A Systematic Analysis of a Mi-RNA Inter-Pathway Regulatory Motif Stefano Di Carlo1*†, Gianfranco Politano1†, Alessandro Savino2† and Alfredo Benso1,2†
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Contig Protein Description Symbol Anterior Posterior Ratio
Table S2. List of proteins detected in anterior and posterior intestine pooled samples. Data on protein expression are mean ± SEM of 4 pools fed the experimental diets. The number of the contig in the Sea Bream Database (http://nutrigroup-iats.org/seabreamdb) is indicated. Contig Protein Description Symbol Anterior Posterior Ratio Ant/Pos C2_6629 1,4-alpha-glucan-branching enzyme GBE1 0.88±0.1 0.91±0.03 0.98 C2_4764 116 kDa U5 small nuclear ribonucleoprotein component EFTUD2 0.74±0.09 0.71±0.05 1.03 C2_299 14-3-3 protein beta/alpha-1 YWHAB 1.45±0.23 2.18±0.09 0.67 C2_268 14-3-3 protein epsilon YWHAE 1.28±0.2 2.01±0.13 0.63 C2_2474 14-3-3 protein gamma-1 YWHAG 1.8±0.41 2.72±0.09 0.66 C2_1017 14-3-3 protein zeta YWHAZ 1.33±0.14 4.41±0.38 0.30 C2_34474 14-3-3-like protein 2 YWHAQ 1.3±0.11 1.85±0.13 0.70 C2_4902 17-beta-hydroxysteroid dehydrogenase 14 HSD17B14 0.93±0.05 2.33±0.09 0.40 C2_3100 1-acylglycerol-3-phosphate O-acyltransferase ABHD5 ABHD5 0.85±0.07 0.78±0.13 1.10 C2_15440 1-phosphatidylinositol phosphodiesterase PLCD1 0.65±0.12 0.4±0.06 1.65 C2_12986 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase delta-1 PLCD1 0.76±0.08 1.15±0.16 0.66 C2_4412 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase gamma-2 PLCG2 1.13±0.08 2.08±0.27 0.54 C2_3170 2,4-dienoyl-CoA reductase, mitochondrial DECR1 1.16±0.1 0.83±0.03 1.39 C2_1520 26S protease regulatory subunit 10B PSMC6 1.37±0.21 1.43±0.04 0.96 C2_4264 26S protease regulatory subunit 4 PSMC1 1.2±0.2 1.78±0.08 0.68 C2_1666 26S protease regulatory subunit 6A PSMC3 1.44±0.24 1.61±0.08 -
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. -
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) -
(12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown Et Al
US 20030082511A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown et al. (43) Pub. Date: May 1, 2003 (54) IDENTIFICATION OF MODULATORY Publication Classification MOLECULES USING INDUCIBLE PROMOTERS (51) Int. Cl." ............................... C12O 1/00; C12O 1/68 (52) U.S. Cl. ..................................................... 435/4; 435/6 (76) Inventors: Steven J. Brown, San Diego, CA (US); Damien J. Dunnington, San Diego, CA (US); Imran Clark, San Diego, CA (57) ABSTRACT (US) Correspondence Address: Methods for identifying an ion channel modulator, a target David B. Waller & Associates membrane receptor modulator molecule, and other modula 5677 Oberlin Drive tory molecules are disclosed, as well as cells and vectors for Suit 214 use in those methods. A polynucleotide encoding target is San Diego, CA 92121 (US) provided in a cell under control of an inducible promoter, and candidate modulatory molecules are contacted with the (21) Appl. No.: 09/965,201 cell after induction of the promoter to ascertain whether a change in a measurable physiological parameter occurs as a (22) Filed: Sep. 25, 2001 result of the candidate modulatory molecule. Patent Application Publication May 1, 2003 Sheet 1 of 8 US 2003/0082511 A1 KCNC1 cDNA F.G. 1 Patent Application Publication May 1, 2003 Sheet 2 of 8 US 2003/0082511 A1 49 - -9 G C EH H EH N t R M h so as se W M M MP N FIG.2 Patent Application Publication May 1, 2003 Sheet 3 of 8 US 2003/0082511 A1 FG. 3 Patent Application Publication May 1, 2003 Sheet 4 of 8 US 2003/0082511 A1 KCNC1 ITREXCHO KC 150 mM KC 2000000 so 100 mM induced Uninduced Steady state O 100 200 300 400 500 600 700 Time (seconds) FIG. -
Rats and Axolotls Share a Common Molecular Signature After Spinal Cord Injury Enriched in Collagen-1
Rats and axolotls share a common molecular signature after spinal cord injury enriched in collagen-1 Athanasios Didangelos1, Katalin Bartus1, Jure Tica1, Bernd Roschitzki2, Elizabeth J. Bradbury1 1Wolfson CARD King’s College London, United Kingdom. 2Centre for functional Genomics, ETH Zurich, Switzerland. Running title: spinal cord injury in rats and axolotls Correspondence: A Didangelos: [email protected] SUPPLEMENTAL FIGURES AND LEGENDS Supplemental Fig. 1: Rat 7 days microarray differentially regulated transcripts. A-B: Protein-protein interaction networks of upregulated (A) and downregulated (B) transcripts identified by microarray gene expression profiling of rat SCI (4 sham versus 4 injured spinal cord samples) 7 days post-injury. Microarray expression data and experimental information is publicly available online (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45006) and is also summarised in Supplemental Table 1. Protein-protein interaction networks were performed in StringDB using the full range of protein interaction scores (0.15 – 0.99) to capture maximum evidence of proteins’ interactions. Networks were then further analysed for betweeness centrality and gene ontology (GO) annotations (BinGO) in Cytoscape. Node colour indicates betweeness centrality while edge colour and thickness indicate interaction score based on predicted functional links between nodes (green: low values; red: high values). C-D: The top 10 upregulated (C) or downregulated (D) transcripts sorted by betweeness centrality score in protein-protein interaction networks shown in A & B. E-F: Biological process GO analysis was performed on networks of upregulated and downregulated genes using BinGO in Cytoscape. Graphs indicate the 20 most significant GO categories and the number of genes in each category. -
Endogenous Protein Interactome of Human UDP-Glucuronosyltransferases Exposed by Untargeted Proteomics
ORIGINAL RESEARCH published: 03 February 2017 doi: 10.3389/fphar.2017.00023 Endogenous Protein Interactome of Human UDP-Glucuronosyltransferases Exposed by Untargeted Proteomics Michèle Rouleau, Yannick Audet-Delage, Sylvie Desjardins, Mélanie Rouleau, Camille Girard-Bock and Chantal Guillemette * Pharmacogenomics Laboratory, Canada Research Chair in Pharmacogenomics, Faculty of Pharmacy, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec, QC, Canada The conjugative metabolism mediated by UDP-glucuronosyltransferase enzymes (UGTs) significantly influences the bioavailability and biological responses of endogenous molecule substrates and xenobiotics including drugs. UGTs participate in the regulation of cellular homeostasis by limiting stress induced by toxic molecules, and by Edited by: controlling hormonal signaling networks. Glucuronidation is highly regulated at genomic, Yuji Ishii, transcriptional, post-transcriptional and post-translational levels. However, the UGT Kyushu University, Japan protein interaction network, which is likely to influence glucuronidation, has received Reviewed by: little attention. We investigated the endogenous protein interactome of human UGT1A Ben Lewis, Flinders University, Australia enzymes in main drug metabolizing non-malignant tissues where UGT expression is Shinichi Ikushiro, most prevalent, using an unbiased proteomics approach. Mass spectrometry analysis Toyama Prefectural University, Japan of affinity-purified UGT1A enzymes and associated protein complexes in liver, -
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 -
PHASE II DRUG METABOLIZING ENZYMES Petra Jancovaa*, Pavel Anzenbacherb,Eva Anzenbacherova
Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2010 Jun; 154(2):103–116. 103 © P. Jancova, P. Anzenbacher, E. Anzenbacherova PHASE II DRUG METABOLIZING ENZYMES Petra Jancovaa*, Pavel Anzenbacherb, Eva Anzenbacherovaa a Department of Medical Chemistry and Biochemistry, Faculty of Medicine and Dentistry, Palacky University, Hnevotinska 3, 775 15 Olomouc, Czech Republic b Department of Pharmacology, Faculty of Medicine and Dentistry, Palacky University, Hnevotinska 3, 775 15 Olomouc E-mail: [email protected] Received: March 29, 2010; Accepted: April 20, 2010 Key words: Phase II biotransformation/UDP-glucuronosyltransferases/Sulfotransferases, N-acetyltransferases/Glutathione S-transferases/Thiopurine S-methyl transferase/Catechol O-methyl transferase Background. Phase II biotransformation reactions (also ‘conjugation reactions’) generally serve as a detoxifying step in drug metabolism. Phase II drug metabolising enzymes are mainly transferases. This