The Small Gtpase Rab35 Is a Novel Oncogenic Regulator
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 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 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
Protein Domain-Level Landscape of Cancer-Type-Specific Somatic We Explored the Protein Domain-Level Landscape of Cancer-Type-Specific Somatic Mutations
RESEARCH ARTICLE Protein Domain-Level Landscape of Cancer- Type-Specific Somatic Mutations Fan Yang1,2,3, Evangelia Petsalaki2,3, Thomas Rolland4,5, David E. Hill4, Marc Vidal4, Frederick P. Roth1,2,3,4,6,7* 1 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, 2 Donnelly Centre, University of Toronto, Toronto, Ontario, Canada, 3 Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario, Canada, 4 Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, 5 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 6 Canadian Institute for Advanced Research, Toronto, Ontario, Canada, 7 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada * [email protected] Abstract OPEN ACCESS Identifying driver mutations and their functional consequences is critical to our understand- Citation: Yang F, Petsalaki E, Rolland T, Hill DE, ing of cancer. Towards this goal, and because domains are the functional units of a protein, Vidal M, Roth FP (2015) Protein Domain-Level Landscape of Cancer-Type-Specific Somatic we explored the protein domain-level landscape of cancer-type-specific somatic mutations. Mutations. PLoS Comput Biol 11(3): e1004147. Specifically, we systematically examined tumor genomes from 21 cancer types to identify doi:10.1371/journal.pcbi.1004147 domains with high mutational density in specific tissues, the positions of mutational hotspots Editor: Mona Singh, Princeton University, United within these domains, and the functional and structural context where possible. While hot- States of America spots corresponding to specific gain-of-function mutations are expected for oncoproteins, Received: August 22, 2014 we found that tumor suppressor proteins also exhibit strong biases toward being mutated in Accepted: January 22, 2015 particular domains. -
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. -
CSNK2B Monoclonal Antibody Catalog Number:67866-1-Ig
For Research Use Only CSNK2B Monoclonal antibody www.ptgcn.com Catalog Number:67866-1-Ig Catalog Number: GenBank Accession Number: CloneNo.: Basic Information 67866-1-Ig BC112017 1B5A6 Size: GeneID (NCBI): Recommended Dilutions: 1000 μg/ml 1460 WB 1:5000-1:20000 Source: Full Name: IF 1:200-1:800 Mouse casein kinase 2, beta polypeptide Isotype: Calculated MW: IgG1 215 aa, 25 kDa Purification Method: Observed MW: Protein G purification 27 kDa Immunogen Catalog Number: AG19180 Applications Tested Applications: Positive Controls: IF, WB,ELISA WB : A549 cells; LNCaP cells, HeLa cells, Jurkat cells, Species Specificity: pig brain tissue, rat brain tissue, mouse brain tissue Human, mouse, rat, pig IF : HeLa cells; CSNK2B is a ubiquitous protein kinase which regulates metabolic pathways, signal transduction, transcription, Background Information translation, and replication. The enzyme is composed of three subunits, alpha, alpha prime and beta, which form a tetrameric holoenzyme. The alpha and alpha prime subunits are catalytic, while the beta subunit serves regulatory functions. The enzyme localizes to the endoplasmic reticulum and the Golgi apparatus. It participates in Wnt signaling, and plays a complex role in regulating the basal catalytic activity of the alpha subunit. Storage: Storage Store at -20ºC. Stable for one year after shipment. Storage Buffer: PBS with 0.02% sodium azide and 50% glycerol pH 7.3. Aliquoting is unnecessary for -20ºC storage For technical support and original validation data for this product please contact: This product is exclusively available under Proteintech T: 4006900926 E: [email protected] W: ptgcn.com Group brand and is not available to purchase from any other manufacturer. -
Multi-Modal Meta-Analysis of 1494 Hepatocellular Carcinoma Samples Reveals
