Cpg Islands and the Regulation of Transcription
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Dna Methylation Post Transcriptional Modification
Dna Methylation Post Transcriptional Modification Blistery Benny backbiting her tug-of-war so protectively that Scot barrel very weekends. Solanaceous and unpossessing Eric pubes her creatorships abrogating while Raymundo bereave some limitations demonstrably. Clair compresses his catchings getter epexegetically or epidemically after Bernie vitriols and piffling unchangeably, hypognathous and nourishing. To explore quantitative and dynamic properties of transcriptional regulation by. MeSH Cochrane Library. In revere last check of man series but left house with various gene expression profile of the effect of. Moreover interpretation of transcriptional changes during COVID-19 has been. In transcriptional modification by post transcriptional repression and posted by selective breeding industry: patterns of dna methylation during gc cells and the study of dna. DNA methylation regulates transcriptional homeostasis of. Be local in two ways Post Translational Modifications of amino acid residues of histone. International journal of cyclic gmp in a chromatin dynamics: unexpected results in alternative splicing of reusing and diagnosis of dmrs has been identified using whole process. Dam in dna methylation to violent outbursts that have originated anywhere in england and post transcriptional gene is regulated at the content in dna methylation post transcriptional modification of. A seven sample which customers post being the dtc company for analysis. Fei zhao y, methylation dynamics and modifications on lysine is an essential that. Tag-based our Generation Sequencing. DNA methylation and histone modifications as epigenetic. Thc content of. Lysine methylation has been involved in both transcriptional activation H3K4. For instance aberrance of DNA methylation andor demethylation has been. Chromosome conformation capture from 3C to 5C and will ChIP-based modification. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Modes of Interaction of KMT2 Histone H3 Lysine 4 Methyltransferase/COMPASS Complexes with Chromatin
cells Review Modes of Interaction of KMT2 Histone H3 Lysine 4 Methyltransferase/COMPASS Complexes with Chromatin Agnieszka Bochy ´nska,Juliane Lüscher-Firzlaff and Bernhard Lüscher * ID Institute of Biochemistry and Molecular Biology, Medical School, RWTH Aachen University, Pauwelsstrasse 30, 52057 Aachen, Germany; [email protected] (A.B.); jluescher-fi[email protected] (J.L.-F.) * Correspondence: [email protected]; Tel.: +49-241-8088850; Fax: +49-241-8082427 Received: 18 January 2018; Accepted: 27 February 2018; Published: 2 March 2018 Abstract: Regulation of gene expression is achieved by sequence-specific transcriptional regulators, which convey the information that is contained in the sequence of DNA into RNA polymerase activity. This is achieved by the recruitment of transcriptional co-factors. One of the consequences of co-factor recruitment is the control of specific properties of nucleosomes, the basic units of chromatin, and their protein components, the core histones. The main principles are to regulate the position and the characteristics of nucleosomes. The latter includes modulating the composition of core histones and their variants that are integrated into nucleosomes, and the post-translational modification of these histones referred to as histone marks. One of these marks is the methylation of lysine 4 of the core histone H3 (H3K4). While mono-methylation of H3K4 (H3K4me1) is located preferentially at active enhancers, tri-methylation (H3K4me3) is a mark found at open and potentially active promoters. Thus, H3K4 methylation is typically associated with gene transcription. The class 2 lysine methyltransferases (KMTs) are the main enzymes that methylate H3K4. KMT2 enzymes function in complexes that contain a necessary core complex composed of WDR5, RBBP5, ASH2L, and DPY30, the so-called WRAD complex. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Genome-Wide Analysis of Transcriptional Bursting-Induced Noise in Mammalian Cells
bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Title: Genome-wide analysis of transcriptional bursting-induced noise in mammalian cells Authors: Hiroshi Ochiai1*, Tetsutaro Hayashi2, Mana Umeda2, Mika Yoshimura2, Akihito Harada3, Yukiko Shimizu4, Kenta Nakano4, Noriko Saitoh5, Hiroshi Kimura6, Zhe Liu7, Takashi Yamamoto1, Tadashi Okamura4,8, Yasuyuki Ohkawa3, Itoshi Nikaido2,9* Affiliations: 1Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-0046, Japan 2Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama, 351-0198, Japan 3Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-0054, Japan 4Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 5Division of Cancer Biology, The Cancer Institute of JFCR, Tokyo, 135-8550, Japan 6Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8503, Japan 7Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, 20147, USA 8Section of Animal Models, Department of Infectious Diseases, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 9Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, Wako, 351-0198, Japan *Corresponding authors Corresponding authors e-mail addresses Hiroshi Ochiai, [email protected] Itoshi Nikaido, [email protected] bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019. -
