DOCSLIB.ORG

Using Weighted Set Cover to Identify Biologically Significant Motifs

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Using Weighted Set Cover to Identify Biologically Significant Motifs A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Master of Science Robert J.M. Schmidt December 2015 © 2015 Robert J.M. Schmidt. All Rights Reserved. 2 This thesis titled Using Weighted Set Cover to Identify Biologically Significant Motifs by ROBERT J.M. SCHMIDT has been approved for the School of Electrical Engineering and Computer Science and the Russ College of Engineering and Technology by Lonnie R. Welch Stuckey Professor of Electrical Engineering and Computer Science Dennis Irwin Dean, Russ College of Engineering and Technology 3 ABSTRACT SCHMIDT, ROBERT J.M., M.S., December 2015, Computer Science Using Weighted Set Cover to Identify Biologically Significant Motifs Director of Thesis: Lonnie R. Welch One of the greatest challenges of mankind is understanding how living organisms operate, and a key step towards understanding this challenge is identifying how genes are regulated. Promoter regions play a key role in the regulation of genes via sequences of DNA base pairs known as transcription factor binding sites. When a transcription factor binding site is activated, the genes associated with the transcription factor binding site are transcribed, the first step towards creating proteins. The identification of transcription factor binding sites has come a long way with the advancements of next generation sequencing technologies and projects like ENCODE, but still relies on motif discovery algorithms to pinpoint the exact binding sites. In this thesis, the motif discovery problem is explored and a novel method based on weighted set cover is presented to identify the minimal set of motifs, with objective functions, that discriminately cover a set of DNA sequences. The results show that some motif set cover methods can more accurately identify biologically significant motifs over simply selecting the top scoring motifs. However, the weighed set cover algorithms did not perform exceptionally well when compared to standard selection methods, which is attributed to the use of a discriminative motif discovery application. Detailed results can be found at http://motifpipeline.com. 4 ACKNOWLEDGMENTS I would like to express my gratitude to Dr. Lonnie Welch, as an advisor and teacher throughout graduate school, for providing solid direction in my research, countless ideas and suggestions, continuous support, and for his work in obtaining the Choose Ohio First for Bioinformatics scholarships. Without Dr. Welch this thesis would not be possible. I would like to thank Dr. Frank Drews for listening to countless research presentations, providing useful guidance and numerous helpful suggestions along the way. I would like to thank Dr. David Juedes for teaching me the majority of what I know about the hardness of problems and the different tools available for solving computationally difficult problems. I also want to thank Dr. Sonsoles de Lacalle for all of her hard work on the rat estrous cycle project, allowing me to work with and analyze her data, and providing crucial insight into the biology behind the numbers. I also want to give a huge thanks to my friend Rami, Al-Ouran, who has given me countless hours of his time, and provided me with so much help and guidance throughout graduate school. I also want to give a huge thanks to Xiaoyu Liang for all of her help throughout graduate school and for always answering my questions. I also want to thank Richard Wolfe, Jeffery Jones, Yichao Li, Ashwini Naik, and the whole bioinformatics lab for all of their help and support throughout graduate school. I also want to give a big thanks to Choose Ohio First for Bioinformatics for help funding me throughout graduate school. Finally, I want to thank my love, Kasia, for always being by my side. 5 TABLE OF CONTENTS Page Abstract ............................................................................................................................... 3 Acknowledgments............................................................................................................... 4 List of Tables ...................................................................................................................... 8 List of Figures ..................................................................................................................... 9 Chapter 1: Introduction ..................................................................................................... 10 1.1 Background ............................................................................................................. 10 1.2 Problem Statement .................................................................................................. 15 Chapter 2: Literature Review ............................................................................................ 17 2.1 Motif Discovery ...................................................................................................... 17 2.1.1 Non-discriminative Motif Discovery Algorithms ............................................ 17 2.1.2 Discriminative Motif Discovery Algorithms ................................................... 19 2.2 Set Cover ................................................................................................................. 21 2.2.1 Set Cover Problem ........................................................................................... 21 2.2.2 Hitting Set Problem .......................................................................................... 23 Chapter 3: Algorithmic Approaches ................................................................................. 24 3.1 Set Cover Methods .................................................................................................. 24 3.1.1 Weighted Greedy Set Cover ............................................................................ 24 3.1.2 Weighted Relaxed Greedy Set Cover .............................................................. 25 3.1.3 Weighted Modified Greedy Approach Set Cover ............................................ 25 3.1.4 Weighted Hill Climbing Set Cover .................................................................. 26 3.1.5 Weighted Random Set Cover ........................................................................... 27 3.1.6 Weighted Simulated Annealing Set Cover ...................................................... 27 3.1.7 Integer Linear Programming Formulation ....................................................... 29 3.1.8 Linear Programming using Branch and Cut .................................................... 29 3.1.9 Linear Programming Relaxation with Randomized Rounding ........................ 30 3.2 Filter Methods ......................................................................................................... 31 3.2.1 Greedy Removal Method ................................................................................. 31 3.3 Weight Schemes ..................................................................................................... 32 3.3.1 Solution Based Weight Schemes ..................................................................... 32 6 3.3.2 Greedy Based Weight Schemes ....................................................................... 33 3.4 Metrics .................................................................................................................... 34 3.4.1 Comparisons .................................................................................................... 34 3.4.2 Classification Metrics ...................................................................................... 36 3.4.3 Ranking ............................................................................................................ 39 3.5 Baseline Motif Selection Methods .......................................................................... 40 Chapter 4: Case Studies .................................................................................................... 41 4.1 ENCODE Case Study ............................................................................................. 41 4.2 Rat Estrous Cycle Case Study ................................................................................ 43 4.3 Brugia Malayi Case Study ...................................................................................... 46 Chapter 5: Evaluation of Algorithms ................................................................................ 49 5.1 Weighted Greedy Set Cover ................................................................................... 57 5.1.1 Results .............................................................................................................. 57 5.1.2 Discussion ........................................................................................................ 61 5.2 Weighted Relaxed Greedy Set Cover ..................................................................... 62 5.2.1 Results .............................................................................................................. 62 5.2.2 Discussion ........................................................................................................ 65 5.3 Weighted Modified Greedy Set Cover ................................................................... 66 5.3.1 Results .............................................................................................................
Recommended publications
  • ARMCX3 (NM 177948) Human Untagged Clone – SC124834

    ARMCX3 (NM 177948) Human Untagged Clone – SC124834

    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 SC124834 ARMCX3 (NM_177948) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: ARMCX3 (NM_177948) Human Untagged Clone Tag: Tag Free Symbol: ARMCX3 Synonyms: ALEX3; dJ545K15.2; GASP6 Vector: pCMV6-XL4 E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: None Fully Sequenced ORF: >NCBI ORF sequence for NM_177948, the custom clone sequence may differ by one or more nucleotides ATGGGCTACGCCAGGAAAGTAGGCTGGGTGACCGCAGGCCTGGTGATTGGGGCTGGCGCCTGCTATTGCA TTTATAGACTGACTAGGGGAAGAAAACAGAACAAGGAAAAAATGGCTGAGGGTGGATCTGGGGATGTGGA TGATGCTGGGGACTGTTCTGGGGCCAGGTATAATGACTGGTCTGATGATGATGATGACAGCAATGAGAGC AAGAGTATAGTATGGTACCCACCTTGGGCTCGGATTGGGACTGAAGCTGGAACCAGAGCTAGGGCCAGGG CAAGGGCCAGGGCTACCCGGGCACGTCGGGCTGTCCAGAAACGGGCTTCCCCCAATTCAGATGATACCGT TTTGTCCCCTCAAGAGCTACAAAAGGTTCTTTGCTTGGTTGAGATGTCTGAAAAGCCTTATATTCTTGAA GCAGCTTTAATTGCTCTGGGTAACAATGCTGCTTATGCATTTAACAGAGATATTATTCGTGATCTGGGTG GTCTCCCAATTGTCGCAAAGATTCTCAATACTCGGGATCCCATAGTTAAGGAAAAGGCTTTAATTGTCCT GAATAACTTGAGTGTGAATGCTGAAAATCAGCGCAGGCTTAAAGTATACATGAATCAAGTGTGTGATGAC ACAATCACTTCTCGCTTGAACTCATCTGTGCAGCTTGCTGGACTGAGATTGCTTACAAATATGACTGTTA CTAATGAGTATCAGCACATGCTTGCTAATTCCATTTCTGACTTTTTTCGTTTATTTTCAGCGGGAAATGA AGAAACCAAACTTCAGGTTCTGAAACTCCTTTTGAATTTGGCTGAAAATCCAGCCATGACTAGGGAACTG CTCAGGGCCCAAGTACCATCTTCACTGGGCTCCCTCTTTAATAAGAAGGAGAACAAAGAAGTTATTCTTA AACTTCTGGTCATATTTGAGAACATAAATGATAATTTCAAATGGGAAGAAAATGAACCTACTCAGAATCA
  • RCAN2 Isoform 2 Recombinant Protein Cat

    RCAN2 Isoform 2 Recombinant Protein Cat

    RCAN2 Isoform 2 Recombinant Protein Cat. No.: 95-114 RCAN2 Isoform 2 Recombinant Protein Specifications SPECIES: Mouse SOURCE SPECIES: E. coli SEQUENCE: aa 2 - 197 FUSION TAG: Fusion Partner: C-terminal His-tag TESTED APPLICATIONS: ELISA, WB APPLICATIONS: This recombinant protein can be used for WB and ELISA. For research use only. PREDICTED MOLECULAR 26 kDa (Calculated) WEIGHT: Properties PURITY: ~95% PHYSICAL STATE: Liquid 100mM sodium phosphate, 10mM Tris, 500mM NaCl, 25 mM imidazole, 2mM MgCl2, 10% BUFFER: gycerol Store in working aliquots at -70˚C. Avoid freeze/thaw cycles. When working with proteins STORAGE CONDITIONS: care should be taken to keep recombinant protein at a cool and stable temperature. September 29, 2021 1 https://www.prosci-inc.com/rcan2-isoform-2-recombinant-protein-95-114.html Additional Info OFFICIAL SYMBOL: Rcan2 RCAN2 Antibody: Csp2, MCIP2, ZAKI-4, Dscr1l1, Zaki4, Calcipressin-2, Calcineurin inhibitory ALTERNATE NAMES: protein ZAKI-4 ACCESSION NO.: AAH62141 PROTEIN GI NO.: 38328420 GENE ID: 53901 Background and References Regulator of calcineurin 2 (RCAN2), also known as ZAKI4 and DSCR1L1, is expressed as two isoforms differing at their N-terminus. The longer of the two (isoform 1) is expressed exclusively in the brain, while isoform 2 is ubiquitously expressed, with highest expression in brain, heart, and muscle (1,2). Both isoforms bind to the catalytic subunit of calcineurin, a Ca++-dependent protein phosphatase involved in several neuronal functions, though BACKGROUND: their C-terminal region and inhibit calcineurin’s activity (3). Unlike isoform 1 of RCAN2, the expression of the second isoform is not induced by the thyroid hormone T3 (3).
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • The Inactive X Chromosome Is Epigenetically Unstable and Transcriptionally Labile in Breast Cancer

    The Inactive X Chromosome Is Epigenetically Unstable and Transcriptionally Labile in Breast Cancer

    Supplemental Information The inactive X chromosome is epigenetically unstable and transcriptionally labile in breast cancer Ronan Chaligné1,2,3,8, Tatiana Popova1,4, Marco-Antonio Mendoza-Parra5, Mohamed-Ashick M. Saleem5 , David Gentien1,6, Kristen Ban1,2,3,8, Tristan Piolot1,7, Olivier Leroy1,7, Odette Mariani6, Hinrich Gronemeyer*5, Anne Vincent-Salomon*1,4,6,8, Marc-Henri Stern*1,4,6 and Edith Heard*1,2,3,8 Extended Experimental Procedures Cell Culture Human Mammary Epithelial Cells (HMEC, Invitrogen) were grown in serum-free medium (HuMEC, Invitrogen). WI- 38, ZR-75-1, SK-BR-3 and MDA-MB-436 cells were grown in Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen) containing 10% fetal bovine serum (FBS). DNA Methylation analysis. We bisulfite-treated 2 µg of genomic DNA using Epitect bisulfite kit (Qiagen). Bisulfite converted DNA was amplified with bisulfite primers listed in Table S3. All primers incorporated a T7 promoter tag, and PCR conditions are available upon request. We analyzed PCR products by MALDI-TOF mass spectrometry after in vitro transcription and specific cleavage (EpiTYPER by Sequenom®). For each amplicon, we analyzed two independent DNA samples and several CG sites in the CpG Island. Design of primers and selection of best promoter region to assess (approx. 500 bp) were done by a combination of UCSC Genome Browser (http://genome.ucsc.edu) and MethPrimer (http://www.urogene.org). All the primers used are listed (Table S3). NB: MAGEC2 CpG analysis have been done with a combination of two CpG island identified in the gene core. Analysis of RNA allelic expression profiles (based on Human SNP Array 6.0) DNA and RNA hybridizations were normalized by Genotyping console.
  • Supplementary Table 1: Adhesion Genes Data Set

    Supplementary Table 1: Adhesion Genes Data Set

    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
  • Atlas of Subcellular RNA Localization Revealed by APEX-Seq

    Atlas of Subcellular RNA Localization Revealed by APEX-Seq

    bioRxiv preprint doi: https://doi.org/10.1101/454470; this version posted October 30, 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-ND 4.0 International license. Atlas of Subcellular RNA Localization Revealed by APEX-seq Furqan M. Fazal1,2,3,*, Shuo Han3,4,5,*, Pornchai Kaewsapsak3,4,5, Kevin R. Parker1,2,3, Jin Xu1,2,3, Alistair N. Boettiger6, Howard Y. Chang1,2,3,7,†, Alice Y. Ting3,4,5,8,9,† 1Center for Personal Dynamics Regulomes, Stanford University School of Medicine, Stanford, CA, USA 2Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA 3Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA 4Department of Chemistry, Stanford University, Stanford, CA, USA 5Department of Biology, Stanford University, Stanford, CA, USA 6Department of Developmental Biology, Stanford University School of Medicine, CA, USA 7Howard Hughes Medical Institute 8Chan Zuckerberg Biohub, San Francisco, California, USA 9Lead contact *These authors contributed equally to this work †Corresponding authors. Email: [email protected] (A.Y.T.); [email protected] (H.Y.C.) SUMMARY We introduce APEX-seq, a method for RNA sequencing based on spatial proximity to the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome, revealing extensive and exquisite patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts.
  • RCAN2 Antibody Cat

    RCAN2 Antibody Cat

    RCAN2 Antibody Cat. No.: 5051 RCAN2 Antibody Immunohistochemistry of RCAN2 in mouse brain tissue Immunofluorescence of RCAN2 in mouse brain tissue with with RCAN2 antibody at 2.5 μg/mL. RCAN2 antibody at 20 μg/mL. Specifications HOST SPECIES: Rabbit SPECIES REACTIVITY: Human, Mouse, Rat HOMOLOGY: Predicted species reactivity based on immunogen sequence: Bovine: (100%) RCAN2 antibody was raised against a 14 amino acid synthetic peptide near the center of human RCAN2. IMMUNOGEN: The immunogen is located within amino acids 70 - 120 of RCAN2. TESTED APPLICATIONS: ELISA, IF, IHC-P, WB September 26, 2021 1 https://www.prosci-inc.com/rcan2-antibody-5051.html RCAN2 antibody can be used for detection of RCAN2 by Western blot at 1 - 2 μg/mL. Antibody can also be used for immunohistochemistry starting at 2.5 μg/mL. For immunofluorescence start at 20 μg/mL. APPLICATIONS: Antibody validated: Western Blot in mouse samples; Immunohistochemistry in mouse samples and Immunofluorescence in mouse samples. All other applications and species not yet tested. POSITIVE CONTROL: 1) Cat. No. 1282 - 3T3 (NIH) Cell Lysate Properties PURIFICATION: RCAN2 Antibody is affinity chromatography purified via peptide column. CLONALITY: Polyclonal ISOTYPE: IgG CONJUGATE: Unconjugated PHYSICAL STATE: Liquid BUFFER: RCAN2 Antibody is supplied in PBS containing 0.02% sodium azide. CONCENTRATION: 1 mg/mL RCAN2 antibody can be stored at 4˚C for three months and -20˚C, stable for up to one STORAGE CONDITIONS: year. As with all antibodies care should be taken to avoid repeated freeze thaw cycles. Antibodies should not be exposed to prolonged high temperatures. Additional Info OFFICIAL SYMBOL: Rcan2 RCAN2 Antibody: Csp2, MCIP2, ZAKI-4, Dscr1l1, Zaki4, Calcipressin-2, Calcineurin inhibitory ALTERNATE NAMES: protein ZAKI-4 ACCESSION NO.: AAH62141 PROTEIN GI NO.: 38328420 GENE ID: 53901 USER NOTE: Optimal dilutions for each application to be determined by the researcher.
  • Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses

    Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses

    medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 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. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses 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 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 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. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened.
  • The RCAN Carboxyl End Mediates Calcineurin Docking-Dependent Inhibition Via a Site That Dictates Binding to Substrates and Regulators

    The RCAN Carboxyl End Mediates Calcineurin Docking-Dependent Inhibition Via a Site That Dictates Binding to Substrates and Regulators

    The RCAN carboxyl end mediates calcineurin docking-dependent inhibition via a site that dictates binding to substrates and regulators Sara Martı´nez-Martı´neza,1, Lali Genesca` b,1,2, Antonio Rodrı´gueza,c, Alicia Rayab, Eula`lia Salichsb, Felipe Werea, Marı´aDolores Lo´pez-Maderueloa, Juan Miguel Redondoa,3, and Susana de la Lunab,d,4 aDepartment of Vascular Biology and Inflammation, Centro Nacional de Investigaciones Cardiovasculares, 28029 Madrid, Spain; bGenes and Disease Program, Centre de Regulacio´Geno`mica, Universitat Pompeu Fabra and CIBER de Enfermedades Raras, 08003 Barcelona, Spain; cDepartamento de Biología Molecular, Facultad de Ciencias, Universidad Auto´noma de Madrid, 28049 Madrid, Spain; and dInstitucio´Catalana de Recerca i Estudis Avanc¸ats, 08010 Barcelona, Spain Edited by Tony Pawson, Mt. Sinai Hospital, Toronto, ON, Canada, and approved February 25, 2009 (received for review December 12, 2008) Specificity of signaling kinases and phosphatases toward their CN activity is also regulated by interaction with anchoring and targets is usually mediated by docking interactions with substrates regulatory proteins (11); however, little is known about how these and regulatory proteins. Here, we characterize the motifs involved proteins form contacts with CN. Among the regulatory proteins, in the physical and functional interaction of the phosphatase one of the most remarkable families is the recently renamed calcineurin with a group of modulators, the RCAN protein family. regulator of calcineurin (RCAN, previously known as DSCR/ Mutation of key residues within the hydrophobic docking-cleft of MCIP/calcipressin/Adapt78 in mammals) (12). RCANs bind to and the calcineurin catalytic domain impairs binding to all human RCAN inhibit CN-mediated activities in vitro (13–18).
  • Regulator of Calcineurin (RCAN): Beyond Down

    Regulator of Calcineurin (RCAN): Beyond Down

    Molecules and Cells Minireview Regulator of Calcineurin (RCAN): Beyond Down Syndrome Critical Region Sun-Kyung Lee1,2,* and Joohong Ahnn1,2,* 1Department of Life Science, 2Research Institute for Natural Sciences, College of Natural Sciences, Hanyang University, Seoul 04763, Korea *Correspondence: [email protected] (SKL); [email protected] (JA) https://doi.org/10.14348/molcells.2020.0060 www.molcells.org The regulator of calcineurin (RCAN) was first reported as RCAN3 a novel gene called DSCR1, encoded in a region termed the Down syndrome critical region (DSCR) of human chromosome 21. Genome sequence comparisons across INTRODUCTION species using bioinformatics revealed three members of the RCAN gene family, RCAN1, RCAN2, and RCAN3, present in The regulator of calcineurin (RCAN) was first reported as a most jawed vertebrates, with one member observed in most Down syndrome critical region 1 (DSCR1), which is encoded invertebrates and fungi. RCAN is most highly expressed in in a region that at that time was thought to participate in the brain and striated muscles, but expression has been reported onset of Down syndrome (DS) (Antonarakis, 2017; Fuentes in many other tissues, as well, including the heart and et al., 1995). Soon after, evidence showed that RCAN binds kidneys. Expression levels of RCAN homologs are responsive to and regulates the Ca2+/calmodulin-dependent serine/thre- to external stressors such as reactive oxygen species, Ca2+, onine phosphatase calcineurin, whose substrates include nu- amyloid β, and hormonal changes and upregulated in clear factor of activated T cells (NFAT), the transcription factor pathological conditions, including Alzheimer’s disease, that regulates gene expression in many cell types, including cardiac hypertrophy, diabetes, and degenerative neuropathy.
  • The Molecular Signature of the Stroma Response in Prostate