Author Manuscript Published OnlineFirst on September 21, 2018; DOI: 10.1158/1078-0432.CCR-18-0088 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals significant impact of consensus driver genes on phenotypes Kumardeep Chaudhary1, Olivier B Poirion1, Liangqun Lu1,2, Sijia Huang1,2, Travers Ching1,2, Lana X Garmire1,2,3* 1Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA 2Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 96822, USA 3Current affiliation: Department of Computational Medicine and Bioinformatics, Building 520, 1600 Huron Parkway, Ann Arbor, MI 48109 Short Title: Impact of consensus driver genes in hepatocellular carcinoma * To whom correspondence should be addressed. Lana X. Garmire, Department of Computational Medicine and Bioinformatics Medical School, University of Michigan Building 520, 1600 Huron Parkway Ann Arbor-48109, MI, USA, Phone: +1-(734) 615-5510 Current email address: [email protected] Grant Support: This research was supported by grants K01ES025434 awarded by NIEHS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (http://datascience.nih.gov/bd2k), P20 COBRE GM103457 awarded by NIH/NIGMS, NICHD R01 HD084633 and NLM R01LM012373 and Hawaii Community Foundation Medical Research Grant 14ADVC-64566 to Lana X Garmire. 1 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 21, 2018; DOI: 10.1158/1078-0432.CCR-18-0088 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. -
Predicting Coupling Probabilities of G-Protein Coupled Receptors Gurdeep Singh1,2,†, Asuka Inoue3,*,†, J
Published online 30 May 2019 Nucleic Acids Research, 2019, Vol. 47, Web Server issue W395–W401 doi: 10.1093/nar/gkz392 PRECOG: PREdicting COupling probabilities of G-protein coupled receptors Gurdeep Singh1,2,†, Asuka Inoue3,*,†, J. Silvio Gutkind4, Robert B. Russell1,2,* and Francesco Raimondi1,2,* 1CellNetworks, Bioquant, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 2Biochemie Zentrum Heidelberg (BZH), Heidelberg University, Im Neuenheimer Feld 328, 69120 Heidelberg, Germany, 3Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan and 4Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA Received February 10, 2019; Revised April 13, 2019; Editorial Decision April 24, 2019; Accepted May 01, 2019 ABSTRACT great use in tinkering with signalling pathways in living sys- tems (5). G-protein coupled receptors (GPCRs) control multi- Ligand binding to GPCRs induces conformational ple physiological states by transducing a multitude changes that lead to binding and activation of G-proteins of extracellular stimuli into the cell via coupling to situated on the inner cell membrane. Most of mammalian intra-cellular heterotrimeric G-proteins. Deciphering GPCRs couple with more than one G-protein giving each which G-proteins couple to each of the hundreds receptor a distinct coupling profile (6) and thus specific of GPCRs present in a typical eukaryotic organism downstream cellular responses. Determining these coupling is therefore critical to understand signalling. Here, profiles is critical to understand GPCR biology and phar- we present PRECOG (precog.russelllab.org): a web- macology. Despite decades of research and hundreds of ob- server for predicting GPCR coupling, which allows served interactions, coupling information is still missing for users to: (i) predict coupling probabilities for GPCRs many receptors and sequence determinants of coupling- specificity are still largely unknown. -
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. -
Pathognomonic and Epistatic Genetic Alterations in B-Cell Non-Hodgkin
bioRxiv preprint doi: https://doi.org/10.1101/674259; this version posted June 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 aCC-BY-NC-ND 4.0 International license. 1 Pathognomonic and epistatic genetic alterations in 2 B-cell non-Hodgkin lymphoma 3 4 Man Chun John Ma1¥, Saber Tadros1¥, Alyssa Bouska2, Tayla B. Heavican2, Haopeng Yang1, 5 Qing Deng1, Dalia Moore3, Ariz Akhter4, Keenan Hartert3, Neeraj Jain1, Jordan Showell1, 6 Sreejoyee Ghosh1, Lesley Street5, Marta Davidson5, Christopher Carey6, Joshua Tobin7, 7 Deepak Perumal8, Julie M. Vose9, Matthew A. Lunning9, Aliyah R. Sohani10, Benjamin J. 8 Chen11, Shannon Buckley12, Loretta J. Nastoupil1, R. Eric Davis1, Jason R. Westin1, Nathan H. 9 Fowler1, Samir Parekh8, Maher K. Gandhi7, Sattva S. Neelapu1, Douglas Stewart5, Javeed 10 Iqbal2, Timothy Greiner2, Scott J. Rodig13, Adnan Mansoor5, Michael R. Green1,14,15* 11 1Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD 12 Anderson Cancer Center, Houston, TX, USA; 2Department of Pathology and Microbiology, University of 13 Nebraska Medical Center, Omaha, NE, USA; 3Eppley Institute for Research in Cancer and Allied 14 Diseases, University of Nebraska Medical Center, Omaha, NE, USA; 4Department of Pathology and 15 Laboratory Medicine, University of Calgary, Calgary, AB, Canada; 5Section of Hematology, Department of 16 Medicine, University -
G Protein-Coupled Receptors
www.aladdin-e.com Address:800 S Wineville Avenue, Ontario, CA 91761,USA Website:www.aladdin-e.com Email USA: [email protected] Email EU: [email protected] Email Asia Pacific: [email protected] G PROTEIN-COUPLED RECEPTORS Overview: The completion of the Human Genome Project allowed the identification of a large family of proteins with a common motif of seven groups of 20–24 hydrophobic amino acids arranged as a-helices. Approximately 800 of these seven transmembrane (7TM) receptors have been identified of which over 300 are non-olfactory receptors (see Fredriksson et al., 2003; Lagerstrom and Schioth, 2008). Subdivision on the basis of sequence homology allows the definition of rhodopsin, secretin, adhesion, glutamate and Frizzled receptor families. NC-IUPHAR recognizes Classes A, B, and C, which equate to the rhodopsin, secretin, and glutamate receptor families. The nomenclature of 7TM receptors is commonly used interchangeably with G protein-coupled receptors (GPCR), although the former nomenclature recognises signalling of 7TM receptors through pathways not involving G proteins. For example, adiponectin and membrane progestin receptors have some sequence homology to 7TM receptors but signal independently of G proteins and appear to reside in membranes in an inverted fashion compared to conventional GPCR. Additionally, the NPR-C natriuretic peptide receptor (see Page S195) has a single transmembrane domain structure, but appears to couple to G proteins to generate cellular responses. The 300+ non-olfactory GPCR are the targets for the majority of drugs in clinical usage (Overington et al., 2006), although only a minority of these receptors are exploited therapeutically. -
HCC and Cancer Mutated Genes Summarized in the Literature Gene Symbol Gene Name References*
HCC and cancer mutated genes summarized in the literature Gene symbol Gene name References* A2M Alpha-2-macroglobulin (4) ABL1 c-abl oncogene 1, receptor tyrosine kinase (4,5,22) ACBD7 Acyl-Coenzyme A binding domain containing 7 (23) ACTL6A Actin-like 6A (4,5) ACTL6B Actin-like 6B (4) ACVR1B Activin A receptor, type IB (21,22) ACVR2A Activin A receptor, type IIA (4,21) ADAM10 ADAM metallopeptidase domain 10 (5) ADAMTS9 ADAM metallopeptidase with thrombospondin type 1 motif, 9 (4) ADCY2 Adenylate cyclase 2 (brain) (26) AJUBA Ajuba LIM protein (21) AKAP9 A kinase (PRKA) anchor protein (yotiao) 9 (4) Akt AKT serine/threonine kinase (28) AKT1 v-akt murine thymoma viral oncogene homolog 1 (5,21,22) AKT2 v-akt murine thymoma viral oncogene homolog 2 (4) ALB Albumin (4) ALK Anaplastic lymphoma receptor tyrosine kinase (22) AMPH Amphiphysin (24) ANK3 Ankyrin 3, node of Ranvier (ankyrin G) (4) ANKRD12 Ankyrin repeat domain 12 (4) ANO1 Anoctamin 1, calcium activated chloride channel (4) APC Adenomatous polyposis coli (4,5,21,22,25,28) APOB Apolipoprotein B [including Ag(x) antigen] (4) AR Androgen receptor (5,21-23) ARAP1 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1 (4) ARHGAP35 Rho GTPase activating protein 35 (21) ARID1A AT rich interactive domain 1A (SWI-like) (4,5,21,22,24,25,27,28) ARID1B AT rich interactive domain 1B (SWI1-like) (4,5,22) ARID2 AT rich interactive domain 2 (ARID, RFX-like) (4,5,22,24,25,27,28) ARID4A AT rich interactive domain 4A (RBP1-like) (28) ARID5B AT rich interactive domain 5B (MRF1-like) (21) ASPM Asp (abnormal -
Novel Driver Strength Index Highlights Important Cancer Genes in TCGA Pancanatlas Patients
medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients Aleksey V. Belikov*, Danila V. Otnyukov, Alexey D. Vyatkin and Sergey V. Leonov Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Moscow Region, Russia *Corresponding author: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Abstract Elucidating crucial driver genes is paramount for understanding the cancer origins and mechanisms of progression, as well as selecting targets for molecular therapy. Cancer genes are usually ranked by the frequency of mutation, which, however, does not necessarily reflect their driver strength. Here we hypothesize that driver strength is higher for genes that are preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer.