Interstitial Microdeletion of the 1P34.3P34.2 Region 9 Authors: Joseph E
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The Transcription Factor Pou3f1 Promotes Neural Fate Commitment
RESEARCH ARTICLE elifesciences.org The transcription factor Pou3f1 promotes neural fate commitment via activation of neural lineage genes and inhibition of external signaling pathways Qingqing Zhu1,2†, Lu Song1†, Guangdun Peng1, Na Sun3, Jun Chen1, Ting Zhang1, Nengyin Sheng1, Wei Tang1, Cheng Qian1, Yunbo Qiao1, Ke Tang4, Jing-Dong Jackie Han3, Jinsong Li1, Naihe Jing1* 1State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; 2Department of Neurosurgery, West China Hospital, Sichuan University, Sichuan, China; 3Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; 4Institute of Life Science, Nanchang University, Nanchang, Jiangxi, China Abstract The neural fate commitment of pluripotent stem cells requires the repression of extrinsic inhibitory signals and the activation of intrinsic positive transcription factors. However, how these two events are integrated to ensure appropriate neural conversion remains unclear. In this study, we showed that Pou3f1 is essential for the neural differentiation of mouse embryonic stem cells (ESCs), specifically during the transition from epiblast stem cells (EpiSCs) to neural progenitor cells (NPCs). Chimeric analysis showed that Pou3f1 knockdown leads to a markedly decreased *For correspondence: njing@ incorporation of ESCs in the neuroectoderm. By contrast, Pou3f1-overexpressing ESC derivatives sibcb.ac.cn preferentially contribute to the neuroectoderm. Genome-wide ChIP-seq and RNA-seq analyses †These authors contributed indicated that Pou3f1 is an upstream activator of neural lineage genes, and also is a repressor of equally to this work BMP and Wnt signaling. -
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. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Functional Genomics Atlas of Synovial Fibroblasts Defining Rheumatoid Arthritis
medRxiv preprint doi: https://doi.org/10.1101/2020.12.16.20248230; this version posted December 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability Xiangyu Ge1*, Mojca Frank-Bertoncelj2*, Kerstin Klein2, Amanda Mcgovern1, Tadeja Kuret2,3, Miranda Houtman2, Blaž Burja2,3, Raphael Micheroli2, Miriam Marks4, Andrew Filer5,6, Christopher D. Buckley5,6,7, Gisela Orozco1, Oliver Distler2, Andrew P Morris1, Paul Martin1, Stephen Eyre1* & Caroline Ospelt2*,# 1Versus Arthritis Centre for Genetics and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK 2Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland 3Department of Rheumatology, University Medical Centre, Ljubljana, Slovenia 4Schulthess Klinik, Zurich, Switzerland 5Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK 6NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, UK 7Kennedy Institute of Rheumatology, University of Oxford Roosevelt Drive Headington Oxford UK *These authors contributed equally #corresponding author: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2020.12.16.20248230; this version posted December 18, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. -
TET2 Binding to Enhancers Facilitates Transcription Factor Recruitment in Hematopoietic Cells
Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press 1 2 TET2 binding to enhancers facilitates 3 transcription factor recruitment in hematopoietic cells 4 5 Kasper D. Rasmussen1,2,7,8*, Ivan Berest3,8, Sandra Keβler1,2,6, Koutarou Nishimura1,4, 6 Lucía Simón-Carrasco1,2, George S. Vassiliou5, Marianne T. Pedersen1,2, 7 Jesper Christensen1,2, Judith B. Zaugg3* and Kristian Helin1,2,4*. 8 9 1Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of 10 Copenhagen, 2200 Copenhagen, Denmark. 2The Novo Nordisk Foundation Center for Stem Cell Biology 11 (Danstem), Faculty or Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, 12 Denmark. 3European Molecular Biology Institute, Structural and Computational Unit. 4Cell Biology Program, 13 Memorial Sloan Kettering Cancer Center, New York, NY, USA. 5Wellcome Trust Sanger Institute, Wellcome 14 Trust Genome Campus, Cambridge, United Kingdom and Department of Haematology, Cambridge 15 University Hospitals NHS Trust, Cambridge, United Kingdom. 6Present address: Friedrich Miescher Institute 16 for Biomedical Research (FMI), CH-4058 Basel, Switzerland. 7Present address: Centre for Gene Regulation 17 and Expression (GRE), School of Life Sciences, University of Dundee, Dundee, UK. 8These authors 18 contributed equally. 19 20 *Correspondence: [email protected], [email protected], [email protected] 21 22 Running title: TET2 and TF binding in enhancers 23 24 Keywords: Acute Myeloid Leukemia; Chromatin; DNA binding; DNA methylation; Epigenetics; 25 Hematopoiesis; TET2; Transcription Factor Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press Rasmussen et al. -
Dissertation
