Plasma cell differentiation is coupled to division-dependent DNA hypomethylation and gene regulation

Abstract

The epigenetic processes that regulate antibody-secreting plasma cells are not well understood. Here, analysis of plasma cell differentiation revealed DNA hypomethylation of 10% of CpG loci that were overrepresented at enhancers. Inhibition of DNA methylation enhanced plasma cell commitment in a cell-division-dependent manner. Analysis of B cells differentiating in vivo stratified by cell division revealed a fivefold increase in mRNA transcription coupled to DNA hypomethylation. Demethylation occurred first at binding motifs for the transcription factors NF-κB and AP-1 and later at those for the transcription factors IRF and Oct-2 and was coincident with activation and differentiation gene-expression programs in a cell-division-dependent manner. These data provide mechanistic insight into cell-division-coupled transcriptional and epigenetic reprogramming and suggest that DNA hypomethylation reflects the cis-regulatory history of plasma cell differentiation.

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: B cell differentiation is coupled to unique transcriptional states.
Figure 2: B220intCD138+ plasmablasts and B220loCD138+ plasma cells undergo DNA hypomethylation.
Figure 3: DML are 'preferentially' present at B cell enhancers and near motifs of transcription factors required for B cell differentiation.
Figure 4: Inhibition of DNA methylation facilitates plasma cell differentiation.
Figure 5: Transcriptional amplification and DNA hypomethylation coincide with cellular division.
Figure 6: Cell-division-coupled changes in gene expression and DNA methylation.
Figure 7: Dynamic gene-expression changes correspond with a hierarchy of DNA hypomethylation.
Figure 8: Gene expression correlates with cell division and DNA methylation.

Accession codes

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

References

  1. 1

    Macallan, D.C. et al. B-cell kinetics in humans: rapid turnover of peripheral blood memory cells. Blood 105, 3633–3640 (2005).

    CAS  PubMed  Google Scholar 

  2. 2

    Kouzine, F. et al. Global regulation of promoter melting in naive lymphocytes. Cell 153, 988–999 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Nutt, S.L., Taubenheim, N., Hasbold, J., Corcoran, L.M. & Hodgkin, P.D. The genetic network controlling plasma cell differentiation. Semin Immunol. 23, 341–349 (2011).

    CAS  PubMed  Google Scholar 

  4. 4

    Hodgkin, P.D., Lee, J.H. & Lyons, A.B. B cell differentiation and isotype switching is related to division cycle number. J. Exp. Med. 184, 277–281 (1996).

    CAS  PubMed  Google Scholar 

  5. 5

    Hasbold, J., Corcoran, L.M., Tarlinton, D.M., Tangye, S.G. & Hodgkin, P.D. Evidence from the generation of immunoglobulin G-secreting cells that stochastic mechanisms regulate lymphocyte differentiation. Nat. Immunol. 5, 55–63 (2004).

    CAS  PubMed  Google Scholar 

  6. 6

    Nutt, S.L., Hodgkin, P.D., Tarlinton, D.M. & Corcoran, L.M. The generation of antibody-secreting plasma cells. Nat. Rev. Immunol. 15, 160–171 (2015).

    CAS  PubMed  Google Scholar 

  7. 7

    Duffy, K.R. et al. Activation-induced B cell fates are selected by intracellular stochastic competition. Science 335, 338–341 (2012).

    CAS  PubMed  Google Scholar 

  8. 8

    Taylor, J.J., Pape, K.A., Steach, H.R. & Jenkins, M.K. Humoral immunity. Apoptosis and antigen affinity limit effector cell differentiation of a single naïve B cell. Science 347, 784–787 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Jones, P.A. & Takai, D. The role of DNA methylation in mammalian epigenetics. Science 293, 1068–1070 (2001).

    CAS  Google Scholar 

  10. 10

    Egger, G., Liang, G., Aparicio, A. & Jones, P.A. Epigenetics in human disease and prospects for epigenetic therapy. Nature 429, 457–463 (2004).

    CAS  PubMed  Google Scholar 

  11. 11

    Bröske, A.-M. et al. DNA methylation protects hematopoietic stem cell multipotency from myeloerythroid restriction. Nat. Genet. 41, 1207–1215 (2009).

    PubMed  Google Scholar 

  12. 12

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    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  PubMed  PubMed Central  Google Scholar 

  14. 14

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

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Kallies, A. et al. Plasma cell ontogeny defined by quantitative changes in blimp-1 expression. J. Exp. Med. 200, 967–977 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Chernova, I. et al. Lasting antibody responses are mediated by a combination of newly formed and established bone marrow plasma cells drawn from clonally distinct precursors. J. Immunol. 193, 4971–4979 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Tarte, K., Zhan, F., De Vos, J., Klein, B. & Shaughnessy, J. Jr. Gene expression profiling of plasma cells and plasmablasts: toward a better understanding of the late stages of B-cell differentiation. Blood 102, 592–600 (2003).

    CAS  PubMed  Google Scholar 

  18. 18

    Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Feng, H., Conneely, K.N. & Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42, e69–e69 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Matsumoto, M. et al. Interleukin-10-producing plasmablasts exert regulatory function in autoimmune inflammation. Immunity 18, 1040–1051 (2014).

    Google Scholar 

  21. 21

    Klein, U. et al. Transcription factor IRF4 controls plasma cell differentiation and class-switch recombination. Nat. Immunol. 7, 773–782 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Ochiai, K. et al. Transcriptional regulation of germinal center B and plasma cell fates by dynamical control of IRF4. Immunity 38, 918–929 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Shi, W. et al. Transcriptional profiling of mouse B cell terminal differentiation defines a signature for antibody-secreting plasma cells. Nat. Immunol. 16, 663–673 (2015).

    CAS  PubMed  Google Scholar 

  24. 24

    Salzer, U. et al. Mutations in TNFRSF13B encoding TACI are associated with common variable immunodeficiency in humans. Nat. Genet. 37, 820–828 (2005).

    CAS  PubMed  Google Scholar 

  25. 25

    Xiong, Z. & Laird, P.W. COBRA: a sensitive and quantitative DNA methylation assay. Nucleic Acids Res. 25, 2532–2534 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Sciammas, R. et al. Graded expression of interferon regulatory factor-4 coordinates isotype switching with plasma cell differentiation. Immunity 25, 225–236 (2006).

    CAS  PubMed  Google Scholar 

  27. 27

    Mittrücker, H.-W. et al. Requirement for the transcription factor LSIRF/IRF4 for mature B and T lymphocyte function. Science 275, 540–543 (1997).

    PubMed  Google Scholar 

  28. 28

    Herrscher, R.F. et al. The immunoglobulin heavy-chain matrix-associating regions are bound by Bright: a B cell-specific trans-activator that describes a new DNA-binding protein family. Genes Dev. 9, 3067–3082 (1995).

    CAS  PubMed  Google Scholar 

  29. 29

    Turner, C.A. Jr., Mack, D.H. & Davis, M.M. Blimp-1, a novel zinc finger-containing protein that can drive the maturation of B lymphocytes into immunoglobulin-secreting cells. Cell 77, 297–306 (1994).

    CAS  PubMed  Google Scholar 

  30. 30

    Creyghton, M.P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 107, 21931–21936 (2010).

    CAS  Google Scholar 

  31. 31

    Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Sabò, A. et al. Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature 511, 488–492 (2014).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Schubart, K. et al. B cell development and immunoglobulin gene transcription in the absence of Oct-2 and OBF-1. Nat. Immunol. 2, 69–74 (2001).

    CAS  PubMed  Google Scholar 

  35. 35

    Grötsch, B. et al. The AP-1 transcription factor Fra1 inhibits follicular B cell differentiation into plasma cells. J. Exp. Med. 211, 2199–2212 (2014).

    PubMed  PubMed Central  Google Scholar 

  36. 36

    Sasaki, Y. et al. Canonical NF-kappaB activity, dispensable for B cell development, replaces BAFF-receptor signals and promotes B cell proliferation upon activation. Immunity 24, 729–739 (2006).

    CAS  PubMed  Google Scholar 

  37. 37

    Yoon, H.S. et al. ZBTB32 is an early repressor of the CIITA and MHC class II gene expression during B cell differentiation to plasma cells. J. Immunol. 189, 2393–2403 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Kitamura, D., Roes, J., Kühn, R. & Rajewsky, K. A B cell-deficient mouse by targeted disruption of the membrane exon of the immunoglobulin μ chain gene. Nature 350, 423–426 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Hu, M. et al. p32 protein levels are integral to mitochondrial and endoplasmic reticulum morphology, cell metabolism and survival. Biochem. J. 453, 381–391 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Bruni, F., Gramegna, P., Oliveira, J.M.A., Lightowlers, R.N. & Chrzanowska-Lightowlers, Z.M.A. REXO2 is an oligoribonuclease active in human mitochondria. PLoS One 8, e64670 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Slifka, M.K., Antia, R., Whitmire, J.K. & Ahmed, R. Humoral immunity due to long-lived plasma cells. Immunity 8, 363–372 (1998).

    CAS  PubMed  Google Scholar 

  42. 42

    Manz, R.A., Thiel, A. & Radbruch, A. Lifetime of plasma cells in the bone marrow. Nature 388, 133–134 (1997).

    CAS  PubMed  Google Scholar 

  43. 43

    Emslie, D. et al. Oct2 enhances antibody-secreting cell differentiation through regulation of IL-5 receptor α chain expression on activated B cells. J. Exp. Med. 205, 409–421 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Tahiliani, M. et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930–935 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Cortellino, S. et al. Thymine DNA glycosylase is essential for active DNA demethylation by linked deamination-base excision repair. Cell 146, 67–79 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Stadler, M.B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).

    CAS  Google Scholar 

  47. 47

    Chang, J.T. et al. Asymmetric T lymphocyte division in the initiation of adaptive immune responses. Science 315, 1687–1691 (2007).

    CAS  Google Scholar 

  48. 48

    Hahne, F. et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10, 106 (2009).

    PubMed  PubMed Central  Google Scholar 

  49. 49

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  50. 50

    Hsu, F. et al. The UCSC Known Genes. Bioinformatics 22, 1036–1046 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    PubMed  PubMed Central  Google Scholar 

  52. 52

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  53. 53

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    PubMed  PubMed Central  Google Scholar 

  54. 54

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 139–140 (2009).

    PubMed  PubMed Central  Google Scholar 

  56. 56

    Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Bowman, S.K. et al. Multiplexed Illumina sequencing libraries from picogram quantities of DNA. BMC Genomics 14, 466 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Krueger, F. & Andrews, S.R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Park, Y. & Wu, H. Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 32, 1446–1453 (2016).

    CAS  PubMed  Google Scholar 

  61. 61

    Fisher, R.A. On the interpretation of x(2) from contingency tables, and the calculation of P. J.R. Stat. Soc. 85, 87–94 (1922).

    Google Scholar 

  62. 62

    Tan, G. & Lenhard, B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555–1556 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Frangini, A. et al. The aurora B kinase and the polycomb protein ring1B combine to regulate active promoters in quiescent lymphocytes. Mol. Cell 51, 647–661 (2013).

    CAS  PubMed  Google Scholar 

  64. 64

    Revilla-I-Domingo, R. et al. The B-cell identity factor Pax5 regulates distinct transcriptional programmes in early and late B lymphopoiesis. EMBO J. 31, 3130–3146 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Jothi, R., Cuddapah, S., Barski, A., Cui, K. & Zhao, K. Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res. 36, 5221–5231 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank R. Martinez for flow cytometry; R. Butler for mouse care; P.M. Vertino, P.A. Wade, L.H. Boise and H.D. Kondilis-Mangum for comments and critique in reading the manuscript; K.N. Conneely and H. Wu for statistical advice; the Genome Technology Center at NYU for Illumina sequencing; the Emory Flow Cytometry Core for flow cytometry; and the Emory Integrated Genomics Core for high-sensitivity DNA Bioanalyzer analysis. Supported by Emory University School of Medicine (institutional funds to J.M.B.) and the US National Institutes of Health (R01 GM47310, R01 AI123733 and U19 AI110483 to J.M.B.; F31 AI112261 to B.G.B.; T32 GM008490 to J.M.B. and B.G.B.; and T32 AI007610 to A.P.R.B.).

Author information

Affiliations

Authors

Contributions

B.G.B. contributed to experiment conception and design, performed the DNA-methylation analyses, mouse experiments, RNA-seq analysis and bioinformatics analyses and wrote the paper; C.D.S. contributed to experimental conception and design and performed RNA microarray analysis; A.P.R.B. provided technical expertise to mouse experiments; J.M.B. contributed to experimental conception and design and wrote the paper; and all authors provided editorial input.

Corresponding author

Correspondence to Jeremy M Boss.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 LPS induces a robust B cell response.

Comparison of spleen (a) size, (b) weight and (c) total splenocytes from naïve and day 3 LPS challenged mice. (d) Representative analysis of splenic B cell frequency in naïve and LPS-challenged mice indicate proportional increase in B cell frequency primarily of the GL7+ activated population, with quantitation of (e) total B220+ B cells and (f) GL7+ activated B cells per spleen. *P <0.001, Welch’s t-test. Data are from two independent experiments with 7 and 8 mice per experiment (b, c, e, f; mean and s.d).

Supplementary Figure 2 Validation of statistically representative and biologically relevant differentially methylated loci (DML).

(a, d, g, j, m) RRBS data are shown for B220+ B cells (B; blue), B220intCD138+ plasmablasts (PB; burgundy), and B220loCD138+ plasma cells (PC; gold) for DML near Irf4, Il10, Tnfrsf13b, Tnfrsf13c, and Egr3. (b, e, h, k, n) Genome schematic of regions near the validated DML. RRBS coverage is shown with vertical black lines and the DML interrogated is indicated with a black arrow. Primer regions are shown below coverage. UCSC KnownGenes are plotted with exonic and intronic sequence denoted with thick and thin black lines, respectively. Scale on the bottom right indicates 1 kb. (c, f, i, l, o) combined bisulfite restriction analysis validation of DML. Enzyme digested and mock digested samples are run adjacent on the agarose gel and indicated by a “+” and “-“ at the bottom of the gel image, respectively. Biological replicates are indicated on top. B220intCD138+ Plasmablasts and B220loCD138+ plasma cells were obtained from the same mice. Six mice were used in this experiment.

Supplementary Figure 3 DNA methylation in B cell subsets.

Plasmablast and plasma cell DML near Cd86, Irf4, Prdm1, and Il10 were validated in naïve B cells (nB), GL7+ activated B cells (aB), B220intCD138+ splenic plasmablasts (PB), B220loCD138+ splenic plasma cells (PC), B220intCD138+ bone marrow cells (BMPB), and B220loCD138+ bone marrow cells (BMPC). DNA methylation was measured as a ratio of mock digested DNA to HpaII digested DNA using qPCR primers. MspI digested DNA serves as a negative control. The experiment was performed once with 4 biological replicates.

Supplementary Figure 4 Tracking of cell division in an in vivo model B cell differentiation.

(a) Schematic of experimental design. (b) Flow cytometry showing CD45.1+ B cells in the spleen of μMT hosts 3 days post-LPS challenge. (c) CFSE staining on CD45.1+ cells from LPS challenged and control mice. (d) Quantification of divisions for LPS and control mice. (e) Flow cytometry analysis of CFSE and viability exclusion dye on CD45.1+ cells transferred into μMT hosts that were challenged with LPS as described in Fig 5. (f) CFSE and B220 expression showing B220+ and B220 populations. (g) Histogram of viability exclusion dye on B220+ and B220 populations. (h) Histogram of CD138 expression on B220+ and B220 populations. Data are from two experiments with 4 and 6 mice per experiment (a-d) or one experiment with 6 mice (e-h) (d; mean and s.d.).

Supplementary Figure 5 Validation of division-specific DNA-methylation and gene-expression changes.

(a) Flow cytometry analysis (top) and post-sort purity (bottom) of transferred splenic CD45.1+B220+ B cells in μMT hosts 3 days post-LPS challenge. (b) Schematic of differentially expressed genes with proximal differentially methylated loci that were interrogated for division-linked gene expression and DNA methylation changes. The interrogated CpG loci are denoted by the black arrows. (c) Gene expression for the genes shown in b and the populations in a. Expression was determined using RT-qPCR and is plotted as % 18S. (d) DNA methylation for the CpG loci denoted in b. DNA methylation was determined using qPCR on HpaII digested DNA relative to mock digested DNA. The methyl-insensitive enzyme MspI was used as a negative control. (e) Flow cytometry analysis as in a, that was used to analyze specific cell divisions using RNA-seq and RRBS. Data is from Fig 5e reproduced here for clarity. (f) Gene expression determined using RNA-seq for the genes in b and the populations in e. (g) DNA methylation determined using RRBS for the CpG loci shown in b and the populations in e. Data are from two with 6 and 4 mice (a), one experiment with 3 mice (c-d) or.one experiment with 2 mice (f-g) (b, d, f, g; mean and s.d.).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 943 kb)

Supplementary Table 1

S1-DEGs.v2.xlsx (XLSX 8983 kb)

Supplementary Table 2

S2-DEG.Gene.Ontology.v2.xlsx (XLSX 1128 kb)

Supplementary Table 3

S3-DEG.GSEA.v3.xlsx (XLSX 1871 kb)

Supplementary Table 4

S4-SequencingStats.v3.xlsx (XLSX 13 kb)

Supplementary Table 5

S5-DML.v3.xlsx (XLSX 59643 kb)

Supplementary Table 6

S6-MetaAnalysis.v2.xlsx (XLSX 13 kb)

Supplementary Table 7

S7-Primers.v1.xlsx (XLSX 16 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barwick, B., Scharer, C., Bally, A. et al. Plasma cell differentiation is coupled to division-dependent DNA hypomethylation and gene regulation. Nat Immunol 17, 1216–1225 (2016). https://doi.org/10.1038/ni.3519

Download citation

Further reading