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Identifying MMP14 and COL12A1 As a Potential Combination of Prognostic Biomarkers in Pancreatic Ductal Adenocarcinoma Using Integrated Bioinformatics Analysis
Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis Jingyi Ding1, Yanxi Liu2 and Yu Lai3 1 Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China 2 University of California, Los Angeles, Los Angeles, CA, United States of America 3 School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China ABSTRACT Background. Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neo- plasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods. The microarray datasets GSE28735, GSE62165, and GSE91035 were down- loaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results. A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI Submitted 25 August 2020 network and then identified key modules. From the PPI network, 225 nodes and 803 Accepted 2 November 2020 edges were selected. The most significant module, which comprised 11 DEGs, was Published 23 November 2020 identified using the Molecular Complex Detection plugin. -
Analyses of Allele-Specific Gene Expression in Highly Divergent
ARTICLES Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance James J Crowley1,10, Vasyl Zhabotynsky1,10, Wei Sun1,2,10, Shunping Huang3, Isa Kemal Pakatci3, Yunjung Kim1, Jeremy R Wang3, Andrew P Morgan1,4,5, John D Calaway1,4,5, David L Aylor1,9, Zaining Yun1, Timothy A Bell1,4,5, Ryan J Buus1,4,5, Mark E Calaway1,4,5, John P Didion1,4,5, Terry J Gooch1,4,5, Stephanie D Hansen1,4,5, Nashiya N Robinson1,4,5, Ginger D Shaw1,4,5, Jason S Spence1, Corey R Quackenbush1, Cordelia J Barrick1, Randal J Nonneman1, Kyungsu Kim2, James Xenakis2, Yuying Xie1, William Valdar1,4, Alan B Lenarcic1, Wei Wang3,9, Catherine E Welsh3, Chen-Ping Fu3, Zhaojun Zhang3, James Holt3, Zhishan Guo3, David W Threadgill6, Lisa M Tarantino7, Darla R Miller1,4,5, Fei Zou2,11, Leonard McMillan3,11, Patrick F Sullivan1,5,7,8,11 & Fernando Pardo-Manuel de Villena1,4,5,11 Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Because regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in further characterizing these mechanisms. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. Effects from these variants influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. -
Hearing Aging Is 14.1±0.4% GWAS-Heritable
medRxiv preprint doi: https://doi.org/10.1101/2021.07.05.21260048; this version posted July 7, 2021. 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. It is made available under a CC-BY-NC 4.0 International license . Predicting age from hearing test results with machine learning reveals the genetic and environmental factors underlying accelerated auditory aging Alan Le Goallec1,2+, Samuel Diai1+, Théo Vincent1, Chirag J. Patel1* 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA 2Department of Systems, Synthetic and Quantitative Biology, Harvard University, Cambridge, MA, 02118, USA +Co-first authors *Corresponding author Contact information: Chirag J Patel [email protected] Abstract With the aging of the world population, age-related hearing loss (presbycusis) and other hearing disorders such as tinnitus become more prevalent, leading to reduced quality of life and social isolation. Unveiling the genetic and environmental factors leading to age-related auditory disorders could suggest lifestyle and therapeutic interventions to slow auditory aging. In the following, we built the first machine learning-based hearing age predictor by training models to predict chronological age from hearing test results (root mean squared error=7.10±0.07 years; R-Squared=31.4±0.8%). We defined hearing age as the prediction outputted by the model on unseen samples, and accelerated auditory aging as the difference between a participant’s hearing age and age. We then performed a genome wide association study [GWAS] and found that accelerated hearing aging is 14.1±0.4% GWAS-heritable. -
Propranolol-Mediated Attenuation of MMP-9 Excretion in Infants with Hemangiomas
Supplementary Online Content Thaivalappil S, Bauman N, Saieg A, Movius E, Brown KJ, Preciado D. Propranolol-mediated attenuation of MMP-9 excretion in infants with hemangiomas. JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2013.4773 eTable. List of All of the Proteins Identified by Proteomics This supplementary material has been provided by the authors to give readers additional information about their work. © 2013 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable. List of All of the Proteins Identified by Proteomics Protein Name Prop 12 mo/4 Pred 12 mo/4 Δ Prop to Pred mo mo Myeloperoxidase OS=Homo sapiens GN=MPO 26.00 143.00 ‐117.00 Lactotransferrin OS=Homo sapiens GN=LTF 114.00 205.50 ‐91.50 Matrix metalloproteinase‐9 OS=Homo sapiens GN=MMP9 5.00 36.00 ‐31.00 Neutrophil elastase OS=Homo sapiens GN=ELANE 24.00 48.00 ‐24.00 Bleomycin hydrolase OS=Homo sapiens GN=BLMH 3.00 25.00 ‐22.00 CAP7_HUMAN Azurocidin OS=Homo sapiens GN=AZU1 PE=1 SV=3 4.00 26.00 ‐22.00 S10A8_HUMAN Protein S100‐A8 OS=Homo sapiens GN=S100A8 PE=1 14.67 30.50 ‐15.83 SV=1 IL1F9_HUMAN Interleukin‐1 family member 9 OS=Homo sapiens 1.00 15.00 ‐14.00 GN=IL1F9 PE=1 SV=1 MUC5B_HUMAN Mucin‐5B OS=Homo sapiens GN=MUC5B PE=1 SV=3 2.00 14.00 ‐12.00 MUC4_HUMAN Mucin‐4 OS=Homo sapiens GN=MUC4 PE=1 SV=3 1.00 12.00 ‐11.00 HRG_HUMAN Histidine‐rich glycoprotein OS=Homo sapiens GN=HRG 1.00 12.00 ‐11.00 PE=1 SV=1 TKT_HUMAN Transketolase OS=Homo sapiens GN=TKT PE=1 SV=3 17.00 28.00 ‐11.00 CATG_HUMAN Cathepsin G OS=Homo -
