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Lösemi Modelinde Tüm Genom RNA Dizileme Analiz Algoritması Geliştirilmesi

Year 2020, Volume: 3 Issue: 2, 26 - 34, 10.07.2020

Abstract

Amaç: RNA Dizileme teknolojisi gen anlatım farklılıkları ve kodlayan bölgedeki varyasyonlar, kodlama yapmayan küçük RNAların anlatımları ve gen füzyonlarının belirlenmesi ile bu farklılıkların nedenlerini sunabilmektedir. Ancak bu kadar enformatik bilgiler sunabilen bu teknolojinin analizlerinin yapılması ve yorumlanması oldukça zorludur. T- hücreli akut lenfoblastik lösemi (T-ALL) de prognostik öneme sahip ve hastalığın takibinde kullanılabilecek güvenilir bir genetik belirteç bulunmamakla birlikte, doğrudan tedavi protokolünü ve tedavide yararlanılacak yeni hedef proteinleri belirlemede esas olacak moleküler alt yapı ve sınıflandırma da bilinmemektedir. Gereç ve Yöntem: Biz de bu çalışmamızda, T-ALL gibi karmaşık bir genomik arka plana sahip lösemi hücrelerinde RNA-dizileme için en uygun enformatik iş akış algoritmasını oluşturmayı amaçladık. Bu çalışmada RNA dizileme ile Jurkat ve Molt 4 hücre hatları dizilenmiştir. Doğrulama ve karşılaştırma amacıyla açık veri bankalarından elde edilen sağlıklı timosit alt grupları ve T-ALL hasta (n=12) örnekleri (GSE48173) kullanılmıştır. Bulgular: Açık erişimli veri araçları ile gerçekleştirdiğimiz enformatik analizlerde doku spesifik alternatif kırpılma ürünlerinin kantitatif tayinini, spesifik gen varyasyonlarını ve global gen anlatım düzeylerini başarılı bir şekilde tespit ettik ve T-ALL hasta verisinde aynı yaklaşımları kullanarak doğrulama yaptık. Sonuç: Çalışmamızın sonucunda lösemi hastalarının veri analizinde kullanılabilecek uygun araçlar ve algoritma belirlenmiştir.

Supporting Institution

Bu çalışma, İstanbul Üniversitesi Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir.

Project Number

Proje No: TYL-2016-20440.

References

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  • 2.Johnsen JM, Nickerson DA, Reiner AP. Massively parallel sequencing: The new frontier of hematologic genomics. Blood. 2013;122(19):3268–75.
  • 3. Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009.
  • 4. Ozsolak F, Milos PM. RNA sequencing: Advances, challenges and opportunities. Nat Rev Genet. 2011;12(2):87–98.
  • 5. Costa V, Angelini C, De Feis I, Ciccodicola A. Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol. 2010;2010:19.
  • 6. David M, Dzamba M, Lister D, Ilie L, Brudno M. SHRiMP2: Sensitive yet Practical Short Read Mapping. Bioinformatics [Internet]. 2011 Apr 1 [cited 2018 Jul 13];27(7):1011–2. Available from: https://academic.oup.com/bioinformatics/ article-lookup/doi/10.1093/bioinformatics/ btr046
  • 7. Williams AG, Thomas S, Wyman SK, Holloway AK. RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis. Curr Protoc Hum Genet. 2014;83.
  • 8. Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 2017;7(6):e577.
  • 9. Van Vlierberghe P, Ferrando A. The molecular basis of T cell acute lymphoblastic leukemia. J Clin Invest. 2012;122(10):3398–406.
  • 10. Galli C, Piemontese M, Lumetti S, Manfredi E, Macaluso GM, Passeri G. GSK3b-inhibitor lithium chloride enhances activation of Wnt canonical signaling and osteoblast differentiation on hydrophilic titanium surfaces. Clin Oral Implants Res. 2013 Aug;24(8):921–7.
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  • 13. Metsalu T, Vilo J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015;43(W1):W566-70.
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  • 15. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012 Apr 4;9(4):357–9.
  • 16. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. Sequence analysis STAR: ultrafast universal RNA-seq aligner. 2013 [cited 2020 Jun 10];29(1):15–21. Available from: http:// code.google.com/p/rna-star/.
  • 17. Zhao S, Liu W, Li Y, Liu P, Li S, Dou D, et al. Alternative splice variants modulates dominantnegative function of Helios in T-cell leukemia. PLoS One. 2016;11(9):e0163328.
  • 18. Adamia S, Pilarski P, Bar-Natan M, Stone R, Griffin J. Alternative Splicing in Chronic Myeloid Leukemia (CML): A Novel Therapeutic Target? Curr Cancer Drug Targets. 2013;13(7):735–48.
  • 19. Bennett JM. The Leukemia-Lymphoma Cell Line Facts Book. Leukemia Research. 2002.
  • 20. Tomov ML, Olmsted ZT, Dogan H, Gongorurler E, Tsompana M, Otu HH, et al. Distinct and Shared Determinants of Cardiomyocyte Contractility in Multi-Lineage Competent Ethnically Diverse Human iPSCs. Sci Rep. 2016;6(37636).
  • 21. Ramsköld D, Kavak E, Sandberg R. How to analyze gene expression using RNA-sequencing data. Methods Mol Biol. 2012;802:259–74.
  • 22. Kalender Atak Z, Gianfelici V, Hulselmans G, De Keersmaecker K, Devasia AG, Geerdens E, et al. Comprehensive Analysis of Transcriptome Variation Uncovers Known and Novel Driver Events in T-Cell Acute Lymphoblastic Leukemia. PLoS Genet. 2013;9(12):e1003997.

Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model

Year 2020, Volume: 3 Issue: 2, 26 - 34, 10.07.2020

Abstract

Objective: RNA Sequencing technology can offer gene expression differences and the reasons for these differences by detecting variations in the coding region, expession of noncoding RNAs and gene fusions. However, it is very difficult to analyze and interpret this technology, which can provide such valuable information. Although there is no reliable genetic marker for T-cell acute lymphoblastic leukemia (T-ALL), which can be used in the follow-up of the disease, the molecular infrastructure and classification that will be directly used in determining the treatment protocol and the new target proteins to be used in treatment are not known. Material and Methods: In this study, we aimed to establish the most suitable workflow algorithm for RNA sequencing in cell lines belonging to a group with a complex genomic background such as T-ALL. With this study, the Jurkat and Molt4 cell lines were sequenced by RNA sequencing. In order to increase the significance of our study, the results of different thymocyte subgroups and 12 T-ALL patient samples (GSE48173) were investigated. Results: We conducted a bioinformatics data approach by using open access data tools, and we successfully detected the tissue specific quantitative alternative splicing gene products, gene specific variations and global gene expression levels, and verified them using the same approach in T-ALL patient data. Conclusion: Aside from these molecular findings that we have achieved, one of our goals in this study was to develop an algorithm of transcriptomic data, which is difficult to work with and to interpret, and showed the correctness of our algorithm by confirming the data described in the literature. 

Project Number

Proje No: TYL-2016-20440.