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). -
RING1 Antibody (N-Term) Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # AP14560A
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 RING1 Antibody (N-term) Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # AP14560A Specification RING1 Antibody (N-term) - Product Information Application WB,E Primary Accession Q06587 Other Accession Q6MGB6, O35730, NP_002922.2 Reactivity Human Predicted Mouse, Rat Host Rabbit Clonality Polyclonal Isotype Rabbit Ig Antigen Region 95-123 RING1 Antibody (N-term) - Additional Information RING1 Antibody (N-term) (Cat. #AP14560a) Gene ID 6015 western blot analysis in MDA-MB453 cell line lysates (35ug/lane).This demonstrates the Other Names RING1 antibody detected the RING1 protein E3 ubiquitin-protein ligase RING1, 632-, (arrow). Polycomb complex protein RING1, RING finger protein 1, Really interesting new gene 1 protein, RING1, RNF1 RING1 Antibody (N-term) - Background Target/Specificity This gene belongs to the RING finger family, This RING1 antibody is generated from members of rabbits immunized with a KLH conjugated synthetic peptide between 95-123 amino which encode proteins characterized by a RING acids from the N-terminal region of human domain, a RING1. zinc-binding motif related to the zinc finger domain. The gene Dilution product can bind DNA and can act as a WB~~1:1000 transcriptional repressor. It is associated with the multimeric polycomb Format group protein complex. Purified polyclonal antibody supplied in PBS The gene product interacts with the polycomb with 0.09% (W/V) sodium azide. This group proteins BMI1, antibody is purified through a protein A EDR1, and CBX4, and colocalizes with these column, followed by peptide affinity proteins in large purification. -
DNA Methylation, Imprinting and Cancer
European Journal of Human Genetics (2002) 10, 6±16 ã 2002 Nature Publishing Group All rights reserved 1018-4813/02 $25.00 www.nature.com/ejhg REVIEW DNA methylation, imprinting and cancer Christoph Plass*,1 and Paul D Soloway*,2 1Division of Human Cancer Genetics and the Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, OH 43210, USA; 2Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, New York, NY 14263, USA It is well known that a variety of genetic changes influence the development and progression of cancer. These changes may result from inherited or spontaneous mutations that are not corrected by repair mechanisms prior to DNA replication. It is increasingly clear that so called epigenetic effects that do not affect the primary sequence of the genome also play an important role in tumorigenesis. This was supported initially by observations that cancer genomes undergo changes in their methylation state and that control of parental allele-specific methylation and expression of imprinted loci is lost in several cancers. Many loci acquiring aberrant methylation in cancers have since been identified and shown to be silenced by DNA methylation. In many cases, this mechanism of silencing inactivates tumour suppressors as effectively as frank mutation and is one of the cancer-predisposing hits described in Knudson's two hit hypothesis. In contrast to mutations which are essentially irreversible, methylation changes are reversible, raising the possibility of developing therapeutics based on restoring the normal methylation state to cancer-associated genes. Development of such therapeutics will require identifying loci undergoing methylation changes in cancer, understanding how their methylation influences tumorigenesis and identifying the mechanisms regulating the methylation state of the genome. -
Expression Profiling of KLF4