Podoplanin Regulates Mammary Stem Cell Function and Tumorigenesis by Potentiating Wnt/Β-Catenin Signaling Laura Bresson1,2,3, Marisa M
© 2018. Published by The Company of Biologists Ltd | Development (2018) 145, dev160382. doi:10.1242/dev.160382 STEM CELLS AND REGENERATION RESEARCH ARTICLE Podoplanin regulates mammary stem cell function and tumorigenesis by potentiating Wnt/β-catenin signaling Laura Bresson1,2,3, Marisa M. Faraldo1,4, Amandine Di-Cicco1, Miguel Quintanilla5, Marina A. Glukhova1,4 and Marie-Ange Deugnier1,4,* ABSTRACT K5/14), P-cadherin, smooth muscle-specific contractile proteins, Δ Stem cells (SCs) drive mammary development, giving rise postnatally and the transcription factors Np63 (an isoform of Trp63) and Slug/ to an epithelial bilayer composed of luminal and basal myoepithelial Snail2, which are essential for the maintenance of basal cell identity cells. Dysregulation of SCs is thought to be at the origin of certain breast (Yalcin-Ozuysal et al., 2010; Guo et al., 2012). The luminal cell cancers; however, the molecular identity of SCs and the factors layer is characterized by the expression of K8/18. It includes a regulating their function remain poorly defined. We identified the subset of hormone-sensing cells that express estrogen, progesterone transmembrane protein podoplanin (Pdpn) as a specific marker of the and prolactin receptors (ER, PR and PrlR, respectively) and produce basal compartment, including multipotent SCs, and found Pdpn local mediators involved in the paracrine control of basal and localized at the basal-luminal interface. Embryonic deletion of Pdpn luminal cell function (Brisken and Ataca, 2015). targeted to basal cells diminished basal and luminal SC activity and It is established that both mammary lineages, basal and luminal, affected the expression of several Wnt/β-catenin signaling components originate from a common embryonic stem cell (SC) expressing basal in basal cells. -
Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
Bioinformatic Analysis of Structure and Function of LIM Domains of Human Zyxin Family Proteins
International Journal of Molecular Sciences Article Bioinformatic Analysis of Structure and Function of LIM Domains of Human Zyxin Family Proteins M. Quadir Siddiqui 1,† , Maulik D. Badmalia 1,† and Trushar R. Patel 1,2,3,* 1 Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, 4401 University Drive, Lethbridge, AB T1K 3M4, Canada; [email protected] (M.Q.S.); [email protected] (M.D.B.) 2 Department of Microbiology, Immunology and Infectious Disease, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive, Calgary, AB T2N 4N1, Canada 3 Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB T6G 2E1, Canada * Correspondence: [email protected] † These authors contributed equally to the work. Abstract: Members of the human Zyxin family are LIM domain-containing proteins that perform critical cellular functions and are indispensable for cellular integrity. Despite their importance, not much is known about their structure, functions, interactions and dynamics. To provide insights into these, we used a set of in-silico tools and databases and analyzed their amino acid sequence, phylogeny, post-translational modifications, structure-dynamics, molecular interactions, and func- tions. Our analysis revealed that zyxin members are ohnologs. Presence of a conserved nuclear export signal composed of LxxLxL/LxxxLxL consensus sequence, as well as a possible nuclear localization signal, suggesting that Zyxin family members may have nuclear and cytoplasmic roles. The molecular modeling and structural analysis indicated that Zyxin family LIM domains share Citation: Siddiqui, M.Q.; Badmalia, similarities with transcriptional regulators and have positively charged electrostatic patches, which M.D.; Patel, T.R. -
Transcriptional Mechanisms of Resistance to Anti-PD-1 Therapy
Author Manuscript Published OnlineFirst on February 13, 2017; DOI: 10.1158/1078-0432.CCR-17-0270 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Transcriptional mechanisms of resistance to anti-PD-1 therapy Maria L. Ascierto1, Alvin Makohon-Moore2, 11, Evan J. Lipson1, Janis M. Taube3,4, Tracee L. McMiller5, Alan E. Berger6, Jinshui Fan6, Genevieve J. Kaunitz3, Tricia R. Cottrell4, Zachary A. Kohutek7, Alexander Favorov8,10, Vladimir Makarov7,11, Nadeem Riaz7,11, Timothy A. Chan7,11, Leslie Cope8, Ralph H. Hruban4,9, Drew M. Pardoll1, Barry S. Taylor11,12,13, David B. Solit13, Christine A Iacobuzio-Donahue2,11, and Suzanne L. Topalian5 From the 1Departments of Oncology, 3Dermatology, 4Pathology, 5Surgery, 6The Lowe Family Genomics Core, 8Oncology Bioinformatics Core, and the 9 Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine and Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21287; the 10Laboratory of System Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia; and 2Pathology, 7Radiation Oncology, 11Human Oncology and Pathogenesis Program, 12Department of Epidemiology and Biostatistics, and the 13Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York NY 10065. MLA, AM-M, EJL, and JMT contributed equally to this work Running title: Transcriptional mechanisms of resistance to anti-PD-1 Key Words: melanoma, cancer genetics, immunotherapy, anti-PD-1 Financial Support: This study was supported by the Melanoma Research Alliance (to SLT and CI-D), the Bloomberg~Kimmel Institute for Cancer Immunotherapy (to JMT, DMP, and SLT), the Barney Family Foundation (to SLT), Moving for Melanoma of Delaware (to SLT), the 1 Downloaded from clincancerres.aacrjournals.org on October 2, 2021. -
Type of the Paper (Article
Cancers 2020, 12, S1 of S11 Supplementary Materials: Inflammatory Proteins HMGA2 and PRTN3 as Drivers of Vulvar Squamous Cell Carcinoma Pro- gression Agnieszka Fatalska, Natalia Rusetska, Elwira Bakuła-Zalewska, Artur Kowalik, Sebastian Zięba, Agnieszka Wroblewska, Kamil Zalewski, Krzysztof Goryca, Dominik Domański and Magdalena Kowalewska Text S1: Supplementary Methods iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) Cell Lysis The samples were snap-frozen in liquid nitrogen immediately after collection and stored at −70°C before pulverization (with the Microdismembrator II, B Braun Biotech In- ternational, Melsungen, Germany). Forty-two (14 control, 16 d-fVSCC and 12 progVSCC) samples were subjected to deep proteomic analysis. Total cell lysates were obtained from approximately 100 mg of pulverized patient tissue samples. The frozen pulverized tissue samples were resuspended in 300 µl of cold Lysis Buffer (1% sodium deoxycholate (NaDOC) in 25 mM HEPES (4-(2-hydroxyethyl)-1-pipera- zineethanesulfonic acid), boiled for 5 min at 100 °C, and cooled to room temperature (RT) on ice. Next, nucleic acids were degraded with 30 µL of benzonase nuclease (2.5 U/µL in 25 mM HEPES) at 4 °C for 30 min. Samples were then centrifuged twice at 12,000 × g for 10 min at 4 °C and the supernatants were stored at −80 °C. Protein concentrations were determined using the Pierce (Appleton, WI, USA) Bicinchoninic Acid (BCA) Assay Kit according to the manufacturer’s instructions, for the 96-well plate format, in duplicate, at two different sample dilutions (ThermoFisher Scientific, Waltham, MA, USA). Sample Protein Extract Digestion and iTRAQ Labeling Protein extract digestion and iTRAQ tag labeling was conducted using the 8-plex iTRAQ assay and iTRAQ Reagent and Buffer Kits (AB SCIEX, Framingham, MA, USA). -
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. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
Podocalyxin Is Required for Maintaining Blood–Brain Barrier Function During Acute Inflammation
Podocalyxin is required for maintaining blood–brain barrier function during acute inflammation Jessica Caita, Michael R. Hughesa,1, Matthew R. Zeglinskib,c,d, Allen W. Chane,f,g,h,i, Sabrina Osterhofa, R. Wilder Scotta,j, Diana Canals Hernaeza, Alissa Caita, A. Wayne Voglj,k, Pascal Bernatchezd,l, T. Michael Underhilla,j, David J. Granvilleb,c,d, Timothy H. Murphye,f,g, Calvin D. Roskelleyj,k, and Kelly M. McNagnya,1 aBiomedical Research Centre, University of British Columbia, Vancouver, BC, Canada V6T 1Z3; bICORD Centre, University of British Columbia, Vancouver, BC, Canada V5Z 1M9; cDepartment of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada V6T 2B5; dCentre for Heart and Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada V6Z 1Y6; eDepartment of Psychiatry, University of British Columbia, Vancouver, BC, Canada V6T 2A1; fKinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, BC, Canada V6T 1Z4; gDjavad Mowafaghian Center for Brain Health, University of British Columbia, Vancouver, BC, Canada V6T 1Z3; hNeuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada T6G 2R3; iNeurochemical Research Unit, Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada T6G 2R3; jDepartment of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, Canada V6T 1Z4; kLife Sciences Institute, University of British Columbia, Vancouver, BC, Canada V6T 1Z4; and lDepartment of Anaesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada V6T 1Z3 Edited by Lawrence Steinman, Stanford University School of Medicine, Stanford, CA, and approved January 15, 2019 (received for review September 6, 2018) Podocalyxin (Podxl) is broadly expressed on the luminal face of and plasma factors to effect repair.