Transcriptional and Metabolomic Analysis of Ascophyllum Nodosum
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Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer Kumardeep Chaudhary1, Olivier B
Published OnlineFirst October 5, 2017; DOI: 10.1158/1078-0432.CCR-17-0853 Statistics in CCR Clinical Cancer Research Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer Kumardeep Chaudhary1, Olivier B. Poirion1, Liangqun Lu1,2, and Lana X. Garmire1,2 Abstract Identifying robust survival subgroups of hepatocellular car- index (C-index) ¼ 0.68]. More aggressive subtype is associated cinoma (HCC) will significantly improve patient care. Current- with frequent TP53 inactivation mutations, higher expression ly, endeavor of integrating multi-omicsdatatoexplicitlypredict of stemness markers (KRT19 and EPCAM)andtumormarker HCC survival from multiple patient cohorts is lacking. To fill BIRC5, and activated Wnt and Akt signaling pathways. We this gap, we present a deep learning (DL)–based model on HCC validated this multi-omics model on five external datasets of that robustly differentiates survival subpopulations of patients various omics types: LIRI-JP cohort (n ¼ 230, C-index ¼ 0.75), in six cohorts. We built the DL-based, survival-sensitive model NCI cohort (n ¼ 221, C-index ¼ 0.67), Chinese cohort (n ¼ on 360 HCC patients' data using RNA sequencing (RNA-Seq), 166, C-index ¼ 0.69), E-TABM-36 cohort (n ¼ 40, C-index ¼ miRNA sequencing (miRNA-Seq), and methylation data from 0.77), and Hawaiian cohort (n ¼ 27, C-index ¼ 0.82). This TheCancerGenomeAtlas(TCGA),whichpredictsprognosis is the first study to employ DL to identify multi-omics features as good as an alternative model where genomics and clinical linked to the differential survival of patients with HCC. Given data are both considered. This DL-based model provides two its robustness over multiple cohorts, we expect this workflow to optimal subgroups of patients with significant survival differ- be useful at predicting HCC prognosis prediction. -
Primary Antibodies Flyer
Primary Antibodies Your choice of size and format Format Concentration Size CF® dye conjugates (13 colors) 0.1 mg/mL 100 or 500 uL Biotin, HRP or AP conjugates 0.1 mg/mL 100 or 500 uL R-PE, APC, or Per-CP conjugates 0.1 mg/mL 250 uL Purified, with BSA 0.1 mg/mL 100 or 500 uL Purified, BSA-free (Mix-n-Stain™ Ready) 1 mg/mL 50 uL Advantages Figure 1. IHC staining of human prostate Figure 2. Flow cytometry analysis of U937 • More than 1000 monoclonal antibodies carcinoma with anti-ODC1 clone cells with anti-CD31/PECAM clone C31.7, • Growing selection of monoclonal rabbit ODC1/485. CF647 conjugate (blue) or isotype control (orange). antibodies • Validated in IHC and other applications Your choice of 13 bright and photostable CF® dyes • Choose from 13 bright and stable CF® dyes CF® dye Ex/Em (nm) Features • Also available with R-PE, APC, PerCP, HRP, AP, CF®405S 404/431 • Better fit for the 450/50 flow cytometer channel than Alexa Fluor® 405 or biotin CF®405M 408/452 • More photostable than Pacific Blue®, with less green spill-over • Purified antibodies available BSA-free, 1 mg/mL, • Compatible with super-resolution imaging by SIM ready to use for Mix-n-Stain™ labeling or other CF®488A 490/515 • Less non-specific binding and spill-over than Alexa Fluor® 488 conjugation • Very photostable and pH-insensitive • Compatible with super-resolution imaging by TIRF • Offered in affordable 100 uL size CF®543 541/560 • Brighter than Alexa Fluor® 546 CF®555 555/565 • Brighter than Cy®3 • Validated in multicolor super-resolution imaging by STORM CF®568 -
