Mediator Complex Interaction Partners Organize the Transcriptional Network That Defines Neural Stem Cells
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Activated Peripheral-Blood-Derived Mononuclear Cells
Transcription factor expression in lipopolysaccharide- activated peripheral-blood-derived mononuclear cells Jared C. Roach*†, Kelly D. Smith*‡, Katie L. Strobe*, Stephanie M. Nissen*, Christian D. Haudenschild§, Daixing Zhou§, Thomas J. Vasicek¶, G. A. Heldʈ, Gustavo A. Stolovitzkyʈ, Leroy E. Hood*†, and Alan Aderem* *Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103; ‡Department of Pathology, University of Washington, Seattle, WA 98195; §Illumina, 25861 Industrial Boulevard, Hayward, CA 94545; ¶Medtronic, 710 Medtronic Parkway, Minneapolis, MN 55432; and ʈIBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598 Contributed by Leroy E. Hood, August 21, 2007 (sent for review January 7, 2007) Transcription factors play a key role in integrating and modulating system. In this model system, we activated peripheral-blood-derived biological information. In this study, we comprehensively measured mononuclear cells, which can be loosely termed ‘‘macrophages,’’ the changing abundances of mRNAs over a time course of activation with lipopolysaccharide (LPS). We focused on the precise mea- of human peripheral-blood-derived mononuclear cells (‘‘macro- surement of mRNA concentrations. There is currently no high- phages’’) with lipopolysaccharide. Global and dynamic analysis of throughput technology that can precisely and sensitively measure all transcription factors in response to a physiological stimulus has yet to mRNAs in a system, although such technologies are likely to be be achieved in a human system, and our efforts significantly available in the near future. To demonstrate the potential utility of advanced this goal. We used multiple global high-throughput tech- such technologies, and to motivate their development and encour- nologies for measuring mRNA levels, including massively parallel age their use, we produced data from a combination of two distinct signature sequencing and GeneChip microarrays. -
In Vivo Studies Using the Classical Mouse Diversity Panel
The Mouse Diversity Panel Predicts Clinical Drug Toxicity Risk Where Classical Models Fail Alison Harrill, Ph.D The Hamner-UNC Institute for Drug Safety Sciences 0 The Importance of Predicting Clinical Adverse Drug Reactions (ADR) Figure: Cath O’Driscoll Nature Publishing 2004 Risk ID PGx Testing 1 People Respond Differently to Drugs Pharmacogenetic Markers Identified by Genome-Wide Association Drug Adverse Drug Risk Allele Reaction (ADR) Abacavir Hypersensitivity HLA-B*5701 Flucloxacillin Hepatotoxicity Allopurinol Cutaneous ADR HLA-B*5801 Carbamazepine Stevens-Johnson HLA-B*1502 Syndrome Augmentin Hepatotoxicity DRB1*1501 Ximelagatran Hepatotoxicity DRB1*0701 Ticlopidine Hepatotoxicity HLA-A*3303 Average preclinical populations and human hepatocytes lack the diversity to detect incidence of adverse events that occur only in 1/10,000 people. Current Rodent Models of Risk Assessment The Challenge “At a time of extraordinary scientific progress, methods have hardly changed in several decades ([FDA] 2004)… Toxicologists face a major challenge in the twenty-first century. They need to embrace the new “omics” techniques and ensure that they are using the most appropriate animals if their discipline is to become a more effective tool in drug development.” -Dr. Michael Festing Quantitative geneticist Toxicol Pathol. 2010;38(5):681-90 Rodent Models as a Strategy for Hazard Characterization and Pharmacogenetics Genetically defined rodent models may provide ability to: 1. Improve preclinical prediction of drugs that carry a human safety risk 2. -
An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Netwo
Washington University School of Medicine Digital Commons@Becker Open Access Publications 2014 An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis Sriram Devanathan Washington University School of Medicine in St. Louis Timothy Whitehead Washington University School of Medicine in St. Louis George G. Schweitzer Washington University School of Medicine in St. Louis Nicole Fettig Washington University School of Medicine in St. Louis Attila Kovacs Washington University School of Medicine in St. Louis See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Recommended Citation Devanathan, Sriram; Whitehead, Timothy; Schweitzer, George G.; Fettig, Nicole; Kovacs, Attila; Korach, Kenneth S.; Finck, Brian N.; and Shoghi, Kooresh I., ,"An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis." PLoS One.9,7. e101900. (2014). https://digitalcommons.wustl.edu/open_access_pubs/3326 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Sriram Devanathan, Timothy Whitehead, George G. Schweitzer, Nicole Fettig, Attila Kovacs, Kenneth S. Korach, Brian N. Finck, and Kooresh I. Shoghi This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/3326 An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Network Analysis Sriram Devanathan1, Timothy Whitehead1, George G. Schweitzer2, Nicole Fettig1, Attila Kovacs3, Kenneth S. -
I LITERATURE-BASED DISCOVERY of KNOWN and POTENTIAL NEW
LITERATURE-BASED DISCOVERY OF KNOWN AND POTENTIAL NEW MECHANISMS FOR RELATING THE STATUS OF CHOLESTEROL TO THE PROGRESSION OF BREAST CANCER BY YU WANG THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics with a concentration in Library and Information Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2019 Urbana, Illinois Adviser: Professor Vetle I. Torvik Professor Erik Russell Nelson i ABSTRACT Breast cancer has been studied for a long period of time and from a variety of perspectives in order to understand its pathogeny. The pathogeny of breast cancer can be classified into two groups: hereditary and spontaneous. Although cancer in general is considered a genetic disease, spontaneous factors are responsible for most of the pathogeny of breast cancer. In other words, breast cancer is more likely to be caused and deteriorated by the dysfunction of a physical molecule than be caused by germline mutation directly. Interestingly, cholesterol, as one of those molecules, has been discovered to correlate with breast cancer risk. However, the mechanisms of how cholesterol helps breast cancer progression are not thoroughly understood. As a result, this study aims to study known and discover potential new mechanisms regarding to the correlation of cholesterol and breast cancer progression using literature review and literature-based discovery. The known mechanisms are further classified into four groups: cholesterol membrane content, transport of cholesterol, cholesterol metabolites, and other. The potential mechanisms, which are intended to provide potential new treatments, have been identified and checked for feasibility by an expert. -
Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces Cerevisiae Received: 26 October 2016 Henriette Uthe, Jens T
www.nature.com/scientificreports OPEN Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces cerevisiae Received: 26 October 2016 Henriette Uthe, Jens T. Vanselow & Andreas Schlosser Accepted: 25 January 2017 Here we present the most comprehensive analysis of the yeast Mediator complex interactome to date. Published: 27 February 2017 Particularly gentle cell lysis and co-immunopurification conditions allowed us to preserve even transient protein-protein interactions and to comprehensively probe the molecular environment of the Mediator complex in the cell. Metabolic 15N-labeling thereby enabled stringent discrimination between bona fide interaction partners and nonspecifically captured proteins. Our data indicates a functional role for Mediator beyond transcription initiation. We identified a large number of Mediator-interacting proteins and protein complexes, such as RNA polymerase II, general transcription factors, a large number of transcriptional activators, the SAGA complex, chromatin remodeling complexes, histone chaperones, highly acetylated histones, as well as proteins playing a role in co-transcriptional processes, such as splicing, mRNA decapping and mRNA decay. Moreover, our data provides clear evidence, that the Mediator complex interacts not only with RNA polymerase II, but also with RNA polymerases I and III, and indicates a functional role of the Mediator complex in rRNA processing and ribosome biogenesis. The Mediator complex is an essential coactivator of eukaryotic transcription. Its major function is to communi- cate regulatory signals from gene-specific transcription factors upstream of the transcription start site to RNA Polymerase II (Pol II) and to promote activator-dependent assembly and stabilization of the preinitiation complex (PIC)1–3. The yeast Mediator complex is composed of 25 subunits and forms four distinct modules: the head, the middle, and the tail module, in addition to the four-subunit CDK8 kinase module (CKM), which can reversibly associate with the 21-subunit Mediator complex. -
Mutational Landscape Differences Between Young-Onset and Older-Onset Breast Cancer Patients Nicole E
Mealey et al. BMC Cancer (2020) 20:212 https://doi.org/10.1186/s12885-020-6684-z RESEARCH ARTICLE Open Access Mutational landscape differences between young-onset and older-onset breast cancer patients Nicole E. Mealey1 , Dylan E. O’Sullivan2 , Joy Pader3 , Yibing Ruan3 , Edwin Wang4 , May Lynn Quan1,5,6 and Darren R. Brenner1,3,5* Abstract Background: The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inherited BRCA1 or BRCA2 mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours. Methods: In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages “deconstructSigs” and “SomaticSignatures” to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset. Results: Older patients had a higher proportion of mutations in PIK3CA, CDH1, and MAP3K1 genes, while young- onset patients had a higher proportion of mutations in GATA3 and CTNNB1. Mutational load was lower for young- onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database. -
The Cyclin-Dependent Kinase 8 Module Sterically Blocks Mediator Interactions with RNA Polymerase II
The cyclin-dependent kinase 8 module sterically blocks Mediator interactions with RNA polymerase II Hans Elmlund*†, Vera Baraznenok‡, Martin Lindahl†, Camilla O. Samuelsen§, Philip J. B. Koeck*¶, Steen Holmberg§, Hans Hebert*ʈ, and Claes M. Gustafsson‡ʈ *Department of Biosciences and Nutrition, Karolinska Institutet and School of Technology and Health, Royal Institute of Technology, Novum, SE-141 87 Huddinge, Sweden; †Department of Molecular Biophysics, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden; ‡Division of Metabolic Diseases, Karolinska Institutet, Novum, SE-141 86 Huddinge, Sweden; §Department of Genetics, Institute of Molecular Biology, Oester Farimagsgade 2A, DK-1353 Copenhagen K, Denmark; and ¶University College of Southern Stockholm, SE-141 57 Huddinge, Sweden Communicated by Roger D. Kornberg, Stanford University School of Medicine, Stanford, CA, August 28, 2006 (received for review February 21, 2006) CDK8 (cyclin-dependent kinase 8), along with CycC, Med12, and Here, we use the S. pombe system to investigate the molecular Med13, form a repressive module (the Cdk8 module) that prevents basis for the distinct functional properties of S and L Mediator. RNA polymerase II (pol II) interactions with Mediator. Here, we We find that the Cdk8 module binds to the pol II-binding cleft report that the ability of the Cdk8 module to prevent pol II of Mediator, where it sterically blocks interactions with the interactions is independent of the Cdk8-dependent kinase activity. polymerase. In contrast to earlier assumptions, the Cdk8 kinase We use electron microscopy and single-particle reconstruction to activity is dispensable for negative regulation of pol II interac- demonstrate that the Cdk8 module forms a distinct structural tions with Mediator. -
Functional Diversification of the Nleg Effector Family in Enterohemorrhagic Escherichia Coli
Functional diversification of the NleG effector family in enterohemorrhagic Escherichia coli Dylan Valleaua, Dustin J. Littleb, Dominika Borekc,d, Tatiana Skarinaa, Andrew T. Quailea, Rosa Di Leoa, Scott Houlistone,f, Alexander Lemake,f, Cheryl H. Arrowsmithe,f, Brian K. Coombesb, and Alexei Savchenkoa,g,1 aDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; bDepartment of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8S 4K1, Canada; cDepartment of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390; dDepartment of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390; ePrincess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G 2M9, Canada; fDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; and gDepartment of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB T2N 4N1, Canada Edited by Ralph R. Isberg, Howard Hughes Medical Institute and Tufts University School of Medicine, Boston, MA, and approved August 15, 2018 (receivedfor review November 6, 2017) The pathogenic strategy of Escherichia coli and many other gram- chains. Depending on the length and nature of the polyubiquitin negative pathogens relies on the translocation of a specific set of chain, it posttranslationally regulates the target protein’s locali- proteins, called effectors, into the eukaryotic host cell during in- zation, activation, or degradation. Ubiquitination is a multistep fection. These effectors act in concert to modulate host cell pro- process which begins with the ubiquitin-activating enzyme (E1) cesses in favor of the invading pathogen. Injected by the type III using ATP to “charge” ubiquitin, covalently binding the ubiquitin secretion system (T3SS), the effector arsenal of enterohemorrhagic C terminus by a thioester linkage. -
COFACTORS of the P65- MEDIATOR COMPLEX
