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Extreme HOT Regions Are Cpg-Dense Promoters in C. Elegans and Humans
Research Extreme HOT regions are CpG-dense promoters in C. elegans and humans Ron A.-J. Chen, Przemyslaw Stempor, Thomas A. Down, Eva Zeiser, Sky K. Feuer, and Julie Ahringer1 The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB3 0DH, United Kingdom Most vertebrate promoters lie in unmethylated CpG-dense islands, whereas methylation of the more sparsely distributed CpGs in the remainder of the genome is thought to contribute to transcriptional repression. Nonmethylated CG di- nucleotides are recognized by CXXC finger protein 1 (CXXC1, also known as CFP1), which recruits SETD1A (also known as Set1) methyltransferase for trimethylation of histone H3 lysine 4, an active promoter mark. Genomic regions enriched for CpGs are thought to be either absent or irrelevant in invertebrates that lack DNA methylation, such as C. elegans; however, a CXXC1 ortholog (CFP-1) is present. Here we demonstrate that C. elegans CFP-1 targets promoters with high CpG density, and these promoters are marked by high levels of H3K4me3. Furthermore, as for mammalian promoters, high CpG content is associated with nucleosome depletion irrespective of transcriptional activity. We further show that highly occupied target (HOT) regions identified by the binding of a large number of transcription factors are CpG-rich pro- moters in C. elegans and human genomes, suggesting that the unusually high factor association at HOT regions may be a consequence of CpG-linked chromatin accessibility. Our results indicate that nonmethylated CpG-dense sequence is a conserved genomic signal that promotes an open chromatin state, targeting by a CXXC1 ortholog, and H3K4me3 modification in both C. -
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. 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. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Universidad Nacional Autónoma De México Plan De Estudios Combinados En Medicina Instituto Nacional De Medicina Genómica
UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO PLAN DE ESTUDIOS COMBINADOS EN MEDICINA INSTITUTO NACIONAL DE MEDICINA GENÓMICA ESTUDIO POST-MORTEM DE LAS ALTERACIONES EN LA EXPRESIÓN DE RNA EN EL CEREBRO DE PACIENTES SUICIDAS TESIS QUE PARA OPTAR POR EL GRADO DE DOCTORA EN MEDICINA PRESENTA: BRENDA CABRERA MENDOZA DIRECTOR DE TESIS: DR. JOSÉ HUMBERTO NICOLINI SÁNCHEZ INSTITUTO NACIONAL DE MEDICINA GENÓMICA COMITÉ TUTOR: DRA. MARTHA PATRICIA OSTROSKY-SHEJET INSTITUTO DE INVESTIGACIONES BIOMÉDICAS DR. DAVID COLIN GLAHN ESCUELA DE MEDICINA DE HARVARD Ciudad Universitaria, CD. MX., diciembre de 2020 TABLA DE CONTENIDOS Resumen ........................................................................................................................................................................ 1 Abstract .......................................................................................................................................................................... 2 Definición y epidemiología del suicidio ............................................................................................................ 3 Epidemiología global del suicidio ..................................................................................................................... 5 Epidemiología del suicidio en América ........................................................................................................... 8 Epidemiología del suicidio en México ............................................................................................................10 -
The Structural Basis for Selective Binding of Non-Methylated Cpg Islands by the CFP1 CXXC Domain
ARTICLE Received 13 Dec 2010 | Accepted 9 Feb 2011 | Published 8 Mar 2011 DOI: 10.1038/ncomms1237 The structural basis for selective binding of non-methylated CpG islands by the CFP1 CXXC domain Chao Xu1,*, Chuanbing Bian1,*, Robert Lam1, Aiping Dong1 & Jinrong Min1,2 CFP1 is a CXXC domain-containing protein and an essential component of the SETD1 histone H3K4 methyltransferase complex. CXXC domain proteins direct different chromatin-modifying activities to various chromatin regions. Here, we report crystal structures of the CFP1 CXXC domain in complex with six different CpG DNA sequences. The crescent-shaped CFP1 CXXC domain is wedged into the major groove of the CpG DNA, distorting the B-form DNA, and interacts extensively with the major groove of the DNA. The structures elucidate the molecular mechanism of the non-methylated CpG-binding specificity of the CFP1 CXXC domain. The CpG motif is confined by a tripeptide located in a rigid loop, which only allows the accommodation of the non-methylated CpG dinucleotide. Furthermore, we demonstrate that CFP1 has a preference for a guanosine nucleotide following the CpG motif. 1 Structural Genomics Consortium, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada. 2 Department of Physiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. *These authors contributed equally to this work. Correspondence and requests for materials should be addressed to J.M. (email: [email protected]). NATURE COMMUNICATIONS | 2:227 | DOI: 10.1038/ncomms1237 | www.nature.com/naturecommunications © 2011 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1237 pG islands contain a high density of CpG content and embrace the promoters of most genes in vertebrate genomes1. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
