A Comprehensive Chip–Chip Analysis of E2F1, E2F4, and E2F6 in Normal and Tumor Cells Reveals Interchangeable Roles of E2F Family Members
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
G2 Phase Cell Cycle Regulation by E2F4 Following Genotoxic Stress
G2 Phase Cell Cycle Regulation by E2F4 Following Genotoxic Stress by MEREDITH ELLEN CROSBY Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Thesis Advisor: Dr. Alex Almasan Department of Environmental Health Sciences CASE WESTERN RESERVE UNIVERSITY May, 2006 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of ______________________________________________________ candidate for the Ph.D. degree *. (signed)_______________________________________________ (chair of the committee) ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. TABLE OF CONTENTS TABLE OF CONTENTS………………………………………………………………….1 LIST OF FIGURES……………………………………………………………………….5 LIST OF TABLES………………………………………………………………………...7 ACKNOWLEDGEMENTS……………………………………………………………….8 LIST OF ABBREVIATIONS……………………………………………………………10 ABSTRACT……………………………………………………………………………...15 CHAPTER 1. INTRODUCTION 1.1. CELL CYCLE REGULATION: HISTORICAL OVERVIEW………………...17 1.2. THE E2F FAMILY OF TRANSCRIPTION FACTORS……………………….22 1.3. E2F AND CELL CYCLE CONTROL 1.3.1. G0/G1 Phase Transition………………………………………………….28 1.3.2. S Phase…………………………………………………………………...28 1.3.3. G2/M Phase Transition…………………………………………………..30 1.4. -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
Supplementary Data
SUPPLEMENTARY DATA A cyclin D1-dependent transcriptional program predicts clinical outcome in mantle cell lymphoma Santiago Demajo et al. 1 SUPPLEMENTARY DATA INDEX Supplementary Methods p. 3 Supplementary References p. 8 Supplementary Tables (S1 to S5) p. 9 Supplementary Figures (S1 to S15) p. 17 2 SUPPLEMENTARY METHODS Western blot, immunoprecipitation, and qRT-PCR Western blot (WB) analysis was performed as previously described (1), using cyclin D1 (Santa Cruz Biotechnology, sc-753, RRID:AB_2070433) and tubulin (Sigma-Aldrich, T5168, RRID:AB_477579) antibodies. Co-immunoprecipitation assays were performed as described before (2), using cyclin D1 antibody (Santa Cruz Biotechnology, sc-8396, RRID:AB_627344) or control IgG (Santa Cruz Biotechnology, sc-2025, RRID:AB_737182) followed by protein G- magnetic beads (Invitrogen) incubation and elution with Glycine 100mM pH=2.5. Co-IP experiments were performed within five weeks after cell thawing. Cyclin D1 (Santa Cruz Biotechnology, sc-753), E2F4 (Bethyl, A302-134A, RRID:AB_1720353), FOXM1 (Santa Cruz Biotechnology, sc-502, RRID:AB_631523), and CBP (Santa Cruz Biotechnology, sc-7300, RRID:AB_626817) antibodies were used for WB detection. In figure 1A and supplementary figure S2A, the same blot was probed with cyclin D1 and tubulin antibodies by cutting the membrane. In figure 2H, cyclin D1 and CBP blots correspond to the same membrane while E2F4 and FOXM1 blots correspond to an independent membrane. Image acquisition was performed with ImageQuant LAS 4000 mini (GE Healthcare). Image processing and quantification were performed with Multi Gauge software (Fujifilm). For qRT-PCR analysis, cDNA was generated from 1 µg RNA with qScript cDNA Synthesis kit (Quantabio). qRT–PCR reaction was performed using SYBR green (Roche). -
Proteogenomic Analysis of Inhibitor of Differentiation 4 (ID4) in Basal-Like Breast Cancer Laura A
Baker et al. Breast Cancer Research (2020) 22:63 https://doi.org/10.1186/s13058-020-01306-6 RESEARCH ARTICLE Open Access Proteogenomic analysis of Inhibitor of Differentiation 4 (ID4) in basal-like breast cancer Laura A. Baker1,2, Holly Holliday1,2†, Daniel Roden1,2†, Christoph Krisp3,4†, Sunny Z. Wu1,2, Simon Junankar1,2, Aurelien A. Serandour5, Hisham Mohammed5, Radhika Nair6, Geetha Sankaranarayanan5, Andrew M. K. Law1,2, Andrea McFarland1, Peter T. Simpson7, Sunil Lakhani7,8, Eoin Dodson1,2, Christina Selinger9, Lyndal Anderson9,10, Goli Samimi11, Neville F. Hacker12, Elgene Lim1,2, Christopher J. Ormandy1,2, Matthew J. Naylor13, Kaylene Simpson14,15, Iva Nikolic14, Sandra O’Toole1,2,9,10, Warren Kaplan1, Mark J. Cowley1,2, Jason S. Carroll5, Mark Molloy3 and Alexander Swarbrick1,2* Abstract