Mouse Gpatch8 Knockout Project (CRISPR/Cas9)
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Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
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. -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
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Download and May While the KEGG Database [18] Was Used As a Basis for the Be Installed As a Local Source Package
BMC Bioinformatics BioMed Central Methodology article Open Access Identifying differential correlation in gene/pathway combinations Rosemary Braun*1, Leslie Cope2 and Giovanni Parmigiani2 Address: 1National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA and 2The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA Email: Rosemary Braun* - [email protected]; Leslie Cope - [email protected]; Giovanni Parmigiani - [email protected] * Corresponding author Published: 18 November 2008 Received: 3 April 2008 Accepted: 18 November 2008 BMC Bioinformatics 2008, 9:488 doi:10.1186/1471-2105-9-488 This article is available from: http://www.biomedcentral.com/1471-2105/9/488 © 2008 Braun et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: An important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level "summaries" for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification. Results: We present a novel method for the analysis of microarray data that identifies joint differential expression in gene-pathway pairs. This method takes advantage of known gene pathway memberships to compute a summary expression level for each pathway as a whole. -
Brno University of Technology Vysoké Učení Technické V Brně
BRNO UNIVERSITY OF TECHNOLOGY VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ FACULTY OF ELECTRICAL ENGINEERING AND COMMUNICATION FAKULTA ELEKTROTECHNIKY A KOMUNIKAČNÍCH TECHNOLOGIÍ DEPARTMENT OF BIOMEDICAL ENGINEERING ÚSTAV BIOMEDICÍNSKÉHO INŽENÝRSTVÍ TRANSCRIPTOMIC CHARACTERIZATION USING RNA-SEQ DATA ANALYSIS TRANSKRIPTOMICKÁ CHARAKTERIZACE POMOCÍ ANALÝZY RNA-SEQ DAT DOCTORAL THESIS DIZERTAČNÍ PRÁCE AUTHOR Layal Abo Khayal AUTOR PRÁCE SUPERVISOR prof. Ing. Ivo Provazník, Ph.D. ŠKOLITEL BRNO 2017 ABSTRACT The high-throughputs sequence technologies produce a massive amount of data, that can reveal new genes, identify splice variants, and quantify gene expression genome-wide. However, the volume and the complexity of data from RNA-seq experiments necessitate a scalable, and mathematical analysis based on a robust statistical model. Therefore, it is challenging to design integrated workflow, that incorporates the various analysis procedures. Particularly, the comparative transcriptome analysis is complicated due to several sources of measurement variability and poses numerous statistical challenges. In this research, we performed an integrated transcriptional profiling pipeline, which generates novel reproducible codes to obtain biologically interpretable results. Starting with the annotation of RNA-seq data and quality assessment, we provided a set of codes to serve the quality assessment visualization needed for establishing the RNA-Seq data analysis experiment. Additionally, we performed comprehensive differential gene expression analysis, presenting -
A Network-Based Approach to Uncover Microrna-Mediated Disease Comorbidities and Potential Pathobiological Implications
www.nature.com/npjsba ARTICLE OPEN A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications Shuting Jin1,9, Xiangxiang Zeng2,9, Jiansong Fang3, Jiawei Lin1, Stephen Y. Chan4, Serpil C. Erzurum5,6 and Feixiong Cheng3,7,8* Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta- path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome. mpDisNet is a meta-path- based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease–disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease–disease relationships with miRNA-mediated pathobiological pathways. 1234567890():,; npj Systems Biology and Applications (2019) ; https://doi.org/10.1038/s41540-019-0115-25:41 INTRODUCTION or completely pairing with their 3′ UTR region, thereby reducing The manifestation and clinical severity of human disease are the stability of the target miRNA or inhibiting translation to 9 affected by myriad factors, including genetic, epigenetic, lifestyle, downregulate the expression of genes of interest. -
Overlapping Genetic Architecture Between Parkinson Disease and Melanoma
