DOCSLIB.ORG

Genome-Wide Screening Identifies Prognostic Long Noncoding Rnas in Hepatocellular Carcinoma

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genome-Wide Screening Identifies Prognostic Long Noncoding Rnas in Hepatocellular Carcinoma Hindawi BioMed Research International Volume 2021, Article ID 6640652, 16 pages https://doi.org/10.1155/2021/6640652 Research Article Genome-Wide Screening Identifies Prognostic Long Noncoding RNAs in Hepatocellular Carcinoma Yujie Feng, Xiao Hu, Kai Ma, Bingyuan Zhang , and Chuandong Sun Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province 266003, China Correspondence should be addressed to Bingyuan Zhang; [email protected] and Chuandong Sun; [email protected] Received 19 October 2020; Revised 18 April 2021; Accepted 23 April 2021; Published 21 May 2021 Academic Editor: Min Tang Copyright © 2021 Yujie Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis. Therefore, there is an urgent call for the investigation of novel biomarkers in HCC. In the present study, we identified 6 upregulated lncRNAs in HCC, including LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1. Higher expression of these lncRNAs was correlated to a more advanced cancer stage and a poorer prognosis in HCC patients. Enrichment analysis revealed that these lncRNAs played a crucial role in HCC progression, possibly through a series of cancer-related biological processes, such as cell cycle, DNA replication, histone acetyltransferase complex, fatty acid oxidation, and lipid modification. Moreover, competing endogenous RNA (ceRNA) network analysis revealed that these lncRNAs could bind to certain miRNAs to promote HCC progression. Loss-of-function assays indicated that silencing of RHPN1-AS1 significantly suppressed HCC proliferation and migration. Though further validations are still needed, these identified lncRNAs could serve as valuable potential biomarkers for HCC prognosis. 1. Introduction link to the regulation of HCC growth and metastasis. Specif- ically, lncRNA XIST regulates HCC tumor growth and Hepatocellular carcinoma (HCC) has ranked as the second migration by sponging miR-497-5p [9]. DANCR retained leading cause of cancer-related deaths worldwide [1], as more HCC stemness by suppressing CTNNB1 expression [11]. than 782,000 HCC-associated deaths are predicted to occur The LINC01138 drives carcinogenesis in HCC via activating annually [2]. Previous studies have demonstrated that the arginine methyltransferase 5 [10]. Meanwhile, several upregulation of oncogenes and downregulation of tumor lncRNAs have been demonstrated as potential prognostic suppressors were related to HCC tumorigenesis. For exam- biomarkers in HCC. For example, overexpression of lncRNA ple, ID1 enhanced tumor growth in HCC patients [3], while ENST00000429227.1, LINC00511, SNHG16, and AK001796 FABP4 inhibited tumor growth and invasion [4]. However, were associated with worse prognosis in HCC patients [12] . there is still an urgent call to explore new biomarkers in A further appreciation of the molecular functions and HCC, as novel therapeutic targets are needed for establishing expression patterns of lncRNAs could help reveal novel bio- future molecular therapies, in the hope of improving progno- markers for HCC. ses of HCC patients. In this study, using gene expression data of HCC patients Noncoding RNAs (ncRNAs) have emerged as a very and healthy controls, we screened possible prognostic promising resource for the identification of prognostic bio- lncRNAs in HCC. Additionally, we utilized bioinformatic markers. ncRNAs longer than 200 bps are regarded as long approaches to explore the potential functions of such noncoding RNAs (lncRNAs) [5]. Growing evidence has con- lncRNAs and constructed competing endogenous RNA firmed the association between HCC and lncRNAs [6]. For (ceRNA) networks to further demonstrate potential mecha- example, XIST [7–9] and LINC01138 [10] were reported to nisms concerning those identified lncRNAs in HCC P er- ff 0.001 < 3 2 1 0 −1 −2 −3 RAD54L MCM10 value CENPK E2F8 CDC25C P CELSR3 RP11−218F10.3 CTD−2349P21.9 AC010761.8 RP5−967N21.11 and WBP2NL The gene list of di RP11−127B20.3 0 RP4−620E11.8 : CTGLF10P RP11−225B17.2 >2 LINC01268 cant association with overall ∣ IGJ fi OLFM1 AXIN1 ADH4 FC BioMed Research International RPL14P3 ∣ SPEF1 ATRIP RP11−932O9.10 RP11−197P3.5 erentially expressed lncRNAs, and DNASE1L3 erential expression analysis, and pro- ff CLEC3B log 2 XCR1 ff OMG STAG3 ZNF131 cally, GEPIA normalized gene expression PSMC3IP erentially expressed genes ranked by the LRP11 fi HEATR5A ff CCZ1B LCN6 Overall survival RP11−709D24.8 Log2 Hazard ratio C2orf66 BFSP1 erentially expressed lncRNAs in HCC and nontumor DDAH2 ff SPATA2 EZH2 MKI67 PARPBP EME1 0 0.5 1 1.5 2 2.5 3 3.5 4 SGOL1 RNFT2 SLC25A19 AP000695.4 CAD ently expressed lncRNAs in HCCGEPIA was downloaded dataset from the newly (http://gepia.cancer-pku.cn/). developed GEPIA interactiveRNA is sequencing web expression a server datanormal of for samples 9,736 from tumors the analyzing TCGA and and the a 8,587 the GTEx projects standard using report processing [13]. Speci pipeline, according to a previous values. lncRNAs with by transcript perpackage to million conduct (TPM), di employed the R limma 2.2. Data Preparation and Processing. vided a list of di XPO5 were considered as di DHX57 BRCA1 MYO19 (a) (b) NUP205 les of the 100 di GPSM2 fi C5orf45 BRD8 RP5−1074L1.4 SNHG20 ZWINT MCM6 CHEK1 CHAF1B 01234 LMNB1 MCM3 NASP CENPH Log2 Hazard ratio STMN1 Disease-free survival KIF2C PTTG1 CCNB1 KPNA2 WDR4 STX1A RBM28 PHF19 NRM UBE2S FAM216A PCNT POLR3F PRR3 NR2C1 TRIM45 RP13−516M14.1 RAD51C NUP85 SRP68 UTP18 EFTUD2 UTP6 C12orf43 Gene symbol CTB−147N14.6 RP11−286H15.1 RP11−295D4.1 RHPN1−AS1 SNRPEP2 LINC01134 NRAV NAP1L1P1 CMB9−22P13.1 MKLN1−AS RP5−864K19.4 CTC−297N7.9 MAPKAPK5−AS1 MIR210HG AP001469.9 RHOT2 TBRG4 From April 2016 to March 2020, 6 MED7 RPL39P3 Non-tumor HCC HIST1H1B SCRT1 WI2−89031B12.1 Tissue Tissue liated Hospital of Qingdao University. The ethics 1: LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 were associated with both disease-free ffi 2. Materials and Methods 2.1. Clinical Samples. paired HCC andthe adjacent normal A tissuescommittee were of collected at thisenrollment hospital of approved patients. this Allinformed study consent. patients before or the their parents signed development and progression. Loss-of-functionperformed assays to were investigate potential functionsin of HCC. key We lncRNAs believemarkers in that HCC this and study inspire could further researches. provide valuable Figure survival and overall survival time in HCC. (a) The expression pro 2 tissues. (b) The two forest plots of disease-free survival and overall survival for the 15 genes with signi survival. The gene symbols with red color are associated with both disease-free survival and overall survival. BioMed Research International 3 Disease-free survival Disease-free survival Disease-free survival 1.0 1.0 1.0 0.8 0.8 0.8 Logrank P = 0.0034 Logrank P = 0.0014 Logrank P = 0.0014 0.6 0.6 0.6 0.4 0.4 0.4 Percent survival 0.2 Percent survival 0.2 Percent survival 0.2 0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Months Low LINC01134 Low RHPN1−AS1 Low NRAV High LINC01134 High RHPN1−AS1 High NRAV Disease-free survival Disease-free survival Disease-free survival 1.0 1.0 1.0 0.8 0.8 0.8 0.6 Logrank P = 0.002 0.6 Logrank P = 0.01 0.6 Logrank P = 0.037 0.4 0.4 0.4 Percent survival 0.2 Percent survival 0.2 Percent survival 0.2 0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Months Low CMB9−22P13.1 Low MKLN1−AS Low MAPKAPK5−AS1 High CMB9−22P13.1 High MKLN1−AS High MAPKAPK5−AS1 (a) Figure 2: Continued. 4 BioMed Research International Overall survival Overall survival Overall survival 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 Percent survival 0.2 Percent survival 0.2 Percent survival 0.2 0.0 Logrank P = 7.9e−06 0.0 Logrank P = 3.5e−06 0.0 Logrank P = 7.1e−06 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Months Low LINC01134 Low RHPN1−AS1 Low NRAV High LINC01134 High RHPN1−AS1 High NRAV Overall survival Overall survival Overall survival 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 Percent survival 0.2 Percent survival 0.2 Percent survival 0.2 Logrank P = 2.4e−05 Logrank P = 3.9e−05 Logrank P = 3.6e−05 0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Months Low CMB9−22P13.1 Low MKLN1−AS Low MAPKAPK5−AS1 High CMB9−22P13.1 High MKLN1−AS High MAPKAPK5−AS1 (b) Figure 2: LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 were related to disease-free and overall survival time in HCC. The Kaplan-Meier (KM) curves of disease-free survival (a) and overall survival (b) for six prognostic lncRNAs in HCC. the top 100 lncRNAs ranked by the P values were selected for HCC. The hypergeometric test was employed to determine downstream analysis. Moreover, those lncRNAs associated the statistical significance. with survival time in HCC were also downloaded from the GEPIA website [13].
