Intronic Architecture Links DNA Methylation to Gene Expression and Helps Drive Subtype-Specific Transcriptional Landscapes in DNMT3A- and IDH1/2-Mutant Acute Myeloid Leukemias (AML)

Abstract Mutations in DNMT3A and IDH1/2 are each found in ~20% of AML patients. 10-15% of AMLs carry mutations in both genes (herein, double mutants), resulting in a unique methylation landscape and upregulation of a signaling signature. In murine models, the presence of both mutations results in greater leukemogenic potential. However, the specific mechanism through which DNA methylation (DNAme) drives gene expression programs in double mutants remains unclear. We hypothesized that the link between DNAme and gene expression would be explained by more than simple proximity, and that the genomic architecture of the affected genes would play a key role. To test this, we first performed an unbiased correlation analysis of gene expression with DNAme at all CpG sites (mCs) located within the same topologically associated domain (TAD). We identified 406 genes with significant (FDR> 5% and absolute rho > 0.5) expression-methylation correlations with mCs proximal to the respective genes (herein the E-M gene set). In addition, another 2,088 genes (the L E-M set) were identified with long-range correlations (>2Kb from the gene body) with mCs in the respective TAD (median distance = 451 Kb). As a set, the E-M genes significantly overlapped (P < 10 -2) with genes identified as either differentially expressed (DE; n=890) or differentially methylated (DM; n= 4,006) between IDH1/2 and DNMT3A mutant AMLs. Notably, a simple overlap analysis of DE and DM genes showed no significant overlap between them, thus demonstrating that correlation analysis performed better in bridging the epigenome with the transcriptome. DAVID and Gene Set Enrichment Analysis on the genes ranked by correlation strength revealed that signaling, fructose and lipid metabolism pathways are enriched in the E-M gene set (FDR < 5%) but not in the L E-M set. Analysis of transcription factor (TF) binding profiles did not reveal a common set of TF(s) binding to the mCs proximal to the genes of the identified pathways. Thus, we hypothesized that the E-M genes have other structural characteristics in common that drive regulation through DNAme, for which we focused on their genomic architecture. This analysis revealed that introns of genes in both the E-M and L E-M sets are significantly denser in Mammalian Interspersed Repeats (MIR) than expected by random chance (P < 10 -2). Additionally, E-M genes were significantly sparser in endogenous retroviruses (ERVL) and primate-specific Alu elements. mCs with significant correlations were also enriched at MIR and depleted from Alu elements (P < 10 -2), thus creating a regulatory network between mCs and genes with MIR sequences as the common denominator. Genome-wide, CpGs within retrotransposons that were differentially methylated among the three AML subtypes were enriched at enhancer regions or coding genes, particularly the E-M genes. Furthermore, the Dnmt3a knock-out (KO) or Idh2 R140Q knock-in mouse models display the same architectural biases at genes correlated with DNAme as the E-M genes identified in the human samples. Next, we sought to put our findings in the context of normal hematopoiesis and found that genes upregulated during normal hematopoietic differentiation are significantly denser in MIR elements and sparser of Alu elements than expected (P < 10 -2). Alignment of the leukemic samples within normal differentiation trajectories revealed that double mutants resembled differentiated cell types more closely, while DNMT3A and IDH1/2 single mutants resembled hematopoietic stem cells. The E-M and L E-M sets significantly overlapped (P < 10 -2) with those genes upregulated during myeloid but not erythroid or lymphoid differentiation, demonstrating that genes regulated by DNAme are at the core of the biology of these AMLs. In summary, our integrative work sheds light on a novel mechanism in which epigenetic modifications can regulate gene expression through MIR sequences within introns of hematopoietic-relevant genes and we posit that overlapping CpG dinucleotides may act as recruiters or substrates of DNMT3A and/or TET proteins. This mechanism seems to also be active in normal hematopoiesis and thus, is hijacked by leukemic cells. Therefore, our findings identify retrotransposons as a missing link in the understanding of epigenetic regulation of gene expression, reveal a previously uncharacterized role for these elements in leukemogenesis, and point to different cells of origin for each AML subtype. Disclosures No relevant conflicts of interest to declare.

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Single Cell RNASeq Demonstrates That Mouse Erythroid Cells Are the Last Lineage to Emerge

Blood ◽

10.1182/blood-2018-99-110318 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1274-1274

Author(s):

Elisabeth F Heuston ◽

Bethan Psaila ◽

Stacie M Anderson ◽

NISC Comparative Sequencing Program ◽

David M. Bodine

Keyword(s):

Gene Expression ◽

Single Cell ◽

Conflicts Of Interest ◽

Gene Set Enrichment Analysis ◽

Erythroid Cells ◽

Erythroid Progenitors ◽

Hematopoietic Stem ◽

Transcriptional Profiles ◽

Gene Set ◽

Rna Signature

Abstract The hierarchical model of hematopoiesis posits that hematopoietic stem cells (HSC) give rise to myeloid progenitors (CMP), that can become further restricted to bipotential granulocyte/monocyte progenitors (GMP) or megakaryocyte/erythroid progenitors (MEP). We and others have shown that this model may not accurately depict hematopoiesis. Recent studies have shown that shown that populations of mouse hematopoietic stem and progenitor cells (LSK) have a strong megakaryocyte (Mk) transcriptional profile (Heuston, 2018, Epig. & Chrom.), and single cell studies have identified lineage committed cells in progenitor populations thought to be multipotent. For example, we recently reported that human MEP contain 3 populations: erythroid (Ery) primed, Mk primed, and bipotential (Psaila, 2016; Gen. Bio.). To determine when Mk and Ery cells emerge during mouse hematopoiesis, we performed single cell RNASeq on 10000 LSK, 12000 CMP, 6000 MEP and 8000 GMP cells. Clustering analysis (Satija, 2018, Nat. Biotech.) of all 4 populations identified 33 transcriptionally distinct clusters. In 30 of 33 clusters, 85% of cells were from a single defined population (e.g. MEP). LSK and CMP clusters grouped closely together. We used gene set profiling (Gene Set Enrichment Analysis, GO and KEGG) to correlate transcriptional profiles of clusters with specific hematopoietic lineages and cellular activities. In LSK, the most common transcriptional profiles correlated with active cell cycling. Mk-associated genes (Meis1, Myct1, and Fli1), were co-expressed with lymphoid genes in 56% of all LSK. Consistent with previous studies, we conclude that cells with Mk transcriptional profiles are abundant in LSK. No cells with an Ery RNA signature were observed in LSK. 23% of all CMP cells expressed Mk genes (e.g., Pf4, Itga2b, and Fli1) and were enriched for processes involved in platelet biology (p < 3E-18). 12% of CMP had an Ery RNA signature (low expression of Gata1, Klf1, and Nfe2) and decreased Mk gene expression (e.g., Gata2 and Gfi1b, [p < 3E-18]) compared to other CMP clusters. The high ratio of Gata2/Gata1 expression (1.90) suggests that this cluster contained immature Ery cells. More than 94% of all mouse MEP had Ery RNA signatures. Clusters could be distinguished by gene expression (e.g., Gata1, Klf1, Tfrc) and biological processes (ribosome synthesis and heme-biology processes [p < 4 E-10]). Based on the transcriptional profiles, we determined the most mature erythroid cells in MEP were late BFU-E. To compare the differentiation of Mk and Ery cells, we pooled our LSK, CMP, and MEP data for analysis using the Monocle software package. GMP contained only clusters expressing granulocytic or monocytic genes and were excluded from the analysis. Monocle arranges cells into trajectories based on their transcriptional profiles, with more differentiated cells positioned further from a common node (Xiaojie, 2017, bioRxiv). We found that LSK cells near the node had overlapping lymphoid and Mk transcriptional profiles. Closest to the node, we found 38% of CMP expressed a profile similar to LSK. An additional 45% of CMP formed one trajectory with lymphoid and granulocyte RNA signatures. Another 17% of CMP formed a second trajectory, with cells expressing an Mk signature closest to the node, cells with a mixed Ery/Mk signature further along the trajectory, and MEP cells with Ery-only signatures furthest from the node. To clarify the Mk/Ery divergence, we focused our analysis on the CMP populations expressing Mk RNAs (Figure1). We observed cells in G1/S phase with an immature Mk signature to the left of the node where the trajectories diverge. On the right, cells with immature Mk signatures were nearest the node and cells with a mixed Ery/Mk signature were at the end of the trajectory (upper right; Mk/Ery). Along the second trajectory, rapidly cycling G2/M Mk cells with an early endomitosis-associated RNA signature (e.g., Pf4, Gp1bb, Gp9, and Vwf) were located at the end of the trajectory (lower right; Mk early endomitosis). Our data are consistent with a model in which two waves of Mk differentiation begin in LSK and progresses to CMP. The Mk lineage is divided in CMP, producing cells that begin endomitosis and cells that have an Mk-repressing/Ery-activating cell program that gives rise to the Ery lineage. We conclude that the erythroid lineage is derived from an Mk-like precursor and is the last lineage to be specified in mouse hematopoiesis. Disclosures No relevant conflicts of interest to declare.

