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DNMT3A and TET2 compete and cooperate to repress lineage-specific transcription factors in hematopoietic stem cells

Abstract

Mutations in the epigenetic modifiers DNMT3A and TET2 non-randomly co-occur in lymphoma and leukemia despite their epistasis in the methylation–hydroxymethylation pathway. Using Dnmt3a and Tet2 double-knockout mice in which the development of malignancy is accelerated, we show that the double-knockout methylome reflects regions of independent, competitive and cooperative activity. Expression of lineage-specific transcription factors, including the erythroid regulators Klf1 and Epor, is upregulated in double-knockout hematopoietic stem cells (HSCs). DNMT3A and TET2 both repress Klf1, suggesting a model of cooperative inhibition by epigenetic modifiers. These data demonstrate a dual role for TET2 in promoting and inhibiting HSC differentiation, the loss of which, along with DNMT3A, obstructs differentiation, leading to transformation.

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Figure 1: Phenotype of Dnmt3aTet2 double-knockout mice.
Figure 2: Synergistic dysregulation of HSC- and RBC-associated genes in double-knockout HSCs.
Figure 3: Knockdown of Klf1 or Epor affects the self-renewal of double-knockout HSPCs in vitro.
Figure 4: Dynamics of DNA methylation in wild-type, Dnmt3a−/− and Tet2−/− HSCs.
Figure 5: Hydroxymethylation in HSCs is associated with active HSC genes and repressed RBC genes.
Figure 6: Hydroxymethylation in HSCs is associated with active HSC genes and repressed RBC genes.
Figure 7: DNMT3A and TET2 cooperate to prevent activation of lineage-specific transcription factors in HSCs.

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References

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Inoue, A. & Zhang, Y. Replication-dependent loss of 5-hydroxymethylcytosine in mouse preimplantation embryos. Science 334, 194 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ley, T.J. et al. DNMT3A mutations in acute myeloid leukemia. N. Engl. J. Med. 363, 2424–2433 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Walter, M.J. et al. Recurrent DNMT3A mutations in patients with myelodysplastic syndromes. Leukemia 25, 1153–1158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Grossmann, V. et al. The molecular profile of adult T-cell acute lymphoblastic leukemia: mutations in RUNX1 and DNMT3A are associated with poor prognosis in T-ALL. Genes Chromosom. Cancer 52, 410–422 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Van Vlierberghe, P. et al. ETV6 mutations in early immature human T cell leukemias. J. Exp. Med. 208, 2571–2579 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pastor, W.A. et al. Genome-wide mapping of 5-hydroxymethylcytosine in embryonic stem cells. Nature 473, 394–397 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Abdel-Wahab, O. et al. Genetic characterization of TET1, TET2, and TET3 alterations in myeloid malignancies. Blood 114, 144–147 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Delhommeau, F. et al. Mutation in TET2 in myeloid cancers. N. Engl. J. Med. 360, 2289–2301 (2009).

    Article  PubMed  Google Scholar 

  10. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  11. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Welch, J.S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shlush, L.I. et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506, 328–333 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Corces-Zimmerman, M.R., Hong, W.J., Weissman, I.L., Medeiros, B.C. & Majeti, R. Preleukemic mutations in human acute myeloid leukemia affect epigenetic regulators and persist in remission. Proc. Natl. Acad. Sci. USA 111, 2548–2553 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Challen, G.A. et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 44, 23–31 (2012).

    Article  CAS  Google Scholar 

  18. Mayle, A. et al. Dnmt3a loss predisposes murine hematopoietic stem cells to malignant transformation. Blood 125, 629–638 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Celik, H. et al. Enforced differentiation of Dnmt3a-null bone marrow leads to failure with c-Kit mutations driving leukemic transformation. Blood 125, 619–628 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ko, M. et al. Ten-Eleven-Translocation 2 (TET2) negatively regulates homeostasis and differentiation of hematopoietic stem cells in mice. Proc. Natl. Acad. Sci. USA 108, 14566–14571 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Moran-Crusio, K. et al. Tet2 loss leads to increased hematopoietic stem cell self-renewal and myeloid transformation. Cancer Cell 20, 11–24 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li, Z. et al. Deletion of Tet2 in mice leads to dysregulated hematopoietic stem cells and subsequent development of myeloid malignancies. Blood 118, 4509–4518 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Quivoron, C. et al. TET2 inactivation results in pleiotropic hematopoietic abnormalities in mouse and is a recurrent event during human lymphomagenesis. Cancer Cell 20, 25–38 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Ko, M. et al. Impaired hydroxylation of 5-methylcytosine in myeloid cancers with mutant TET2. Nature 468, 839–843 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Figueroa, M.E. et al. Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell 18, 553–567 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Madzo, J. et al. Hydroxymethylation at gene regulatory regions directs stem/early progenitor cell commitment during erythropoiesis. Cell Rep. 6, 231–244 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Russler-Germain, D.A. et al. The R882H DNMT3A mutation associated with AML dominantly inhibits wild-type DNMT3A by blocking its ability to form active tetramers. Cancer Cell 25, 442–454 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Palomero, T. et al. Recurrent mutations in epigenetic regulators, RHOA and FYN kinase in peripheral T cell lymphomas. Nat. Genet. 46, 166–170 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Couronné, L., Bastard, C. & Bernard, O.A. TET2 and DNMT3A mutations in human T-cell lymphoma. N. Engl. J. Med. 366, 95–96 (2012).