review covers the major phase II enzymes: UDP-glucuronosyltransferases, sulfotransferases, N-acetyltransferases, glutathione S-transferases and methyltransferases (mainly thiopurine S-methyl transferase and catechol O-methyl transferase). The focus is on the presence of various forms, on tissue and cellular distribution, on the respective substrates, on genetic polymorphism and finally on the interspecies differences in these enzymes. Methods and Results. A literature search using the following databases PubMed, Science Direct and EBSCO for the years, 1969–2010. Conclusions. Phase II drug metabolizing enzymes play an important role in biotransformation of endogenous compounds and xenobiotics to more easily excretable forms as well as in the metabolic inactivation of pharmacologi- cally active compounds. Reduced metabolising capacity of Phase II enzymes can lead to toxic effects of clinically used drugs. Gene polymorphism/ lack of these enzymes may often play a role in several forms of cancer. -
Endogenous Protein Interactome of Human
Human UGT1A interaction network 1 Endogenous protein interactome of human UDP- 2 glucuronosyltransferases exposed by untargeted proteomics 3 4 5 Michèle Rouleau, Yannick Audet-Delage, Sylvie Desjardins, Mélanie Rouleau, Camille Girard- 6 Bock and Chantal Guillemette* 7 8 Pharmacogenomics Laboratory, Canada Research Chair in Pharmacogenomics, Centre 9 Hospitalier Universitaire (CHU) de Québec Research Center and Faculty of Pharmacy, Laval 10 University, G1V 4G2, Québec, Canada 11 12 13 14 15 *Corresponding author: 16 Chantal Guillemette, Ph.D. 17 Canada Research Chair in Pharmacogenomics 18 Pharmacogenomics Laboratory, CHU de Québec Research Center, R4720 19 2705 Boul. Laurier, Québec, Canada, G1V 4G2 20 Tel. (418) 654-2296 Fax. (418) 654-2298 21 E-mail: [email protected] 22 23 24 25 26 27 28 29 30 31 32 Running title: Human UGT1A interaction network 33 1 Human UGT1A interaction network 1 Number of: Pages: 26 2 Tables: 2 3 Figures: 5 4 References: 62 5 Supplemental Tables: 7 6 Supplemental Figures: 5 7 8 Number of words: Total: 7882 9 Abstract: 229 10 Introduction: 549 11 Results: 1309 12 Discussion: 1403 13 Body Text: 3261 14 15 16 17 18 Abbreviations: AP: affinity purification; UGT, UDP-glucuronosyltransferases; IP, immuno- 19 precipitation; PPIs, protein-protein interactions; UDP-GlcA, Uridine diphospho-glucuronic acid; 20 ER, endoplasmic reticulum; MS, mass spectrometry. 21 22 Keywords: UGT; Proteomics; Protein-protein interaction; Affinity purification; Mass 23 spectrometry; Metabolism; Human tissues; 24 2 Human UGT1A interaction network 1 ABSTRACT 2 3 The conjugative metabolism mediated by UDP-glucuronosyltransferase enzymes (UGTs) 4 significantly influences the bioavailability and biological responses of endogenous molecule 5 substrates and xenobiotics including drugs. -
Glycomic and Transcriptomic Response of GSC11 Glioblastoma Stem Cells to STAT3 Phosphorylation Inhibition and Serum- Induced Differentiation
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/41720955 Glycomic and Transcriptomic Response of GSC11 Glioblastoma Stem Cells to STAT3 Phosphorylation Inhibition and Serum- Induced Differentiation Article in Journal of Proteome Research · March 2010 Impact Factor: 4.25 · DOI: 10.1021/pr900793a · Source: PubMed CITATIONS READS 21 107 11 authors, including: Yongjie Ji Waldemar Priebe University of Texas MD Anderson Cancer C… University of Texas MD Anderson Cancer C… 14 PUBLICATIONS 550 CITATIONS 275 PUBLICATIONS 6,260 CITATIONS SEE PROFILE SEE PROFILE Frederick F Lang Charles A Conrad University of Texas MD Anderson Cancer C… University of Texas MD Anderson Cancer C… 254 PUBLICATIONS 10,474 CITATIONS 84 PUBLICATIONS 2,169 CITATIONS SEE PROFILE SEE PROFILE Available from: Frederick F Lang Retrieved on: 26 May 2016 Glycomic and Transcriptomic Response of GSC11 Glioblastoma Stem Cells to STAT3 Phosphorylation Inhibition and Serum-Induced Differentiation Huan He,†,‡ Carol L. Nilsson,*,†,# Mark R. Emmett,†,‡ Alan G. Marshall,†,‡ Roger A. Kroes,§ Joseph R. Moskal,§ Yongjie Ji,| Howard Colman,| Waldemar Priebe,⊥ Frederick F. Lang,| and Charles A. Conrad| Ion Cyclotron Resonance Program, National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306-43903, Falk Center for Molecular Therapeutics, Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60201, Department of Neuro-oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, and Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030 Received September 05, 2009 A glioblastoma stem cell (GSC) line, GSC11, grows as neurospheres in serum-free media supplemented with EGF (epidermal growth factor) and bFGF (basic fibroblast growth factor), and, if implanted in nude mice brains, will recapitulate high-grade glial tumors. -