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Establishment of Enhancer Elements during Erythro-Megakaryopoiesis

Blood ◽

10.1182/blood.v128.22.1486.1486 ◽

2016 ◽

Vol 128 (22) ◽

pp. 1486-1486

Author(s):

Elisabeth F. Heuston ◽

Cheryl A. Keller ◽

Jens Lichtenberg ◽

Stacie M. Anderson ◽

NIH Intramural Sequencing Center ◽

...

Keyword(s):

Gene Expression ◽

Progenitor Cells ◽

Histone H3 ◽

Gene Set Enrichment Analysis ◽

Lineage Commitment ◽

Open Chromatin ◽

Hematopoietic Stem ◽

Gene Set ◽

Primary Mouse ◽

Accessible Chromatin

Abstract Enhancers are cis acting regulatory modules associated with lineage-specific gene expression. The Encyclopedia of DNA Elements project (ENCODE) showed that enhancers are in open chromatin regions identified by the Assay for Transposable-Accessible Chromatin (ATAC) and bound with histone H3 is mono-methylated at lysine 4 (H3K4me1). Chromatin regions marked by H3K4me1 alone identifies "poised" enhancers (not active), while the additional presence and histone H3 acetylated at lysine 27 (H3K27ac) identifies "active" enhancers. To establish a genome-wide enhancer map in the erythro-megakaryocytic lineage, we performed ChIPSeq of H3K4me1 and H3K27ac in primary erythroblasts (EB) and megakaryocytes (MEG) isolated from mouse bone marrow. We also assayed primary mouse EB, MEG, hematopoietic stem and progenitor cells (LSK), and common myeloid progenitor cells (CMP) for open chromatin regions with ATAC and transcriptome profiling by RNASeq. Finally, we compared histone-defined enhancers in mature cells with the corresponding ATAC regions in progenitor cells to identify the preservation of poised and active enhancers through hematopoiesis. We identified 6565 and 3543 active enhancers in EB and MEG respectively; approximately 10% (434) were shared. We further refined our enhancer set to the ~90% of EB and MEG active enhancers that overlap with ATAC regions (AER, histone-marked active enhancer within an ATAC region). To identify enhancers in the open chromatin of progenitor cells, we overlaid EB and MEG AER with CMP ATAC sites. This revealed that 82% (5226/6399) of EB AER and 87% (1437/3302) of MEG AER were present in CMP. Overlaying the EB and MEG AER onto LSK ATAC showed that 67% (4278/6399) of EB-specific AER and 79% (2594/3302) of MEG-specific AER overlapped with LSK ATAC sites. To identify the EB and MEG AER in LSK-accessible chromatin that are active (not poised), we compared our LSK enhancer set with the indexing-first ChIP (iChIP) histone marks identified by Lara-Astiaso et al., (Science, 2014). 1840 of the 4278 (43%) LSK-accessible EB AER overlapped with LSK iChIP H3K4me1 marks; 632 of these (15% overall) also had the active H3K27ac mark. 1083 of the 2594 (42%) MEG AER that were present in LSK overlapped with LSK iChIP H3K4me1 marks; 241 of these (9% overall) had the H3K27ac mark. For both EB and MEG, AER not marked by iChIP K4me1 were within gene bodies. To further characterize enhancer roles in lineage commitment, we profiled super enhancers (SE), which have highly lineage-specific activity. We defined SE as the 2% of AER with the highest H3K27ac levels (Hnisz et al., Cell, 2013) and identified 101 EB and 98 MEG SE; all of these were cell-specific. We found that 65% (66/101) of EB SE and 87% (85/98) of MEG SE overlapped with LSK ATAC sites. 30 of the 66 (45%) LSK-accessible EB SE overlapped with LSK iChIP H3K4me1 marks; 9 of these (14% overall) also had the active H3K27ac mark. In comparison, 15 of the 85 (18%) LSK-accessible MEG SE overlapped with LSK iChIP H3K4me1 marks; 4 of these (5% overall) also had the active H3K27ac mark. We correlated our LSK-accessible, iChIP-marked active AER with gene expression by assigning each AER to the nearest gene. We then used RNASeq data to perform gene set enrichment analysis via Ingenuity Pathway Analysis. We found that the LSK-accessible EB-specific AER gene set included erythropoietin-regulated genes (p £ 9x10-5) and genes associated with Fanconi anemia (6x10-4). Conversely, LSK-accessible and iChIP-active MEG AER were associated an increase of progenitor cell populations and proliferation activities for several hematopoietic lineages (p £ 2x10-5). However, the genes in the non-megakaryocyte pathways were significantly down-regulated as LSK committed to the megakaryocyte lineage. In summary, our results demonstrate the establishment of poised and active enhancers in hematopoietic progenitors and their preservation through erythro-megakaryopoiesis. We show that >40% of EB and MEG enhancers were also enhancers in LSK and CMP; the EB and MEG enhancers that were not LSK enhancers were primarily within gene bodies. We also found that MEG, but not EB, super enhancers were less likely than conventional enhancers to be established in LSK. Finally, our data show that, while LSK-established EB enhancers target EB-specific functions, LSK-established MEG enhancers have more universal hematopoietic functions that are down-regulated during megakaryocytic lineage commitment. Disclosures No relevant conflicts of interest to declare.

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Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1574888x13666180105125347 ◽

2018 ◽

Vol 21 (2) ◽

pp. 74-83

Author(s):

Tzu-Hung Hsiao ◽

Yu-Chiao Chiu ◽

Yu-Heng Chen ◽

Yu-Ching Hsu ◽

Hung-I Harry Chen ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Therapy ◽

Cancer Treatment ◽

Cancer Survival ◽

Expression Profiles ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

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Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893615999200629124444 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chen-An Tsai ◽

James J. Chen

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Data ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Superior Performance ◽

Expression Data ◽

Gene Set ◽

Gene Sets ◽

Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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Gene set enrichment analysis for genome-wide DNA methylation data

Genome Biology ◽

10.1186/s13059-021-02388-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jovana Maksimovic ◽

Alicia Oshlack ◽

Belinda Phipson

Keyword(s):

Dna Methylation ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Methylation Array ◽

Gene Set ◽

Genome Wide ◽

Genome Methylation ◽

Unbiased Gene ◽

Gene Set Testing

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

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Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits

Journal of the American Society of Nephrology ◽

10.1681/asn.2020010051 ◽

2020 ◽

Vol 31 (10) ◽

pp. 2326-2340 ◽

Cited By ~ 2

Author(s):

Yong Li ◽

Stefan Haug ◽

Pascal Schlosser ◽

Alexander Teumer ◽

Adrienne Tin ◽

...

Keyword(s):

Gene Expression ◽

Kidney Function ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Rna Seq ◽

Cell Type ◽

Gene Set Enrichment ◽

Gene Set

BackgroundGenetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.MethodsThe CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score–based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.ResultsAcross 53 tissues, genes in kidney function–associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=1.2E-5 for urate) and liver (P=6.8·10-5 for eGFR). In the kidney, proximal tubule was enriched in humans (P=8.5E-5 for eGFR; P=7.8E-6 for urate) and mice (P=0.0003 for eGFR; P=0.0002 for urate) and confirmed as the primary cell type in microdissected tubules and organoids. Gene set enrichment analysis supported this and showed enrichment of genes implicated in monogenic glomerular diseases in podocytes. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type–specific expression.ConclusionsIntegration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.

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Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

Cancer Informatics ◽

10.4137/cin.s13882 ◽

2014 ◽

Vol 13s1 ◽

pp. CIN.S13882 ◽

Cited By ~ 4

Author(s):

Binghuang Cai ◽

Xia Jiang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Expression Data ◽

Lung Squamous Cell Carcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Expression Data ◽

Gene Set Enrichment ◽

Gene Set ◽

Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

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Genome Wide DNA Methylation and Transcriptome Analysis in HSC Aging

Blood ◽

10.1182/blood.v118.21.2367.2367 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2367-2367

Author(s):

Mira Jeong ◽

Deqiang Sun ◽

Min Luo ◽

Aysegul Ergen ◽

Hongcang Gu ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Differentially Expressed ◽

Mouse Bone Marrow ◽

Rna Seq ◽

Differentiation Potential ◽

Hematopoietic Stem ◽

Exon Arrays ◽

Age Related ◽

Genome Wide

Abstract Abstract 2367 Hematopoietic stem cell (HSC) Aging is a complex process linked to number of changes in gene expression and functional decline of self-renewal and differentiation potential. While epigenetic changes have been implicated in HSC aging, little direct evidence has been generated. DNA methylation is one of the major underlying mechanisms associated with the regulation of gene expression, but changes in DNA methylation patterns with HSC aging have not been characterized. We hypothesize that revealing the genome-wide DNA methylation and transcriptome signatures will lead to a greater understanding of HSC aging. Here, we report the first genome-scale study of epigenomic dynamics during normal mouse HSC aging. We isolated SP-KSL-CD150+ HSC populations from 4, 12, 24 month-old mouse bone marrow and carried out genome-wide reduced representative bisulfite sequencing (RRBS) and identified aging-associated differentially methylated CpGs. Three biological samples were sequenced from each aging group and we obtained 30–40 million high-quality reads with over 30X total coverage on ∼1.1M CpG sites which gives us adequate statistical power to infer methylation ratios. Bisulfite conversion rate of non-CpG cytosines was >99%. We analyzed a variety of genomic features to find that CpG island promoters, gene bodies, 5'UTRs, and 3'UTRs generally were associated with hypermethylation in aging HSCs. Overall, out of 1,777 differentially methylated CpGs, 92.8% showed age-related hypermethylation and 7.2% showed age-related hypomethylation. Gene ontology analyses have revealed that differentially methylated CpGs were significantly enriched near genes associated with alternative splicing, DNA binding, RNA-binding, transcription regulation, Wnt signaling and pathways in cancer. Most interestingly, over 579 splice variants were detected as candidates for age-related hypermethylation (86%) and hypomethylation (14%) including Dnmt3a, Runx1, Pbx1 and Cdkn2a. To quantify differentially expressed RNA-transcripts across the entire transcriptome, we performed RNA-seq and analyzed exon arrays. The Spearman's correlation between two different methods was good (r=0.80). From exon arrays, we identified 586 genes that were down regulated and 363 gene were up regulated with aging (p<0.001). Most interestingly, overall expression of DNA methyl transferases Dnmt1, Dnmt3a, Dnmt3b were down regulated with aging. We also found that Dnmt3a2, the short isoform of Dnmt3a, which lacks the N-terminal region of Dnmt3a and represents the major isoform in ES cells, is more expressed in young HSC. For the RNA-seq analysis, we focused first on annotated transcripts derived from cloned mRNAs and we found 307 genes were down regulated and 1015 gene were up regulated with aging (p<0.05). Secondly, we sought to identify differentially expressed isoforms and also novel transcribed regions (antisense and novel genes). To characterize the genes showing differential regulation, we analyzed their functional associations and observed that the highest scoring annotation cluster was enriched in genes associated with translation, the immune network and hematopoietic cell lineage. We expect that the results of these experiments will reveal the global effect of DNA methylation on transcript stability and the translational state of target genes. Our findings will lend insight into the molecular mechanisms responsible for the pathologic changes associated with aging in HSCs. Disclosures: No relevant conflicts of interest to declare.