Expression Profiling of KLF4 AJCR0000006 Supplemental Data Figure S1. Snapshot of enriched gene sets identified by GSEA in Klf4-null MEFs. Figure S2. Snapshot of enriched gene sets identified by GSEA in wild type MEFs. 98 Am J Cancer Res 2011;1(1):85-97 Table S1: Functional Annotation Clustering of Genes Up-Regulated in Klf4 -Null MEFs ILLUMINA_ID Gene Symbol Gene Name (Description) P -value Fold-Change Cell Cycle 8.00E-03 ILMN_1217331 Mcm6 MINICHROMOSOME MAINTENANCE DEFICIENT 6 40.36 ILMN_2723931 E2f6 E2F TRANSCRIPTION FACTOR 6 26.8 ILMN_2724570 Mapk12 MITOGEN-ACTIVATED PROTEIN KINASE 12 22.19 ILMN_1218470 Cdk2 CYCLIN-DEPENDENT KINASE 2 9.32 ILMN_1234909 Tipin TIMELESS INTERACTING PROTEIN 5.3 ILMN_1212692 Mapk13 SAPK/ERK/KINASE 4 4.96 ILMN_2666690 Cul7 CULLIN 7 2.23 ILMN_2681776 Mapk6 MITOGEN ACTIVATED PROTEIN KINASE 4 2.11 ILMN_2652909 Ddit3 DNA-DAMAGE INDUCIBLE TRANSCRIPT 3 2.07 ILMN_2742152 Gadd45a GROWTH ARREST AND DNA-DAMAGE-INDUCIBLE 45 ALPHA 1.92 ILMN_1212787 Pttg1 PITUITARY TUMOR-TRANSFORMING 1 1.8 ILMN_1216721 Cdk5 CYCLIN-DEPENDENT KINASE 5 1.78 ILMN_1227009 Gas2l1 GROWTH ARREST-SPECIFIC 2 LIKE 1 1.74 ILMN_2663009 Rassf5 RAS ASSOCIATION (RALGDS/AF-6) DOMAIN FAMILY 5 1.64 ILMN_1220454 Anapc13 ANAPHASE PROMOTING COMPLEX SUBUNIT 13 1.61 ILMN_1216213 Incenp INNER CENTROMERE PROTEIN 1.56 ILMN_1256301 Rcc2 REGULATOR OF CHROMOSOME CONDENSATION 2 1.53 Extracellular Matrix 5.80E-06 ILMN_2735184 Col18a1 PROCOLLAGEN, TYPE XVIII, ALPHA 1 51.5 ILMN_1223997 Crtap CARTILAGE ASSOCIATED PROTEIN 32.74 ILMN_2753809 Mmp3 MATRIX METALLOPEPTIDASE -
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. -
RING1 Proteins Contribute to Early Proximal-Distal Specification of The
© 2016. Published by The Company of Biologists Ltd | Development (2016) 143, 276-285 doi:10.1242/dev.127506 RESEARCH ARTICLE RING1 proteins contribute to early proximal-distal specification of the forelimb bud by restricting Meis2 expression Nayuta Yakushiji-Kaminatsui1,*,‡, Takashi Kondo1,2,3, Takaho A. Endo4, Yoko Koseki1,2, Kaori Kondo1,2,3, Osamu Ohara4, Miguel Vidal5 and Haruhiko Koseki1,2,‡ ABSTRACT subunits to catalyze histone H2A monoubiquitylation at lysine 119 Polycomb group (PcG) proteins play a pivotal role in silencing (H2AK119ub1). PRC1 also targets chromatin compaction by SAM developmental genes and help to maintain various stem and domain polymerization of PHC1 and PHC2, a mammalian PH precursor cells and regulate their differentiation. PcG factors also ortholog, via H3K27me3 recognition by chromodomain proteins regulate dynamic and complex regional specification, particularly in such as CBX7 and CBX2 (Cao et al., 2002; Endoh et al., 2012; mammals, but this activity is mechanistically not well understood. In Isono et al., 2013; Kuzmichev et al., 2002). Recent studies identified this study, we focused on proximal-distal (PD) patterning of the a variant PRC1 complex containing RYBP (Ring1 and YY1 mouse forelimb bud to elucidate how PcG factors contribute to a binding protein)/YAF (YY1-associated factor), RING1A/B and a regional specification process that depends on developmental distinct PCGF subunit (Gao et al., 2012; Tavares et al., 2012), and signals. Depletion of the RING1 proteins RING1A (RING1) and demonstrated that H2AK119ub1 mediated by this variant PRC1 RING1B (RNF2), which are essential components of Polycomb leads to the recruitment of PRC2 and placement of H3K27me3 to repressive complex 1 (PRC1), led to severe defects in forelimb initiate Polycomb repression (Blackledge et al., 2014). -
Supplemental Table S1 (A): Microarray Datasets Characteristics
Supplemental table S1 (A): Microarray datasets characteristics Title Summary Samples Literature ref. GEO ref. Acquisition of granule Gene expression profiling of 27 (1) GSE 11859 neuron precursor identity cerebellar tumors generated and Hedgehog‐induced from various early and late medulloblastoma in mice. stage CNS progenitor cells Medulloblastomas derived Study of mouse 5 (2) GSE 7212 from Cxcr6 mutant mice medulloblastoma in response respond to treatment with to inhibitor of Smoothened a Smoothened inhibitor Expression profiles of Identification of distinct classes 10 (3) GSE 9299 mouse medulloblastoma of up‐regulated or down‐ 339 & 340 regulated genes during Hh dependent tumorigenesis Genetic alterations in Identification of differently 10 (4) GSE 6463 mouse medulloblastomas expressed genes among CGNPs 339 & and generation of tumors and CGNPs transfected with 340 from cerebellar granule retroviruses that express nmyc neuron precursors or cyclin‐d1 Patched heterozygous Analysis of granule cell 14 (5) GSE 2426 model of medulloblastoma precursors, pre‐neoplastic cells, GDS1110 and tumor cells 1. Schuller U, Heine VM, Mao J, Kho AT, Dillon AK, Han YG, et al. Acquisition of granule neuron precursor identity is a critical determinant of progenitor cell competence to form Shh‐induced medulloblastoma. Cancer Cell 2008;14:123‐134. 2. Sasai K, Romer JT, Kimura H, Eberhart DE, Rice DS, Curran T. Medulloblastomas derived from Cxcr6 mutant mice respond to treatment with a smoothened inhibitor. Cancer Res 2007;67:3871‐3877. 3. Mao J, Ligon KL, Rakhlin EY, Thayer SP, Bronson RT, Rowitch D, et al. A novel somatic mouse model to survey tumorigenic potential applied to the Hedgehog pathway. Cancer Res 2006;66:10171‐10178.