scholarly journals Combined Loss of Tet1 and Tet2 Promotes B-Cell, but Not Myeloid Malignancies in Mice

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3650-3650
Author(s):  
Zhigang Zhao ◽  
Lin Li ◽  
Meelad Dawlaty ◽  
Feng Pan ◽  
Zhe Li ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Cultures ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Hematological Malignancies ◽  
Cell Populations ◽  
Double Knockout ◽  
B Cell Malignancies ◽  
Myeloid Malignancies ◽  
Genome Wide

Abstract Objective: Tet1/2/3 are methylcytosine dioxygenases regulating cytosine methylation in the genome. Tet1 and Tet2 are abundantly expressed in HSC/HPCs and implicated in hematological malignancies. Tet2 -deletion in mice causes myeloid malignancies, while Tet1 -null mice are overtly normal early in life. Here, we investigated the overlapping and non-redundant functions of Tet1/Tet2 in HSC maintenance and hematological malignancies using Tet1/2 double knockout (DKO) mice. Methods: 1) Kinetic analysis of the hematologicalparameters on WT, Tet1-/-, Tet2-/- and DKO mice; 2) Analysis of HSC, myeloid and lymphoid progenitors and various maturation stages of B-cell populations; 3) Competitive bone marrow reconstitution assay; 4) RAN-Seq on LK cells and B220+ cells from young/undiseased and diseased DKO mice respectively; 5) Chemical labeling and affinity purification method coupled with high-throughput sequencing (hMe-Seal) to profile the genome-wide distribution of 5hmC, and methylated DNA immunoprecipitation coupled with high-throughput sequencing (MeDIP-seq) to profile 5mC in BM LK cells from young WT, Tet2-/- and DKO mice; 6) q-PCR analysis of the mRNA expression levels of Tet1 and Tet2 on BM CD19+ cells from B-ALL patients and compared to that of CD19+ B-cells from healthy controls. Results: We found that T et 1 and T et 2 are often concomitantly down-regulatedin patients with B-ALL. Therefore, it is important to investigate the effects of combined loss of Tet1 and Tet2 on the hematopoietic phenotype and development of hematological malignancies in vivo. The LSK and CMP/GMP/MEP cell populations are comparable in yound WT, Tet1-/- and DKO mice, while were significantly increasedin Tet2-/- mice. When a replating assay was performed using LSK cells, Tet2-/- LSK cell cultures had a significant higher colony formation in each round of replating, while Tet1-/- and DKO LSK cell cultures only exhibited a moderate increase in the number of colonies at P2, but not P3 and P4. Furthermore, young DKO mice had an increased percentage of CLP, BLP and Pro-/Pre-/Immature-B cell populations in their BM as compared to WT, Tet1-/- and Tet2-/- mice. Consistent to the B-lineage phenotypic analysis, DKO BM cells contained higher pre-B cell colony forming cells than the three genotypes of control mice. Interestingly, DKO mice exhibited a strikingly decreased incidence and delayed onset of myeloid malignancies compared to Tet2-/- mice and in contrast developed lethal B-cell malignancies, most closely resembling B-ALL. The loss of Tet2 or DKO leads to genome-wide alterations of both 5mC and 5hmC. Significant overlaps between the differential hydroxymethylated regions (DhMRs) or differential methylated regions (DMRs) of two genotypes of LK cells were observed. However, intriguingly, the overlaps between DhMRs and DMRs within each genotype of LK cells were minimal, indicating that DhMRs and DMRs might represent distinct loci with altered epigenetic modifications under these conditions. When the expression of a pool of 654 genes that are known to be involved in regulating hematopoietic cell development and/or promoting leukemogenesis were overlap with DhMRs and DMRs identified above, we observed significant numbers of these genes with altered either 5hmC or 5mC modifications which however did not alter their gene expression. Furthermore, RNA-Seq on B-ALL DKO B220+ cells showed alteration of a set of genes involved in B-cell development and B-cell lymphoma/leukemogenesis. Conclusion: Using Tet1/2 double knockout mice, we found that Tet1 is required for Tet2 -deletion mediated HSC dysregulation, myeloid skewing and myeloid malignancy, indicating distinct roles of the two enzymes. Tet1 loss modulates the Tet2 -deletion mediated disease phenotype, not only decreasing the incidence and delaying the onset of myeloid malignancies, but also promoting the pathogenesis of B-cell malignancies. Furthermore, our observations highlight the roles of distinct cytosine modifications, particularly 5hmC, could play in marking the specific genes and enabling cells to fate decision change upon stimulation signals. These findings provide a pathological framework for further elucidating the molecular mechanisms and critical cross talks between Tet1 and Tet2 in the pathogenesis of hematological malignancies. Disclosures No relevant conflicts of interest to declare.

scholarly journals Common Viral Integration Sites Identified in Avian Leukosis Virus-Induced B-Cell Lymphomas

mBio ◽  
2015 ◽  
Vol 6 (6) ◽  
Author(s):  
James F. Justice ◽  
Robin W. Morgan ◽  
Karen L. Beemon
Keyword(s):  
B Cell ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Consensus Sequence ◽  
B Cell Lymphoma ◽  
Avian Leukosis Virus ◽  
B Cell Lymphomas ◽  
Genome Wide ◽  
Integration Sites

ABSTRACTAvian leukosis virus (ALV) induces B-cell lymphoma and other neoplasms in chickens by integrating within or near cancer genes and perturbing their expression. Four genes—MYC,MYB,Mir-155, andTERT—have previously been identified as common integration sites in these virus-induced lymphomas and are thought to play a causal role in tumorigenesis. In this study, we employ high-throughput sequencing to identify additional genes driving tumorigenesis in ALV-induced B-cell lymphomas. In addition to the four genes implicated previously, we identify other genes as common integration sites, includingTNFRSF1A,MEF2C,CTDSPL,TAB2,RUNX1,MLL5,CXorf57, andBACH2. We also analyze the genome-wide ALV integration landscapein vivoand find increased frequency of ALV integration near transcriptional start sites and within transcripts. Previous work has shown ALV prefers a weak consensus sequence for integration in cultured human cells. We confirm this consensus sequence for ALV integrationin vivoin the chicken genome.IMPORTANCEAvian leukosis virus induces B-cell lymphomas in chickens. Earlier studies showed that ALV can induce tumors through insertional mutagenesis, and several genes have been implicated in the development of these tumors. In this study, we use high-throughput sequencing to reveal the genome-wide ALV integration landscape in ALV-induced B-cell lymphomas. We find elevated levels of ALV integration near transcription start sites and use common integration site analysis to greatly expand the number of genes implicated in the development of these tumors. Interestingly, we identify several genes targeted by viral insertions that have not been previously shown to be involved in cancer.

A Comprehensive Identification of the Microrna Transcriptome and Its Application in B Cell Malignancies.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2403-2403
Author(s):  
Cassandra L. Jacobs ◽  
Dereje D Jima ◽  
Jenny Zhang ◽  
Cherie Dunphy ◽  
Kristy L. Richards ◽  
...  
Keyword(s):  
B Cells ◽  
B Cell ◽  
High Throughput ◽  
Germinal Center ◽  
High Throughput Sequencing ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Microrna Expression ◽  
B Cell Malignancies

