Define Senescence-Accelerated SAMP8 Mice, a Model of a Geriatric
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Recombinant Human ARFIP2 Protein Catalog Number: ATGP1695
Recombinant human ARFIP2 protein Catalog Number: ATGP1695 PRODUCT INPORMATION Expression system E.coli Domain 1-341aa UniProt No. P53365 NCBI Accession No. NP_036534 Alternative Names Arfaptin 2, POR1 PRODUCT SPECIFICATION Molecular Weight 40.2 kDa (364aa) confirmed by MALDI-TOF Concentration 0.25mg/ml (determined by Bradford assay) Formulation Liquid in. 20mM Tris-HCl buffer (pH 8.0) containing 0.2M NaCl, 40% glycerol, 1mM DTT Purity > 90% by SDS-PAGE Tag His-Tag Application SDS-PAGE Storage Condition Can be stored at +2C to +8C for 1 week. For long term storage, aliquot and store at -20C to -80C. Avoid repeated freezing and thawing cycles. BACKGROUND Description Arfaptin 2, also known as ARFIP2, is a Rac1 binding protein necessary for Rac-mediated actin polymerization and the subsequent formation of membrane ruffles and lamellipodia. ARFIP2 has also been shown to interact with the ADP ribosylation factor ARF6, a GTPase that associates with the plasma membrane and intracellular endosome vesicles, in a GTP dependent manner. Arfaptin 2 also regulates the aggregation of mutant Huntingtin protein by possibly impairing proteasome function. Expression of ARFIP2 was shown to be increased at sites of neurodegeneration. Recombinant human ARFIP2 protein, fused to His-tag at N-terminus, was expressed in E. coli 1 Recombinant human ARFIP2 protein Catalog Number: ATGP1695 and purified by using conventional chromatography techniques. Amino acid Sequence MGSSHHHHHH SSGLVPRGSH MGSMTDGILG KAATMEIPIH GNGEARQLPE DDGLEQDLQQ VMVSGPNLNE TSIVSGGYGG SGDGLIPTGS GRHPSHSTTP SGPGDEVARG IAGEKFDIVK KWGINTYKCT KQLLSERFGR GSRTVDLELE LQIELLRETK RKYESVLQLG RALTAHLYSL LQTQHALGDA FADLSQKSPE LQEEFGYNAE TQKLLCKNGE TLLGAVNFFV SSINTLVTKT MEDTLMTVKQ YEAARLEYDA YRTDLEELSL GPRDAGTRGR LESAQATFQA HRDKYEKLRG DVAIKLKFLE ENKIKVMHKQ LLLFHNAVSA YFAGNQKQLE QTLQQFNIKL RPPGAEKPSW LEEQ General References D'Souza Schorey C., et al. -
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. -
AVILA-DISSERTATION.Pdf
LOGOS EX MACHINA: A REASONED APPROACH TOWARD CANCER by Andrew Avila, B. S., M. S. A Dissertation In Biological Sciences Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Approved Lauren Gollahon Chairperson of the Committee Rich Strauss Sean Rice Boyd Butler Richard Watson Peggy Gordon Miller Dean of the Graduate School May, 2012 c 2012, Andrew Avila Texas Tech University, Andrew Avila, May 2012 ACKNOWLEDGEMENTS I wish to acknowledge the incredible support given to me by my major adviser, Dr. Lauren Gollahon. Without your guidance surely I would not have made it as far as I have. Furthermore, the intellectual exchange I have shared with my advisory committee these long years have propelled me to new heights of inquiry I had not dreamed of even in the most lucid of my imaginings. That their continual intellectual challenges have provoked and evoked a subtle sense of natural wisdom is an ode to their efficacy in guiding the aspirant to the well of knowledge. For this initiation into the mysteries of nature I cannot thank my advisory committee enough. I also wish to thank the Vice President of Research for the fellowship which sustained the initial couple years of my residency at Texas Tech. Furthermore, my appreciation of the support provided to me by the Biology Department, financial and otherwise, cannot be understated. Finally, I also wish to acknowledge the individuals working at the High Performance Computing Center, without your tireless support in maintaining the cluster I would have not have completed the sheer amount of research that I have. -
Extensive Variation Between Inbred Mouse Strains Due to Endogenous L1 Retrotransposition
Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Extensive variation between inbred mouse strains due to endogenous L1 retrotransposition Keiko Akagi,1,5 Jingfeng Li,2,5 Robert M. Stephens,3,5 Natalia Volfovsky,3 and David E. Symer2,4,6 1Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, USA; 2Basic Research Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, USA; 3Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick, Inc., Frederick, Maryland 21702, USA; 4Laboratory of Biochemistry and Molecular Biology, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, USA Numerous inbred mouse strains comprise models for human diseases and diversity, but the molecular differences between them are mostly unknown. Several mammalian genomes have been assembled, providing a framework for identifying structural variations. To identify variants between inbred mouse strains at a single nucleotide resolution, we aligned 26 million individual sequence traces from four laboratory mouse strains to the C57BL/6J reference genome. We discovered and analyzed over 10,000 intermediate-length genomic variants (from 100 nucleotides to 10 kilobases), distinguishing these strains from the C57BL/6J reference. Approximately 85% of such variants are due to recent mobilization of endogenous retrotransposons, predominantly L1 elements, greatly exceeding that reported in humans. Many genes’ structures and expression are altered directly by polymorphic L1 retrotransposons, including Drosha (also called Rnasen), Parp8, Scn1a, Arhgap15, and others, including novel genes. L1 polymorphisms are distributed nonrandomly across the genome, as they are excluded significantly from the X chromosome and from genes associated with the cell cycle, but are enriched in receptor genes. -
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L
www.nature.com/scientificreports OPEN A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L. Patel-Murray1, Miriam Adam2, Nhan Huynh2, Brook T. Wassie2, Pamela Milani2 & Ernest Fraenkel 2,3* High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with benefcial efects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these efects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specifc assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. Unknown modes of action of drug candidates can lead to unpredicted consequences on efectiveness and safety. Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that afect a specifc target or phenotype1–3. However, these advances have not dramatically changed the rate of drug approvals. Between 2000 and 2015, 86% of drug can- didates failed to earn FDA approval, with toxicity or a lack of efcacy being common reasons for their clinical trial termination4,5. -
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Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes. -
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, -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Prediction of Host-Virus Interaction Networks
Prediction of host-virus interaction networks Janet M. Doolittle A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Bioinformatics and Computational Biology. Chapel Hill 2012 Approved by: Tim Elston, PhD Shawn Gomez, Eng.Sc.D. Klaus Hahn, PhD Keith Burridge, PhD Anne Taylor, PhD Jennifer Webster-Cyriaque, PhD ©2012 Janet M. Doolittle ALL RIGHTS RESERVED ii ABSTRACT JANET DOOLITTLE: Prediction of host-virus interaction networks (Under the direction of Shawn Gomez) As with other viral pathogens, HIV-1 and dengue virus (DENV) are dependent on their hosts for the bulk of the functions necessary for viral survival and replication. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects, while avoiding elimination by the immune system. Protein-protein interactions are one avenue through which viruses can connect with and exploit these host cellular pathways and processes. We developed a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2,000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. -
Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing