Cell Growth-Regulated Expression of Mammalian MCM5 and MCM6 Genes Mediated by the Transcription Factor E2F
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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. -
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. -
Introduction to the Cell Cell History Cell Structures and Functions
Introduction to the cell cell history cell structures and functions CK-12 Foundation December 16, 2009 CK-12 Foundation is a non-profit organization with a mission to reduce the cost of textbook materials for the K-12 market both in the U.S. and worldwide. Using an open-content, web-based collaborative model termed the “FlexBook,” CK-12 intends to pioneer the generation and distribution of high quality educational content that will serve both as core text as well as provide an adaptive environment for learning. Copyright ©2009 CK-12 Foundation This work is licensed under the Creative Commons Attribution-Share Alike 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Contents 1 Cell structure and function dec 16 5 1.1 Lesson 3.1: Introduction to Cells .................................. 5 3 www.ck12.org www.ck12.org 4 Chapter 1 Cell structure and function dec 16 1.1 Lesson 3.1: Introduction to Cells Lesson Objectives • Identify the scientists that first observed cells. • Outline the importance of microscopes in the discovery of cells. • Summarize what the cell theory proposes. • Identify the limitations on cell size. • Identify the four parts common to all cells. • Compare prokaryotic and eukaryotic cells. Introduction Knowing the make up of cells and how cells work is necessary to all of the biological sciences. Learning about the similarities and differences between cell types is particularly important to the fields of cell biology and molecular biology. -
Supplementary Table S5. Differentially Expressed Gene Lists of PD-1High CD39+ CD8 Tils According to 4-1BB Expression Compared to PD-1+ CD39- CD8 Tils
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Immunother Cancer Supplementary Table S5. Differentially expressed gene lists of PD-1high CD39+ CD8 TILs according to 4-1BB expression compared to PD-1+ CD39- CD8 TILs Up- or down- regulated genes in Up- or down- regulated genes Up- or down- regulated genes only PD-1high CD39+ CD8 TILs only in 4-1BBneg PD-1high CD39+ in 4-1BBpos PD-1high CD39+ CD8 compared to PD-1+ CD39- CD8 CD8 TILs compared to PD-1+ TILs compared to PD-1+ CD39- TILs CD39- CD8 TILs CD8 TILs IL7R KLRG1 TNFSF4 ENTPD1 DHRS3 LEF1 ITGA5 MKI67 PZP KLF3 RYR2 SIK1B ANK3 LYST PPP1R3B ETV1 ADAM28 H2AC13 CCR7 GFOD1 RASGRP2 ITGAX MAST4 RAD51AP1 MYO1E CLCF1 NEBL S1PR5 VCL MPP7 MS4A6A PHLDB1 GFPT2 TNF RPL3 SPRY4 VCAM1 B4GALT5 TIPARP TNS3 PDCD1 POLQ AKAP5 IL6ST LY9 PLXND1 PLEKHA1 NEU1 DGKH SPRY2 PLEKHG3 IKZF4 MTX3 PARK7 ATP8B4 SYT11 PTGER4 SORL1 RAB11FIP5 BRCA1 MAP4K3 NCR1 CCR4 S1PR1 PDE8A IFIT2 EPHA4 ARHGEF12 PAICS PELI2 LAT2 GPRASP1 TTN RPLP0 IL4I1 AUTS2 RPS3 CDCA3 NHS LONRF2 CDC42EP3 SLCO3A1 RRM2 ADAMTSL4 INPP5F ARHGAP31 ESCO2 ADRB2 CSF1 WDHD1 GOLIM4 CDK5RAP1 CD69 GLUL HJURP SHC4 GNLY TTC9 HELLS DPP4 IL23A PITPNC1 TOX ARHGEF9 EXO1 SLC4A4 CKAP4 CARMIL3 NHSL2 DZIP3 GINS1 FUT8 UBASH3B CDCA5 PDE7B SOGA1 CDC45 NR3C2 TRIB1 KIF14 TRAF5 LIMS1 PPP1R2C TNFRSF9 KLRC2 POLA1 CD80 ATP10D CDCA8 SETD7 IER2 PATL2 CCDC141 CD84 HSPA6 CYB561 MPHOSPH9 CLSPN KLRC1 PTMS SCML4 ZBTB10 CCL3 CA5B PIP5K1B WNT9A CCNH GEM IL18RAP GGH SARDH B3GNT7 C13orf46 SBF2 IKZF3 ZMAT1 TCF7 NECTIN1 H3C7 FOS PAG1 HECA SLC4A10 SLC35G2 PER1 P2RY1 NFKBIA WDR76 PLAUR KDM1A H1-5 TSHZ2 FAM102B HMMR GPR132 CCRL2 PARP8 A2M ST8SIA1 NUF2 IL5RA RBPMS UBE2T USP53 EEF1A1 PLAC8 LGR6 TMEM123 NEK2 SNAP47 PTGIS SH2B3 P2RY8 S100PBP PLEKHA7 CLNK CRIM1 MGAT5 YBX3 TP53INP1 DTL CFH FEZ1 MYB FRMD4B TSPAN5 STIL ITGA2 GOLGA6L10 MYBL2 AHI1 CAND2 GZMB RBPJ PELI1 HSPA1B KCNK5 GOLGA6L9 TICRR TPRG1 UBE2C AURKA Leem G, et al. -
