Transcriptional Regulation of the Col1a2 Gene in Kidney

Total Page:16

File Type:pdf, Size:1020Kb

Transcriptional Regulation of the Col1a2 Gene in Kidney TRANSCRIPTIONAL REGULATION OF THE COL1A2 GENE IN KIDNEY FIBROBLASTS A thesis submitted to Imperial College London in candidature for the degree of Doctor of Philosophy by: MARIA FRAGIADAKI Imperial College London, Renal Section, Division of Medicine Commonwealth Building, Du Cane Road London, W12 0NN JULY 2009 1 ABSTRACT Renal tubulointerstitial fibrosis is a major predictor of progressive glomerular disease. It occurs as a result of persistent inflammation and is characterised by excessive deposition of extracellular matrix (ECM) proteins, including accumulation of type I collagen, the most abundant protein of the ECM. Type I collagen is encoded by two genes, COL1A1 and COL1A2, that are tightly regulated at a transcriptional level. A key aim of this study was to use the previously identified COL1A2 promoter and enhancer sequences in order to identify novel regulatory cis- acting elements and the relevant transcription factors that regulate collagen transcription in cells derived from healthy or diseased kidneys. Moreover, the effects of hypoxia and transforming growth factor beta (TGFβ), which are both pro- fibrotic stimuli, on collagen transcription were studied. TGFβ is known to activate CDP/cux transcription factors which can suppress gene activation; based on this finding the role of CDP/cux in COL1A2 transcriptional regulation was assessed. In conclusion, the work presented in this thesis provides an insight into the complex control mechanisms that regulate collagen transcription in both physiological and pathological conditions. 2 ACKNOWLEDGEMENTS I would like to thank my supervisor Dr George Bou-Gharios for his discussion, throughout my PhD. Moreover Professor Patrick Maxwell for the intellectual stimulation and guidance and Dr Paul C Evans for reviewing my thesis. Numerous members of the laboratory, past and present, that have become friends and provided help and assistance, especially Hiroyusi Nakamura, Tetsuro Ikeda and Jenny Smith. I would also like to thank Kidney Research UK for funding my study. Finally, I would like to dedicate this thesis to all my family and friends for their continued belief and unfailing confidence in my abilities. 3 CONTENTS ABSTRACT 2 ACKNOWLEDGEMENTS 3 CONTENTS 4 LIST OF FIGURES 8 ABBREVIATIONS 10 PUBLISHED AND PRESENTED WORK 14 CHAPTER 1- GENERAL INTRODUCTION 16 1.1 INTRODUCTION 16 1.2 TISSUE FIBROSIS: THE KIDNEY 20 The nephron 20 TGFβ and Epithelial- to mesenchymal transition (EMT) 26 Chronic hypoxia hypothesis 33 1.3 EXTRACELLULAR MATRIX AND THE COLLAGEN FAMILY OF PROTEINS 38 Collagen, ECM and fibrosis 38 Cells that synthesise collagen 44 The collagen family of proteins 52 Type I Collagen 55 Genetic disease and type I Collagen 57 1.3 EUKARYOTIC TRANSCRIPTIONAL CONTROL 59 The pre-initiation complex 61 Enhancers and co-ordinately expressed genes 64 The collagen genes 66 Transcriptional control of type I collagen 71 1.5 PROJECT AIMS 79 CHAPTER 2- METHODS AND MATERIALS 81 2.1 MAMMALIAN CELL CULTURE 83 Cell lines 83 Maintenance of cells 83 Cryopreservation 84 2.2 TRANSIENT DNA TRANSFECTIONS 84 Seeding of the cells 84 Transfection protocol 85 Reporter gene assay 86 4 2.3 PREPARATION OF NUCLEIC ACIDS 87 Small-scale DNA extraction 87 Large-scale DNA extraction 89 Total RNA extraction and quantification 90 Oligonucleotides 91 2.4 STANDARD MANIPULATIONS OF DNA 92 Agarose gel electrophoresis 92 DNA isolation from agarose gel 93 Restriction enzyme digestion of plasmid DNA 93 2.5 RECOMBINANT DNA PROCEDURES 94 Blunt-ended DNA ligation 94 Competent DH5α cell preparation 95 Transformation of DH5α 96 Screening for recombinant plasmid 96 2.6 POLYMERASE CHAIN REACTION 97 Standard PCR 97 Colony PCR 99 cDNA synthesis 100 Real-time PCR 100 2.7 CLONING VECTORS 101 pBluescript 101 pβgal-Basic 101 2.8 GENERATION OF TRANSGENIC ANIMALS 104 Mice and microinjection 104 Preparation of embryos and detection of the transgene 106 2.9 ELECTROPHORETIC MOBILITY SHIFT ASSAY (EMSA) 107 Nuclear extract preparation 107 Quantification of nuclear protein 107 Radio-labelling of the DNA 108 Binding reaction and SDS-PAGE 108 Autoradiography 109 2.10 WESTERN BLOTTING 110 Preparation of lysates 110 Electrophoresis, transfer and developing 110 2.10 STATISTICAL ANALYSIS 112 CHAPTER 3- INVESTIGATION OF THE HUMAN COL1A2 FAR- UPSTREAM ENHANCER 113 3.1 INTRODUCTION 114 3.2 IDENTIFICATION OF THE MINIMAL DNA SEQUENCE REQUIRED TO DRIVE KIDNEY-SPECIFIC EXPRESSION OF COL1A2 IN VITRO 118 Aims & and experimental design 118 Results 119 3.3 THE ROLE OF AP1 AND TGFβ IN COL1A2 ENHANCER ACTIVATION 130 Aims & experimental design 130 Results 131 5 3.4 GENERATION OF MOUSE TRANSGENIC EMBRYOS USING THE COL1A2 ENHANCER REGION 136 Aims & experimental design 136 Results 137 3.5 DISCUSSION 140 CHAPTER 4- REGULATION OF COL1A2 BY CDP/CUX: A ROLE FOR TGFΒ 148 4.1 INTRODUCTION 149 4.2 SUPPRESSION OF COL1A2 mRNA BY CDP/CUX 150 Aim and Experimental design 150 Results 151 4.3 ANTAGONISTIC RELATIONSHIP BETWEEN CDP/CUX AND TGFβ ON COL1A2 mRNA PRODUCTION 159 Aim and Experimental design 159 Results 160 4.4 IN SILICO ANALYSIS OF COL1A2 PUTATIVE CDP/CUX CIS-ACTING ELEMENTS AND BINDING ASSAYS 165 Aim and Experimental design 165 Results 167 CHAPTER 5: HYPOXIC REGULATION OF COLLAGEN 182 5.1 INTRODUCTION 183 5.2 HYPOXIA INDUCES INCREASED mRNA EXPRESSION OF THE COL1A2 GENE 186 Aim and Experimental design 186 Results 187 5.3 BIOINFORMATICS ANALYSIS OF COL1A2 5’ UTR FOR PUTATIVE HYPOXIA RESPONSE ELEMENTS AND SP1 191 Aim and Experimental design 191 Results 192 5.4 MINIMAL DNA REGION REQUIRED FOR THE HYPOXIA-INDUCED COL1A2 INCREASED EXPRESSION 195 Aim and Experimental design 195 Results 195 5.5 DISCUSSION 199 CHAPTER 6- GENERAL DISCUSSION 207 6.1 DISCUSSION 207 6.2 FUTURE DIRECTIONS 221 6.3 LIMITATIONS OF THE STUDY 224 REFERENCE LIST 227 6 APPENDIX A- SUPPLIERS 261 APPENDIX B- BUFFERS AND SOLUTIONS 263 APPENDIX C- TF EXTENDED LIST 266 APPENDIX D- CONSERVATION OF THE HUMAN COL1A2 5’ UTR 289 7 LIST OF FIGURES Figure 1.1: A simplified schematic illustration of a kidney nephron. 22 Figure 1.2: Normal versus fibrotic kidney. 25 Figure 1.3: The canonical TGFβ signalling pathway. 29 Figure 1.4: Cellular modifications associated with EMT. 32 Figure 1.5: The chronic hypoxia hypothesis. 34 Figure 1.6: Representation of the constituents of ECM. 41 Figure 1.7: The roles of different components of the ECM. 43 Figure 1.8: Origins of collagen-producing cells. 50 Figure 1.9: Normal versus activated fibroblasts. 51 Figure 1.11: Cartoon emphasizing the importance of collagen. 53 Figure 1.12: The collagen triple helix. 53 Figure 1.13: The collagen structure. 56 Figure 1.14: Fundamental elements of eukaryotic transcriptional control. 63 Figure 1.15: Representation of the trans-acting factors and cis-acting regulatory elements of COL1A2. 79 Figure 2.1: Schematic representation of the cloning strategy. 103 Figure 2.2: Generation of transgenics. 105 Figure 3.1: Transgenic animal analysis of human COL1A2 upstream DNA sequence. 116 Figure 3.2: Part of the human COL1A2 enhancer can drive-kidney specific expression in vivo. 117 Figure 3.4: Testing COL1A2 promoter and promoter with full length activity in mammalian collagen-producing cells. 124 Figure 3.5: Further characterisation of COL1A2 enhancer activity by testing sequential deletion constructs. 127 Figure 3.6: Identification of minimal DNA sequence required for promoter/enhancer activity. 128 Figure 3.7: TGFβ stimulation enhanced basal COL1A2 promoter/enhancer activity. 133 Figure 3.8: Site-directed mutagenesis revealed a cis-acting element important in regulation of basal COL1A2 expression. 135 Figure 3.9: Generation of COL1A2-reporter transgenics. 139 Figure 4.1: Validation, by qPCR, of CDP/cux overexpression in fibroblasts. 153 Figure 4.2: Overexpression of CDP/cux suppressed COL1A2 mRNA expression in fibroblasts. 156 Figure 4.4: Overexpression of CDP/cux suppresses COL1A2 promoter activity in renal fibroblasts, though the proximal promoter. 158 8 Figure 4.5: Stimulation of fibroblasts with TGFβ induced an increased production of CDP/cux which correlated with reduced expression of COL1A2. 163 Figure 4.6 CDP/cux overexpression suppresses COL1A2 transcriptional activity through the promoter and reverses TGFβ-induced COL1A2 activation. 164 Figure 4.7: In silico analysis revealed 5 putative CDP/cux binding sites 170 Figure 4.8: CBF binds to the COL1A2 proximal promoter at position -80bp relative to the transcriptional start site. 172 Figure 4.9: CDP binds to the COL1A2 proximal promoter through a cis-acting element located -200bp relative to the transcriptional start site. 173 Figure 4.10: Overexpression of CDP/cux with binding to the -80bp site. 174 Figure 5.1: HIF response in the presence and absence of oxygen. 185 Figure 5.2: COL1A2 expression in kidney and lung cells. 189 Figure 5.3: VEGF expression and HIF activity in human kidney fibroblasts in response to hypoxia. 190 Figure 5.4: Putative hypoxia response elements and SP1 motifs on COL1A2 regulatory DNA sequences. 194 Figure 5.5: The COL1A2 promoter/enhancer is activated in response to hypoxia. 197 Figure 6.1: TGFβ mediates COL1A2 transcription in a dose-dependent manner via induction of CDP/cux. 219 9 ABBREVIATIONS α-SMA alpha-smooth muscle actin AMP ampicillin AP1 activator protein-1 APS ammonium persulphate BMP-7 bone morphogenic protein 7/ osteogenic protein-1 bp base pair BSA bovine serum albumin CAT chloroamphenicol acetyl-transferase CBF CCAAT binding factor CDP/cux CCAAT displacement protein CME collagen modulating element COL1A1 / 2 collagen 1 alpha 1 / 2 COL4A3/4/5 collagen
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]
  • A Flexible Microfluidic System for Single-Cell Transcriptome Profiling
    www.nature.com/scientificreports OPEN A fexible microfuidic system for single‑cell transcriptome profling elucidates phased transcriptional regulators of cell cycle Karen Davey1,7, Daniel Wong2,7, Filip Konopacki2, Eugene Kwa1, Tony Ly3, Heike Fiegler2 & Christopher R. Sibley 1,4,5,6* Single cell transcriptome profling has emerged as a breakthrough technology for the high‑resolution understanding of complex cellular systems. Here we report a fexible, cost‑efective and user‑ friendly droplet‑based microfuidics system, called the Nadia Instrument, that can allow 3′ mRNA capture of ~ 50,000 single cells or individual nuclei