S-Nitrosylation in Organs of Mice Exposed to Low Or High Doses of Γ-Rays
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International Journal of Molecular Sciences Article Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions Elizabeth C. Lee y and Valerie W. Hu * Department of Biochemistry and Molecular Medicine, The George Washington University, School of Medicine and Health Sciences, Washington, DC 20037, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-202-994-8431 Current address: W. Harry Feinstone Department of Molecular Microbiology and Immunology, y Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. Received: 25 August 2020; Accepted: 17 September 2020; Published: 19 September 2020 Abstract: Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group. -
United States Patent 19 11 Patent Number: 5,780,253 Subramanian Et Al
III USOO5780253A United States Patent 19 11 Patent Number: 5,780,253 Subramanian et al. (45) Date of Patent: Jul. 14, 1998 54 SCREENING METHOD FOR DETECTION OF 4.433.999 2/1984 Hyzak ....................................... 71.03 HERBCDES 4.6–552 2/1987 Anoti et al. if O3. 4,802,912 2/1989 Baker ........................................ 7/103 Inventors: Wenkiteswaran Subramanian Danville: Anne G. Toschi. Burlingame. OTHERTHER PPUBLICATION CATIONS both of Calif. Heim et al. Pesticide Biochem & Physiol; vol. 53, pp. 138-145 (1995). 73) Assignee: Sandoz Ltd., Basel. Switzerland Hatch. MD.: Phytochem. vol. 6... pp. 115 to 119, (1967). Haworth et al. J. Agric. Food Chem, vol. 38, pp. 1271-1273. 21 Appl. No.:752.990 1990. Nishimura et al: Phytochem: vol. 34, pp. 613-615. (1993). 22 Filed: Nov. 21, 1996 Primary Examiner-Louise N. Leary Related U.S. Application Data Attorney, Agent, or Firm-Lynn Marcus-Wyner: Michael P. Morris 63 Continuation of Ser. No. 434.826, May 4, 1995, abandoned. 6 57 ABSTRACT 51 Int. Cl. ............................... C12Q 1/48: C12Q 1/32: C12Q 1/37; C12O 1/00 This invention relates to novel screening methods for iden 52 U.S. Cl. ................................. 435/15:435/18: 435/26: tifying compounds that specifically inhibit a biosynthetic 435/23: 435/4, 536/23.6:536/23.2:536/24.3 pathway in plants. Enzymes which are specifically affected 536/26.11:536/26.12:536/26.13 by the novel screening method include plant purine biosyn 58 Field of Search .................................. 435/15, 8, 26, thetic pathway enzymes and particularly the enzymes 435/23 4: 536/23.6, 23.2, 24.3, 26.1, involved in the conversion of inosine monophosphate to 26.12, 26.13 adenosine monophosphate and inosine monophosphate to guanosine monophosphate. -
Global-Scale Analysis of the Dynamic Transcriptional Adaptations Within Skeletal Muscle During Hypertrophic Growth
University of Kentucky UKnowledge Theses and Dissertations--Physiology Physiology 2015 GLOBAL-SCALE ANALYSIS OF THE DYNAMIC TRANSCRIPTIONAL ADAPTATIONS WITHIN SKELETAL MUSCLE DURING HYPERTROPHIC GROWTH Tyler Kirby University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Kirby, Tyler, "GLOBAL-SCALE ANALYSIS OF THE DYNAMIC TRANSCRIPTIONAL ADAPTATIONS WITHIN SKELETAL MUSCLE DURING HYPERTROPHIC GROWTH" (2015). Theses and Dissertations--Physiology. 22. https://uknowledge.uky.edu/physiology_etds/22 This Doctoral Dissertation is brought to you for free and open access by the Physiology at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Physiology by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless an embargo applies. -
Human ADAM12 Quantikine ELISA
Quantikine® ELISA Human ADAM12 Immunoassay Catalog Number DAD120 For the quantitative determination of A Disintegrin And Metalloproteinase domain- containing protein 12 (ADAM12) concentrations in cell culture supernates, serum, plasma, and urine. This package insert must be read in its entirety before using this product. For research use only. Not for use in diagnostic procedures. TABLE OF CONTENTS SECTION PAGE INTRODUCTION .....................................................................................................................................................................1 PRINCIPLE OF THE ASSAY ...................................................................................................................................................2 LIMITATIONS OF THE PROCEDURE .................................................................................................................................2 TECHNICAL HINTS .................................................................................................................................................................2 MATERIALS PROVIDED & STORAGE CONDITIONS ...................................................................................................3 OTHER SUPPLIES REQUIRED .............................................................................................................................................3 PRECAUTIONS .........................................................................................................................................................................4 -
