Integrated Genomic, Epigenomic, and Expression Analyses of Ovarian Cancer Cell Lines
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DNA Methylation of GHSR, GNG4, HOXD9 and SALL3 Is a Common Epigenetic Alteration in Thymic Carcinoma
INTERNATIONAL JOURNAL OF ONCOLOGY 56: 315-326, 2020 DNA methylation of GHSR, GNG4, HOXD9 and SALL3 is a common epigenetic alteration in thymic carcinoma REINA KISHIBUCHI1, KAZUYA KONDO1, SHIHO SOEJIMA1, MITSUHIRO TSUBOI2, KOICHIRO KAJIURA2, YUKIKIYO KAWAKAMI2, NAOYA KAWAKITA2, TORU SAWADA2, HIROAKI TOBA2, MITSUTERU YOSHIDA2, HIROMITSU TAKIZAWA2 and AKIRA TANGOKU2 1Department of Oncological Medical Services, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8509; 2Department of Thoracic, Endocrine Surgery and Oncology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan Received August 17, 2019; Accepted October 25, 2019 DOI: 10.3892/ijo.2019.4915 Abstract. Thymic epithelial tumors comprise thymoma, promoter methylation of the 4 genes was not significantly thymic carcinoma and neuroendocrine tumors of the thymus. higher in advanced-stage tumors than in early-stage tumors in Recent studies have revealed that the incidence of somatic all thymic epithelial tumors. Among the 4 genes, relapse-free non‑synonymous mutations is significantly higher in thymic survival was significantly worse in tumors with a higher DNA carcinoma than in thymoma. However, limited information methylation than in those with a lower DNA methylation in all is currently available on epigenetic alterations in these types thymic epithelial tumors. Moreover, relapse-free survival was of cancer. In this study, we thus performed genome-wide significantly worse in thymomas with a higher DNA methyla- screening of aberrantly methylated CpG islands in thymoma tion of HOXD9 and SALL3 than in those with a lower DNA and thymic carcinoma using Illumina HumanMethylation450 methylation. On the whole, the findings of this study indicated K BeadChip. We identified 92 CpG islands significantly that the promoter methylation of cancer-related genes was hypermethylated in thymic carcinoma in relation to thymoma significantly higher in thymic carcinoma than in thymoma and and selected G protein subunit gamma 4 (GNG4), growth the thymus. -
Core Transcriptional Regulatory Circuitries in Cancer
Oncogene (2020) 39:6633–6646 https://doi.org/10.1038/s41388-020-01459-w REVIEW ARTICLE Core transcriptional regulatory circuitries in cancer 1 1,2,3 1 2 1,4,5 Ye Chen ● Liang Xu ● Ruby Yu-Tong Lin ● Markus Müschen ● H. Phillip Koeffler Received: 14 June 2020 / Revised: 30 August 2020 / Accepted: 4 September 2020 / Published online: 17 September 2020 © The Author(s) 2020. This article is published with open access Abstract Transcription factors (TFs) coordinate the on-and-off states of gene expression typically in a combinatorial fashion. Studies from embryonic stem cells and other cell types have revealed that a clique of self-regulated core TFs control cell identity and cell state. These core TFs form interconnected feed-forward transcriptional loops to establish and reinforce the cell-type- specific gene-expression program; the ensemble of core TFs and their regulatory loops constitutes core transcriptional regulatory circuitry (CRC). Here, we summarize recent progress in computational reconstitution and biologic exploration of CRCs across various human malignancies, and consolidate the strategy and methodology for CRC discovery. We also discuss the genetic basis and therapeutic vulnerability of CRC, and highlight new frontiers and future efforts for the study of CRC in cancer. Knowledge of CRC in cancer is fundamental to understanding cancer-specific transcriptional addiction, and should provide important insight to both pathobiology and therapeutics. 1234567890();,: 1234567890();,: Introduction genes. Till now, one critical goal in biology remains to understand the composition and hierarchy of transcriptional Transcriptional regulation is one of the fundamental mole- regulatory network in each specified cell type/lineage. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Early B-Cell Factors Are Required for Specifying Multiple Retinal Cell Types and Subtypes from Postmitotic Precursors
11902 • The Journal of Neuroscience, September 8, 2010 • 30(36):11902–11916 Development/Plasticity/Repair Early B-Cell Factors Are Required for Specifying Multiple Retinal Cell Types and Subtypes from Postmitotic Precursors Kangxin Jin,1,2 Haisong Jiang,1,2 Zeqian Mo,3 and Mengqing Xiang1,2 1Center for Advanced Biotechnology and Medicine and Department of Pediatrics, 2Graduate Program in Molecular Genetics, Microbiology and Immunology, and 3Department of Cell Biology and Neuroscience, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, New Jersey 08854 The establishment of functional retinal circuits in the mammalian retina depends critically on the proper generation and assembly of six classes of neurons, five of which consist of two or more subtypes that differ in morphologies, physiological properties, and/or sublaminar positions. How these diverse neuronal types and subtypes arise during retinogenesis still remains largely to be defined at the molecular level. Here we show that all four family members of the early B-cell factor (Ebf) helix-loop-helix transcription factors are similarly expressedduringmouseretinogenesisinseveralneuronaltypesandsubtypesincludingganglion,amacrine,bipolar,andhorizontalcells, and that their expression in ganglion cells depends on the ganglion cell specification factor Brn3b. Misexpressed Ebfs bias retinal precursors toward the fates of non-AII glycinergic amacrine, type 2 OFF-cone bipolar and horizontal cells, whereas a dominant-negative Ebf suppresses the differentiation of these cells as well as ganglion cells. Reducing Ebf1 expression by RNA interference (RNAi) leads to an inhibitory effect similar to that of the dominant-negative Ebf, effectively neutralizes the promotive effect of wild-type Ebf1, but has no impact on the promotive effect of an RNAi-resistant Ebf1. -
Watsonjn2018.Pdf (1.780Mb)
UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support. -
Tepzz¥ 6Z54za T
(19) TZZ¥ ZZ_T (11) EP 3 260 540 A1 (12) EUROPEAN PATENT APPLICATION (43) Date of publication: (51) Int Cl.: 27.12.2017 Bulletin 2017/52 C12N 15/113 (2010.01) A61K 9/127 (2006.01) A61K 31/713 (2006.01) C12Q 1/68 (2006.01) (21) Application number: 17000579.7 (22) Date of filing: 12.11.2011 (84) Designated Contracting States: • Sarma, Kavitha AL AT BE BG CH CY CZ DE DK EE ES FI FR GB Philadelphia, PA 19146 (US) GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO • Borowsky, Mark PL PT RO RS SE SI SK SM TR Needham, MA 02494 (US) • Ohsumi, Toshiro Kendrick (30) Priority: 12.11.2010 US 412862 P Cambridge, MA 02141 (US) 20.12.2010 US 201061425174 P 28.07.2011 US 201161512754 P (74) Representative: Clegg, Richard Ian et al Mewburn Ellis LLP (62) Document number(s) of the earlier application(s) in City Tower accordance with Art. 76 EPC: 40 Basinghall Street 11840099.3 / 2 638 163 London EC2V 5DE (GB) (71) Applicant: The General Hospital Corporation Remarks: Boston, MA 02114 (US) •Thecomplete document including Reference Tables and the Sequence Listing can be downloaded from (72) Inventors: the EPO website • Lee, Jeannie T •This application was filed on 05-04-2017 as a Boston, MA 02114 (US) divisional application to the application mentioned • Zhao, Jing under INID code 62. San Diego, CA 92122 (US) •Claims filed after the date of receipt of the divisional application (Rule 68(4) EPC). (54) POLYCOMB-ASSOCIATED NON-CODING RNAS (57) This invention relates to long non-coding RNAs (IncRNAs), libraries of those ncRNAs that bind chromatin modifiers, such as Polycomb Repressive Complex 2, inhibitory nucleic acids and methods and compositions for targeting IncRNAs. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Single Cell Regulatory Landscape of the Mouse Kidney Highlights Cellular Differentiation Programs and Disease Targets
