DOCSLIB.ORG

Glygen As a Case Study by Jeet Vora BS In

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Infrastructure for Data Collection and Integration for Biomedical Knowledgebases – GlyGen as a Case Study by Jeet Vora B.S. in Microbiology, April 2012, University of Pune M.S. in Microbiology, April 2014, Savitribai Phule Pune University A Thesis submitted to The Faculty of The Columbian College of Arts and Sciences of The George Washington University in partial fulfillment of the requirements for the degree of Master of Science May 20, 2018 Thesis directed by Raja Mazumder Associate Professor of Biochemistry and Molecular Biology © Copyright 2018 by Jeet Vora All rights reserved ii Acknowledgments The author expresses his sincere gratitude to his mentor, Dr. Raja Mazumder, for providing valuable time, guidance and suggestions throughout the project. The author also wishes to thank Dr. Nagarajan Pattabiraman for his comments and suggestions on the thesis, Dr. Robel Kahsay for his help in understanding the semantic web technologies and IT infrastructure, Hayley Dingerdissen for her continuous support and help in familiarizing glycobiology concepts, Dacian Recce-Stremtan for providing details of GlyGen servers, Rahi Navelkar and Reza Mousavi for helping in creating datasets and Amanraj Singh for his help in using Cytoscape. The author also expresses sincere gratitude to Charles Hadley King, Amanda Bell, and all other members of the lab for their support and encouragement. The author wishes to acknowledge the members of the GlyGen project for their contribution of data and the technical discussions of this project. GlyGen Project is funded by National Institutes of Health Common Fund Glycoscience Fund program (NIH Award #U01GM125267-01; PI- William York & Raja Mazumder) iii Abstract of Thesis Infrastructure for Data Collection and Integration for Biomedical Knowledgebases – GlyGen as a Case Study The ongoing acceleration in the use of omics technologies is generating petabytes of data that has resulted in the development of several knowledgebases and tools. Even though a vast amount of knowledge is present in these resources, much of it is redundant heterogeneous and scattered. Despite of the availability of considerable number of resources, there is still a need for manual literature search and manual collection of data from multiple resources to find an answer to a specific scientific question. Hence, there is a greater need for collection and integration of such biomedical data. The slow progress in biomedical data integration is because of the data in the knowledgebases are in different file formats, have multiple identifiers for the same entity, lack of machine- readable schemas, ill-defined Application Programming Interfaces (APIs) and data licensing issues. The biomedical community is making substantial progress by implementing new infrastructure technologies and standards comprising of Semantic Web technologies, common formats and global linked identifiers for data collection, integration and retrieval. The field of glycomics is generating data at a fast pace from the high-throughput projects, and as a result, many tools and databases have been developed for the glycoinformatics community. Even in the glycobiology domain, the relevant data is scattered in various databases and knowledgebases giving rise to a need of having a global and comprehensive glycobiology knowledgebase. GlyGen, a glycoinformatics iv knowledgebase that is a free, extendable and multidisciplinary resource for glycobiology aims to address these needs. GlyGen includes data and knowledge related to glycobiology which comprises of glycans, genes, proteins, diseases, expression, and mutation. For GlyGen, the data based on pre-defined data model derived from use-cases developed from the input of more than 50 scientists are being collected and integrated from various data resources. In the initial phase of the GlyGen project, we have collected and integrated mouse and human data from resources such as UniProt, PDB, PubChem, GlyTouCan and UniCarbKB and other individual data generators based on the workflow that incorporated semantic web technologies and standards. We created 74 datasets and further categorized them as protein centric, proteoform centric and glycan centric datasets based on the content of the data. Detailed readme for each dataset was created based on the BioCompute Object specification document. A dataset collection viewer page was developed and view the dataset collection and to understand the relationship and linking of data in the dataset categories; dataset networks were created using Cytoscape. The datasets in CSV (Comma Separated Value) format were later rdfized using an RDF (Resource Description Framework) model based on the existing RDF models. The rdfized data was stored in the GlyGen triplestore in the form of triples. The GlyGen triplestore was made available to be accessed by web service APIs and by SPARQL queries. To collect, integrate and retrieve data, high availability clusters server configuration comprising three servers with preinstalled software were used. For GlyGen data, we chose Creative Commons Attribution 4.0 International (CC BY 4.0) license, and v for source code, we chose, GNU General Public License v3. A private GitHub repository was created for sharing source code with the public. As it is challenging to retrieve a list if Glycoside hydrolases (GHs) from a single resource developed a workflow that retrieved the GHs from UniProtKB and validated the entries through QuickGO, Carbohydrate-Active Enzymes database (CAZy) and Pfam. The workflow retrieved a list of 83 GHs classified by GH families. When GlyGen is fully developed and functional, we will make it available to the public through the link – www.glygen.org Data integration is challenging and a difficult task that requires meticulous planning, creative approaches to tackle issues and concentrated efforts in implementing solutions. We firmly believe that the rest of the biomedical community will take inspiration from GlyGen to collect and integrate data for a biomedical domain from diverse resources. vi Contents Acknowledgments.............................................................................................................. iii Abstract of Thesis .............................................................................................................. iv List of Figures .................................................................................................................. viii List of Tables ..................................................................................................................... ix List of Abbreviations .......................................................................................................... x Introduction ......................................................................................................................... 1 Literature Review................................................................................................................ 4 Methods............................................................................................................................... 8 Results ............................................................................................................................... 13 Discussion ......................................................................................................................... 17 References ......................................................................................................................... 38 vii List of Figures Figure 1. Data collection and integration workflow for GlyGen ...................................... 18 Figure 2: GlyGen high availability cluster server configuration ...................................... 19 Figure 3: GlyGen dataset collection viewer ...................................................................... 20 Figure 4: Protein centric network ..................................................................................... 21 Figure 5: Proteoform centric network ............................................................................... 22 Figure 6: Glycan centric network ..................................................................................... 23 Figure 7: Biocompute Object example representation...................................................... 24 Figure 8: Detailed dataset readme for Human glycoside hydrolased ............................... 25 viii List of Tables Table 1. Information on licenses that cover the biomedical resources ............................. 26 Table 2. List of human glycosidases ................................................................................. 27 Table 3. List of datasets in GlyGen .................................................................................. 33 ix List of Abbreviations API – Application programming interface BCO – BioCompute Object CAZy – Carbohydrate-Active Enzymes CPL – Common Programming Language CSV – Comma Separated Value EMBL-EBI - The European Molecular Biology Laboratory - European Bioinformatics Institute FAQ – Frequently Asked Questions GB – Gigabyte GHs- Glycoside Hydrolases GO – Gene ontology GTs – Glycosyltransferases HTML – HyperText Markup Language JSON – JavaScript Object Notation NCBI - The National Center for Biotechnology Information NFS – Network File Server OBO – Open biological and biomedical Ontology OWL – Ontology Web Language RDF – Resource Description Format RDFS – Resource Description Format Schema SPARQL – SPARQL Protocol and RDF Query Language
Recommended publications
  • The Rise and Fall of the Bovine Corpus Luteum

    The Rise and Fall of the Bovine Corpus Luteum

    University of Nebraska Medical Center DigitalCommons@UNMC Theses & Dissertations Graduate Studies Spring 5-6-2017 The Rise and Fall of the Bovine Corpus Luteum Heather Talbott University of Nebraska Medical Center Follow this and additional works at: https://digitalcommons.unmc.edu/etd Part of the Biochemistry Commons, Molecular Biology Commons, and the Obstetrics and Gynecology Commons Recommended Citation Talbott, Heather, "The Rise and Fall of the Bovine Corpus Luteum" (2017). Theses & Dissertations. 207. https://digitalcommons.unmc.edu/etd/207 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]. THE RISE AND FALL OF THE BOVINE CORPUS LUTEUM by Heather Talbott 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 John S. Davis University of Nebraska Medical Center Omaha, Nebraska May, 2017 Supervisory Committee: Carol A. Casey, Ph.D. Andrea S. Cupp, Ph.D. Parmender P. Mehta, Ph.D. Justin L. Mott, Ph.D. i ACKNOWLEDGEMENTS This dissertation was supported by the Agriculture and Food Research Initiative from the USDA National Institute of Food and Agriculture (NIFA) Pre-doctoral award; University of Nebraska Medical Center Graduate Student Assistantship; University of Nebraska Medical Center Exceptional Incoming Graduate Student Award; the VA Nebraska-Western Iowa Health Care System Department of Veterans Affairs; and The Olson Center for Women’s Health, Department of Obstetrics and Gynecology, Nebraska Medical Center.
  • 1General Introduction and Outline Glycosphingolipids, Carbohydrate

    1General Introduction and Outline Glycosphingolipids, Carbohydrate

    Lipophilic iminosugars : synthesis and evaluation as inhibitors of glucosylceramide metabolism Wennekes, T. Citation Wennekes, T. (2008, December 15). Lipophilic iminosugars : synthesis and evaluation as inhibitors of glucosylceramide metabolism. Retrieved from https://hdl.handle.net/1887/13372 Version: Corrected Publisher’s Version Licence agreement concerning inclusion of doctoral thesis in the License: Institutional Repository of the University of Leiden Downloaded from: https://hdl.handle.net/1887/13372 Note: To cite this publication please use the final published version (if applicable). General Introduction and Outline Glycosphingolipids, Carbohydrate- 1 processing Enzymes and Iminosugar Inhibitors General Introduction The study described in this thesis was conducted with the aim of developing lipophilic iminosugars as selective inhibitors for three enzymes involved in glucosylceramide metabolism. Glucosylceramide, a β-glycoside of the lipid ceramide and the carbohydrate d-glucose, is a key member of a class of biomolecules called the glycosphingolipids (GSLs). One enzyme, glucosylceramide synthase (GCS), is responsible for its synthesis and the two other enzymes, glucocerebrosidase (GBA1) and β-glucosidase 2 (GBA2), catalyze its degradation. Being able to influence glucosylceramide biosynthesis and degradation would greatly facilitate the study of GSL functioning in (patho)physiological processes. This chapter aims to provide background information and some history on the various subjects that were involved in this study. The chapter will start out with a brief overview of the discovery of GSLs and the evolving view of the biological role of GSLs and carbohydrate containing biomolecules in general during the last century. Next, the topology and dynamics of mammalian GSL biosynthesis and degradation will be described with special attention for the involved carbohydrate-processing enzymes.
  • Enzymatic Encoding Methods for Efficient Synthesis Of

    Enzymatic Encoding Methods for Efficient Synthesis Of

    (19) TZZ__T (11) EP 1 957 644 B1 (12) EUROPEAN PATENT SPECIFICATION (45) Date of publication and mention (51) Int Cl.: of the grant of the patent: C12N 15/10 (2006.01) C12Q 1/68 (2006.01) 01.12.2010 Bulletin 2010/48 C40B 40/06 (2006.01) C40B 50/06 (2006.01) (21) Application number: 06818144.5 (86) International application number: PCT/DK2006/000685 (22) Date of filing: 01.12.2006 (87) International publication number: WO 2007/062664 (07.06.2007 Gazette 2007/23) (54) ENZYMATIC ENCODING METHODS FOR EFFICIENT SYNTHESIS OF LARGE LIBRARIES ENZYMVERMITTELNDE KODIERUNGSMETHODEN FÜR EINE EFFIZIENTE SYNTHESE VON GROSSEN BIBLIOTHEKEN PROCEDES DE CODAGE ENZYMATIQUE DESTINES A LA SYNTHESE EFFICACE DE BIBLIOTHEQUES IMPORTANTES (84) Designated Contracting States: • GOLDBECH, Anne AT BE BG CH CY CZ DE DK EE ES FI FR GB GR DK-2200 Copenhagen N (DK) HU IE IS IT LI LT LU LV MC NL PL PT RO SE SI • DE LEON, Daen SK TR DK-2300 Copenhagen S (DK) Designated Extension States: • KALDOR, Ditte Kievsmose AL BA HR MK RS DK-2880 Bagsvaerd (DK) • SLØK, Frank Abilgaard (30) Priority: 01.12.2005 DK 200501704 DK-3450 Allerød (DK) 02.12.2005 US 741490 P • HUSEMOEN, Birgitte Nystrup DK-2500 Valby (DK) (43) Date of publication of application: • DOLBERG, Johannes 20.08.2008 Bulletin 2008/34 DK-1674 Copenhagen V (DK) • JENSEN, Kim Birkebæk (73) Proprietor: Nuevolution A/S DK-2610 Rødovre (DK) 2100 Copenhagen 0 (DK) • PETERSEN, Lene DK-2100 Copenhagen Ø (DK) (72) Inventors: • NØRREGAARD-MADSEN, Mads • FRANCH, Thomas DK-3460 Birkerød (DK) DK-3070 Snekkersten (DK) • GODSKESEN,
  • Unicarbkb: Building a Standardised and Scalable Informatics Platform for Glycosciences Research

    Unicarbkb: Building a Standardised and Scalable Informatics Platform for Glycosciences Research