in a single run. The precise pressure‑based system demonstrates highly reproducible droplet size, low doublet rates and high mRNA capture efciencies that compare favorably in the feld. Moreover, when combined with the Nadia Innovate, the system can be transformed into an adaptable setup that enables use of diferent bufers and barcoded bead confgurations to facilitate diverse applications. Finally, by 3′ mRNA profling asynchronous human and mouse cells at diferent phases of the cell cycle, we demonstrate the system’s ability to readily distinguish distinct cell populations and infer underlying transcriptional regulatory networks. Notably this provided supportive evidence for multiple transcription factors that had little or no known link to the cell cycle (e.g. DRAP1, ZKSCAN1 and CEBPZ). In summary, the Nadia platform represents a promising and fexible technology for future transcriptomic studies, and other related applications, at cell resolution. Single cell transcriptome profling has recently emerged as a breakthrough technology for understanding how cellular heterogeneity contributes to complex biological systems. Indeed, cultured cells, microorganisms, biopsies, blood and other tissues can be rapidly profled for quantifcation of gene expression at cell resolution.
    [Show full text]
  • Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
    Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002)
    [Show full text]
  • Gene Networks Activated by Specific Patterns of Action Potentials in Dorsal Root Ganglia Neurons Received: 10 August 2016 Philip R
    www.nature.com/scientificreports OPEN Gene networks activated by specific patterns of action potentials in dorsal root ganglia neurons Received: 10 August 2016 Philip R. Lee1,*, Jonathan E. Cohen1,*, Dumitru A. Iacobas2,3, Sanda Iacobas2 & Accepted: 23 January 2017 R. Douglas Fields1 Published: 03 March 2017 Gene regulatory networks underlie the long-term changes in cell specification, growth of synaptic connections, and adaptation that occur throughout neonatal and postnatal life. Here we show that the transcriptional response in neurons is exquisitely sensitive to the temporal nature of action potential firing patterns. Neurons were electrically stimulated with the same number of action potentials, but with different inter-burst intervals. We found that these subtle alterations in the timing of action potential firing differentially regulates hundreds of genes, across many functional categories, through the activation or repression of distinct transcriptional networks. Our results demonstrate that the transcriptional response in neurons to environmental stimuli, coded in the pattern of action potential firing, can be very sensitive to the temporal nature of action potential delivery rather than the intensity of stimulation or the total number of action potentials delivered. These data identify temporal kinetics of action potential firing as critical components regulating intracellular signalling pathways and gene expression in neurons to extracellular cues during early development and throughout life. Adaptation in the nervous system in response to external stimuli requires synthesis of new gene products in order to elicit long lasting changes in processes such as development, response to injury, learning, and memory1. Information in the environment is coded in the pattern of action-potential firing, therefore gene transcription must be regulated by the pattern of neuronal firing.
    [Show full text]
  • Downloaded from Here
    bioRxiv preprint doi: https://doi.org/10.1101/017566; this version posted November 19, 2015. 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-NC-ND 4.0 International license. 1 1 Testing for ancient selection using cross-population allele 2 frequency differentiation 1;∗ 3 Fernando Racimo 4 1 Department of Integrative Biology, University of California, Berkeley, CA, USA 5 ∗ E-mail: [email protected] 6 1 Abstract 7 A powerful way to detect selection in a population is by modeling local allele frequency changes in a 8 particular region of the genome under scenarios of selection and neutrality, and finding which model is 9 most compatible with the data. Chen et al. [2010] developed a composite likelihood method called XP- 10 CLR that uses an outgroup population to detect departures from neutrality which could be compatible 11 with hard or soft sweeps, at linked sites near a beneficial allele. However, this method is most sensitive 12 to recent selection and may miss selective events that happened a long time ago. To overcome this, 13 we developed an extension of XP-CLR that jointly models the behavior of a selected allele in a three- 14 population tree. Our method - called 3P-CLR - outperforms XP-CLR when testing for selection that 15 occurred before two populations split from each other, and can distinguish between those events and 16 events that occurred specifically in each of the populations after the split.
    [Show full text]
  • Epigenetic Biomarkers in Obesity, Weight Loss and Inflammation: a Role for Circadian Rhythm and Methyl Donors
    Facultad de Farmacia y Nutrición Epigenetic biomarkers in obesity, weight loss and inflammation: a role for circadian rhythm and methyl donors Mirian Samblas García Pamplona, 2018 Facultad de Farmacia y Nutrición Memoria presentada por Dña. Mirian Samblas García para aspirar al grado de Doctor por la Universidad de Navarra. Mirian Samblas García El presente trabajo ha sido realizado bajo nuestra dirección en el Departamento de Ciencias de la Alimentación y Fisiología de la Facultad de Farmacia y Nutrición de la Universidad de Navarra y autorizamos su presentación ante el Tribunal que lo ha de juzgar. Pamplona, 26 de Febrero de 2018 VºBº Director VºBº Co-Director Dr. Fermín I. Milagro Yoldi Prof. J. Alfredo Martínez Hernández Este trabajo ha sido posible gracias a la financiación de diversas entidades: Ministerio de Economía y Competitividad (AGL2013-45554-R), Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituo de Salud Carlos III (ISCIII), Centro de Investigación en Nutrición (Universidad de Navarra). La investigación que ha dado lugar a estos resultados ha sido impulsada por la beca predoctoral 2014-2015 del Centro de Investigación en Nutrición y las becas predoctoral 2015-2018 y de movilidad del Ministerio de Educación, Cultura y Deporte. “Necesitamos especialmente de la imaginación en las ciencias. No todo es matemáticas y no todo es simple lógica, también se trata de un poco de belleza y poesía” Maria Montessori Dedicado a las dos personas que me lo han dado todo, Mi aita Ray, Mi ama Ana Con especial cariño a, Mi hermana, Bea Mi sobrina, Aroa Mi abu, Esther Agradecimientos/Acknowledgements En estas líneas quisiera mostrar mi más sincero agradecimiento a todas aquellas personas e instituciones que han hecho posible la actual tesis.
    [Show full text]
  • Detection of Genetic Variation and Activity Analysis of the Promoter Region of the Cattle Trna-Modified Gene TRDMT1
    Original study Arch. Anim. Breed., 64, 147–155, 2021 https://doi.org/10.5194/aab-64-147-2021 Open Access © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Archives Animal Breeding Detection of genetic variation and activity analysis of the promoter region of the cattle tRNA-modified gene TRDMT1 Xiaohua Yi1;, Shuai He1;, Shuhui Wang1, Haidong Zhao1, Mingli Wu1, Shirong Liu1, and Xiuzhu Sun2 1College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, PR China 2College of Grassland Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, PR China These authors contributed equally to this work. Correspondence: Xiuzhu Sun ([email protected]) Received: 21 September 2020 – Revised: 9 March 2021 – Accepted: 26 March 2021 – Published: 30 April 2021 Abstract. The tRNA modification gene in eukaryotes is relatively conservative. As an important modification gene, the TRDMT1 gene plays an important role in maintaining tRNA structural maintenance and reducing mis- translation of protein translation by methylation of specific tRNA subpopulations. Mouse and zebrafish TRDMT1 knockout experiments indicate that it may mediate growth and development through tRNA modification. How- ever, there are no systematic reports on the function of tRNA-modified genes in livestock. In this study, Qinchuan cattle DNA pool sequencing technology was used. A G>C mutation in the −1223 bp position upstream of the TRDMT1 translation initiator codon was found. At this locus, the dual-luciferase assay indicated that different genotypes cause differences in transcriptional activity (P < 0:05). Our experiment detected a natural genetic variation of a tRNA modification gene TRDMT1, which may provide potential natural molecular materials for the study of tRNA modification.
    [Show full text]
  • (12) Patent Application Publication (10) Pub. No.: US 2009/0269772 A1 Califano Et Al
    US 20090269772A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2009/0269772 A1 Califano et al. (43) Pub. Date: Oct. 29, 2009 (54) SYSTEMS AND METHODS FOR Publication Classification IDENTIFYING COMBINATIONS OF (51) Int. Cl. COMPOUNDS OF THERAPEUTIC INTEREST CI2O I/68 (2006.01) CI2O 1/02 (2006.01) (76) Inventors: Andrea Califano, New York, NY G06N 5/02 (2006.01) (US); Riccardo Dalla-Favera, New (52) U.S. Cl. ........... 435/6: 435/29: 706/54; 707/E17.014 York, NY (US); Owen A. (57) ABSTRACT O'Connor, New York, NY (US) Systems, methods, and apparatus for searching for a combi nation of compounds of therapeutic interest are provided. Correspondence Address: Cell-based assays are performed, each cell-based assay JONES DAY exposing a different sample of cells to a different compound 222 EAST 41ST ST in a plurality of compounds. From the cell-based assays, a NEW YORK, NY 10017 (US) Subset of the tested compounds is selected. For each respec tive compound in the Subset, a molecular abundance profile from cells exposed to the respective compound is measured. (21) Appl. No.: 12/432,579 Targets of transcription factors and post-translational modu lators of transcription factor activity are inferred from the (22) Filed: Apr. 29, 2009 molecular abundance profile data using information theoretic measures. This data is used to construct an interaction net Related U.S. Application Data work. Variances in edges in the interaction network are used to determine the drug activity profile of compounds in the (60) Provisional application No. 61/048.875, filed on Apr.
    [Show full text]
  • ZNF35 Mouse Monoclonal Antibody [Clone ID: OTI3H5] Product Data
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for CF807143 ZNF35 Mouse Monoclonal Antibody [Clone ID: OTI3H5] Product data: Product Type: Primary Antibodies Clone Name: OTI3H5 Applications: IHC, WB Recommended Dilution: WB 1:2000, IHC 1:150 Reactivity: Human Host: Mouse Isotype: IgG2b Clonality: Monoclonal Immunogen: Human recombinant protein fragment corresponding to amino acids 1-258 of human ZNF35(NP_003411) produced in E.coli. Formulation: Lyophilized powder (original buffer 1X PBS, pH 7.3, 8% trehalose) Reconstitution Method: For reconstitution, we recommend adding 100uL distilled water to a final antibody concentration of about 1 mg/mL. To use this carrier-free antibody for conjugation experiment, we strongly recommend performing another round of desalting process. (OriGene recommends Zeba Spin Desalting Columns, 7KMWCO from Thermo Scientific) Purification: Purified from mouse ascites fluids or tissue culture supernatant by affinity chromatography (protein A/G) Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 58.9 kDa Gene Name: Homo sapiens zinc finger protein 35 (ZNF35), mRNA. Database Link: NP_003411 Entrez Gene 7584 Human P13682 Synonyms: HF.10; HF10; Zfp105 Protein Families: Transcription Factors This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 3 ZNF35 Mouse Monoclonal Antibody [Clone ID: OTI3H5] – CF807143 Product images: HEK293T cells were transfected with the pCMV6- ENTRY control (Cat# [PS100001], Left lane) or pCMV6-ENTRY ZNF35 (Cat# [RC203770], Right lane) cDNA for 48 hrs and lysed.
