Methods in and Applications of the Sequencing of Short Non-Coding Rnas" (2013)
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Human 28S Rrna 5' Terminal Derived Small RNA Inhibits Ribosomal Protein Mrna Levels Shuai Li 1* 1. Department of Breast Cancer
bioRxiv preprint doi: https://doi.org/10.1101/618520; this version posted April 25, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Human 28s rRNA 5’ terminal derived small RNA inhibits ribosomal protein mRNA levels Shuai Li 1* 1. Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China. * To whom correspondence should be addressed. Tel.: +86 22 23340123; Fax: +86 22 23340123; Email: [email protected] & [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/618520; this version posted April 25, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 Recent small RNA (sRNA) high-throughput sequencing studies reveal ribosomal RNAs (rRNAs) as major resources of sRNA. By reanalyzing sRNA sequencing datasets from Gene Expression Omnibus (GEO), we identify 28s rRNA 5’ terminal derived sRNA (named 28s5-rtsRNA) as the most abundant rRNA-derived sRNAs. These 28s5-rtsRNAs show a length dynamics with identical 5’ end and different 3’ end. Through exploring sRNA sequencing datasets of different human tissues, 28s5-rtsRNA is found to be highly expressed in bladder, macrophage and skin. We also show 28s5-rtsRNA is independent of microRNA biogenesis pathway and not associated with Argonaut proteins. Overexpression of 28s5-rtsRNA could alter the 28s/18s rRNA ratio and decrease multiple ribosomal protein mRNA levels. -
The ELIXIR Core Data Resources: Fundamental Infrastructure for The
Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that mention Core Data Resource by name or include specific data record accession numbers. -
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. -
Molecular Profiling of Peripheral Blood Is Associated with Circulating Tumor Cells Content and Poor Survival in Metastatic Castration-Resistant Prostate Cancer
www.impactjournals.com/oncotarget/ Oncotarget, Vol. 6, No. 12 Molecular profiling of peripheral blood is associated with circulating tumor cells content and poor survival in metastatic castration-resistant prostate cancer Mercedes Marín-Aguilera1, Òscar Reig1,2, Juan José Lozano3, Natalia Jiménez1, Susana García-Recio1,4, Nadina Erill5, Lydia Gaba2, Andrea Tagliapietra2, Vanesa Ortega2, Gemma Carrera6, Anna Colomer5, Pedro Gascón4 and Begoña Mellado1,2 1 Translational Genomics Group and Targeted Therapeutics in Solid Tumors Group, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 2 Medical Oncology Department, Hospital Clínic, Barcelona, Spain 3 Bioinformatics Platform Department, Centro de Investigación Biomédica en Red en el Área temática de Enfermedades Hepáticas y Digestivas (CIBEREHD), Hospital Clínic, Barcelona, Spain 4 Laboratory of Translational Oncology, Fundació Clínic per a la Recerca Biomèdica, Barcelona, Spain 5 Althia, Barcelona, Spain 6 Medical Oncology Department, Hospital Plató, Barcelona, Spain Correspondence to: Begoña Mellado, email: [email protected] Keywords: circulating tumor cells, peripheral blood, microarrays, cell search system Received: January 22, 2015 Accepted: February 14, 2015 Published: March 12, 2015 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT The enumeration of circulating -
Genome-Wide Association Study for Circulating Fibroblast Growth Factor
www.nature.com/scientificreports OPEN Genome‑wide association study for circulating fbroblast growth factor 21 and 23 Gwo‑Tsann Chuang1,2,14, Pi‑Hua Liu3,4,14, Tsui‑Wei Chyan5, Chen‑Hao Huang5, Yu‑Yao Huang4,6, Chia‑Hung Lin4,7, Jou‑Wei Lin8, Chih‑Neng Hsu8, Ru‑Yi Tsai8, Meng‑Lun Hsieh9, Hsiao‑Lin Lee9, Wei‑shun Yang2,9, Cassianne Robinson‑Cohen10, Chia‑Ni Hsiung11, Chen‑Yang Shen12,13 & Yi‑Cheng Chang2,9,12* Fibroblast growth factors (FGFs) 21 and 23 are recently identifed hormones regulating metabolism of glucose, lipid, phosphate and vitamin D. Here we conducted a genome‑wide association study (GWAS) for circulating FGF21 and FGF23 concentrations to identify their genetic determinants. We enrolled 5,000 participants from Taiwan Biobank for this GWAS. After excluding participants with diabetes mellitus and quality control, association of single nucleotide polymorphisms (SNPs) with log‑transformed FGF21 and FGF23 serum concentrations adjusted for age, sex and principal components of ancestry were analyzed. A second model additionally adjusted for body mass index (BMI) and a third model additionally adjusted for BMI and estimated glomerular fltration rate (eGFR) were used. A total of 4,201 participants underwent GWAS analysis. rs67327215, located within RGS6 (a gene involved in fatty acid synthesis), and two other SNPs (rs12565114 and rs9520257, located between PHC2-ZSCAN20 and ARGLU1-FAM155A respectively) showed suggestive associations with serum FGF21 level (P = 6.66 × 10–7, 6.00 × 10–7 and 6.11 × 10–7 respectively). The SNPs rs17111495 and rs17843626 were signifcantly associated with FGF23 level, with the former near PCSK9 gene and the latter near HLA-DQA1 gene (P = 1.04 × 10–10 and 1.80 × 10–8 respectively). -
Bioinformatics Analyses of Genomic Imprinting
Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________ -
Controls Homeostatic Splicing of ARGLU1 Mrna Stephan P
Published online 28 November 2016 Nucleic Acids Research, 2017, Vol. 45, No. 6 3473–3486 doi: 10.1093/nar/gkw1140 An Ultraconserved Element (UCE) controls homeostatic splicing of ARGLU1 mRNA Stephan P. Pirnie, Ahmad Osman, Yinzhou Zhu and Gordon G. Carmichael* Department of Genetics and Genome Sciences, UCONN Health Center, 400 Farmington Avenue, Farmington, CT 06030, USA Received January 13, 2016; Revised October 25, 2016; Editorial Decision October 28, 2016; Accepted October 31, 2016 ABSTRACT or inhibit the usage of a particular splice site (1,2). The ex- pression of trans-acting factors in a developmental and tis- Arginine and Glutamate-Rich protein 1 (ARGLU1) is sue specific manner results in regulated splicing that isof- a protein whose function is poorly understood, but ten cell specific. Most notably, trans-acting proteins such as may act in both transcription and pre-mRNA splicing. the NOVA (3–6), the RBFOX (7) and SR protein (8–11) We demonstrate that the ARGLU1 gene expresses families, and a number of hnRNP (12,13) proteins compete at least three distinct RNA splice isoforms – a fully to bind nascent RNAs at specific motifs and drive regula- spliced isoform coding for the protein, an isoform tion of alternative splicing in a tissue and developmentally containing a retained intron that is detained in the specific manner. Alternative splicing is an important pro- nucleus, and an isoform containing an alternative cess that is seen in at least 95% of multi-exon genes in the exon that targets the transcript for nonsense medi- human transcriptome (14). Furthermore, alternative splic- ated decay. -
Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage. -
HIC1 Modulates Prostate Cancer Progression by Epigenetic Modification
Author Manuscript Published OnlineFirst on January 22, 2013; DOI: 10.1158/1078-0432.CCR-12-2888 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. HIC1 modulates prostate cancer progression by epigenetic modification Jianghua Zheng#1, 2, Jinglong Wang#1, Xueqing Sun#1,Mingang Hao1,Tao Ding3, Dan Xiong 1, Xiumin Wang1, Yu Zhu 4, Gang Xiao 1, Guangcun Cheng1, Meizhong Zhao5, Jian Zhang6, Jianhua Wang1* 1 Department of Biochemistry and Molecular Cell Biology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. 2 Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China. 3 Department of Urology, Shanghai Putuo Hospital, Shanghai Traditional Chinese Medicine University, Shanghai, 200062, China. 4 Department of Urology, Shanghai Ruijin Hospital, Shanghai, 200025, China. 5 Shanghai Ruijin Hospital, Comprehensive Breast Health Center, Shanghai, 200025, China. 6 Guangxi Medical University, Nanning, 530021, China. # J Zheng, J Wang and X Sun contributed equally to this work. *Corresponding author: Jianhua Wang, PhD, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. Phone: 021-54660871; Fax: 021-63842157; E-mail:[email protected]. No potential conflicts of interest were disclosed 1 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2013 American Association for Cancer Research. Author Manuscript Published OnlineFirst on January 22, 2013; DOI: 10.1158/1078-0432.CCR-12-2888 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Statement of Translational Relevance This study aimed to further our understanding of the role that hypermethylatioted in cancer 1 (HIC1) plays in prostate cancer (PCa) progression. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
(KPNA7), a Divergent Member of the Importin a Family of Nuclear Import
Kelley et al. BMC Cell Biology 2010, 11:63 http://www.biomedcentral.com/1471-2121/11/63 RESEARCH ARTICLE Open Access Karyopherin a7 (KPNA7), a divergent member of the importin a family of nuclear import receptors Joshua B Kelley1, Ashley M Talley1, Adam Spencer1, Daniel Gioeli2, Bryce M Paschal1,3* Abstract Background: Classical nuclear localization signal (NLS) dependent nuclear import is carried out by a heterodimer of importin a and importin b. NLS cargo is recognized by importin a, which is bound by importin b. Importin b mediates translocation of the complex through the central channel of the nuclear pore, and upon reaching the nucleus, RanGTP binding to importin b triggers disassembly of the complex. To date, six importin a family members, encoded by separate genes, have been described in humans. Results: We sequenced and characterized a seventh member of the importin a family of transport factors, karyopherin a 7 (KPNA7), which is most closely related to KPNA2. The domain of KPNA7 that binds Importin b (IBB) is divergent, and shows stronger binding to importin b than the IBB domains from of other importin a family members. With regard to NLS recognition, KPNA7 binds to the retinoblastoma (RB) NLS to a similar degree as KPNA2, but it fails to bind the SV40-NLS and the human nucleoplasmin (NPM) NLS. KPNA7 shows a predominantly nuclear distribution under steady state conditions, which contrasts with KPNA2 which is primarily cytoplasmic. Conclusion: KPNA7 is a novel importin a family member in humans that belongs to the importin a2 subfamily. KPNA7 shows different subcellular localization and NLS binding characteristics compared to other members of the importin a family. -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US).