bioRxiv preprint doi: https://doi.org/10.1101/740589; this version posted August 28, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 Overlapping Genetic Architecture between Parkinson Disease and Melanoma 2 Umber Dube1,2,3,4; Laura Ibanez2,4; John P Budde2,4; Bruno A Benitez2,4; Albert A Davis3; Oscar 3 Harari2,4; Mark M Iles5; Matthew H Law6; Kevin M Brown7; 23andMe Research Team; 4 Melanoma-Meta-analysis Consortium; and Carlos Cruchaga2,3,4* 5 6 1Medical Scientist Training Program, Washington University School of Medicine, 660 S. Euclid 7 Ave, St. Louis, MO 63110, USA. 8 2Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave. 9 CB8134, St. Louis, MO 63110, USA. 10 3Department of Neurology, Washington University School of Medicine, St Louis, MO, USA 11 4NeuroGenomics and Informatics. Washington University School of Medicine, 660 S. Euclid 12 Ave. B8111, St. Louis, MO 63110, USA. 13 5Leeds Institute for Data Analytics, University of Leeds, Leeds, UK 14 6Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia 15 7Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of 16 Health, Bethesda, Maryland, USA 17 18 Corresponding Author: Carlos Cruchaga, Neurogenetics and Informatics, Department of 19 Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, 20 Missouri 63110. E-mail: [email protected] . Phone: 314-286-0546 21 1 bioRxiv preprint doi: https://doi.org/10.1101/740589; this version posted August 28, 2019. -
Identification of Antiviral Roles for the Exon-Junction Complex and Nonsense-Mediated Decay in Flaviviral Infection
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Microbiol Manuscript Author . Author manuscript; Manuscript Author available in PMC 2019 September 04. Published in final edited form as: Nat Microbiol. 2019 June ; 4(6): 985–995. doi:10.1038/s41564-019-0375-z. Identification of Antiviral Roles for the Exon-Junction Complex and Nonsense-Mediated Decay in Flaviviral Infection Minghua Li1, Jeffrey R. Johnson2,3,4, Billy Truong1, Grace Kim1, Nathan Weinbren1, Mark Dittmar1, Priya S. Shah2,3,4,5, John Von Dollen2,3,4, Billy W. Newton2,3,4, Gwendolyn M. Jang2,3,4, Nevan J. Krogan2,3,4,*, Sara Cherry1,*, and Holly Ramage1,*,† 1.Department of Microbiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA 2.Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, USA 3.Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA 4.The J. David Gladstone Institutes, San Francisco, California, USA 5.Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA Abstract West Nile virus (WNV) is an emerging mosquito-borne flavivirus, related to dengue virus and Zika virus. To gain insight into host pathways involved in WNV infection, we performed a systematic affinity-tag purification mass spectrometry (AP-MS) study to identify 259 WNV- interacting human proteins. RNAi screening revealed 26 genes that both interact with WNV proteins and influence WNV infection. We found that WNV, dengue and Zika virus capsids interact with a conserved subset of proteins that impact infection. These include the exon-junction complex (EJC) recycling factor, PYM1, which is antiviral against all three viruses. -
Genomic Landscape of Metastatic Colorectal Cancer
ARTICLE Received 22 Feb 2014 | Accepted 2 Oct 2014 | Published 14 Nov 2014 DOI: 10.1038/ncomms6457 OPEN Genomic landscape of metastatic colorectal cancer Josien C. Haan1, Mariette Labots2, Christian Rausch1, Miriam Koopman3, Jolien Tol4, Leonie J.M. Mekenkamp5, Mark A. van de Wiel6, Danielle Israeli1, Hendrik F. van Essen1, Nicole C.T. van Grieken1, Quirinus J.M. Voorham1, Linda J.W. Bosch1, Xueping Qu7, Omar Kabbarah7, Henk M.W. Verheul2, Iris D. Nagtegaal5, Cornelis J.A. Punt8, Bauke Ylstra1,* & Gerrit A. Meijer1,* Response to drug therapy in individual colorectal cancer (CRC) patients is associated with tumour biology. Here we describe the genomic landscape of tumour samples of a homogeneous well-annotated series of patients with metastatic CRC (mCRC) of two phase III clinical trials, CAIRO and CAIRO2. DNA copy number aberrations of 349 patients are determined. Within three treatment arms, 194 chromosomal subregions are associated with progression-free survival (PFS; uncorrected single-test P-values o0.005). These subregions are filtered for effect on messenger RNA expression, using an independent data set from The Cancer Genome Atlas which returned 171 genes. Three chromosomal regions are associated with a significant difference in PFS between treatment arms with or without irinotecan. One of these regions, 6q16.1–q21, correlates in vitro with sensitivity to SN-38, the active metabolite of irinotecan. This genomic landscape of mCRC reveals a number of DNA copy number aberrations associated with response to drug therapy. 1 Department of Pathology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands. 2 Department of Medical Oncology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands. -
1 Supplementary Material Figure S1. Volcano Plot of Differentially
