High-Resolution Gene Maps of Horse Chromosomes 14 and 21: Additional Insights Into Evolution and Rearrangements of HSA5 Homologs in Mammals
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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. -
Figure S1. DMD Module Network. the Network Is Formed by 260 Genes from Disgenet and 1101 Interactions from STRING. Red Nodes Are the Five Seed Candidate Genes
Figure S1. DMD module network. The network is formed by 260 genes from DisGeNET and 1101 interactions from STRING. Red nodes are the five seed candidate genes. Figure S2. DMD module network is more connected than a random module of the same size. It is shown the distribution of the largest connected component of 10.000 random modules of the same size of the DMD module network. The green line (x=260) represents the DMD largest connected component, obtaining a z-score=8.9. Figure S3. Shared genes between BMD and DMD signature. A) A meta-analysis of three microarray datasets (GSE3307, GSE13608 and GSE109178) was performed for the identification of differentially expressed genes (DEGs) in BMD muscle biopsies as compared to normal muscle biopsies. Briefly, the GSE13608 dataset included 6 samples of skeletal muscle biopsy from healthy people and 5 samples from BMD patients. Biopsies were taken from either biceps brachii, triceps brachii or deltoid. The GSE3307 dataset included 17 samples of skeletal muscle biopsy from healthy people and 10 samples from BMD patients. The GSE109178 dataset included 14 samples of controls and 11 samples from BMD patients. For both GSE3307 and GSE10917 datasets, biopsies were taken at the time of diagnosis and from the vastus lateralis. For the meta-analysis of GSE13608, GSE3307 and GSE109178, a random effects model of effect size measure was used to integrate gene expression patterns from the two datasets. Genes with an adjusted p value (FDR) < 0.05 and an │effect size│>2 were identified as DEGs and selected for further analysis. A significant number of DEGs (p<0.001) were in common with the DMD signature genes (blue nodes), as determined by a hypergeometric test assessing the significance of the overlap between the BMD DEGs and the number of DMD signature genes B) MCODE analysis of the overlapping genes between BMD DEGs and DMD signature genes. -
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. -
Transcriptional Control of Tissue-Resident Memory T Cell Generation
Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells. -
Fine Mapping of the Hereditary Haemorrhagic Telangiectasia (HHT)3 Locus on Chromosome 5 Excludes VE-Cadherin-2, Sprouty4 And
Govani and Shovlin Journal of Angiogenesis Research 2010, 2:15 http://www.jangiogenesis.com/content/2/1/15 JOURNAL OF ANGIOGENESIS RESEARCH RESEARCH Open Access Fine mapping of the hereditary haemorrhagic telangiectasia (HHT)3 locus on chromosome 5 excludes VE-Cadherin-2, Sprouty4 and other interval genes Fatima S Govani, Claire L Shovlin* Abstract Background: There is significant interest in new loci for the inherited condition hereditary haemorrhagic telangiectasia (HHT) because the known disease genes encode proteins involved in vascular transforming growth factor (TGF)-b signalling pathways, and the disease phenotype appears to be unmasked or provoked by angiogenesis in man and animal models. In a previous study, we mapped a new locus for HHT (HHT3) to a 5.7 Mb region of chromosome 5. Some of the polymorphic markers used had been uninformative in key recombinant individuals, leaving two potentially excludable regions, one of which contained loci for attractive candidate genes encoding VE Cadherin-2, Sprouty4 and FGF1, proteins involved in angiogenesis. Methods: Extended analyses in the interval-defining pedigree were performed using informative genomic sequence variants identified during candidate gene sequencing. These variants were amplified by polymerase chain reaction; sequenced on an ABI 3730xl, and analysed using FinchTV V1.4.0 software. Results: Informative genomic sequence variants were used to construct haplotypes permitting more precise citing of recombination breakpoints. These reduced the uninformative centromeric region from 141.2-144 Mb to between 141.9-142.6 Mb, and the uninformative telomeric region from 145.2-146.9 Mb to between 146.1-146.4 Mb. Conclusions: The HHT3 interval on chromosome 5 was reduced to 4.5 Mb excluding 30% of the coding genes in the original HHT3 interval. -
Mir-29C Is Downregulated in Gastric Carcinomas and Regulates Cell
Matsuo et al. Molecular Cancer 2013, 12:15 http://www.molecular-cancer.com/content/12/1/15 RESEARCH Open Access MiR-29c is downregulated in gastric carcinomas and regulates cell proliferation by targeting RCC2 Mitsuhiro Matsuo1†, Chisato Nakada1†, Yoshiyuki Tsukamoto1*, Tsuyoshi Noguchi2, Tomohisa Uchida1, Naoki Hijiya1, Keiko Matsuura1 and Masatsugu Moriyama1 Abstract Background: Previously, using miRNA microarray, we have found that miR-29c is significantly downregulated in advanced gastric carcinoma. In the present study, we investigated whether miR-29c functions as a tumor- suppressor miRNA in gastric carcinoma cells. For this purpose, we verified the downregulation of miR-29c in gastric carcinoma tissues, and assessed the biological effect of miR-29c on gastric carcinoma cells. Results: In miR-29c-transfected cells, both proliferation and colony formation ability on soft agar were significantly decreased. Although apoptosis was not induced, BrdU incorporation and the proportion of cells positive for phospho-histone H3 (S10) were significantly decreased in miR-29c-transfected cells, indicating that miR-29c may be involved in the regulation of cell proliferation. To explain the mechanism of growth suppression by miR-29c, we explored differentially expressed genes (>2-fold) in miR-29c-transfected cells in comparison with negative control transfected cells using microarray. RCC2, PPIC and CDK6 were commonly downregulated in miR-29c-transfected MKN45, MKN7 and MKN74 cells, and all of the genes harbored miR-29c target sequences in the 3’-UTR of their mRNA. RCC2 and PPIC were actually upregulated in gastric carcinoma tissues, and therefore both were identified as possible targets of miR-29c in gastric carcinoma. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts
Author Manuscript Published OnlineFirst on June 13, 2019; DOI: 10.1158/2159-8290.CD-19-0094 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts Ela Elyada1,2, Mohan Bolisetty3,4,14, Pasquale Laise5,14, William F. Flynn3,14, Elise T. Courtois3, Richard A. Burkhart6, Jonathan A. Teinor6, Pascal Belleau1, Giulia Biffi1,2, Matthew S. Lucito1,2, Santhosh Sivajothi3, Todd D. Armstrong6, Dannielle D. Engle1,2,7, Kenneth H. Yu8, Yuan Hao1, Christopher L. Wolfgang6, Youngkyu Park1,2, Jonathan Preall1, Elizabeth M. Jaffee6, Andrea Califano5,9-12, Paul Robson3,13 and David A. Tuveson1,2. 1Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA 2Lustgarten Foundation Pancreatic Cancer Research Laboratory, Cold Spring Harbor, NY 11724, USA. 3The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA 4Bristol-Myers Squibb, Pennington, NJ, USA 5Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA 6Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA. 7Salk institute for Biological Studies, La Jolla, CA 92037 8Memorial Sloan Kettering Cancer Center, New York, NY, USA 9Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA 10J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY 10032, USA 11Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA 12Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA 13Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA 14Equal contribution Running title Antigen-presenting CAFs in PDAC Keywords Single-cell analysis, pancreatic cancer, CAFs, MHC class II, heterogeneity *Co-corresponding authors: David A. -
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. -
High Resolution Physical and Comparative Maps of Horse