COFACTORS OF THE April 5 p65- MEDIATOR 2011 COMPLEX Honors Thesis Department of Chemistry and Biochemistry NICHOLAS University of Colorado at Boulder VICTOR Faculty Advisor: Dylan Taatjes, PhD PARSONNET Committee Members: Rob Knight, PhD; Robert Poyton, PhD Table of Contents Abstract ......................................................................................................................................................... 3 Introduction .................................................................................................................................................. 4 The Mediator Complex ............................................................................................................................. 6 The NF-κB Transcription Factor ................................................................................................................ 8 Hypothesis............................................................................................................................................... 10 Results ......................................................................................................................................................... 11 Discussion.................................................................................................................................................... 15 p65-only factors ...................................................................................................................................... 16 p65-enriched factors .............................................................................................................................. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
FBXL19 Recruits CDK-Mediator to Cpg Islands of Developmental Genes Priming Them for Activation During Lineage Commitment
RESEARCH ARTICLE FBXL19 recruits CDK-Mediator to CpG islands of developmental genes priming them for activation during lineage commitment Emilia Dimitrova1, Takashi Kondo2†, Angelika Feldmann1†, Manabu Nakayama3, Yoko Koseki2, Rebecca Konietzny4‡, Benedikt M Kessler4, Haruhiko Koseki2,5, Robert J Klose1* 1Department of Biochemistry, University of Oxford, Oxford, United Kingdom; 2Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; 3Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Japan; 4Nuffield Department of Medicine, TDI Mass Spectrometry Laboratory, Target Discovery Institute, University of Oxford, Oxford, United Kingdom; 5CREST, Japan Science and Technology Agency, Kawaguchi, Japan Abstract CpG islands are gene regulatory elements associated with the majority of mammalian promoters, yet how they regulate gene expression remains poorly understood. Here, we identify *For correspondence: FBXL19 as a CpG island-binding protein in mouse embryonic stem (ES) cells and show that it [email protected] associates with the CDK-Mediator complex. We discover that FBXL19 recruits CDK-Mediator to †These authors contributed CpG island-associated promoters of non-transcribed developmental genes to prime these genes equally to this work for activation during cell lineage commitment. We further show that recognition of CpG islands by Present address: ‡Agilent FBXL19 is essential for mouse development. Together this reveals a new CpG island-centric Technologies, Waldbronn, mechanism for CDK-Mediator recruitment to developmental gene promoters in ES cells and a Germany requirement for CDK-Mediator in priming these developmental genes for activation during cell lineage commitment. Competing interests: The DOI: https://doi.org/10.7554/eLife.37084.001 authors declare that no competing interests exist. -
CCAR1 Polyclonal Antibody
PRODUCT DATA SHEET Bioworld Technology,Inc. CCAR1 polyclonal antibody Catalog: BS8140 Host: Rabbit Reactivity: Human BackGround: by affinity-chromatography using epitope-specific im- CARP-1 (cell division cycle and apoptosis regulator 1), munogen and the purity is > 95% (by SDS-PAGE). also known as CCAR1 or DIS, is a 1,150 amino acid pro- Applications: tein that localizes to the perinuclear region of the cyto- WB 1:500 - 1:2000 plasm and contains one SAP domain. Expressed in sever- IF 1:50 - 1:200 al epithelial cancer cell lines, including breast, colon, IP 1:50 - 1:100 prostate and leukemia, CARP-1 is involved in apoptotic Storage&Stability: signaling, as well as in cell cycle progression and cell Store at 4°C short term. Aliquot and store at -20°C long proliferation via interaction with c-Myc and cyclin B1. term. Avoid freeze-thaw cycles. CARP-1 is subject to DNA damage-induced phosphory- Specificity: lation, probably by ATM or ATR. The gene encoding CCAR1 polyclonal antibody detects endogenous levels of CARP-1 maps to human chromosome 10, which houses CCAR1 protein. over 1,200 genes and comprises nearly 4.5% of the hu- DATA: man genome. Defects in some of the genes that map to chromosome 10 are associated with Charcot-Marie Tooth disease, Jackson-Weiss syndrome, Usher syndrome, non- syndromatic deafness, Wolman’s syndrome, Cowden syndrome, multiple endocrine neoplasia type 2 and por- phyria. Product: Rabbit IgG, 1mg/ml in PBS with 0.02% sodium azide, WesternBlot (WB) analysis of CCAR1 polyclonal antibody 50% glycerol, pH7.2 Note: Molecular Weight: For research use only, not for use in diagnostic procedure.