DOCSLIB.ORG

Study of the G Protein Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Study of the G Protein Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis Hindawi BioMed Research International Volume 2021, Article ID 4873678, 12 pages https://doi.org/10.1155/2021/4873678 Research Article Study of the G Protein Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis Yiwei Dong,1 Qianqian Cai,2 Lisheng Fu,1 Haojie Liu,1 Mingzhe Ma,3 and Xingzhong Wu 1 1Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Key Lab of Glycoconjugate Research, Ministry of Public Health, Shanghai 200032, China 2Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China 3Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai 200025, China Correspondence should be addressed to Xingzhong Wu; [email protected] Received 10 May 2021; Accepted 29 June 2021; Published 20 July 2021 Academic Editor: Tao Huang Copyright © 2021 Yiwei Dong 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. LIHC (liver hepatocellular carcinoma) mostly occurs in patients with chronic liver disease. It is primarily induced by a vicious cycle of liver injury, inflammation, and regeneration that usually last for decades. The G protein nucleolar 2 (GNL2), as a protein- encoding gene, is also known as NGP1, Nog2, Nug2, Ngp-1, and HUMAUANTIG. Few reports are shown towards the specific biological function of GNL2. Meanwhile, it is still unclear whether it is related to the pathogenesis of carcinoma up to date. Here, our study attempts to validate the role and function of GNL2 in LIHC via multiple databases and functional assays. After analysis of gene expression profile from The Cancer Genome Atlas (TCGA) database, GNL2 was largely heightened in LIHC, and its overexpression displayed a close relationship with different stages and poor prognosis of carcinoma. After enrichment analysis, the data revealed that the genes coexpressed with GNL2 probably participated in ribosome biosynthesis which was essential for unrestricted growth of carcinoma. Cell functional assays presented that GNL2 knockdown by siRNA in LIHC cells MHCC97-H and SMCC-7721 greatly reduced cell proliferation, migration, and invasion ability. All in all, these findings capitulated that GNL2 could be a promising treatment target and prognosis biomarker for LIHC. 1. Background been the most commonly used biomarker in LIHC man- agement [4, 13, 14]. However, LIHC is classified as a Liver cancer is composed of primary carcinoma and meta- complicated disease accompanied by several risk factors static carcinoma [1, 2]. As far as the most common caused pathogenic mechanisms. Thus, to characterize inducers of carcinoma-associated mortality in the world, LIHC only by a single biomarker is not feasible. To explore primary liver carcinoma ranks second, thus becoming a the pathogenesis of LIHC and uncover the candidate bio- great public health challenge [3–5]. Primary liver carci- markers become urgently needed. noma consists of liver hepatocellular carcinoma (LIHC), As a protein-encoding gene, the G protein nucleolar 2 intrahepatic cholangiocarcinoma (iCCA), and other rarely (GNL2) is also named by nucleostemin (NS), which is essen- seen neoplasms [6, 7]. Mostly, LIHC occurs in patients tial for stem cell growth and development [15, 16]. GNL2 is with chronic liver disease, and it is generally caused by a highly expressed in tissues, such as testis and bone marrow vicious cycle of liver damage, inflammation, and regenera- [17, 18]. Presently, there are no researches on the particular tion that usually span several decades [8–11]. Like other biological function of GNL2, and little is known about its malignant neoplasms, the widely generated biological alter- relationship with carcinoma pathogenesis. ation in LIHC pathogenesis comprises activated oncogene Here, our study is aimed at investigating whether there is and inactivated neoplasm inhibitor genes [12]. Since