HIGH RESOLUTION PHYSICAL AND COMPARATIVE MAPS OF HORSE CHROMOSOMES 14 (ECA14) AND 21 (ECA21) A Thesis by GLENDA GOH Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 2005 Major Subject: Genetics HIGH RESOLUTION PHYSICAL AND COMPARATIVE MAPS OF HORSE CHROMOSOMES 14 (ECA14) AND 21 (ECA21) A Thesis by GLENDA GOH Submitted to Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved as to style and content by: _________________________ _________________________ Bhanu P. Chowdhary Loren C. Skow (Chair of Committee) (Member) _________________________ _________________________ James N. Derr James E. Womack (Member) (Member) _________________________ _________________________ Geoffrey Kapler Evelyn Tiffany-Castiglioni (Chair of Genetics Faculty) (Head of Department) May 2005 Major Subject: Genetics iii ABSTRACT High Resolution Physical and Comparative Maps of Horse Chromosomes 14 (ECA14) and 21 (ECA21). (May 2005) Glenda Goh, B.S. (Hons.), Flinders University of South Australia Chair of Advisory Committee: Dr. Bhanu P. Chowdhary In order to identify genes or markers responsible for economically important traits in the horse, the development of high resolution gene maps of individual equine chromosomes is essential. We herein report the construction of high resolution physically ordered radiation hybrid (RH) and comparative maps for horse chromosomes 14 and 21 (ECA14 and ECA21). These chromosomes predominantly share correspondence with human chromosome 5 (HSA5), though a small region on the proximal part of ECA21 corresponds to a ~5Mb region from the short arm of HSA19. The map for ECA14 consists of 128 markers (83 Type I and 45 Type II) and spans a total of 1828cR.Compared to this, the map of ECA21 is made up of 90 markers (64 Type I and 26 Type II), that segregate into two linkage groups spanning 278 and 760cR each. -
Mai Muudatuntuu Ti on Man Mini
MAIMUUDATUNTUU US009809854B2 TI ON MAN MINI (12 ) United States Patent ( 10 ) Patent No. : US 9 ,809 ,854 B2 Crow et al. (45 ) Date of Patent : Nov . 7 , 2017 Whitehead et al. (2005 ) Variation in tissue - specific gene expression ( 54 ) BIOMARKERS FOR DISEASE ACTIVITY among natural populations. Genome Biology, 6 :R13 . * AND CLINICAL MANIFESTATIONS Villanueva et al. ( 2011 ) Netting Neutrophils Induce Endothelial SYSTEMIC LUPUS ERYTHEMATOSUS Damage , Infiltrate Tissues, and Expose Immunostimulatory Mol ecules in Systemic Lupus Erythematosus . The Journal of Immunol @(71 ) Applicant: NEW YORK SOCIETY FOR THE ogy , 187 : 538 - 552 . * RUPTURED AND CRIPPLED Bijl et al. (2001 ) Fas expression on peripheral blood lymphocytes in MAINTAINING THE HOSPITAL , systemic lupus erythematosus ( SLE ) : relation to lymphocyte acti vation and disease activity . Lupus, 10 :866 - 872 . * New York , NY (US ) Crow et al . (2003 ) Microarray analysis of gene expression in lupus. Arthritis Research and Therapy , 5 :279 - 287 . * @(72 ) Inventors : Mary K . Crow , New York , NY (US ) ; Baechler et al . ( 2003 ) Interferon - inducible gene expression signa Mikhail Olferiev , Mount Kisco , NY ture in peripheral blood cells of patients with severe lupus . PNAS , (US ) 100 ( 5 ) : 2610 - 2615. * GeneCards database entry for IFIT3 ( obtained from < http : / /www . ( 73 ) Assignee : NEW YORK SOCIETY FOR THE genecards. org /cgi - bin / carddisp .pl ? gene = IFIT3 > on May 26 , 2016 , RUPTURED AND CRIPPLED 15 pages ) . * Navarra et al. (2011 ) Efficacy and safety of belimumab in patients MAINTAINING THE HOSPITAL with active systemic lupus erythematosus : a randomised , placebo FOR SPECIAL SURGERY , New controlled , phase 3 trial . The Lancet , 377 :721 - 731. * York , NY (US ) Abramson et al . ( 1983 ) Arthritis Rheum . -
Improved Detection of Gene Fusions by Applying Statistical Methods Reveals New Oncogenic RNA Cancer Drivers
bioRxiv preprint doi: https://doi.org/10.1101/659078; this version posted June 3, 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. Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers Roozbeh Dehghannasiri1, Donald Eric Freeman1,2, Milos Jordanski3, Gillian L. Hsieh1, Ana Damljanovic4, Erik Lehnert4, Julia Salzman1,2,5* Author affiliation 1Department of Biochemistry, Stanford University, Stanford, CA 94305 2Department of Biomedical Data Science, Stanford University, Stanford, CA 94305 3Department of Computer Science, University of Belgrade, Belgrade, Serbia 4Seven Bridges Genomics, Cambridge, MA 02142 5Stanford Cancer Institute, Stanford, CA 94305 *Corresponding author [email protected] Short Abstract: The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function.