Binding Specificities of Human RNA Binding Proteins Towards Structured and Linear RNA Sequences
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Entrez Symbols Name Termid Termdesc 117553 Uba3,Ube1c
Entrez Symbols Name TermID TermDesc 117553 Uba3,Ube1c ubiquitin-like modifier activating enzyme 3 GO:0016881 acid-amino acid ligase activity 299002 G2e3,RGD1310263 G2/M-phase specific E3 ubiquitin ligase GO:0016881 acid-amino acid ligase activity 303614 RGD1310067,Smurf2 SMAD specific E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 308669 Herc2 hect domain and RLD 2 GO:0016881 acid-amino acid ligase activity 309331 Uhrf2 ubiquitin-like with PHD and ring finger domains 2 GO:0016881 acid-amino acid ligase activity 316395 Hecw2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 361866 Hace1 HECT domain and ankyrin repeat containing, E3 ubiquitin protein ligase 1 GO:0016881 acid-amino acid ligase activity 117029 Ccr5,Ckr5,Cmkbr5 chemokine (C-C motif) receptor 5 GO:0003779 actin binding 117538 Waspip,Wip,Wipf1 WAS/WASL interacting protein family, member 1 GO:0003779 actin binding 117557 TM30nm,Tpm3,Tpm5 tropomyosin 3, gamma GO:0003779 actin binding 24779 MGC93554,Slc4a1 solute carrier family 4 (anion exchanger), member 1 GO:0003779 actin binding 24851 Alpha-tm,Tma2,Tmsa,Tpm1 tropomyosin 1, alpha GO:0003779 actin binding 25132 Myo5b,Myr6 myosin Vb GO:0003779 actin binding 25152 Map1a,Mtap1a microtubule-associated protein 1A GO:0003779 actin binding 25230 Add3 adducin 3 (gamma) GO:0003779 actin binding 25386 AQP-2,Aqp2,MGC156502,aquaporin-2aquaporin 2 (collecting duct) GO:0003779 actin binding 25484 MYR5,Myo1e,Myr3 myosin IE GO:0003779 actin binding 25576 14-3-3e1,MGC93547,Ywhah -
High Throughput Circrna Sequencing Analysis Reveals Novel Insights Into
Xiong et al. Cell Death and Disease (2019) 10:658 https://doi.org/10.1038/s41419-019-1890-9 Cell Death & Disease ARTICLE Open Access High throughput circRNA sequencing analysis reveals novel insights into the mechanism of nitidine chloride against hepatocellular carcinoma Dan-dan Xiong1, Zhen-bo Feng1, Ze-feng Lai2,YueQin2, Li-min Liu3,Hao-xuanFu2, Rong-quan He4,Hua-yuWu5, Yi-wu Dang1, Gang Chen 1 and Dian-zhong Luo1 Abstract Nitidine chloride (NC) has been demonstrated to have an anticancer effect in hepatocellular carcinoma (HCC). However, the mechanism of action of NC against HCC remains largely unclear. In this study, three pairs of NC-treated and NC-untreated HCC xenograft tumour tissues were collected for circRNA sequencing analysis. In total, 297 circRNAs were differently expressed between the two groups, with 188 upregulated and 109 downregulated, among which hsa_circ_0088364 and hsa_circ_0090049 were validated by real-time quantitative polymerase chain reaction. The in vitro experiments showed that the two circRNAs inhibited the malignant biological behaviour of HCC, suggesting that they may play important roles in the development of HCC. To elucidate whether the two circRNAs function as “miRNA sponges” in HCC, we identified circRNA-miRNA and miRNA-mRNA interactions by using the CircInteractome and miRwalk, respectively. Subsequently, 857 miRNA-associated differently expressed genes in HCC were selected for weighted gene co-expression network analysis. Module Eigengene turquoise with 423 genes was found to be significantly related to the survival time, pathology grade and TNM stage of HCC patients. Gene functional enrichment 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; analysis showed that the 423 genes mainly functioned in DNA replication- and cell cycle-related biological processes and signalling cascades. -
Molecular Mechanisms Underlying Noncoding Risk Variations in Psychiatric Genetic Studies
OPEN Molecular Psychiatry (2017) 22, 497–511 www.nature.com/mp REVIEW Molecular mechanisms underlying noncoding risk variations in psychiatric genetic studies X Xiao1,2, H Chang1,2 and M Li1 Recent large-scale genetic approaches such as genome-wide association studies have allowed the identification of common genetic variations that contribute to risk architectures of psychiatric disorders. However, most of these susceptibility variants are located in noncoding genomic regions that usually span multiple genes. As a result, pinpointing the precise variant(s) and biological mechanisms accounting for the risk remains challenging. By reviewing recent progresses in genetics, functional genomics and neurobiology of psychiatric disorders, as well as gene expression analyses of brain tissues, here we propose a roadmap to characterize the roles of noncoding risk loci in the pathogenesis of psychiatric illnesses (that is, identifying the underlying molecular mechanisms explaining the genetic risk conferred by those genomic loci, and recognizing putative functional causative variants). This roadmap involves integration of transcriptomic data, epidemiological and bioinformatic methods, as well as in vitro and in vivo experimental approaches. These tools will promote the translation of genetic discoveries to physiological mechanisms, and ultimately guide the development of preventive, therapeutic and prognostic measures for psychiatric disorders. Molecular Psychiatry (2017) 22, 497–511; doi:10.1038/mp.2016.241; published online 3 January 2017 RECENT GENETIC ANALYSES OF NEUROPSYCHIATRIC neurodevelopment and brain function. For example, GRM3, DISORDERS GRIN2A, SRR and GRIA1 were known to involve in the neuro- Schizophrenia, bipolar disorder, major depressive disorder and transmission mediated by glutamate signaling and synaptic autism are highly prevalent complex neuropsychiatric diseases plasticity. -
