Tubulin Gene (TUBB) in the HLA Class I Region May Provide the Genetic Basis for HLA-Linked Microtubule Dysfunction
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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. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Blood-Based Molecular Biomarker Signatures in Alzheimer's
bioRxiv preprint doi: https://doi.org/10.1101/481879; this version posted December 31, 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. Article Blood-based molecular biomarker signatures in Alzheimer’s disease: insights from systems biomedicine perspective Tania Islam 1,#, Md. Rezanur Rahman 2,#,*, Md. Shahjaman3, Toyfiquz Zaman2, Md. Rezaul Karim2, Julian M.W. Quinn4, R.M. Damian Holsinger5,6, and Mohammad Ali Moni 6,* 1Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh; [email protected](T.I.). 2 Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj, Bangladesh; [email protected](M.R.R.). 3 Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh; [email protected] (M.S.). 4 Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia;[email protected](J.W.W.Q.). 5 Laboratory of Molecular Neuroscience and Dementia, Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia; [email protected] (R.M.D.H.). 6 Discipline of Biomedical Science, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia; [email protected] (M.A.M.). #These two authors have made an equal contribution and hold joint first authorship for this work. * Correspondence: [email protected] (M.R.R.) or [email protected] (M.A.M.). Abstract: Background and objectives: Alzheimer’s disease (AD) is the progressive neurodegenerative disease characterized by dementia, but no peripheral biomarkers available yet that can detect the AD. -
Snipa Snpcard
SNiPAcard Block annotations Block info genomic range chr6:29,970,960-30,792,117 block size 821,158 bp variant count 107 variants Basic features Conservation/deleteriousness Linked genes μ = -0.141 [-3.802 – ABCF1 , AL662797.1 , AL662800.2 , ATAT1 , C6orf136 , DHX16 , FLOT1 4.141] , GNL1 , HCG17 , HCG18 , HCG19P , HCG20 , HCG4P3 , HLA-J , HLA-L , HLA-N , IER3 , LINC00243 , MDC1 , MICC , MRPS18B , NRM , PAIP1P1 , PPP1R10 , PPP1R11 , PPP1R18 , PTMAP1 , RN7SL353P , phyloP gene(s) hit or close-by RNF39 , RPP21 , SUCLA2P1 , TRIM10 , TRIM15 , TRIM26 , TRIM26BP , TRIM31 , TRIM31-AS1 , TRIM39 , TRIM39-RPP21 , TRIM40 , TUBB , UBQLN1P1 , XXbac-BPG252P9.10 , XXbac-BPG252P9.9 , XXbac-BPG283O16.9 , Y_RNA , ZNRD1 , ZNRD1-AS1 , ZNRD1-AS1_3 μ = 0.130 [0 – 1] BTN3A2 , BTN3A3 , C2 , CCHCR1 , CFB , FLOT1 , GABBR1 , HCG17 , HCG20 , HCG22 , HCP5 , HIST1H3I , HLA-A , HLA-B , HLA-C , HLA-F- AS1 , HLA-G , HLA-H , HLA-J , HLA-K , HLA-L , HLA-U , HLA-V , IER3 phastCons eQTL gene(s) , LINC00243 , MICB , NDUFS1 , OR5V1 , PPP1R18 , PSORS1C1 , RNF39 , RPP21 , TMEM154 , TRIM26BP , TRIM31 , TRIM39-RPP21 , VARS2 , WASF5P , ZFP57 , ZNRD1 , ZNRD1-AS1_3 μ = -0.462 [-9.98 – 5.1] ATAT1 , ATAT1 , ATAT1 , ATAT1 , ATAT1 , ATAT1 , ATAT1 , DDR1 , DDR1 , DDR1 , DDR1 , DDR1 , DDR1 , DDR1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , HCG19P , HCG19P potentially regulated , HCG19P , HCG19P , HCG19P , HCG19P , HCG19P , NRM , NRM , GERP++ gene(s) NRM , NRM , NRM , NRM , NRM , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , TRIM26 , TRIM26 , TRIM26 -
Whole Exome Sequencing Reveals NOTCH1 Mutations in Anaplastic Large Cell Lymphoma and Points to Notch Both As a Key Pathway and a Potential Therapeutic Target
Non-Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing reveals NOTCH1 mutations in anaplastic large cell lymphoma and points to Notch both as a key pathway and a potential therapeutic target Hugo Larose, 1,2 Nina Prokoph, 1,2 Jamie D. Matthews, 1 Michaela Schlederer, 3 Sandra Högler, 4 Ali F. Alsulami, 5 Stephen P. Ducray, 1,2 Edem Nuglozeh, 6 Mohammad Feroze Fazaludeen, 7 Ahmed Elmouna, 6 Monica Ceccon, 2,8 Luca Mologni, 2,8 Carlo Gambacorti-Passerini, 2,8 Gerald Hoefler, 9 Cosimo Lobello, 2,10 Sarka Pospisilova, 2,10,11 Andrea Janikova, 2,11 Wilhelm Woessmann, 2,12 Christine Damm-Welk, 2,12 Martin Zimmermann, 13 Alina Fedorova, 14 Andrea Malone, 15 Owen Smith, 15 Mariusz Wasik, 2,16 Giorgio Inghirami, 17 Laurence Lamant, 18 Tom L. Blundell, 5 Wolfram Klapper, 19 Olaf Merkel, 2,3 G. A. Amos Burke, 20 Shahid Mian, 6 Ibraheem Ashankyty, 21 Lukas Kenner 2,3,22 and Suzanne D. Turner 1,2,10 1Department of Pathology, University of Cambridge, Cambridge, UK; 2European Research Initiative for ALK Related Malignancies (ERIA; www.ERIALCL.net ); 3Department of Pathology, Medical University of Vienna, Vienna, Austria; 4Unit of Laboratory Animal Pathology, Uni - versity of Veterinary Medicine Vienna, Vienna, Austria; 5Department of Biochemistry, University of Cambridge, Tennis Court Road, Cam - bridge, UK; 6Molecular Diagnostics and Personalised Therapeutics Unit, Colleges of Medicine and Applied Medical Sciences, University of Ha’il, Ha’il, Saudi Arabia; 7Neuroinflammation Research Group, Department of Neurobiology, A.I Virtanen Institute -
Effect of Cigarette Smoke on DNA Methylation and Lung Function
de Vries et al. Respiratory Research (2018) 19:212 https://doi.org/10.1186/s12931-018-0904-y RESEARCH Open Access From blood to lung tissue: effect of cigarette smoke on DNA methylation and lung function Maaike de Vries1,2* , Diana A van der Plaat1,2, Ivana Nedeljkovic3, Rikst Nynke Verkaik-Schakel4, Wierd Kooistra2,5, Najaf Amin3, Cornelia M van Duijn3, Corry-Anke Brandsma2,5, Cleo C van Diemen6, Judith M Vonk1,2 and H Marike Boezen1,2 Abstract Background: Genetic and environmental factors play a role in the development of COPD. The epigenome, and more specifically DNA methylation, is recognized as important link between these factors. We postulate that DNA methylation is one of the routes by which cigarette smoke influences the development of COPD. In this study, we aim to identify CpG-sites that are associated with cigarette smoke exposure and lung function levels in whole blood and validate these CpG-sites in lung tissue. Methods: The association between pack years and DNA methylation was studied genome-wide in 658 current smokers with >5 pack years using robust linear regression analysis. Using mediation analysis, we subsequently selected the CpG-sites that were also associated with lung function levels. Significant CpG-sites were validated in lung tissue with pyrosequencing and expression quantitative trait methylation (eQTM) analysis was performed to investigate the association between DNA methylation and gene expression. Results: 15 CpG-sites were significantly associated with pack years and 10 of these were additionally associated with lung function levels. We validated 5 CpG-sites in lung tissue and found several associations between DNA methylation and gene expression. -
Encoded on Chromosome 6P21.33 in Human Breast Cancers Revealed by Transcrip- Tome Analysis Yan A
Journal of Cancer 2010, 1 38 Journal of Cancer 2010; 1:38-50 © Ivyspring International Publisher. All rights reserved Research Paper Undetectable and Decreased Expression of KIAA1949 (Phostensin) Encoded on Chromosome 6p21.33 in Human Breast Cancers Revealed by Transcrip- tome Analysis Yan A. Su1 , Jun Yang1, Lian Tao1, and Hein Nguyen1, and Ping He2 1. GenProMarkers Inc., Rockville, Maryland 20850, USA; 2. Division of Hematology, Center for Biological Evaluation and Research, Food and Drug Administration, Bethesda, MD 20892, USA Corresponding author: Yan A. Su, MD, PhD, GenProMarkers Inc., 9700 Great Seneca Highway, Suite 182, Rockville, Maryland 20850. Phone: (301) 326-6523; FAX: (240) 453-6208; Email:[email protected] Published: 2010.06.21 Abstract Cytogenetic aberration and loss of heterozygosity (LOH) are documented on chromosome 6 in many cancers and the introduction of a neo-tagged chromosome 6 into breast cancer cell lines mediates suppression of tumorigenicity. In this study, we described the identification of KIAA1949 (phostensin) as a putative tumor suppressor gene. Our microarray analysis screened 25,985 cDNAs between a tumorigenic and metastatic breast cancer cell line MDA-MB-231 and the chromosome 6-mediated suppressed, non-tumorigenic and non-metastatic derivative cell line MDA/H6, resulting in the identification of 651 differentially expressed genes. Using customized microarrays containing these 651 cDNAs and 117 con- trols, we identified 200 frequently dysregulated genes in 10 breast cancer cell lines and 5 tumor tissues using MDA/H6 as reference. Our bioinformatics analysis revealed that chro- mosome 6 encodes 25 of these 200 genes, with 4 downregulation and 21 upergulation. -
The RNA Base-Pairing Problem and Base- Pairing Solutions
Downloaded from http://cshperspectives.cshlp.org/ on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press The RNA Base-Pairing Problem and Base- Pairing Solutions Zhipeng Lu and Howard Y. Chang Center for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305 Correspondence: [email protected] SUMMARY RNA molecules are folded into structures and complexes to perform a wide variety of functions. Determination of RNA structures and their interactions is a fundamental problem in RNA biology. Most RNA molecules in living cells are large and dynamic, posing unique challenges to structure analysis. Here we review progress in RNA structure analysis, focusing on methods that use the “cross-link, proximally ligate, and sequence” principle for high-throughput detec- tion of base-pairing interactions in living cells. Beginning with a comparison of commonly used methods in structure determination and a brief historical account of psoralen cross-linking studies, we highlight the important features of cross-linking methods and new biological in- sights into RNA structures and interactions from recent studies. Further improvement of these cross-linking methods and application to previously intractable problems will shed new light on the mechanisms of the “modern RNA world.” Outline 1 The everlasting RNA structure problem and 7 Dynamic and complex structures: Catching ever-evolving solutions RNA in action 2 Historical use of psoralen to analyze RNA 8 Architecture of the XIST RNP: Modular structures and interactions structures for modular functions 3 The next generation: Cross-linking 9 Novel RNA–RNA interactions: The molecular with proximity ligation and social network high-throughput sequencing 10 Limitations of the psoralen cross-linking 4 Basic analysis of RNA structure cross-linking methods and future directions data 11 Integrating methods to solve complex RNA 5 Prevalent long-range structures structures and interactions in the transcriptome References 6 Conservation analysis of the RNA structures Editors: Thomas R. -
Network-Based Method for Drug Target Discovery at the Isoform Level
www.nature.com/scientificreports OPEN Network-based method for drug target discovery at the isoform level Received: 20 November 2018 Jun Ma1,2, Jenny Wang2, Laleh Soltan Ghoraie2, Xin Men3, Linna Liu4 & Penggao Dai 1 Accepted: 6 September 2019 Identifcation of primary targets associated with phenotypes can facilitate exploration of the underlying Published: xx xx xxxx molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited efort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specifc isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path’ drug target prioritization method. We used a leukemia cancer network and diferential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defned by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. -
Integrative Analysis of Next Generation Sequencing for Small Non-Coding
Beck et al. BMC Medical Genomics 2011, 4:19 http://www.biomedcentral.com/1755-8794/4/19 RESEARCHARTICLE Open Access Integrative analysis of next generation sequencing for small non-coding RNAs and transcriptional regulation in Myelodysplastic Syndromes Dominik Beck1,2, Steve Ayers4, Jianguo Wen3, Miriam B Brandl1,2, Tuan D Pham1, Paul Webb4, Chung-Che Chang3*, Xiaobo Zhou1* Abstract Background: Myelodysplastic Syndromes (MDSS) are pre-leukemic disorders with increasing incident rates worldwide, but very limited treatment options. Little is known about small regulatory RNAs and how they contribute to pathogenesis, progression and transcriptome changes in MDS. Methods: Patients’ primary marrow cells were screened for short RNAs (RNA-seq) using next generation sequencing. Exon arrays from the same cells were used to profile gene expression and additional measures on 98 patients obtained. Integrative bioinformatics algorithms were proposed, and pathway and ontology analysis performed. Results: In low-grade MDS, observations implied extensive post-transcriptional regulation via microRNAs (miRNA) and the recently discovered Piwi interacting RNAs (piRNA). Large expression differences were found for MDS- associated and novel miRNAs, including 48 sequences matching to miRNA star (miRNA*) motifs. The detected species were predicted to regulate disease stage specific molecular functions and pathways, including apoptosis and response to DNA damage. In high-grade MDS, results suggested extensive post-translation editing via transfer RNAs (tRNAs), providing a potential link for reduced apoptosis, a hallmark for this disease stage. Bioinformatics analysis confirmed important regulatory roles for MDS linked miRNAs and TFs, and strengthened the biological significance of miRNA*. The “RNA polymerase II promoters” were identified as the tightest controlled biological function. -