References

  • 1. Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed. 2013;98(6):236–8.
  • 2.Johnsen JM, Nickerson DA, Reiner AP. Massively parallel sequencing: The new frontier of hematologic genomics. Blood. 2013;122(19):3268–75.
  • 3. Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009.
  • 4. Ozsolak F, Milos PM. RNA sequencing: Advances, challenges and opportunities. Nat Rev Genet. 2011;12(2):87–98.
  • 5. Costa V, Angelini C, De Feis I, Ciccodicola A. Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol. 2010;2010:19.
  • 6. David M, Dzamba M, Lister D, Ilie L, Brudno M. SHRiMP2: Sensitive yet Practical Short Read Mapping. Bioinformatics [Internet]. 2011 Apr 1 [cited 2018 Jul 13];27(7):1011–2. Available from: https://academic.oup.com/bioinformatics/ article-lookup/doi/10.1093/bioinformatics/ btr046
  • 7. Williams AG, Thomas S, Wyman SK, Holloway AK. RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis. Curr Protoc Hum Genet. 2014;83.
  • 8. Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 2017;7(6):e577.
  • 9. Van Vlierberghe P, Ferrando A. The molecular basis of T cell acute lymphoblastic leukemia. J Clin Invest. 2012;122(10):3398–406.
  • 10. Galli C, Piemontese M, Lumetti S, Manfredi E, Macaluso GM, Passeri G. GSK3b-inhibitor lithium chloride enhances activation of Wnt canonical signaling and osteoblast differentiation on hydrophilic titanium surfaces. Clin Oral Implants Res. 2013 Aug;24(8):921–7.
  • 11. Gottardi CJ, Gumbiner BM. Distinct molecular forms of β-catenin are targeted to adhesive or transcriptional complexes. J Cell Biol. 2004 Oct 25;167(2):339–49.
  • 12. Cufflinks [Internet]. [cited 2020 Jun 23]. Available from: http://cole-trapnell-lab.github. io/cufflinks/
  • 13. Metsalu T, Vilo J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015;43(W1):W566-70.
  • 14. DAVID Functional Annotation Bioinformatics Microarray Analysis [Internet]. [cited 2020 Jun 23]. Available from: https://david.ncifcrf.gov/
  • 15. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012 Apr 4;9(4):357–9.
  • 16. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. Sequence analysis STAR: ultrafast universal RNA-seq aligner. 2013 [cited 2020 Jun 10];29(1):15–21. Available from: http:// code.google.com/p/rna-star/.
  • 17. Zhao S, Liu W, Li Y, Liu P, Li S, Dou D, et al. Alternative splice variants modulates dominantnegative function of Helios in T-cell leukemia. PLoS One. 2016;11(9):e0163328.
  • 18. Adamia S, Pilarski P, Bar-Natan M, Stone R, Griffin J. Alternative Splicing in Chronic Myeloid Leukemia (CML): A Novel Therapeutic Target? Curr Cancer Drug Targets. 2013;13(7):735–48.
  • 19. Bennett JM. The Leukemia-Lymphoma Cell Line Facts Book. Leukemia Research. 2002.
  • 20. Tomov ML, Olmsted ZT, Dogan H, Gongorurler E, Tsompana M, Otu HH, et al. Distinct and Shared Determinants of Cardiomyocyte Contractility in Multi-Lineage Competent Ethnically Diverse Human iPSCs. Sci Rep. 2016;6(37636).
  • 21. Ramsköld D, Kavak E, Sandberg R. How to analyze gene expression using RNA-sequencing data. Methods Mol Biol. 2012;802:259–74.
  • 22. Kalender Atak Z, Gianfelici V, Hulselmans G, De Keersmaecker K, Devasia AG, Geerdens E, et al. Comprehensive Analysis of Transcriptome Variation Uncovers Known and Novel Driver Events in T-Cell Acute Lymphoblastic Leukemia. PLoS Genet. 2013;9(12):e1003997.
There are 22 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

Eda Sun This is me 0000-0003-0320-5784

Müge Sayitoğlu 0000-0002-8648-213X

Project Number Proje No: TYL-2016-20440.
Publication Date July 10, 2020
Submission Date May 14, 2020
Published in Issue Year 2020 Volume: 3 Issue: 2

Cite

MLA Sun, Eda and Müge Sayitoğlu. “Lösemi Modelinde Tüm Genom RNA Dizileme Analiz Algoritması Geliştirilmesi”. Sağlık Bilimlerinde İleri Araştırmalar Dergisi, vol. 3, no. 2, 2020, pp. 26-34.