Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2016 May 12.
Published in final edited form as: Cell Rep. 2016 Apr 28;15(6):1190–1201. doi: 10.1016/j.celrep.2016.04.004

Promoter hypomethylation and expression is conserved in mouse chronic lymphocytic leukemia induced by decreased or inactivated Dnmt3a

Staci L Haney 1, G Michael Upchurch 2, Jana Opavska 2, David Klinkebiel 3, Ryan A Hlady 4, Abhinav Suresh 2, Samuel J Pirruccello 5,6, Vipul Shukla 1, Runqing Lu 1, Stefan Costinean 5, Angie Rizzino 2,3,5, Adam R Karpf 2, Shantaram Joshi 1,6, Patrick Swanson 7, Rene Opavsky 1,2,6,#
PMCID: PMC4864108  NIHMSID: NIHMS776428  PMID: 27134162

SUMMARY

DNA methyltransferase 3a (DNMT3A) catalyzes the formation of 5-methyl-cytosine in mammalian genomic DNA and it is frequently mutated in human hematologic malignancies. Bi-allelic loss of Dnmt3a in mice results in leukemia and lymphoma, including chronic lymphocytic leukemia (CLL). Here we investigate whether mono-allelic loss of Dnmt3a is sufficient to induce disease. We show that by 16 months of age, 65% of Dnmt3a+/− mice develop a CLL-like disease and 15% of mice develop non-malignant myeloproliferation. Genome-wide methylation analysis reveals that reduced Dnmt3a levels induce promoter hypomethylation at similar loci in Dnmt3a+/− and Dnmt3aΔ/Δ CLL, suggesting that promoters are particularly sensitive to Dnmt3a levels. Gene-expression analysis identified 26 hypomethylated and over-expressed genes common to both Dnmt3a+/− and Dnmt3aΔ/Δ CLL as putative oncogenic drivers. Our data provide evidence that Dnmt3a is a haplo-insufficient tumor suppressor in CLL and highlights the importance of deregulated molecular events in disease pathogenesis.

INTRODUCTION

DNA methyltransferase 3a (Dnmt3a) is an enzyme important for the generation and maintenance of 5-methyl-cytosine in mammalian genomic DNA. The methylation of gene promoters is typically associated with gene repression and plays an important role in silencing of endogenous retroviral elements, X-chromosome inactivation, imprinting and differentiation. In particular, cytosine methylation plays a critical role in hematopoiesis and its deregulation contributes to hematologic malignancies (Yang et al., 2015). This is highlighted by the presence of mutations in the DNMT3A gene in a wide variety of human hematologic malignancies of myeloid and T cell origin (Yang et al., 2015). Although the precise biological and molecular functions by which DNMT3A prevents cellular transformation are poorly understood, functional studies in mice have begun to uncover the role of Dnmt3a in hematopoiesis. Long-term Dnmt3a deficiency inhibits the ability of hematopoietic stem cells (HSCs) to differentiate into hematopoietic lineages, promoting the development of various hematologic malignancies, including myelodysplastic syndrome, acute myeloid leukemia, and acute lymphoblastic leukemia of T and B cell origin (Challen et al., 2011; Challen, G. et al., 2014, Mayle et al., 2015). Introduction of genetic alterations found in hematologic malignancies into a Dnmt3a deficient background often results in enhanced phenotypes. For example, Dnmt3a deficiency in combination with c-kit overexpression induces acute T and B cell leukemia (Celik et al, 2015), and when associated with KrasG12D/+ promotes progression of juvenile and chronic myelomonocytic leukemia (Chang et al., 2015). Given the multiple genome-wide activities associated with Dnmt3a, such as de novo methylation (Okano et al, 1999), maintenance methylation (Jeong, M. et al., 2014), and methylation-independent repression (Datta et al., 2005), it is not surprising that under some circumstances Dnmt3a may promote the development of hematologic malignancies. For example, upregulation of Dnmt3a promotes AML/ETO induced leukemia through de novo hypermethylation (Gao et al., 2015) and methylation-independent repressor function enhances T cell lymphomagenesis (Haney et al., 2015). Such studies highlight the importance of context-dependent activities of Dnmt3a in hematologic malignancies. DNMT3A mutations in human hematologic malignancies are usually heterozygous and most commonly occur at amino acid 882. DNMT3A R882H mutants show both decreased methyltransferase activity (Russler-Germain et al., 2014) and dominant negative functions as its overexpression results in hypomethylation (Kim et al., 2013). Therefore, a partial, rather than complete inactivation of DNMT3A is likely more relevant in the pathogenesis of human hematologic malignancies. The effects of decreased levels of Dnmt3a in prevention of hematologic malignancies, however, are poorly understood.

We have previously utilized EμSRα-tTA;Teto-Cre;Dnmt3afl/fl;Rosa26 LOXPEGFP/EGFP quadruple transgenic mice (designated here as Dnmt3aΔ/Δ mice) to conditionally inactivate Dnmt3a in hematopoietic stem cells and hematopoietic lineages (Peters et al., 2014). Surprisingly, all Dnmt3aΔ/Δ mice developed disease similar to chronic lymphocytic leukemia (CLL) with a median survival of 371 days characterized by an expansion of EGFP+ mature B220+CD19+CD5+ B cells (B-1a cells) in hematopoietic organs. Here we asked whether Dnmt3a haploinsufficiency can result in the development of a CLL-like disease or other hematologic malignancies by observing Dnmt3a+/− mice. We show that whereas a decrease in Dnmt3a levels is insufficient to immediately induce cellular transformation of hematopoietic cells, long-term Dnmt3a decrease results in the development of a CLL-like disease in 65% of mice and myeloproliferative disease in 15% of mice within 16 months. Whole-genome bisulfite sequencing (WGBS) and RNA-seq revealed that a significant cohort of methylation and expression changes were conserved in Dnmt3a+/− and Dnmt3aΔ/Δ CLL. Ingenuity Pathway Analysis (IPA) analysis revealed a signature of putative oncogenes that may drive CLL development. Altogether, our data demonstrate that a small reduction of Dnmt3a levels has profound phenotypic consequence on both cellular and molecular levels, identifying Dnmt3a as a critical gene preventing B-1a cell transformation.

RESULTS

The majority of Dnmt3a+/− mice develop a CLL-like disease

During the course of our studies utilizing Dnmt3aΔ/Δ mice (Peters et al., Leukemia 2014) we also observed an EμSRα-tTA;Teto-Cre;Dnmt3+/fl;Rosa26LOXPEGFP/EGFP mouse in which only one allele of Dnmt3a was conditionally inactivated (referred herein as Dnmt3a+/Δ or conditional heterozygous mouse). This mouse became moribund at 16 months and analysis of the organs revealed expansion of EGFP-positive (EGFP+) B220+CD19+CD5+ (B-1a) cells in the spleen, suggesting that this mouse developed a CLL-like disease (Figure 1A). Serial transplantation of Dnmt3a+/Δ splenic cells induced CLL within 4 months in primary, secondary and tertiary transplanted mice, illustrating their selective advantage to grow and induce disease (Figures S1A and S1B). Dnmt3a+/Δ CLL cells showed reduced Dnmt3a protein and mRNA levels (Figures 1B and 1C), suggesting that decreased Dnmt3a dosage is sufficient to promote B-1a cell transformation. Dnmt3a+/Δ tumor cells incorporated BrdU in vivo less efficiently than Dnmt3aΔ/Δ cells (Figure S1C). These data suggest that Dnmt3a haploinsufficiency in hematopoietic cells, like full Dnmt3a inactivation, might be sufficient to induce a less aggressive CLL-like disease in mice. To test this we generated Dnmt3a+/− mice harboring a conventional Dnmt3a knockout allele (referred to herein as Dnmt3a−) via germline cre-mediated excision of Dnmt3a exons 18–20 (Figure S2A) (Dnmt3afl; Nguyen et al., 2007). Generation of the Dnmt3a− allele was confirmed by PCR-based genotyping (Figure S2B). Analysis of protein levels in normal thymus and spleen isolated from 6 week-old Dnmt3a+/− mice showed ~50% reduction in Dnmt3a protein levels (Figure 1D). This decrease had no measurable effect on hematopoiesis in 6 week old Dnmt3a+/− mice (Figures S2C–2G).

Figure 1. Dnmt3a heterozygous mice develop CLL.

Figure 1

Open in a new tab

A. Flow diagram of CD5 and CD19 expression is shown for EGFP-negative (black) and EGFP-positive (green) cells from a Dnmt3a+/Δ spleen. The percentage of positive cells is indicated in quadrants. B. Dnmt3a expression in Dnmt3a+/Δ spleen as determined by immunoblot using anti-Dnmt3a antibody. N indicates Dnmt3a+/+ CD19+ splenic cells. +/Δ and Δ/Δ indicate splenic CLL cells from Dnmt3a+/Δ and Dnmt3aΔ/Δ mice, respectively. 3a represents a positive control in which Dnmt3a protein was overexpressed in Dnmt3a−/− cells. γ-tubulin served as a loading control. C. qRT-PCR analysis of Dnmt3a expression in normal splenic B-1a (B1) cells and Dnmt3a+/Δ (+/Δ) CLL. Average of two independent experiment is presented. Error bars represent ± standard deviation (SD). P<0.05, Student's t-test. Statistically significant difference is indicated by (*). D. Dnmt3a expression in thymi (Th) and spleens of 6 week old Dnmt3a+/+ (+/+) and Dnmt3a+/− (+/−) mice as determined by immunoblot using anti-Dnmt3a antibody. (−/−) represents Dnmt3a-defficient cells. γ-tubulin served as a loading control. E. Disease spectrum observed in ~16 months old Dnmt3a+/− mice (n=20). MBL/CLL – monoclonal B cell lymphopoiesis/CLL-like disease, MPD – myeloproliferative disease, no disease – disease free mice. F. Flow diagram of CD5 and CD19 expression in spleens of Dnmt3a+/− (+/−) and aged-matched Dnmt3a+/+ (+/+) mice. G. H&E stained sections of Dnmt3a+/+ (normal) and Dnmt3a+/− (CLL) spleens (200X). H. Percentage of B-1a cells in the spleens and blood of ~16 months old Dnmt3a+/+ (blue) and Dnmt3a+/− (red) mice as determined by FACS. Number of mice (n) is shown. P<0.05 is indicated by (*), Student's t-test. I. Flow diagram of CD5 and CD19 expression in the spleen of a Dnmt3a+/− mouse with Monoclonal B cell lymphopoeisis (MBL) (top) and terminally ill FVB recipient mouse injected with MBL splenic cells. J. Kaplan-Meier survival curves for FVB mice injected with Dnmt3a+/− MBL/CLL splenic cells (3 mice per line). Five primary MBL/CLL mice are shown. K. Dnmt3a expression in spleens of Dnmt3a+/− (+/−) and Dnmt3aΔ/Δ (Δ/Δ) mice as determined by immunoblot using anti-Dnmt3a antibody. N indicates Dnmt3a+/+ CD19+ splenic cells. γ-tubulin served as a loading control. L. qRTPCR analysis of Dnmt3a expression in normal and leukemic Dnmt3a+/− B-1a cells. Average of two independent experiment is shown. Error bars represent ± SD.

At 16 months of age, all Dnmt3a+/+ control mice were healthy with no signs of deregulated hematopoiesis. In contrast, only 20% of Dnmt3a+/− mice were disease-free at 16 months (Figure 1E and data not shown). Five out of 20 Dnmt3a+/− mice developed a CLL-like disease, characterized by B-1a cell expansion greater than 20% in the blood, spleen and bone marrow (Figures 1E–1I, and data not shown). Eight Dnmt3a+/− mice showed signs of monoclonal B cell lymphocytosis (MBL) – a less progressed form of CLL – in which the percentage of B-1a cells in the blood are between 2% to 20%, with simultaneous expansion in the spleen and bone marrow (Figures 1E–1I and data not shown). Importantly, splenic cells either from mice with MBL or CLL were able to induce disease in recipient mice (Figures 1I–1J), demonstrating that both populations contain true leukemic cells. Therefore, we refer to both conditions as CLL-like disease. Similar to Dnmt3a+/Δ CLL, leukemic B-1a cells isolated from Dnmt3a+/− mice retained approximately 50% expression of Dnmt3a, suggesting that the remaining allele is expressed in fully transformed cells (Figures 1K–L). Importantly, sequencing analysis of cDNA generated from three independent Dnmt3a+/− CLL revealed no mutations in the coding sequence of Dnmt3a (data not shown), demonstrating that the expressed Dnmt3a allele is in the wild-type configuration. Altogether these data suggest that Dnmt3a is a haploinsufficient tumor suppressor gene in prevention of CLL in mice.

Dnmt3a+/− mice also develop myeloproliferative disease

In addition to CLL, we also observed the development of a myeloproliferative disease (MPD) in 15% of Dnmt3a+/− mice (Figure 1E). These mice showed expansion of Gr-1+CD11b+ myeloid cells in the blood, spleen and bone marrow (Figure 2A–2C and data not shown). In contrast to CLL-like cells, Gr-1+CD11b+ splenic cells did not induce disease upon injection into sublethally irradiated FVB recipient mice, suggesting that this population of cells do not contain leukemia initiating cells (Figure 2D–E). Thus, our studies of Dnmt3a+/− mice show that long-term mono-allelic loss of Dnmt3a can induce a frank B cell malignancy, non-malignant myeloproliferative disorder with a combined 80% penetrance by 16 months of age. In order to understand why Dnmt3a heterozygosity affects primarily B-1a and myeloid cells, we measured Dnmt3a mRNA levels in FACS-sorted normal Dnmt3a+/+ B-1a, B2, T cells and myeloid cells and found that Dnmt3a expression was lower in B-1a cells when compared to B2 cells and relatively equal when compared to T cells (Figure 2F). Thus, the preferential transformation of B-1a cells or myeloid cells does not seem to be associated with significantly different Dnmt3a levels relative to other normal hematopoietic cells. The reason why B-1a and, to some extent, myeloid cells are in particular sensitive to transformation upon decreased Dnmt3a levels therefore remains unclear.

Figure 2. Dnmt3a+/− mice also develop non-malignant myeloproliferative disorder.

Figure 2

Open in a new tab

A. Flow diagram of CD11b and Gr-1 expression in spleens of normal Dnmt3a+/+ (+/+) and Dnmt3a+/− (+/−) mice with MPD. B. H&E stained sections of Dnmt3a+/+ and Dnmt3a+/− spleens (200×). C. H&E stained sections of Dnmt3a+/+ and Dnmt3a+/− femurs (200×). D. Flow diagram of Gr-1 and CD11b expression in spleen of Dnmt3a+/− mouse with MPD (left). Gr-1 and CD11b expression 9 months post injection (right). E. Kaplan-Meier survival curves for FVB recipients injected with two independent Dnmt3a+/− MPD lines (3 mice per line). F. qRT-PCR analysis of Dnmt3a expression in sorted B-1a, B2, CD4+ T cells, CD8+ T cells, and myeloid cells isolated from FVB spleen. Two biological replicates are shown. Data was normalized to Gapdh and error bars represent ± SD. G. qRT-PCR analysis of DNMT3A expression in five human CLL samples (1–5). Two biological replicates for CD19+ peripheral blood from healthy donors were used as controls. Average of two independent experiments is shown. Error bars represent ± SD.

Given that Dnmt3a+/− mice develop CLL, we next asked whether or not DNMT3A deficiency is observed in human CLL. Previous analysis of available gene expression data (Haferlach et al., 2010) identified DNMT3A as belonging to the top 1% of underexpressed genes in CLL (Peters et al., 2014). Our further analysis showed that in 4/5 cases of primary human CLL, DNMT3A expression was significantly decreased relative to normal human CD19+ B cells (Figure 2G). These data support the idea that decreased DNMT3A may promote the development of human CLL. Further investigation is needed to more carefully address this point.

DNA methylome and transcriptome of normal mouse B-1a cells

To identify DNA methylation and transcriptional changes associated with CLL development, we performed global methylation profiling by WGBS and gene expression profiling by RNA-seq of B-1a cells isolated from normal Dnmt3a+/+ spleens, as this cellular population is immunophenotypically the closest normal counterpart of CLL cells. We focused on splenic B-1a cells rather than B-1a cells residing in the intraperitoneal cavity because: a.) the observed CLL disease consistently presented with splenomegaly and the spleen served as a source of tumor cells b.) the microenvironment can effect gene expression and presumably methylation patterns. WGBS analysis of normal B-1a cells revealed that out of 22,452,960 CpG dinucleotides covered by our analysis 20,316,133 CpGs were methylated > 50%, whereas only 2,136,827 CpGs were methylated < 50% and only 133,765 CpGs ≤ 20% (Figure 3A). When we analyzed only CpG dinculeotides that aligned to core promoter regions (−300 to +150bp relative to transcription start site; TSS) we found that from the 25,742 promoters analyzed, 15,203 were methylated at > 50%. (Figure 3B; Table S1) and only 7,436 promoters were methylated at ≤ 20%. A combined analysis of gene expression and methylation revealed that 50% of genes with ≤ 20% promoter methylation were expressed (FPKM > 5; Figure 3C, Table S2). In contrast, 84% of genes with promoter hypermethylation (≥ 50%) were not expressed (FPKM ≤ 5; Figure 3C, Table S2). Consistently, the degree of promoter methylation inversely correlated with gene expression (Figure 3D). For example, promoters with less than 25% methylation were expressed at significantly higher levels than promoters with higher levels of methylation (Figure 3D). IPA of 3,700 highly expressed genes (FPKM ≥ 10) revealed a significant enrichment in genes relating to hematopoiesis and lymphoid tissue structure and development (Figure 3E). Altogether, these data demonstrates that most gene promoters in normal splenic B-1a cells are hypermethylated and silenced whereas promoters that are hypomethylated are largely expressed and their physiological relevance is linked to the hematopoietic system.

Figure 3. DNA methylome and transcriptome of normal mouse B-1a cells.

Figure 3

Open in a new tab

A. Methylation status of 22,452,960 CpG dinucleotides in normal B-1a cells as determined by whole-genome bisulfite sequencing (WGBS). B. A heat map displaying methylation status of 25,742 promoters as determined by WGBS. Methylation percentage for individual CpGs were annotated to the promoter regions −300bp to +150bp relative to the transcription start site (TSS). Methylation percentages for all CpGs across the 450bp region were averaged to give a mean methylation value for each gene promoter. C. Heat map presentation of promoter methylation (analyzed as in Figure 2B) and corresponding gene expression (average FPKM values from RNA-seq) in mouse splenic B-1a cells for 16,770 genes. Genes with high FPKM values are shown in red and genes with low FPKM values are shown in green. Upper limit for color-coding in gene expression heat map is FPKM ≥ 5 as indicated. Heat maps are organized in the same gene order to match data for methylation and gene expression. D. Analysis of promoter (−300 to +150bp) methylation in relation to gene expression in mouse splenic B-1a cells for 16,770 genes. Genes were divided into four groups based on percentage of promoter methylation (0–25%, 26–50%, 51-75% and 76-100%). P<0.05 for all pairwise comparisons except 51-75 to 76-100 group (Bonferroni's multiple comparison test). E. Ingenuity Pathway analysis of 3,700 highly expressed genes (FPKM ≥ 10). The top subcategories obtained in “Physiological System, Development and Functions” are shown with the number of genes indicated above individual bars (P<0.05, for all subcategories).

DNA methylome of Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL

We next performed WGBS on DNA isolated from Dnmt3a+/Δ CLL cells and Dnmt3aΔ/Δ CLL cells to determine the effects of loss of Dnmt3a on the CLL methylome. Monoallelic loss of Dnmt3a resulted in a ~1% relative hypomethylation and a 0.1% hypermethylation in individual CpGs relative to B-1a (Figure 4A, Table S3). Bi-allelic loss of Dnmt3a resulted in a substantial 4.1% decrease and a 0.1% increase CpG methylation (Figure 4A, Table S3). Relative to Dnmt3a+/Δ CLL, hypomethylation in the Dnmt3a-deficient CLL genome was most pronounced in repetitive elements and gene bodies (3.5 to 5.5 fold) (Figures 4B, S3A and B). In contrast, the increased incidence of hypomethylated long promoters (−1500 bp to +500 bp relative to TSS) was only 2.2 fold greater in Dnmt3aΔ/Δ CLL compared to Dnmt3a+/Δ CLL (Figures 4C, S3B, Table S4). This ratio was even smaller (1.9 fold) when analysis was restricted to core promoters (−300 bp to +150 bp) (Figures 4C, S3B, Table S4). These data suggest that Dnmt3a levels are more critical for promoter methylation than other parts of the genome, such as repetitive elements or gene bodies and that 699 hypomethylated promoters in Dnmt3a+/Δ are likely hypersensitive to levels of Dnmt3a (Figure 4C). Overall, 386 hypomethylated promoters and 43 hypermethylated promoters were shared between Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL (Figure 4D and Table S4). Differentially methylated promoters were equally distributed across the genome, with exception of the X chromosome in which no hypo- or hypermethylated promoters were shared between Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL cells relative to B-1a control cells (Figure 4D, Table S5). In contrast, chromosome 11 and 19 had the highest number of commonly hypomethylated promoters, with 48 and 23, respectively (Figure 4D, Table S5). Commonly hypomethylated promoters represent putative targets of Dnmt3a maintenance methylation function, whereas hypermethylated promoters in Dnmt3aΔ/Δ CLL are likely de novo methylated by other Dnmts.

Figure 4. DNA methylome of CLL induced by decrease or absence of Dnmt3a.

