Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control

Abstract

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation–positive Burkitt lymphoma, nine BCL2 translocation–positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Loss of methylation in lymphoma.
Figure 2: Differentially methylated regions.
Figure 3: Sphingosine-1-phosphate signaling is affected by complementary DNA mutation and methylation in germinal center B lymphomas.
Figure 4: Enrichment of transcription factor binding sites in cDMRs.
Figure 5: SMARCA4 genome architecture and protein expression.

Accession codes

Primary accessions

European Nucleotide Archive

References

  1. 1

    Lai, A.Y. et al. DNA methylation profiling in human B cells reveals immune regulatory elements and epigenetic plasticity at Alu elements during B-cell activation. Genome Res. 23, 2030–2041 (2013).

    CAS  Article  Google Scholar 

  2. 2

    Lee, S.T. et al. A global DNA methylation and gene expression analysis of early human B-cell development reveals a demethylation signature and transcription factor network. Nucleic Acids Res. 40, 11339–11351 (2012).

    CAS  Article  Google Scholar 

  3. 3

    Shaknovich, R. et al. DNA methyltransferase 1 and DNA methylation patterning contribute to germinal center B-cell differentiation. Blood 118, 3559–3569 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Basso, K. & Dalla-Favera, R. Germinal centres and B cell lymphomagenesis. Nat. Rev. Immunol. 15, 172–184 (2015).

    CAS  Article  Google Scholar 

  5. 5

    Lenz, G. & Staudt, L.M. Aggressive lymphomas. N. Engl. J. Med. 362, 1417–1429 (2010).

    CAS  Article  Google Scholar 

  6. 6

    Küppers, R. & Dalla-Favera, R. Mechanisms of chromosomal translocations in B cell lymphomas. Oncogene 20, 5580–5594 (2001).

    Article  Google Scholar 

  7. 7

    Dave, S.S. et al. Molecular diagnosis of Burkitt's lymphoma. N. Engl. J. Med. 354, 2431–2442 (2006).

    CAS  Article  Google Scholar 

  8. 8

    Richter, J. et al. Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing. Nat. Genet. 44, 1316–1320 (2012).

    CAS  Article  Google Scholar 

  9. 9

    Schmitz, R. et al. Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics. Nature 490, 116–120 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Loeffler, M. et al. Genomic and epigenomic co-evolution in follicular lymphomas. Leukemia 29, 456–463 (2015).

    CAS  Article  Google Scholar 

  11. 11

    Morin, R.D. et al. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nat. Genet. 42, 181–185 (2010).

    CAS  Article  Google Scholar 

  12. 12

    Morin, R.D. et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 476, 298–303 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Okosun, J. et al. Integrated genomic analysis identifies recurrent mutations and evolution patterns driving the initiation and progression of follicular lymphoma. Nat. Genet. 46, 176–181 (2014).

    CAS  Article  Google Scholar 

  14. 14

    Pasqualucci, L. et al. Inactivating mutations of acetyltransferase genes in B-cell lymphoma. Nature 471, 189–195 (2011).

    CAS  Article  Google Scholar 

  15. 15

    Victora, G.D. et al. Identification of human germinal center light and dark zone cells and their relationship to human B-cell lymphomas. Blood 120, 2240–2248 (2012).

    CAS  Article  Google Scholar 

  16. 16

    Otto, C., Stadler, P.F. & Hoffmann, S. Fast and sensitive mapping of bisulfite-treated sequencing data. Bioinformatics 28, 1698–1704 (2012).

    CAS  Article  Google Scholar 

  17. 17

    Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    CAS  Article  Google Scholar 

  18. 18

    Hahn, M.A. et al. Loss of the Polycomb mark from bivalent promoters leads to activation of cancer-promoting genes in colorectal tumors. Cancer Res. 74, 3617–3629 (2014).

    CAS  Article  Google Scholar 

  19. 19

    Hovestadt, V. et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510, 537–541 (2014).

    CAS  Article  Google Scholar 

  20. 20

    Weidensdorfer, D. et al. Control of c-myc mRNA stability by IGF2BP1-associated cytoplasmic RNPs. RNA 15, 104–115 (2009).

    CAS  Article  Google Scholar 

  21. 21

    Hummel, M. et al. A biologic definition of Burkitt's lymphoma from transcriptional and genomic profiling. N. Engl. J. Med. 354, 2419–2430 (2006).

    CAS  Article  Google Scholar 

  22. 22

    Love, C. et al. The genetic landscape of mutations in Burkitt lymphoma. Nat. Genet. 44, 1321–1325 (2012).

    CAS  Article  Google Scholar 

  23. 23

    Muppidi, J.R. et al. Loss of signalling via Galpha13 in germinal centre B-cell–derived lymphoma. Nature 516, 254–258 (2014).

    CAS  Article  Google Scholar 

  24. 24

    Rohde, M. et al. Recurrent RHOA mutations in pediatric Burkitt lymphoma treated according to the NHL-BFM protocols. Genes Chromosom. Cancer 53, 911–916 (2014).

    CAS  Article  Google Scholar 

  25. 25

    O'Hayre, M. et al. The emerging mutational landscape of G proteins and G-protein–coupled receptors in cancer. Nat. Rev. Cancer 13, 412–424 (2013).

    CAS  Article  Google Scholar 

  26. 26

    Takuwa, N. et al. Tumor-suppressive sphingosine-1-phosphate receptor-2 counteracting tumor-promoting sphingosine-1-phosphate receptor-1 and sphingosine kinase 1—Jekyll hidden behind Hyde. Am. J. Cancer Res. 1, 460–481 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Morin, R.D. et al. Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing. Blood 122, 1256–1265 (2013).

    CAS  Article  Google Scholar 

  28. 28

    Abraham, B.J., Cui, K., Tang, Q. & Zhao, K. Dynamic regulation of epigenomic landscapes during hematopoiesis. BMC Genomics 14, 193 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Scott, C.L. et al. Role of the chromobox protein CBX7 in lymphomagenesis. Proc. Natl. Acad. Sci. USA 104, 5389–5394 (2007).

    CAS  Article  Google Scholar 

  30. 30

    Dykhuizen, E.C. et al. BAF complexes facilitate decatenation of DNA by topoisomerase IIα. Nature 497, 624–627 (2013).

    CAS  Article  Google Scholar 

  31. 31

    Klapper, W. et al. Patient age at diagnosis is associated with the molecular characteristics of diffuse large B-cell lymphoma. Blood 119, 1882–1887 (2012).

    CAS  Article  Google Scholar 

  32. 32

    Hasselblatt, M. et al. Nonsense mutation and inactivation of SMARCA4 (BRG1) in an atypical teratoid/rhabdoid tumor showing retained SMARCB1 (INI1) expression. Am. J. Surg. Pathol. 35, 933–935 (2011).

    Article  Google Scholar 

  33. 33

    Hasselblatt, M. et al. SMARCA4-mutated atypical teratoid/rhabdoid tumors are associated with inherited germline alterations and poor prognosis. Acta Neuropathol. 128, 453–456 (2014).

    Article  Google Scholar 

  34. 34

    Schneppenheim, R. et al. Germline nonsense mutation and somatic inactivation of SMARCA4/BRG1 in a family with rhabdoid tumor predisposition syndrome. Am. J. Hum. Genet. 86, 279–284 (2010).

    CAS  Article  Google Scholar 

  35. 35

    Witkowski, L. et al. Germline and somatic SMARCA4 mutations characterize small cell carcinoma of the ovary, hypercalcemic type. Nat. Genet. 46, 438–443 (2014).

    CAS  Article  Google Scholar 

  36. 36

    Betts, M.J. et al. Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions. Nucleic Acids Res. 43, e10 (2015).

    Article  Google Scholar 

  37. 37

    Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat. Genet. 47, 746–756 (2015).

    CAS  Article  Google Scholar 

  38. 38

    Lister, R. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68–73 (2011).

    CAS  Article  Google Scholar 

  39. 39

    Bibikova, M. et al. High density DNA methylation array with single CpG site resolution. Genomics 98, 288–295 (2011).

    CAS  Article  Google Scholar 

  40. 40

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  41. 41

    Jones, D.T. et al. Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma. Nat. Genet. 45, 927–932 (2013).

    CAS  Article  Google Scholar 

  42. 42

    Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).

    CAS  Article  Google Scholar 

  43. 43

    Hoffmann, S. et al. Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput. Biol. 5, e1000502 (2009).

    Article  Google Scholar 

  44. 44

    Zhang, N.R. & David, O.S. Model selection for high-dimensional, multisequence change-point problems. Stat. Sin. 22, 1507 (2012).

    Google Scholar 

  45. 45

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  Article  Google Scholar 

  46. 46

    Karolchik, D. et al. The UCSC Genome Browser database: 2014 update. Nucleic Acids Res. 42, D764–D770 (2014).

    CAS  Article  Google Scholar 

  47. 47

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  48. 48

    Flicek, P. et al. Ensembl 2014. Nucleic Acids Res. 42, D749–D755 (2014).