    Platforms EOI: UniCarbKB: Building a Standardised and Scalable Informatics Platform for Glycosciences Research 20 September 2019 at 13:02 Project title UniCarbKB: Building a Standardised and Scalable Informatics Platform for Glycosciences Research Field of Research code(s) 03 CHEMICAL SCIENCES 06 BIOLOGICAL SCIENCES 08 INFORMATION AND COMPUTING SCIENCES 11 MEDICAL AND HEALTH SCIENCES EOI Lead Name Matthew Campbell EOI lead Research Group Institute for Glycomics EOI lead Organisation Institute for Glycomics, Griffith University EOI lead Email Collaborator details Name Research Group Organisation Malcolm Wolski eResearch Services Griffith University Project description Over the last few years the glycomics knowledge platform UniCarbKB has contributed to the growth of international glycoinformatics resources by providing access to data collections, web services and software applications supported by biocuration activities. However, UniCarbKB has continued to develop as a single monolithic resource. This proposed project will improve the growth and scalability of UniCarbKB by breaking down the platform into smaller and composable pieces. This will be achieved through the adoption of a more decentralised approach and a microservice architecture that will allow components of the platform to scale at different rates. This architecture will allow us is to build an extendable and cross-disciplinary resource by integrating existing data with new analysis tools. The adoption of international resources (e.g. NIH GlyGen), will allow not just mapping of glycan data to genes and proteins but also pathways, gene ontology, publications and a wide variety of data from EBI, NCBI and other sources. Additionally, we propose to integrate glycan array data and develop ontologies that can serve as tools for integration and subsequently analysis of glycan-associated Existing technology Adopt The proposed project will adopt a multi-tier application architecture in which applications will be developed and deployed as microservices.
  • Glycoproteomics-Based Signatures for Tumor Subtyping and Clinical Outcome Prediction of High-Grade Serous Ovarian Cancer

    Glycoproteomics-Based Signatures for Tumor Subtyping and Clinical Outcome Prediction of High-Grade Serous Ovarian Cancer

    ARTICLE https://doi.org/10.1038/s41467-020-19976-3 OPEN Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer Jianbo Pan 1,2,3, Yingwei Hu1,3, Shisheng Sun 1,3, Lijun Chen1, Michael Schnaubelt1, David Clark1, ✉ Minghui Ao1, Zhen Zhang1, Daniel Chan1, Jiang Qian2 & Hui Zhang 1 1234567890():,; Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post- translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans cor- respond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy.

  • Hierarchical Classification of Gene Ontology Terms Using the Gostruct

    Hierarchical classification of Gene Ontology terms using the GOstruct method Artem Sokolov and Asa Ben-Hur Department of Computer Science, Colorado State University Fort Collins, CO, 80523, USA Abstract Protein function prediction is an active area of research in bioinformatics. And yet, transfer of annotation on the basis of sequence or structural similarity remains widely used as an annotation method. Most of today's machine learning approaches reduce the problem to a collection of binary classification problems: whether a protein performs a particular function, sometimes with a post-processing step to combine the binary outputs. We propose a method that directly predicts a full functional annotation of a protein by modeling the structure of the Gene Ontology hierarchy in the framework of kernel methods for structured-output spaces. Our empirical results show improved performance over a BLAST nearest-neighbor method, and over algorithms that employ a collection of binary classifiers as measured on the Mousefunc benchmark dataset. 1 Introduction Protein function prediction is an active area of research in bioinformatics [25]; and yet, transfer of annotation on the basis of sequence or structural similarity remains the standard way of assigning function to proteins in newly sequenced organisms [20]. The Gene Ontology (GO), which is the current standard for annotating gene products and proteins, provides a large set of terms arranged in a hierarchical fashion that specify a gene-product's molecular function, the biological process it is involved in, and its localization to a cellular component [12]. GO term prediction is therefore a hierarchical classification problem, made more challenging by the thousands of annotation terms available.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • Localization of Heparanase in Esophageal Cancer Cells: Respective Roles in Prognosis and Differentiation

    Localization of Heparanase in Esophageal Cancer Cells: Respective Roles in Prognosis and Differentiation

    Laboratory Investigation (2004) 84, 1289–1304 & 2004 USCAP, Inc All rights reserved 0023-6837/04 $30.00 www.laboratoryinvestigation.org Localization of heparanase in esophageal cancer cells: respective roles in prognosis and differentiation Takaomi Ohkawa1, Yoshio Naomoto1, Munenori Takaoka1, Tetsuji Nobuhisa1, Kazuhiro Noma1, Takayuki Motoki1, Toshihiro Murata1, Hirokazu Uetsuka1, Masahiko Kobayashi1, Yasuhiro Shirakawa1, Tomoki Yamatsuji1, Nagahide Matsubara1, Junji Matsuoka1, Minoru Haisa1, Mehmet Gunduz2, Hidetsugu Tsujigiwa2, Hitoshi Nagatsuka2, Masao Hosokawa3, Motowo Nakajima4 and Noriaki Tanaka1 1Department of Gastroenterological Surgery, Transplant, and Surgical Oncology; 2Department of Oral Pathology and Medicine, Graduate School of Medicine and Dentistry, Okayama University, Okayama, Japan; 3Keiyukai Sapporo Hospital, Sapporo, Japan and 4Tsukuba Research Institute, Novartis Pharma KK Tsukuba, Japan In this study, we examined the distribution of heparanase protein in 75 esophageal squamous cell carcinomas by immunohistochemistry and analyzed the relationship between heparanase expression and clinicopatho- logical characteristics. In situ hybridization showed that the mRNA expression pattern of heparanase was similar to that of the protein, suggesting that increased expression of the heparanase protein at the invasive front was caused by an increase of heparanase mRNA in tumor cells. Heparanase expression correlated significantly with depth of tumor invasion, lymph node metastasis, tumor node metastasis (TNM) stage and lymphatic
  • Mannosidases Are the Putative Catabolic Enzymes Which

    Mannosidases Are the Putative Catabolic Enzymes Which

    THE CHARACTERIZATION OF A NOVEL HUMAN CORE-SPECIFIC LYSOSOMAL α1-6MANNOSIDASE INVOLVED IN N-GLYCAN CATABOLISM by CHAEHO PARK (Under the Direction of Kelley W. Moremen) ABSTRACT In humans and rodents lysosomal catabolism of Man3GlcNAc2 core N-glycan structures results from the concerted actions of exoglycosidases including the broad specificity lysosomal α- mannosidase (LysMan), a core-specific α1-6mannosidase, and β-mannosidase, as well as the core chitobiose cleavage by an di-N-acetylchitobiase. In ungulates and carnivora, both the chitobiase and the α1-6mannosidase enzyme activities are absent suggesting a co-regulation of the two enzymes. We describe here the cloning, expression, purification and characterization of the human core-specific α1-6mannosidase with similarity to members of the glycosylhydrolase family 38. The recombinant enzyme had a pH optimum of 4.0, was potently inhibited by swainsonine and 1,4-dideoxy1,4-imino-D-mannitol, and was stimulated by Co+2. NMR-based time course substrate specificity studies comparing the α1-6mannosidase with human LysMan revealed that the former enzyme selectively cleaved the α1-6mannose residue from Man3GlcNAc, but not Man3GlcNAc2 or other larger high mannose structures, indicating the requirement for chitobiase action prior to α1-6mannosidase cleavage. In contrast, LysMan cleaved all of the α-linked mannose residues from Man9-5GlcNAc2, Man3GlcNAc2, or Man3GlcNAc structures except the core α1-6mannose residue. Transcripts encoding the α1-6mannosidase were ubiquitously expressed in human tissues and expressed sequence tag searches in various mammalian species demonstrated a similar distribution in species-specific expression as the chitobiase. No expressed sequence tags were identified for bovine α1-6mannosidase despite the identification of two homologs in the bovine genome.
  • Ultrasensitive Small Molecule Fluorogenic Probe for Human Heparanase

    Ultrasensitive Small Molecule Fluorogenic Probe for Human Heparanase

    bioRxiv preprint doi: https://doi.org/10.1101/2020.03.26.008730; this version posted March 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Ultrasensitive small molecule fluorogenic probe for human heparanase Jun Liu1, 2, Kelton A. Schleyer1, 2, Tyrel L. Bryan2, Changjian Xie2, Gustavo Seabra1, Yongmei Xu3, Arjun Kafle2, Chao Cui1, 2, Ying Wang2, Kunlun Yin2, Benjamin Fetrow2, Paul K. P. Henderson2, Peter Z. Fatland2, Jian Liu3, Chenglong Li1, Hua Guo2, and Lina Cui1, 2, * 1Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA (Current) 2Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA 3Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA *Correspondence should be addressed to L.C. E-mail: [email protected] Abstract Heparanase is a critical enzyme involved in the remodeling of the extracellular matrix (ECM), and its elevated expression has been linked with diseases such as cancer and inflammation. The detection of heparanase enzymatic activity holds tremendous value in the study of the cellular microenvironment, and search of molecular therapeutics targeting heparanase, however, assays developed for this enzyme so far have suffered prohibitive drawbacks. Here we present an ultrasensitive fluorogenic small-molecule probe for heparanase enzymatic activity. The probe exhibits a 756-fold fluorescence turn-on response in the presence of human heparanase, allowing one-step detection of heparanase activity in real-time with a picomolar detection limit.
  • Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations

    Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations

    The American Journal of Pathology, Vol. 180, No. 6, June 2012 Copyright © 2012 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ajpath.2012.01.044 Tumorigenesis and Neoplastic Progression Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations John M. Krill-Burger,* Maureen A. Lyons,*† The annual incidence of renal cell carcinoma (RCC) has Lori A. Kelly,*† Christin M. Sciulli,*† increased steadily in the United States for the past three Patricia Petrosko,*† Uma R. Chandran,†‡ decades, with approximately 58,000 new cases diag- 1,2 Michael D. Kubal,§ Sheldon I. Bastacky,*† nosed in 2010, representing 3% of all malignancies. Anil V. Parwani,*†‡ Rajiv Dhir,*†‡ and Treatment of RCC is complicated by the fact that it is not a single disease but composes multiple tumor types with William A. LaFramboise*†‡ different morphological characteristics, clinical courses, From the Departments of Pathology* and Biomedical and outcomes (ie, clear-cell carcinoma, 82% of RCC ‡ Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania; cases; type 1 or 2 papillary tumors, 11% of RCC cases; † the University of Pittsburgh Cancer Institute, Pittsburgh, chromophobe tumors, 5% of RCC cases; and collecting § Pennsylvania; and Life Technologies, Carlsbad, California duct carcinoma, approximately 1% of RCC cases).2,3 Benign renal neoplasms are subdivided into papillary adenoma, renal oncocytoma, and metanephric ade- Copy number variant (CNV) analysis was performed on noma.2,3 Treatment of RCC often involves surgical resec- renal cell carcinoma (RCC) specimens (chromophobe, tion of a large renal tissue component or removal of the clear cell, oncocytoma, papillary type 1, and papillary entire affected kidney because of the relatively large size of type 2) using high-resolution arrays (1.85 million renal tumors on discovery and the availability of a life-sus- probes).
  • Salivary Alpha Amylase (AMY1C) (NM 001008219) Human Tagged ORF Clone Product Data

    Salivary Alpha Amylase (AMY1C) (NM 001008219) Human Tagged ORF Clone Product Data

    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RG215827 Salivary alpha amylase (AMY1C) (NM_001008219) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: Salivary alpha amylase (AMY1C) (NM_001008219) Human Tagged ORF Clone Tag: TurboGFP Symbol: AMY1C Synonyms: AMY1 Vector: pCMV6-AC-GFP (PS100010) E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: Neomycin This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 5 Salivary alpha amylase (AMY1C) (NM_001008219) Human Tagged ORF Clone – RG215827 ORF Nucleotide >RG215827 representing NM_001008219 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGAAGCTCTTTTGGTTGCTTTTCACCATTGGGTTCTGCTGGGCTCAGTATTCCTCAAATACACAACAAG GACGAACATCTATTGTTCATCTGTTTGAATGGCGATGGGTTGATATTGCTCTTGAATGTGAGCGATATTT AGCTCCCAAGGGATTTGGAGGGGTTCAGGTCTCTCCACCAAATGAAAATGTTGCCATTCACAACCCTTTC AGACCTTGGTGGGAAAGATACCAACCAGTTAGCTATAAATTATGCACAAGATCTGGAAATGAAGATGAAT TTAGAAACATGGTGACTAGATGCAACAATGTTGGGGTTCGTATTTATGTGGATGCTGTAATTAATCATAT GTGTGGTAATGCTGTGAGTGCAGGAACAAGCAGTACCTGTGGAAGTTACTTCAACCCTGGAAGTAGGGAC TTTCCAGCAGTCCCATATTCTGGATGGGATTTTAATGATGGTAAATGTAAAACTGGAAGTGGAGATATCG AGAACTATAATGATGCTACTCAGGTCAGAGATTGTCGTCTGTCTGGTCTTCTCGATCTTGCACTGGGGAA