Using Regulatory Genomics Data to Interpret the Function of Disease Variants and Prioritise Genes from Expression Studies[Versio
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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. -
Inhibiting TRK Proteins in Clinical Cancer Therapy
cancers Review Inhibiting TRK Proteins in Clinical Cancer Therapy Allison M. Lange 1 and Hui-Wen Lo 1,2,* 1 Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; [email protected] 2 Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA * Correspondence: [email protected] Received: 25 February 2018; Accepted: 29 March 2018; Published: 4 April 2018 Abstract: Gene rearrangements resulting in the aberrant activity of tyrosine kinases have been identified as drivers of oncogenesis in a variety of cancers. The tropomyosin receptor kinase (TRK) family of tyrosine receptor kinases is emerging as an important target for cancer therapeutics. The TRK family contains three members, TRKA, TRKB, and TRKC, and these proteins are encoded by the genes NTRK1, NTRK2, and NTRK3, respectively. To activate TRK receptors, neurotrophins bind to the extracellular region stimulating dimerization, phosphorylation, and activation of downstream signaling pathways. Major known downstream pathways include RAS/MAPK/ERK, PLCγ, and PI3K/Akt. While being rare in most cancers, TRK fusions with other proteins have been well-established as oncogenic events in specific malignancies, including glioblastoma, papillary thyroid carcinoma, and secretory breast carcinomas. TRK protein amplification as well as alternative splicing events have also been described as contributors to cancer pathogenesis. For patients harboring alterations in TRK expression or activity, TRK inhibition emerges as an important therapeutic target. To date, multiple trials testing TRK-inhibiting compounds in various cancers are underway. In this review, we will summarize the current therapeutic trials for neoplasms involving NTKR gene alterations, as well as the promises and setbacks that are associated with targeting gene fusions. -
Overlapping Role of SCYL1 and SCYL3 in Maintaining Motor Neuron Viability
The Journal of Neuroscience, March 7, 2018 • 38(10):2615–2630 • 2615 Neurobiology of Disease Overlapping Role of SCYL1 and SCYL3 in Maintaining Motor Neuron Viability Emin Kuliyev,1 Sebastien Gingras,4 XClifford S. Guy,1 Sherie Howell,2 Peter Vogel,3 and XStephane Pelletier1 1Departments of Immunology, 2Pathology, 3Veterinary Pathology Core, Advanced Histology Core, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, and 4Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213 Members of the SCY1-like (SCYL) family of protein kinases are evolutionarily conserved and ubiquitously expressed proteins character- ized by an N-terminal pseudokinase domain, centrally located Huntingtin, elongation factor 3, protein phosphatase 2A, yeast kinase TOR1 repeats, and an overall disorganized C-terminal segment. In mammals, three family members encoded by genes Scyl1, Scyl2, and Scyl3 have been described. Studies have pointed to a role for SCYL1 and SCYL2 in regulating neuronal function and viability in mice and humans, but little is known about the biological function of SCYL3. Here, we show that the biochemical and cell biological properties of SCYL3aresimilartothoseofSCYL1andbothproteinsworkinconjunctiontomaintainmotorneuronviability.Specifically,althoughlack of Scyl3 in mice has no apparent effect on embryogenesis and postnatal life, it accelerates the onset of the motor neuron disorder caused by Scyl1 deficiency. Growth abnormalities, motor dysfunction, hindlimb paralysis, muscle wasting, neurogenic atrophy, motor neuron degeneration, and loss of large-caliber axons in peripheral nerves occurred at an earlier age in Scyl1/Scyl3 double-deficient mice than in Scyl1-deficientmice.DiseaseonsetalsocorrelatedwiththemislocalizationofTDP-43inspinalmotorneurons,suggestingthatSCYL1and SCYL3 regulate TDP-43 proteostasis. Together, our results demonstrate an overlapping role for SCYL1 and SCYL3 in vivo and highlight the importance the SCYL family of proteins in regulating neuronal function and survival. -
Genome‑Wide Investigation of the Clinical Implications and Molecular Mechanism of Long Noncoding RNA LINC00668 and Protein‑Coding Genes in Hepatocellular Carcinoma