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Epigenetic Effects of IDH1/IDH2 Mutations

Blood ◽

10.1182/blood.v118.21.sci-33.sci-33 ◽

2011 ◽

Vol 118 (21) ◽

pp. SCI-33-SCI-33 ◽

Cited By ~ 1

Author(s):

Ari M. Melnick ◽

Ross L Levine ◽

Maria E Figueroa ◽

Craig B. Thompson ◽

Omar Abdel-Wahab

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Stem Cells ◽

Hematopoietic Stem Cells ◽

Malignant Transformation ◽

Covalent Modification ◽

Specific Gene ◽

Gene Promoters ◽

Loss Of Function ◽

Hematopoietic Stem

Abstract Abstract SCI-33 Epigenetic deregulation of gene expression through aberrant DNA methylation or histone modification plays an important role in the malignant transformation of hematopoietic cells. In particular, acute myeloid leukemias (AMLs) can be classified according to epigenetic signatures affecting DNA methylation or histone modifications affecting specific gene sets. Heterozygous somatic mutations in the loci encoding isocitrate dehydrogenase 1 and 2 (IDH1/2) occur in ∼20% of AMLs and are accompanied by global DNA hypermethylation and hypermethylation and silencing of a number of specific gene promoters. IDH1/2 mutations are almost completely mutually exclusive with somatic loss-of-function mutations in TET2, which hydroxylates methylcytosine (mCpG). DNA hydroxymethylation can function as an intermediate step in mCpG demethylation. TET2 mutant de novo AMLs also display global and promoter specific hypermethylation partially overlapping with IDH1/2 mutant cases. Mutations in the IDH1/2 loci result in a neomorphic enzyme that generates the aberrant oncometabolite 2-hydroxyglutarate (2HG) using α-ketoglutarate (αKG) as a substrate. 2HG can disrupt the activity of enzymes that use αKG as a cofactor, including TET2 and the jumonji family of histone demethylases. Expression of mutant IDH isoforms inhibits TET2 hydroxymethylation and jumonji histone demethylase functions. IDH and TET2 mutant AMLs accordingly exhibit reduced levels of hydroxymethylcytosine and a trend towards increased histone methylation. Mutant IDH or TET2 loss of function causes differentiation blockade and expansion of hematopoietic stem cells and TET2 knockout results in a myeloproliferative phenotype in mice. Hydroxymethylcytosine is in abundance in hematopoietic stem cells and displays specific distribution patterns, yet the function of this covalent modification is not fully understood. Recent data link TET2 with the function of cytosine deaminases as a pathway towards DNA demethylation, which has implications as well for B cell lymphomas and CML lymphoid blast crisis, which are linked with the actions of activation induced cytosine deaminase. Altogether, the available data implicate mutations in IDH1/2 and TET2 in promoting malignant transformation in several tissues, by disrupting epigenomics programming and altering gene expression patterning. Disclosures: Thompson: Agios Pharmaceuticals: Consultancy.

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Large Conserved Domains Of Low DNA Methylation Maintained By 5-Hydroxymethycytosine and Dnmt3a

Blood ◽

10.1182/blood.v122.21.2406.2406 ◽

2013 ◽

Vol 122 (21) ◽

pp. 2406-2406

Author(s):

Mira Jeong ◽

Deqiang Sun ◽

Min Luo ◽

Yun Huang ◽

Myunggon Ko ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Hox Genes ◽

Conflicts Of Interest ◽

P Value ◽

Genomic Feature ◽

Conserved Domains ◽

Hematopoietic Stem ◽

Genome Wide ◽

The Impact

Abstract Identification of recurrent leukemia-associated mutations in genes encoding regulators of DNA methylation such as DNMT3A and TET2 have underscored the critical importance of DNA methylation in maintenance of normal physiology. To gain insight into how DNA methylation exerts the central role, we sought to determine the genome-wide pattern of DNA methylation in the normal precursors of leukemia cells: the hematopoietic stem cell (HSC), and investigate the factors that affect alterations in DNA methylation and gene expression. We performed whole genome bisulfite sequencing (WGBS) on purified murine HSCs achieving a total of 1,121M reads, resulting in a combined average of 40X coverage. Using Hidden Markov Model we identified 32,325 under-methylated regions (UMRs) with average proportion of methylation ≤ 10% and by inspecting the UMR size distribution, we discovered exceptionally large “methylation Canyons” which span highly conserved domains frequently containing transcription factors and are quite distinct from CpG islands and shores. Methylation Canyons are a distinct genomic feature that is stable, albeit with subtle differences, across cell-types and species. Canyon-associated genes showed a striking pattern of enrichment for genes involved in transcriptional regulation (318 genes, P=6.2 x 10-123), as well as genes containing a homeobox domain (111 genes, P=3.9 x 10-85). We compared Canyons with TF binding sites as identified from more than 150 ChIP-seq data sets across a variety of blood lineages (>10)19 and found that TF binding peaks for 10 HSC pluripotency TFs are significantly enriched in entirety of Canyons compared with their surrounding regions. Low DNA methylation is usually associated with active gene expression. However, half of Canyon genes associated with H3K27me3 showed low or no expression regardless of their H3K4me3 association while H3K4me3-only Canyon genes were highly expressed. Because DNMT3A is mutated in a high frequency of human leukemias24, we examined the impact of loss of Dnmt3a on Canyon size. Upon knockout of Dnmt3a, the edges of the Canyons are hotspots of differential methylation while regions inside of Canyon are relatively resistant. The methylation loss in Dnmt3a KO HSCs led Canyon edge erosion, Canyon size expansion and addition of 861 new Canyons for a total of 1787 Canyons. Canyons marked with H3K4me3 only were most likely to expand after Dnmt3a KO and the canyons marked only with H3K27me3 or with both marks were more likely to contract. This suggests Dnmt3a specifically is acting to restrain Canyon size where active histone marks (and active transcription) are already present. WGBS cannot distinguish between 5mC and 5hmC, so we determined the genome-wide distribution of 5hmC in WT and Dnmt3a KO HSCs using the cytosine-5-methylenesulphonate (CMS)-Seq method in which sodium bisulfate treatment convert 5hmC to CMS; CMS-containing DNA fragments are then immunoprecipitated using a CMS specific antiserum. Strikingly, 5hmC peaks were enriched specifically at the borders of Canyons. In particular, expanding Canyons, typically associated with highest H3K4me3 marking, were highly enriched at the edges for the 5hmC signal suggesting a model in which Tet proteins and Dnmt3a act concomitantly on Canyon borders opposing each other in alternately effacing and restoring methylation at the edges, particularly at sites of active chromatin marks. Using Oncomine data, we tested whether Canyon-associated genes were likely to be associated with hematologic malignancy development and found Canyon genes were highly enriched in seven signatures of genes over-expressed in Leukemia patients compared to normal bone marrow; in contrast, four sets of control genes were not similarly enriched. Further using TCGA data, we found that expressed canyon genes are significantly enriched for differentially expressed genes between patients with and without DNMT3A mutation (p value<0.05) Overall, 76 expressed canyon genes, including multiple HOX genes, are significantly changed in patients with DNMT3A mutation (p=0.0031). Methylation Canyons, the novel epigenetic landscape we describe may provide a mechanism for the regulation of hematopoiesis and may contribute to leukemia development. Disclosures: No relevant conflicts of interest to declare.

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