    Article  PubMed  Google Scholar 

  30. Sakata-Yanagimoto, M. et al. Somatic RHOA mutation in angioimmunoblastic T cell lymphoma. Nat. Genet. 46, 171–175 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Scourzic, L. et al. DNMT3AR882H mutant and Tet2 inactivation cooperate in the deregulation of DNA methylation control to induce lymphoid malignancies in mice. Leukemia 30, 1388–1398 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chambers, S.M. et al. Hematopoietic fingerprints: an expression database of stem cells and their progeny. Cell Stem Cell 1, 578–591 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tallack, M.R. et al. Novel roles for KLF1 in erythropoiesis revealed by mRNA-seq. Genome Res. 22, 2385–2398 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tallack, M.R. et al. A global role for KLF1 in erythropoiesis revealed by ChIP-seq in primary erythroid cells. Genome Res. 20, 1052–1063 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hasemann, M.S. et al. C/EBPα is required for long-term self-renewal and lineage priming of hematopoietic stem cells and for the maintenance of epigenetic configurations in multipotent progenitors. PLoS Genet. 10, e1004079 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Schwickert, T.A. et al. Stage-specific control of early B cell development by the transcription factor Ikaros. Nat. Immunol. 15, 283–293 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Shu, J. et al. Induction of pluripotency in mouse somatic cells with lineage specifiers. Cell 153, 963–975 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Orkin, S.H. Priming the hematopoietic pump. Immunity 19, 633–634 (2003).

    Article  CAS  PubMed  Google Scholar 

  39. Lessene, G. et al. Structure-guided design of a selective BCL-X(L) inhibitor. Nat. Chem. Biol. 9, 390–397 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Jeong, M. et al. Large conserved domains of low DNA methylation maintained by Dnmt3a. Nat. Genet. 46, 17–23 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Ziller, M.J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rasmussen, K.D. et al. Loss of TET2 in hematopoietic cells leads to DNA hypermethylation of active enhancers and induction of leukemogenesis. Genes Dev. 29, 910–922 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lara-Astiaso, D. et al. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Huang, Y. et al. The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing. PLoS One 5, e8888 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Huang, Y., Pastor, W.A., Zepeda-Martínez, J.A. & Rao, A. The anti-CMS technique for genome-wide mapping of 5-hydroxymethylcytosine. Nat. Protoc. 7, 1897–1908 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Shen, L. et al. Genome-wide analysis reveals TET- and TDG-dependent 5-methylcytosine oxidation dynamics. Cell 153, 692–706 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Williams, K. et al. TET1 and hydroxymethylcytosine in transcription and DNA methylation fidelity. Nature 473, 343–348 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wu, H. et al. Dual functions of Tet1 in transcriptional regulation in mouse embryonic stem cells. Nature 473, 389–393 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ficz, G. et al. Dynamic regulation of 5-hydroxymethylcytosine in mouse ES cells and during differentiation. Nature 473, 398–402 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Challen, G.A. et al. Dnmt3a and Dnmt3b have overlapping and distinct functions in hematopoietic stem cells. Cell Stem Cell 15, 350–364 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tsagaratou, A. et al. Dissecting the dynamic changes of 5-hydroxymethylcytosine in T-cell development and differentiation. Proc. Natl. Acad. Sci. USA 111, E3306–E3315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhao, Z. et al. Combined loss of Tet1 and Tet2 promotes B cell, but not myeloid malignancies, in mice. Cell Rep. 13, 1692–1704 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. An, J. et al. Acute loss of TET function results in aggressive myeloid cancer in mice. Nat. Commun. 6, 10071 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Cimmino, L. et al. TET1 is a tumor suppressor of hematopoietic malignancy. Nat. Immunol. 16, 653–662 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang, L. et al. Molecular basis for 5-carboxycytosine recognition by RNA polymerase II elongation complex. Nature 523, 621–625 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Guo, G. et al. Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell 13, 492–505 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Nimmo, R.A., May, G.E. & Enver, T. Primed and ready: understanding lineage commitment through single cell analysis. Trends Cell Biol. 25, 459–467 (2015).

    Article  PubMed  Google Scholar 

  58. Mayle, A., Luo, M., Jeong, M. & Goodell, M.A. Flow cytometry analysis of murine hematopoietic stem cells. Cytometry A 83, 27–37 (2013).

    Article  PubMed  CAS  Google Scholar 

  59. Pollier, J., Rombauts, S. & Goossens, A. Analysis of RNA-Seq data with TopHat and Cufflinks for genome-wide expression analysis of jasmonate-treated plants and plant cultures. Methods Mol. Biol. 1011, 305–315 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Sun, D. et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 15, R38 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Zhang, Y. et al. QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Res. 39, e58 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Quinlan, A.R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014).

    Article  Google Scholar 

  64. Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Chen, K. et al. DANPOS: dynamic analysis of nucleosome position and occupancy by sequencing. Genome Res. 23, 341–351 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Huang, D.W. et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, R183 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We thank M. Kampmann for discussions at AACR, C. Gillespie for editing, and R. Nitsal, S. Hexige and Y. Zheng for assistance with histology, pathology and BMT, respectively. X.Z. is supported by the Wellcome Trust Gene–Environment training program. This work was supported by the US NIH (DK092883, CA183252, HG007538, CA193466, CA125123, P50CA126752 and CA151535), the Samuel Waxman Medical Research Foundation, the Edward P. Evans Foundation, the Adrienne Helis Malvin Medical Research Foundation, the Leukemia and Lymphoma Society (Translational Research Program to A.R. and Special Fellow Award to M.K.), and CPRIT (RP140001) and a CPRIT Scholar award to Y.H. (RP140053).

Author information

Authors and Affiliations

Authors

Contributions

X.Z. and M.A.G. conceived and discussed the project with Y.H., M.K. and A.R. X.Z. analyzed phenotypes and performed shRNA knockdown with M.J. J.S. analyzed the WGBS and RNA-seq data with H.J.P. M.J. performed HSC sorting and WGBS and RNA-seq analyses. M.K. and A.R. provided Tet2−/− mice. Y.H. generated CMS libraries. A.G., Y.L. and Y.-H.H. performed experiments. X.Z. led the project and drafted the manuscript. All authors participated in discussions, data interpretation and manuscript editing. M.A.G. and W.L. provided funding and supervision.

Corresponding authors

Correspondence to Wei Li or Margaret A Goodell.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Experimental procedure and replating assay in vitro.

(a) The experimental scheme depicting bone marrow transplantation from mice with all four genotypes. (b) Serial replating assay with WT, Dnmt3a−/−, Tet2–/ and DKO bone marrow cells. (c) Representative Giemsa-stained images after serial plating cells from WT and DKO bone marrow. 400× magnification. Scale bar, 20 μm. (d) RNA-seq results from the indicated genotypes. The blue shaded region indicates exons 7–9 of Tet2, absent in Tet2−/− and DKO HSCs. (e) RNA-seq result across the Dnmt3a gene from HSCs with the indicated genotypes. The pink shaded area indicates exon 17, which is deleted in the Dnmt3a transcript after Cre-mediated recombination in Dnmt3a−/− and DKO HSCs.

Supplementary Figure 2 Histology analysis of WT, Dnmt3a−/−, Tet2−/− and DKO recipient mice.

(a) Representative bone marrow hematoxylin and eosin staining of age-matched recipient mice of four genotypes. A representative DKO mouse depicts bone marrow hyperplasia (i). Scale bar, 20 μm. Representative DKO mice depict dysmegakaryopoiesis with features of emperipolesis (ii, left) and hypolobulated nuclei (ii, right). Scale bar, 20 μm. Bone marrow fibrosis is also observed in DKO mice (iii). (b) Representative spleen hematoxylin and eosin staining of age-matched recipient mice of four genotypes. Scale bar, 50 μm. (c) Representative liver hematoxylin and eosin staining of age-matched recipient mice of four genotypes. Scale bar, 50 μm. (d) Representative lung hematoxylin and eosin staining of age-matched recipient mice of four genotypes. Scale bar, 100 μm.

Supplementary Figure 3 DNMT3A and TET2 loss synergistically induces lymphoid disease.

(a) A recipient of DKO bone marrow after 7 months showing B cell infiltration of the salivary gland. A WT mouse in the cohort was used as control (left). Middle, FACS analysis of infiltrated cells. Right, histology comparing the normal gland (from a Dnmt3a−/− recipient) to a gland with infiltration (the dotted line shows the remaining epithelial gland structure). Scale bar, 50 μm. (b) A DKO mouse without transplantation developed B cell lymphoma. Right, FACS analysis with B cell and T cell markers. (c) Hematological analysis from a representative DKO-transplanted mouse that developed B-ALL 9 months after bone marrow transplantation and a Tet2−/− transplanted mouse for comparison. Top and middle scale bars, 20 μm. Bottom, FACS analysis of peripheral blood. (d,e) Analysis of representative secondary recipients of DKO cells demonstrating T cell thymic lymphoma with cell surface marker for immature cells (e) sacrificed at the same time point. The black dotted line indicates the position of the thymus.

