Skip to main content
SpringerLink
Log in
Book cover

Chronic Lymphocytic Leukemia pp 83–99Cite as

The Development and Use of Scalable Systems for Studying Aberrant Splicing in SF3B1-Mutant CLL

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1881))

Abstract

Mutational landscape of CLL is now known to include recurrent non-synonymous mutations in SF3B1, a core splicing factor. About 5–10% of newly diagnosed CLL harbor these mutations which are typically limited to HEAT domains in the carboxyl-terminus of the protein. Importantly, the mutations are not specific to CLL but also present in several unrelated clonal disorders. Analysis of patient samples and cell lines has shown the primary splicing aberration in SF3B1-mutant cells to the use of novel or “cryptic” 3′ splice sites (3SS). Advances in genome-editing and next-generation sequencing (NGS) have allowed development of isogenic models and detailed analysis of changes to the transcriptome with relative ease. In this manuscript, we focus on two relevant methods to study splicing factor mutations in CLL: development of isogenic scalable cell lines and informatics analysis of RNA-Seq datasets.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Amin NA, Malek SN (2016) Gene mutations in chronic lymphocytic leukemia. Semin Oncol 43:215–221. https://doi.org/10.1053/j.seminoncol.2016.02.002

    Article  CAS  PubMed  Google Scholar 

  2. Quesada V, Conde L, Villamor N et al (2011) Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat Genet 44:47–52. https://doi.org/10.1038/ng.1032

    Article  CAS  PubMed  Google Scholar 

  3. Wang L, Lawrence MS, Wan Y et al (2011) SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N Engl J Med 365:2497–2506. https://doi.org/10.1056/NEJMoa1109016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Biankin AV, Waddell N, Kassahn KS et al (2012) Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491:399–405. https://doi.org/10.1038/nature11547

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ellis MJ, Ding L, Shen D et al (2012) Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486:353–360. https://doi.org/10.1038/nature11143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Harbour JW, Roberson EDO, Anbunathan H et al (2013) Recurrent mutations at codon 625 of the splicing factor SF3B1 in uveal melanoma. Nat Genet 45:133–135. https://doi.org/10.1038/ng.2523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Papaemmanuil E, Cazzola M, Boultwood J et al (2011) Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med 365:1384–1395. https://doi.org/10.1056/NEJMoa1103283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yoshida K, Sanada M, Shiraishi Y et al (2011) Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478:64–69. https://doi.org/10.1038/nature10496

    Article  CAS  PubMed  Google Scholar 

  9. Cancer Genome Atlas Research Network. Electronic address: wheeler@bcm.edu, Cancer Genome Atlas Research Network (2017) Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169:1327–1341.e23. https://doi.org/10.1016/j.cell.2017.05.046

    Article  CAS  Google Scholar 

  10. Hahn CN, Scott HS (2011) Spliceosome mutations in hematopoietic malignancies. Nat Genet 44:9–10. https://doi.org/10.1038/ng.1045

    Article  CAS  PubMed  Google Scholar 

  11. Strefford JC, Sutton L-A, Baliakas P et al (2013) Distinct patterns of novel gene mutations in poor-prognostic stereotyped subsets of chronic lymphocytic leukemia: the case of SF3B1 and subset #2. Leukemia 27:2196–2199. https://doi.org/10.1038/leu.2013.98

    Article  CAS  PubMed  Google Scholar 

  12. Nadeu F, Delgado J, Royo C et al (2016) Clinical impact of clonal and subclonal TP53, SF3B1, BIRC3, NOTCH1, and ATM mutations in chronic lymphocytic leukemia. Blood 127:2122–2130. https://doi.org/10.1182/blood-2015-07-659144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Te Raa GD, Derks IAM, Navrkalova V et al (2015) The impact of SF3B1 mutations in CLL on the DNA-damage response. Leukemia 29:1133–1142. https://doi.org/10.1038/leu.2014.318

    Article  CAS  Google Scholar 

  14. DeBoever C, Ghia EM, Shepard PJ et al (2015) Transcriptome sequencing reveals potential mechanism of cryptic 3′ splice site selection in SF3B1-mutated cancers. PLoS Comput Biol 11:e1004105. https://doi.org/10.1371/journal.pcbi.1004105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang L, Brooks AN, Fan J et al (2016) Transcriptomic characterization of SF3B1 mutation reveals its pleiotropic effects in chronic lymphocytic leukemia. Cancer Cell 30:750–763. https://doi.org/10.1016/j.ccell.2016.10.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Alsafadi S, Houy A, Battistella A et al (2016) Cancer-associated SF3B1 mutations affect alternative splicing by promoting alternative branchpoint usage. Nat Commun 7:10615. https://doi.org/10.1038/ncomms10615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Darman RB, Seiler M, Agrawal AA et al (2015) Cancer-associated SF3B1 hotspot mutations induce cryptic 3? Splice site selection through use of a different branch point. Cell Rep 13:1033–1045. https://doi.org/10.1016/j.celrep.2015.09.053

    Article  CAS  PubMed  Google Scholar 

  18. Kesarwani AK, Ramirez O, Gupta AK et al (2016) Cancer-associated SF3B1 mutants recognize otherwise inaccessible cryptic 3′ splice sites within RNA secondary structures. Oncogene. https://doi.org/10.1038/onc.2016.279

    Article  PubMed Central  PubMed  Google Scholar 

  19. Mupo A, Seiler M, Sathiaseelan V et al (2016) Hemopoietic-specific Sf3b1-K700E knock-in mice display the splicing defect seen in human MDS but develop anemia without ring sideroblasts. Leukemia. https://doi.org/10.1038/leu.2016.251

    Article  PubMed Central  PubMed  Google Scholar 

  20. Obeng EA, Chappell RJ, Seiler M et al (2016) Physiologic expression of Sf3b1(K700E) causes impaired erythropoiesis, aberrant splicing, and sensitivity to therapeutic spliceosome modulation. Cancer Cell 30:404–417. https://doi.org/10.1016/j.ccell.2016.08.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang C, Chua K, Seghezzi W et al (1998) Phosphorylation of spliceosomal protein SAP 155 coupled with splicing catalysis. Genes Dev 12:1409–1414

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Effenberger KA, Urabe VK, Jurica MS (2017) Modulating splicing with small molecular inhibitors of the spliceosome. Wiley Interdiscip Rev RNA 8:n/a–n/a. https://doi.org/10.1002/wrna.1381

    Article  CAS  Google Scholar 

  23. Cretu C, Schmitzová J, Ponce-Salvatierra A et al (2016) Molecular architecture of SF3b and structural consequences of its cancer-related mutations. Mol Cell 64:307–319. https://doi.org/10.1016/j.molcel.2016.08.036

    Article  CAS  PubMed  Google Scholar 

  24. Fica SM, Nagai K (2017) Cryo-electron microscopy snapshots of the spliceosome: structural insights into a dynamic ribonucleoprotein machine. Nat Struct Mol Biol 24:791–799. https://doi.org/10.1038/nsmb.3463

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shi Y (2017) Mechanistic insights into precursor messenger RNA splicing by the spliceosome. Nat Rev Mol Cell Biol 18:655–670. https://doi.org/10.1038/nrm.2017.86

    Article  CAS  PubMed  Google Scholar 

  26. Will CL, Lührmann R (2011) Spliceosome structure and function. Cold Spring Harb Perspect Biol 3. https://doi.org/10.1101/cshperspect.a003707

    Google Scholar 

  27. Cavellán E, Asp P, Percipalle P, Farrants A-KO (2006) The WSTF-SNF2h chromatin remodeling complex interacts with several nuclear proteins in transcription. J Biol Chem 281:16264–16271. https://doi.org/10.1074/jbc.M600233200

    Article  CAS  PubMed  Google Scholar 

  28. de Almeida SF, Grosso AR, Koch F et al (2011) Splicing enhances recruitment of methyltransferase HYPB/Setd2 and methylation of histone H3 Lys36. Nat Struct Mol Biol 18:977–983. https://doi.org/10.1038/nsmb.2123

    Article  CAS  PubMed  Google Scholar 

  29. Isono K, Mizutani-Koseki Y, Komori T et al (2005) Mammalian polycomb-mediated repression of Hox genes requires the essential spliceosomal protein Sf3b1. Genes Dev 19:536–541. https://doi.org/10.1101/gad.1284605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Listerman I, Sapra AK, Neugebauer KM (2006) Cotranscriptional coupling of splicing factor recruitment and precursor messenger RNA splicing in mammalian cells. Nat Struct Mol Biol 13:815–822. https://doi.org/10.1038/nsmb1135

    Article  CAS  PubMed  Google Scholar 

  31. Kfir N, Lev-Maor G, Glaich O et al (2015) SF3B1 association with chromatin determines splicing outcomes. Cell Rep. https://doi.org/10.1016/j.celrep.2015.03.048

    Article  CAS  PubMed  Google Scholar 

  32. Sánchez-Rivera FJ, Jacks T (2015) Applications of the CRISPR-Cas9 system in cancer biology. Nat Rev Cancer 15:387–395. https://doi.org/10.1038/nrc3950

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zhao W, He X, Hoadley KA et al (2014) Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics 15:419. https://doi.org/10.1186/1471-2164-15-419

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. https://doi.org/10.1186/s13059-016-0881-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Alamancos GP, Agirre E, Eyras E (2014) Methods to study splicing from high-throughput RNA sequencing data. Methods Mol Biol 1126:357–397. https://doi.org/10.1007/978-1-62703-980-2_26

    Article  CAS  PubMed  Google Scholar 

  36. Shen S, Park JW, Lu Z et al (2014) rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A 111:E5593–E5601. https://doi.org/10.1073/pnas.1419161111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ran FA, Hsu PD, Wright J et al (2013) Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8:2281–2308. https://doi.org/10.1038/nprot.2013.143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. https://doi.org/10.1093/bioinformatics/btu170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21. https://doi.org/10.1093/bioinformatics/bts635

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Driskill Graduate Program, Northwestern University, Chicago, IL, USA

    Tushar Murthy

  2. Yale Cancer Center, Yale University, New Haven, CT, USA

    Kiran V. Paul & Manoj M. Pillai

  3. Blood Research Institute, BloodCenter of Wisconsin, Milwaukee, WI, USA

    Alexander C. Minella

Authors
  1. Tushar Murthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kiran V. Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander C. Minella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manoj M. Pillai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj M. Pillai .

Editor information

Editors and Affiliations

  1. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Sami N. Malek

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Murthy, T., Paul, K.V., Minella, A.C., Pillai, M.M. (2019). The Development and Use of Scalable Systems for Studying Aberrant Splicing in SF3B1-Mutant CLL. In: Malek, S. (eds) Chronic Lymphocytic Leukemia. Methods in Molecular Biology, vol 1881. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8876-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8876-1_7

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8875-4

  • Online ISBN: 978-1-4939-8876-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation