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. Author manuscript; available in PMC: 2019 Sep 5.
Published in final edited form as: Blood Cells Mol Dis. 2018 Jan 4;69:10–22. doi: 10.1016/j.bcmd.2017.12.002

Abnormal RNA splicing and genomic instability after induction of DNMT3A mutations by CRISPR/Cas9 gene editing

Lauren G Banaszak 1, Valentina Giudice 1, Xin Zhao 1, Zhijie Wu 1, Shouguo Gao 1, Kohei Hosokawa 1, Keyvan Keyvanfar 1, Danielle M Townsley 1, Fernanda Gutierrez-Rodrigues 1, Maria del Pilar Fernandez Ibanez 1, Sachiko Kajigaya 1, Neal S Young 1
PMCID: PMC6728079  NIHMSID: NIHMS933305  PMID: 29324392

Abstract

DNA methyltransferase 3A (DNMT3A) mediates de novo DNA methylation. Mutations in DNMT3A are associated with hematological malignancies, most frequently acute myeloid leukemia. DNMT3A mutations are hypothesized to establish a pre-leukemic state, rendering cells vulnerable to secondary oncogenic mutations and malignant transformation. However, the mechanisms by which DNMT3A mutations contribute to leukemogenesis are not well-defined. Here, we successfully created four DNMT3A-mutated K562 cell lines with frameshift mutations resulting in truncated DNMT3A proteins. DNMT3A-mutated cell lines exhibited significantly impaired growth and increased apoptotic activity compared to wild-type (WT) cells. Consistent with previous studies, DNMT3A-mutated cells displayed impaired differentiation capacity. RNA-seq was used to compare transcriptomes of DNMT3A-mutated and WT cells; DNMT3A ablation resulted in downregulation of genes involved in spliceosome function, causing dysfunction of RNA splicing. Unexpectedly, we observed DNMT3A-mutated cells to exhibit marked genomic instability and an impaired DNA damage response compared to WT. CRISPR/Cas9-mediated DNMT3A-mutated K562 cells may be used to model effects of DNMT3A mutations in human cells. Our findings implicate aberrant splicing and induction of genomic instability as potential mechanisms by which DNMT3A mutations might predispose to malignancy.

Keywords: DNMT3A, CRISPR/Cas9, leukemia, genomic instability, RNA splicing

INTRODUCTION

DNA methyltransferase 3A (DNMT3A) is the member of the DNA methyltransferase family primarily involved in de novo gene methylation (Okana et al., 1998). DNA methylation is an epigenetic modification involving the addition of a methyl group to cytosine residues to form 5-methylcytosine (5-mC), usually in the context of a cytosine-guanine (CpG) dinucleotide pair (Holliday and Grigg, 1993). DNA methylation of CpGs in gene regulatory regions influences gene expression, with high levels of DNA methylation generally associated with gene silencing (You and Jones, 2012).

Aberrant DNA methylation has been broadly implicated in the pathogenesis of cancer (Galm et al., 2006). In particular, mutations in the DNMT3A gene are associated with a wide range of hematological malignancies. DNMT3A mutations are found in 20–40% of acute myeloid leukemia (AML) patients (Ley et al., 2010; Roller et al., 2013) and are also reported in myelodysplastic syndrome (MDS), myeloproliferative neoplasms, and T-cell acute lymphoblastic leukemia (Yang et al., 2015). Dnmt3a loss in mouse hematopoietic stem cells (HSCs) predisposes to malignant transformation, further supporting a role of DNMT3A in preventing malignancy (Mayle et al., 2015). Clinically, many studies have demonstrated that the presence of somatic DNMT3A mutations is associated with poor patient prognosis in myeloid neoplasia (Ribeiro et al., 2012; Walter et al., 2011).

DNMT3A mutations may act as driver mutations, producing a pre-leukemic state by rendering cells vulnerable to secondary oncogenic mutations and malignant transformation. DNMT3A mutations are typically present at higher variant-allele frequencies in patients with hematological malignancies, suggesting they occur early, perhaps arising months or years before the development of disease (Welch et al., 2012). In AML patients, mutations in DNMT3A often coexist with secondary lesions in leukemia-related genes such as ASXL1, FLT3, IDH1/2, and TET2, supporting the hypothesis that DNMT3A mutations predispose to secondary oncogenic lesions (Ley et al., 2010). Furthermore, AML patients harbor phenotypically normal HSCs with DNMT3A mutations but without coincident NPM1 mutations present in peripheral blasts, and these HSCs retain the ability to differentiate into multiple lineages, suggesting that DNMT3A mutations confer a pre-leukemic state (Shlush et al., 2014). Similarly, clonal hematopoiesis driven by leukemia-associated genes, with DNMT3A being the most common driver mutation, is common in humans and increases with age. Healthy individuals with such clonal hematopoiesis are at increased risk of developing leukemia and all-cause mortality (Jaiswal et al., 2014; Genovese et al., 2014). We have also recently described a large cohort of aplastic anemia (AA) patients, in whom the presence of adverse somatic mutations, including DNMT3A, was associated with inferior overall survival and more frequent progression to MDS or AML (Yoshizato et al., 2015).

The mechanisms by which DNMT3A mutations contribute to malignant transformation and ultimately to poor patient outcomes are not well-defined. In mice, Dnmt3a-mutated HSCs preferentially self-renew rather than differentiate, leading to accumulation of mutated clones in the bone marrow (Challen et al., 2012). Further, Dnmt3a loss drives hypomethylation and subsequent activation of leukemia-related genes (Lu et al., 2016; Yang et al., 2016). However, these findings have not been recapitulated using human tissue.

The goals of our study were to determine the effects of DNMT3A mutations which contribute to malignant transformation in human cells. To this end, we created DNMT3A mutated (MT) human cell lines using the gene-editing technology CRISPR/Cas9. Compared to conventional gene editing techniques such as RNA interference, CRISPR/Cas9 leads to permanent and complete loss of gene function by altering the genetic code, analogous to mutations that occur in vivo during the development of hematologic malignancy. Our generation of DNMT3A-mutated K562 clones establishes this cell line as a model that can be used to study human DNMT3A-driven leukemogenesis. In addition, we provide potential mechanisms by which DNMT3A mutations predispose to malignancy, including the novel association of DNMT3A loss with spliceosomal dysregulation and genomic instability.

MATERIALS AND METHODS

Cell culture and cytogenetic analysis

The K562 cell line and HAP1 GeneArt engineered DNMT3A KO cell line were purchased from the American Type Culture Collection (ATCC) and Thermo Fisher Scientific, respectively. All cell lines were cultured in IMDM medium supplemented with 10% fetal bovine serum and antibiotics and were incubated at 37°C in a humidified atmosphere of 95% air and 5% CO2. Cells in the logarithmic growth stage were cytospun on slides using the Shandon Cytospin 4 and subjected to staining with the StainRITE® Wright-Giemsa Stain Solution (Polysciences, Warrington, PA) to examine their morphologies. Standard G-band karyotype analysis was performed using passage-matched parental cells within 7 days of thawing (Karyologic, Inc., Durham, NC, USA).

Genome editing

Two pU6-based plasmids were purchased from Santa Cruz Biotechnologies (sc-400323 and sc-418922): a plasmid containing a DNMT3A-targeting guide RNA (gRNA) and Cas9-GFP for generation of DNMT3A MT cell lines; and a non-targeting 20-nucleotide scramble gRNA and Cas9-GFP for creating transfected K562 DNMT3A WT cell lines. Each plasmid (2 µg) was transfected into K562 cells using the Amaxa Cell Line Nucleofector kit according to the manufacturer’s instructions. After transfection and electroporation, cells were seeded onto 12-well plates, and GFP-expressing cells were sorted singly into 96-well plates by fluorescence-activated cell sorting (FACS). Individual single-cell clones were subsequently expanded and genotyped via Sanger sequencing.

Validation of DNMT3A mutations

Sanger sequencing was utilized to validate DNMT3A gene ablation and to determine the mutation induced by the CRISPR/Cas9 system. DNA extracted using the Qiagen DNeasy Blood and Tissue kit was amplified by PCR with primers targeting genomic regions surrounding the CRISPR/Cas9 gRNA target sites using the TaKaRa LA Taq polymerase kit. Purified PCR amplicons were then subjected to TA cloning via the TOPO TA Cloning kit (Invitrogen, Carlsbad, CA). The TempliPhi 100 Amplification Kit (GE Healthcare Life Sciences, Pittsburgh, PA) was used to prepare the DNA templates for sequencing, and the amplified product was subsequently sequenced with adequate primers using the BigDye Terminator v3.1 Cycle Sequencing kit and the 3130xl Genetic Analyzer (Applied Biosystems, Foster City, CA).

Whole-exome sequencing of extracted DNA was performed to confirm DNMT3A mutations. Library construction, exon capture, and sequencing was performed by Otogenetics (Atlanta, GA, USA). In brief, paired-end libraries were generated using the Illumina TruSeq DNA sample preparation kit. Exons were enriched using the Agilent Human All Exon V5 (51 Mb) capture system. Illumina HiSeq2500 was used for sequencing with a paired-end sequencing length of 100–125 bp and approximately 70 million reads per sample (data deposited in the National Center for Biotechnology Information [NCBI] Gene Expression Omnibus [GEO] database [GSE96635, subseries GSE96625]).

Protein extraction and immunoblotting

Cells (1 × 107) were directly lysed using 300 µL of the protein gel loading buffer, boiled at 95°C for 10 minutes, and subjected to gel electrophoresis by loading 45 µL (1.5 × 106 cells/well) of cell lysate onto 8% SDS gels (12-well Novex WedgeWell gels). After transferring onto PVDF membranes, immunoblotting was performed. Briefly, the membranes were incubated with the following primary antibodies (Abs): anti-DNMT3A (C-12; Santa Cruz; Dallas, TX, USA) and anti-β-tubulin (9F3; Cell Signaling; Danvers, MA, USA).Subsequently, membranes were subjected to incubation with anti-mouse IgG (Santa Cruz) or anti-rabbit IgG (Cell Signaling) conjugated with horseradish peroxidase. Signals were detected with a 1:1 mixture of the SuperSignal West Dura Extended Duration Substrate and the SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher, Waltham, MA) using the Image Quant LAS 4000 system (GE Healthcare Life Sciences).

Functional assays

To assess cell growth, four replicates of each K562 cell line were seeded into a 75 mm2 flask at 0.2 × 106 cells/mL and incubated for 8 days without changing the media. Cell numbers were measured daily by the Beckman-Coulter Vi-Cell XR Cell Viability Analyzer. All experiments were conducted using passage-matched parental cells within 7 days of thawing.

To assess apoptosis and the DNA-damage response, exponentially-growing K562 cells were seeded at a density of 5.0 × 105 cells/mL in a 12-well plate and treated with 5-fluorouracil (5-FU) at concentrations of 50 µM, 100 µM, and 250 µM. After 48 hours of culture, apoptotic activity was measured using the FITC Annexin V Apoptosis Detection kit with Propidium Iodide (BioLegend) according to the manufacturer’s instructions. For DNA damage, cells were washed twice with PBS, fixed, and permeabilized. Cells were then stained with anti-pH2AX-FITC (BD Biosciences, San Jose, CA) for 30 minutes at room temperature followed by flow cytometry analysis.

To assess megakaryocytic differentiation, K562 cells in the exponential growth phase were seeded at a density of 5.0 × 105 cells/mL in a 12-well plate and treated with 10 ng/mL phorbal 12-myristate 13-acetate (PMA, Sigma) for 16 and 24 hours. Cells were harvested, stained with anti-CD61-PE (BD Biosciences) for 30 minutes at room temperature, and subjected to flow cytometry analysis. To assess erythroid differentiation, cells in the exponential growth phase were seeded at a density of 4.0 × 105 cells/mL in 3.5mL/well in a 6-well plate and treated with 40 µM Hemin (Sigma Aldrich, Saint Louis, MO) for 48 and 72 hours. Cells were harvested, stained with anti-CD71-APC and Glycophorin A-PE (BD Biosciences) for 20 minutes at room temperature and then subjected to flow cytometry analysis. Samples stained with isotype controls (BD Biosciences) were used as a reference

Regional DNA methylation

Methylated DNA immunoprecipitation (MeDIP) was used to detect regional differences in DNA methylation between DNMT3A WT and MT clones. DNA extraction of WTblk, WT1, MT1, MT2, and MT4 cell lines was performed as previously described. 5-mC profiling, fragmentation, library preparation and sequencing were performed by Otogenetics (Atlanta, GA, USA). In brief, paired-end libraries were generated using the Illumina TruSeq DNA sample preparation kit. Illumina HiSeq2500 was used for sequencing with a paired-end sequencing length of 100–125 bp and approximately 40 million reads per sample (data deposited in NCBI GEO database [GSE96635, subseries GSE96577]).

Raw reads were aligned to the human reference genome (hg19) using Bowtie (version 1.1.1). ngs.plot was used to create genome overviews of binding at gene bodies (Shen et al., 2014). Model-based Analysis of ChIP-Seq (MACS) was used to predict highly methylated regions, and these regions were annotated using PAVIS as previously described (Huang et al., 2013). For differential analysis, the reference genome was divided into non-overlapping 1000-bp windows, and reads in each window for each sample were counted. Differential analysis was then performed between conditions using edgeR in bioconductor. Only windows with a combined coverage of 10 or more reads were included in the analysis. Windows that showed a 4-fold or greater difference (edgeR logFC >2) and p-value < 0.0001 were retained and annotated to the nearest or overlapping gene using ChIPpeakAnno[4] (version 3.2.2) or refseq for 5’ upstream and 5’UTR regions.

RNA sequencing and differential gene expression

Total RNA was extracted from cells using the Qiagen RNeasy Plus Mini kit at cells in the logarithmic and plateau stages of growth. Library construction and sequencing were performed by GeneWiz (South Plainfield, NJ, USA). In brief, paired-end libraries were generated using the Illumina TruSeq RNA sample preparation kit. Illumina HiSeq2500 was used for sequencing with a paired-end sequencing length of 100 bp and approximately 70 million reads per sample (data deposited in NCBI GEO database [GSE96635, subseries GSE96634]).

Sequence reads were trimmed to remove possible adapter sequences and nucleotides with poor quality at the ends. Remaining sequence reads were then aligned to the human reference genome (hg19) using Tophat2. Gene read counts were measured using FeatureCounts and FPKM values were calculated with cufflinks. edgeR was used to identify differentially expressed genes between conditions, and topGO was used for annotation (Alexa, Rahnenfuhrer, and Lengauer, 2006). Sample comparison for differential gene expression was as follows: WTblk and WT1 versus MT2, MT3, MT4, and MT5. Gene enrichment set analysis (GSEA) was conducted with KEGG, Biocarta, and Reactome pathway datasets (Subramanian et al., 2005).

RNA stability and splicing

Sample comparison for differential gene expression was as follows: WTblk and WT1 versus MT2, MT3, MT4, and MT5. The transcriptomic distance between growth conditions for each cell line was calculated by plotMDS in edgeR, which first converts the counts to log-counts-per-million and then calculates distances based on log2-fold changes between samples. miso was used to detect RNA splicing differences, specifically differentially regulated exons or isoforms, between DNMT3A WT and MT cell lines.

Quantitative real-time PCR

Total RNA was extracted from cells as described above. RNA was reverse-transcribed using the SuperScript III reverse transcriptase kit with oligo(dT)18 primers (Invitrogen), and cDNA was used for qPCR analysis using the Fast SYBR Green Master Mix (Life Technologies). TaqMan probes for target genes were purchased from ThermoFisher Scientific. GAPDH and beta-tubulin (TUBB) were used as loading controls. Triplicate samples were analyzed using the 7900HT Fast Real-Time PCR System (Applied Biosystems), and expression ratios were calculated using the ΔΔCT method.

RESULTS

Establishment of CRISPR/Cas9-mediated DNMT3A-mutated K562 cell lines

To elucidate effects of DNMT3A gene ablation, we introduced DNMT3A frameshift mutations into K562 cells using the CRISPR/Cas9 gene-editing system. In brief, K562 cells were transfected with a plasmid encoding Cas9, a CRISPR guide-RNA (gRNA) targeting the DNMT3A gene, and green fluorescent protein (GFP) followed by single-cell sorting. GFP-positive single cell clones were genotyped to confirm the presence of DNMT3A mutations. We generated five DNMT3A-mutated (MT) cell lines, denoted MT1, MT2, MT3, MT4, and MT5, which harbored mutations in exon 2 and/or exon 3 (Figure 1A–C). Genomic DNA mutations were defined by direct sequencing based on TA cloning (Figure 1C). Cell lines MT2-MT5 with frameshift mutations yielded truncated DNMT3A proteins lacking the catalytically active C-terminal domain. In MT1, a mutation induced in exon 3 of one allele cooperated to reverse the initial frameshift mutation in exon 2, producing a DNMT3A protein with an altered N-terminus but an intact C-terminal catalytic domain. Thus, MT1 was considered to be more like WT in its DNMT3A activity and used as an internal WT control. Eight transfected wild-type K562 cell lines (WT1-WT8) were generated by transfection of a non-targeting 20-nucleotide scramble gRNA. Bulk parental K562 cells (WTblk) that did not undergo single-cell sorting were also used as a control. gRNA-target sites of DNMT3A exons 2 and 3 in WTblk and WT1 cells, illustrated in Supplemental Figure 1, confirm the absence of mutations. Further, the DNMT3A mutations in MT2-MT5 were verified by analysis of whole-exome sequencing data (Supplemental Figure 1B), and WTblk and WT1 were again confirmed to have no mutations in the DNMT3A gene. As expected, MT cell lines produced less DNMT3A protein compared WT; interestingly, MT1 produced a smaller amount of DNMT3A protein but at a slightly lower molecular weight, consistent with some functional DNMT3A protein being produced in this cell line as predicted by our genomic analysis (Figure 1D). Morphologically, DNMT3A MT cell lines appeared to have more cytoplasmic vacuoles and were slightly larger but otherwise no dramatic changes in morphology by conventional microscopy were detected compared to DNMT3A WT cells (Figure 1E).

Figure 1. Creation of DNMT3A-mutated cell lines.

Figure 1

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(A) Structures of DNMT3A mRNA and protein. Shown are two gRNA sequences and their associated target sites in the mRNA. The corresponding mutated site is indicated in the translated DNMT3A protein. DNMT3A functional regions and domains are shown. (B) Confirmation of DNMT3A mutations by Sanger sequencing; as examples, mutations in exons 2 and 3 in MT4 are shown. Exon 2 harbored a homozygous four-nucleotide deletion (underlined in red), and exon 3 carried a homozygous two-nucleotide insertion (underlined in green). (C) Summary of CRISPR/Cas9-mediated DNMT3A mutations in exons 2 and 3 of all DNMT3A-mutated cell lines (MT1-MT5) with the associated change in DNMT3A protein. All genomic and protein sequence variants are described using Human Genome Variation Society (HGVS) nomenclature. WT represents a wild-type sequence with no mutations. (D) Immunoblot of WT and MT cell lines. Samples were loaded onto SDS 8% gel and submitted to electrophoresis. β-tubulin was used as a loading control. (E) Representative Wright-Giemsa stains of WT1, MT2, MT3, and MT4 cell lines.

DNMT3A ablation leads to impaired growth, increased apoptotic activity, and decreased differentiation capacity

We first assessed the effects of DNMT3A loss on cell growth, apoptosis, and differentiation. Upon growth curve analysis, DNMT3A MT clones (MT1-MT5) exhibited altered growth patterns as compared to WT cell lines (WTblk and WT1; Figure 2A), but growth rates were clone-specific, making it difficult to discern an overall trend. However, averaging of the WT and MT growth curves revealed that DNMT3A loss in general led to significantly impaired growth (Figure 2A). DNMT3A MT cell lines also displayed decreased cell viability in culture compared to WTblk and WT1 cell lines (Figure 2B). To better elucidate the effects of DNMT3A mutations on cell fitness, we assessed apoptosis of DNMT3A WT and MT cells before and after induction of cell stress with 5-fluorouracil (5-FU), a chemotherapeutic agent. DNMT3A MT cell lines 2, 3, 4, and 5 had significantly increased levels of apoptotic cells after exposure to 5-FU, and they showed increased apoptosis even when untreated (Figure 2C; Supplemental Figure 2). MT1, with one allele encoding a catalytically-active C-terminal domain of DNMT3A, had similar apoptotic rates to WT, suggesting the presence of functional DNMT3A protein in this cell line. Collectively, DNMT3A loss appears to impair cell growth and to render cells prone to apoptosis, but not to confer a proliferative phenotype.

Figure 2. DNMT3A mutants exhibit altered cell growth, increased apoptotic activity, and impaired differentiation capacity.

Figure 2

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(A) Growth curves of WTblk, WT1, and MT (MT1-MT5) cell lines (upper panel; mean ± SD). The average cell number of WTblk and WT1 cells was compared to the average cell number of MT1-MT5 cell lines at each time point (lower panel; mean ± SD). Data shown are representative of three separate experiments. (B) Viability of WTblk, WT1, and MT cell lines in culture (upper panel; mean ± SD). The average viability of WT and MT cell lines at indicated time points were measured in a similar manner as described above (lower panel; mean ± SD). (C) Apoptosis. WTblk, WT1, and MT (MT1-MT5) cell lines were treated with various concentrations (0, 50, 100, and 250, µM) of 5-FU for 48 hours. Apoptotic activity was measured by annexin V (mean ± SD) using flow cytometry. Data shown are representative of two separate experiments, and the average apoptosis of WTblk and WT1 are represented together as WT. The lower panel shows representative flow cytometry data for WTblk, WT1, and MT2 cell lines. (D) Differentiation. WTblk, WT1, and MT (MT1-MT5) cell lines were stimulated with 10 ng/mL of PMA for 16 and 24 hours. Megakaryocytic differentiation was measured by CD61 expression (mean ± SD) using flow cytometry. Data shown are representative of two separate experiments, and the average apoptotic activity of WTblk and WT1 are represented together as WT. The lower panel shows representative flow cytometry data after 16 hours of PMA treatment for all cell lines. *p < 0.05 (Student’s T test)

Previous studies have reported that DNMT3A ablation in hematopoietic stem cells (HSCs) results in a differentiation block, such that DNMT3A-mutated cells preferentially undergo self-renewal and accumulate in the bone marrow (Challen et al., 2012; Koya et al., 2016). We sought to recapitulate these effects by treating our cell lines with phorbal 12-myristate 13-acetate (PMA), which induces megakaryocytic differentiation of K562 cells. After 16 hours of treatment with PMA, four MT cell lines (MT2-MT5) exhibited less CD61 expression, a marker of megakaryocytic differentiation, than did WTblk and WT1, but MT1 had a differentiation pattern similar to WT cells. However, after 24-hour treatment, all five MT cell lines exhibited identical degrees of differentiation (Figure 2D; Supplemental Figure 3). In regards to erythroid differentiation, the MT cell lines on average displayed higher concentrations of erythroid markers at baseline and displayed similar if not more robust differentiation potential compared to WT (Supplemental Figure 4). Thus, our data demonstrate that DNMT3A loss affects megakaryocytic differentiation of K562 cells, supporting the role of DNMT3A in the differentiation of multipotent progenitors in addition to HSCs. However, this differentiation defect was not apparent for erythropoiesis, possibly because DNMT3A-mutated cells are more skewed to erythroid differentiate at baseline than are WT cells.

No alteration of regional DNA methylation patterns observed in DNMT3A MT cell lines

We used methylated DNA immunoprecipitation (MeDIP) to detect regional differences in DNA methylation between DNMT3A WT and MT cell lines. First, methylation patterns common to all samples were interrogated. We found methylation to be significantly enriched upstream of genes, in the 5’UTR, and in exonic sequences, consistent with knowledge of the high CpG content of these regions (Figure 3A; Supplemental Figure 5). In contrast, a decrease in methylation was observed upstream of transcription start sites (TSSs) and at transcription end sites (TESs) of genes, with a gradual increase in methylation seen throughout the gene body, as previously described (Staunstrup et al., 2016; Figure 3B). However, this pattern did not differ between DNMT3A WT and MT cell lines. We attempted to identify differentially methylated genes based on DNMT3A mutation status and found very few regions of differential methylation between WT and MT cell lines (Figure 3C), which did not exceed the number of regions expected to exhibit differential methylation by chance alone. These data suggest that regional methylation patterns do not differ between DNMT3A MT and WT cell lines in our gene-edited K562 cells.

Figure 3. DNMT3A mutation not associated with changes in regional DNA methylation patterns.

Figure 3

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To quantify regional differences in DNA methylation, DNA was extracted from WTblk, WT1, MT1, MT2, and MT4 cell lines and subjected to methylated DNA immunoprecipitation (MeDIP). Approximately 40 million reads per sample were generated using a paired-end sequencing length of 100–125 bp using the Illumina HiSeq2500 system. (A) Pie charts showing proportions of methylated CpG sites in annotated genomic DNA regions of WTblk, MT2, and MT4. (B) Comparison of methylated genomic DNA patterns of WTblk, WT1, MT1, MT2, and MT4 cell lines. Methylated DNA profiling was achieved by calculating an average read count (read count/106 mapped reads) across transcribed genomic regions ± 2kb. Transcription start site (TSS) and transcription end site (TES) are highlighted in blue. (C) Differentially methylated genes, with red referring to hypermethylation in MT cell lines and blue indicating hypermethylation in WT cell lines.

DNMT3A mutation leads to dysregulation of genes involved in spliceosome function and disruption in RNA splicing

RNA-seq was employed to investigate differential gene expression between DNMT3A WT (WTblk, WT1) and MT (MT2-MT5) cell lines. Twenty-one genes were found to be differentially expressed between WT and MT cell lines (Figure 4A; Supplemental Table 1). Gene set enrichment analysis (GSEA) was then performed to detect perturbations in specific gene pathways. GSEA is designed to identify modest but coordinate regulation of functionally-related genes even when individual expression analysis fails to detect a significant change, as previously described (Mootha et al., 2003). In our study, GSEA revealed significantly downregulated genes involved in spliceosome maintenance and RNA degradation pathways (Figure 4A–B); this was confirmed by qPCR (Figure 4C). We also found that genes related to ribosome function and processing were downregulated in DNMT3A MT cell lines (data not shown).

Figure 4. DNMT3A loss leads to downregulation of spliceosome and RNA degradation genes.

Figure 4

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Total RNA extracted from WTblk, WT1, and MT2-MT5 cell lines were subjected to RNA sequencing. Approximately 70 million reads per sample were generated using a paired-end sequencing length of 100 bp using the Illumina HiSeq2500 system. edgeR was used to identify differentially expressed genes between conditions. (A) Heat map of differentially expressed genes. (B) Gene set enrichment analysis (GSEA) showing downregulation of genes involved in spliceosome maintenance and RNA degradation in MT cell lines. (C) qPCR confirmation of GSEA results. TaqMan qPCR was performed to verify gene expression levels. The following genes were analyzed based on our RNA sequencing results: PRPF40A, PRPF40B, SF3B1, SNRNP40, DDX23, and SRSF2.

Splicing variation and transcriptome instability are molecular markers of malignancy, such as in colorectal, breast, and lung cancers (Venables, 2004; Sveen et al., 2011; Sveen et al., 2014). Thus, we sought to determine if observed downregulation of spliceosome and RNA degradation genes are associated with concomitant changes in RNA splicing and stability. To quantify RNA stability, we measured the transcriptomic distance between RNA extracted from cells during logarithmic growth and plateau growth stages of each cell line. Larger transcriptomic distances indicate greater variability in the transcriptome between conditions. The average distance between these growth stages was larger within MT cell lines, indicating greater transcriptome instability with cell growth, although this difference did not reach statistical significance with our limited number of samples (Figure 5A).

Figure 5. DNMT3A-mutated cell lines exhibit transcriptome instability.

Figure 5

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(A) Average transcriptomic distance between RNA extracted from cells during logarithmic growth and plateau growth stages in WTblk, WT1, and MT (MT2-MT5) cell lines. The transcriptomic distance was calculated by plotMDS in edgeR; first, reads are converted to log-counts-per-million, and the distance is then calculated based on log2-fold changes between samples. A larger distance indicates greater variability between growth conditions. (B) Frequency of DNMT3A mRNA splicing abnormalities detected using the TA cloning-based method. cDNA converted from mRNA extracted from WTblk, WT1, and MT (MT1-MT5) cell lines was subjected to TA cloning and sequence analysis. (C) Summary of DNMT3A mRNA splicing abnormalities in WTblk, WT1, and MT (MT1-MT5) cell lines. All genomic sequence variants are described using Human Genome Variation Society (HGVS) nomenclature. *A sequence identical to the that of exon 2 was inserted in between exons 6 and 7 in this clone.

We next used TA cloning to analyze RNA splicing by sequencing DNMT3A mRNA derived from WTblk, WT1, and MT (MT1-5) cell lines. In these experiments, aberrant mRNA processing was detected in all MT clones, most frequently as large deletions involving exons 2 and 3 adjacent to the CRISPR gRNA target sites. The predominant mRNA transcript in MT1 harbored a large deletion involving exons 2 and 3 but contained nucleotides 134–141 not expected to be present based on the observed genomic DNA sequence. Surprisingly, we also observed abnormal splicing downstream, with abnormal retention of intronic sequences being the most frequently observed splicing error (Figure 5B–C). The mRNA sequences of seven other transfected WT (WT2-WT8) cell lines were scrutinized using the TA cloning method, and no splicing abnormalities were detected (data not shown).

We next compared global RNA splicing patterns in DNMT3A MT and WT cell lines. First, we calculated the proportion of read counts in exon and intron fractions of genes. If decreased spliceosome function in DNMT3A MT cell lines were leading to abnormal splicing, we would expect a greater number of reads containing intronic sequences in DNMT3A MT cell lines, indicating abnormal pre-RNA processing and intron retention. In our cell lines, the mapping ratios to exonic regions were comparable to published studies, suggesting little DNA contamination. Interestingly, DNMT3A MT cell lines exhibited a trend toward lower proportion of reads mapping to exonic sequences and a greater proportion of reads mapping to intronic sequences as compared to WT cells (Figure 6A). However, our study was not powered sufficiently for this to reach statistical significance for most MT cell lines. We next determined the splicing ratio for each cell line, which is the proportion of spliced transcripts in all spliced and non-spliced reads in a sample. Thus, cell lines exhibiting aberrant splicing and a high proportion of non-spliced reads would be expected to have a low splicing ratio. Indeed, we observed a statistically significant reduced splicing ratio in all MT cells compared to WT (Figure 6B). When specific splicing patterns were analyzed, 338 aberrant splicing events in the pooled DNMT3A MT cell lines were observed compared to WT, among which 234 (69%) represented abnormal exon skipping and/or retention (Figure 6C–D). Thus, we inferred DNMT3A mutations to lead to decreased RNA stability and abnormal RNA splicing.

Figure 6. DNMT3A mutations are associated with abnormal RNA splicing.

Figure 6

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RNA extracted from WTblk, WT1, and MT (M2-MT5) cell lines was subjected to RNA sequencing. Approximately 70 million reads per sample were generated using a paired-end sequencing length of 100 bp using the Illumina HiSeq2500 system. Sequence reads were aligned to hg19 human reference genome using Tophat2, read counts were quantified using FeatureCounts, and annotation was performed using topGO. (A) Proportion of read counts in exon and intron fractions of WTblk, WT1, and MT (MT2-MT5) cell lines (left and middle panels; mean ± SD). Averaging of WT and MT cell lines showed significantly fewer exonic read counts and increased intronic read counts in MT cell lines (right panel; mean ± SD). (B) Proportions of spliced reads (left panel; mean ± SD) in WTblk, WT1, and MT (MT2-MT5) cell lines. Averaging of WT and MT cell lines showed significantly fewer spliced transcripts in MT cell lines (right panel; mean ± SD). (C) miso was used to detect RNA splicing differences between WT (WTblk, WT1) and pooled MT (MT2-MT5) cell lines. 338 aberrant splicing events specific to MT cell lines were detected: 234 (69%) exon skipping or retention, 19 (6%) intron retention, 49 (14%) alternative 3’ splice site usage, and 36 (11%) alternative 5’ splice site usage. (D) Representative examples of altered RNA splicing in MT cell lines showing aberrant exon skipping (upper left panel, upper right panel, lower right panel) and exon retention (lower left panel) in MT cell lines in genes located on chromosomes 1, 3, 11, and 16. *p < 0.05 (Student’s T test)

Genomic DNMT3A ablation is associated with cytogenetic variability and alters the DNA-damage response

To address whether DNMT3A loss affected genomic integrity, we performed karyotype analysis of DNMT3A WT and MT cell lines (Figure 7A–B). Surprisingly, four MT cell lines (MT2-MT5) exhibited profound cytogenetic variability, with karyotypes differing markedly from the parental WTblk cell line (Figure 7A; Supplemental Figure 6 and Supplemental Table 2). Compared to WTblk and WT1 cells, extensive alterations of chromosome number and structure, especially of chromosomes 2 and 7, were observed in MT cells, with the exception of MT1 (Figure 7B; Supplemental Figure 6 and Supplemental Table 2). Additionally, most DNMT3A MT cells contained dicentric chromosomes and ring forms in multiple spreads, most notably 14/20 spreads in MT2 (Figure 7B). Karyotype analysis was performed on five additional WT cell lines (WT2-WT6) generated by transfection of a non-targeting gRNA and confirmed to harbor a WT DNMT3A gene, and we observed almost identical karyotypes to the parental WTblk cell line (data not shown). Collectively, our data suggest that DNMT3A mutations lead to karyotypic abnormalities that cannot be attributed to the inherent instability of the K562 cell line.

Figure 7. DNMT3A ablation is associated with genomic instability.

Figure 7

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(A) Representative karyotypes of WTblk, MT2, MT4, and MT5. Red circles indicate ploidy different from the parental WTblk cell line. (B) Summary of karyotype data. Twenty spreads were analyzed for each cell line. Shading indicates alterations in chromosome number or structure that differ from WTblk. Blue, no change from WTblk; green, alterations in 4–6 spreads; orange, alterations in 7–9 spreads; and red, alterations in ≥ 10 spreads. The presence of dicentric chromosomes in any number of spreads is indicated by purple shading. (C) DNA double-stranded breaks quantified by pH2AX positivity. WTblk, WT1, and MT (MT1-MT5) cell lines were subjected to treatment with various concentrations (0, 50, 100, and 250 µM) of 5-FU for 48 hours. DNA damage was measured by pH2AX staining of DNA double-stranded breaks (mean ± SD) using flow cytometry. The left panel shows representative flow cytometry data of WTblk, WT1, and MT4 cell lines. The percentage of pH2AX expression in individual cell lines is shown in the left panel. WT data represents the average of WTblk and WT1. Data are representative of two separate experiments. *p < 0.05 (Student’s T test)

We next sought to validate these findings using a different DNMT3A-mutated cell line (HAP1), which contains a CRISPR/Cas9-mediated 17-base pair deletion in a coding exon. The parental HAP1 cell line derived from KBM-7 cells (a chronic myelogenous leukemia cell line) is known to display a near-haploid karyotype except for a heterozygous 30Mb fragment of chromosome 15 integrated onto the long arm of chromosome 19. When karyotyping was performed on the HAP1 DNMT3A KO and parental WT cell lines, we again observed cytogenetic variability and even a diploid karyotype in multiple metaphase spreads of the DNMT3A KO cell line, consistent with our observations in the K562 DNMT3A MT cell lines, strongly supporting that DNMT3A loss leads to abnormal ploidy and aberrant chromosomal structures (Supplemental Figure 7 and Supplemental Table 3). To exclude the CRISPR/Cas9 gene editing process as a cause of this genomic instability, we performed karyotype analysis of K562 cells with a CRISPR/Cas9-induced BCOR mutation (somatic lesion associated with good prognosis in aplastic anemia patients; Yoshizato et al., 2015). We found no differences in karyotypes between K562 BCOR WT and BCOR-mutated cell lines (data not shown), indicating that Cas9-induced DNA mutagenesis did not itself cause altered karyotypes in our model. Taken together, these results suggest that loss of DNMT3A confers chromosomal instability and subsequent cytogenetic variability.

To further test this hypothesis, we examined the DNA-damage response of our cell lines by measuring DNA double-stranded breaks before and after treatment with 5-FU. As expected, DNMT3A MT (MT2-MT5) cells were more susceptible to DNA damage than were WTblk and WT1, whereas MT1 displayed significantly more DNA double-stranded breaks compared to WT only when treated at the highest concentration of 5-FU (Figure 7C; Supplemental Figure 8). These results again support a role of DNMT3A in maintaining genomic integrity.

DISCUSSION

In the current study, we created four DNMT3A-mutated cell lines with frameshift mutations that generated truncated DNMT3A proteins missing their catalytic active sites. Additionally, we independently produced one cell line with cooperating mutations such that the DNMT3A protein had an altered N-terminus but an intact catalytic domain, potentially serving as a DNMT3A mutant with residual WT DNMT3A activity. Rather than conferring a proliferative phenotype, DNMT3A mutations in our model led to impaired cell growth and increased apoptosis. In addition to the established role of DNMT3A in HSC differentiation, we describe maintenance of RNA splicing and genomic integrity as additional functions that become strikingly dysregulated when DNMT3A is mutated. DNMT3A MT cell lines exhibited cytogenetic variability upon karyotyping, transcriptomic instability, and vulnerability to DNA damage. Additionally, DNMT3A loss led to downregulation of spliceosome genes and abnormal RNA splicing, both of the DNMT3A mRNA and globally. Indel mutations induced by the Cas9 endonuclease have been shown to cause splicing abnormalities of the affected gene, presumably by altering exonic splicing enhancers and/or exonic splicing silencers (Kapahnke, Banning, and Tikkanen, 2016). It is therefore possible that alteration of DNMT3A transcripts in our MT cell lines is due to CRISPR-mediated disruption of splice-regulatory sequences, but this would not explain the widespread splicing alterations we observed in genes not targeted by the Cas9 endonuclease. Thus, our data support DNMT3A mutations as leading to global dysfunction of RNA splicing.

Spliceosome gene mutations are common in cancer, especially hematological malignancies. Somatic mutations in genes encoding components of the splicing machinery, such as U2AF, SF3B1, and SRSF2, are frequent in myeloid neoplasms, especially when myelodysplastic features are present, and lead to aberrant RNA splicing (Makishima et al., 2012; Yoshida et al., 2011). Similarly, germline mutations in DDX41, an RNA helicase involved in RNA splicing and processing, cause a hereditary AML and MDS syndrome (Polprasert et al., 2015). Mutations in splicing factor genes affect patient outcome, as AML patients with these mutations experience a lower complete remission rate and poor overall survival (Hou et al., 2016). The association between DNMT3A mutations, abnormal RNA splicing, and genomic instability presented in our study is particularly intriguing, as RNA processing defects and aberrant splicing can lead to genome instability phenotypes (Li and Manley, 2005; Paulsen et al., 2009; Chan, Hieter, and Sterling, 2014). It has been proposed that splicing factor mutations lead to “spliceosome sickness,” in which abnormal RNA transcripts undergo non-sense mediated decay, causing abnormal gene expression, impaired cell signaling, and intracellular stress (Darman et al., 2015; Shirai et al., 2017). Our data implicate possible roles for DNMT3A in spliceosome maintenance, suggesting the potential mechanism of DNMT3A mutations leading to abnormal RNA splicing and subsequent genomic instability.

Mutation of DNMT3A in our model was not associated with changes in regional DNA methylation patterns. Although contradictory to the established role of DNMT3A as a methyltransferase, previous studies convey a lack of correlation between DNMT3A loss and a consistent epigenetic signature. In Dnmt3a-null HSCs, global 5-mC levels were unchanged compared to WT HSCs, while regional methylation analysis showed paradoxical hypermethylation of most differentially-methylated regions (Challen et al., 2012). Similarly, mice transplanted with Dnmt3a mutant-transduced cells exhibited downregulation of differentiation-associated genes without concomitant changes in promoter methylation (Koya et al., 2016). Furthermore, genomic DNA derived from the bone marrow of DNMT3A-mutated AML patients displayed no changes in global or regional methylation patterns compared to AML patients with WT DNMT3A (Ley et al., 2010). These data and ours suggest that DNMT3A may have other functions additional to its role as a methyltransferase that become dysregulated when DNMT3A is mutated, and loss of these functions lead to the development of hematologic disease. Indeed, the MT1 cell line with CRISPR/Cas9-mediated DNMT3A deletions but an intact DNMT3A catalytic domain, although phenotypically similar to WT in many respects, displayed behaviors different from WT, such as impaired growth and increased susceptibility to DNA double-stranded breaks at high concentrations of 5-FU, again consistent with pleomorphic DNMT3Aassociated functions and/or protein-protein interactions. We acknowledge that our approach to epigenomic profiling may have biases which hampered the detection of changes in global methylation patterns between DNMT3A WT and MT cell lines. It is possible that global rather than regional variation in methylation contributes to the observed phenotype in our DNMT3A MT cell lines, which would be consistent with previous studies of DNMT3A KO (Raddatz et al., 2012; Russler-Germain et al., 2014). Further studies utilizing more advanced sequencing technologies are required to address whether loss of DNMT3A in our model leads to hypomethylation of the genome.

A possible limitation in this study is that most DNMT3A mutations in patients are heterozygous and missense rather than frameshift, with ~60% of AML patients harboring the R882H mutation (Ley et al., 2010). However, R882H DNMT3A is thought to act as dominant-negative by disrupting the ability of WT DNMT3A to form a tetramer, thus reducing overall methyltransferase activity (Russler-Germain et al., 2014). Furthermore, Dnmt3a-null mice recapitulate the phenotypic manifestations of patients with DNMT3A missense mutations, implying that loss of function rather than haploinsufficiency is required for DNMT3A-mediated leukemogenesis (Mayle et al., 2015). Additionally, no clinical differences have been described between DNMT3A-mutated AML patients with and without R882 mutations (Im et al., 2014). Therefore, while the mutations present in our DNMT3A MT cells lines are not representative of clinical cases, the effects we observed could most likely be extrapolated to leukemogenesis in vivo. Again, MT1, predicted to have residual DNMT3A activity, was more phenotypically similar to the WT cell lines, supporting the hypothesis that DNMT3A-mutated leukemogenesis is a result of complete or near-complete loss of DNMT3A activity rather than haploinsufficiency.

Another limitation of our approach is the use of the K562 cell line. This cell line was obtained from a chronic myeloid leukemia patient in blast crisis and harbors the BCR-ABL chromosomal rearrangement, and it is possible that the inherent instability of K562 cells might influence our results, especially given its aneuploidy. This was most apparent in MT1, in which sequences not present in the detected genomic DNA were present in the majority of mRNA transcripts. Since this cell line harbors three copies of chromosome 2, and thus potentially three DNMT3A alleles, we postulate that a large deletion occurred in an intronic region upstream of exon 3 in one allele, hindering our ability to amplify exon 3 of this allele during the genomic DNA TA cloning process. Regardless, the presence of DNMT3A protein in this cell line is established with our Western blot data, and this cell line was used as an internal control because of this; thus, the potential presence of a WT DNMT3A allele should not influence our results. For the remaining MT cell lines, we took numerous measures to control for K562 variability and aneuploidy, such as the use of both Sanger sequencing and next-generation sequencing to confirm genomic DNA and mRNA mutations, the production of multiple K562 WT cell lines, and the reproduction of our results in the HAP1 cell line. However, our findings must be validated using a non-leukemic cell line, ideally human CD34+ human hematopoietic stem cells. Although utilizing primary cells is the best model for studying leukemogenesis, this experimental approach is technically difficult for multiple reasons, including procuring adequate numbers of CD34+ cells, isolating a homogenous population of CRISPR-mutated cells, and manipulating cells in vitro without inducing differentiation.

In summary, we developed a human model of DNMT3A-mutated leukemogenesis using CRISPR/Cas9-engineered K562 cells. In addition to the established role of DNMT3A in differentiation, we demonstrated dysregulated splicing and genomic instability as novel mechanisms by which DNMT3A mutations might predispose to the development of malignancy. We propose a model in which DNMT3A mutations lead to aberrant splicing, causing “spliceosome sickness” and subsequent genomic instability, which thereby predisposes to the acquisition of deleterious mutations and development of myeloid neoplasia. In this model, splicing abnormalities in DNMT3A-mutated HSCs may represent a molecular process that can be targeted in treating DNMT3A-associated hematologic malignancies as well as in preventing disease in healthy individuals with DNMT3A-driven clonal hematopoiesis. However, it is possible that DNMT3A mutations may drive genomic instability independent of the spliceosome, and the observed abnormalities in RNA splicing may merely be reflective of cell stress. Thus, further studies are required to better define the mechanism by which DNMT3A mutations trigger genomic instability and to elucidate the relationship between DNMT3A and the splicing machinery. Clarification of these mechanisms will allow for a better understanding of DNMT3A-associated leukemogenesis and identification of targets that can be utilized for future therapeutics, such as splicing modulators currently being tested in clinical trials, to prevent and treat DNMT3A-associated hematologic malignancies.

Supplementary Material

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Acknowledgments

We thank Marie Desierto, Xingmin Feng, James Cooper, and Uimook Choi for technical assistance. This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute, and the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the American Association for Dental Research, the Howard Hughes Medical Institute, and the Colgate-Palmolive Company, as well as other private donors.

Footnotes

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AUTHOR CONTRIBUTIONS

L.G. Banaszak, V. Giudice, X. Zhao, Z. Wu, S. Kajigaya, D.M. Townsley, and N.S. Young participated in the design of the study. L.G. Banaszak, V. Giudice, F. Gutierrez-Rodrigues, P. Fernandez, and S. Kajigaya conducted the experiments and analyzed the data. L.G. Banaszak interpreted the results and drafted the manuscript. S. Gao performed bioinformatics analysis. K. Keyvanfar conducted the singlecell sorting. S. Kajigaya edited the manuscript. N.S. Young conceptualized the experiments, participated in periodic discussions regarding interpretations of the results, and edited the paper. All authors critically reviewed the manuscript content and agreed with the final submission of the manuscript.

DISCLOSURES

No relevant disclosures.

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NIHMS933305-supplement-1.docx (25.3KB, docx)
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NIHMS933305-supplement-2.pptx (2.3MB, pptx)

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