Abstract Abstract 2403 Poster Board II-380 Background MicroRNAs are 18-22 nucleotide-long RNA molecules that regulate expression of genes. We and others have previously demonstrated a role for microRNAs in the pathogenesis of B cell malignancies. Computational predictions suggest that the human genome encodes several thousand microRNAs. Thus far, about 700 microRNAs have been discovered in humans, including over 200 new microRNAs in the past year alone. The ongoing discovery of microRNAs makes it difficult to comprehensively study their role in a disease group. The advent of high throughput sequencing allows the simultaneous identification of millions of transcripts, thereby providing a sensitivity that is several orders of magnitude higher than conventional methods. We hypothesized that high throughput sequencing would be an effective tool to comprehensively identify microRNAs in normal and malignant B cells. While there is an overlap between diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL) in morphology, immunophenotype and cytogenetics, distinguishing between BL and DLBCL is critical because there are important differences in their clinical management. We investigated whether microRNA expression could be used to reliably distinguish BL from DLBCL. Methods and Results We carefully chose 31 human samples to represent the spectrum of normal and malignant B cells including FACS-sorted naive, germinal center, memory, plasma cells, EBV transformed and activated B cells. Samples derived from B cell malignancies included B-lymphoblastic lymphoma, chronic lymphocytic leukemia (immunoglobulin gene mutated and unmutated), mantle cell lymphoma, marginal zone lymphomas, HIV-related lymphoma, BL, DLBCL (activated and germinal center type), primary mediastinal B cell lymphoma, Hodgkin lymphoma, and multiple myeloma. We applied massively parallel, high-throughput sequencing of the 18-22 nt RNAs from these cases and generated a total of 255,624,785 sequences (∼5 billion bases). Using a computational approach that we have previously validated with normal B cells, we identified the expression of 429 known microRNAs in normal and malignant B cells, a number that is over three times higher than previously recognized in any tissue type. We also identified the expression of 302 novel microRNAs in normal and malignant B cells. The vast majority of these microRNAs were highly conserved in multiple species. As a proof of principle, we generated a custom microarray that included all the known human, and viral microRNAs, as well as 302 novel microRNAs identified by sequencing, and applied it to the clinically important distinction of BL from DLBCL. Biopsy samples were collected from 104 patients (BL, N=25, DLBCL, N=79) treated at 9 institutions that comprise an international consortium. All cases were reviewed for pathology diagnosis and profiled for microRNA expression. We constructed a Bayesian predictor to distinguish BL from DLBCL based on the microRNA expression. The predictor performance was tested using leave-one-out cross-validation. We also applied gene expression profiling to the cases of DLBCL to identify the molecular subsets of DLBCL: activated B cell like and germinal center B cell like DLBCL. The microRNA profiles of these cases were equally efficacious in distinguishing the DLBCL subsets. The predictor constructed based on microRNA expression was over 90% accurate in distinguishing BL from DLBCL, using pathology diagnosis as the gold standard. Further, microRNA-based predictor was also over 90% accurate in the distinction of the molecular subsets of DLBCL, compared to the gold standard of gene expression-profiling. As additional validation, we performed in situ hybridization of selected microRNAs to directly visualize their expression using methods that are easily accessible in conventional pathology laboratories. We found excellent concordance between the expression results derived from microarrays and in situ hybridization suggesting a ready path to clinical translation. Conclusion Our study represents the first comprehensive delineation of microRNA expression in B cell malignancies using high throughput sequencing. Our data suggest that microRNAs are a promising marker for the distinction of aggressive lymphomas. Disclosures: No relevant conflicts of interest to declare.

Massively Parallel High Throughput Sequencing Identifies Novel Micrornas in Normal and Malignant B Cells.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3350-3350
Author(s):  
Jenny Zhang ◽  
Dereje D. Jima ◽  
Yuan Gao ◽  
Han Wu ◽  
Jun Zhu ◽  
...  
Keyword(s):  
B Cells ◽  
B Cell ◽  
High Throughput ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Microrna Expression ◽  
Massively Parallel ◽  
B Cell Malignancies

Abstract Background: MicroRNAs (miRNAs) are small non coding RNAs that have been shown to play a regulatory role in a number of different settings including development, hematopoiesis and lineage-selection. The expression patterns of miRNAs in various cellular processes and in various normal and malignant tissues are an area of active exploration. Bioinformatic analyses of the genome suggest that there might be thousands of miRNAs encoded in the genome. However, thus far only about 600 unique miRNAs have been identified in humans. The role of microRNAs in B cell malignancies is poorly understood. Mature B cells comprise naive, germinal center, memory and plasma cells. These B cell stages comprise the majority of leukemias and lymphomas. We have previously demonstrated a role for microRNAs in the regulation of key transcription factors and oncogenes including PRDM1, LMO2 and MYBL1. We hypothesized that microRNAs play a role in the pathogenesis of lymphomas and have applied high throughput sequencing to understand the pattern and function of microRNA expression in normal B cells and their malignant counterparts. Methods: CD19+ mature B cells were obtained from normal patients undergoing tonsillectomy and sorted using flow cytometry into naive, germinal center, memory and plasma cells. We also obtained cells from tumor cell lines derived from mantle cell lymphoma (Mino, JVM2), Burkitt lymphoma (Ramos, BL41) and multiple myeloma (H929, U266), as well as patient tumor samples derived from Burkitt lymphoma and diffuse large B cell lymphomas. From these cells, we purified small RNAs (18-25 nucleotides) and ligated sequencing adapters to these small RNAs and subjected them to 15 cycles of PCR amplification. The constructs were then subjected to massively parallel high throughput sequencing (Illumina) in picoliter wells to identify millions of sequences per sample. Sequences thus identified were matched to the genome and microRNAs were identified based on their characteristic stem-loop secondary structure, thermodynamic stability, and evidence of processing with the microRNA-related enzymes drosha and dicer. Results: Using massively parallel high-throughput sequencing of small RNAs isolated from these B cell subsets, we analyzed a total of 62,293,147 sequences (> 1.6 billion bases). We found that 261 known microRNAs are expressed in normal and malignant B cells, a number that is three times higher than previously recognized. Our work also identified the expression of 86 novel miRNAs in normal and malignant B cells, many of which appear to target genes important in B cell differentiation including BCL6, NLK, EBF, as well as oncogenes including MYC, LMO2, and CCND1. We found no evidence of decreased expression of microRNAs in B cell malignancies, in contrast to the described down-regulation of microRNAs in tumors from other lineages. On the other hand, there were striking differences between the microRNA expression patterns in normal and malignant B cells, although B cell malignancies still frequently express miRNAs that are characteristic of their normal B cell counterpart. Each malignancy had a characteristic pattern of microRNA expression that was distinct from other malignancies and normal B cells. Conclusion: Through high throughput sequencing, we have discovered novel microRNAs that target important oncogenes including BCL6, MYC, LMO2, and CCND1, suggesting a role for microRNAs in B cell lymphomas.

scholarly journals PRIME-3D2D is a 3D2D model to predict binding sites of protein–RNA interaction

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Juan Xie ◽  
Jinfang Zheng ◽  
Xu Hong ◽  
Xiaoxue Tong ◽  
Shiyong Liu
Keyword(s):  
Full Advantage ◽  
High Throughput ◽  
Binding Sites ◽  
High Throughput Sequencing ◽  
Secondary Structures ◽  
Biological Processes ◽  
Genome Wide ◽  
Rna Interaction ◽  
Rna Complexes ◽  
Better Than

AbstractProtein-RNA interaction participates in many biological processes. So, studying protein–RNA interaction can help us to understand the function of protein and RNA. Although the protein–RNA 3D3D model, like PRIME, was useful in building 3D structural complexes, it can’t be used genome-wide, due to lacking RNA 3D structures. To take full advantage of RNA secondary structures revealed from high-throughput sequencing, we present PRIME-3D2D to predict binding sites of protein–RNA interaction. PRIME-3D2D is almost as good as PRIME at modeling protein–RNA complexes. PRIME-3D2D can be used to predict binding sites on PDB data (MCC = 0.75/0.70 for binding sites in protein/RNA) and transcription-wide (MCC = 0.285 for binding sites in RNA). Testing on PDB and yeast transcription-wide data show that PRIME-3D2D performs better than other binding sites predictor. So, PRIME-3D2D can be used to predict the binding sites both on PDB and genome-wide, and it’s freely available.

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