Exercise Rapidly Alters Proteomes in Mice Following Spinal Cord
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Identifying Novel Disease-Associated Variants and Understanding The
Identifying Novel Disease-variants and Understanding the Role of the STAT1-STAT4 Locus in SLE A dissertation submitted to the Graduate School of University of Cincinnati In partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Immunology Graduate Program of the College of Medicine by Zubin H. Patel B.S., Worcester Polytechnic Institute, 2009 John B. Harley, M.D., Ph.D. Committee Chair Gurjit Khurana Hershey, M.D., Ph.D Leah C. Kottyan, Ph.D. Harinder Singh, Ph.D. Matthew T. Weirauch, Ph.D. Abstract Systemic Lupus Erythematosus (SLE) or lupus is an autoimmune disorder caused by an overactive immune system with dysregulation of both innate and adaptive immune pathways. It can affect all major organ systems and may lead to inflammation of the serosal and mucosal surfaces. The pathogenesis of lupus is driven by genetic factors, environmental factors, and gene-environment interactions. Heredity accounts for a substantial proportion of SLE risk, and the role of specific genetic risk loci has been well established. Identifying the specific causal genetic variants and the underlying molecular mechanisms has been a major area of investigation. This thesis describes efforts to develop an analytical approach to identify candidate rare variants from trio analyses and a fine-mapping analysis at the STAT1-STAT4 locus, a well-replicated SLE-risk locus. For the STAT1-STAT4 locus, subsequent functional biological studies demonstrated genotype dependent gene expression, transcription factor binding, and DNA regulatory activity. Rare variants are classified as variants across the genome with an allele-frequency less than 1% in ancestral populations. -
A Cell Line P53 Mutation Type UM
A Cell line p53 mutation Type UM-SCC 1 wt UM-SCC5 Exon 5, 157 GTC --> TTC Missense mutation by transversion (Valine --> Phenylalanine UM-SCC6 wt UM-SCC9 wt UM-SCC11A wt UM-SCC11B Exon 7, 242 TGC --> TCC Missense mutation by transversion (Cysteine --> Serine) UM-SCC22A Exon 6, 220 TAT --> TGT Missense mutation by transition (Tyrosine --> Cysteine) UM-SCC22B Exon 6, 220 TAT --> TGT Missense mutation by transition (Tyrosine --> Cysteine) UM-SCC38 Exon 5, 132 AAG --> AAT Missense mutation by transversion (Lysine --> Asparagine) UM-SCC46 Exon 8, 278 CCT --> CGT Missense mutation by transversion (Proline --> Alanine) B 1 Supplementary Methods Cell Lines and Cell Culture A panel of ten established HNSCC cell lines from the University of Michigan series (UM-SCC) was obtained from Dr. T. E. Carey at the University of Michigan, Ann Arbor, MI. The UM-SCC cell lines were derived from eight patients with SCC of the upper aerodigestive tract (supplemental Table 1). Patient age at tumor diagnosis ranged from 37 to 72 years. The cell lines selected were obtained from patients with stage I-IV tumors, distributed among oral, pharyngeal and laryngeal sites. All the patients had aggressive disease, with early recurrence and death within two years of therapy. Cell lines established from single isolates of a patient specimen are designated by a numeric designation, and where isolates from two time points or anatomical sites were obtained, the designation includes an alphabetical suffix (i.e., "A" or "B"). The cell lines were maintained in Eagle's minimal essential media supplemented with 10% fetal bovine serum and penicillin/streptomycin. -
Supplementary Information
Supplementary Information PathwayMatcher: multi-omics pathway mapping and proteoform network generation Luis Francisco Hernández Sánchez1,2,3, Bram Burger4,5, Carlos Horro4,5, Antonio Fabregat3, Stefan Johansson1,2, Pål Rasmus Njølstad1,6, Harald Barsnes4,5, Henning Hermjakob3,7, and Marc Vaudel1,2,* 1 K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway 2 Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway 3 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom 4 Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway 5 Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway 6 Department of Pediatrics, Haukeland University Hospital, Bergen, Norway 7 Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing, China * To whom correspondence should be addressed Abstract Mapping biomedical data to functional knowledge is an essential task in biomedicine and can be achieved by querying gene or protein identifiers in pathway knowledgebases. Here, we demonstrate that including fine-granularity information such as post-translational modifications greatly increases the specificity of the analysis. We present PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher), a bioinformatic application for mapping multi-omics data to pathways and show how this enables the -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Nuclear Organization and the Epigenetic Landscape of the Mus Musculus X-Chromosome Alicia Liu University of Connecticut - Storrs, [email protected]
University of Connecticut OpenCommons@UConn Doctoral Dissertations University of Connecticut Graduate School 8-9-2019 Nuclear Organization and the Epigenetic Landscape of the Mus musculus X-Chromosome Alicia Liu University of Connecticut - Storrs, [email protected] Follow this and additional works at: https://opencommons.uconn.edu/dissertations Recommended Citation Liu, Alicia, "Nuclear Organization and the Epigenetic Landscape of the Mus musculus X-Chromosome" (2019). Doctoral Dissertations. 2273. https://opencommons.uconn.edu/dissertations/2273 Nuclear Organization and the Epigenetic Landscape of the Mus musculus X-Chromosome Alicia J. Liu, Ph.D. University of Connecticut, 2019 ABSTRACT X-linked imprinted genes have been hypothesized to contribute parent-of-origin influences on social cognition. A cluster of imprinted genes Xlr3b, Xlr4b, and Xlr4c, implicated in cognitive defects, are maternally expressed and paternally silent in the murine brain. These genes defy classic mechanisms of autosomal imprinting, suggesting a novel method of imprinted gene regulation. Using Xlr3b and Xlr4c as bait, this study uses 4C-Seq on neonatal whole brain of a 39,XO mouse model, to provide the first in-depth analysis of chromatin dynamics surrounding an imprinted locus on the X-chromosome. Significant differences in long-range contacts exist be- tween XM and XP monosomic samples. In addition, XM interaction profiles contact a greater number of genes linked to cognitive impairment, abnormality of the nervous system, and abnormality of higher mental function. This is not a pattern that is unique to the imprinted Xlr3/4 locus. Additional Alicia J. Liu - University of Connecticut - 2019 4C-Seq experiments show that other genes on the X-chromosome, implicated in intellectual disability and/or ASD, also produce more maternal contacts to other X-linked genes linked to cognitive impairment. -
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 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 Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
The Inactive X Chromosome Is Epigenetically Unstable and Transcriptionally Labile in Breast Cancer
Supplemental Information The inactive X chromosome is epigenetically unstable and transcriptionally labile in breast cancer Ronan Chaligné1,2,3,8, Tatiana Popova1,4, Marco-Antonio Mendoza-Parra5, Mohamed-Ashick M. Saleem5 , David Gentien1,6, Kristen Ban1,2,3,8, Tristan Piolot1,7, Olivier Leroy1,7, Odette Mariani6, Hinrich Gronemeyer*5, Anne Vincent-Salomon*1,4,6,8, Marc-Henri Stern*1,4,6 and Edith Heard*1,2,3,8 Extended Experimental Procedures Cell Culture Human Mammary Epithelial Cells (HMEC, Invitrogen) were grown in serum-free medium (HuMEC, Invitrogen). WI- 38, ZR-75-1, SK-BR-3 and MDA-MB-436 cells were grown in Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen) containing 10% fetal bovine serum (FBS). DNA Methylation analysis. We bisulfite-treated 2 µg of genomic DNA using Epitect bisulfite kit (Qiagen). Bisulfite converted DNA was amplified with bisulfite primers listed in Table S3. All primers incorporated a T7 promoter tag, and PCR conditions are available upon request. We analyzed PCR products by MALDI-TOF mass spectrometry after in vitro transcription and specific cleavage (EpiTYPER by Sequenom®). For each amplicon, we analyzed two independent DNA samples and several CG sites in the CpG Island. Design of primers and selection of best promoter region to assess (approx. 500 bp) were done by a combination of UCSC Genome Browser (http://genome.ucsc.edu) and MethPrimer (http://www.urogene.org). All the primers used are listed (Table S3). NB: MAGEC2 CpG analysis have been done with a combination of two CpG island identified in the gene core. Analysis of RNA allelic expression profiles (based on Human SNP Array 6.0) DNA and RNA hybridizations were normalized by Genotyping console. -
The Intrinsically Disordered Proteins of Myelin in Health and Disease
cells Review Flexible Players within the Sheaths: The Intrinsically Disordered Proteins of Myelin in Health and Disease Arne Raasakka 1 and Petri Kursula 1,2,* 1 Department of Biomedicine, University of Bergen, Jonas Lies vei 91, NO-5009 Bergen, Norway; [email protected] 2 Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Aapistie 7A, FI-90220 Oulu, Finland * Correspondence: [email protected] Received: 30 January 2020; Accepted: 16 February 2020; Published: 18 February 2020 Abstract: Myelin ensheathes selected axonal segments within the nervous system, resulting primarily in nerve impulse acceleration, as well as mechanical and trophic support for neurons. In the central and peripheral nervous systems, various proteins that contribute to the formation and stability of myelin are present, which also harbor pathophysiological roles in myelin disease. Many myelin proteins have common attributes, including small size, hydrophobic segments, multifunctionality, longevity, and regions of intrinsic disorder. With recent advances in protein biophysical characterization and bioinformatics, it has become evident that intrinsically disordered proteins (IDPs) are abundant in myelin, and their flexible nature enables multifunctionality. Here, we review known myelin IDPs, their conservation, molecular characteristics and functions, and their disease relevance, along with open questions and speculations. We place emphasis on classifying the molecular details of IDPs in myelin, and we correlate these with their various functions, including susceptibility to post-translational modifications, function in protein–protein and protein–membrane interactions, as well as their role as extended entropic chains. We discuss how myelin pathology can relate to IDPs and which molecular factors are potentially involved. Keywords: myelin; intrinsically disordered protein; multiple sclerosis; peripheral neuropathies; myelination; protein folding; protein–membrane interaction; protein–protein interaction 1. -
DNA Methylation Changes in Down Syndrome Derived Neural Ipscs Uncover Co-Dysregulation of ZNF and HOX3 Families of Transcription
Laan et al. Clinical Epigenetics (2020) 12:9 https://doi.org/10.1186/s13148-019-0803-1 RESEARCH Open Access DNA methylation changes in Down syndrome derived neural iPSCs uncover co- dysregulation of ZNF and HOX3 families of transcription factors Loora Laan1†, Joakim Klar1†, Maria Sobol1, Jan Hoeber1, Mansoureh Shahsavani2, Malin Kele2, Ambrin Fatima1, Muhammad Zakaria1, Göran Annerén1, Anna Falk2, Jens Schuster1 and Niklas Dahl1* Abstract Background: Down syndrome (DS) is characterized by neurodevelopmental abnormalities caused by partial or complete trisomy of human chromosome 21 (T21). Analysis of Down syndrome brain specimens has shown global epigenetic and transcriptional changes but their interplay during early neurogenesis remains largely unknown. We differentiated induced pluripotent stem cells (iPSCs) established from two DS patients with complete T21 and matched euploid donors into two distinct neural stages corresponding to early- and mid-gestational ages. Results: Using the Illumina Infinium 450K array, we assessed the DNA methylation pattern of known CpG regions and promoters across the genome in trisomic neural iPSC derivatives, and we identified a total of 500 stably and differentially methylated CpGs that were annotated to CpG islands of 151 genes. The genes were enriched within the DNA binding category, uncovering 37 factors of importance for transcriptional regulation and chromatin structure. In particular, we observed regional epigenetic changes of the transcription factor genes ZNF69, ZNF700 and ZNF763 as well as the HOXA3, HOXB3 and HOXD3 genes. A similar clustering of differential methylation was found in the CpG islands of the HIST1 genes suggesting effects on chromatin remodeling. Conclusions: The study shows that early established differential methylation in neural iPSC derivatives with T21 are associated with a set of genes relevant for DS brain development, providing a novel framework for further studies on epigenetic changes and transcriptional dysregulation during T21 neurogenesis. -
Investigation of Candidate Genes and Mechanisms Underlying Obesity
Prashanth et al. BMC Endocrine Disorders (2021) 21:80 https://doi.org/10.1186/s12902-021-00718-5 RESEARCH ARTICLE Open Access Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules G. Prashanth1 , Basavaraj Vastrad2 , Anandkumar Tengli3 , Chanabasayya Vastrad4* and Iranna Kotturshetti5 Abstract Background: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. Methods: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. Results: A total of 820 DEGs were identified between -
Cell Reprogramming Technologies for Treatment And
CELL REPROGRAMMING TECHNOLOGIES FOR TREATMENT AND UNDERSTANDING OF GENETIC DISORDERS OF MYELIN by ANGELA MARIE LAGER Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Thesis advisor: Paul J Tesar, PhD Department of Genetics and Genome Sciences CASE WESTERN RESERVE UNIVERSITY May 2015 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Angela Marie Lager Candidate for the Doctor of Philosophy degree*. (signed) Ronald A Conlon, PhD (Committee Chair) Paul J Tesar, PhD (Advisor) Craig A Hodges, PhD Warren J Alilain, PhD (date) 31 March 2015 *We also certify that written approval has been obtained from any proprietary material contained therein. TABLE OF CONTENTS Table of Contents……………………………………………………………………….1 List of Figures……………………………………………………………………………4 Acknowledgements……………………………………………………………………..7 Abstract…………………………………………………………………………………..8 Chapter 1: Introduction and Background………………………………………..11 1.1 Overview of mammalian oligodendrocyte development in the spinal cord and myelination of the central nervous system…………………..11 1.1.1 Introduction……………………………………………………..11 1.1.2 The establishment of the neuroectoderm and ventral formation of the neural tube…………………………………..12 1.1.3 Ventral patterning of the neural tube and specification of the pMN domain in the spinal cord……………………………….15 1.1.4 Oligodendrocyte progenitor cell production through the process of gliogenesis ………………………………………..16 1.1.5 Oligodendrocyte progenitor cell to oligodendrocyte differentiation…………………………………………………...22 -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,