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. -
Proteome Analysis of Isolated Podocytes Reveals Stress Responses in Glomerular Sclerosis
BASIC RESEARCH www.jasn.org Proteome Analysis of Isolated Podocytes Reveals Stress Responses in Glomerular Sclerosis Sybille Koehler,1,2 Alexander Kuczkowski,1 Lucas Kuehne,1 Christian Jüngst ,3 Martin Hoehne ,1,3 Florian Grahammer,4 Sean Eddy ,5 Matthias Kretzler ,5,6 Bodo B. Beck,7 Jörg Höhfeld,8 Bernhard Schermer,1,3 Thomas Benzing,1,3 Paul T. Brinkkoetter,1 and Markus M. Rinschen1,3,9 Due to the number of contributing authors, the affiliations are listed at the end of this article. ABSTRACT Background Understanding podocyte-specific responses to injury at a systems level is difficult because injury leads to podocyte loss or an increase of extracellular matrix, altering glomerular cellular composi- tion. Finding a window into early podocyte injury might help identify molecular pathways involved in the podocyte stress response. Methods We developed an approach to apply proteome analysis to very small samples of purified podo- cyte fractions. To examine podocytes in early disease states in FSGS mouse models, we used podocyte fractions isolated from individual mice after chemical induction of glomerular disease (with Doxorubicin or LPS). We also applied single-glomerular proteome analysis to tissue from patients with FSGS. Results Transcriptome and proteome analysis of glomeruli from patients with FSGS revealed an under- representation of podocyte-specific genes and proteins in late-stage disease. Proteome analysis of puri- fied podocyte fractions from FSGS mouse models showed an early stress response that includes perturbations of metabolic, mechanical, and proteostasis proteins. Additional analysis revealed a high correlation between the amount of proteinuria and expression levels of the mechanosensor protein Filamin-B. -
Sox2-RNA Mechanisms of Chromosome Topological Control in Developing Forebrain
bioRxiv preprint doi: https://doi.org/10.1101/2020.09.22.307215; this version posted September 22, 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. Title: Sox2-RNA mechanisms of chromosome topological control in developing forebrain Ivelisse Cajigas1, Abhijit Chakraborty2, Madison Lynam1, Kelsey R Swyter1, Monique Bastidas1, Linden Collens1, Hao Luo1, Ferhat Ay2,3, Jhumku D. Kohtz1,4 1Department of Pediatrics, Northwestern University, Feinberg School of Medicine, Department of Human Molecular Genetics, Stanley Manne Children's Research Institute 2430 N Halsted, Chicago, IL 60614 2Centers for Autoimmunity and Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA 3School of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA 4To whom correspondence should be addressed: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.22.307215; this version posted September 22, 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. Summary Precise regulation of gene expression networks requires the selective targeting of DNA enhancers. The Evf2 long non-coding RNA regulates Dlx5/6 ultraconserved enhancer(UCE) interactions with long-range target genes, controlling gene expression over a 27Mb region in mouse developing forebrain. Here, we show that Evf2 long range gene repression occurs through multi-step mechanisms involving the transcription factor Sox2, a component of the Evf2 ribonucleoprotein complex (RNP). -
Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79
www.nature.com/scientificreports OPEN Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment Jifeng Liu1,2, Tengfei Ma1,2, Mingzhong Gao3, Yilin Liu4, Jun Liu1, Shichao Wang2, Yike Xie2, Ling Wang2, Juan Cheng2, Shixi Liu1*, Jian Zou1,2*, Jiang Wu2, Weimin Li2 & Heping Xie2,3,5 As resources in the shallow depths of the earth exhausted, people will spend extended periods of time in the deep underground space. However, little is known about the deep underground environment afecting the health of organisms. Hence, we established both deep underground laboratory (DUGL) and above ground laboratory (AGL) to investigate the efect of environmental factors on organisms. Six environmental parameters were monitored in the DUGL and AGL. Growth curves were recorded and tandem mass tag (TMT) proteomics analysis were performed to explore the proliferative ability and diferentially abundant proteins (DAPs) in V79 cells (a cell line widely used in biological study in DUGLs) cultured in the DUGL and AGL. Parallel Reaction Monitoring was conducted to verify the TMT results. γ ray dose rate showed the most detectable diference between the two laboratories, whereby γ ray dose rate was signifcantly lower in the DUGL compared to the AGL. V79 cell proliferation was slower in the DUGL. Quantitative proteomics detected 980 DAPs (absolute fold change ≥ 1.2, p < 0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. KEGG pathway analysis revealed that seven pathways (e.g. -
Mapping of Leptin and Its Syntenic Genes to Chicken Chromosome
Edinburgh Research Explorer Mapping of leptin and its syntenic genes to chicken chromosome 1p Citation for published version: Seroussi, E, Pitel, F, Leroux, S, Morisson, M, Bornelöv, S, Miyara, S, Yosefi, S, Cogburn, LA, Burt, DW, Anderson, L & Friedman-Einat, M 2017, 'Mapping of leptin and its syntenic genes to chicken chromosome 1p', BMC Genetics, vol. 18, no. 1, pp. 77. https://doi.org/10.1186/s12863-017-0543-1 Digital Object Identifier (DOI): 10.1186/s12863-017-0543-1 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: BMC Genetics General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 07. Oct. 2021 Seroussi et al. BMC Genetics (2017) 18:77 DOI 10.1186/s12863-017-0543-1 RESEARCHARTICLE Open Access Mapping of leptin and its syntenic genes to chicken chromosome 1p Eyal Seroussi1*, Frédérique Pitel2, Sophie Leroux2, Mireille Morisson2, Susanne Bornelöv3, Shoval Miyara1, Sara Yosefi1, Larry A. Cogburn4, David W. Burt5, Leif Anderson3,6,7 and Miriam Friedman-Einat1* Abstract Background: Misidentification of the chicken leptin gene has hampered research of leptin signaling in this species for almost two decades. -
Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage. -
Nucleolin and Its Role in Ribosomal Biogenesis
NUCLEOLIN: A NUCLEOLAR RNA-BINDING PROTEIN INVOLVED IN RIBOSOME BIOGENESIS Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf vorgelegt von Julia Fremerey aus Hamburg Düsseldorf, April 2016 2 Gedruckt mit der Genehmigung der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf Referent: Prof. Dr. A. Borkhardt Korreferent: Prof. Dr. H. Schwender Tag der mündlichen Prüfung: 20.07.2016 3 Die vorgelegte Arbeit wurde von Juli 2012 bis März 2016 in der Klinik für Kinder- Onkologie, -Hämatologie und Klinische Immunologie des Universitätsklinikums Düsseldorf unter Anleitung von Prof. Dr. A. Borkhardt und in Kooperation mit dem ‚Laboratory of RNA Molecular Biology‘ an der Rockefeller Universität unter Anleitung von Prof. Dr. T. Tuschl angefertigt. 4 Dedicated to my family TABLE OF CONTENTS 5 TABLE OF CONTENTS TABLE OF CONTENTS ............................................................................................... 5 LIST OF FIGURES ......................................................................................................10 LIST OF TABLES .......................................................................................................12 ABBREVIATION .........................................................................................................13 ABSTRACT ................................................................................................................19 ZUSAMMENFASSUNG -
Binding Specificities of Human RNA Binding Proteins Towards Structured
bioRxiv preprint doi: https://doi.org/10.1101/317909; this version posted March 1, 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. 1 Binding specificities of human RNA binding proteins towards structured and linear 2 RNA sequences 3 4 Arttu Jolma1,#, Jilin Zhang1,#, Estefania Mondragón4,#, Teemu Kivioja2, Yimeng Yin1, 5 Fangjie Zhu1, Quaid Morris5,6,7,8, Timothy R. Hughes5,6, Louis James Maher III4 and Jussi 6 Taipale1,2,3,* 7 8 9 AUTHOR AFFILIATIONS 10 11 1Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden 12 2Genome-Scale Biology Program, University of Helsinki, Helsinki, Finland 13 3Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom 14 4Department of Biochemistry and Molecular Biology and Mayo Clinic Graduate School of 15 Biomedical Sciences, Mayo Clinic College of Medicine and Science, Rochester, USA 16 5Department of Molecular Genetics, University of Toronto, Toronto, Canada 17 6Donnelly Centre, University of Toronto, Toronto, Canada 18 7Edward S Rogers Sr Department of Electrical and Computer Engineering, University of 19 Toronto, Toronto, Canada 20 8Department of Computer Science, University of Toronto, Toronto, Canada 21 #Authors contributed equally 22 *Correspondence: [email protected] 23 24 25 SUMMARY 26 27 Sequence specific RNA-binding proteins (RBPs) control many important 28 processes affecting gene expression. They regulate RNA metabolism at multiple 29 levels, by affecting splicing of nascent transcripts, RNA folding, base modification, 30 transport, localization, translation and stability. Despite their central role in most 31 aspects of RNA metabolism and function, most RBP binding specificities remain 32 unknown or incompletely defined. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.