Figure 4

Open in a new tab

A. A graphical presentation of differentially methylated cytosines (DMCS) in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL relative to B-1a control. Methylation changes were evaluated in 15,533,510 CpGs and are shown in both absolute numbers and percentages. B. The number of differentially methylated regions (DMRS) associated with gene bodies in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL samples when compared to B-1a control sample. C. The number of DMRs associated with long promoters (−1500 to +500 bp relative to the TSS) and core promoters (TSS; −300 to +150bp relative to the TSS) in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL relative to B-1a control. D. Circos plot of DMRs associated with long promoters in Dnmt3a+/Δ or Dnmt3aΔ/Δ CLL. Outer circle is a graphical presentation of mouse chromosomes and inner circles indicate DMRs and their positions on mouse chromosomes observed in Dnmt3a+/Δ (+/Δ) or Dnmt3aΔ/Δ CLL (Δ/Δ) relative to B-1a control. Yellow lines indicate hypomethylated promoters whereas blue lines indicate hypermethylated promoters. Circle `C' represents hypo- (red lines) and hypermethylated (green lines) promoters commonly observed in both CLL samples. E. COBRA analysis of putative Dnmt3a target gene promoters in splenic B-1a cells (B1), Dnmt3a+/Δ CLL (+/Δ), Dnmt3a+/− CLL (+/−) and Dnmt3aΔ/Δ CLL (Δ/Δ) samples. Undigested (U) and digested (D) fragments correspond to unmethylated and methylated DNA, respectively. CpG and M indicates a fully methylated control and undigested PCR fragments, respectively. F. (left) Heatmap showing 249 hypomethylated and 104 hypermethylated promoters identified through WGBS and confirmed by RRBS. Data is shown as an average percent methylation for Dnmt3a+/+ B-1a (n=2) and Dnmt3aΔ/Δ CLL (n=2) for differentially methylated CpGs (minimum 30% change in methylation) annotated to the promoter region (−1500 to +500bp). (right) Heatmap showing 336 hypomethylated and 34 hypermethylated promoters identified through WGBS and confirmed by MSCC. Data is shown as a fold change in methylation for Dnmt3aΔ/Δ CLL (n=3) relative to Dnmt3a+/+ B-1a (n=2). Differentially methylated CpGs (fold change > 2 and a P < 0.05, negative binomial analysis) were annotated to the promoter region. G. RRBS and MSCC confirmation of differentially methylated promoters (red) identified through WGBS. The number and percent of confirmed hypomethylated (top) and hypermethylated (bottom) genes by RRBS (yellow), MSCC (blue) or both (green) is shown. Gray represents genes identified through WGBS not confirmed by MSCC or RRBS.

To determine whether the methylation landscape generated by WGBS is specific to the CLL samples profiled or rather represents common changes that occur in Dnmt3a-deficient CLL, we validated hypo- and hypermethylated promoters using locus-specific Combined Bisulfite Restriction Analysis (COBRA) assays, as well as two global methods – methyl sensitive cut counting (MSCC) and reduced representation bisulfite sequencing (RRBS). COBRA analysis of 6 promoters confirmed the hypomethylation identified by WGBS, suggesting that changes in promoter methylation likely represent common events occurring in mouse CLL (Figure 4G, Figure S3C). Next, we validated hypo- and hypermethylated promoters using two global methods – MSCC and RRBS. The choice of two independent approaches was driven by the fact that each method has lower genome-wide coverage and is biased against regions with low-CG content (Hirst and Marra, 2010). Thus, their concurrent use allowed us to obtain a more comprehensive and complementary methylation dataset. Importantly, were confirmed 53% of hypomethylated and 98% of hypermethyated DMRS in promoters in Dnmt3aΔ/Δ CLL identified by WGBS using one or both methods (Fig. 4G, Table S6). These data demonstrate that methylation changes detected by WGBS on a small sample set likely represent events shared among larger sets of Dnmt3aΔ/Δ CLL.

Promoter hypomethylation in CLL is likely due to the lack of cancer-specific maintenance activity of Dnmt3a and is independent of proliferation

Promoter hypomethylation observed in Dnmt3a+/− and Dnmt3aΔ/Δ CLL could results from Dnmt3a inactivation in normal B-1a cells, either due to lack of Dnmt3a's de novo or maintenance activity. In such a scenario, promoters would be hypomethylated in normal B-1a cells prior to CLL development. To address this, we performed COBRA assays to analyze promoter methylation of 14 genes in normal Dnmt3a+/+ and Dnmt3a+/− B-1a cells. Interestingly, we have not seen any evidence of decreased promoter methylation in any of loci tested in Dnmt3a+/− B-1a cells, suggesting that a partial inactivation of Dnmt3a does not affect the methylation status of promoters during the development of normal B-1a cells (Figure 5A). Thus, the decrease in methylation observed in Dnmt3a+/− and Dnmt3aΔ/Δ CLL appears to be tumor-specific.

Figure 5. Dnmt3a has a cancer-specific maintenance function.

Figure 5

Open in a new tab

A. COBRA analysis of 14 putative Dnmt3a target gene promoters in splenic Dnmt3a+/+ and Dnmt3a+/Δ B-1a cells. Undigested (U) and digested (D) fragments correspond to unmethylated and methylated DNA, respectively. CpG and M indicates a fully methylated control and undigested PCR fragments, respectively. B. qRT-PCR analysis of Dnmt3a expression in normal B-1a and IRF4−/−;Vh11 CLL samples. Average of two independent experiments is presented. Error bars represent ± SD. C. COBRA analysis of 4 putative Dnmt3a target gene promoters in Dnmt3a+/Δ (+/Δ), Dnmt3aΔ/Δ (Δ/Δ), Eμ-TCL1, and IRF4−/−;Vh11 CLL. D. qRT-PCR analysis of Nfam1 expression in B-1a, IRF4−/−;Vh11, Dnmt3a+/− (+/−), and Dnmt3aΔ/Δ (Δ/Δ) CLL. Average of two independent experiments is presented. Error bars represent ± SD.

We next sought to determine if promoter hypomethylation observed in Dnmt3a+/− and Dnmt3aΔ/Δ CLL could be the result of increased proliferation of CLL cells rather than the result of Dnmt3a-deficiency. We therefore examined promoter methylation in selected loci in two independent mouse models of CLL, IRF4−/−;Vh11 and Eμ-TCL1 (Shukla et al., 2013; Nganga et al, Blood). In IRF4−/−;Vh11 CLL samples, no changes in Dnmt3a levels were observed by global gene expression (data not shown). To verify this, we measured transcript levels of Dnmt3a but did not observe changes in Dnmt3a transcript levels in IRF4−/−;Vh11 CLL (Figure 5B). Thus, this CLL model likely represent one in which Dnmt3a activity is not altered. In contrast, it has been reported recently, that TCL-1 binds to and inhibits Dnmt3a activity, resulting in suppression of Dnmt3a activity and hypomethylation in the Eμ-TCL1 mice (Palamarchuck et al., 2012). Thus, Eμ-TCL1 CLL represents a model in which biochemical inhibition of Dnmt3a occurs. We analyzed promoter methylation of four genes found to be hypomethylated in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL using DNA from IRF4−/−;Vh11 and Eμ-TCL1 CLL samples. This analysis revealed that all tested promoters were partially hypomethylated in Eμ-TCL1 CLL and not hypomethylated in IRF4−/−;Vh11 CLL samples (Figure 5C), suggesting that promoter hypomethylation in Dnmt3a+/− and Dnmt3aΔ/Δ CLL is directly linked to the lack of Dnmt3a rather than to increased cellular proliferation. Expression of Nfam1 correlated with its promoter methylation, as this gene was expressed in Dnmt3a+/− and Dnmt3aΔ/Δ CLL but not in IRF4−/−;Vh11 CLL, suggesting that at least some genes that become hypomethylated during CLL development become overexpressed (Figure 5D). These data suggest that Dnmt3a may have a tumor-specific maintenance activity similar to the one we described previously for Dnmt3b in MYC-induced T cell lymphomagenesis (Hlady et al., 2012).

Gene expression is conserved in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL

To better understand the molecular basis for CLL development, global gene expression profiles of normal B-1a cells, Dnmt3a+/− and Dnmt3aΔ/Δ CLL cells were determined by RNA-seq. We identified 413 overexpressed and 282 underexpressed genes in Dnmt3a+/− CLL relative to B-1a cells (Figure 6A, Table S7). Inactivation of both Dnmt3a alleles in Dnmt3aΔ/Δ CLL resulted in overexpression of 790 genes and underexpression of 398 genes. Interestingly, the majority of genes upregulated in Dnmt3a+/− CLL were also upregulated in Dnmt3aΔ/Δ CLL cells (67%), whereas downregulated genes were less conserved between the two genetic settings (57%; Figure 6B, Table S7). To gain insight into the nature of deregulated processes in CLL induced by decreased levels of Dnmt3a, we next performed IPA analysis of differentially expressed genes between Dnmt3a+/− and Dnmt3aΔ/Δ CLL relative to control B-1a cells. Notably, the top five subcategories under “Disease and disorder” and “Physiological System Development and Functions” were identical in Dnmt3a+/− and Dnmt3aΔ/Δ CLL (Figure S4A). In addition, three out of five subcategories under “Canonical pathways” were conserved between Dnmt3a+/− and Dnmt3aΔ/Δ CLL. Altogether, this suggests that despite the higher number of differentially expressed genes present in Dnmt3aΔ/Δ CLL, similar pathways are affected in both settings. However, the physiological relevance of these categories to CLL development is less clear as this analysis did not provide clear links to pathways or molecules driving CLL development.

Figure 6. Decreased Dnmt3a levels result in deregulated transcription in Dnmt3a+/Δ CLL similar to Dnmt3aΔ/Δ CLL.

Figure 6

Open in a new tab

A. Heat maps derived from RNA-seq analysis displaying 413 overexpressed and 282 underexpressed genes in Dnmt3a+/ Δ and Dnmt3a+/− CLL (n=4) relative to control B-1a cells (n=2) (left) and 790 overexpressed and 398 underexpressed genes in Dnmt3aΔ/Δ CLL (n=8). Only genes with a fold change ≥ 2 and a q-value < 0.05 are shown (CuffDiff analysis). B. The number of genes differentially expressed in Dnmt3aΔ/Δ CLL (white box), Dnmt3a+/− CLL (black box) and genes common between Dnmt3aΔ/Δ and Dnmt3a+/− CLL (grey box).

Genes commonly hypomethylated and overexpressed in Dnmt3a+/− and Dnmt3aΔ/Δ CLL (HOC) are putative drivers of CLL

Since loss of one allele of Dnmt3a is sufficient to induce CLL, we hypothesized that genes most likely involved in pathogenesis of the disease are those whose promoter methylation affect gene expression in both Dnmt3a+/− and Dnmt3aΔ/Δ CLL. A comparison of promoter methylation and gene expression revealed that 10% of genes in both Dnmt3a+/− and Dnmt3aΔ/Δ CLL were hypomethylated and overexpressed (Figure 7A, Table S8). In contrast, only 3% and 1% of hypermethylated genes showed reduced expression in Dnmt3a+/− and Dnmt3aΔ/Δ CLL, respectively (Figure 7A). We next performed IPA using genes whose expression and methylation was commonly changed in both Dnmt3a+/− and Dnmt3aΔ/Δ CLL. This analysis identified a signature of 26 genes commonly hypomethylated and overexpressed in both Dnmt3a+/− and Dnmt3aΔ/Δ CLL (Hypomethylated and overexpressed in CLL; HOC genes; Figure 7B). With exception of Mgmt, promoter hypomethylation of all HOC genes was confirmed by locus-specific COBRA assay in the vast majority Dnmt3a+/− CLL and Dnmt3aΔ/Δ CLL samples tested, suggesting that HOC promoter hypomethylation is a conserved event in CLL induced by a decrease in Dnmt3a. These genes therefore may play a role in disease development. Consistently, IPA analysis placed twenty-four genes in the category `cancer' (Figure S4B). In contrast to the strong association of HOC genes with the cancer category, IPA analysis of multiple randomly selected groups of 26 overexpressed genes in Dnmt3a+/− CLL did not identify the category of `cancer' as top category and failed to yield a single gene associated with cancer (data not shown), further supporting the idea that HOC genes may promote the development of CLL. Thus, combined methylation and gene expression analysis identified genes likely regulated by Dnmt3a maintenance methylation activity that have strong association to cancer and may contain oncogenic drivers of CLL.

Figure 7. Genes commonly hypomethylated and overexpressed in Dnmt3a+/− and Dnmt3aΔ/Δ CLL (HOC) are putative drivers of CLL.

Figure 7

Open in a new tab

A. The number of genes differentially expressed and differentially methylated at the promoter region in Dnmt3aΔ/Δ CLL and Dnmt3a+/− CLL. The number of genes with corresponding methylation and expression changes are shown in the grey boxes. B. Promoter methylation for HOC genes in B-1a, Dnmt3a+/Δ CLL, and Dnmt3aΔ/Δ CLL is shown within boxes. Similarly, fold differences in gene expression between Dnmt3a+/Δ CLL relative to B-1a and Dnmt3aΔ/Δ CLL relative to B-1a is shown. (*) denotes genes overexpressed in human CLL. C. COBRA analysis of 26 HOC gene promoters in Dnmt3a+/Δ CLL (+/Δ), Dnmt3a+/− CLL (+/−) and Dnmt3aΔ/Δ CLL (Δ/Δ) samples. Undigested (U) and digested (D) fragments correspond to unmethylated and methylated DNA, respectively. CpG and M indicates a fully methylated control and undigested PCR fragments, respectively. (*) denotes unconfirmed target.

DISCUSSION

Identification of Dnmt3a's molecular targets and understanding how they regulate cellular functions will enrich our understanding of the pathogenesis of human blood cancers given the broad scope of hematologic malignancies in which the function of Dnmt3a is altered. Mouse models represent an invaluable tool in such efforts as they allow us to evaluate the phenotypic and molecular consequences of changes in levels of Dnmt3a in normal and malignant hematopoiesis. Through functional studies in mice, we have previously expanded the known disease spectrum in which Dnmt3a plays a role, as we showed that complete inactivation of Dnmt3a in cells of the hematopoietic system resulted in the development of a CLL-like disease: a malignancy of B cell origin that in humans is not known to harbor genetic alterations of DNMT3A locus. Rather, decreased levels of DNMT3A are a common feature of CLL (Haferlach et al., 2010; Peters et al., 2014) and wide-spread promoter and gene-body hypomethylation is characteristic of the CLL methylome in humans (Kulis et al., 2012). Similarly, in Eμ-TCL1 mice Dnmt3a levels are both decreased and inhibited in early stages of CLL development, suggesting a role for Dnmt3a in tumor initiation (Chen et al., 2009, Palamarchuk et al., 2012).

In this study, we asked whether a more physiologically relevant setting – a decrease in Dnmt3a, rather than complete deficiency – can drive the development of hematologic malignancies. By analysis of Dnmt3a+/− mice, we identified Dnmt3a as a haploinsufficient tumor suppressor gene in the prevention of CLL and MPD. The overall penetrance of disease in Dnmt3a heterozygotes (80%) might be even greater, as the 16 month old healthy Dnmt3a+/− mice could have developed disease at a later time. We further demonstrate that Dnmt3a+/− CLL cells are fully transformed and capable of inducing disease in wild-type recipient mice, whereas MPD cells fail to engraft in recipients, and appear to represent an expanded population of non-tumorigenic cells.

To gain insight into the pathogenesis of CLL in mice with decreased or completely absent Dnmt3a protein, we analyzed high resolution methylomes and transcriptomes of normal splenic B-1a cells, Dnmt3a+/Δ (or Dnmt3a+/−), and Dnmt3aΔ/Δ CLL cells. These analyses resulted in several interesting observations. First, the methylome of normal B-1a cells consisted largely of hypermethylated promoters, most of which were associated with transcriptional repression. This observation could explain why only B-1a cells become fully transformed in Dnmt3aΔ/Δ or Dnmt3a+/− mice despite the decrease in Dnmt3a levels in all hematopoietic cells. Unlike other terminally differentiated hematopoietic cells, B-1a cells are believed to maintain their population through self-renewal. These added cell divisions, along with Dnmt3a deficiency, may further increase the chances for the accumulation of epi-mutations over time. This idea is supported by the recently identified maintenance methylation activity for Dnmt3a (Jeong et al., 2014) and findings that all active DNA methyltransferases seem to have cancer-specific maintenance functions (Hlady et al., 2012, Peters et al., 2013). Further studies focusing on molecular changes in other B cell subtypes need to be performed to clarify this point.

