Gain of Additional BIRC3 Protein Functions Through 3ʹ-UTR-Mediated Protein Complex Formation
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Topological Scoring of Protein Interaction Networks
bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Topological Scoring of Protein Interaction Networks Mihaela E. Sardiu1, Joshua M. Gilmore1,2, Brad D. Groppe1,3, Arnob Dutta1,4, Laurence Florens1, and Michael P. Washburn1,5‡ 1Stowers Institute for Medical Research, Kansas City, MO 64110 U.S.A. 2Current Address: Boehringer Ingelheim Vetmedica, St. Joseph, MO 64506 U.S.A. 3Current Address: Thermo Fisher Scientific, Waltham, MA 02451, U.S.A. 4Current Address: Department of Cell and Molecular Biology, University of Rhode Island, 287 CBLS, 120 Flagg Road, Kingston, RI 02881. 5 Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, Kansas 66160, USA ‡To whom correspondence should be addressed: Michael Washburn, Ph.D. Stowers Institute for Medical Research 1000 E. 50th St. Kansas City, MO 64110 Phone: 816-926-4457 E-mail: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic analyses of affinity purifications. -
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. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Nuclear PTEN Safeguards Pre-Mrna Splicing to Link Golgi Apparatus for Its Tumor Suppressive Role
ARTICLE DOI: 10.1038/s41467-018-04760-1 OPEN Nuclear PTEN safeguards pre-mRNA splicing to link Golgi apparatus for its tumor suppressive role Shao-Ming Shen1, Yan Ji2, Cheng Zhang1, Shuang-Shu Dong2, Shuo Yang1, Zhong Xiong1, Meng-Kai Ge1, Yun Yu1, Li Xia1, Meng Guo1, Jin-Ke Cheng3, Jun-Ling Liu1,3, Jian-Xiu Yu1,3 & Guo-Qiang Chen1 Dysregulation of pre-mRNA alternative splicing (AS) is closely associated with cancers. However, the relationships between the AS and classic oncogenes/tumor suppressors are 1234567890():,; largely unknown. Here we show that the deletion of tumor suppressor PTEN alters pre-mRNA splicing in a phosphatase-independent manner, and identify 262 PTEN-regulated AS events in 293T cells by RNA sequencing, which are associated with significant worse outcome of cancer patients. Based on these findings, we report that nuclear PTEN interacts with the splicing machinery, spliceosome, to regulate its assembly and pre-mRNA splicing. We also identify a new exon 2b in GOLGA2 transcript and the exon exclusion contributes to PTEN knockdown-induced tumorigenesis by promoting dramatic Golgi extension and secretion, and PTEN depletion significantly sensitizes cancer cells to secretion inhibitors brefeldin A and golgicide A. Our results suggest that Golgi secretion inhibitors alone or in combination with PI3K/Akt kinase inhibitors may be therapeutically useful for PTEN-deficient cancers. 1 Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China. 2 Institute of Health Sciences, Shanghai Institutes for Biological Sciences of Chinese Academy of Sciences and SJTU-SM, Shanghai 200025, China. -
Integrative Genomic Analysis of Hnrnp L Splicing Regulation
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2015 Terrae Incognitae: Integrative Genomic Analysis of Hnrnp L Splicing Regulation Brian Sebastian Cole University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Allergy and Immunology Commons, Biochemistry Commons, Bioinformatics Commons, Immunology and Infectious Disease Commons, and the Medical Immunology Commons Recommended Citation Cole, Brian Sebastian, "Terrae Incognitae: Integrative Genomic Analysis of Hnrnp L Splicing Regulation" (2015). Publicly Accessible Penn Dissertations. 