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. -
UCP1-Independent Thermogenesis in Brown/Beige Adipocytes: Classical Creatine Kinase/Phosphocreatine Shuttle Instead of “Futile Creatine Cycling”
UCP1-independent thermogenesis in brown/beige adipocytes: classical creatine kinase/phosphocreatine shuttle instead of “futile creatine cycling”. Theo Wallimann1*), Malgorzata Tokarska-Schlattner2) Laurence Kay2) and Uwe Schlattner2,3*) 1) Biology Dept. ETH-Zurich, Switzerland, emeritus, E-mail address: [email protected] 2) University Grenoble Alpes and Inserm U1055, Laboratory of Fundamental and Applied Bioenergetics & SFR Environmental and Systems Biology, Grenoble, France, E-mail address: [email protected] 3) Institut Universitaire de France (IUF), Paris, France *) joint corresponding authors Abstract Various studies have identified creatine kinase (CK) and creatine (Cr) as important players for thermogenesis. More recently, they have been specifically linked to UCP1-independent thermogenesis in beige/brown adipocytes, and a “Cr-driven futile cycle” within mitochondria was proposed as the mechanistic basis. Here, we provide a critical appraisal of such a mechanism, which would require a rather undefined phosphocreatine phosphatase. As alternative explanation, we suggest instead that the well-known functions of the CK system, that is ATP buffering and shuttling of high-energy phosphocreatine (PCr) from sites of ATP generation to sites of ATP utilization, are also working in brown/beige adipocytes. There, the CK/PCr system would be shunted between ATP generation, at the mitochondria and/or glycolysis, and ATP hydrolysis at the ER/SR. This would largely facilitate high-throughput calcium pumping by the ATP-dependent Ca2+ pump (SERCA) as described also in skeletal and cardiac muscle. This very CK/PCr system would then support adipocyte SERCA2b function and, in tandem with adipocyte ryanodine receptor (RyR2) and/or inositol 1,4,5- 2+ triphosphate receptor (IP3-R3), facilitate thermogenic futile Ca cycling that has been described to operate in UCP1-independent, but ATP-dependent non-shivering thermogenesis. -
RNA Editing at Baseline and Following Endoplasmic Reticulum Stress
RNA Editing at Baseline and Following Endoplasmic Reticulum Stress By Allison Leigh Richards A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Human Genetics) in The University of Michigan 2015 Doctoral Committee: Professor Vivian G. Cheung, Chair Assistant Professor Santhi K. Ganesh Professor David Ginsburg Professor Daniel J. Klionsky Dedication To my father, mother, and Matt without whom I would never have made it ii Acknowledgements Thank you first and foremost to my dissertation mentor, Dr. Vivian Cheung. I have learned so much from you over the past several years including presentation skills such as never sighing and never saying “as you can see…” You have taught me how to think outside the box and how to create and explain my story to others. I would not be where I am today without your help and guidance. Thank you to the members of my dissertation committee (Drs. Santhi Ganesh, David Ginsburg and Daniel Klionsky) for all of your advice and support. I would also like to thank the entire Human Genetics Program, and especially JoAnn Sekiguchi and Karen Grahl, for welcoming me to the University of Michigan and making my transition so much easier. Thank you to Michael Boehnke and the Genome Science Training Program for supporting my work. A very special thank you to all of the members of the Cheung lab, past and present. Thank you to Xiaorong Wang for all of your help from the bench to advice on my career. Thank you to Zhengwei Zhu who has helped me immensely throughout my thesis even through my panic. -
Mammalian Protein Expression and Characterization Tools for Next Generation Biologics
Mammalian protein expression and characterization tools for next generation biologics next generation for tools characterization and expression protein Mammalian Doctoral Thesis in Biotechnology Mammalian protein expression and characterization tools for next generation biologics NIKLAS THALÉN ISBN TRITACBHFOU: KTH www.kth.se Stockholm, Sweden Mammalian protein expression and characterization tools for next generation biologics NIKLAS THALÉN Academic Dissertation which, with due permission of the KTH Royal Institute of Technology, is submitted for public defence for the Degree of Doctor of Philosophy on June 11th, 2021 at 10:00, F3, Lindstedsvägen 26, våningsplan 2, Sing-Sing, KTH campus, Stockholm. Doctoral Thesis in Biotechnology KTH Royal Institute of Technology Stockholm, Sweden 2021 © Niklas Thalén ISBN 978-91-7873-927-1 TRITA-CBH-FOU-2021:21 Printed by: Universitetsservice US-AB, Sweden 2021 Till Sofia Abstract Protein therapeutics are increasingly important for modern medicine. Novel recombinant proteins developed today can bind towards their target with high specificity and with low adverse effect. This has enabled the treatment of diseases that for a few years ago were deemed uncurable. Discovery of therapeutic proteins is driven through protein engineering, a field that is in constant expansion. And, through artificial construction of recombinant proteins, a large array of diseases can be defeated. The function and quality of these protein therapeutics rely on the correct folding, assembly and residue modification that occurs during their production within a living production cell host. Furthermore, producing them in large quantities are essential for accessibility of the best biopharmaceuticals available. Commonly, mammalian cells are the production host of choice when it comes to production of biopharmaceuticals. -
A-To-I RNA Editing Does Not Change with Age in the Healthy Male Rat Brain
Biogerontology (2013) 14:395–400 DOI 10.1007/s10522-013-9433-8 RESEARCH ARTICLE A-to-I RNA editing does not change with age in the healthy male rat brain Andrew P. Holmes • Shona H. Wood • Brian J. Merry • Joa˜o Pedro de Magalha˜es Received: 18 January 2013 / Accepted: 15 May 2013 / Published online: 26 May 2013 Ó The Author(s) 2013. This article is published with open access at Springerlink.com Abstract RNA editing is a post-transcriptional pro- Introduction cess, which results in base substitution modifications to RNA. It is an important process in generating Adenosine to inosine (A-to-I) RNA editing is a post- protein diversity through amino acid substitution and transcriptional process that alters the sequences of the modulation of splicing events. Previous studies RNA molecules. The adenosine deaminases ADAR have suggested a link between gene-specific reduc- and ADARB1 convert specific adenosine residues on tions in adenosine to inosine RNA editing and aging in RNA to inosine bases. During translation, sequencing, the human brain. Here we demonstrate that changes in and splicing, inosine is recognized as guanosine. RNA editing observed in humans with age are not Therefore, A-to-I RNA editing has important impli- observed during aging in healthy rats. Furthermore, we cations in altering specific amino acids, miRNA identify a conserved editing site in rats, in Cog3.We targeting, and in the modulation of alternative splicing propose that either age-related changes in RNA (Nishikura 2010). editing are specific to primates or humans, or that Targets of A-to-I RNA editing are often genes they are the manifestation of disease pathology. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Comparative Genomics of the Major Parasitic Worms
Comparative genomics of the major parasitic worms International Helminth Genomes Consortium Supplementary Information Introduction ............................................................................................................................... 4 Contributions from Consortium members ..................................................................................... 5 Methods .................................................................................................................................... 6 1 Sample collection and preparation ................................................................................................................. 6 2.1 Data production, Wellcome Trust Sanger Institute (WTSI) ........................................................................ 12 DNA template preparation and sequencing................................................................................................. 12 Genome assembly ........................................................................................................................................ 13 Assembly QC ................................................................................................................................................. 14 Gene prediction ............................................................................................................................................ 15 Contamination screening ............................................................................................................................