860 INTERNATIONAL JOURNAL OF ONCOLOGY 55: 860-878 2019 Genome‑wide investigation of the clinical implications and molecular mechanism of long noncoding RNA LINC00668 and protein‑coding genes in hepatocellular carcinoma XIANGKUN WANG1, XIN ZHOU1, JUNQI LIU1, ZHENGQIAN LIU1, LINBO ZHANG2, YIZHEN GONG3, JIANLU HUANG4, LONG YU1,5, QIAOQI WANG6, CHENGKUN YANG1, XIWEN LIAO1, TINGDONG YU1, CHUANGYE HAN1, GUANGZHI ZHU1, XINPING YE1 and TAO PENG1 Department of 1Hepatobiliary Surgery, 2Health Management and Division of Physical Examination and 3Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021; 4Department of Hepatobiliary Surgery, Third Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530031; 5Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000; 6Department of Medical Cosmetology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530000, P.R. China Received April 12, 2019; Accepted July 31, 2019 DOI: 10.3892/ijo.2019.4858 Abstract. Hepatocellular carcinoma (HCC) is one of the clinical factors and PCGs were used to construct a nomo- leading causes of tumor-related mortalities worldwide. gram for predicting prognosis in HCC. A Connectivity Map Long noncoding RNAs have been reported to be associ- was constructed to identify candidate target drugs for HCC. ated with tumor initiation, progression and prognosis. The The top 10 PCGs identified were: Pyrimidineregic receptor present study aimed to explore the association between long P2Y4 (P2RY4), signal peptidase complex subunit 2 (SPCS2), noncoding RNA LINC00668 and its co-expression correlated family with sequence similarity 86 member C1 (FAM86C1), protein-coding genes (PCGs) in HCC. -
Mutations in Disordered Regions Cause Disease by Creating Endocytosis Motifs
bioRxiv preprint doi: https://doi.org/10.1101/141622; this version posted May 24, 2017. 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. Mutations in disordered regions cause disease by creating endocytosis motifs Katrina Meyer1, Bora Uyar2, Marieluise Kirchner3, Jingyuan Cheng1, Altuna Akalin2, Matthias Selbach1,4 1 Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13092 Berlin, Germany 2 Bioinformatics Platform, Max Delbrück Center for Molecular Medicine, Robert-Rössle- Str. 10, D-13092 Berlin, Germany 3 BIH Core Facility Proteomics, Robert-Rössle-Str. 10, D-13092 Berlin, Germany 4 Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany Corresponding author: Matthias Selbach Max Delbrück Centrum for Molecular Medicine Robert-Rössle-Str. 10, D-13092 Berlin, Germany Tel.: +49 30 9406 3574 Fax.: +49 30 9406 2394 email: [email protected] Word count: 2741 bioRxiv preprint doi: https://doi.org/10.1101/141622; this version posted May 24, 2017. 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 Abstract 2 Mutations in intrinsically disordered regions (IDRs) of proteins can cause a wide 3 spectrum of diseases. Since IDRs lack a fixed three-dimensional structure, the 4 mechanism by which such mutations cause disease is often unknown. Here, we employ 5 a proteomic screen to investigate the impact of mutations in IDRs on protein-protein 6 interactions. We find that mutations in disordered cytosolic regions of three 7 transmembrane proteins (GLUT1, ITPR1 and CACNA1H) lead to an increased binding 8 of clathrins. -
Recount Brain Example with Data from SRP027383
recount_brain example with data from SRP027383 true Abstract This is an example on how to use recount_brain applied to the SRP027383 study. We show how to download data from recount2, add the sample metadata from recount_brain, explore the sample metadata and the gene expression data, and perform a gene expression analysis. Introduction This document is an example of how you can use recount_brain. We will use the data from the SRA study SRP027383 which is described in “RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas” (Bao, Chen, Yang, Zhang, et al., 2014). As you can see in Figure @ref(fig:runselector) a lot of the metadata for these samples is missing from the SRA Run Selector which makes it a great case for using recount_brain. We will show how to add the recount_brain metadata and perform a gene differential expression analysis using this information. Sample metadata Just like any study in recount2 (Collado-Torres, Nellore, Kammers, Ellis, et al., 2017), we first need to download the gene count data using recount::download_study(). Since we will be using many functions from the recount package, lets load it first1. ## Load the package library('recount') Download gene data Having loaded the package, we next download the gene-level data. if(!file.exists(file.path('SRP027383', 'rse_gene.Rdata'))) { download_study('SRP027383') } load(file.path('SRP027383', 'rse_gene.Rdata'), verbose = TRUE) ## Loading objects: ## rse_gene 1If you are a first time recount user, we recommend first reading the package vignette at bioconductor.org/packages/recount. 1 Figure 1: SRA Run Selector information for study SRP027383. -
Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: a Feature Selection Approach
International Journal of Molecular Sciences Article Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach Simona De Summa 1,* , Antonio Palazzo 2 , Mariapia Caputo 1, Rosa Maria Iacobazzi 3 , Brunella Pilato 1, Letizia Porcelli 3, Stefania Tommasi 1 , Angelo Virgilio Paradiso 4,† and Amalia Azzariti 3,† 1 Molecular Diagnostics and Pharmacogenetics Unit, IRCCS IstitutoTumori Giovanni Paolo II, 70124 Bari, Italy; [email protected] (M.C.); [email protected] (B.P.); [email protected] (S.T.) 2 Laboratory of Nanotechnology, IRCCS IstitutoTumori Giovanni Paolo II, 70124 Bari, Italy; [email protected] 3 Laboratory of Experimental Pharmacology, IRCCS Istituto Tumori Giovanni Paolo II, 70124 Bari, Italy; [email protected] (R.M.I.); [email protected] (L.P.); [email protected] (A.A.) 4 Scientific Directorate, IRCCS Istituto Tumori Giovanni Paolo II, 70124 Bari, Italy; [email protected] * Correspondence: [email protected] † Co-senior authors. Abstract: Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, Citation: De Summa, S.; Palazzo, A.; were demonstrated to be positive prognostic biomarkers in ERG-positive patients. -
Cisgenome User's Manual 1. Overview 1.1 Basic Framework Of
CisGenome User’s Manual 1. Overview 1.1 Basic Framework of CisGenome 1.2 Installation 1.3 Summary of All Functions 1.4 A Quick Start – Analysis of a ChIP-chip Experiment 2. Genomics Toolbox I – Establishing Local Genome Databases 2.1 Introduction to Local Genome Database 2.2 List of Functions 2.3 Establishing Database Step I – Download Sequences and Annotation 2.4 Establishing Database Step II – Coding Genome Sequences 2.5 Establishing Database Step III – Coding Gene Annotations 2.6 Establishing Database Step IV – Creating Markov Background 2.7 Establishing Database Step V – Coding Conservation Scores 2.8 Establishing Database Step VI – Creating Coding Region Indicators 3. Genomics Toolbox II – Sequence and Conservation Score Retrieval 3.1 Introduction 3.2 List of Functions 3.3 Sequence Manipulation I – Retrieving Sequences for Specified Genomic Regions. 3.4 Sequence Manipulation II – Retrieving Sequences and Conservation Scores for Specified Genomic Regions 3.5 Sequence Manipulation III – Retrieving Sequences in which Protein Coding Regions and/or Non-Conserved Regions are masked. 3.6 Sequence Manipulation IV – Filtering Genomic Regions according to Repeats, Conservation Scores and CDS Status. 3.7 Sequence Manipulation V – Masking Certain Regions from FASTA Sequences. 4. Genomics Toolbox III – Obtaining Annotations and Orthologs For Specified Regions 4.1 Introduction 4.2 List of Functions 4.3 Getting Base Occurrence Frequencies and Conservation Score Distributions for Specified Genomic Regions 4.4 Associating Genomic Regions with Neighboring Genes 4.5 Getting Regions Surrounding Transcriptional Starts 4.6 Getting Affymetrix Probeset IDs for Specified Genes 4.7 Identifying Ortholog (Homolog) Genes 5. -
Small Non-Coding-RNA in Gynecological Malignancies
cancers Review Small Non-Coding-RNA in Gynecological Malignancies Shailendra Kumar Dhar Dwivedi 1 , Geeta Rao 2, Anindya Dey 1, Priyabrata Mukherjee 2,3, Jonathan D. Wren 4,5,6 and Resham Bhattacharya 1,3,7,* 1 Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; [email protected] (S.K.D.D.); [email protected] (A.D.) 2 Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; [email protected] (G.R.); [email protected] (P.M.) 3 Peggy and Charles Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA 4 Biochemistry and Molecular Biology Department, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; [email protected] 5 Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA 6 Genes & Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA 7 Department of Cell Biology, University of Oklahoma Health Science Center, Oklahoma City, OK 73104, USA * Correspondence: [email protected] Simple Summary: Gynecologic malignancies are among the leading cause of female mortality worldwide, and their management is complicated by late diagnosis and acquired therapy resistance. Although altered DNA code, leading to aberrant protein expression, is indispensable for cancer Citation: Dwivedi, S.K.D.; Rao, G.; initiation and progression, from the current literature it is clear that, not only proteins, but also Dey, A.; Mukherjee, P.; Wren, J.D.; noncoding RNA, which does not translate into proteins, can also be instrumental. -
