I IDENTIFICATION of a PHOSPHO

Total Page:16

File Type:pdf, Size:1020Kb

I IDENTIFICATION of a PHOSPHO IDENTIFICATION OF A PHOSPHO-HNRNP E1 NUCLEIC ACID CONSENSUS SEQUENCE MEDIATING EPITHELIAL TO MESENCHYMAL TRANSITION (138 PP.) Dissertation Advisor: Philip H. Howe Protein translational regulation by RNA binding proteins (RBPs) is a critical process in maintaining homeostasis. Epithelial to mesenchymal transition (EMT) is a process in which epithelial cells de-differentiate and become mesenchymal, increasing the propensity toward tumorigenesis and/or metastasis. We have identified a heterogeneous nuclear riboprotein E1 (hnRNP E1)-mediated post-transcriptional operon that controls transcript-selective translational regulation of epithelial / mesenchymal transition (EMT)-associated genes. In this regulatory mechanism, hnRNPE1 binds to the 3’-UTR of select transcripts and silences their translation. TGFβ reverses translational silencing through Akt2-dependent phosphorylation of hnRNP E1 at Ser-43, resulting in loss of hnRNP E1 binding to RNA. We have identified approximately forty pro-EMT / metastatic mRNAs that are regulated by this hnRNP E1 operon and our preliminary studies have revealed a short stretch of nucleic acids, that we have termed the BAT element (TGF-beta activated translational (BAT) element), present in their respective 3’-UTRs that may be responsible for hnRNP E1 binding. Herein, through the use of in-vitro and in-vivo assays, we demonstrate the contribution of BAT element mutations and constitutively high levels of pSer43 hnRNP E1 to cancer tumorigenesis and metastasis. i IDENTIFICATION OF A PHOSPHO-HNRNP E1 NUCLEIC ACID CONSENSUS SEQUENCE MEDIATING EPITHELIAL TO MESENCHYMAL TRANSITION A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Andrew S. Brown August 2015 © Copyright All rights reserved Except for previously published materials ii Dissertation written by Andrew S. Brown B.S., Youngstown State University, 2008 M.S., Youngstown State University, 2010 Ph.D., Kent State University, 2015 Approved by Philip H. Howe, Ph.D., Chairman, Department of Biochemistry, Doctoral Advisor Derek S. Damron, Ph.D., Professor, Department of Cellular and Molecular Biology Srinivasan Vijayaraghavan, Ph.D. Professor, Department of Cellular and Molecular Biology Olena Piontkivska, Ph.D., Associate Professor, Department of Biology Bidyut K. Mohanty, Ph.D., Assistant Professor, Department of Biochemistry Accepted by Ernest J. Freeman, Ph.D., Director, School of Biomedical Science James L. Blank, Ph.D., Dean, College of Arts and Sciences iii TABLE OF CONTENTS TABLE OF CONTENTS ............................................................................................................ iv LIST OF FIGURES ..................................................................................................................... vi LIST OF TABLES ..................................................................................................................... viii ACKNOWLEDGEMENTS ......................................................................................................... x CHAPTER I .................................................................................................................................. 1 INTRODUCTION .................................................................................................................... 1 CHAPTER II ................................................................................................................................. 5 Computational Identification of Post Translational Modification Regulated RNA Binding Protein Motifs ............................................................................................................. 5 ABSTRACT ............................................................................................................................... 6 INTRODUCTION .................................................................................................................... 7 EXPERIMENTAL PROCEDURES ..................................................................................... 10 RESULTS ................................................................................................................................ 15 DISCUSSION .......................................................................................................................... 33 ACKNOWLEDGEMENTS ................................................................................................... 36 FUNDING ................................................................................................................................ 37 REFERENCES ........................................................................................................................ 38 CHAPTER III ............................................................................................................................. 42 Identification and Characterization of an hnRNP E1 Translational Silencing Motif ...... 42 iv ABSTRACT ............................................................................................................................. 43 INTRODUCTION .................................................................................................................. 44 EXPERIMENTAL PROCEDURES ..................................................................................... 48 RESULTS ................................................................................................................................ 53 DISCUSSION .......................................................................................................................... 77 ACKNOWLEDGMENTS ...................................................................................................... 80 FUNDING ................................................................................................................................ 81 REFERENCES ........................................................................................................................ 82 CHAPTER IV ............................................................................................................................. 86 Development of a Phosphorylated Serine 43 (hnRNP E1) Specific Antibody and Implications of Expression in Metastatic Cancer ................................................................ 86 ABSTRACT ............................................................................................................................. 87 INTRODUCTION .................................................................................................................. 88 EXPERIMENTAL PROCEDURES ..................................................................................... 93 RESULTS ................................................................................................................................ 96 DISCUSSION ........................................................................................................................ 110 ACKNOWLEDGEMENTS ................................................................................................. 112 FUNDING .............................................................................................................................. 113 REFERENCES ...................................................................................................................... 114 CHAPTER V ............................................................................................................................. 118 GENERAL SUMMARY ...................................................................................................... 118 REFERECES: ....................................................................................................................... 125 v LIST OF FIGURES Figure 1. Systems overview of RPTS Application Capabilities ............................................. 20 Figure 2. 3’-UTRs become divergent across evolution while conserving regulatory motifs. ....................................................................................................................................................... 24 Figure 3. RPTS predicted motif-containing genes interact with hnRNP E1. ....................... 27 Figure 4. Predicted motif-containing genes global set enrichment (GSEA) analysis. ......... 30 Supplementary Figure 1. Additional RBP motif training sets. .............................................. 32 Figure 5. Translational regulation of EMT-inducing genes is upregulated by TGFβ and knockout of hnRNP E1. .............................................................................................................. 55 Figure 6. EMT-inducing genes bind hnRNP E1 through their 3’UTR and are protected from exonuclease degradation. .................................................................................................. 59 Figure 7. hnRNP E1 binding nucleic acid motifs show a total loss of affinity for hnRNP E1 when in its phosphorylated state. .............................................................................................. 62 Figure 8. Unbiased exonuclease digestion of hnRNP E1 bound target mRNA allows for high-throughput analysis of protected RNA fragment sequences. ......................................... 65 Figure 9. Genomic exonuclease ION torrent analysis reveals 37 consensus motifs with conserved pyrimidine residues interspaced by variable structural regions. ......................... 68 Figure 10. Conservation of descriptor
Recommended publications
  • 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.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • 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).
    [Show full text]
  • Mir-17-92 Fine-Tunes MYC Expression and Function to Ensure
    ARTICLE Received 31 Mar 2015 | Accepted 22 Sep 2015 | Published 10 Nov 2015 DOI: 10.1038/ncomms9725 OPEN miR-17-92 fine-tunes MYC expression and function to ensure optimal B cell lymphoma growth Marija Mihailovich1, Michael Bremang1, Valeria Spadotto1, Daniele Musiani1, Elena Vitale1, Gabriele Varano2,w, Federico Zambelli3, Francesco M. Mancuso1,w, David A. Cairns1,w, Giulio Pavesi3, Stefano Casola2 & Tiziana Bonaldi1 The synergism between c-MYC and miR-17-19b, a truncated version of the miR-17-92 cluster, is well-documented during tumor initiation. However, little is known about miR-17-19b function in established cancers. Here we investigate the role of miR-17-19b in c-MYC-driven lymphomas by integrating SILAC-based quantitative proteomics, transcriptomics and 30 untranslated region (UTR) analysis upon miR-17-19b overexpression. We identify over one hundred miR-17-19b targets, of which 40% are co-regulated by c-MYC. Downregulation of a new miR-17/20 target, checkpoint kinase 2 (Chek2), increases the recruitment of HuR to c- MYC transcripts, resulting in the inhibition of c-MYC translation and thus interfering with in vivo tumor growth. Hence, in established lymphomas, miR-17-19b fine-tunes c-MYC activity through a tight control of its function and expression, ultimately ensuring cancer cell homeostasis. Our data highlight the plasticity of miRNA function, reflecting changes in the mRNA landscape and 30 UTR shortening at different stages of tumorigenesis. 1 Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, Milan 20139, Italy. 2 Units of Genetics of B cells and lymphomas, IFOM, FIRC Institute of Molecular Oncology Foundation, Milan 20139, Italy.
    [Show full text]
  • Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
    BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in
    [Show full text]
  • The FAM3C Locus That Encodes Interleukin-Like EMT Inducer (ILEI) Is Frequently Co-Amplifed in MET- Amplifed Cancers and Contributes to Invasiveness
    The FAM3C Locus that Encodes Interleukin-like EMT Inducer (ILEI) is Frequently Co-amplied in MET- amplied Cancers and Contributes to Invasiveness Ulrike Schmidt Research Institute of Molecular Pathology and Ecological Biochemistry: Naucno-issledovatel'skij institut molekularnoj biologii i bioziki Gerwin Heller Christian Medical College Department of Medical Oncology Gerald Timelthaler Medical University of Vienna Institute of Cancer Research Petra Heffeter Medical University of Vienna Institute of Cancer Research Zsolt Somodi Deparment of Oncology, Bacs-Kiskun County Teaching Hospital Norbert Schweifer Boehringer Ingelheim RCV GmbH & Co KG: Boehringer Ingelheim RCV GmbH und Co KG Maria Sibilia Medical University of Vienna Institute of Cancer Research Walter Berger Medical University of Vienna Institute of Cancer Research Agnes Csiszar ( [email protected] ) Medical University of Vienna Institute of Cancer Research https://orcid.org/0000-0001-7911-3427 Research Keywords: Interleukin-like EMT inducer (ILEI), FAM3C, c-MET, gene amplication, invasion, matrix metalloproteinase (MMP), cancer Posted Date: November 4th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-100297/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/34 Version of Record: A version of this preprint was published on February 17th, 2021. See the published version at https://doi.org/10.1186/s13046-021-01862-5. Page 2/34 Abstract Background Gene amplication of MET, which encodes for the receptor tyrosine kinase c-MET, occurs in a variety of human cancers. High c-MET levels often correlate with poor cancer prognosis. Interleukin-like EMT inducer (ILEI) is also overexpressed in many cancers and is associated with metastasis and poor survival.
    [Show full text]
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
    [Show full text]
  • Microarray Bioinformatics and Its Applications to Clinical Research
    Microarray Bioinformatics and Its Applications to Clinical Research A dissertation presented to the School of Electrical and Information Engineering of the University of Sydney in fulfillment of the requirements for the degree of Doctor of Philosophy i JLI ··_L - -> ...·. ...,. by Ilene Y. Chen Acknowledgment This thesis owes its existence to the mercy, support and inspiration of many people. In the first place, having suffering from adult-onset asthma, interstitial cystitis and cold agglutinin disease, I would like to express my deepest sense of appreciation and gratitude to Professors Hong Yan and David Levy for harbouring me these last three years and providing me a place at the University of Sydney to pursue a very meaningful course of research. I am also indebted to Dr. Craig Jin, who has been a source of enthusiasm and encouragement on my research over many years. In the second place, for contexts concerning biological and medical aspects covered in this thesis, I am very indebted to Dr. Ling-Hong Tseng, Dr. Shian-Sehn Shie, Dr. Wen-Hung Chung and Professor Chyi-Long Lee at Change Gung Memorial Hospital and University of Chang Gung School of Medicine (Taoyuan, Taiwan) as well as Professor Keith Lloyd at University of Alabama School of Medicine (AL, USA). All of them have contributed substantially to this work. In the third place, I would like to thank Mrs. Inge Rogers and Mr. William Ballinger for their helpful comments and suggestions for the writing of my papers and thesis. In the fourth place, I would like to thank my swim coach, Hirota Homma.
    [Show full text]
  • Prognostic Significance of FAM3C in Esophageal Squamous Cell Carcinoma
    Zhu et al. Diagnostic Pathology (2015) 10:192 DOI 10.1186/s13000-015-0424-8 RESEARCH Open Access Prognostic significance of FAM3C in esophageal squamous cell carcinoma Ying-Hui Zhu1†, Baozhu Zhang1†, Mengqing Li1, Pinzhu Huang5, Jian Sun1, Jianhua Fu1,3,4* and Xin-Yuan Guan1,2* Abstract Background: Family with sequence similarity 3, member C (FAM3C) has been identified as a novel regulator in epithelial-mesenchymal transition (EMT) and metastatic progression. However, the role of FAM3C in esophageal squamous cell carcinoma (ESCC) remains unexplored. The purpose of present study is to illustrate the role of FAM3C in predicting outcomes of patients with ESCC. Methods: FAM3C expression was measured in ESCC tissues and the matched adjacent nontumorous tissues by quantitative real-time RT-PCR and Western blot analysis. The relationship between FAM3C expression and prognosis of ESCC patients was further evaluated by univariate and multivariate regression analyses. Univariate and multivariate analyses of the prognostic factors were performed using Cox proportional hazards model. Results: The FAM3C mRNA expression was remarkably upregulated in ESCC compared with their nontumor counterparts (P < 0.001). In addition, high expression of FAM3C was significantly associated with pT stage (P = 0.014) , pN stage (P = 0.026) and TNM stage (P = 0.003). Kaplan-Meier analysis showed that the 7-year overall survival rate in the group with high expression of FAM3C was poorer than that in low expression group (32.0 versus 70.9 %; P <0.001). Univariate and multivariate analyses demonstrated that FAM3C was an independent risk factor for overall survival. Moreover, Stratified analysis revealed that FAM3C expression could differentiate the prognosis of patients in early clinical stage (TNM stage I-II).