1664. https://repository.upenn.edu/edissertations/1664 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1664 For more information, please contact [email protected]. Terrae Incognitae: Integrative Genomic Analysis of Hnrnp L Splicing Regulation Abstract Alternative splicing is a critical component of human gene control that generates functional diversity from a limited genome. Defects in alternative splicing are associated with disease in humans. Alternative splicing is regulated developmentally and physiologically by the combinatorial actions of cis- and trans- acting factors, including RNA binding proteins that regulate splicing through sequence-specific interactions with pre-mRNAs. In T cells, the splicing regulator hnRNP L is an essential factor that regulates alternative splicing of physiologically important mRNAs, however the broader physical and functional impact of hnRNP L remains unknown. In this dissertation, I present analysis of hnRNP L-RNA interactions with CLIP-seq, which identifies transcriptome-wide binding sites and uncovers novel functional targets. I then use functional genomics studies to define pre-mRNA processing alterations induced by hnRNP L depletion, chief among which is cassette-type alternative splicing. -
Splicing Graphs and RNA-Seq Data
Splicing graphs and RNA-seq data Herv´ePag`es Daniel Bindreither Marc Carlson Martin Morgan Last modified: January 2015; Compiled: April 30, 2018 Contents 1 Introduction 1 2 Splicing graphs 2 2.1 Definitions and example . .2 2.2 Details about nodes and edges . .2 2.3 Uninformative nodes . .5 3 Computing splicing graphs from annotations 6 3.1 Choosing and loading a gene model . .6 3.2 Generating a SplicingGraphs object . .6 3.3 Basic manipulation of a SplicingGraphs object . .8 3.4 Extracting and plotting graphs from a SplicingGraphs object . 12 4 Splicing graph bubbles 14 4.1 Some definitions . 14 4.2 Computing the bubbles of a splicing graph . 17 4.3 AScodes ............................................... 18 4.4 Tabulating all the AS codes for Human chromosome 14 . 18 5 Counting reads 20 5.1 Assigning reads to the edges of a SplicingGraphs object . 21 5.2 How does read assignment work? . 22 5.3 Counting and summarizing the assigned reads . 24 6 Session Information 25 1 Introduction The SplicingGraphs package allows the user to create and manipulate splicing graphs [1] based on annotations for a given organism. Annotations must describe a gene model, that is, they need to contain the following information: The exact exon/intron structure (i.e., genomic coordinates) for the known transcripts. The grouping of transcripts by gene. Annotations need to be provided as a TxDb or GRangesList object, which is how a gene model is typically represented in Bioconductor. 1 The SplicingGraphs package defines the SplicingGraphs container for storing the splicing graphs together with the gene model that they are based on. -
14Q11.2 Deletions FTNP
Support and Information Rare Chromosome Disorder Support Group, G1, The Stables, Station Road West, Oxted, Surrey RH8 9EE, United Kingdom Tel/Fax: +44(0)1883 723356 [email protected] III www.rarechromo.org Unique is a charity without government funding, existing entirely on donations 14q11.2 deletions and grants. If you are able to support our work in any way, however small, please make a donation via our website at www.rarechromo.org/html/MakingADonation.asp Please help us to help you! This information guide is not a substitute for personal medical advice. Families should consult a medically qualified clinician in all matters relating to genetic diagnosis, management and health. Information on genetic changes is a very fast-moving field and while the information in this guide is believed to be the best available at the time of publication, some facts may later change. Unique does its best to keep abreast of changing information and to review its published guides as needed. It was compiled by Unique and reviewed by Jana Drábová, Division of Medical Cytogenetics, Department of Biology and Medical Genetics, Charles University 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic. 2016 Version 1 (PM) Copyright © Unique 2016 20 Rare Chromosome Disorder Support Group Charity Number 1110661 Registered in England and Wales Company Number 5460413 rarechromo.org 14q11.2 deletions Genes A chromosome 14 deletion means that part of one of the body’s chromosomes (chromosome 14) has been lost or deleted. If the material that has been deleted contains important genes, learning disability, developmental delay and health HNRNPC problems may occur. -
Autocrine IFN Signaling Inducing Profibrotic Fibroblast Responses by a Synthetic TLR3 Ligand Mitigates