    CAS  Article  Google Scholar 

  49. 49

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    CAS  Article  Google Scholar 

  50. 50

    Dürr, H. et al. X-ray structures of the Sulfolobus solfataricus SWI2/SNF2 ATPase core and its complex with DNA. Cell 121, 363–373 (2005).

    Article  Google Scholar 

  51. 51

    Eswar, N. et al. Comparative protein structure modeling using Modeller. Curr. Protoc. Bioinformatics 47, 5.6.1–5.6.32 (2006).

    Article  Google Scholar 

  52. 52

    Sharma, V. et al. Crystal structure of Mycobacterium tuberculosis SecA, a preprotein translocating ATPase. Proc. Natl. Acad. Sci. USA 100, 2243–2248 (2003).

    CAS  Article  Google Scholar 

  53. 53

    Huber, W. et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96–S104 (2002).

    Article  Google Scholar 

  54. 54

    Bentink, S. et al. Pathway activation patterns in diffuse large B-cell lymphomas. Leukemia 22, 1746–1754 (2008).

    CAS  Article  Google Scholar 

  55. 55

    Wright, G. et al. A gene expression–based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc. Natl. Acad. Sci. USA 100, 9991–9996 (2003).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This study has been supported by the German Ministry of Science and Education (BMBF) in the framework of the ICGC MMML-Seq project (01KU1002A-J) and the MMML-MYC-SYS project (036166B), the European Union in the framework of the BLUEPRINT Project (HEALTH-F5-2011-282510) and the KinderKrebsInitiative Buchholz/Holm-Seppensen and LIFE (Leipzig Research Center for Civilization Diseases), Leipzig University. LIFE is funded by the European Union, the European Regional Development Fund (ERDF), the European Social Fund (ESF) and the Free State of Saxony. WGBS was additionally supported by NGFNplus (BMBF, 01GS0883) and the DKFZ–Heidelberg Center for Personalized Oncology (DKFZ-HIPO). Former grant support of MMML by the Deutsche Krebshilfe (2003–2011) is gratefully acknowledged. J.R. is supported by the Dr. Werner Jackstädt Foundation in the framework of a Junior Excellence Research Group on 'Mechanisms of B-Cell Lymphomagenesis in the Senium as Basic Principle for the Development of Age-Adjusted Therapy Regimes' (S134-10.100).

Author information

Affiliations

Authors

Consortia

Contributions

A.H., O.A., R.K., P.L., R.S., S. Hoffmann and B.R. conceived and designed the experiments. W.W., A.H., A.K.B., J.G., J.R., J.K., R.W., S.E., H.H.D.K., W.K., D.L., C. López, S.P., I.V., P.R., M. Schilhabel, M. Szczepanowski, L.T. and R.K. performed the experiments. H.K., S.H.B., M.J.B., B.H., R.E., P.F., C. Lawerenz, J.H.A.M., M. Schlesner, P.F.S., H.G.S. and S. Hoffmann performed statistical analysis. H.K., S.H.B., G.D., V.H., D.R., F.J., C.O., M.H., M. Kreuz, M. Kulis, I.N., M. Rosolowski, R.B.R., M. Schlesner, S. Hoffmann and B.R. analyzed the data. H.K., M.A.W., M.J.B., E.C.-d.-S.-P., G.D., B.H., F.J., Q.L., C.O., J.A., B.B., A.C., H.G.D., S.E., R.E., P.F., S. Haas, D.K., H.H.D.K., W.K., M. Kreuz, C. Lawerenz, D.L., M.L., R.A.F.M., J.H.A.M., J.I.M.-S., P.M., M. Rohde, P.R., M. Schilhabel, M. Schlesner, L.T., H.G.S. and S. Hoffmann contributed reagents, materials and/or analysis tools. H.K., S.H.B., W.W., A.H., P.L., R.S., S. Hoffmann and B.R. wrote the manuscript.

Corresponding authors

Correspondence to Reiner Siebert, Steve Hoffmann or Bernhard Radlwimmer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Figures 1–23 and Supplementary Tables 1–4, 6, 9–11, 13, 14 and 16–18. (PDF 4797 kb)

Supplementary Table 5

Statistics of whole-genome sequencing. (XLSX 15 kb)

Supplementary Table 7

Annotation of DMRs. (XLSX 7648 kb)

Supplementary Table 8

Correlating DMRs. (XLSX 990 kb)

Supplementary Table 12

Statistics of transcriptome arrays. (XLSX 29 kb)

Supplementary Table 15

SWI/SNF SNVs. (XLSX 14 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kretzmer, H., Bernhart, S., Wang, W. et al. DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control. Nat Genet 47, 1316–1325 (2015). https://doi.org/10.1038/ng.3413

Download citation

Further reading

Search

Quick links