 2019;47(1):106-13. google scholar
- Hymowitz SG, Patel DR, Wallweber HJ, Runyon S, Yan M, Yin J, et al. Structures of APRIL-receptor complexes: like BCMA, TACI employs only a single cysteine-rich domain for high affinity ligand binding. J Biol Chem 2005;280(8):7218-27. google scholar
- Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2(9):1511-9. google scholar
- Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253(5016):164-70. google scholar
- Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356(6364):83-5. google scholar
- Pontius J, Richelle J, Wodak SJ. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 1996;264(1):121-36. google scholar
- Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 1996;8(4):477-86. google scholar
- Hooft RW, Vriend G, Sander C, and Abola EE. Errors in protein structures. Nature 1996;381(6):272-6. google scholar
- Parthiban V, Gromiha MM, and Schomburg D. CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 2006;34(Web Server issue):W239-42. google scholar
- Chen Y, Lu H, Zhang N, Zhu Z, Wang S, Li M. PremPS: Predicting the impact of missense mutations on protein stability. PLoS Comput Biol 2020;16(12):100-5. google scholar
- Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 1995;23(4):566-79. google scholar
- Aksu G, Genel F, Koturoglu G, Kurugol Z, Kutukculer N. Serum immunoglobulin (IgG, IgM, IgA) and IgG subclass concentrations in healthy children: a study using nephelometric technique. Turk J Pediatr 2006;48(1):19-24. google scholar
- Ozcan E, Rauter I, Garibyan L, Dillon SR, Geha RS. Toll-like receptor 9, transmembrane activator and calcium-modulating cyclophilin ligand interactor, and CD40 synergize in causing B-cell activation. J Allergy Clin Immunol 2011;128(3):601-9. google scholar
- Salzer U, Bacchelli C, Buckridge S, Pan-Hammarstrom Q, Jennings S, Lougaris V, et al. Relevance of biallelic versus monoallelic TNFRSF13B mutations in distinguishing disease-causing from risk-increasing TNFRSF13B variants in antibody deficiency syndromes. Blood 2009;113(9):1967-76. google scholar
- Liao M, Ye F, Zhang B, Huang L, Xiao Q, Qin M, et al. Genome-wide association study identifies common variants at TNFRSF13B associated with IgG level in a healthy Chinese male population. Genes Immun 2012;13(6):509-13. google scholar
- Speletas M, Mamara A, Papadopoulou-Alataki E, Iordanakis G, Liadaki K, Bardaka F, et al. TNFRSF13B/TACI alterations in Greek patients with antibody deficiencies. J Clin Immunol 2011;31(4):550-9. google scholar
TNFRSF13B VARIANTS ACT AS MODIFIERS TO CLINICAL PHENOTYPES IN COMMON VARIABLE IMMUNE DEFICIENCY DISORDERS
Öz
Objective: The TNF receptor gene 13B (TNFSRF13B) is a member of the TNF superfamily which is crucial for B cell maturation, plasma cell differentiation, and antibody response. Impaired expression of the TNFRSF13B gene is associated with common variable immune deficiency (CVID), autoimmunity, and lymphoproliferation disorders. Besides the disease-causing variants of this gene, its different isoforms are associated with strong and weak TNFRSF13B expression that leads to an unbalanced B cell response.
Materials and Methods: The study detected 26 variants (three synonymous, five missenses, eleven UTR, and seven intronic variants) in the TNFRSF13B gene by screening 68 CVID patients with targeted next generation sequencing. An integrative bioinformatics approach was utilized to provide a plausible explanation for CVID associations from different perspectives and to investigate the associations from the clinical findings.
Results: Fifty-eight percent (15/26) of the detected variants were altered regulatory elements, such as transcription factor binding, miRNA binding sites, splice site regions or the thermodynamic impact on protein. We observed that patients who suffered from the potential splicing variants had significantly low IgA levels (p=0.009), autoimmunity (p=0.02) and gastrointestinal findings (p=0.05). In addition, the c.*79A>G 3-UTR variant was found with the low IgA and IgE levels. Thirteen variants found to have at least tenfold increased allele frequencies as compared to global databases indicating that the TNFRSF13B variants, which have a potential regulatory effect, are more common in CVID patients.
Conclusions: All findings suggested that these variants may not be the causative variant for the CVID phenotype but the unbalanced TNFRSF13B alternative splices could contribute to the pathogenesis of patients independent from the underlying genetic background of CVID.
Anahtar Kelimeler
TNFRSF13B in silico analysis CVID integrated bioinformatics PID
Destekleyen Kurum
Istanbul University Cerrahpasa Research Fund
Proje Numarası
project no: TYO-2017-24271
Teşekkür
We would like to thank to Dr. Ozkan Ozdemir for his interpretation for creating a graphical figure 1 with Biorender program.