Regulation of gene silencing: From microRNA biogenesis to post-translational modifications of TNRC6 complexes DISSERTATION zur Erlangung des DOKTORGRADES DER NATURWISSENSCHAFTEN (Dr. rer. nat.) der Fakultät Biologie und Vorklinische Medizin der Universität Regensburg vorgelegt von Johannes Danner aus Eggenfelden im Jahr 2017 Das Promotionsgesuch wurde eingereicht am: 12.09.2017 Die Arbeit wurde angeleitet von: Prof. Dr. Gunter Meister Johannes Danner Summary ‘From microRNA biogenesis to post-translational modifications of TNRC6 complexes’ summarizes the two main projects, beginning with the influence of specific RNA binding proteins on miRNA biogenesis processes. The fate of the mature miRNA is determined by the incorporation into Argonaute proteins followed by a complex formation with TNRC6 proteins as core molecules of gene silencing complexes. miRNAs are transcribed as stem-loop structured primary transcripts (pri-miRNA) by Pol II. The further nuclear processing is carried out by the microprocessor complex containing the RNase III enzyme Drosha, which cleaves the pri-miRNA to precursor-miRNA (pre-miRNA). After Exportin-5 mediated transport of the pre-miRNA to the cytoplasm, the RNase III enzyme Dicer cleaves off the terminal loop resulting in a 21-24 nt long double-stranded RNA. One of the strands is incorporated in the RNA-induced silencing complex (RISC), where it directly interacts with a member of the Argonaute protein family. The miRNA guides the mature RISC complex to partially complementary target sites on mRNAs leading to gene silencing. During this process TNRC6 proteins interact with Argonaute and recruit additional factors to mediate translational repression and target mRNA destabilization through deadenylation and decapping leading to mRNA decay. -
Transitions in Cell Potency During Early Mouse Development Are Driven by Notch
bioRxiv preprint doi: https://doi.org/10.1101/451922; this version posted October 24, 2018. 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 4.0 International license. Menchero et al. Transitions in cell potency during early mouse development are driven by Notch Sergio Menchero1, Antonio Lopez-Izquierdo1, Isabel Rollan1, Julio Sainz de Aja1, Ϯ, Maria Jose Andreu1, Minjung Kang2, Javier Adan1, Rui Benedito3, Teresa Rayon1, †, Anna-Katerina Hadjantonakis2 and Miguel Manzanares1,* 1 Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Melchor Fernández Almagro 3, 28029 Madrid, Spain. 2 Developmental Biology Program, Sloan Kettering Institute, New York NY 10065, USA. 3 Molecular Genetics of Angiogenesis Group, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Melchor Fernández Almagro 3, 28029 Madrid, Spain. Ϯ Current address: Children's Hospital, Stem Cell Program, 300 Longwood Ave, Boston MA 02115, USA. † Current address: The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK. * Corresponding author ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/451922; this version posted October 24, 2018. 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 4.0 International license. Menchero et al. Abstract The Notch signalling pathway plays fundamental roles in diverse developmental processes in metazoans, where it is important in driving cell fate and directing differentiation of various cell types. -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7 -
Genome-Wide Investigation of Gene-Cancer Associations to Predict Novel Therapeutic Targets in Oncology
Supplementary Material for: Genome-wide investigation of gene-cancer associations to predict novel therapeutic targets in oncology by Adrian´ Bazaga, Dan Leggate and Hendrik Weisser 1 Supplementary Table S1: Details of the artificial neural network architecture used in this work. Layer Type Number of neurons (output) Activation function 1 Fully-connected 64 ReLU 2 Fully-connected 128 ReLU 3 Fully-connected 16 ReLU 4 Fully-connected 2 Sigmoid 2 Supplementary Table S2: Performance in terms of test set AUC achieved by each of the five different machine learning methods across cancer types. Cancer type Bladder Breast Colon Kidney Leukemia Liver Lung Ovarian Pancreatic Method Logistic Regression 0.78 0.77 0.69 0.86 0.75 0.84 0.81 0.79 0.75 Support Vector Machine 0.77 0.78 0.72 0.88 0.72 0.84 0.87 0.8 0.73 Gradient Boosting Machine 0.75 0.7 0.74 0.75 0.71 0.81 0.73 0.74 0.73 Neural Network 0.67 0.72 0.71 0.71 0.7 0.86 0.75 0.75 0.72 Random Forests 0.76 0.75 0.76 0.79 0.74 0.85 0.83 0.77 0.76 3 Distributions of predicted probabilities Distributions of standard scores 3.0 Cancer type: Cancer type: bladder 0.6 bladder breast breast colon colon 2.5 kidney kidney leukemia 0.5 leukemia liver liver lung lung 2.0 ovarian ovarian 0.4 pancreatic pancreatic 1.5 0.3 Density Density 1.0 0.2 0.5 0.1 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 −2 −1 0 1 2 3 P(target) z−score Supplementary Figure S1: Distributions (kernel density estimates) of genome-wide predicted probabilities for different cancer types, before (left) and after (right) scaling per cancer type. -
A Novel Smad Nuclear Interacting Protein, SNIP1, Suppresses P300-Dependent TGF- Signal Transduction
Downloaded from genesdev.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press A novel Smad nuclear interacting protein, SNIP1, suppresses p300-dependent TGF- signal transduction Richard H. Kim,1 David Wang,1 Michael Tsang,2 Jennifer Martin,3,5 Carla Huff,1 Mark P. de Caestecker,1 W. Tony Parks,2 Xianwang Meng,3,5 Robert J. Lechleider,4 Tongwen Wang,3,5 and Anita B. Roberts2,6 1Laboratory of Cell Regulation and Carcinogenesis, National Cancer Institute, Bethesda, Maryland 20892 USA; 2Laboratory of Molecular Genetics, National Institute of Child Health and Human Development, Bethesda, Maryland 20892 USA; 3Department of Surgery, Massachusetts General Hospital, Department of Genetics, Harvard Medical School, Boston, Massachusetts 02114 USA Members of the transforming growth factor- superfamily play critical roles in controlling cell growth and differentiation. Effects of TGF- family ligands are mediated by Smad proteins. To understand the mechanism of Smad function, we sought to identify novel interactors of Smads by use of a yeast two-hybrid system. A 396-amino acid nuclear protein termed SNIP1 was cloned and shown to harbor a nuclear localization signal (NLS) and a Forkhead-associated (FHA) domain. The carboxyl terminus of SNIP1 interacts with Smad1 and Smad2 in yeast two-hybrid as well as in mammalian overexpression systems. However, the amino terminus of SNIP1 harbors binding sites for both Smad4 and the coactivator CBP/p300. Interaction between endogenous levels of SNIP1 and Smad4 or CBP/p300 is detected in NMuMg cells as well as in vitro. Overexpression of full-length SNIP1 or its amino terminus is sufficient to inhibit multiple gene responses to TGF- and CBP/p300, as well as the formation of a Smad4/p300 complex. -
Durbinharly.Pdf (9.770Mb)
GENOMICS OF SEASONAL HAIR SHEDDING AND ECOREGION-SPECIFIC GROWTH TO IDENTIFY ENVIRONMENTALLY-ADAPTED BEEF CATTLE _______________________________________ A Dissertation presented to the Faculty of the Graduate School at the University of Missouri-Columbia _______________________________________________________ In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy _____________________________________________________ by HARLY JANE DURBIN Dr. Jared Decker, Dissertation Supervisor December 2020 APPROVAL PAGE The undersigned, appointed by the Dean of the Graduate School, have examined the dissertation entitled: GENOMICS OF SEASONAL HAIR SHEDDING AND ECO-REGION SPECIFIC GROWTH TO IDENTIFY ENVIRONMENTALLY-ADAPTED BEEF CATTLE Presented by Harly Jane Durbin, a candidate for the degree of Doctor of Philosophy, and hereby certify that in their opinion it is worthy of acceptance. _____________________________________ Dr. Jared E. Decker, Animal Sciences, UMC _____________________________________ Dr. Robert D. Schnabel, Animal Sciences, UMC _____________________________________ Dr. Jeremy F. Taylor, Animal Sciences, UMC _____________________________________ Dr. Elizabeth G. King, Biological Sciences, UMC _____________________________________ Dr. Stephen P. Miller, Angus Genetics, Inc. DEDICATION Momma – thank you for teaching me compassion and also how to approach life with a sense of humor. You do such an incredible amount for other people and I strive to be as selfless every day. Daddy – thank you for encouraging me to think independently and for teaching me how to stand up for myself. Thank you also for the years of FFA (also known sometimes as Father Feeds Animal) and getting up early to haul heifers to jackpot shows. Darby – thank you for holding me to a high standard. I often think there was some mix-up and that you were meant to be the older sister for how much you hold me up. -
Smad Regulation in TGF-(Beta) Signal Transduction
COMMENTARY 4359 Smad regulation in TGF-β signal transduction Aristidis Moustakas, Serhiy Souchelnytskyi and Carl-Henrik Heldin Ludwig Institute for Cancer Research, Box 595, SE-751 24 Uppsala, Sweden Corresponding author (e-mail: [email protected]) Journal of Cell Science 114, 4359-4369 (2001) © The Company of Biologists Ltd Summary Smad proteins transduce signals from transforming growth promote degradation of transcriptional repressors, thus factor-β (TGF-β) superfamily ligands that regulate cell facilitating target gene regulation by TGF-β. Smads proliferation, differentiation and death through activation themselves can also become ubiquitinated and are of receptor serine/threonine kinases. Phosphorylation of degraded by proteasomes. Finally, the inhibitory Smads (I- receptor-activated Smads (R-Smads) leads to formation of Smads) block phosphorylation of R-Smads by the receptors complexes with the common mediator Smad (Co-Smad), and promote ubiquitination and degradation of receptor which are imported to the nucleus. Nuclear Smad complexes, thus inhibiting signalling. oligomers bind to DNA and associate with transcription factors to regulate expression of target genes. Alternatively, Key words: Phosphorylation, Signal transduction, Smad, nuclear R-Smads associate with ubiquitin ligases and Transforming growth factor-β, Ubiquitination Introduction chromosome 15q21-22, and MADH5, MADH1 and MADH8 Members of the transforming growth factor-β (TGF-β) family to chromosomes 15q31, 4 and 13, respectively (Gene control growth, differentiation