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. -
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. -
Glioma Cell Secretion: a Driver of Tumor Progression and a Potential Therapeutic Target Damian A
Published OnlineFirst October 17, 2018; DOI: 10.1158/0008-5472.CAN-18-0345 Cancer Review Research Glioma Cell Secretion: A Driver of Tumor Progression and a Potential Therapeutic Target Damian A. Almiron Bonnin1,2, Matthew C. Havrda1,2, and Mark A. Israel1,2,3 Abstract Cellular secretion is an important mediator of cancer progres- ple oncogenic pathologies. In this review, we describe tumor cell sion. Secreted molecules in glioma are key components of secretion in high-grade glioma and highlight potential novel complex autocrine and paracrine pathways that mediate multi- therapeutic opportunities. Cancer Res; 78(21); 6031–9. Ó2018 AACR. Introduction Glioma-Secreted Molecules Impact Disease Glial cells in the central nervous system (CNS) provide trophic Progression support for neurons (1). In glial tumors, this trophic support is Glioma cells modify their microenvironment by introducing dysregulated creating a pro-oncogenic microenvironment medi- diverse molecules into the extracellular space (Table 1). To exem- ated by a heterogeneous array of molecules secreted into the plify the pro-oncogenic role that secreted molecules can have on – extracellular space (2 15). The glioma secretome includes pro- glioma pathology, we review the functional impact of specific teins, nucleic acids, and metabolites that are often overexpressed cytokines, metabolites, and nucleic acids on glioma biology. By in malignant tissue and contribute to virtually every aspect of describing some of the potent antitumorigenic effects observed in – cancer pathology (Table 1; Fig. 1; refs. 2 15), providing a strong preclinical therapeutic studies targeting tumor cell secretion, we – rationale to target the cancer cell secretory mechanisms. also highlight how blocking secreted molecules might be of fi Although the speci c mechanisms regulating secretion in therapeutic impact in gliomas, as well as other tumors. -
Complete Genome of the Cellyloytic Thermophile Acidothermus Cellulolyticus 11B Provides Insights Into Its Ecophysiological and Evloutionary Adaptations
Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Title Complete genome of the cellyloytic thermophile Acidothermus cellulolyticus 11B provides insights into its ecophysiological and evloutionary adaptations Permalink https://escholarship.org/uc/item/5xg662d7 Author Barabote, Ravi D. Publication Date 2009-08-25 eScholarship.org Powered by the California Digital Library University of California Title: Complete genome of the cellyloytic thermophile Acidothermus cellulolyticus 11B provides insights into its ecophysiological and evolutionary adaptations Author(s): R. Barabote1,†, G. Xie1, D. Leu2, P. Normand3, A. Necsulea4, V. Daubin4, C. Médigue5, W. Adney6, X. Xu2, A. Lapidus7, C. Detter1, P. Pujic3, D. Bruce1, C. Lavire3, J. Challacombe1, T. Brettin1 and Alison M. Berry2. Author Affiliations: 1 DOE Joint Genome Institute, Bioscience Division, Los Alamos National Laboratory, 2 Department of Plant Sciences, University of California, Davis, 3 Centre National de la Recherche Scientifique (CNRS), UMR5557, Écologie Microbienne, Université Lyon I, Villeurbanne, 4 Centre National de la Recherche Scientifique (CNRS), UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Université Lyon I, Villeurbanne, 5 Centre National de la Recherche Scientifique (CNRS), UMR8030 and CEA/DSV/IG/Genoscope, Laboratoire de Génomique Comparative, 6 National Renewable Energy Laboratory 7 DOE Joint Genome Institute Date: 06/10/09 Funding: This work was performed under the auspices of the US Department of Energy's Office of Science, Biological and Environmental Research Program, and by the University of California, Lawrence Berkeley National Laboratory under contract No. DE-AC02- 05CH11231, Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344, and Los Alamos National Laboratory under contract No. DE-AC02-06NA25396. R. D. Barabote Complete genome of the cellulolytic thermophile Acidothermus cellulolyticus 11B provides insights into its ecophysiological and evolutionary adaptations. -
Modulating Hallmarks of Cholangiocarcinoma
University of Nebraska Medical Center DigitalCommons@UNMC Theses & Dissertations Graduate Studies Fall 12-14-2018 Modulating Hallmarks of Cholangiocarcinoma Cody Wehrkamp University of Nebraska Medical Center Follow this and additional works at: https://digitalcommons.unmc.edu/etd Part of the Molecular Biology Commons Recommended Citation Wehrkamp, Cody, "Modulating Hallmarks of Cholangiocarcinoma" (2018). Theses & Dissertations. 337. https://digitalcommons.unmc.edu/etd/337 This Dissertation is brought to you for free and open access by the Graduate Studies at DigitalCommons@UNMC. It has been accepted for inclusion in Theses & Dissertations by an authorized administrator of DigitalCommons@UNMC. For more information, please contact [email protected]. MODULATING HALLMARKS OF CHOLANGIOCARCINOMA by Cody J. Wehrkamp A DISSERTATION Presented to the Faculty of the University of Nebraska Graduate College in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Biochemistry and Molecular Biology Graduate Program Under the Supervision of Professor Justin L. Mott University of Nebraska Medical Center Omaha, Nebraska November 2018 Supervisory Committee: Kaustubh Datta, Ph.D. Melissa Teoh‐Fitzgerald, Ph.D. Richard G. MacDonald, Ph.D. Acknowledgements This endeavor has led to scientific as well as personal growth for me. I am indebted to many for their knowledge, influence, and support along the way. To my mentor, Dr. Justin L. Mott, you have been an incomparable teacher and invaluable guide. You upheld for me the concept that science is intrepid, even when the experience is trying. Through my training, and now here at the end, I can say that it has been an honor to be your protégé. When you have shaped your future graduates to be and do great, I will be privileged to say that I was your first one. -
Predicting Coupling Probabilities of G-Protein Coupled Receptors Gurdeep Singh1,2,†, Asuka Inoue3,*,†, J
Published online 30 May 2019 Nucleic Acids Research, 2019, Vol. 47, Web Server issue W395–W401 doi: 10.1093/nar/gkz392 PRECOG: PREdicting COupling probabilities of G-protein coupled receptors Gurdeep Singh1,2,†, Asuka Inoue3,*,†, J. Silvio Gutkind4, Robert B. Russell1,2,* and Francesco Raimondi1,2,* 1CellNetworks, Bioquant, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 2Biochemie Zentrum Heidelberg (BZH), Heidelberg University, Im Neuenheimer Feld 328, 69120 Heidelberg, Germany, 3Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan and 4Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA Received February 10, 2019; Revised April 13, 2019; Editorial Decision April 24, 2019; Accepted May 01, 2019 ABSTRACT great use in tinkering with signalling pathways in living sys- tems (5). G-protein coupled receptors (GPCRs) control multi- Ligand binding to GPCRs induces conformational ple physiological states by transducing a multitude changes that lead to binding and activation of G-proteins of extracellular stimuli into the cell via coupling to situated on the inner cell membrane. Most of mammalian intra-cellular heterotrimeric G-proteins. Deciphering GPCRs couple with more than one G-protein giving each which G-proteins couple to each of the hundreds receptor a distinct coupling profile (6) and thus specific of GPCRs present in a typical eukaryotic organism downstream cellular responses. Determining these coupling is therefore critical to understand signalling. Here, profiles is critical to understand GPCR biology and phar- we present PRECOG (precog.russelllab.org): a web- macology. Despite decades of research and hundreds of ob- server for predicting GPCR coupling, which allows served interactions, coupling information is still missing for users to: (i) predict coupling probabilities for GPCRs many receptors and sequence determinants of coupling- specificity are still largely unknown. -
Chuanxiong Rhizoma Compound on HIF-VEGF Pathway and Cerebral Ischemia-Reperfusion Injury’S Biological Network Based on Systematic Pharmacology
ORIGINAL RESEARCH published: 25 June 2021 doi: 10.3389/fphar.2021.601846 Exploring the Regulatory Mechanism of Hedysarum Multijugum Maxim.-Chuanxiong Rhizoma Compound on HIF-VEGF Pathway and Cerebral Ischemia-Reperfusion Injury’s Biological Network Based on Systematic Pharmacology Kailin Yang 1†, Liuting Zeng 1†, Anqi Ge 2†, Yi Chen 1†, Shanshan Wang 1†, Xiaofei Zhu 1,3† and Jinwen Ge 1,4* Edited by: 1 Takashi Sato, Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of 2 Tokyo University of Pharmacy and Life Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China, Galactophore Department, The First 3 Sciences, Japan Hospital of Hunan University of Chinese Medicine, Changsha, China, School of Graduate, Central South University, Changsha, China, 4Shaoyang University, Shaoyang, China Reviewed by: Hui Zhao, Capital Medical University, China Background: Clinical research found that Hedysarum Multijugum Maxim.-Chuanxiong Maria Luisa Del Moral, fi University of Jaén, Spain Rhizoma Compound (HCC) has de nite curative effect on cerebral ischemic diseases, *Correspondence: such as ischemic stroke and cerebral ischemia-reperfusion injury (CIR). However, its Jinwen Ge mechanism for treating cerebral ischemia is still not fully explained. [email protected] †These authors share first authorship Methods: The traditional Chinese medicine related database were utilized to obtain the components of HCC. The Pharmmapper were used to predict HCC’s potential targets. Specialty section: The CIR genes were obtained from Genecards and OMIM and the protein-protein This article was submitted to interaction (PPI) data of HCC’s targets and IS genes were obtained from String Ethnopharmacology, a section of the journal database. -
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,