    [Show full text]
  • A Whole-Genome Sequencing Association Study of Low Bone Mineral Density Identifies New Susceptibility Loci in the Phase I Qatar Biobank Cohort
    Journal of Personalized Medicine Article A Whole-Genome Sequencing Association Study of Low Bone Mineral Density Identifies New Susceptibility Loci in the Phase I Qatar Biobank Cohort Nadin Younes 1, Najeeb Syed 2, Santosh K. Yadav 2, Mohammad Haris 2, Atiyeh M. Abdallah 3,4 and Marawan Abu-Madi 1,3,4,* 1 Biomedical Research Center, College of Health Sciences-QU Health, Qatar University, Doha 2713, Qatar; [email protected] 2 Biomedical Informatics Division, Sidra Medicine, Doha 26999, Qatar; [email protected] (N.S.); [email protected] (S.K.Y.); [email protected] (M.H.) 3 Department of Biomedical Sciences, College of Health Sciences-QU Health, Doha 2713, Qatar; [email protected] 4 Biomedical and Pharmaceutical Research Unit-QU Health, Qatar University, Doha 2713, Qatar * Correspondence: [email protected]; Tel.: +974-4403-7578; Fax: +974-4403-4801 Abstract: Bone density disorders are characterized by a reduction in bone mass density and strength, which lead to an increase in the susceptibility to sudden and unexpected fractures. Despite the serious consequences of low bone mineral density (BMD) and its significant impact on human health, most affected individuals may not know that they have the disease because it is asymptomatic. Therefore, understanding the genetic basis of low BMD and osteoporosis is essential to fully elucidate its pathobiology and devise preventative or therapeutic approaches. Here we sequenced the whole genomes of 3000 individuals from the Qatar Biobank and conducted genome-wide association analyses to identify genetic risk factors associated with low BMD in the Qatari population. Fifteen variants were significantly associated with total body BMD (p < 5 × 10−8).
    [Show full text]
  • A Network Inference Approach to Understanding Musculoskeletal
    A NETWORK INFERENCE APPROACH TO UNDERSTANDING MUSCULOSKELETAL DISORDERS by NIL TURAN A thesis submitted to The University of Birmingham for the degree of Doctor of Philosophy College of Life and Environmental Sciences School of Biosciences The University of Birmingham June 2013 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Musculoskeletal disorders are among the most important health problem affecting the quality of life and contributing to a high burden on healthcare systems worldwide. Understanding the molecular mechanisms underlying these disorders is crucial for the development of efficient treatments. In this thesis, musculoskeletal disorders including muscle wasting, bone loss and cartilage deformation have been studied using systems biology approaches. Muscle wasting occurring as a systemic effect in COPD patients has been investigated with an integrative network inference approach. This work has lead to a model describing the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. This model has shown for the first time that oxygen dependent changes in the expression of epigenetic modifiers and not chronic inflammation may be causally linked to muscle dysfunction.
    [Show full text]
  • Table S1: Gene Symbol Full Gene Name Entrez Gene ID Refseq A2M
    Table S1: Gene Symbol Full Gene Name Entrez Gene ID RefSeq A2M alpha-2-macroglobulin 2 NM_000014 ABHD15 abhydrolase domain containing 15 116236 NM_198147 ACADVL acyl-Coenzyme A dehydrogenase, very long chain 37 NM_000018 ACSS1 acyl-CoA synthetase short-chain family member 1 84532 NM_032501 ACY3 aspartoacylase (aminocyclase) 3 91703 NM_080658 ADAM33 ADAM metallopeptidase domain 33 80332 NM_153202 AFF2 AF4/FMR2 family, member 2 2334 NM_002025 ALX1 ALX homeobox 1 8092 NM_006982 ANGPTL4 angiopoietin-like 4 51129 NM_001039667 ANKRD20A3 ankyrin repeat domain 20 family, member A3 441425 NM_001012419 ANKRD45 ankyrin repeat domain 45 339416 NM_198493 ANXA1 annexin A1 301 NM_000700 ANXA5 annexin A5 308 NM_001154 APBB1IP amyloid beta (A4) precursor protein-binding, family B, member 1 interacting protein 54518 NM_019043 ARAP3 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 3 64411 NM_022481 ARF3 ADP-ribosylation factor 3 377 NM_001659 ARF5 ADP-ribosylation factor 5 381 NM_001662 ARHGAP1 Rho GTPase activating protein 1 392 NM_004308 ARHGAP6 Rho GTPase activating protein 6 395 NM_006125 ARHGDIA Rho GDP dissociation inhibitor (GDI) alpha 396 NM_004309 ARMC8 armadillo repeat containing 8 25852 NM_014154 ATP2A2 ATPase, Ca++ transporting, cardiac muscle, slow twitch 2 488 NM_170665 ATP6AP2 ATPase, H+ transporting, lysosomal accessory protein 2 10159 NM_005765 ATP6V1B2 ATPase, H+ transporting, lysosomal 56/58kDa, V1 subunit B2 526 NM_001693 B3GNT8 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 8 374907 NM_198540 B4GALNT1 beta-1,4-N-acetyl-galactosaminyl
    [Show full text]