    The Molecular Signature of the Stroma Response in Prostate

    RESEARCH ARTICLE The Molecular Signature of the Stroma Response in Prostate Cancer-Induced Osteoblastic Bone Metastasis Highlights Expansion of Hematopoietic and Prostate Epithelial Stem Cell Niches Berna C. O¨ zdemir1., Janine Hensel1., Chiara Secondini1, Antoinette Wetterwald1, Ruth Schwaninger1, Achim Fleischmann2, Wolfgang Raffelsberger3, OPEN ACCESS Olivier Poch4, Mauro Delorenzi5, Ramzi Temanni6, Ian G. Mills7, Gabri van der Citation: O¨ zdemir BC, Hensel J, Secondini C, Pluijm8, George N. Thalmann1, Marco G. Cecchini1* Wetterwald A, Schwaninger R, et al. (2014) The Molecular Signature of the Stroma Response in 1. Urology Research Laboratory, Department of Urology and Department of Clinical Research, University of Prostate Cancer-Induced Osteoblastic Bone Bern, Bern, Switzerland, 2. Institute of Pathology, University of Bern, Bern, Switzerland, 3. Institut Ge´ne´tique Metastasis Highlights Expansion of Hematopoietic Biologie Mole´culaire Cellulaire (IGBMC), Strasbourg, France, 4. ICube UMR7357, University of Strasbourg, and Prostate Epithelial Stem Cell Niches. PLoS Strasbourg, France, 5. Ludwig Center for Cancer Research, Department of Oncology, University of Lausanne ONE 9(12): e114530. doi:10.1371/journal.pone. and Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland, 6. Biomedical Informatics Division, Sidra 0114530 Medical and Research Center, Doha, Qatar, 7. Prostate Cancer Research Group, Norway Centre for Editor: Adriano Angelucci, University of L9Aquila, Molecular Medicine (NCMM), University of Oslo, Oslo, Norway,
  • A Master Autoantigen-Ome Links Alternative Splicing, Female Predilection, and COVID-19 to Autoimmune Diseases

    A Master Autoantigen-Ome Links Alternative Splicing, Female Predilection, and COVID-19 to Autoimmune Diseases

    bioRxiv preprint doi: https://doi.org/10.1101/2021.07.30.454526; this version posted August 4, 2021. 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 4.0 International license. A Master Autoantigen-ome Links Alternative Splicing, Female Predilection, and COVID-19 to Autoimmune Diseases Julia Y. Wang1*, Michael W. Roehrl1, Victor B. Roehrl1, and Michael H. Roehrl2* 1 Curandis, New York, USA 2 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA * Correspondence: [email protected] or [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.30.454526; this version posted August 4, 2021. 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 4.0 International license. Abstract Chronic and debilitating autoimmune sequelae pose a grave concern for the post-COVID-19 pandemic era. Based on our discovery that the glycosaminoglycan dermatan sulfate (DS) displays peculiar affinity to apoptotic cells and autoantigens (autoAgs) and that DS-autoAg complexes cooperatively stimulate autoreactive B1 cell responses, we compiled a database of 751 candidate autoAgs from six human cell types. At least 657 of these have been found to be affected by SARS-CoV-2 infection based on currently available multi-omic COVID data, and at least 400 are confirmed targets of autoantibodies in a wide array of autoimmune diseases and cancer.