    [Show full text]
  • Autocrine IFN Signaling Inducing Profibrotic Fibroblast Responses By
    Downloaded from http://www.jimmunol.org/ by guest on September 23, 2021 Inducing is online at: average * The Journal of Immunology , 11 of which you can access for free at: 2013; 191:2956-2966; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication August 2013; doi: 10.4049/jimmunol.1300376 http://www.jimmunol.org/content/191/6/2956 A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Autocrine IFN Signaling Feng Fang, Kohtaro Ooka, Xiaoyong Sun, Ruchi Shah, Swati Bhattacharyya, Jun Wei and John Varga J Immunol cites 49 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/2013/08/20/jimmunol.130037 6.DC1 This article http://www.jimmunol.org/content/191/6/2956.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 © 2013 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 23, 2021. The Journal of Immunology A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Inducing Autocrine IFN Signaling Feng Fang,* Kohtaro Ooka,* Xiaoyong Sun,† Ruchi Shah,* Swati Bhattacharyya,* Jun Wei,* and John Varga* Activation of TLR3 by exogenous microbial ligands or endogenous injury-associated ligands leads to production of type I IFN.
    [Show full text]
  • Target Genes Regulated by Hsa-Mir-21, by Hsa-Mir-203, by Hsa-Mir-21 and by Hsa-Mir-143
    Supplemental table 1: Target genes regulated by hsa-miR-205 Index Target gene Index Target gene Index Target gene Index Target gene Index Target gene 1 KCTD20 35 UBE2Z 69 SLC38A1 103 LPCAT1 137 STK38L 2 MAPK14 36 YWHAH 70 ANGPTL7 104 MARCKS 138 C1orf123 3 TXNL1 37 RBBP4 71 CTGF 105 MED13 139 GUCD1 4 SPDL1 38 LRP1 72 CYR61 106 IPO7 140 CDK6 5 TCF20 39 IMPAD1 73 TP73 107 PHC2 141 CDKN2AIPNL 6 RAN 40 GNAS 74 EGLN2 108 PICALM 142 CLIP1 7 RGS6 41 MED1 75 ERBB2 109 PLAGL2 143 CUL5 8 HOXA11 42 INPPL1 76 PRRG4 110 NDUFA4 144 C6orf201 9 PAPPA-AS1 43 DDX5 77 F2RL2 111 NDUFB2 145 VTI1A 10 PRR15 44 E2F1 78 GOT1 112 NIPA2 146 SLC5A12 11 ACTRT3 45 E2F5 79 NUFIP2 113 NOTCH2 147 MAML2 12 YES1 46 ZEB2 80 IL24 114 PANK1 148 MAP3K9 13 SRC 47 ERBB3 81 IL32 115 PARD6B 149 NUDT21 14 NPRL3 48 PRKCE 82 RNF217 116 TMEM66 150 DNAJA1 15 NFAT5 49 SLC41A1 83 ZNF585B 117 EZR 151 CCDC108 16 XPOT 50 SLC7A2 84 SIGMAR1 118 ENPP4 152 SHISA6 17 KCTD16 51 ZEB1 85 VEGFA 119 LRRTM4 153 ACP1 18 TMSB4X 52 PHF8 86 BCL9L 120 KCNJ10 154 BCL2 19 PLCXD2 53 TMEM201 87 CREB1 121 PHLPP2 155 NCAPG 20 TNFSF8 54 PTPRJ 88 SERINC3 122 YEATS2 156 KLHL5 21 SLC25A25 55 ETNK1 89 HMGB3 123 VAMP1 157 ACSL4 22 C11orf74 56 XPR1 90 SRD5A1 124 RTN3 158 BCL6 23 GM2A 57 MRPL44 91 PTEN 125 RFX7 159 ITGA5 24 SMNDC1 58 TM9SF2 92 ESRRG 126 RAP2B 160 ACSL1 25 BAMBI 59 PAIP2B 93 PRLR 127 TRAF3IP1 161 EID2B 26 LCOR 60 NEK9 94 ICK 128 SERTAD2 162 TEX35 27 TMEM239 61 NOX5 95 LOH12CR1 129 TOLLIP 163 YY1 28 AMOT 62 DMXL2 96 SLC39A14 130 TMEM55B 164 SMAD1 29 CDK1 63 ETF1 97 BDP1 131 TMEM123 165 SMAD4 30 SQLE 64
    [Show full text]
  • Tablegen Xu.Xlsx
    Transcription factors IsoformID # of isoforms ARID5A, CEBPB, CREB1, MZF1, SATB1, SMAD3, STAT3, ZBTB3, ZNF219, ZNF263, ZNF35 ENST00000261654 1 ARID5A, CEBPB, KLF6, MEF2C, MEIS1, MZF1, NF1, SATB1, SMAD3, SPI1, STAT5B, ZBTB3, ZNF219 ENST00000272928 1 ARID5A, CEBPD, HHEX, HSF2, IRF3, MZF1, ZNF219 ENST00000378138, ENST00000378142 2 ARID5A, CEBPD, HHEX, SATB1, TEF ENST00000402328 1 ARID5A, E2F4, KLF6, MAZ, MZF1, NF1, RFX5, SF1, SP1, ZBTB7, ZNF263, ZNF354C, ENST00000392627 1 ARID5A, E2F4, MZF1, SATB1, SMAD3, USF1, ZNF217, ZNF354C ENST00000257267 1 ARID5A, HHEX, MEF2C, MZF1, SPI1 ENST00000302548 1 ARID5A, HHEX, MEIS1, PBX3, SMAD4, SPI1, STAT5B, ZBTB3, ZFX, ZNF219, ZNF35 ENST00000322241 1 ARID5A, HSF2, MZF1, NF1, SMAD3, ZBTB3, ENST00000370758 1 ARNT, HSF2, IRF7, STAT5A, ENST00000400926 1 ARNT, KLF6, MZF1, SPI1, ZNF354C ENST00000345941 1 ARNT, MZF1, SPI1, ZFX, ENST00000474396 1 ASCL2, BARX2, GATA4, HOXB9, ZFP57 ENST00000393294 1 ASCL2, EOMES, LEF1, NRL, SALL2, ZNF300 ENST00000412676 1 ASCL2, ETV1, HIC1, LEF1, SALL2 ENST00000330120 1 ASCL2, ETV1, HIC1, TEAD4, ZKSCAN3 ENST00000392964 1 ATF6, E2F3, E2F4, EGR1, HSF2, KLF6, KLF7, MZF1, SMAD3, SP1, STAT5B, ZBTB3, ZFX, ZNF219, ZNF263 ENST00000354791 1 ATF6, E2F4, KAISO, KLF7, NFKAPPAB, PBX3, SP1, ZBTB3, ZFX ENST00000344575 1 ATF6, E2F4, KLF6, KLF7, MZF1, SP1, SPI1, ZBTB3, ZNF219 ENST00000393597 1 ATF6, E2F4, KLF6, MZF1, SF1, TCF12, ZBTB3, ZBTB7 ENST00000323289 1 ATF6, E2F4, KLF7, MZF1, NFKAPPAB, SMAD3, SP1, SPI1, ZFX, ZNF219, ZNF263, ZNF35 ENST00000292513 1 ATF6, ELK1, KLF6, MAZ, MEIS1, MZF1, SPI1, TCF7L2,
    [Show full text]