Bioinformatics-Based Screening of Key Genes for Transformation of Liver
Jiang et al. J Transl Med (2020) 18:40 https://doi.org/10.1186/s12967-020-02229-8 Journal of Translational Medicine RESEARCH Open Access Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma Chen Hao Jiang1,2, Xin Yuan1,2, Jiang Fen Li1,2, Yu Fang Xie1,2, An Zhi Zhang1,2, Xue Li Wang1,2, Lan Yang1,2, Chun Xia Liu1,2, Wei Hua Liang1,2, Li Juan Pang1,2, Hong Zou1,2, Xiao Bin Cui1,2, Xi Hua Shen1,2, Yan Qi1,2, Jin Fang Jiang1,2, Wen Yi Gu4, Feng Li1,2,3 and Jian Ming Hu1,2* Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver tumour, and is closely related to liver cirrhosis. Previous studies have focussed on the pathogenesis of liver cirrhosis developing into HCC, but the molecular mechanism remains unclear. The aims of the present study were to identify key genes related to the transformation of cirrhosis into HCC, and explore the associated molecular mechanisms. Methods: GSE89377, GSE17548, GSE63898 and GSE54236 mRNA microarray datasets from Gene Expression Omni- bus (GEO) were analysed to obtain diferentially expressed genes (DEGs) between HCC and liver cirrhosis tissues, and network analysis of protein–protein interactions (PPIs) was carried out. String and Cytoscape were used to analyse modules and identify hub genes, Kaplan–Meier Plotter and Oncomine databases were used to explore relationships between hub genes and disease occurrence, development and prognosis of HCC, and the molecular mechanism of the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis. -
Supplementary Table S1. Correlation Between the Mutant P53-Interacting Partners and PTTG3P, PTTG1 and PTTG2, Based on Data from Starbase V3.0 Database
Supplementary Table S1. Correlation between the mutant p53-interacting partners and PTTG3P, PTTG1 and PTTG2, based on data from StarBase v3.0 database. PTTG3P PTTG1 PTTG2 Gene ID Coefficient-R p-value Coefficient-R p-value Coefficient-R p-value NF-YA ENSG00000001167 −0.077 8.59e-2 −0.210 2.09e-6 −0.122 6.23e-3 NF-YB ENSG00000120837 0.176 7.12e-5 0.227 2.82e-7 0.094 3.59e-2 NF-YC ENSG00000066136 0.124 5.45e-3 0.124 5.40e-3 0.051 2.51e-1 Sp1 ENSG00000185591 −0.014 7.50e-1 −0.201 5.82e-6 −0.072 1.07e-1 Ets-1 ENSG00000134954 −0.096 3.14e-2 −0.257 4.83e-9 0.034 4.46e-1 VDR ENSG00000111424 −0.091 4.10e-2 −0.216 1.03e-6 0.014 7.48e-1 SREBP-2 ENSG00000198911 −0.064 1.53e-1 −0.147 9.27e-4 −0.073 1.01e-1 TopBP1 ENSG00000163781 0.067 1.36e-1 0.051 2.57e-1 −0.020 6.57e-1 Pin1 ENSG00000127445 0.250 1.40e-8 0.571 9.56e-45 0.187 2.52e-5 MRE11 ENSG00000020922 0.063 1.56e-1 −0.007 8.81e-1 −0.024 5.93e-1 PML ENSG00000140464 0.072 1.05e-1 0.217 9.36e-7 0.166 1.85e-4 p63 ENSG00000073282 −0.120 7.04e-3 −0.283 1.08e-10 −0.198 7.71e-6 p73 ENSG00000078900 0.104 2.03e-2 0.258 4.67e-9 0.097 3.02e-2 Supplementary Table S2. -
Cell Cycle-Dependent Modification of Pot1 and Its Effects on Telomere
Cell cycle-dependent modification of Pot1 and its effects on telomere function Vitaliy Kuznetsov Thesis submitted towards the degree of Doctor of Philosophy University College of London Department of Biology London, WC1E 6BT The program of research was carried out at Cancer Research U.K. Laboratory of Telomere Biology 44 Lincoln’s Inn Fields, London, U.K. WC2A 3PX Supervisor: Dr Julia Promisel Cooper September 2008 I, Vitaliy Kuznetsov, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. ABSTRACT Telomere functions are tightly controlled throughout the cell cycle to allow telomerase access while suppressing a bona fide DNA damage response (DDR) at linear chromosome ends. However, the mechanisms that link cell cycle progression with telomere functions are largely unknown. Here we show that a key S-phase kinase, DDK (Dbf4-dependent protein kinase), phosphorylates the telomere binding protein Pot1, and that this phosphorylation is crucial for DNA damage checkpoint inactivation, the suppression of homologous recombination (HR) at telomeres, and the prevention of telomere loss. DDK phosphorylates Pot1 in a very conserved region of its most amino-terminal-proximal OB fold, suggesting that this regulation of telomere function may be widely conserved. Mutation of Pot1 phosphorylation sites leads to telomerase independent telomere maintenance through constant HR, as well as a dependence of telomere maintenance proteins involved in checkpoint activation and HR. These results uncover a novel and important link between DDR suppression and telomere maintenance. The failure in Pot1 phosphorylation and DDR inactivation could potentially lead to uncontrolled cell proliferation without a requirement for telomerase by switching cells to HR dependent telomere homeostasis. -
Prognostic Significance of Minichromosome Maintenance Mrna Expression in Human Lung Adenocarcinoma
ONCOLOGY REPORTS 42: 2279-2292, 2019 Prognostic significance of minichromosome maintenance mRNA expression in human lung adenocarcinoma SHU LI1,2, ZHOU JIANG2, YIRUN LI3 and YANG XU1 1Department of Hematology, 2Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009; 3Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, P.R. China Received February 20, 2019; Accepted August 9, 2019 DOI: 10.3892/or.2019.7330 Abstract. The minichromosome maintenance (MCM) gene protein-like 4 and anaplastic lymphoma kinase, respec- family plays an essential role in DNA replication and cell cycle tively (4-7). More recently, immunotherapy has been developed progression. However, MCM gene expression has not been and increasingly used in patients with lung cancer, including well-studied in lung adenocarcinoma (LUAD). In the present immune checkpoint inhibitors that target programmed cell study, the expression, prognostic value and functions of MCMs death 1 ligand 1 (PD-L1)-expressing tumor cells by blocking in LUAD were investigated using several databases and PD-L1/PD-1 signaling (8,9). Despite recent advances in bioinformatic tools, including Oncomine, GEPIA, cBioPortal, targeted therapy and immunotherapy, the prognosis of LUAD CancerSEA and Kaplan-Meier plotter. It was demonstrated remains poor (10). It has become a research trend to explore that the mRNA expression of MCM2, MCM4 and MCM10 novel molecular biomarkers or therapeutic targets in the era of were significantly increased in patients with LUAD. High precision medicine (11). In fact, databases based on large-scale, mRNA expression of MCM2-5, MCM8 and MCM10 were genome-wide association studies have facilitated the discovery associated with poor overall survival and progression-free of new biomarkers for cancer management (12). -
A Peripheral Blood Gene Expression Signature to Diagnose Subclinical Acute Rejection