Supplementary Tables S1-S3
Supplementary Table S1: Real time RT-PCR primers COX-2 Forward 5’- CCACTTCAAGGGAGTCTGGA -3’ Reverse 5’- AAGGGCCCTGGTGTAGTAGG -3’ Wnt5a Forward 5’- TGAATAACCCTGTTCAGATGTCA -3’ Reverse 5’- TGTACTGCATGTGGTCCTGA -3’ Spp1 Forward 5'- GACCCATCTCAGAAGCAGAA -3' Reverse 5'- TTCGTCAGATTCATCCGAGT -3' CUGBP2 Forward 5’- ATGCAACAGCTCAACACTGC -3’ Reverse 5’- CAGCGTTGCCAGATTCTGTA -3’ Supplementary Table S2: Genes synergistically regulated by oncogenic Ras and TGF-β AU-rich probe_id Gene Name Gene Symbol element Fold change RasV12 + TGF-β RasV12 TGF-β 1368519_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 42.22 5.53 75.28 1373000_at sushi-repeat-containing protein, X-linked 2 (predicted) Srpx2 19.24 25.59 73.63 1383486_at Transcribed locus --- ARE 5.93 27.94 52.85 1367581_a_at secreted phosphoprotein 1 Spp1 2.46 19.28 49.76 1368359_a_at VGF nerve growth factor inducible Vgf 3.11 4.61 48.10 1392618_at Transcribed locus --- ARE 3.48 24.30 45.76 1398302_at prolactin-like protein F Prlpf ARE 1.39 3.29 45.23 1392264_s_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 24.92 3.67 40.09 1391022_at laminin, beta 3 Lamb3 2.13 3.31 38.15 1384605_at Transcribed locus --- 2.94 14.57 37.91 1367973_at chemokine (C-C motif) ligand 2 Ccl2 ARE 5.47 17.28 37.90 1369249_at progressive ankylosis homolog (mouse) Ank ARE 3.12 8.33 33.58 1398479_at ryanodine receptor 3 Ryr3 ARE 1.42 9.28 29.65 1371194_at tumor necrosis factor alpha induced protein 6 Tnfaip6 ARE 2.95 7.90 29.24 1386344_at Progressive ankylosis homolog (mouse) -
Cancer Sequencing Service Data File Formats File Format V2.4 Software V2.4 December 2012
Cancer Sequencing Service Data File Formats File format v2.4 Software v2.4 December 2012 CGA Tools, cPAL, and DNB are trademarks of Complete Genomics, Inc. in the US and certain other countries. All other trademarks are the property of their respective owners. Disclaimer of Warranties. COMPLETE GENOMICS, INC. PROVIDES THESE DATA IN GOOD FAITH TO THE RECIPIENT “AS IS.” COMPLETE GENOMICS, INC. MAKES NO REPRESENTATION OR WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE, OR ANY OTHER STATUTORY WARRANTY. COMPLETE GENOMICS, INC. ASSUMES NO LEGAL LIABILITY OR RESPONSIBILITY FOR ANY PURPOSE FOR WHICH THE DATA ARE USED. Any permitted redistribution of the data should carry the Disclaimer of Warranties provided above. Data file formats are expected to evolve over time. Backward compatibility of any new file format is not guaranteed. Complete Genomics data is for Research Use Only and not for use in the treatment or diagnosis of any human subject. Information, descriptions and specifications in this publication are subject to change without notice. Copyright © 2011-2012 Complete Genomics Incorporated. All rights reserved. RM_DFFCS_2.4-01 Table of Contents Table of Contents Preface ...........................................................................................................................................................................................1 Conventions ................................................................................................................................................................................................. -
Genome-Wide Meta-Analysis Identifies Five New Susceptibility Loci for Pancreatic Cancer
Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Klein, A. P., B. M. Wolpin, H. A. Risch, R. Z. Stolzenberg-Solomon, E. Mocci, M. Zhang, F. Canzian, et al. 2018. “Genome-wide meta- analysis identifies five new susceptibility loci for pancreatic cancer.” Nature Communications 9 (1): 556. doi:10.1038/ s41467-018-02942-5. http://dx.doi.org/10.1038/s41467-018-02942-5. Published Version doi:10.1038/s41467-018-02942-5 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:35015015 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA ARTICLE DOI: 10.1038/s41467-018-02942-5 OPEN Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer Alison P. Klein et al.# In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European 1234567890():,; ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10−8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PAN- DoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10−14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10−10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10−8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10−8).