Load more

Recommended publications
  • Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
    School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage.
    [Show full text]
  • Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
    Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int.
    [Show full text]
  • The Role of Non-Coding Rnas in Uveal Melanoma
    cancers Review The Role of Non-Coding RNAs in Uveal Melanoma Manuel Bande 1,2,*, Daniel Fernandez-Diaz 1,2, Beatriz Fernandez-Marta 1, Cristina Rodriguez-Vidal 3, Nerea Lago-Baameiro 4, Paula Silva-Rodríguez 2,5, Laura Paniagua 6, María José Blanco-Teijeiro 1,2, María Pardo 2,4 and Antonio Piñeiro 1,2 1 Department of Ophthalmology, University Hospital of Santiago de Compostela, Ramon Baltar S/N, 15706 Santiago de Compostela, Spain; [email protected] (D.F.-D.); [email protected] (B.F.-M.); [email protected] (M.J.B.-T.); [email protected] (A.P.) 2 Tumores Intraoculares en el Adulto, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain; [email protected] (P.S.-R.); [email protected] (M.P.) 3 Department of Ophthalmology, University Hospital of Cruces, Cruces Plaza, S/N, 48903 Barakaldo, Vizcaya, Spain; [email protected] 4 Grupo Obesidómica, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain; [email protected] 5 Fundación Pública Galega de Medicina Xenómica, Clinical University Hospital, SERGAS, 15706 Santiago de Compostela, Spain 6 Department of Ophthalmology, University Hospital of Coruña, Praza Parrote, S/N, 15006 La Coruña, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-981951756; Fax: +34-981956189 Received: 13 September 2020; Accepted: 9 October 2020; Published: 12 October 2020 Simple Summary: The development of uveal melanoma is a multifactorial and multi-step process, in which abnormal gene expression plays a key role.
    [Show full text]
  • Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
    Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................
    [Show full text]
  • The Role of Lncrnas in Uveal Melanoma
    cancers Review The Role of LncRNAs in Uveal Melanoma Paula Milán-Rois 1 , Anan Quan 2, Frank J. Slack 2 and Álvaro Somoza 1,* 1 Instituto Madrileño de Estudios Avanzados en Nanociencia (IMDEA Nanociencia), Unidad Asociada al Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain; [email protected] 2 Department of Pathology, Beth Israel Deaconess Medical Center (BIDMC)/Harvard Medical School, Boston, MA 02215, USA; [email protected] (A.Q.); [email protected] (F.J.S.) * Correspondence: [email protected]; Tel.: +34-91-299-8856 Simple Summary: Uveal melanoma is a rare cancer with a bad prognosis that needs new therapeutic and diagnostic approaches. In this regard, long non-coding RNAs (lncRNAs) play a pivotal role in cancer, among other diseases, and could be used as therapeutic targets of diagnostic markers. In this review, lncRNAs related to uveal melanoma are revealed to better understand their mechanism of action, and inspire the development of novel treatment and diagnostic approaches. In addition, the interaction of lncRNA with other non-coding RNAs (ncRNAs) is also discussed since it might be one of the most relevant mechanisms of action. The compiled information is helpful not only for uveal melanoma experts, but also for ncRNA cancer researchers. Abstract: Uveal melanoma (UM) is an intraocular cancer tumor with high metastatic risk. It is considered a rare disease, but 90% of affected patients die within 15 years. Non-coding elements (ncRNAs) such as long non-coding RNAs (lncRNAs) have a crucial role in cellular homeostasis maintenance, taking part in many critical cellular pathways.