CLINICAL RESEARCH www.jasn.org A Peripheral Blood Gene Expression Signature to Diagnose Subclinical Acute Rejection Weijia Zhang,1 Zhengzi Yi,1 Karen L. Keung,2 Huimin Shang,3 Chengguo Wei,1 Paolo Cravedi,1 Zeguo Sun,1 Caixia Xi,1 Christopher Woytovich,1 Samira Farouk,1 Weiqing Huang,1 Khadija Banu,1 Lorenzo Gallon,4 Ciara N. Magee,5 Nader Najafian,5 Milagros Samaniego,6 Arjang Djamali ,7 Stephen I. Alexander,2 Ivy A. Rosales,8 Rex Neal Smith,8 Jenny Xiang,3 Evelyne Lerut,9 Dirk Kuypers,10,11 Maarten Naesens ,10,11 Philip J. O’Connell,2 Robert Colvin,8 Madhav C. Menon,1 and Barbara Murphy1 Due to the number of contributing authors, the affiliations are listed at the end of this article. ABSTRACT Background In kidney transplant recipients, surveillance biopsies can reveal, despite stable graft function, histologic features of acute rejection and borderline changes that are associated with undesirable graft outcomes. Noninvasive biomarkers of subclinical acute rejection are needed to avoid the risks and costs associated with repeated biopsies. Methods We examined subclinical histologic and functional changes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study who underwent surveillance biopsies over 2 years, identifying those with subclinical or borderline acute cellular rejection (ACR) at 3 months (ACR-3) post-transplant. We performed RNA sequencing on whole blood collected from 88 indi- viduals at the time of 3-month surveillance biopsy to identify transcripts associated with ACR-3, developed a novel sequencing-based targeted expression assay, and validated this gene signature in an independent cohort. -
Cell Life Cycle and Reproduction the Cell Cycle (Cell-Division Cycle), Is a Series of Events That Take Place in a Cell Leading to Its Division and Duplication
Cell Life Cycle and Reproduction The cell cycle (cell-division cycle), is a series of events that take place in a cell leading to its division and duplication. The main phases of the cell cycle are interphase, nuclear division, and cytokinesis. Cell division produces two daughter cells. In cells without a nucleus (prokaryotic), the cell cycle occurs via binary fission. Interphase Gap1(G1)- Cells increase in size. The G1checkpointcontrol mechanism ensures that everything is ready for DNA synthesis. Synthesis(S)- DNA replication occurs during this phase. DNA Replication The process in which DNA makes a duplicate copy of itself. Semiconservative Replication The process in which the DNA molecule uncoils and separates into two strands. Each original strand becomes a template on which a new strand is constructed, resulting in two DNA molecules identical to the original DNA molecule. Gap 2(G2)- The cell continues to grow. The G2checkpointcontrol mechanism ensures that everything is ready to enter the M (mitosis) phase and divide. Mitotic(M) refers to the division of the nucleus. Cell growth stops at this stage and cellular energy is focused on the orderly division into daughter cells. A checkpoint in the middle of mitosis (Metaphase Checkpoint) ensures that the cell is ready to complete cell division. The final event is cytokinesis, in which the cytoplasm divides and the single parent cell splits into two daughter cells. Reproduction Cellular reproduction is a process by which cells duplicate their contents and then divide to yield multiple cells with similar, if not duplicate, contents. Mitosis Mitosis- nuclear division resulting in the production of two somatic cells having the same genetic complement (genetically identical) as the original cell. -
Deciphering Genetic Determinants of Cell Growth by Single-Cell RNA Sequencing Deregulated Growth Is a Hallmark of Human Diseases