ARTICLE https://doi.org/10.1038/s41467-021-22266-1 OPEN Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets Zhen Miao 1,2,3,8, Michael S. Balzer 1,2,8, Ziyuan Ma 1,2,8, Hongbo Liu1,2, Junnan Wu 1,2, Rojesh Shrestha 1,2, Tamas Aranyi1,2, Amy Kwan4, Ayano Kondo 4, Marco Pontoglio 5, Junhyong Kim6, ✉ Mingyao Li 7, Klaus H. Kaestner2,4 & Katalin Susztak 1,2,4 1234567890():,; Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene- regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development. 1 Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 2 Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. -
CRISPR/Cas9 Interrogation of the Mouse Pcdhg Gene Cluster 4 Reveals a Crucial Isoform-Specific Role for Pcdhgc4
bioRxiv preprint doi: https://doi.org/10.1101/739508; this version posted August 19, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 CRISPR/Cas9 interrogation of the mouse Pcdhg gene cluster 4 reveals a crucial isoform-specific role for Pcdhgc4 5 6 Andrew M. Garrett1, 3,*, Peter J. Bosch2, David M. Steffen2, Leah C. Fuller2, Charles G. Marcucci2, Alexis A. 7 Koch1, Preeti Bais3, Joshua A. Weiner2,*, and Robert W. Burgess3,* 8 9 1 Wayne State University, Department of Pharmacology, Department of Ophthalmology, Visual, and 10 Anatomical Sciences, Detroit, MI. 11 2 University of Iowa, Department of Biology and Iowa Neuroscience Institute, Iowa City, IA 12 3 The Jackson Laboratory, Bar Harbor, ME 13 * Co-corresponding authors 1 bioRxiv preprint doi: https://doi.org/10.1101/739508; this version posted August 19, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 14 ABSTRACT 15 The mammalian Pcdhg gene cluster encodes a family of 22 cell adhesion molecules, the gamma- 16 Protocadherins (γ-Pcdhs), critical for neuronal survival and neural circuit formation. The extent to which 17 isoform diversity–a γ-Pcdh hallmark–is required for their functions remains unclear. We used a 18 CRISPR/Cas9 approach to reduce isoform diversity, targeting each Pcdhg variable exon with pooled 19 sgRNAs to generate an allelic series of 26 mouse lines with 1 to 21 isoforms disrupted via discrete indels 20 at guide sites and/or larger deletions/rearrangements. -
Epigenetic Services Citations
Active Motif Epigenetic Services Publications The papers below contain data generated by Active Motif’s Epigenetic Services team. To learn more about our services, please give us a call or visit us at www.activemotif.com/services. Technique Target Journal Year Reference Justin C. Boucher et al. CD28 Costimulatory Domain- ATAC-Seq, Cancer Immunol. Targeted Mutations Enhance Chimeric Antigen Receptor — 2021 RNA-Seq Res. T-cell Function. Cancer Immunol. Res. doi: 10.1158/2326- 6066.CIR-20-0253. Satvik Mareedu et al. Sarcolipin haploinsufficiency Am. J. Physiol. prevents dystrophic cardiomyopathy in mdx mice. RNA-Seq — Heart Circ. 2021 Am J Physiol Heart Circ Physiol. doi: 10.1152/ Physiol. ajpheart.00601.2020. Gabi Schutzius et al. BET bromodomain inhibitors regulate Nature Chemical ChIP-Seq BRD4 2021 keratinocyte plasticity. Nat. Chem. Biol. doi: 10.1038/ Biology s41589-020-00716-z. Siyun Wang et al. cMET promotes metastasis and ChIP-qPCR FOXO3 J. Cell Physiol. 2021 epithelial-mesenchymal transition in colorectal carcinoma by repressing RKIP. J. Cell Physiol. doi: 10.1002/jcp.30142. Sonia Iyer et al. Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of ATAC-Seq — Cancer Discovery 2021 Combination Immunotherapy. Cancer Discov. doi: doi: 10.1158/2159-8290 Vinod Krishna et al. Integration of the Transcriptome and Genome-Wide Landscape of BRD2 and BRD4 Binding BRD2, BRD4, RNA Motifs Identifies Key Superenhancer Genes and Reveals ChIP-Seq J. Immunol. 2021 Pol II the Mechanism of Bet Inhibitor Action in Rheumatoid Arthritis Synovial Fibroblasts. J. Immunol. doi: doi: 10.4049/ jimmunol.2000286. Daniel Haag et al.