its an association of GNL2 expression with the prognosis in discovery over 60 years ago, alpha-fetoprotein (AFP) has LIHC patients. In addition, our study also wants to validate 2 BioMed Research International 10 8 6 value P 10 4 –Log 2 0 –4 –2 0 2 4 Log2 fold change Figure 1: Volcano plot illustrated the DEGs existing in liver carcinoma samples and normal ones based on TCGA dataset. Genes with upregulation were presented as the red dot, while those with downregulation were shown as the blue dot. its inhibitory effect on LIHC cell proliferation, migration, and annotation enrichment, we utilized the online tool Enrichr invasion by in vitro experiments. (https://maayanlab.cloud/Enrichr/). Enrichment annota- tions were presented as Kyoto Encyclopedia of Genes and 2. Material and Methods Genomes (KEGG) pathway and Gene Ontology (GO) bio- logical process. Data were plotted and visualized using an fi ff 2.1. Identi cation of the Di erentially Expressed Genes online platform http://www.bioinformatics.com.cn. (DEGs) in TCGA Database. The Cancer Genome Atlas (TCGA) database, as the National Cancer Institute (NCI) 2.5. Cell Culture and Transfection. The human LIHC cell and the National Human Genome Institute (NHGRI) sup- lines MHCC97-H and SMCC-7721 were obtained from the ported the project, was employed to obtain mRNA expres- China Center for Type Culture Collection in 2020 and sion profile of liver carcinoma and normal samples. It could authenticated by short tandem repeat analysis. All cells were offer all-inclusive maps of the main alterations of genomic routinely cultured in DMEM medium (Thermo Fisher Scien- in multiple sorts of carcinomas. ∣log fold change ðFCÞ ∣ >1 2 tific, Inc., USA) supplemented with 10% FBS (Thermo Fisher and P value <0.05 were considered to be parameters for iden- Scientific, Inc., USA) in an incubator with 5% CO at 37°C. tifying the DEGs. The protein-protein network (PPI) of the 2 SiRNA targeting GNL2 (si-GNL2) and a negative control selected DEGs was constructed by the Search Tool for the siRNA (si-NC) were acquired from GeneCopoeia, Inc. Retrieval of Interacting Genes (STRING) database. (USA). When MHCC97-H and SMCC-7721 cells confluence 2.2. Gene Expression Profiling Interactive Analysis (GEPIA) reached up to 80% on the following day, they were trans- Database. GEPIA is an interacting online server for the anal- fected with indicated siRNAs utilizing Lipofectamine 2000 ffi ysis of RNA sequencing data of tumorous and healthy sam- (Invitrogen, USA) as the manual described. Reduced e - ples from the Genotype-Tissue Expression (GTEx) and ciency of GNL2 was determined by qRT-PCR at 48 h post- TCGA datasets. The URL of GEPIA is http://gepia.cancer- transfection. The siRNAs used were as follows: si-GNL2-1, ′ ′ pku.cn. In this study, we used GEPIA to determine GNL2 5 -CACGTGTGATTAAGCAGTCATCATT-3 ; si-GNL2-2, expression in different stages of liver carcinoma and then 5′-CCATACAAAGTTGTCATGAAGCAAA-3′; si-NC, 5′ perform patient survival analysis and identify similar genes. -UUCUCCGAACGUGUCACGUTT-3′. 2.3. Kaplan-Meier Plotter Database. The Kaplan-Meier plot- 2.6. Quantitative Reverse-Transcription PCR (qRT-PCR) ter (https://kmplot.com/analysis/), functioning as a web tool, Assay. Overall RNA was harvested from indicated cells utiliz- is used to validate survival biomarkers in the light of meta- ing TRIzol reagent (Invitrogen, USA). qRT-PCR was carried analysis. The information of gene expression profile and out on a Bio-Rad Single Color Real-Time PCR system (Bio- various survival data comprising overall survival (OS), Rad Laboratories, Inc., USA) with SYBR-Green Real-Time relapse-free survival (RFS), and disease-specific survival PCR Master Mix (Toyobo Life Science, Japan). β-Actin was (DSS) come from the well-known public databases, including used as an internal control. GEO (only Affymetrix microarrays), EGA, and TCGA. Kaplan-Meier plotter was taken to generate the survival curves of GNL2, and log rank P values, as well as hazard ratio 2.7. Cell Proliferation Assay. 2,000 of si-NC- and si-GNL2- (HR) with 95% confidence intervals, were calculated. transfected cells were plated in per well of 96-well plates, followed by maintaining 0, 24, 48, and 72 h at 37°C. 10 μlof 2.4. Biological Process and Pathway Enrichment Analysis. The CCK-8 reagent was added into each well at the indicated top 1000 genes coexpressed with GNL2 (sorted by Pearson time, followed by another 2 h incubation. The wavelength correlation coefficient) in LICH-tumor and LICH-normal at 450 nm was read in a Varioskan Flash spectrophotometer dataset were identified by GEPIA. To assess the gene- (Thermo Fisher Scientific, Inc., USA). BioMed Research International 3 GPM6A ICM5 TBXA2R CLEC4M GRPR DKKL1 CHRM3 MARCO SGCD ADRA1A STAB2 WDFY2 CDC37L1 TSPO PVALB DBH SP2 ADRA2B CRHBP TUBDNASE1L3 NAAA ECM1 UCN2 TRIB1 NTF3 MAT1A CIDEB GNL2 MAP2K1 INS-IGF2 SPN ALB ZFP36 CXCL12 MMP14 HIP1 TBX21 PLG IL7R ETS2 EGR1 LGALS1ADAMTS13 POSTN LCAT CCL23 DUSP3 APOF CSRNP1 FCN2 TSLP DSG1 RCAN1 FCN3 CFP LIFR C1RL MED1 C1R CSTA MASP1 C8A IL5RA GHR COLEC10 C6 GYS2 CPED1 CYP4A11 ALDH8A1 TTC36 FMO1 CYP2C8 ALDH2 ALDH6A1 ADH4 ACADSB UGT3A1 CNDP1 LRAT GLYATL1 ACADS ERLIN1 ETFDH NAT2 BCO2 MRO DCDC2 MAP7D2 GPR20 MRPL43 VIPR1 KIF13B GDPD3 RPL27A BMPER PTH1R KLC3 RPS25 GDF2 PNPT1 CLEC4G SENP7 PFIA1 CLEC1B PIAS1 CASKIN1 CPEB3 Figure 2: PPI network of the DEGs. Nodes displayed proteins, and edges exhibited the interactions between proteins. 2.8. Transwell Assay. For cell migration assay, Matrigel needed with 10% FBS, followed by maintaining 24 h at 37°C. Then, not to be precoated in the Transwell chamber (Corning, NY). migrated cells underside were rinsed by PBS, fixed by metha- The Transwell upper chamber was added 50,000 si-NC- or si- nol, and stained by DAPI. Positive cells were photographed GNL2-transfected cells suspended in serum-free DMEM and counted under a microscope. Five random fields were medium, and the lower chamber was added DMEM medium selected and counted the average.
Load more

Recommended publications
  • 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.
    [Show full text]
  • Proteomics Provides Insights Into the Inhibition of Chinese Hamster V79
    www.nature.com/scientificreports OPEN Proteomics provides insights into the inhibition of Chinese hamster V79 cell proliferation in the deep underground environment Jifeng Liu1,2, Tengfei Ma1,2, Mingzhong Gao3, Yilin Liu4, Jun Liu1, Shichao Wang2, Yike Xie2, Ling Wang2, Juan Cheng2, Shixi Liu1*, Jian Zou1,2*, Jiang Wu2, Weimin Li2 & Heping Xie2,3,5 As resources in the shallow depths of the earth exhausted, people will spend extended periods of time in the deep underground space. However, little is known about the deep underground environment afecting the health of organisms. Hence, we established both deep underground laboratory (DUGL) and above ground laboratory (AGL) to investigate the efect of environmental factors on organisms. Six environmental parameters were monitored in the DUGL and AGL. Growth curves were recorded and tandem mass tag (TMT) proteomics analysis were performed to explore the proliferative ability and diferentially abundant proteins (DAPs) in V79 cells (a cell line widely used in biological study in DUGLs) cultured in the DUGL and AGL. Parallel Reaction Monitoring was conducted to verify the TMT results. γ ray dose rate showed the most detectable diference between the two laboratories, whereby γ ray dose rate was signifcantly lower in the DUGL compared to the AGL. V79 cell proliferation was slower in the DUGL. Quantitative proteomics detected 980 DAPs (absolute fold change ≥ 1.2, p < 0.05) between V79 cells cultured in the DUGL and AGL. Of these, 576 proteins were up-regulated and 404 proteins were down-regulated in V79 cells cultured in the DUGL. KEGG pathway analysis revealed that seven pathways (e.g.
    [Show full text]
  • Supplementary Table 3: Genes Only Influenced By
    Supplementary Table 3: Genes only influenced by X10 Illumina ID Gene ID Entrez Gene Name Fold change compared to vehicle 1810058M03RIK -1.104 2210008F06RIK 1.090 2310005E10RIK -1.175 2610016F04RIK 1.081 2610029K11RIK 1.130 381484 Gm5150 predicted gene 5150 -1.230 4833425P12RIK -1.127 4933412E12RIK -1.333 6030458P06RIK -1.131 6430550H21RIK 1.073 6530401D06RIK 1.229 9030607L17RIK -1.122 A330043C08RIK 1.113 A330043L12 1.054 A530092L01RIK -1.069 A630054D14 1.072 A630097D09RIK -1.102 AA409316 FAM83H family with sequence similarity 83, member H 1.142 AAAS AAAS achalasia, adrenocortical insufficiency, alacrimia 1.144 ACADL ACADL acyl-CoA dehydrogenase, long chain -1.135 ACOT1 ACOT1 acyl-CoA thioesterase 1 -1.191 ADAMTSL5 ADAMTSL5 ADAMTS-like 5 1.210 AFG3L2 AFG3L2 AFG3 ATPase family gene 3-like 2 (S. cerevisiae) 1.212 AI256775 RFESD Rieske (Fe-S) domain containing 1.134 Lipo1 (includes AI747699 others) lipase, member O2 -1.083 AKAP8L AKAP8L A kinase (PRKA) anchor protein 8-like -1.263 AKR7A5 -1.225 AMBP AMBP alpha-1-microglobulin/bikunin precursor 1.074 ANAPC2 ANAPC2 anaphase promoting complex subunit 2 -1.134 ANKRD1 ANKRD1 ankyrin repeat domain 1 (cardiac muscle) 1.314 APOA1 APOA1 apolipoprotein A-I -1.086 ARHGAP26 ARHGAP26 Rho GTPase activating protein 26 -1.083 ARL5A ARL5A ADP-ribosylation factor-like 5A -1.212 ARMC3 ARMC3 armadillo repeat containing 3 -1.077 ARPC5 ARPC5 actin related protein 2/3 complex, subunit 5, 16kDa -1.190 activating transcription factor 4 (tax-responsive enhancer element ATF4 ATF4 B67) 1.481 AU014645 NCBP1 nuclear cap
    [Show full text]
  • Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
    Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo
    [Show full text]
  • Tepzz 8Z6z54a T
    (19) TZZ ZZ_T (11) EP 2 806 054 A1 (12) EUROPEAN PATENT APPLICATION (43) Date of publication: (51) Int Cl.: 26.11.2014 Bulletin 2014/48 C40B 40/06 (2006.01) C12Q 1/68 (2006.01) C40B 30/04 (2006.01) C07H 21/00 (2006.01) (21) Application number: 14175049.7 (22) Date of filing: 28.05.2009 (84) Designated Contracting States: (74) Representative: Irvine, Jonquil Claire AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HGF Limited HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL 140 London Wall PT RO SE SI SK TR London EC2Y 5DN (GB) (30) Priority: 28.05.2008 US 56827 P Remarks: •Thecomplete document including Reference Tables (62) Document number(s) of the earlier application(s) in and the Sequence Listing can be downloaded from accordance with Art. 76 EPC: the EPO website 09753364.0 / 2 291 553 •This application was filed on 30-06-2014 as a divisional application to the application mentioned (71) Applicant: Genomedx Biosciences Inc. under INID code 62. Vancouver, British Columbia V6J 1J8 (CA) •Claims filed after the date of filing of the application/ after the date of receipt of the divisional application (72) Inventor: Davicioni, Elai R.68(4) EPC). Vancouver British Columbia V6J 1J8 (CA) (54) Systems and methods for expression- based discrimination of distinct clinical disease states in prostate cancer (57) A system for expression-based discrimination of distinct clinical disease states in prostate cancer is provided that is based on the identification of sets of gene transcripts, which are characterized in that changes in expression of each gene transcript within a set of gene transcripts can be correlated with recurrent or non- recur- rent prostate cancer.
    [Show full text]
  • Chromosome Studies and Genetic Analysis of Natural and Synthetic Apomictic Model Species
    Chromosome studies and genetic analyses of natural and synthetic apomictic model species Promotor: Prof. dr. S.C. de Vries - Hoogleraar in de Biochemie Co-promotoren: Dr. J.H. de Jong - Universitair hoofddocent, Laboratorium voor Genetica Dr. E. Russinova - Postdoc, Laboratorium voor Biochemie Promotiecommissie: Prof.dr. U. Grossniklaus, University of Zürich, Switzerland Prof.dr. M. Koornneef, Wageningen Universiteit Dr. P.J. van Dijk, NIOO-CTO, Heteren Dr. T. Sharbel, PKI, Gatersleben, Germany Dit onderzoek werd uitgevoerd binnen de onderzoekschool Experimentele Plantenwetenschappen (EPW) Laksana Kantama Chromosome studies and genetic analyses of natural and synthetic apomictic model species Proefschrift ter verkrijging van de graad van doctor op gezag van de rector magnificus van Wageningen Universiteit Prof.dr. M.J. Kropff in het openbaar te verdedigen op dinsdag 20 december 2005 des namiddags te vier uur in de Aula Laksana Kantama (2005) Chromosome studies and genetic analyses of natural and synthetic apomictic model species Thesis Wageningen University - with references - with summaries in English, Dutch and Thai ISBN 90-8504-321-2 Contents Chapter 1 General Introduction 7 Chapter 2 Karyotype analysis and FISH analysis of sexual diploids and apomictic accessions of Boechera 15 Chapter 3 Microsporogenesis and flow cytometric seed screens of sexual and apomictic Boechera accessions 31 Chapter 4 Genome painting of Boechera reveals different parental chromosomes combinations in apomictic accessessions 47 Chapter 5 A cytogenetic view on
    [Show full text]
  • Downloaded, Preprocessed and Analysed Case-Control Gene Expression Datasets from Gene Ex- Pression Omnibus (GEO)
    bioRxiv preprint doi: https://doi.org/10.1101/2020.03.23.002972; this version posted March 25, 2020. 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-ND 4.0 International license. Finding associations in a heterogeneous setting: Statistical test for aberration enrichment Aziz M. Mezlini1,2,3,4*, Sudeshna Das 1,2, Anna Goldenberg 3,4 1 Harvard Medical School. Boston 2 Massachusetts General Hospital. Boston 3 Department of Computer Science. University of Toronto. 4 Hospital for sick children. Toronto * [email protected] Abstract Most two-group statistical tests are implicitly looking for a broad pattern such as an overall shift in mean, median or variance between the two groups. Therefore, they operate best in settings where the effect of interest is uniformly affecting everyone in one group versus the other. In real-world applications, there are many scenarios where the effect of interest is heterogeneous. For example, a drug that works very well on only a proportion of patients and is equivalent to a placebo on the remaining patients, or a disease associated gene expression dysregulation that only occurs in a proportion of cases whereas the remaining cases have expression levels indistinguishable from the controls for the considered gene. In these examples with heterogeneous effect, we believe that using classical two-group statistical tests may not be the most powerful way to detect the signal. In this paper, we developed a statistical test targeting heterogeneous effects and demonstrated its power in a controlled simulation setting compared to existing methods.
    [Show full text]
  • Gene Expression Patterns Induced by HPV-16 L1 Virus-Like Particles in Leukocytes from Vaccine Recipients
    Gene Expression Patterns Induced by HPV-16 L1 Virus-Like Particles in Leukocytes from Vaccine Recipients This information is current as Alfonso J. García-Piñeres, Allan Hildesheim, Lori Dodd, of October 3, 2021. Troy J. Kemp, Jun Yang, Brandie Fullmer, Clayton Harro, Douglas R. Lowy, Richard A. Lempicki and Ligia A. Pinto J Immunol 2009; 182:1706-1729; ; doi: 10.4049/jimmunol.182.3.1706 http://www.jimmunol.org/content/182/3/1706 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2009/01/15/182.3.1706.DC1 Material http://www.jimmunol.org/ References This article cites 53 articles, 16 of which you can access for free at: http://www.jimmunol.org/content/182/3/1706.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on October 3, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2009 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Gene Expression Patterns Induced by HPV-16 L1 Virus-Like Particles in Leukocytes from Vaccine Recipients1 Alfonso J.
    [Show full text]
  • Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility.
    [Show full text]
  • Identification of Common Molecular Biomarker Signatures in Blood And
    bioRxiv preprint doi: https://doi.org/10.1101/482828; this version posted January 14, 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. Identification of common molecular biomarker signatures in blood and brain of Alzheimer's disease Md. Rezanur Rahmana,y,∗, Tania Islamb,y, Md. Shahjamanc, Julian M.W. Quinnd, R. M. Damian Holsingere,f, Mohammad Ali Monid,e,∗ aDepartment of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj, Bangladesh bDepartment of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh cDepartment of Statistics, Begum Rokeya University, Rangpur, Bangladesh dBone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia eDiscipline of Biomedical Science, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia fLaboratory of Molecular Neuroscience and Dementia, Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia Abstract Background: Alzheimers disease (AD) is a progressive neurodegenerative disease charac- terized by memory loss and confusion. Neuroimaging and cerebrospinal fluid-based early detection is limited in sensitivity and specificity as well as by cost. Therefore, detecting AD from blood cell analysis could improve early diagnosis and treatment of the disease. The present study aimed to identify blood cell transcripts that reflect brain expression levels of factors linked to AD progression. Methods: We analyzed blood cell and brain microarray gene expression datasets from NCBI-GEO for AD association and expression in blood and brain. We also used eQTL and epigenetics data to identify AD-related genes that were regulated similarly in blood and brain.
    [Show full text]
  • Common Molecular Biomarker Signatures in Blood and Brain of Alzheimer’S Disease
    bioRxiv preprint doi: https://doi.org/10.1101/482828; this version posted November 29, 2018. 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. Common molecular biomarker signatures in blood and brain of Alzheimer’s disease Md. Rezanur Rahman1,a,*, Tania Islam2,a, Md. Shahjaman3, Damian Holsinger4, and Mohammad Ali Moni4,* 1Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj, Bangladesh 2Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh 3Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh 4The University of Sydney, Sydney Medical School, School of Medical Sciences, Discipline of Biomedical Science, Sydney, New South Wales, Australia aThese two authors made equal contribution and hold joint first authorhsip for this work. *Corresponding author: E-mail: [email protected] (Md. Rezanur Rahman) . E-mail: [email protected] (Mohammad Ali Moni, PhD) 1 bioRxiv preprint doi: https://doi.org/10.1101/482828; this version posted November 29, 2018. 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. Abstract Background: The Alzheimer’s is a progressive neurodegenerative disease of elderly peoples characterized by dementia and the fatality is increased due to lack of early stage detection. The neuroimaging and cerebrospinal fluid based detection is limited with sensitivity and specificity and cost. Therefore, detecting AD from blood transcriptsthat mirror the expression of brain transcripts in the AD would be one way to improve the diagnosis and treatment of AD.
    [Show full text]
  • Ii. Runx1-Eto
    %NVUEDELOBTENTIONDU %0$503"5%&-6/*7&34*5² %&506-064& $ÏLIVRÏPAR Université Toulouse III Paul Sabatier (UT3 Paul Sabatier) $ISCIPLINEOUSPÏCIALITÏ Gènes, Cellules et Développement 0RÏSENTÏEETSOUTENUEPAR Stéphanie BRAS LE vendredi 8 juin 2012 4ITRE Etude des fonctions normales et pathologiques des facteurs de transcription de type RUNX au cours de l’hématopoïèse chez Drosophila melanogaster. %COLEDOCTORALE Biologie, Santé, Biotechnologies (BSB) 5NITÏDERECHERCHE Centre de Biologie du développement, CNRS UMR 5547 $IRECTEURS DE4HÒSE Dr. Lucas Waltzer Dr. Marc Haenlin 2APPORTEURS Dr. Marie Meister Dr. Georges Lacaud MEMBRES DUJURY: Pr David Cribbs, président Dr. Marie Meister, rapportrice Dr. Georges Lacaud, rapporteur Dr. Estelle Duprez, examinatrice Dr. Lucas Waltzer, directeur de thèse Dr. Marc Haenlin, directeur de thèse Je voudrais tout d’abord remercier les membres du jury : David Cribbs, Georges Lacaud, Marie Meister et Estelle Duprez qui ont accepté de lire et de juger mes travaux de thèse. Je remercie également mes deux directeurs de thèse, Lucas et Marc pour ces six ans passés au sein de leur équipe. Et oui, je n’étais pas rassasiée de mes deux années de master passée dans votre équipe, il a fallu que je m’accroche encore 4ans pour faire ma thèse dans votre équipe ! Ma première pensée va vers Lucas : Merci Lucas pour toutes ces discussions qui m’ont permis de me recentrer sur les questions essentielles à des moments où les résultats n’étaient pas des plus clairs. Tu m’as soutenu. Tu as été toujours plus disponible au fil des années pour m’encadrer et me permettre de mener à bien mes deux projets de thèse.
    [Show full text]