Tanısı Zor Tek Gen Hastalıklarında Hedefe Yönelik Yeni Nesil Dizileme Panel Tasarımı: Primer İmmün Yetersizlik Örneği

Yıl 2020, Cilt: 3 Sayı: 3, 93 - 101, 05.11.2020

Sinem Fırtına Özden Hatırnaz Ng , Müge Sayitoğlu , Yuk Yin Ng

Öz

Amaç: Yeni nesil dizileme teknolojileri bugün çok sayıda aday genin, genomun tüm kodlayan bölgelerinin hatta tüm genomun analizini tek seferde ve kısa süre içerisinde düşük maliyet ve yüksek hassasiyette, güvenilir bir şekilde mümkün kılmaktadır. Hedefe yönelik yeni nesil dizileme sistemleri genomda sadece belirli bölgenin dizilenmesine imkan veren, tüm genom dizilemelere göre uygulaması ve analizi daha kolay, hızlı ve yüksek güvenirlilikte bir yöntem olarak pek çok rutin genetik tanı uygulamalarında yerini bulmuştur. Gereç ve Yöntem: Çalışmamızda primer immün yetersizliklerin en yaygın grubu olan primer antikor yetersizlikleri (PAY) ve en ağır seyirli grubu ağır kombine immün yetersizlikler (AKİY) için hastalık ile ilişkili olduğu bilinen genleri kapsayan PZR temelli genetik tanı panelleri geliştirilmiş ve ortaya çıkan yüksek verinin yorumlanması için bir analiz akışı oluşturulmuştur. Bulgular: Tasarlanan paneller ile toplam 112 hasta (PAY:64, AKİY:48) dizilenmiş ve AKİY hastalarının %58’i ve PAY hastalarının %14,2’sinde hastalık ile ilişkili varyantlar tespit edilmiştir. Tüm varyantlar Sanger dizileme ile doğrulanarak oluşturulan moleküler tanı panellerinin ve analiz algoritmasının doğruluğu kontrol edilmiştir. Sonuç: Hedefe yönelik yeni nesil dizileme panellerinin hedeflenen bölgeye uygun olarak doğru yöntemle tasarlanması ve çıkan ham datanın doğru iş akışı ile analiz edilmesi panelin başarısını arttırmaktadır.

Anahtar Kelimeler

yeni nesil dizileme, varyant yorumlama, hedefe yönelik panel tasarımı

Destekleyen Kurum

istanbul üniversitesi BAP ve İstanbul Bilgi Üniversitesi araştırma fonu

Proje Numarası

BAP: 52575 ve 20499, Bilgi Üniversitesi:NGYY-2018.01.0006

Teşekkür

Çalışmaya dahil edilen hasta örneklerini sağlayan Doç. Dr. Selda Hançerli Torun, Prof. Dr. Elif Aydıner, Doç. Dr. Ayca Kiykim, Prof. Dr. Yildiz Camcioglu, Prof. Dr. Safa Baris ve Prof. Dr. Ahmet Ozen’e, Çalışmaya bilimsel katkıları için Prof. Dr. Ugur Ozbek’e teşekkürlerimizi sunarız.

Targeted Next Generation Sequencing Panel Design in Complex Monogenic Diseases: The Example of Primary Immunodeficiency

Öz

Objective: Next-generation sequencing technologies can generate an analysis of a large number of candidate genes, all the coding regions of a genome, or whole genomes with a high degree of accuracy and within a short amount of time. Targeted next generation sequencing systems have been found in many routine genetic diagnosis applications that allow the sequencing of only the candidate regions of the genome. Materials and Methods: In this study, we designed PCR-based targeted next generation sequencing (NGS) panels for severe combined immunodeficiency (SCID) and primary antibody deficiency (PAD) and created an algorithm for analysing high-throughput data. Results: We screened 112 patients (48 SCID and 64 PAD) and we detected genetic variations in 58% of the SCID and in 14.2% of the PAD patients. All variants were validated by Sanger sequencing to validate the accuracy of the NGS panel and analysis algorithm. Conclusion: Designing targeted next generation sequencing panels with an appropriate method, in accordance with the targeted region, and analysing the raw data with a suitable workflow, increases the success of the panel.

Anahtar Kelimeler

Next generation sequencing, variant annotation, targeted-ngs panel design

Proje Numarası

BAP: 52575 ve 20499, Bilgi Üniversitesi:NGYY-2018.01.0006

Kaynakça

• 1. Goodwin S, JD, McPherson, WR McCombie. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016; 17(6):333-51.
• 2. Shendure J, H Ji. Next-generation DNA sequencing. Nat Biotechnol, 2008; 26(10):1135-45.
• 3. Mardis ER. DNA sequencing technologies: 2006-2016. Nat Protoc 2017; 12(2):213-8.
• 4. Giani AM, Gallo L GR. Gianfranceschi G. Formenti, Long walk to genomics: History and current approaches to genome sequencing and assembly. Comput Struct Biotechnol J 2020; 18:9-19.
• 5. Pettersson E, Lundeberg J, Ahmadian A. Generations of sequencing technologies. Genomics 2009; 93(2):105-11.
• 6. Cifaldi C, Brigida I, Barzaghi F, Zoccolillo M, Ferradini V, Petricone D et al. Targeted NGS Platforms for Genetic Screening and Gene Discovery in Primary Immunodeficiencies. Front Immunol 2019;10:316.
• 7. Raje N S, Soden D, Swanson CE, Ciaccio SF, Kingsmore DL, Dinwiddie. Utility of next generation sequencing in clinical primary immunodeficiencies. Curr Allergy Asthma Rep 2014; 14(10):468.
• 8. Notarangelo LD. Primary immunodeficiencies. J Allergy Clin Immunol 2010; 125(2 Suppl 2):S182-94.
• 9. Parvaneh N, Casanova JL, Notarangelo LD, Conley ME. Primary immunodeficiencies: a rapidly evolving story. J Allergy Clin Immunol 2013; 131(2):314-23.
• 10. Stoddard JL, Niemela JE, Fleisher TA, Rosenzweig SD. Targeted NGS: A Cost-Effective Approach to Molecular Diagnosis of PIDs. Front Immunol 2014;5:531.
• 11. Al-Herz, Bousfiha WA, Casanova JL, Chatila T, Conley ME, Cunningham-Rundles C, et al. Primary immunodeficiency diseases: an update on the classification from the international union of immunological societies expert committee for primary immunodeficiency. Front Immunol 2014; 5:162.
• 12. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25(14):1754-60.
• 13. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30(15):2114-20.
• 14. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol 2011; 29(1):24-6.
• 15. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009; 4(7):1073-81.
• 16. Adzhubei IA, Schmidt S, L. Peshkin, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7(4):248-9.
• 17. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol 2016; 17(1):122.
• 18. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deepsequencing age. Nat Methods 2014; 11(4):361-2.
• 19. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 2012; 6(2):80-92.
• 20. Pertea M, Lin X, Salzberg SL. GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 2001; 29(5):1185-90.
• 21. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 2014; 46(3):310-5.
• 22. Nijman IJ, Montfrans JM van, Hoogstraat M, Boes ML, van de Corput L, Renner ED, et al. Targeted next-generation sequencing: a novel diagnostic tool for primary immunodeficiencies. J Allergy Clin Immunol 2014; 133(2):529-34.
• 23. Al-Mousa H, Abouelhoda M, Monies DM, Al-Tassan N, Al-Ghonaium A, Al-Saud B, et al. Unbiased targeted next-generation sequencing molecular approach for primary immunodeficiency diseases. J Allergy Clin Immunol 2016; 137(6):1780-7.
• 24. Yu H, Zhang VW, Stray-Pedersen A, Hanson IC, Forbes LR, de la Morena MT, et al. Rapid molecular diagnostics of severe primary immunodeficiency determined by using targeted next-generation sequencing. J Allergy Clin Immunol 2016; 138(4):1142-1151 e2.
• 25. Fang M, Abolhassani H, Lim CK, Zhang J, Hammarstrom L. Next Generation Sequencing Data Analysis in Primary Immunodeficiency Disorders - Future Directions. J Clin Immunol 2016; 36 Suppl 1:68-75.
• 26. Erman B, Bilic I, Hirschmugl T, Salzer E, Boztug H, Sanal O, et al. Investigation of Genetic Defects in Severe Combined Immunodeficiency Patients from Turkey by Targeted Sequencing. Scand J Immunol 2017; 85(3):227-34.
• 27. 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;00:1-10.
• 28. Firtina S, Ng YY, Ng OH, Nepesov S, Yesilbas O, Kilercik M, et al. A novel pathogenic frameshift variant of CD3E gene in two T-B+ NK+ SCID patients from Turkey. Immunogenetics 2017; 69: 653–9.
• 29. Firtina S, Cipe F, Ng YY, Kiykim A, Ng OH, Sudutan T, et al. A Novel FOXN1 Variant Is Identified in Two Siblings with Nude Severe Combined Immunodeficiency. J Clin Immunol 2019; 39(2):144-147.
• 30. Mamanova L, Coffey AJ, Scott CE, Kozarewa I, Turner EH, Kumar A, et al. Target-enrichment strategies for next-generation sequencing. Nat Methods 2010; 7(2):111-8.
• 31. Pannicke U, Honig M, Schulze I, Rohr J, Heinz GA, Braun S, et al. The most frequent DCLRE1C (ARTEMIS) mutations are based on homologous recombination events. Hum Mutat 2010; 31(2):197-207.
• 32. Mu W, Lu HM, Chen J, Li S, Elliott AM. Sanger Confirmation Is Required to Achieve Optimal Sensitivity and Specificity in Next-Generation Sequencing Panel Testing. J Mol Diagn 2016; 18(6):923-32.
• 33. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of proteincoding genetic variation in 60,706 humans. Nature 2016; 536(7616):285-91.