The Proteins of Keratoconus: a Literature Review Exploring Their Contribution to the Pathophysiology of the Disease
Adv Ther (2019) 36:2205–2222 https://doi.org/10.1007/s12325-019-01026-0 REVIEW The Proteins of Keratoconus: a Literature Review Exploring Their Contribution to the Pathophysiology of the Disease Eleftherios Loukovitis . Nikolaos Kozeis . Zisis Gatzioufas . Athina Kozei . Eleni Tsotridou . Maria Stoila . Spyros Koronis . Konstantinos Sfakianakis . Paris Tranos . Miltiadis Balidis . Zacharias Zachariadis . Dimitrios G. Mikropoulos . George Anogeianakis . Andreas Katsanos . Anastasios G. Konstas Received: June 11, 2019 / Published online: July 30, 2019 Ó The Author(s) 2019 ABSTRACT abnormalities primarily relate to the weakening of the corneal collagen. Their understanding is Introduction: Keratoconus (KC) is a complex, crucial and could contribute to effective man- genetically heterogeneous multifactorial agement of the disease, such as with the aid of degenerative disorder characterized by corneal corneal cross-linking (CXL). The present article ectasia and thinning. Its incidence is approxi- critically reviews the proteins involved in the mately 1/2000–1/50,000 in the general popula- pathophysiology of KC, with particular tion. KC is associated with moderate to high emphasis on the characteristics of collagen that myopia and irregular astigmatism, resulting in pertain to CXL. severe visual impairment. KC structural Methods: PubMed, MEDLINE, Google Scholar and GeneCards databases were screened for rel- evant articles published in English between January 2006 and June 2018. Keyword combi- Enhanced Digital Features To view enhanced digital nations of the words ‘‘keratoconus,’’ ‘‘risk features for this article go to https://doi.org/10.6084/ m9.figshare.8427200. E. Loukovitis K. Sfakianakis Hellenic Army Medical Corps, Thessaloniki, Greece Division of Surgical Anatomy, Laboratory of Anatomy, Medical School, Democritus University of E. -
Loss of LMOD1 Impairs Smooth Muscle Cytocontractility and Causes
Loss of LMOD1 impairs smooth muscle cytocontractility PNAS PLUS and causes megacystis microcolon intestinal hypoperistalsis syndrome in humans and mice Danny Halima,1, Michael P. Wilsonb,1, Daniel Oliverc, Erwin Brosensa, Joke B. G. M. Verheijd, Yu Hanb, Vivek Nandab, Qing Lyub, Michael Doukase, Hans Stoope, Rutger W. W. Brouwerf, Wilfred F. J. van IJckenf, Orazio J. Slivanob, Alan J. Burnsa,g, Christine K. Christieb, Karen L. de Mesy Bentleyh, Alice S. Brooksa, Dick Tibboeli, Suowen Xub, Zheng Gen Jinb, Tono Djuwantonoj, Wei Yanc, Maria M. Alvesa, Robert M. W. Hofstraa,g,2, and Joseph M. Mianob,2 aDepartment of Clinical Genetics, Erasmus University Medical Center, 3015 CN Rotterdam, The Netherlands; bAab Cardiovascular Research Institute, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642; cDepartment of Physiology and Cell Biology, University of Nevada School of Medicine, Reno, NV 89557; dDepartment of Genetics, University Medical Center, University of Groningen, 9700 RB Groningen, The Netherlands; eDepartment of Pathology, Erasmus University Medical Center, 3015 CN Rotterdam, The Netherlands; fCenter for Biomics, Erasmus University Medical Center, 3015 CN Rotterdam, The Netherlands; gStem Cells and Regenerative Medicine, Birth Defects Research Centre, University College London Institute of Child Health, London WC1N 1EH, United Kingdom; hDepartment of Pathology and Laboratory Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642; iDepartment of Pediatric Surgery, Erasmus University Medical Center, 3015 CN Rotterdam, The Netherlands; and jDepartment of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia Edited by Eric N. Olson, University of Texas Southwestern Medical Center, Dallas, TX, and approved February 21, 2017 (received for review December 13, 2016) Megacystis microcolon intestinal hypoperistalsis syndrome (MMIHS) is lacking (6, 7).