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. -
WO 2007/056812 Al
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (43) International Publication Date (10) International Publication Number 24 May 2007 (24.05.2007) PCT WO 2007/056812 Al (51) International Patent Classification: Victoria 3941 (AU). WHITTAKER, Jason, S. [AU/AU]; C07K 14/54 (2006.01) C07H 21/04 (2006.01) 24 Maddison Street, Redfern East, New South Wales A61K 38/19 (2006.01) C07K 14/52 (2006.01) 2016 (AU). DOMAGALA, Teresa, A. [AU/AU]; 14/30 A61K 38/20 (2006.01) C07K 14/61 (2006.01) Garden Street, Alexandria, New South Wales 2015 (AU). A61K 38/27 (2006.01) C07K 14/715 (2006.01) SIMPSON, Raina, J. [AU/AU]; 10 Snowgum Street, A61P 7/00 (2006.01) C07 /4/72 (2006.01) Acacia Gardens, New South Wales 2763 (AU). A61P 35/00 (2006.01) C07K 19/00 (2006.01) A61P 37/00 (2006.01) GOlN 33/543 (2006.01) (74) Agents: HUGHES, John, E., L. et al.; DAVIES COLLI- C07H 21/02 (2006.01) SON CAVE, 1Nicholson Street, Melbourne, Victoria 3000 (AU). (21) International Application Number: PCT/AU2006/001718 (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, (22) International Filing Date: AT,AU, AZ, BA, BB, BG, BR, BW, BY, BZ, CA, CH, CN, 16 November 2006 (16.1 1.2006) CO, CR, CU, CZ, DE, DK, DM, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, (25) Filing Language: English JP, KE, KG, KM, KN, KP, KR, KZ, LA, LC, LK, LR, LS, LT, LU, LV,LY,MA, MD, MG, MK, MN, MW, MX, MY, (26) -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
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. -
CDG and Immune Response: from Bedside to Bench and Back Authors
CDG and immune response: From bedside to bench and back 1,2,3 1,2,3,* 2,3 1,2 Authors: Carlota Pascoal , Rita Francisco , Tiago Ferro , Vanessa dos Reis Ferreira , Jaak Jaeken2,4, Paula A. Videira1,2,3 *The authors equally contributed to this work. 1 Portuguese Association for CDG, Lisboa, Portugal 2 CDG & Allies – Professionals and Patient Associations International Network (CDG & Allies – PPAIN), Caparica, Portugal 3 UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal 4 Center for Metabolic Diseases, UZ and KU Leuven, Leuven, Belgium Word count: 7478 Number of figures: 2 Number of tables: 3 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jimd.12126 This article is protected by copyright. All rights reserved. Abstract Glycosylation is an essential biological process that adds structural and functional diversity to cells and molecules, participating in physiological processes such as immunity. The immune response is driven and modulated by protein-attached glycans that mediate cell-cell interactions, pathogen recognition and cell activation. Therefore, abnormal glycosylation can be associated with deranged immune responses. Within human diseases presenting immunological defects are Congenital Disorders of Glycosylation (CDG), a family of around 130 rare and complex genetic diseases. In this review, we have identified 23 CDG with immunological involvement, characterised by an increased propensity to – often life-threatening – infection. -
©Ferrata Storti Foundation
Original Articles T-cell/histiocyte-rich large B-cell lymphoma shows transcriptional features suggestive of a tolerogenic host immune response Peter Van Loo,1,2,3 Thomas Tousseyn,4 Vera Vanhentenrijk,4 Daan Dierickx,5 Agnieszka Malecka,6 Isabelle Vanden Bempt,4 Gregor Verhoef,5 Jan Delabie,6 Peter Marynen,1,2 Patrick Matthys,7 and Chris De Wolf-Peeters4 1Department of Molecular and Developmental Genetics, VIB, Leuven, Belgium; 2Department of Human Genetics, K.U.Leuven, Leuven, Belgium; 3Bioinformatics Group, Department of Electrical Engineering, K.U.Leuven, Leuven, Belgium; 4Department of Pathology, University Hospitals K.U.Leuven, Leuven, Belgium; 5Department of Hematology, University Hospitals K.U.Leuven, Leuven, Belgium; 6Department of Pathology, The Norwegian Radium Hospital, University of Oslo, Oslo, Norway, and 7Department of Microbiology and Immunology, Rega Institute for Medical Research, K.U.Leuven, Leuven, Belgium Citation: Van Loo P, Tousseyn T, Vanhentenrijk V, Dierickx D, Malecka A, Vanden Bempt I, Verhoef G, Delabie J, Marynen P, Matthys P, and De Wolf-Peeters C. T-cell/histiocyte-rich large B-cell lymphoma shows transcriptional features suggestive of a tolero- genic host immune response. Haematologica. 2010;95:440-448. doi:10.3324/haematol.2009.009647 The Online Supplementary Tables S1-5 are in separate PDF files Supplementary Design and Methods One microgram of total RNA was reverse transcribed using random primers and SuperScript II (Invitrogen, Merelbeke, Validation of microarray results by real-time quantitative Belgium), as recommended by the manufacturer. Relative reverse transcriptase polymerase chain reaction quantification was subsequently performed using the compar- Ten genes measured by microarray gene expression profil- ative CT method (see User Bulletin #2: Relative Quantitation ing were validated by real-time quantitative reverse transcrip- of Gene Expression, Applied Biosystems). -
Function Study of Arabidopsis Thaliana Core Alpha1,3-Fucosyltransferase (Fucta) Peter Both
Structure – function study of Arabidopsis thaliana core alpha1,3-fucosyltransferase (FucTA) Peter Both To cite this version: Peter Both. Structure – function study of Arabidopsis thaliana core alpha1,3-fucosyltransferase (FucTA). Biomolecules [q-bio.BM]. Université Joseph-Fourier - Grenoble I, 2009. English. tel- 00449431 HAL Id: tel-00449431 https://tel.archives-ouvertes.fr/tel-00449431 Submitted on 21 Jan 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. UNIVERSITE JOSEPH FOURIER (GRENOBLE I ) ECOLE DOCTORALE CHIMIE ET SCIENCES DU VIVANT UNIVERSITE SLOVAQUE DE TECHNOLOGIE DE BRATISLAVA (STUBA) THESE Pour l’obtention du Diplôme de DOCTEUR DE L’UNIVERSITE JOSEPH FOURIER Discipline: Biologie Présentée et soutenue publiquement le 29 octobre 2009 par PETER BOTH ETUDE STRUCTURE-FUNCTION D’UNE FUCOSYLTRANSFERASE (FUCT-A) DE ARABIDOPSIS THALIANA JURY Rapporteurs : Prof. Abderrahman MAFTAH, Université de Limoges Dr Eva HOSTINOVA, Slovak Academy of Sciences, Bratislava Examinateurs : Dr Eva KUTEJOVA, Slovak Academy of Sciences, Bratislava Dr Serge PEREZ, ESRF, Grenoble Dr Jan MUCHA, Slovak Academy of Sciences, Bratislava Prof. Christelle BRETON, Université Grenoble I Thèse préparée au CERMAV (Grenoble) et ICHSAS (Bratislava) 1 I would like to express my gratitude to all those who gave me the possibility to complete this thesis. -
PP1-Associated Signaling and − B/AP-1 Κ Inhibition of NF- Tolerance
Downloaded from http://www.jimmunol.org/ by guest on October 3, 2021 is online at: average * and − B/AP-1 κ The Journal of Immunology published online 26 February 2014 from submission to initial decision 4 weeks from acceptance to publication http://www.jimmunol.org/content/early/2014/02/26/jimmun ol.1301610 Identification of Two Forms of TNF Tolerance in Human Monocytes: Differential Inhibition of NF- PP1-Associated Signaling Johannes Günther, Nico Vogt, Katharina Hampel, Rolf Bikker, Sharon Page, Benjamin Müller, Judith Kandemir, Michael Kracht, Oliver Dittrich-Breiholz, René Huber and Korbinian Brand J Immunol 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/2014/02/26/jimmunol.130161 0.DCSupplemental 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 © 2014 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of October 3, 2021. Published February 26, 2014, doi:10.4049/jimmunol.1301610 The Journal of Immunology Identification of Two Forms of TNF Tolerance in Human Monocytes: Differential Inhibition of NF-kB/AP-1– and PP1-Associated Signaling Johannes Gunther,*€ ,1 Nico Vogt,*,1 Katharina Hampel,*,1 Rolf Bikker,* Sharon Page,* Benjamin Muller,*€ Judith Kandemir,* Michael Kracht,† Oliver Dittrich-Breiholz,‡ Rene´ Huber,* and Korbinian Brand* The molecular basis of TNF tolerance is poorly understood. -
Transcriptome Profiling Reveals the Complexity of Pirfenidone Effects in IPF
ERJ Express. Published on August 30, 2018 as doi: 10.1183/13993003.00564-2018 Early View Original article Transcriptome profiling reveals the complexity of pirfenidone effects in IPF Grazyna Kwapiszewska, Anna Gungl, Jochen Wilhelm, Leigh M. Marsh, Helene Thekkekara Puthenparampil, Katharina Sinn, Miroslava Didiasova, Walter Klepetko, Djuro Kosanovic, Ralph T. Schermuly, Lukasz Wujak, Benjamin Weiss, Liliana Schaefer, Marc Schneider, Michael Kreuter, Andrea Olschewski, Werner Seeger, Horst Olschewski, Malgorzata Wygrecka Please cite this article as: Kwapiszewska G, Gungl A, Wilhelm J, et al. Transcriptome profiling reveals the complexity of pirfenidone effects in IPF. Eur Respir J 2018; in press (https://doi.org/10.1183/13993003.00564-2018). This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Copyright ©ERS 2018 Copyright 2018 by the European Respiratory Society. Transcriptome profiling reveals the complexity of pirfenidone effects in IPF Grazyna Kwapiszewska1,2, Anna Gungl2, Jochen Wilhelm3†, Leigh M. Marsh1, Helene Thekkekara Puthenparampil1, Katharina Sinn4, Miroslava Didiasova5, Walter Klepetko4, Djuro Kosanovic3, Ralph T. Schermuly3†, Lukasz Wujak5, Benjamin Weiss6, Liliana Schaefer7, Marc Schneider8†, Michael Kreuter8†, Andrea Olschewski1, -
Supplemental Figures 04 12 2017
Jung et al. 1 SUPPLEMENTAL FIGURES 2 3 Supplemental Figure 1. Clinical relevance of natural product methyltransferases (NPMTs) in brain disorders. (A) 4 Table summarizing characteristics of 11 NPMTs using data derived from the TCGA GBM and Rembrandt datasets for 5 relative expression levels and survival. In addition, published studies of the 11 NPMTs are summarized. (B) The 1 Jung et al. 6 expression levels of 10 NPMTs in glioblastoma versus non‐tumor brain are displayed in a heatmap, ranked by 7 significance and expression levels. *, p<0.05; **, p<0.01; ***, p<0.001. 8 2 Jung et al. 9 10 Supplemental Figure 2. Anatomical distribution of methyltransferase and metabolic signatures within 11 glioblastomas. The Ivy GAP dataset was downloaded and interrogated by histological structure for NNMT, NAMPT, 12 DNMT mRNA expression and selected gene expression signatures. The results are displayed on a heatmap. The 13 sample size of each histological region as indicated on the figure. 14 3 Jung et al. 15 16 Supplemental Figure 3. Altered expression of nicotinamide and nicotinate metabolism‐related enzymes in 17 glioblastoma. (A) Heatmap (fold change of expression) of whole 25 enzymes in the KEGG nicotinate and 18 nicotinamide metabolism gene set were analyzed in indicated glioblastoma expression datasets with Oncomine. 4 Jung et al. 19 Color bar intensity indicates percentile of fold change in glioblastoma relative to normal brain. (B) Nicotinamide and 20 nicotinate and methionine salvage pathways are displayed with the relative expression levels in glioblastoma 21 specimens in the TCGA GBM dataset indicated. 22 5 Jung et al. 23 24 Supplementary Figure 4.