Downloaded from http://www.jimmunol.org/ by guest on September 28, 2021 Inducing is online at: average * The Journal of Immunology published online 16 August 2013 from submission to initial decision 4 weeks from acceptance to publication http://www.jimmunol.org/content/early/2013/08/16/jimmun ol.1300376 A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Autocrine IFN Signaling Feng Fang, Kohtaro Ooka, Xiaoyong Sun, Ruchi Shah, Swati Bhattacharyya, Jun Wei and John Varga J Immunol Submit online. Every submission reviewed by practicing scientists ? is published twice each month by http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://www.jimmunol.org/content/suppl/2013/08/20/jimmunol.130037 6.DC1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 28, 2021. Published August 16, 2013, doi:10.4049/jimmunol.1300376 The Journal of Immunology A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Inducing Autocrine IFN Signaling Feng Fang,* Kohtaro Ooka,* Xiaoyong Sun,† Ruchi Shah,* Swati Bhattacharyya,* Jun Wei,* and John Varga* Activation of TLR3 by exogenous microbial ligands or endogenous injury-associated ligands leads to production of type I IFN. -
Mir-744-5P Inducing Cell Death by Directly Targeting HNRNPC
www.nature.com/scientificreports OPEN MiR-744-5p inducing cell death by directly targeting HNRNPC and NFIX in ovarian cancer cells Received: 14 December 2017 Michael Kleemann1,2, Helga Schneider1, Kristian Unger3, Philip Sander4, E. Marion Schneider4, Accepted: 4 June 2018 Pamela Fischer-Posovszky5, René Handrick1 & Kerstin Otte1 Published: xx xx xxxx MicroRNAs (miRNAs) play an important role in the regulation of gene expression. The binding to target messenger RNAs (mRNAs) results in mRNA cleavage or inhibition of the translational machinery leading to decreased protein levels. Various signalling pathways, including apoptosis are modulated by miRNAs. Here, we investigated the role of miR-744-5p in apoptosis signalling in ovarian cancer cell lines. MiR-744-5p expression was reduced in the cancer cell lines independent of the host gene MAP2K4. Overexpression of miR-744-5p activated the intrinsic apoptotic pathway in SKOV3, OVCAR3 and Cisplatin resistant (A2780-cis) and non-resistant A2780 cells leading to cell death. Notably, miR-744-5p overexpression together with Carboplatin treatment led to at least additive pro-apoptotic efects. Investigation of the apoptotic signalling pathways mediated by miR-744-5p revealed that its elevated expression directly downregulated mRNA and protein expression of nuclear factor I X (NFIX) and heterogeneous nuclear ribonucleoprotein C (HNRNPC). HNRNPC caused diminished miR-21 expression and AKT phosphorylation, while NFIX decreased Bcl2 levels, leading to the detected pro-apoptotic efects. Finally, Kaplan-Meier-Plots showed a prolonged median disease-free survival in ovarian serous cystadenocarcinoma patients with high miR-744 expression. Apoptosis is a form of programmed cell death considered to destroy only single cells without damaging surround- ing tissues1. -
Identification of RNA-Binding Proteins As Targetable Putative Oncogenes
International Journal of Molecular Sciences Article Identification of RNA-Binding Proteins as Targetable Putative Oncogenes in Neuroblastoma Jessica L. Bell 1,2,*, Sven Hagemann 1, Jessica K. Holien 3,4 , Tao Liu 2, Zsuzsanna Nagy 2,5, Johannes H. Schulte 6,7, Danny Misiak 1 and Stefan Hüttelmaier 1,* 1 Institute of Molecular Medicine, Sect. Molecular Cell Biology, Martin Luther University Halle-Wittenberg, Charles Tanford Protein Center, 06120 Halle Saale, Germany; [email protected] (S.H.); [email protected] (D.M.) 2 Children’s Cancer Institute Australia, Randwick, NSW 2031, Australia; [email protected] (T.L.); [email protected] (Z.N.) 3 St. Vincent’s Institute of Medical Research, Fitzroy, Victoria 3065, Australia; [email protected] 4 Biosciences and Food Technology, School of Science, College of Science, Engineering and Health, RMIT University, Melbourne, Victoria 3053, Australia 5 School of Women’s & Children’s Health, UNSW Sydney, Randwick, NSW 2031, Australia 6 Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; [email protected] 7 German Consortium for Translational Cancer Research (DKTK), Partner Site Charité Berlin, 10117 Berlin, Germany * Correspondence: [email protected] (J.L.B.); [email protected] (S.H.) Received: 23 April 2020; Accepted: 14 July 2020; Published: 19 July 2020 Abstract: Neuroblastoma is a common childhood cancer with almost a third of those affected still dying, thus new therapeutic strategies need to be explored. Current experimental therapies focus mostly on inhibiting oncogenic transcription factor signalling. Although LIN28B, DICER and other RNA-binding proteins (RBPs) have reported roles in neuroblastoma development and patient outcome, the role of RBPs in neuroblastoma is relatively unstudied. -
Analysis of Genetically Regulated Gene Expression Identifies A
bioRxiv preprint doi: https://doi.org/10.1101/581124; this version posted March 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 Huckins et al. aCC-BY-NC-ND 4.0 International license. Analysis of Genetically Regulated Gene Expression identifies a trauma type specific PTSD gene, SNRNP35 Authors: Laura M Huckins1,2,3,4*, Michael S Breen1,2,5#, Chris Chatzinakos6#, Jakob Hartmann7#, Torsten Klengel7,8#, Ana C da Silva Almeida9$, Amanda Dobbyn1,2,3,4$, Kiran 5 Girdhar1,2,3,4$, Gabriel E Hoffman1,2,3,4$, Claudia Klengel7$, Mark W Logue10,11,12,13$, Adriana Lori14$, Filomene G Morrison10,11$, Hoang T Nguyen 1,2,3,4$, Yongjin Park15,16$, Douglas Ruderfer17$, Laura G Sloofman1,2,3,4$, Sanne JH van Rooij14$, PTSD Working Group of Psychiatric Genomics Consortium18, Dewleen G Baker19,20,21¥, Chia-Yen Chen22,23,24¥, Nancy Cox17¥, Laramie E Duncan25¥, Mark A Geyer19,20,26¥, Stephen J. Glatt27¥, Hae Kyung Im28,29¥, 10 Adam X Maihofer19,20,26¥, Victoria B Risbrough19,20,26¥, Jordan W Smoller22,24¥, Dan J Stein30¥, Rachel Yehuda1,31,32¥, Israel Liberzon33, Karestan C Koenen22,24,34, Tanja Jovanovic14,35, Manolis Kellis15,16, Mark W Miller10,11, Silviu-Alin Bacanu6, Caroline M Nievergelt19,20,26, Joseph D Buxbaum1,2,4,5,31, Pamela Sklar1,2,3,4,31, Kerry J Ressler7, Eli A Stahl1,2,3,4, and Nikolaos P Daskalakis1,7,15,16,32* 15 Affiliations: 1Department of Psychiatry, Icahn School of Medicine -
RNA Binding Proteins Involved in Regulation of Protein Synthesis to Initiate Biogenesis of Secondary Tumor in Hepatocellular Carcinoma in Mice
RNA binding proteins involved in regulation of protein synthesis to initiate biogenesis of secondary tumor in hepatocellular carcinoma in mice Genliang Li, Anni Ni, Yulian Tang, Shubo Li and Lingzhang Meng Department of Biochemistry and Molecular Biology, Youjiang Medical University for Nationalities, Baise, Guangxi, China ABSTRACT Background. The tumor microenvironment (TM) in close contact with cancer cells is highly related to tumor growth and cancer metastasis. This study is to explore the biogenesis mechanism of a secondary hepatocellular carcinoma (HCC) based on the function of RNA binding proteins (RBPs)-encoding genes in the physiological microenvironment (PM). Methods. The healthy and HCC mice were used to isolate the PM, pre-tumor microenvironment (PTM), and TM. The samples were analyzed using the technology of RNA-seq and bioinformatics. The differentially expressed RBPs-encoding genes (DERs) and differentially expressed DERs-associated genes (DEDs) were screened to undergo GO and KEGG analysis. Results. 18 DERs and DEDs were identified in the PTM vs. PM, 87 in the TM vs. PTM, and 87 in the TM vs. PM. Those DERs and DEDs participated in the regulation of gene expression at the levels of chromatin conformation, gene activation and silencing, splicing and degradation of mRNA, biogenesis of piRNA and miRNA, ribosome assemble, and translation of proteins. Conclusion. The genes encoding RBPs and the relevant genes are involved in the transformation from PM to PTM, then constructing the TM by regulating protein synthesis. This regulation included whole process of biological genetic information Submitted 20 August 2019 transmission from chromatin conformation to gene activation and silencing to mRNA Accepted 3 February 2020 splicing to ribosome assemble to translation of proteins and degradation of mRNA.