Background: Basal-like breast cancer (BLBC) is a poorly characterised, heterogeneous disease. Patients are diagnosed with aggressive, high-grade tumours and often relapse with chemotherapy resistance. Detailed understanding of the molecular underpinnings of this disease is essential to the development of personalised therapeutic strategies. Inhibitor of differentiation 4 (ID4) is a helix-loop-helix transcriptional regulator required for mammary gland development. ID4 is overexpressed in a subset of BLBC patients, associating with a stem-like poor prognosis phenotype, and is necessary for the growth of cell line models of BLBC through unknown mechanisms. Methods: Here, we have defined unique molecular insights into the function of ID4 in BLBC and the related disease high-grade serous ovarian cancer (HGSOC), by combining RIME proteomic analysis, ChIP-seq mapping of genomic binding sites and RNA-seq. -
Cytoplasmic E2f4 Forms Organizing Centres for Initiation of Centriole Amplification During Multiciliogenesis
ARTICLE Received 13 Feb 2017 | Accepted 8 May 2017 | Published 4 Jul 2017 DOI: 10.1038/ncomms15857 OPEN Cytoplasmic E2f4 forms organizing centres for initiation of centriole amplification during multiciliogenesis Munemasa Mori1, Renin Hazan2, Paul S. Danielian2, John E. Mahoney1,w, Huijun Li1, Jining Lu1, Emily S. Miller2, Xueliang Zhu3, Jacqueline A. Lees2 & Wellington V. Cardoso1 Abnormal development of multiciliated cells is a hallmark of a variety of human conditions associated with chronic airway diseases, hydrocephalus and infertility. Multiciliogenesis requires both activation of a specialized transcriptional program and assembly of cytoplasmic structures for large-scale centriole amplification that generates basal bodies. It remains unclear, however, what mechanism initiates formation of these multiprotein complexes in epithelial progenitors. Here we show that this is triggered by nucleocytoplasmic translocation of the transcription factor E2f4. After inducing a transcriptional program of centriole biogenesis, E2f4 forms apical cytoplasmic organizing centres for assembly and nucleation of deuterosomes. Using genetically altered mice and E2F4 mutant proteins we demonstrate that centriole amplification is crucially dependent on these organizing centres and that, without cytoplasmic E2f4, deuterosomes are not assembled, halting multiciliogenesis. Thus, E2f4 integrates nuclear and previously unsuspected cytoplasmic events of centriole amplification, providing new perspectives for the understanding of normal ciliogenesis, ciliopathies and cancer. 1 Columbia Center for Human Development, Department of Medicine, Pulmonary Allergy Critical Care, Columbia University Medical Center, New York City, New York 10032, USA. 2 David H. Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139, USA. 3 State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. -
Table of Contents Methods
SUPPLEMENTARY MATERIAL FOR: Intrinsic specificity differences between transcription factor paralogs partly explain their differential in vivo binding Ning Shen1,2,3, Jingkang Zhao1,3,4, Joshua Schipper1,3, Yuning Zhang1,4,Tristan Bepler1, Dan Leehr1, John Bradley1, John Horton1,3, Hilmar Lapp1, Raluca Gordan1,3,5* 1Center for Genomic and Computational Biology, 2Department of Pharmacology and Cancer Biology, 3Department of Biostatistics and Bioinformatics, 4Program in Computational Biology and Bioinformatics, 5Department of Computer Science, Duke University, Durham, NC 27708 * Corresponding author. E-mail: [email protected] Table of Contents Methods ................................................................................................................................................... 1 1. ChIP-seq and DNase-seq data ................................................................................................................. 1 2. Design of DNA libraries for gcPBM assays .......................................................................................... 1 3. Protein expression and purification .................................................................................................... 3 4. Universal and genomic-context PBM assays ..................................................................................... 4 5. gcPBM data processing. Identifying core motifs .............................................................................. 4 6. Building core-stratified SVR models of TF-DNA binding specificity -