Second, we observe that promoter methylation appears to be more dependent on high Dnmt3a expression levels than other parts of the genome. Loss of one allele of Dnmt3a induced hypomethylation of 699 2kb-long promoters (−1500 bp to +500 bp relative to the TSS) in Dnmt3a+/Δ CLL. The number of hypomethylated promoters was increased only 2.2 fold in Dnmt3aΔ/Δ CLL relative to Dnmt3a+/Δ CLL. In contrast, hypomethylation of CpGs distributed across the genome, gene bodies and repetitive elements was much more pronounced in Dnmt3aΔ/Δ CLL (3.5–5 fold) relative to Dnmt3a+/Δ CLL. Such data suggest that a select number of promoters are particularly sensitive to Dnmt3a levels and that the complete absence of Dnmt3a is not necessary for hypomethylation to occur at these loci.

Lastly, the number of hypomethylated promoters was 4.9 and 13.2 fold greater than the number of hypermethylated promoters seen in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL, respectively. Promoter hypermethylation was somewhat suppressed in Dnmt3aΔ/Δ CLL, suggesting that Dnmt3a might have tumor specific de novo activity. Interestingly, although 3% of hypermethylated promoters were associated with gene down-regulation, these events were not shared in Dnmt3a+/Δ and Dnmt3aΔ/Δ CLL, suggesting that promoter hypermethylation either contributes to their pathogenesis differently or it does not play a significant role. Unlike hypermethylation, promoter hypomethylation was highly conserved between methylomes of Dnmt3a+/Δ CLL and Dnmt3aΔ/Δ CLL (60% overlap) and the effects on gene expression were broader, as 10% of hypomethylated promoters were associated with overexpression. These data demonstrate that loss of Dnmt3a in CLL results in genome-wide deregulation of DNA methylation and this is primarily due to hypomethylation.

Based on three simple points - Dnmt3a is a DNA methyltransferase, promoter methylation is associated with gene repression, and loss of one Dnmt3a allele is sufficient to induce CLL - we speculated that genes hypomethylated and overexpressed in both mouse Dnmt3a+/− and Dnmt3aΔ/Δ CLL likely represent oncogenic drivers. Using statistical approaches, we identified a signature of 26 genes commonly hypomethylated and overexpressed in both mouse Dnmt3a+/− and Dnmt3aΔ/Δ CLL (HOC genes).

The hypomethylation of HOC gene promoters in Dnmt3a+/− and Dnmt3aΔ/Δ CLL could be linked to Dnmt3a inactivation in normal cells either due to lack of Dnmt3a's de novo or maintenance activity during normal development of B-1a cells. However, we found that HOC gene promoters are hypermethylated in both normal Dnmt3a+/+ and Dnmt3a+/− B-1a cells, suggesting that loss of one allele of Dnmt3a does not affect the methylation status of promoters in normal B-1a cells. Lack of promoter methylation could also result from increased proliferation of tumor cells without a direct link to Dnmt3a. However, analysis of promoter methylation of several HOC genes revealed while these promoters are hypomethylated in Dnmt3a+/− and Dnmt3aΔ/Δ CLL, they remain hypermethylated in IRF4−/−;Vh11 CLL. Such results suggest that the lack of Dnmt3a maintenance methylation activity, rather than proliferation of tumor cells, is responsible for the promoter hypomethylation in HOC genes. It is however, still important to point out that we do not have definitive evidence that observed hypomethylated changes are strictly dependent on Dnmt3a. Further studies will need to address whether promoter hypomethylation is a function of Dnmt3a-defficiency, proliferation, aging or their combinations.

Interestingly, IPA using expression data for the HOC signature placed 24 genes into the category of “cancer”. A closer examination of this signature revealed a number of genes with potential to transform cells, including those of hematopoietic origin. For example, Zbtb32 belongs to a list of genes whose increased expression was recently identified to have positive-predictive value in determining whether patients will develop CLL later in life (Chadeau-Hyam et al., 2014). Gas7, a growth arrest–specific gene is overexpressed in hairy cell leukemia, a slow growing malignancy thought to, like CLL, arise from memory B cells (Dunphy CH, 2006). PVT1 gene locus encodes a long non-coding RNA and several microRNA's with predicted oncogenic functions, as it is a target of tumorigenic translocations and retroviral insertions, and its overexpression correlates with upregulation of MYC. Specifically, PVT1 encodes miR-1206 and miR-1204. The former, miR-1206, is upregulated in tumors of B cell origin such as Burkitt's lymphomas in humans and plasmacytomas in mice (Beck-Engeser et al., 2008; Guan et al., 2007; Huppi et al., 2008). In mice, miR-1204 is overexpressed in retrovirally induced T cell lymphomas. (Beck-Engeser et al., 2008). PDCD1LG2 (PD-L2) is aberrantly upregulated in a significant number of patients with AML (Yang et al., 2013). We also found that several HOC genes were reported to be overexpressed in the Eμ-TCL1 mouse model as well as other mouse models of CLL. Slc7a7, Pstpip2, Pon3, Il5ra and the uncharacterized gene 1810046K07Rik (C11orf53) are among the Top 25 overexpressed genes in the Eμ-TCL1 mouse model of CLL (Nganga et al., 2013). Slc7a7, Arid3b and Ppil1 are overexpressed in CLL/B cell malignancies that develop in Eμ-miR-17~92 transgenic mice overexpressing miR-17~92 polycistronic microRNA (Sandhu et al., 2013). Some of these genes or their close relatives show putative oncogenic functions. For example, human PON3 is markedly overexpressed in a variety of human neoplasias and has antiapoptotic function (Schweikert et al., 2012). The role of another HOC gene, SLC7A7, is poorly understood but its relative SLC7A5 (LAT1) is associated with high proliferation and poor prognosis in newly diagnosed patients with multiple myeloma – a B cell malignancy (Isoda et al., 2014). Altogether, these data strongly suggest that HOC signature contains oncogenic drivers of CLL that contribute to the transformation of B-1a cells. However, the genes that are oncogenic drivers of CLL do not necessarily need to be limited to only hypomethylated and overexpressed genes. Indeed, other cancer-associated genes are found in gene expression profiles obtained from Dnmt3a+/− and Dnmt3aΔ/Δ CLL. Whatever the case, at a minimum, the HOC signature represents genes likely regulated by Dnmt3a-dependent methylation, providing an opportunity to study the nature of deregulated methylation during disease development and progression. Future functional studies will dissect the potential contribution of these genes to the development of CLL.

Experimental Procedures

Mouse Studies

All mice used in these studies were of the FVB/N background and were generated using standard genetic crosses. To obtain mice with a germline transmission of the Dnmt3a− allele, we crossed EμSRα-tTA;Teto-Cre;Dnmt3afl/fl mice with FVB mice, taking advantage of our observation that the EμSRα-tTA transgene is expressed in germ cells (data not shown). To generate Dnmt3a+/− we subsequently bread out transgenes by crossing obtained mice with FVB mice. Mice were harvested at the experimental end point of 16 months.

FACS analysis

Mice diagnosed with MBL had between 2 to 20% B-1a in the blood at time of harvest, while those diagnosed with CLL had greater than 20% B-1a in the blood. Mice diagnosed with MPD had profound expansion of CD11b+Gr-1+ cells in the blood (>70%), and spleen (>40%).