    [Show full text]
  • A Yeast Bifc-Seq Method for Genome-Wide Interactome Mapping
    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.16.154146; this version posted June 17, 2020. 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 A yeast BiFC-seq method for genome-wide interactome mapping 2 3 Limin Shang1,a, Yuehui Zhang1,b, Yuchen Liu1,c, Chaozhi Jin1,d, Yanzhi Yuan1,e, 4 Chunyan Tian1,f, Ming Ni2,g, Xiaochen Bo2,h, Li Zhang3,i, Dong Li1,j, Fuchu He1,*,k & 5 Jian Wang1,*,l 6 1State Key Laboratory of Proteomics, Beijing Proteome Research Center, National 7 Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, 8 China; 2Department of Biotechnology, Beijing Institute of Radiation Medicine, 9 Beijing 100850, China; 3Department of Rehabilitation Medicine, Nan Lou; 10 Department of Key Laboratory of Wound Repair and Regeneration of PLA, College 11 of Life Sciences, Chinese PLA General Hospital, Beijing 100853, China; 12 Correspondence should be addressed to F.H. ([email protected]) and J.W. 13 ([email protected]). 14 * Corresponding authors. 15 E-mail：[email protected] (Fuchu He)，[email protected] (Jian Wang) 16 a ORCID: 0000-0002-6371-1956. 17 b ORCID: 0000-0001-5257-1671 18 c ORCID: 0000-0003-4691-4951 19 d ORCID: 0000-0002-1477-0255 20 e ORCID: 0000-0002-6576-8112 21 f ORCID: 0000-0003-1589-293X 22 g ORCID: 0000-0001-9465-2787 23 h ORCID: 0000-0003-3490-5812 24 i ORCID: 0000-0002-3477-8860 25 j ORCID: 0000-0002-8680-0468 26 k ORCID: 0000-0002-8571-2351 27 l ORCID: 0000-0002-8116-7691 28 Total word counts:4398 (from “Introduction” to “Conclusions”) 29 Total Keywords: 5 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.16.154146; this version posted June 17, 2020.
    [Show full text]
  • Supporting Information Supporting Information Corrected July 15 , 2014 Yi Et Al
    Supporting Information Supporting Information Corrected July 15 , 2014 Yi et al. 10.1073/pnas.1404943111 SI Materials and Methods which no charge state could be determined were excluded MS/ Tumorsphere Growth Assay. Single-cell tumorsphere growth assays MS selection). Three independent experiments were performed. of human mammary epithelial (HMLER) (CD44high/CD24low)SA and HMLER (CD44high/CD24low)FA population cells were per- Phosphorylation Site Localization. The probability of correct phos- formed with ultra-low attachment surface six-well plates (Corn- phorylation site localization for each phosphorylation site was ing) and the mammary epithelial cell basal medium (MEBM) measured using an Ascore algorithm (1). This algorithm con- medium (Lonza) was supplemented with B27 (Invitrogen), 20 ng/mL siders all phosphoforms of a peptide and uses the presence or EGF, and 20 ng/mL basic FGF (BD Biosciences), and 4 μg/mL absence of experimental fragment ions unique to each to create an ambiguity score (Ascore). Parameters included a window size of heparin (Sigma). Single cells were cultured for 8 d and images were ± taken with a Nikon camera. 100 m/z units and a fragment ion tolerance of 0.6 m/z units. Sites with an Ascore of ≥13 (P ≤ 0.05) were considered to be ≥ ≤ Soft Agar Colony Growth Assay. Single-cell soft agar colony for- confidently localized, and those with an Ascore of 19 (P 0.01) mation and growth assays were performed to identify the tumor- were considered to be localized with near certainty. More than ≥ igenesis capacity of HMLER (CD44high/CD24low)SA and HMLER a 2.5-fold ratio ( 60.02%) was considered as a significant (CD44high/CD24low)FA population cells in vitro.
    [Show full text]
  • 1 Novel Expression Signatures Identified by Transcriptional Analysis
    ARD Online First, published on October 7, 2009 as 10.1136/ard.2009.108043 Ann Rheum Dis: first published as 10.1136/ard.2009.108043 on 7 October 2009. Downloaded from Novel expression signatures identified by transcriptional analysis of separated leukocyte subsets in SLE and vasculitis 1Paul A Lyons, 1Eoin F McKinney, 1Tim F Rayner, 1Alexander Hatton, 1Hayley B Woffendin, 1Maria Koukoulaki, 2Thomas C Freeman, 1David RW Jayne, 1Afzal N Chaudhry, and 1Kenneth GC Smith. 1Cambridge Institute for Medical Research and Department of Medicine, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0XY, UK 2Roslin Institute, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK Correspondence should be addressed to Dr Paul Lyons or Prof Kenneth Smith, Department of Medicine, Cambridge Institute for Medical Research, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0XY, UK. Telephone: +44 1223 762642, Fax: +44 1223 762640, E-mail: [email protected] or [email protected] Key words: Gene expression, autoimmune disease, SLE, vasculitis Word count: 2,906 The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non-exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in Annals of the Rheumatic Diseases and any other BMJPGL products to exploit all subsidiary rights, as set out in their licence (http://ard.bmj.com/ifora/licence.pdf). http://ard.bmj.com/ on September 29, 2021 by guest. Protected copyright. 1 Copyright Article author (or their employer) 2009.
    [Show full text]
  • PRODUCTS and SERVICES Target List
    PRODUCTS AND SERVICES Target list Kinase Products P.1-11 Kinase Products Biochemical Assays P.12 "QuickScout Screening Assist™ Kits" Kinase Protein Assay Kits P.13 "QuickScout Custom Profiling & Panel Profiling Series" Targets P.14 "QuickScout Custom Profiling Series" Preincubation Targets Cell-Based Assays P.15 NanoBRET™ TE Intracellular Kinase Cell-Based Assay Service Targets P.16 Tyrosine Kinase Ba/F3 Cell-Based Assay Service Targets P.17 Kinase HEK293 Cell-Based Assay Service ～ClariCELL™ ～ Targets P.18 Detection of Protein-Protein Interactions ～ProbeX™～ Stable Cell Lines Crystallization Services P.19 FastLane™ Structures ～Premium～ P.20-21 FastLane™ Structures ～Standard～ Kinase Products For details of products, please see "PRODUCTS AND SERVICES" on page 1～3. Tyrosine Kinases Note: Please contact us for availability or further information. Information may be changed without notice. Expression Protein Kinase Tag Carna Product Name Catalog No. Construct Sequence Accession Number Tag Location System HIS ABL(ABL1) 08-001 Full-length 2-1130 NP_005148.2 N-terminal His Insect (sf21) ABL(ABL1) BTN BTN-ABL(ABL1) 08-401-20N Full-length 2-1130 NP_005148.2 N-terminal DYKDDDDK Insect (sf21) ABL(ABL1) [E255K] HIS ABL(ABL1)[E255K] 08-094 Full-length 2-1130 NP_005148.2 N-terminal His Insect (sf21) HIS ABL(ABL1)[T315I] 08-093 Full-length 2-1130 NP_005148.2 N-terminal His Insect (sf21) ABL(ABL1) [T315I] BTN BTN-ABL(ABL1)[T315I] 08-493-20N Full-length 2-1130 NP_005148.2 N-terminal DYKDDDDK Insect (sf21) ACK(TNK2) GST ACK(TNK2) 08-196 Catalytic domain
    [Show full text]
  • Views for Entrez
    BASIC RESEARCH www.jasn.org Phosphoproteomic Analysis Reveals Regulatory Mechanisms at the Kidney Filtration Barrier †‡ †| Markus M. Rinschen,* Xiongwu Wu,§ Tim König, Trairak Pisitkun,¶ Henning Hagmann,* † † † Caroline Pahmeyer,* Tobias Lamkemeyer, Priyanka Kohli,* Nicole Schnell, †‡ †† ‡‡ Bernhard Schermer,* Stuart Dryer,** Bernard R. Brooks,§ Pedro Beltrao, †‡ Marcus Krueger,§§ Paul T. Brinkkoetter,* and Thomas Benzing* *Department of Internal Medicine II, Center for Molecular Medicine, †Cologne Excellence Cluster on Cellular Stress | Responses in Aging-Associated Diseases, ‡Systems Biology of Ageing Cologne, Institute for Genetics, University of Cologne, Cologne, Germany; §Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; ¶Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; **Department of Biology and Biochemistry, University of Houston, Houston, Texas; ††Division of Nephrology, Baylor College of Medicine, Houston, Texas; ‡‡European Molecular Biology Laboratory–European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom; and §§Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany ABSTRACT Diseases of the kidney filtration barrier are a leading cause of ESRD. Most disorders affect the podocytes, polarized cells with a limited capacity for self-renewal that require tightly controlled signaling to maintain their integrity, viability, and function. Here, we provide an atlas of in vivo phosphorylated, glomerulus- expressed
