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

Nature Genetics volume 48, pages 1014–1023 (2016)Cite this article

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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 Dnmt3a–Tet2 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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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).

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Author notes

  1. Xiaotian Zhang, Jianzhong Su and Mira Jeong: These authors contributed equally to this work.

Authors and Affiliations

  1. Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, Texas, USA

    Xiaotian Zhang, Mira Jeong, Anna Guzman, Yong Lei, Yung-Hsin Huang & Margaret A Goodell

  2. Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, Texas, USA

    Xiaotian Zhang, Mira Jeong, Anna Guzman, Yong Lei, Yung-Hsin Huang & Margaret A Goodell

  3. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA

    Xiaotian Zhang & Margaret A Goodell

  4. Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, USA

    Jianzhong Su, Hyun Jung Park & Wei Li

  5. La Jolla Institute for Allergy and Immunology, La Jolla, California, USA

    Myunggon Ko & Anjana Rao

  6. School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

    Myunggon Ko

  7. Institute of Biosciences and Technology, Texas A&M Health Science Center, Houston, Texas, USA

    Yun Huang

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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.

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Correspondence to Wei Li or Margaret A Goodell.

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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.

(a–d) Expression (FPKM) of major HSC transcription factors (Gata2, Meis1, Runx1, Lmo2) that are not differentially expressed. (e–g) 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 d–n 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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