Systems Physiology and Nutrition In
SYSTEMS PHYSIOLOGY AND NUTRITION IN DAIRY CATTLE: APPLICATIONS OF OMICS AND BIOINFORMATICS TO BETTER UNDERSTAND THE HEPATIC METABOLOMICS AND TRANSCRIPTOMICS ADAPTATIONS IN TRANSITION DAIRY COWS BY KHURAM SHAHZAD DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Informatics in the Graduate College of the University of Illinois at Urbana-Champaign, 2017 Urbana, Illinois Doctoral Committee: Associate Professor, Juan J. Loor, Chair Professor Gustavo Caetano-Anolles Associate Professor, Juan Steibel, Michigan State University Assistant Professor Phil Cardoso ABSTRACT Application of systems concepts to better understand physiological and metabolic changes in dairy cows during the transition into lactation could enhance our understanding about the role of nutrients in helping to meet the animal’s requirements for optimal production and health. Four different analyses focused on the liver were conducted to analyze metabolic disorder or thermal stress. The first three analyses dealt with supplementation of methionine to prevent clinical ketosis development in high-genetic merit dairy cows. Four groups of cows were formed retrospectively based on clinical health evaluated at 1 week postpartum: cows that remained healthy (OVE), cows that developed ketosis (K), and healthy cows supplemented with one of two commercial methionine products [Smartamine M (SM), and MetaSmart (MS)]. The liver tissue samples (n = 6/group) were harvested at -10 d before calving, and were used for metabolomics (GC-MS, LC-MS; Metabolon Inc.) and transcriptomics (44K-whole-transcriptome microarray; Agilent) analyses. Therefore, the main goals of the analyses were to 1) uncover metabolome and transcriptome patterns in the prepartum liver that were unique to those cows that became ketotic postpartum, and to 2) uncover unique patterns affected by supplemental methionine. -
Straightjacket/Α2δ3 Deregulation Is Associated with Cardiac Conduction Defects in Myotonic Dystrophy Type 1
bioRxiv preprint doi: https://doi.org/10.1101/431569; this version posted October 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Straightjacket/α2δ3 deregulation is associated with cardiac conduction defects in Myotonic Dystrophy type 1 Emilie Plantié1, Masayuki Nakamori2, Yoan Renaud3, Aline Huguet4, Caroline Choquet5, Cristiana Dondi1, Lucile Miquerol5, Masanori Takahashi6, Geneviève Gourdon4, Guillaume Junion1, Teresa Jagla1, Monika Zmojdzian1* and Krzysztof Jagla1* 1 GReD, CNRS UMR6293, INSERM U1103, University of Clermont Auvergne, 28, Place Henri Dunant, 63000 Clermont-Ferrand, France 2 Department of Neurology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan 3 BYONET (www.byonet.fr) 4 Imagine Institute, Inserm UMR1163, 24, boulevard de Montparnasse, 75015 Paris, France 5 Aix-Marseille University, CNRS UMR7288, IBDM Luminy Campus Case 907, 13288 Marseille cedex 9, France 6 Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka 565-0871, Japan • Correspondence to: Krzysztof Jagla [email protected] and Monika Zmojdzian [email protected] tel. +33 473178181; GReD, CNRS UMR6293, INSERM U1103, University of Clermont Auvergne, 28, Place Henri Dunant, 63000 Clermont-Ferrand, France 1 bioRxiv preprint doi: https://doi.org/10.1101/431569; this version posted October 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ABSTRACT Cardiac conduction defects decrease life expectancy in myotonic dystrophy type 1 (DM1), a complex toxic CTG repeat disorder involving misbalance between two RNA- binding factors, MBNL1 and CELF1.