Safety and Efficacy of C9ORF72-Repeat RNA Nuclear Export Inhibition in Amyotrophic Lateral Sclerosis
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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. -
Microrna Expression Signature of Oral Squamous Cell Carcinoma: Functional Role of Microrna-26A&Sol;B in the Modulation of No
FULL PAPER British Journal of Cancer (2015) 112, 891–900 | doi: 10.1038/bjc.2015.19 Keywords: microRNA; oral squamous cell carcinoma; miR-26a; miR-26b; tumour suppressor; TMEM184B; expression signature MicroRNA expression signature of oral squamous cell carcinoma: functional role of microRNA-26a/b in the modulation of novel cancer pathways I Fukumoto1,2, T Hanazawa2, T Kinoshita1,2, N Kikkawa2, K Koshizuka1,2, Y Goto1, R Nishikawa1, T Chiyomaru3, H Enokida3, M Nakagawa3, Y Okamoto2 and N Seki*,1 1Department of Functional Genomics, Chiba University Graduate School of Medicine, Chiba, Japan; 2Department of Otorhinolaryngology/Head and Neck Surgery, Chiba University Graduate School of Medicine, Chiba, Japan and 3Department of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan Background: MicroRNAs (miRNAs) have been shown to play major roles in carcinogenesis in a variety of cancers. The aim of this study was to determine the miRNA expression signature of oral squamous cell carcinoma (OSCC) and to investigate the functional roles of miR-26a and miR-26b in OSCC cells. Methods: An OSCC miRNA signature was constructed by PCR-based array methods. Functional studies of differentially expressed miRNAs were performed to investigate cell proliferation, migration, and invasion in OSCC cells. In silico database and genome-wide gene expression analyses were performed to identify molecular targets and pathways mediated by miR-26a/b. Results: miR-26a and miR-26b were significantly downregulated in OSCC. Restoration of both miR-26a and miR-26b in cancer cell lines revealed that these miRNAs significantly inhibited cancer cell migration and invasion. Our data demonstrated that the novel transmembrane TMEM184B gene was a direct target of miR-26a/b regulation. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Profiling Cellular Processes in Adipose Tissue During Weight Loss Using Time Series Gene Expression
G C A T T A C G G C A T genes Article Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression Samar H. K. Tareen 1,* , Michiel E. Adriaens 1,* , Ilja C. W. Arts 1,2, Theo M. de Kok 1,3, Roel G. Vink 4, Nadia J. T. Roumans 4, Marleen A. van Baak 4, Edwin C. M. Mariman 4, Chris T. Evelo 1,5,* and Martina Kutmon 1,5,* 1 Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands; [email protected] (I.C.W.A.); [email protected] (T.M.d.K.) 2 Department of Epidemiology, CARIM School for Cardiovascular Diseases, Maastricht University, 6211ER Maastricht, The Netherlands 3 Department of Toxicogenomics, GROW School of Oncology and Developmental Biology, Maastricht University, 6211ER Maastricht, The Netherlands 4 Department of Human Biology, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands; [email protected] (R.G.V.); [email protected] (N.J.T.R.); [email protected] (M.A.v.B.); [email protected] (E.C.M.M.) 5 Department of Bioinformatics—BiGCaT, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands * Correspondence: [email protected] (S.H.K.T.); [email protected] (M.E.A.); [email protected] (C.T.E.); [email protected] (M.K.) Received: 28 September 2018; Accepted: 22 October 2018; Published: 29 October 2018 Abstract: Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. -
Evolution of Vertebrate Solute Carrier Family 9B
etics & E en vo g lu t lo i y o h n a P r f y Journal of Phylogenetics & Holmes et al., J Phylogen Evolution Biol 2016, 4:3 o B l i a o n l r o DOI: 10.4172/2329-9002.1000167 u g o y J Evolutionary Biology ISSN: 2329-9002 Research Article Open Access Evolution of Vertebrate Solute Carrier Family 9B Genes and Proteins (SLC9B): Evidence for a Marsupial Origin for Testis Specific SLC9B1 from an Ancestral Vertebrate SLC9B2 Gene Roger S Holmes1*,2, Kimberly D Spradling-Reeves2 and Laura A Cox2 1Eskitis Institute for Drug Discovery and School of Natural Sciences, Griffith University, Nathan, QLD, Australia 2Department of Genetics and Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, USA Abstract SLC9B genes and proteins are members of the sodium/lithium hydrogen antiporter family which function as solute exchangers within cellular membranes of mammalian tissues. SLC9B2 and SLC9B1 amino acid sequences and structures and SLC9B-like gene locations were examined using bioinformatic data from several vertebrate genome projects. Vertebrate SLC9B2 sequences shared 56-98% identity as compared with ~50% identities with mammalian SLC9B1 sequences. Sequence alignments, key amino acid residues and conserved predicted transmembrane structures were also studied. Mammalian SLC9B2 and SLC9B1 genes usually contained 11 or 12 coding exons with differential tissue expression patterns: SLC9B2, broad tissue distribution; and SLC9B1, being testis specific. Transcription factor binding sites and CpG islands within the human SLC9B2 and SLC9B1 gene promoters were identified. Phylogenetic analyses suggested thatSLC9B1 originated in an ancestral marsupial genome from a SLC9B2 gene duplication event. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations
IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. X, NO. X, MONTHXXX 20XX 1 Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations Davide Chicco, Fernando Palluzzi, and Marco Masseroli Abstract—Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand their test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and may contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new biomolecular annotations are both effective and efficient, especially if compared with the time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritizing their results, we introduce here a new element that can improve the accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of ”newness” (or ”originality”) of the annotations predicted for a specific gene to Gene Ontology terms, and to help prioritizing the most novel and interesting annotations predicted. We performed a thorough biological functional analysis of the prioritized annotations predicted with high accuracy by using this indicator and our previously proposed prediction algorithms. The relevance -
Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts
University of Calgary PRISM: University of Calgary's Digital Repository Graduate Studies The Vault: Electronic Theses and Dissertations 2017 Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts Zabinyakov, Nikita Zabinyakov, N. (2017). Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/27775 http://hdl.handle.net/11023/3639 master thesis University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts by Nikita Zabinyakov A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN BIOMEDICAL ENGINEERING CALGARY, ALBERTA JANUARY 2017 © Nikita Zabinyakov 2017 Abstract Applied mechanical forces, such as those resulting from fluid flow, trigger cells to change their functional behavior or phenotype. However, there is little known about how fluid flow affects fibroblasts. The hypothesis of this thesis is that dermal fibroblasts undergo significant changes of expression of differentiation genes after exposure to fluid flow (or shear stress). To test the hypothesis, human dermal fibroblasts were exposed to laminar steady fluid flow for 20 and 40 hours and RNA was collected for microarray analysis. -
Supplementary Material Computational Prediction of SARS
Supplementary_Material Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets Sheet_1 List of potential stem-loop structures in SARS-CoV-2 genome as predicted by VMir. Rank Name Start Apex Size Score Window Count (Absolute) Direct Orientation 1 MD13 2801 2864 125 243.8 61 2 MD62 11234 11286 101 211.4 49 4 MD136 27666 27721 104 205.6 119 5 MD108 21131 21184 110 204.7 210 9 MD132 26743 26801 119 188.9 252 19 MD56 9797 9858 128 179.1 59 26 MD139 28196 28233 72 170.4 133 28 MD16 2934 2974 76 169.9 71 43 MD103 20002 20042 80 159.3 403 46 MD6 1489 1531 86 156.7 171 51 MD17 2981 3047 131 152.8 38 87 MD4 651 692 75 140.3 46 95 MD7 1810 1872 121 137.4 58 116 MD140 28217 28252 72 133.8 62 122 MD55 9712 9758 96 132.5 49 135 MD70 13171 13219 93 130.2 131 164 MD95 18782 18820 79 124.7 184 173 MD121 24086 24135 99 123.1 45 176 MD96 19046 19086 75 123.1 179 196 MD19 3197 3236 76 120.4 49 200 MD86 17048 17083 73 119.8 428 223 MD75 14534 14600 137 117 51 228 MD50 8824 8870 94 115.8 79 234 MD129 25598 25642 89 115.6 354 Reverse Orientation 6 MR61 19088 19132 88 197.8 271 10 MR72 23563 23636 148 188.8 286 11 MR11 3775 3844 136 185.1 116 12 MR94 29532 29582 94 184.6 271 15 MR43 14973 15028 109 183.9 226 27 MR14 4160 4206 89 170 241 34 MR35 11734 11792 111 164.2 37 52 MR5 1603 1652 89 152.7 118 53 MR57 18089 18132 101 152.7 139 94 MR8 2804 2864 122 137.4 38 107 MR58 18474 18508 72 134.9 237 117 MR16 4506 4540 72 133.8 311 120 MR34 10010 10048 82 132.7 245 133 MR7 2534 2578 90 130.4 75 146 MR79 24766 24808 75 127.9 59 150 MR65 21528 21576 99 127.4 83 180 MR60 19016 19049 70 122.5 72 187 MR51 16450 16482 75 121 363 190 MR80 25687 25734 96 120.6 75 198 MR64 21507 21544 70 120.3 35 206 MR41 14500 14542 84 119.2 94 218 MR84 26840 26894 108 117.6 94 Sheet_2 List of stable stem-loop structures based on MFE. -
(Q36.1;Q24) with a Concurrent Submicroscopic Del(4)(Q23q24) in an Adult with Polycythemia Vera
cancers Case Report A Novel Acquired t(2;4)(q36.1;q24) with a Concurrent Submicroscopic del(4)(q23q24) in An Adult with Polycythemia Vera Eigil Kjeldsen Cancer Cytogenetic Section, HemoDiagnostic Laboratory, Department of Hematology, Aarhus University Hospital, Tage-Hansens Gade 2, DK-8000 Aarhus C, Denmark; [email protected]; Tel.: +45-7846-7398; Fax: +45-7846-7399 Received: 6 June 2018; Accepted: 21 June 2018; Published: 25 June 2018 Abstract: Background: Polycythemia vera (PV) is a clonal myeloid stem cell disease characterized by a growth-factor independent erythroid proliferation with an inherent tendency to transform into overt acute myeloid malignancy. Approximately 95% of the PV patients harbor the JAK2V617F mutation while less than 35% of the patients harbor cytogenetic abnormalities at the time of diagnosis. Methods and Results: Here we present a JAK2V617F positive PV patient where G-banding revealed an apparently balanced t(2;4)(q35;q21), which was confirmed by 24-color karyotyping. Oligonucleotide array-based Comparative Genomic Hybridization (aCGH) analysis revealed an interstitial 5.4 Mb large deletion at 4q23q24. Locus-specific fluorescent in situ hybridization (FISH) analyses confirmed the mono-allelic 4q deletion and that it was located on der(4)t(2;4). Additional locus-specific bacterial artificial chromosome (BAC) probes and mBanding refined the breakpoint on chromosome 2. With these methods the karyotype was revised to 46,XX,t(2;4)(q36.1;q24)[18]/46,XX[7]. Conclusions: This is the first report on a PV patient associated with an acquired novel t(2;4)(q36.1;q24) and a concurrent submicroscopic deletion del(4)(q23q24). -
Poster Listings
Posters A-Z Akiyama, Yasutoshi Evidence that angiogenin does not cleave CCA termini of tRNAs in vivo 64 Cancelled 65 Albanese, Tanino Recycling of stalled ribosome complexes in the absence of 66 trans-translation Aleksashin, Nikolay Fully orthogonal translation system built on the dissociable ribosome 67 Alexandrova, Jana NKRF RNA binding protein implicated in ribosome biogenesis 68 Alves Guerra, Beatriz Adipocyte-specific GCN1 knockout mice exhibit decreased fat mass 69 and impaired adipose tissue function Andersen, Kasper Langebjerg Ribosome specialization by changes in the 2’-O-methylation pattern – a 70 target for an anti-cancer drug? Andreev, D E. The uORF controls translation of two long overlapping reading frames in 71 the single mRNA Annibaldis, Giuditta Ribosome profiling in mammalian cells to reveal the role of NMD factors 72 in translation termination Barba Moreno, Laura Regulation of Ribosomal Protein Gene expression by DYRK1A 73 Barbosa, Natália M eIF5A impacts the synthesis of mitochondrial complexes proteins in 74 Saccharomyces cerevisiae Page 19 EMBO Conference: Protein Synthesis and Translational Control Belsham, Graham J. Requirements for the co-translational “cleavage” at the 2A/2B junction 75 of the FMDV polyprotein Biffo, Stefano Phosphorylation of eIF6 in vivo is necessary for efficient translation, 76 metabolic remodelling and tumorigenesis Blasco, Bernat The 5´-3´exonuclease Xrn1 promotes translation of viral and cellular 77 mRNAs Bochler, Anthony Interacting networks of ribosomal RNA expansion segments from 78 -
Downloaded, Each with Over 20 Samples for AD-Specific Pathways, Biological Processes, and Driver Each Specific Brain Region in Each Condition
Xiang et al. BMC Medical Genomics 2018, 11(Suppl 6):115 https://doi.org/10.1186/s12920-018-0431-1 RESEARCH Open Access Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients Shunian Xiang1,2, Zhi Huang4, Wang Tianfu1, Zhi Han3, Christina Y. Yu3,5, Dong Ni1*, Kun Huang3* and Jie Zhang2* From 29th International Conference on Genome Informatics Yunnan, China. 3-5 December 2018 Abstract Background: Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions. Methods: In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules. Results: We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal- specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples.