Genetic Regulation of Disease Risk and Endometrial Gene Expression
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation. -
Supporting Information for Proteomics DOI 10.1002/Pmic.200400896
Supporting Information for Proteomics DOI 10.1002/pmic.200400896 Odette Prat, Frdric Berenguer, Vronique Malard, Emmanuelle Tavan, Nicole Sage, Grard Steinmetz and Eric Quemeneur Transcriptomic and proteomic responses of human renal HEK293 cells to uranium toxicity ª 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.de Table 1 : Differentially expressed genes in HEK293 cells treated with uranium at CI50 , CI30 and CI20. GENE ID GENE DESCRIPTION CI50 CI30 CI20 AANAT arylalkylamine N-acetyltransferase 1.66 AASDHPPT aminoadipate-semialdehyde dehydrogenase-phosphopantetheinyl transferase -1.73 ABCC8 ATP-binding cassette, sub-family C (CFTR/MRP), member 8 2.96 2.9 ABCF2 ATP-binding cassette, sub-family F (GCN20), member 2 1.86 ACAT2 acetyl-Coenzyme A acetyltransferase 2 (acetoacetyl Coenzyme A thiolase) -2.47 ACTB actin, beta -2.12 ACTR2 ARP2 actin-related protein 2 homolog (yeast) -1.94 ADAR adenosine deaminase, RNA-specific 1.87 1.92 ADNP activity-dependent neuroprotector -1.03 ADPRTL1 ADP-ribosyltransferase (NAD+; poly (ADP-ribose) polymerase)-like 1 1.48 2.31 2.1 AKAP1 A kinase (PRKA) anchor protein 1 1.59 AKR1C3 aldo-keto reductase family 1, member C3 -1.37 (3-alpha hydroxysteroid dehydrogenase, type II) ALS2CR3 amyotrophic lateral sclerosis 2 (juvenile) chromosome region, candidate 3 -1.21 APBB1 amyloid beta (A4) precursor protein-binding, family B, member 1 (Fe65) 4.41 APC adenomatosis polyposis coli -1.66 APP amyloid beta (A4) precursor protein (protease nexin-II, Alzheimer disease) -1.32 APPBP1 amyloid beta precursor -
Meta-Analysis of Nasopharyngeal Carcinoma
BMC Genomics BioMed Central Research article Open Access Meta-analysis of nasopharyngeal carcinoma microarray data explores mechanism of EBV-regulated neoplastic transformation Xia Chen†1,2, Shuang Liang†1, WenLing Zheng1,3, ZhiJun Liao1, Tao Shang1 and WenLi Ma*1 Address: 1Institute of Genetic Engineering, Southern Medical University, Guangzhou, PR China, 2Xiangya Pingkuang associated hospital, Pingxiang, Jiangxi, PR China and 3Southern Genomics Research Center, Guangzhou, Guangdong, PR China Email: Xia Chen - [email protected]; Shuang Liang - [email protected]; WenLing Zheng - [email protected]; ZhiJun Liao - [email protected]; Tao Shang - [email protected]; WenLi Ma* - [email protected] * Corresponding author †Equal contributors Published: 7 July 2008 Received: 16 February 2008 Accepted: 7 July 2008 BMC Genomics 2008, 9:322 doi:10.1186/1471-2164-9-322 This article is available from: http://www.biomedcentral.com/1471-2164/9/322 © 2008 Chen 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: Epstein-Barr virus (EBV) presumably plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC), but the molecular mechanism of EBV-dependent neoplastic transformation is not well understood. The combination of bioinformatics with evidences from biological experiments paved a new way to gain more insights into the molecular mechanism of cancer. Results: We profiled gene expression using a meta-analysis approach. Two sets of meta-genes were obtained. Meta-A genes were identified by finding those commonly activated/deactivated upon EBV infection/reactivation. -
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. -
Defining the Kv2.1–Syntaxin Molecular Interaction Identifies a First-In-Class Small Molecule Neuroprotectant
Defining the Kv2.1–syntaxin molecular interaction identifies a first-in-class small molecule neuroprotectant Chung-Yang Yeha,b,1, Zhaofeng Yec,d,1, Aubin Moutale, Shivani Gaura,b, Amanda M. Hentonf,g, Stylianos Kouvarosf,g, Jami L. Salomana, Karen A. Hartnett-Scotta,b, Thanos Tzounopoulosa,f,g, Rajesh Khannae, Elias Aizenmana,b,g,2, and Carlos J. Camachoc,2 aDepartment of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261; bPittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261; cDepartment of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261; dSchool of Medicine, Tsinghua University, Beijing 100871, China; eDepartment of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ 85724; fDepartment of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261; and gPittsburgh Hearing Research Center, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261 Edited by Lily Yeh Jan, University of California, San Francisco, CA, and approved June 19, 2019 (received for review February 27, 2019) + The neuronal cell death-promoting loss of cytoplasmic K follow- (13). The Kv2.1-dependent cell death pathway is normally initiated ing injury is mediated by an increase in Kv2.1 potassium channels in by the oxidative liberation of zinc from intracellular metal-binding the plasma membrane. This phenomenon relies on Kv2.1 binding to proteins (14), leading to the sequential phosphorylation of syntaxin 1A via 9 amino acids within the channel intrinsically disor- Kv2.1 residues Y124 and S800 by Src and p38 kinases, respectively dered C terminus. Preventing this interaction with a cell and blood- (15–17). -
Produktinformation
Produktinformation Diagnostik & molekulare Diagnostik Laborgeräte & Service Zellkultur & Verbrauchsmaterial Forschungsprodukte & Biochemikalien Weitere Information auf den folgenden Seiten! See the following pages for more information! Lieferung & Zahlungsart Lieferung: frei Haus Bestellung auf Rechnung SZABO-SCANDIC Lieferung: € 10,- HandelsgmbH & Co KG Erstbestellung Vorauskassa Quellenstraße 110, A-1100 Wien T. +43(0)1 489 3961-0 Zuschläge F. +43(0)1 489 3961-7 [email protected] • Mindermengenzuschlag www.szabo-scandic.com • Trockeneiszuschlag • Gefahrgutzuschlag linkedin.com/company/szaboscandic • Expressversand facebook.com/szaboscandic SANTA CRUZ BIOTECHNOLOGY, INC. MAGE-D4/MAGE-D4B CRISPR/Cas9 KO Plasmid (h): sc-418394 BACKGROUND APPLICATIONS The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and MAGE-D4/MAGE-D4B CRISPR/Cas9 KO Plasmid (h) is recommended for the CRISPR-associated protein (Cas9) system is an adaptive immune response disruption of gene expression in human cells. defense mechanism used by archea and bacteria for the degradation of foreign genetic material (4,6). This mechanism can be repurposed for other 20 nt non-coding RNA sequence: guides Cas9 functions, including genomic engineering for mammalian systems, such as to a specific target location in the genomic DNA gene knockout (KO) (1,2,3,5). CRISPR/Cas9 KO Plasmid products enable the U6 promoter: drives gRNA scaffold: helps Cas9 identification and cleavage of specific genes by utilizing guide RNA (gRNA) expression of gRNA bind to target DNA sequences derived from the Genome-scale CRISPR Knock-Out (GeCKO) v2 library developed in the Zhang Laboratory at the Broad Institute (3,5). Termination signal Green Fluorescent Protein: to visually REFERENCES verify transfection CRISPR/Cas9 Knockout Plasmid CBh (chicken β-Actin 1. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
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, -
Elastin Biology and Tissue Engineering with Adult Cells
DOI 10.1515/bmc-2012-0040 BioMol Concepts 2013; 4(2): 173–185 Review Cassandra B. Saitow * , Steven G. Wise, Anthony S. Weiss , John J. Castellot Jr. and David L. Kaplan Elastin biology and tissue engineering with adult cells Abstract: The inability of adult cells to produce well-organ- collagen to allow for repeated cycles of expansion and ized, robust elastic fibers has long been a barrier to the contraction in response to pulsatile flow (3) . This review successful engineering of certain tissues. In this review, focuses primarily on blood vessel tissue engineering, we focus primarily on elastin with respect to tissue-engi- particularly the challenge of inducing sufficient elastin neered vascular substitutes. To understand elastin regu- expression from cells that colonize vascular constructs. lation during normal development, we describe the role Developmental regulation of elastin dictates that of various elastic fiber accessory proteins. Biochemical synthesis is predominantly in utero and early childhood pathways regulating expression of the elastin gene are (4) . New elastic fibers are not produced in appreciable addressed, with particular focus on tissue-engineering amounts under normal physiological circumstances in research using adult-derived cells. adult cells. This deficit presents a significant challenge in tissue engineering of vascular constructs that seek Keywords: elastin; heparin; smooth muscle cell; vascular to replicate the elastin content of natural vessels. This tissue engineering. review considers factors involved in elastic fiber forma- tion during normal development and addresses recent advances in the induction of elastin expression by cells *Corresponding author : Cassandra B. Saitow, Tufts University, from adult donors. Department of Biomedical Engineering, Medford, MA, 02155 , USA, e-mail: [email protected] Steven G. -
Learning from Cadherin Structures and Sequences: Affinity Determinants and Protein Architecture
Learning from cadherin structures and sequences: affinity determinants and protein architecture Klára Fels ıvályi 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 Klara Felsovalyi All rights reserved ABSTRACT Learning from cadherin structures and sequences: affinity determinants and protein architecture Klara Felsovalyi Cadherins are a family of cell-surface proteins mediating adhesion that are important in development and maintenance of tissues. The family is defined by the repeating cadherin domain (EC) in their extracellular region, but they are diverse in terms of protein size, architecture and cellular function. The best-understood subfamily is the type I classical cadherins, which are found in vertebrates and have five EC domains. Among the five different type I classical cadherins, the binding interactions are highly specific in their homo- and heterophilic binding affinities, though their sequences are very similar. As previously shown, E- and N-cadherins, two prototypic members of the subfamily, differ in their homophilic K D by about an order of magnitude, while their heterophilic affinity is intermediate. To examine the source of the binding affinity differences among type I cadherins, we used crystal structures, analytical ultracentrifugation (AUC), surface plasmon resonance (SPR), and electron paramagnetic resonance (EPR) studies. Phylogenetic analysis and binding affinity behavior show that the type I cadherins can be further divided into two subgroups, with E- and N-cadherin representing each. In addition to the affinity differences in their wild-type binding through the strand-swapped interface, a second interface also shows an affinity difference between E- and N-cadherin. -
High-Resolution Analysis of Chromosomal Breakpoints and Genomic Instability Identifies PTPRD As a Candidate Tumor Suppressor Gene in Neuroblastoma
Research Article High-Resolution Analysis of Chromosomal Breakpoints and Genomic Instability Identifies PTPRD as a Candidate Tumor Suppressor Gene in Neuroblastoma Raymond L. Stallings,1 Prakash Nair,1 John M. Maris,2 Daniel Catchpoole,3 Michael McDermott,4 Anne O’Meara,5 and Fin Breatnach5 1Children’s Cancer Research Institute and Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, Texas; 2Division of Oncology, Children’s Hospital of Philadelphia and Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; 3The Tumor Bank, Children’s Hospital at Westmead, Sydney, New South Wales, Australia; and Departments of 4Pathology and 5Oncology, Our Lady’s Hospital for Sick Children, Dublin, Ireland Abstract and death from disease. Patient age, tumor stage, and several Although neuroblastoma is characterized by numerous different genetic abnormalities are important factors that influence clinical outcome. Loss of 1p and 11q, gain of 17q, and amplification recurrent, large-scale chromosomal imbalances, the genes MYCN targeted by such imbalances have remained elusive. We have of the oncogene are particularly strong genetic indicators of poor disease outcome (2–5). Two of these abnormalities, loss of 11q applied whole-genome oligonucleotide array comparative MYCN genomic hybridization (median probe spacing 6 kb) to 56 and amplification, form the basis for dividing advanced- stage neuroblastomas into genetic subtypes due to their rather neuroblastoma tumors and cell lines to identify genes involved with disease pathogenesis. This set oftumors was selected for striking inverse distribution in tumors (6, 7). Many other recurrent having either 11q loss or MYCN amplification, abnormalities partial chromosomal imbalances, including loss of 3p, 4p, 9p, and that define the two most common genetic subtypes of 14q and gain of 1q, 2p, 7q, and 11p, have been identified by metastatic neuroblastoma. -
PRODUCT SPECIFICATION Product Datasheet
Product Datasheet QPrEST PRODUCT SPECIFICATION Product Name QPrEST K1841 Mass Spectrometry Protein Standard Product Number QPrEST29029 Protein Name Uncharacterized protein KIAA1841 Uniprot ID Q6NSI8 Gene KIAA1841 Product Description Stable isotope-labeled standard for absolute protein quantification of Uncharacterized protein KIAA1841. Lys (13C and 15N) and Arg (13C and 15N) metabolically labeled recombinant human protein fragment. Application Absolute protein quantification using mass spectrometry Sequence (excluding EQCIQYCHKNMNAIVATPCNMNCINANLLTRIADLFSHNEVDDLKDKKDK fusion tag) FKSKLFCKKIERLFDPEYLNPDSRSNAA Theoretical MW 26883 Da including N-terminal His6ABP fusion tag Fusion Tag A purification and quantification tag (QTag) consisting of a hexahistidine sequence followed by an Albumin Binding Protein (ABP) domain derived from Streptococcal Protein G. Expression Host Escherichia coli LysA ArgA BL21(DE3) Purification IMAC purification Purity >90% as determined by Bioanalyzer Protein 230 Purity Assay Isotopic Incorporation >99% Concentration >5 μM after reconstitution in 100 μl H20 Concentration Concentration determined by LC-MS/MS using a highly pure amino acid analyzed internal Determination reference (QTag), CV ≤10%. Amount >0.5 nmol per vial, two vials supplied. Formulation Lyophilized in 100 mM Tris-HCl 5% Trehalose, pH 8.0 Instructions for Spin vial before opening. Add 100 μL ultrapure H2O to the vial. Vortex thoroughly and spin Reconstitution down. For further dilution, see Application Protocol. Shipping Shipped at ambient temperature Storage Lyophilized product shall be stored at -20°C. See COA for expiry date. Reconstituted product can be stored at -20°C for up to 4 weeks. Avoid repeated freeze-thaw cycles. Notes For research use only Product of Sweden. For research use only. Not intended for pharmaceutical development, diagnostic, therapeutic or any in vivo use.