    [Show full text]
  • PRAK (MAPKAPK5) Antibody (T182) Peptide Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # Ap7216a
    9765 Clairemont Mesa Blvd, Suite C San Diego, CA 92124 Tel: 858.875.1900 Fax: 858.622.0609 PRAK (MAPKAPK5) Antibody (T182) Peptide Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # AP7216a Specification PRAK (MAPKAPK5) Antibody (T182) - Product Information Application WB,E Primary Accession Q8IW41 Reactivity Human Host Rabbit Clonality Polyclonal Isotype Rabbit Ig Clone Names RB13233 Calculated MW 54220 Antigen Region 160-189 PRAK (MAPKAPK5) Antibody (T182) - Additional Information Gene ID 8550 Other Names MAP kinase-activated protein kinase 5, Western blot analysis of MAPKAPK5 Antibody MAPK-activated protein kinase 5, MAPKAP (T182) (Cat.#AP7216a) in Hela cell line lysates kinase 5, MAPKAP-K5, MAPKAPK-5, MK-5, (35ug/lane). MAPKAPK5 (arrow) was detected MK5, p38-regulated/activated protein kinase, using the purified Pab. PRAK, MAPKAPK5, PRAK Target/Specificity This PRAK(MAPKAPK5) antibody is generated from rabbits immunized with a KLH conjugated synthetic peptide between 160-189 amino acids from human PRAK(MAPKAPK5). Dilution WB~~1:1000 Format Purified polyclonal antibody supplied in PBS with 0.09% (W/V) sodium azide. This antibody is purified through a protein A column, followed by peptide affinity purification. Storage Maintain refrigerated at 2-8°C for up to 6 Western blot analysis of MAPKAPK5 (arrow) months. For long term storage store at -20°C using rabbit polyclonal MAPKAPK5 Antibody in small aliquots to prevent freeze-thaw (T182) (Cat.#AP7216a). 293 cell lysates (2 cycles. ug/lane) either nontransfected (Lane 1) or transiently transfected (Lane 2) with the Precautions MAPKAPK5 gene. PRAK (MAPKAPK5) Antibody (T182) is for research use only and not for use in diagnostic or therapeutic procedures.
    [Show full text]
  • Inhibitors of MBNL1-CUG RNA Binding with Distinct Cellular Effects Jason W
    Published online 20 April 2014 Nucleic Acids Research, 2014, Vol. 42, No. 10 6591–6602 doi: 10.1093/nar/gku275 Lomofungin and dilomofungin: inhibitors of MBNL1-CUG RNA binding with distinct cellular effects Jason W. Hoskins1, Leslie O. Ofori2, Catherine Z. Chen3,AmitKumar4, Krzysztof Sobczak1, Masayuki Nakamori1, Noel Southall3, Samarjit Patnaik3, Juan J. Marugan3, Wei Zheng3, Christopher P. Austin3, Matthew D. Disney4, Benjamin L. Miller5 and Charles A. Thornton1,* 1Department of Neurology, University of Rochester, Rochester, NY 14642, USA, 2Department of Chemistry, University of Rochester, Rochester, NY 14642, USA, 3Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA, 4Department of Chemistry, Scripps Florida, Jupiter, FL 33458, USA and 5Department of Dermatology, University of Rochester, Rochester, NY 14642, USA Received May 8, 2013; Revised March 21, 2014; Accepted March 24, 2014 ABSTRACT INTRODUCTION Myotonic dystrophy type 1 (DM1) is a dominantly Myotonic dystrophy type 1 (DM1) is the prototypical ge- inherited neuromuscular disorder resulting from ex- netic disease resulting from a toxic gain-of-function by mu- pression of RNA containing an expanded CUG repeat tant RNA (1). RNA toxicity has been implicated in several (CUGexp). The pathogenic RNA is retained in nuclear other disorders that share a distinctive set of genetic fea- foci. Poly-(CUG) binding proteins in the Muscleblind- tures: the causal mutations are large polymorphic expan- sions of simple tandem repeats that are genetically unsta- like (MBNL) family are sequestered in foci, caus- ble and located in non-coding regions of the genome (2–9). ing misregulated alternative splicing of specific pre- The underlying disease mechanisms are heterogeneous and exp mRNAs.
    [Show full text]