Deciphering genetic determinants of cell growth by single-cell RNA sequencing Deregulated growth is a hallmark of human diseases such as cancer. A wide range of therapeutic approaches target proliferating cells effectively through inhibition of pathways related to growth and division. However, cells can often escape arrest due to the genetic and phenotypic heterogeneity present in cancer cell populations leading to relapse of the disease. Likewise, heterogeneity in gene expression makes treatment of persistent microbial infections an important challenge for human health. Identifying genetic and non-genetic features that can drive individual cells to overcome growth inhibition is therefore required to understand disease progression and resistance. Recent experimental protocols made it possible to characterise the transcriptomes and genotypes of single cells simultaneously. These opened the door to investigate the dynamics and heterogeneity of gene expression in population of cells with variable genotypes. These methods are technically challenging, generate large amounts of complex data, and require the development of state-of-the-art computational methods for their analysis. Yet, when coupled with mathematical modelling of cell physiology, these single cells approaches have an unprecedented potential to unravel complex genetic and gene expression programmes that underlie growth dynamics of cell populations. This project aims at: i) understanding the genetic basis of cell growth under conditions were gene expression or proliferation are compromised by associating high fitness genotypes to transcriptome signatures in single cells, ii) develop computational tools to integrate single-cell and population level genotyping and gene expression data, iii) improve selected aspect of the laboratory single-cell RNA-seq protocol to improve data quality. -
Stem Cells® Original Article
® Stem Cells Original Article Properties of Pluripotent Human Embryonic Stem Cells BG01 and BG02 XIANMIN ZENG,a TAKUMI MIURA,b YONGQUAN LUO,b BHASKAR BHATTACHARYA,c BRIAN CONDIE,d JIA CHEN,a IRENE GINIS,b IAN LYONS,d JOSEF MEJIDO,c RAJ K. PURI,c MAHENDRA S. RAO,b WILLIAM J. FREEDa aCellular Neurobiology Research Branch, National Institute on Drug Abuse, Department of Health and Human Services (DHHS), Baltimore, Maryland, USA; bLaboratory of Neuroscience, National Institute of Aging, DHHS, Baltimore, Maryland, USA; cLaboratory of Molecular Tumor Biology, Division of Cellular and Gene Therapies, Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, Maryland, USA; dBresaGen Inc., Athens, Georgia, USA Key Words. Embryonic stem cells · Differentiation · Microarray ABSTRACT Human ES (hES) cell lines have only recently been compared with pooled human RNA. Ninety-two of these generated, and differences between human and mouse genes were also highly expressed in four other hES lines ES cells have been identified. In this manuscript we (TE05, GE01, GE09, and pooled samples derived from describe the properties of two human ES cell lines, GE01, GE09, and GE07). Included in the list are genes BG01 and BG02. By immunocytochemistry and reverse involved in cell signaling and development, metabolism, transcription polymerase chain reaction, undifferenti- transcription regulation, and many hypothetical pro- ated cells expressed markers that are characteristic of teins. Two focused arrays designed to examine tran- ES cells, including SSEA-3, SSEA-4, TRA-1-60, TRA-1- scripts associated with stem cells and with the 81, and OCT-3/4. Both cell lines were readily main- transforming growth factor-β superfamily were tained in an undifferentiated state and could employed to examine differentially expressed genes.