Whole genome Bisulfite sequencing (WGBS)

Splenic B-1a cells (EGFP+CD5+CD19+B220+) were FACS sorted from Dnmt3a+/Δ (EμSRα-tTA;Teto-Cre;Dnmt3+/fl;Rosa26LOXPEGFP/EGFP; n=1) and Dnmt3aΔ/Δ (EμSRα-tTA;Teto-Cre;Dnmt3fl/fl;Rosa26LOXPEGFP/EGFP; n=1) mice suffering from CLL. Control B-1a cells were isolated from healthy age-matched Dnmt3a+/+ control mice (EμSRα-tTA;Teto-Cre;Dnmt3a+/+;Rosa26LOXPEGFP/EGFP; n=1). Data is available at the NCBI Gene Expression Omnibus (GSE78146). Details regarding the number of aligned sequencing reads per sample can be found in Table S9. Additional description of the data analysis is provided in the supplemental methods.

Statistical Analysis

In Figures 1C, 1H, 2F, 2G, 5B, and 5D, P-values were calculated using a 2-sample student t tests in Excel. For Figure 3D, a Bonferroni's multiple comparison test was used to assess statistical difference between groups. For RNA-seq data, CuffDiff software was used for statistical analysis of differentially expressed genes (q-value <0.05). DMRS were determined using the R software package DSS (Wu et al., 2015). A Wald test was performed at each CpG site to obtain P-values. For MSCC, count data was analyzed by edgeR which uses Bayesian estimation and exact tests based on the negative binomial distribution to make pairwise comparisons. P-values were estimated using the Benjamini Hochberg method. Only a P<0.05 was considered significant.

Supplementary Material

1
6
7
8
9
10
11
12
13
2
3
4
5

Acknowledgements

The authors thank the University of Nebraska Epigenomics Core Facility, UNMC Flow Cytometry Research Facility and the UNMC Tissue Sciences Facilities. The authors also thank to DNA Services facility at the University of Illinois at Urbana-Champaign, Roy J. Carver Biotechnology Center / W.M. Keck Center for performing WGBS. This work was supported by NIH/NCI: 1R01CA188561-01A1 grant (RO), an Eppley Cancer Center pilot project grant (RO), an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health 5P30GM106397, and funding from the LB595 Nebraska Cancer and Smoking Disease Research Program (PS). SLH and RAH were supported by UNMC fellowship. SLH was supported by NIH T32 award CA009476.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Author Contributions: Conceptualization, S.L.H. and R.O.; Methodology, S.L.H. and J.O.; Investigation, S.L.H., G.M.U, J.O., R.A.H., A.S., and R.O.; Formal Analysis S.L.H., G.M.U., D.K. S.C. and S.J.P.; Data Curation, D.K., Visualization, J.O.; Resources S.J., V.S., R.L., P.S.,; Writing – Original Draft S.L.H., G.M.U. and RO; Writing – Review & Editing, S.L.H., A.K. A.R. and R.O.; Supervision R.O; Project Administration, R.O.; Funding Acquisition, S.L.H., R.A.H., and R.O.

Citations

  1. Beck-Engeser GB, Lum AM, Huppi K, Caplen NJ, Wang BB, Wabl M. Pvt1-encoded microRNAs in oncogenesis. Retrovirology. 2008;5:4. doi: 10.1186/1742-4690-5-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Celik H, Mallaney C, Kothari A, Ostrander EL, Eultgen E, Martens A, Miller CA, Hundal J, Klco JM, Challen GA. Enforced differentiation of Dnmt3a-null bone marrow leads to failure with c-Kit mutations driving leukemic transformation. Blood. 2015;125:619–628. doi: 10.1182/blood-2014-08-594564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chadeau-Hyam M, Vermeulen RC, Hebels DG, Castagné R, Campanella G, Portengen L, Kelly RS, Bergdahl IA, Melin B, Hallmans G, et al. Prediagnostic transcriptomic markers of Chronic lymphocytic leukemia reveal perturbations 10 years before diagnosis. Ann. Oncol. 2014;25:1065–1072. doi: 10.1093/annonc/mdu056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Challen GA, Sun D, Jeong M, Luo M, Jelinek J, Berg JS, Bock C, Vasanthakumar A, Gu H, Xi Y, et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 2011;44:23–31. doi: 10.1038/ng.1009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Challen GA, Sun D, Mayle A, Jeong M, Luo M, Rodriguez B, Mallaney C, Celik H, Yang L, Xia Z, et al. Dnmt3a and Dnmt3b have overlapping and distinct functions in hematopoietic stem cells. Cell Stem Cell. 2014;15:350–364. doi: 10.1016/j.stem.2014.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chang YI, You X, Kong G, Ranheim E,A, Wang J, Du J, Liu Y, Zhou Y, Ryu MJ, Zhang J. Loss of Dnmt3a and endogenous KrasG12D/+ cooperate to regulate hematopoietic stem and progenitor cell functions in leukemogenesis. Leukemia. 2015;29:1847–1856. doi: 10.1038/leu.2015.85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen S, Sherman MH, Hertlein E, Johnson AJ, Teitell MA, Byrd JC, Plass C. Epigenetic alterations in a murine model for chronic lymphocytic leukemia. Cell Cycle. 2009;8:3663–3667. doi: 10.4161/cc.8.22.9957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Datta J, Majumder S, Bai S, Ghoshal K, Kutay H, Smith DS, Crabb JW, Jacob ST. Physical and functional interaction of DNA methyltransferase 3A with Mbd3 and Brg1 in mouse lymphosarcoma cells. Cancer Res. 2005;65:10891–10900. doi: 10.1158/0008-5472.CAN-05-1455. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  9. Dunphy CH. Gene expression profiling data in lymphoma and leukemia: review of the literature and extrapolation of pertinent clinical applications. Arch. Pathol. Lab. Med. 2006;130:483–520. doi: 10.5858/2006-130-483-GEPDIL. [DOI] [PubMed] [Google Scholar]
  10. Gao XN, Yan F, Lin J, Gao L, Lu XL, Wei SC, Shen N, Pang JX, Ning QY, Komeno Y, et al. AML1/ETO cooperates with HIF1α to promote leukemogenesis through DNMT3a transactivation. Leukemia. 2015;29:1730–1740. doi: 10.1038/leu.2015.56. [DOI] [PubMed] [Google Scholar]
  11. Guan Y, Kuo WL, Stilwell JL, Takano H, Lapuk AV, Fridlyand J, Mao JH, Yu M, Miller MA, Santos JL, et al. Amplification of PVT1 contributes to the pathophysiology of ovarian and breast cancer. Clin. Cancer Res. 2007;13:5745–5755. doi: 10.1158/1078-0432.CCR-06-2882. [DOI] [PubMed] [Google Scholar]
  12. Haferlach T, Kohlmann A, Wieczorek L, Basso G, Kronnie GT, Béné MC, De Vos J, Hernández JM, Hofmann WK, Mills KI, et al. Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. J. Clin. Oncol. 2010;28:2529–2537. doi: 10.1200/JCO.2009.23.4732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Haney SL, Hlady RA, Opavska J, Klinkebiel D, Pirruccello SJ, Dutta S, Datta K, Simpson MA, Wu L, Opavsky R. Methylation-independent repression of Dnmt3b contributes to oncogenic activity of Dnmt3a in mouse MYC-induced T-cell lymphomagenesis. Oncogene. 2015;34:5436–5446. doi: 10.1038/onc.2014.472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hirst M, Marra MA. Next generation sequencing based approaches to epigenomics. Brief Funct Genomics. 2010;9:455–465. doi: 10.1093/bfgp/elq035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hlady RA, Novakova S, Opavska J, Klinkebiel D, Peters SL, Bies J, Hannah J, Iqbal J, Anderson KM, Siebler HM, et al. Loss of Dnmt3b function upregulates the tumor modifier Ment and accelerates mouse lymphomagenesis. J. Clin. Invest. 2012;122:163–177. doi: 10.1172/JCI57292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Huppi K, Volfovsky N, Runfola T, Jones TL, Mackiewicz M, Martin SE, Mushinski JF, Stephens R, Caplen NJ. The identification of microRNAs in a genomically unstable region of human chromosome 8q24. Mol. Cancer Res. 2008;6:212–221. doi: 10.1158/1541-7786.MCR-07-0105. [DOI] [PubMed] [Google Scholar]
  17. Isoda A, Kaira K, Iwashina M, Oriuchi N, Tominaga H, Nagamori S, Kanai Y, Oyama T, Asao T, Matsumoto M, et al. Expression of L-type amino acid transporter 1 (LAT1) as a prognostic and therapeutic indicator in multiple myeloma. Cancer Sci. 2014;105:1496–1502. doi: 10.1111/cas.12529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jeong M, Sun D, Luo M, Huang Y, Challen GA, Rodriguez B, Zhang X, Chavez L, Wang H, Hannah R, et al. Large conserved domains of low DNA methylation maintained by Dnmt3a. Nature Genet. 2014;46:17–23. doi: 10.1038/ng.2836. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kim SJ, Zhao H, Hardikar S, Singh AK, Goodell MA, Chen T. A DNMT3A mutation common in AML exhibits dominant-negative effects in murine ES cells. Blood. 2013;122:4086–4089. doi: 10.1182/blood-2013-02-483487. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kulis M, Heath S, Bibikova M, Queirós AC, Navarro A, Clot G, Martínez-Trillos A, Castellano G, Brun-Heath I, Pinyol M, et al. Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nat. Genet. 2012;44:1236–1242. doi: 10.1038/ng.2443. [DOI] [PubMed] [Google Scholar]
  21. Mayle A, Yang L, Rodriguez B, Zhou T, Chang E, Curry CV, Challen GA, Li W, Wheeler D, Rebel VI, et al. Dnmt3a loss predisposes murine hematopoietic stem cells to malignant transformation. Blood. 2015;125:629–638. doi: 10.1182/blood-2014-08-594648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Nganga VK, Palmer VL, Naushad H, Kassmeier MD, Anderson DK, Perry GA, Schabla NM, Swanson PC. Accelerated progression of chronic lymphocytic leukemia in Emu-TCL1 mice expressing catalytically inactive RAG1. Blood. 2013;121:3855–3866. doi: 10.1182/blood-2012-08-446732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Nguyen S, Meletis K, Fu D, Jhaveri S, Jaenisch R. Ablation of de novo DNA methyltransferase Dnmt3a in the nervous system leads to neuromuscular defects and shortened lifespan. Dev. Dyn. 2007;236:1663–1676. doi: 10.1002/dvdy.21176. [DOI] [PubMed] [Google Scholar]
  24. Okano M, Bell DW, Haber DA, Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999;99:247–257. doi: 10.1016/s0092-8674(00)81656-6. [DOI] [PubMed] [Google Scholar]
  25. Palamarchuk A, Yan PS, Zanesi N, Wang L, Rodrigues B, Murphy M, Balatti V, Bottoni A, Nazaryan N, Alder H, et al. Tcl1 protein functions as an inhibitor of de novo DNA methylation in B-cell chronic lymphocytic leukemia (CLL) Proc Natl Acad Sci U S A. 2012;109:2555–2560. doi: 10.1073/pnas.1200003109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Peters SL, Hlady RA, Opavska J, Klinkebiel D, Novakova S, Smith LM, Lewis RE, Karpf AR, Simpson MA, Wu L, et al. Essential role for Dnmt1 in the prevention and maintenance of MYC-induced T-cell lymphomas. Mol. Cell Biol. 2013;33:4321–4333. doi: 10.1128/MCB.00776-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Peters SL, Hlady RA, Opavska J, Klinkebiel D, Pirruccello SJ, Talmon GA, Sharp JG, Wu L, Jaenisch R, Simpson MA, et al. Tumor suppressor functions of Dnmt3a and Dnmt3b in the prevention of malignant mouse lymphopoiesis. Leukemia. 2014;28:1138–1142. doi: 10.1038/leu.2013.364. [DOI] [PubMed] [Google Scholar]
  28. Russler-Germain DA, Spencer DH, Young MA, Lamprecht TL, Miller CA, Fulton R, Meyer MR, Erdmann-Gilmore P, Townsend RR, Wilson RK, et al. The R882H DNMT3A mutation associated with AML dominantly inhibits wild-type DNMT3A by blocking its ability to form active tetramers. Cancer Cell. 2014;25:442–454. doi: 10.1016/j.ccr.2014.02.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Sandhu SK, Fassan M, Volinia S, Lovat F, Balatti V, Pekarsky Y, Croce CM. B-cell malignancies in microRNA Emu-miR-17~92 transgenic mice. Proc. Natl. Acad. Sci. U.S.A. 2013;110:18208–18213. doi: 10.1073/pnas.1315365110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Schweikert EM, Devarajan A, Witte I, Wilgenbus P, Amort J, Förstermann U, Shabazian A, Grijalva V, Shih DM, Farias-Eisner R, et al. PON3 is upregulated in cancer tissues and protects against mitochondrial superoxide-mediated cell death. Cell Death Differ. 2012;19:1549–1560. doi: 10.1038/cdd.2012.35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Shukla V, Ma S, Hardy RR, Joshi SS, Lu R. A role for IRF4 in the development of CLL. Blood. 2013;122:2848–55. doi: 10.1182/blood-2013-03-492769. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wu H, Xu T, Feng H, Chen L, Li B, Yao B, Qin Z, Jin P, Conneely KN. Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic Acids Res. 2015 doi: 10.1093/nar/gkv715. Epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Yang H, Bueso-Ramos C, DiNardo C, Estecio MR, Davanlou M, Geng QR, Fang Z, Nguyen M, Pierce S, Wei Y, et al. Expression of PD-L1, PD-L2, PD-1 and CTLA4 in myelodysplastic syndromes is enhanced by treatment with hypomethylating agents. Leukemia. 2013;28:1280–1288. doi: 10.1038/leu.2013.355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Yang L, Rau R, Goodell MA. DNMT3A in hematological malignancies Nat. Rev. Cancer. 2015;15:152–165. doi: 10.1038/nrc3895. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS776428-supplement-1.pdf (766.7KB, pdf)
6
NIHMS776428-supplement-6.xlsx (38.6MB, xlsx)
7
NIHMS776428-supplement-7.xlsx (2.6MB, xlsx)
8
NIHMS776428-supplement-8.xlsx (16.5KB, xlsx)
9
NIHMS776428-supplement-9.xlsx (41.4KB, xlsx)
10
NIHMS776428-supplement-10.xlsx (253.7KB, xlsx)
11
NIHMS776428-supplement-11.xlsx (26.4KB, xlsx)
12
NIHMS776428-supplement-12.xlsx (9.9KB, xlsx)
13
NIHMS776428-supplement-13.xlsx (11KB, xlsx)
2
NIHMS776428-supplement-2.docx (15.3KB, docx)
3
NIHMS776428-supplement-3.docx (22.2KB, docx)
4
NIHMS776428-supplement-4.xlsx (454.1KB, xlsx)
5
NIHMS776428-supplement-5.xlsx (531KB, xlsx)

RESOURCES