Kaynakça
- Fried AJ, Bonilla FA. Pathogenesis, diagnosis, and management of primary antibody deficiencies and infections. Clin Microbiol Rev 2009;22(3):396-414. google scholar
- Karaca NE, Severcan EU, Guven B, Azarsiz E, Aksu G, Kutukculer N. TNFRSF13B/TACI Alterations in Turkish Patients with Common Variable Immunodeficiency and IgA Deficiency. Avicenna J Med Biotechnol 2018;10(3):192-5. google scholar
- Wu Y, Bressette D, Carrell JA, Kaufman T, Feng P, Taylor K, et al. Tumor necrosis factor (TNF) receptor superfamily member TACI is a high affinity receptor for TNF family members APRIL and BLyS. J Biol Chem 2000;275(45):35478-85. google scholar
- Salzer U, Jennings S, Grimbacher B. To switch or not to switch--the opposing roles of TACI in terminal B cell differentiation. Eur J Immunol 2007;37(1):17-20. google scholar
- Ou X, Xu S, Lam KP. Deficiency in TNFRSF13B (TACI) expands T-follicular helper and germinal center B cells via increased ICOS-ligand expression but impairs plasma cell survival. Proc Natl Acad Sci U S A 2012;109(38):15401-6. google scholar
- Figgett WA, Fairfax K, Vincent FB, Le Page MA, Katik I, Deliyanti D, et al. The TACI receptor regulates T-cell-independent marginal zone B cell responses through innate activation-induced cell death. Immunity 2013;39(3):573-83. google scholar
- Salzer U, Grimbacher B. TACI deficiency - a complex system out of balance. Curr Opin Immunol 2021;71:81-8. google scholar
- He B, Santamaria R, Xu W, Cols M, Chen K, Puga I, et al. The transmembrane activator TACI triggers immunoglobulin class switching by activating B cells through the adaptor MyD88. Nat Immunol 2010;11(9):836-45. google scholar
- Bogaert DJ, Dullaers M, Lambrecht BN, Vermaelen KY, De Baere E, Haerynck F. Genes associated with common variable immunodeficiency: one diagnosis to rule them all? J Med Genet 2016;53(9):575-90. google scholar
- Ameratunga R, Koopmans W, Woon ST, Leung E, Lehnert K, Slade CA, et al. Epistatic interactions between mutations of TACI (TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus. Clin Transl Immunology 2017;6(10):159-65. google scholar
- Platt JL, de Mattos Barbosa MG, Huynh D, Lefferts AR, Katta J, Kharas C, et al. TNFRSF13B polymorphisms counter microbial adaptation to enteric IgA. JCI Insight 2021;(6):14-7. google scholar
- Firtina S, Ng YY, Ng OH, Kiykim A, Ozek EY, Kara M, et al. Primary antibody deficiencies in Turkey: molecular and clinical aspects. Immunol Res 2022;70(1):44-55. google scholar
- Firtina S, Yin Ng Y, Hatirnaz Ng O, Kiykim A, Aydiner E, Nepesov S, et al. Mutational landscape of severe combined immunodeficiency patients from Turkey. Int J Immunogenet 2020;47(6):529-38. google scholar
- Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 2020;48(1):127-31. google scholar
- Kozomara A, Birgaoanu M, and Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res 2019;47(1):155-62. google scholar
- Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015;(4):1-3. google scholar
- Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 2009;37(9):67-70. google scholar
- Pertea M, Lin X, Salzberg SL. GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 2001;29(5):1185-90. google scholar
- Ittisoponpisan S, Islam SA, Khanna T, Alhuzimi E, David A, Sternberg MJE. Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated? J Mol Biol2019;431(11):2197-212. google scholar
- Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph 1996;14(1):27-33. google scholar
- Schwarz JM, Hombach D, Kohler S, Cooper DN, Schuelke M, Seelow D. RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants. Nucleic Acids Res 2019;47(1):106-13. google scholar
- Hymowitz SG, Patel DR, Wallweber HJ, Runyon S, Yan M, Yin J, et al. Structures of APRIL-receptor complexes: like BCMA, TACI employs only a single cysteine-rich domain for high affinity ligand binding. J Biol Chem 2005;280(8):7218-27. google scholar
- Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2(9):1511-9. google scholar
- Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253(5016):164-70. google scholar
- Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356(6364):83-5. google scholar
- Pontius J, Richelle J, Wodak SJ. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 1996;264(1):121-36. google scholar
- Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 1996;8(4):477-86. google scholar
- Hooft RW, Vriend G, Sander C, and Abola EE. Errors in protein structures. Nature 1996;381(6):272-6. google scholar
- Parthiban V, Gromiha MM, and Schomburg D. CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 2006;34(Web Server issue):W239-42. google scholar
- Chen Y, Lu H, Zhang N, Zhu Z, Wang S, Li M. PremPS: Predicting the impact of missense mutations on protein stability. PLoS Comput Biol 2020;16(12):100-5. google scholar
- Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 1995;23(4):566-79. google scholar
- Aksu G, Genel F, Koturoglu G, Kurugol Z, Kutukculer N. Serum immunoglobulin (IgG, IgM, IgA) and IgG subclass concentrations in healthy children: a study using nephelometric technique. Turk J Pediatr 2006;48(1):19-24. google scholar
- Ozcan E, Rauter I, Garibyan L, Dillon SR, Geha RS. Toll-like receptor 9, transmembrane activator and calcium-modulating cyclophilin ligand interactor, and CD40 synergize in causing B-cell activation. J Allergy Clin Immunol 2011;128(3):601-9. google scholar
- Salzer U, Bacchelli C, Buckridge S, Pan-Hammarstrom Q, Jennings S, Lougaris V, et al. Relevance of biallelic versus monoallelic TNFRSF13B mutations in distinguishing disease-causing from risk-increasing TNFRSF13B variants in antibody deficiency syndromes. Blood 2009;113(9):1967-76. google scholar
- Liao M, Ye F, Zhang B, Huang L, Xiao Q, Qin M, et al. Genome-wide association study identifies common variants at TNFRSF13B associated with IgG level in a healthy Chinese male population. Genes Immun 2012;13(6):509-13. google scholar
- Speletas M, Mamara A, Papadopoulou-Alataki E, Iordanakis G, Liadaki K, Bardaka F, et al. TNFRSF13B/TACI alterations in Greek patients with antibody deficiencies. J Clin Immunol 2011;31(4):550-9. google scholar
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Klinik Tıp Bilimleri (Diğer) |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Proje Numarası | project no: TYO-2017-24271 |
| Yayımlanma Tarihi | 24 Ekim 2023 |
| Gönderilme Tarihi | 19 Ağustos 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 6 Sayı: 3 |