Supplementary Figure 4 Expression of major HSC transcription factors.

(ad) Expression (FPKM) of major HSC transcription factors (Gata2, Meis1, Runx1, Lmo2) that are not differentially expressed. (eg) The expression (FPKM mean) of DNA methylation regulators is not significantly altered in HSCs from single and double knockouts relative to WT. (h,i) Cebpe and Ikzf1 show significantly different expression. All data are shown as mean ± s.d. *P < 0.05. **P < 0.01; ns, not significant; n =2. P values were calculated by cuffdiff in the RNA-seq analysis pipeline. (j) Correlation of the transcriptomes of HSCs of all genotypes with differentiated lineages and committed progenitors. The boxed region shows the stronger correlation between the progenitor population and Tet2−/− and DKO HSCs43. (k,l) Box plots depicting the range of expression of Cebpa (k) and Ebf1 (l) target genes from previous ChIP-seq and gene expression data35,36. All data in dn are plotted from RNA-seq analysis with duplicates for each genotype.

Supplementary Figure 5 Patients with AML from TCGA carrying both TET2 and DNMT3A mutations show activation of KLF1 and its downstream targets.

(a) RBC gene overexpression in patients carrying both TET2 and DNMT3A mutations. (b) Venn diagrams showing the overlap of genes activated in patients and genes upregulated in DKO HSCs. The P value was calculated by two-tailed Fisher’s exact test.

Supplementary Figure 6 DNMT3A and TET2 counteract each other at canyon edges.

(a) Basic quality control statistics for the WGBS sequencing results. (b) Methylation levels of all DMRs of six major DMAPs in HSCs of all four genotypes. (c) Pie chart showing the makeup of genomic regions with dynamic DMR patterns. (d) Distribution of DMRs of DMAPs in genomic regions. (e) Distribution of DMRs of DMAPs in coordination with the position of WT UMRs. (f) Number of UMRs and DNA methylation canyons in HSCs of all genotypes analyzed. (g) Canyon regions of four genotypes at the Gata2 locus, which contains 2 type VI DMRs. The blue shaded area indicates the canyon region in WT HSCs.

Supplementary Figure 7 Enhancer methylation analysis.

(a) The DNA methylation levels of HSCs of all four genotypes in enhancer regions marked by H3K27ac in all blood progenitors and lineages. (b) The methylation levels of DMRs of six DMAPs overlapped with H3K27ac distribution in HSCs of all four genotypes. (c) Example of hypomethylated RBC progenitor enhancer DMRs in Dnmt3a−/− and DKO HSCs.

Supplementary Figure 8 Differential 5hmC alteration is associated with both gene activation and repression in HSCs.

(a) Overlapping distribution of type IV DMR and cluster 4 DhMRs at the canyon edges of the Gata2 gene. Orange shading indicates the canyon edges; gray shading shows the overlapping region for type IV DMRs and cluster 4 DhMRs. (b) Genes with type III DMRs and cluster 3 DhMRs are significantly overlapped. The P value was calculated by Fisher’s exact test. (c) 5hmC signal distribution in HSC- and RBC-specific fingerprint genes in all four genotypes. (d) Loss of TSS 5hmC signal in the Cebpe locus in DKO and Tet2−/− HSCs. The blue shaded area indicates the TSS region.

Supplementary Figure 9 Differential 5hmC analysis and the relationship between altered 5hmC and gene expression.

(a) Gene expression of all lineage-specific fingerprint genes in all four genotypes. Mean FPKM values are plotted. (b) 5hmC signal distribution in lineage-specific fingerprint genes in all four genotypes with no clear correlation with gene expression. (c) Dynamic 5hmC enrichment alteration in the promoter of the Ikzf1 locus. The blue shaded area shows the overlap of a type III DMR and a cluster 3 DhMR in the promoter region of Ikzf1.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9. (PDF 2198 kb)

Supplementary Table 1

Differentially expressed genes of four genotypes among m12_HSC(WT), Dnmt3a_HSC,Tet2KO_HSC and DKO_HSC. (XLS 3931 kb)

Supplementary Table 2

DMRs (differentially methylated regions) in all three genotypes versus WT. (XLS 4264 kb)

Supplementary Table 3

DhMRs (differentially hydroxylmethylated regions) in all three genotypes versus WT. (XLS 7728 kb)

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Zhang, X., Su, J., Jeong, M. et al. DNMT3A and TET2 compete and cooperate to repress lineage-specific transcription factors in hematopoietic stem cells. Nat Genet 48, 1014–1023 (2016). https://doi.org/10.1038/ng.3610

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