Transcriptional and Post-Transcriptional Regulation of ATP-Binding Cassette Transporter Expression
Transcriptional and Post-transcriptional Regulation of ATP-binding Cassette Transporter Expression by Aparna Chhibber DISSERTATION Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Pharmaceutical Sciences and Pbarmacogenomies in the Copyright 2014 by Aparna Chhibber ii Acknowledgements First and foremost, I would like to thank my advisor, Dr. Deanna Kroetz. More than just a research advisor, Deanna has clearly made it a priority to guide her students to become better scientists, and I am grateful for the countless hours she has spent editing papers, developing presentations, discussing research, and so much more. I would not have made it this far without her support and guidance. My thesis committee has provided valuable advice through the years. Dr. Nadav Ahituv in particular has been a source of support from my first year in the graduate program as my academic advisor, qualifying exam committee chair, and finally thesis committee member. Dr. Kathy Giacomini graciously stepped in as a member of my thesis committee in my 3rd year, and Dr. Steven Brenner provided valuable input as thesis committee member in my 2nd year. My labmates over the past five years have been incredible colleagues and friends. Dr. Svetlana Markova first welcomed me into the lab and taught me numerous laboratory techniques, and has always been willing to act as a sounding board. Michael Martin has been my partner-in-crime in the lab from the beginning, and has made my days in lab fly by. Dr. Yingmei Lui has made the lab run smoothly, and has always been willing to jump in to help me at a moment’s notice. -
Mammalian Atypical E2fs Link Endocycle Control to Cancer
Mammalian Atypical E2Fs Link Endocycle Control to Cancer DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Hui-Zi Chen Graduate Program in Integrated Biomedical Science Program The Ohio State University 2011 Dissertation Committee: Gustavo Leone, PhD, Advisor Michael Ostrowski, PhD Clay Marsh, MD Tsonwin Hai, PhD Kathryn Wikenheiser-Brokamp, MD PhD Copyright by Hui-Zi Chen 2011 Abstract The endocycle is a developmentally programmed variant cell cycle consisting of successive S (DNA synthesis) and G (Gap) phases without an intervening M phase or cytokinesis. As a consequence of the regulated “decoupling” of DNA replication and mitosis, which are two central processes of the traditional cell division program, endocycling cells acquire highly polyploid genomes after having undergone multiple rounds of whole genome reduplication. Although essential for metazoan development, relatively little is known about the regulation of endocycle or its physiologic role in higher vertebrates such as the mammal. A substantial body of work in the model organism Drosophila melanogaster has demonstrated an important function for dE2Fs in the control of endocycle. Genetic studies showed that both endocycle initiation and progression is severely disrupted by altering the expression of the fly E2F activator (dE2F1) or repressor (dE2F2). In mammals, the E2F family is comprised of nine structurally related proteins, encoded by eight distinct genes, that can be classified into transcriptional activators (E2f1, E2f2, E2f3a and E2f3b) or repressors (E2f4, E2f5, E2f6, E2f7 and E2f8). The repressor subclass may then be further divided into canonical (E2f4, E2f5 and E2f6) or atypical E2fs (E2f7 and E2f8). -
Dialogue Between Estrogen Receptor and E2F Signaling Pathways: the Transcriptional Coregulator RIP140 at the Crossroads*
Advances in Bioscience and Biotechnology, 2013, 4, 45-54 ABB http://dx.doi.org/10.4236/abb.2013.410A3006 Published Online October 2013 (http://www.scirp.org/journal/abb/) Dialogue between estrogen receptor and E2F signaling pathways: The transcriptional coregulator RIP140 at the * crossroads Marion Lapierre1,2,3,4, Aurélie Docquier1,2,3,4, Audrey Castet-Nicolas1,2,3,4, Stéphan Jalaguier1,2,3,4, Catherine Teyssier1,2,3,4, Patrick Augereau1,2,3,4, Vincent Cavaillès1,2,3,4 1IRCM—Institut de Recherche en Cancérologie de Montpellier, Montpellier, France 2INSERM, U896, Montpellier, France 3Université Montpellier1, Montpellier, France 4Institut Régional du Cancer Montpellier, Montpellier, France Email: [email protected] Received 25 July 2013; revised 25 August 2013; accepted 19 September 2013 Copyright © 2013 Marion Lapierre 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. ABSTRACT Estrogen Receptors; Gene Expression; Cell Proliferation; Breast Cancer; Endocrine Therapies Estrogen receptors and E2F transcription factors are the key players of two nuclear signaling pathways which exert a major role in oncogenesis, particularly 1. ESTROGEN RECEPTOR AND E2F in the mammary gland. Different levels of dialogue SIGNALING PATHWAYS between these two pathways have been deciphered 1.1. Estrogen Signaling in Breast Cancer Cells and deregulation of the E2F pathway has been shown to impact the response of breast cancer cells to endo- Estrogens are steroid hormones that regulate growth and crine therapies. The present review focuses on the differentiation of a large number of target tissues such as transcriptional coregulator RIP140/NRIP1 which is the mammary gland, the reproductive tract and skeletal involved in several regulatory feed-back loops and and cardiovascular systems [1]. -
A Scalable Disease Module Identification Algorithm with Application to Glioma Progression
DiME: A Scalable Disease Module Identification Algorithm with Application to Glioma Progression Yunpeng Liu1, Daniel A. Tennant2, Zexuan Zhu4, John K. Heath3, Xin Yao1, Shan He1,3* 1 School of Computer Science, University of Birmingham, Birmingham, United Kingdom, 2 School of Cancer Sciences, University of Birmingham, Birmingham, United Kingdom, 3 Centre for Systems Biology, School of Biological Sciences, University of Birmingham, Birmingham, United Kingdom, 4 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China Abstract Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B- score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II) - and high (GBM) - grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. -
P53 and E2f: Partners in Life and Death
REVIEWS p53 and E2f: partners in life and death Shirley Polager and Doron Ginsberg Abstract | During tumour development cells sustain mutations that disrupt normal mechanisms controlling proliferation. Remarkably, the Rb–E2f and MDM2–p53 pathways are both defective in most, if not all, human tumours, which underscores the crucial role of these pathways in regulating cell cycle progression and viability. A simple interpretation of the observation that both pathways are deregulated is that they function independently in the control of cell fate. However, a large body of evidence indicates that, in addition to their independent effects on cell fate, there is extensive crosstalk between these two pathways, and specifically between the transcription factors E2F1 and p53, which influences vital cellular decisions. This Review discusses the molecular mechanisms that underlie the intricate interactions between E2f and p53. Autophagy The tumour suppressor p53 is a transcription factor suppressor and have a pivotal role in controlling cell cycle A catabolic process involving that is activated in response to virtually all cancer- progression (FIG. 1b). Initially, studies revealed that E2fs the degradation of a cell’s own associated stress signals, including DNA damage determine the timely expression of many genes that components by the lysosomal and oncogene activation. Normally, the levels of p53 are required for entry into and progression through machinery. protein are low, owing to rapid ubiquitin-dependent S phase of the cell cycle. However, it has become clear degradation largely directed by the E3 ubiquitin ligase that transcriptional activation of S phase-associated MDM2, which is also a target of transcriptional regu- genes is only one facet of E2f activity: we now know lation by p53 (REF.