Supplementary material Figure S1. Volcano Plot of differentially expressed genes between preterm infants fed own mother’s milk (OMM) or pasteurized donated human milk (DHM). Table S1. The 10 most representative biological processes filtered for enrichment p- value in preterm infants. Biological Processes p-value Quantity of DEG* Transcription, DNA-templated 3.62x10-24 189 Regulation of transcription, DNA-templated 5.34x10-22 188 Transport 3.75x10-17 140 Cell cycle 1.03x10-13 65 Gene expression 3.38x10-10 60 Multicellular organismal development 6.97x10-10 86 1 Protein transport 1.73x10-09 56 Cell division 2.75x10-09 39 Blood coagulation 3.38x10-09 46 DNA repair 8.34x10-09 39 Table S2. Differential genes in transcriptomic analysis of exfoliated epithelial intestinal cells between preterm infants fed own mother’s milk (OMM) and pasteurized donated human milk (DHM). Gene name Gene Symbol p-value Fold-Change (OMM vs. DHM) (OMM vs. DHM) Lactalbumin, alpha LALBA 0.0024 2.92 Casein kappa CSN3 0.0024 2.59 Casein beta CSN2 0.0093 2.13 Cytochrome c oxidase subunit I COX1 0.0263 2.07 Casein alpha s1 CSN1S1 0.0084 1.71 Espin ESPN 0.0008 1.58 MTND2 ND2 0.0138 1.57 Small ubiquitin-like modifier 3 SUMO3 0.0037 1.54 Eukaryotic translation elongation EEF1A1 0.0365 1.53 factor 1 alpha 1 Ribosomal protein L10 RPL10 0.0195 1.52 Keratin associated protein 2-4 KRTAP2-4 0.0019 1.46 Serine peptidase inhibitor, Kunitz SPINT1 0.0007 1.44 type 1 Zinc finger family member 788 ZNF788 0.0000 1.43 Mitochondrial ribosomal protein MRPL38 0.0020 1.41 L38 Diacylglycerol -
Table SI. Phosphorylated Proteins Identified in Rats with Cancer-Induced Bone Pain
Table SI. Phosphorylated proteins identified in rats with cancer-induced bone pain. Accession Description Microtubule-associated protein 1A OS=Rattus norvegicus GN=Map1a PE=1 P34926 SV=1 - [MAP1A_RAT] Microtubule-associated protein 1B OS=Rattus norvegicus GN=Map1b PE=1 F1LRL9 SV=1 - [F1LRL9_RAT] Microtubule-associated protein 1B OS=Rattus norvegicus GN=Map1b PE=1 P15205 SV=2 - [MAP1B_RAT] Neurofilament medium polypeptide OS=Rattus norvegicus GN=Nefm PE=1 G3V7S2 SV=3 - [G3V7S2_RAT] Microtubule associated protein 1A (Fragment) OS=Rattus norvegicus Q63330 GN=Map1a PE=2 SV=1 - [Q63330_RAT] 14-3-3 protein gamma OS=Rattus norvegicus GN=Ywhag PE=1 SV=2 - P61983 [1433G_RAT] Q62745 CD81 antigen OS=Rattus norvegicus GN=Cd81 PE=1 SV=1 - [CD81_RAT] A-kinase anchor protein 12 OS=Rattus norvegicus GN=Akap12 PE=1 SV=1 - Q5QD51 [AKA12_RAT] Erythrocyte membrane protein band 4.1-like 3 OS=Rattus norvegicus G3V874 GN=Epb41l3 PE=1 SV=1 - [G3V874_RAT] 60S acidic ribosomal protein P2 OS=Rattus norvegicus GN=Rplp2 PE=1 SV=2 - P02401 [RLA2_RAT] Gap junction gamma-2 protein OS=Rattus norvegicus GN=Gjc2 PE=1 SV=2 - Q80XF7 [CXG2_RAT] Prostaglandin E synthase 3 OS=Rattus norvegicus GN=Ptges3 PE=1 SV=2 - P83868 [TEBP_RAT] Adenosylhomocysteinase 2 OS=Rattus norvegicus GN=Ahcyl1 PE=1 SV=1 - A0A140TAI8 [A0A140TAI8_RAT] Uncharacterized protein OS=Rattus norvegicus PE=4 SV=1 - A0A0G2K4Q1 [A0A0G2K4Q1_RAT] F1LQN3 Reticulon OS=Rattus norvegicus GN=Rtn4 PE=1 SV=1 - [F1LQN3_RAT] Band 4.1-like protein 1 OS=Rattus norvegicus GN=Epb41l1 PE=1 SV=1 - A0A0G2K0F3 [A0A0G2K0F3_RAT] -
Identification and Characterisation of the Underlying Defects in Patients with Inherited Platelet Bleeding Disorders
Identification and Characterisation of the Underlying Defects in Patients with Inherited Platelet Bleeding Disorders By: Maryam Ahmed Aldossary A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy The University of Sheffield Faculty of Medicine, Dentistry and Health Department of Infection, Immunity & Cardiovascular Disease Submission Date January 2019 Abstract The underlying genetic defects remain unknown in about 50% of inherited platelet bleeding disorders (IPDs). This study investigated the use of whole exome sequencing (WES) to identify candidate gene defects in 34 index cases enrolled in the UK Genotyping and Phenotyping of Platelets study with a history of bleeding, whose platelets demonstrated defects in agonist-induced dense granule secretion or Gi- signalling. WES analysis identified a median of 98 candidate disease-causing variants per index case highlighting the complexity of IPDs. Sixteen variants were in genes that had previously been associated with IPDs, two of which were selected for further characterisation. Two novel FLI1 alterations, predicting p.Arg340Cys/His substitutions in the DNA binding domain of FLI1 were shown to reduce transcriptional activity and nuclear accumulation of FLI1, suggesting that these variants interfere with the regulation of essential megakaryocyte genes by FLI1 and may explain the bleeding tendency in affected patients. Expression of a novel truncated p.Arg430* variant of ETV6 revealed it to be stably expressed, possessing normal repressor activity in HEK 293T cells and a slight reduction in repressor activity in megakaryocytic cells. Further studies are required to confirm the pathogenicity of this variant. To identify novel genes involved in platelet granule biogenesis and secretion, gene expression was examined in megakaryocytic cells before and after knockdown of FLI1, defects in which are associated with platelet granule abnormalities.